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index 75fc2296d..7174d42eb 100644 --- a/doc/LectureNotes/DataFiles/cancer.dot +++ b/doc/LectureNotes/DataFiles/cancer.dot @@ -10,7 +10,7 @@ edge [fontname="helvetica"] ; 2 -> 3 ; 4 [label="gini = 0.0\nsamples = 239\nvalue = [[239, 0]\n[0, 239]]", fillcolor="#e58139"] ; 3 -> 4 ; -5 [label="mean texture <= 18.935\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ; +5 [label="mean radius <= 12.265\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ; 3 -> 5 ; 6 [label="gini = 0.0\nsamples = 1\nvalue = [[0, 1]\n[1, 0]]", fillcolor="#e58139"] ; 5 -> 6 ; @@ -22,7 +22,7 @@ edge [fontname="helvetica"] ; 8 -> 9 ; 10 [label="gini = 0.0\nsamples = 3\nvalue = [[0, 3]\n[3, 0]]", fillcolor="#e58139"] ; 8 -> 10 ; -11 [label="mean texture <= 16.22\ngini = 0.278\nsamples = 6\nvalue = [[1, 5]\n[5, 1]]", fillcolor="#f4caac"] ; +11 [label="area error <= 13.475\ngini = 0.278\nsamples = 6\nvalue = [[1, 5]\n[5, 1]]", fillcolor="#f4caac"] ; 1 -> 11 ; 12 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; 11 -> 12 ; @@ -30,11 +30,11 @@ edge [fontname="helvetica"] ; 11 -> 13 ; 14 [label="worst texture <= 20.645\ngini = 0.202\nsamples = 167\nvalue = [[19, 148]\n[148, 19]]", fillcolor="#f0b68c"] ; 0 -> 14 [labeldistance=2.5, labelangle=-45, headlabel="False"] ; -15 [label="worst radius <= 17.74\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ; +15 [label="worst area <= 964.4\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ; 14 -> 15 ; 16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139"] ; 15 -> 16 ; -17 [label="worst concave points <= 0.104\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ; +17 [label="worst texture <= 18.445\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ; 15 -> 17 ; 18 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; 17 -> 18 ; @@ -42,7 +42,7 @@ edge [fontname="helvetica"] ; 17 -> 19 ; 20 [label="mean concave points <= 0.049\ngini = 0.088\nsamples = 151\nvalue = [[7, 144]\n[144, 7]]", fillcolor="#ea985d"] ; 14 -> 20 ; -21 [label="compactness error <= 0.016\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#ffffff"] ; +21 [label="concave points error <= 0.01\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#ffffff"] ; 20 -> 21 ; 22 [label="gini = 0.0\nsamples = 9\nvalue = [[0, 9]\n[9, 0]]", fillcolor="#e58139"] ; 21 -> 22 ; diff --git a/doc/LectureNotes/DataFiles/cancer.png b/doc/LectureNotes/DataFiles/cancer.png index 00a3090d55d8e5af35b1753ae06fae23eecf5f54..4de674f5f2ec8dad654ec1e9658009cdc6778d6f 100644 GIT binary patch literal 246077 zcmbrmby$_#*EPHmg(IM-pfo53B1or{3P^*rl1g`XT7ZIpNH+q4fV7mfbW3-4ch@(! z8$7?~{hsG}|9JPgt|PMD_rCA7=9+ViF~?dTo=OT`z#_&%AP^Tsgau>}2+SM=0^Ri= z4ERmv`bTZ}14BbhNC0t+{4b_B(I0`hjt~)eBxe_~FlhU%tMj;Kb^87r68v+5q@lD; 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@@ -1056,13 +1066,13 @@ example of the functionality of Scikit-Learn.

The intercept alpha: 
- [1.94485679]
+ [2.07549007]
 Coefficient beta : 
- [[5.13059483]]
-Mean squared error: 0.32
-Variance score: 0.87
+ [[5.14029264]]
+Mean squared error: 0.23
+Variance score: 0.89
 Mean squared log error: 0.01
-Mean absolute error: 0.45
+Mean absolute error: 0.38
 
_images/chapter1_19_1.png @@ -1162,7 +1172,7 @@ a linear \(x\)-dependence we s
_images/chapter1_33_0.png -
0.004999999999999996
+
0.004999999999999987
 
diff --git a/doc/LectureNotes/_build/html/chapter10.html b/doc/LectureNotes/_build/html/chapter10.html index 2f2d9e60e..dd0d76d05 100644 --- a/doc/LectureNotes/_build/html/chapter10.html +++ b/doc/LectureNotes/_build/html/chapter10.html @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +
  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • @@ -1373,7 +1383,7 @@ the Hadamard product, meaning element-wise multiplication.

    Old accuracy on training data: 0.1440501043841336
     
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
     
    @@ -1707,7 +1717,7 @@ Lambda = 10.0 Accuracy score on test set: 0.19166666666666668
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
     
    @@ -1716,7 +1726,7 @@ Lambda = 1e-05 Accuracy score on test set: 0.10555555555555556
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
     
    @@ -1725,7 +1735,7 @@ Lambda = 0.0001 Accuracy score on test set: 0.08611111111111111
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
     
    @@ -1734,7 +1744,7 @@ Lambda = 0.001 Accuracy score on test set: 0.10555555555555556
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
     
    @@ -1743,7 +1753,7 @@ Lambda = 0.01 Accuracy score on test set: 0.08888888888888889
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
     
    @@ -1752,7 +1762,7 @@ Lambda = 0.1 Accuracy score on test set: 0.08611111111111111
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
     
    @@ -1761,191 +1771,34 @@ Lambda = 1.0 Accuracy score on test set: 0.08888888888888889
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
     
    -
    Learning rate  =  0.1
    -Lambda =  10.0
    -Accuracy score on test set:  0.09166666666666666
    -
    -
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    -  return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    -  exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    -  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
    -
    -
    -
    Learning rate  =  1.0
    -Lambda =  1e-05
    -Accuracy score on test set:  0.07777777777777778
    -
    -
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    -  return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    -  exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    -  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
    -
    -
    -
    Learning rate  =  1.0
    -Lambda =  0.0001
    -Accuracy score on test set:  0.07777777777777778
    -
    -
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    -  return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    -  exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    -  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
    -
    -
    -
    Learning rate  =  1.0
    -Lambda =  0.001
    -Accuracy score on test set:  0.07777777777777778
    -
    -
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    -  return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    -  exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    -  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
    -
    -
    -
    Learning rate  =  1.0
    -Lambda =  0.01
    -Accuracy score on test set:  0.07777777777777778
    -
    -
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    -  return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    -  exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    -  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
    -
    -
    -
    Learning rate  =  1.0
    -Lambda =  0.1
    -Accuracy score on test set:  0.07777777777777778
    -
    -
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    -  return 1/(1 + np.exp(-x))
    -
    -
    -
    Learning rate  =  1.0
    -Lambda =  1.0
    -Accuracy score on test set:  0.10555555555555556
    -
    -
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    -  return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    -  exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    -  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
    -
    -
    -
    Learning rate  =  1.0
    -Lambda =  10.0
    -Accuracy score on test set:  0.07777777777777778
    -
    -
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    -  return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    -  exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    -  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
    -
    -
    -
    Learning rate  =  10.0
    -Lambda =  1e-05
    -Accuracy score on test set:  0.07777777777777778
    -
    -
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    -  return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    -  exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    -  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
    -
    -
    -
    Learning rate  =  10.0
    -Lambda =  0.0001
    -Accuracy score on test set:  0.07777777777777778
    -
    -
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    -  return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    -  exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    -  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
    -
    -
    -
    Learning rate  =  10.0
    -Lambda =  0.001
    -Accuracy score on test set:  0.07777777777777778
    -
    -
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    -  return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    -  exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    -  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
    -
    -
    -
    Learning rate  =  10.0
    -Lambda =  0.01
    -Accuracy score on test set:  0.07777777777777778
    -
    -
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    -  return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    -  exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    -  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
    -
    -
    -
    Learning rate  =  10.0
    -Lambda =  0.1
    -Accuracy score on test set:  0.07777777777777778
    -
    -
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    -  return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    -  exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    -  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
    -
    -
    -
    Learning rate  =  10.0
    -Lambda =  1.0
    -Accuracy score on test set:  0.07777777777777778
    -
    -
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    -  return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    -  exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    -  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
    -
    -
    -
    Learning rate  =  10.0
    -Lambda =  10.0
    -Accuracy score on test set:  0.07777777777777778
    +
    ---------------------------------------------------------------------------
    +KeyboardInterrupt                         Traceback (most recent call last)
    +Cell In[8], line 11
    +      8 for j, lmbd in enumerate(lmbd_vals):
    +      9     dnn = NeuralNetwork(X_train, Y_train_onehot, eta=eta, lmbd=lmbd, epochs=epochs, batch_size=batch_size,
    +     10                         n_hidden_neurons=n_hidden_neurons, n_categories=n_categories)
    +---> 11     dnn.train()
    +     13     DNN_numpy[i][j] = dnn
    +     15     test_predict = dnn.predict(X_test)
    +
    +Cell In[6], line 98, in NeuralNetwork.train(self)
    +     95 self.X_data = self.X_data_full[chosen_datapoints]
    +     96 self.Y_data = self.Y_data_full[chosen_datapoints]
    +---> 98 self.feed_forward()
    +     99 self.backpropagation()
    +
    +Cell In[6], line 38, in NeuralNetwork.feed_forward(self)
    +     36 def feed_forward(self):
    +     37     # feed-forward for training
    +---> 38     self.z_h = np.matmul(self.X_data, self.hidden_weights) + self.hidden_bias
    +     39     self.a_h = sigmoid(self.z_h)
    +     41     self.z_o = np.matmul(self.a_h, self.output_weights) + self.output_bias
    +
    +KeyboardInterrupt: 
     
    @@ -1991,22 +1844,6 @@ Accuracy score on test set: 0.07777777777777778
    -
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    -  return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    -  return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    -  return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    -  return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
    -  return 1/(1 + np.exp(-x))
    -
    -
    -_images/chapter10_59_1.png -_images/chapter10_59_2.png -
    @@ -2042,333 +1879,6 @@ performance overall.

    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  1e-05
    -Lambda =  1e-05
    -Accuracy score on test set:  0.18333333333333332
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  1e-05
    -Lambda =  0.0001
    -Accuracy score on test set:  0.18611111111111112
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  1e-05
    -Lambda =  0.001
    -Accuracy score on test set:  0.13055555555555556
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  1e-05
    -Lambda =  0.01
    -Accuracy score on test set:  0.24444444444444444
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  1e-05
    -Lambda =  0.1
    -Accuracy score on test set:  0.23333333333333334
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  1e-05
    -Lambda =  1.0
    -Accuracy score on test set:  0.12777777777777777
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  1e-05
    -Lambda =  10.0
    -Accuracy score on test set:  0.1527777777777778
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  0.0001
    -Lambda =  1e-05
    -Accuracy score on test set:  0.9111111111111111
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  0.0001
    -Lambda =  0.0001
    -Accuracy score on test set:  0.8888888888888888
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  0.0001
    -Lambda =  0.001
    -Accuracy score on test set:  0.8722222222222222
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  0.0001
    -Lambda =  0.01
    -Accuracy score on test set:  0.8305555555555556
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  0.0001
    -Lambda =  0.1
    -Accuracy score on test set:  0.8888888888888888
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  0.0001
    -Lambda =  1.0
    -Accuracy score on test set:  0.8805555555555555
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  0.0001
    -Lambda =  10.0
    -Accuracy score on test set:  0.8944444444444445
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  0.001
    -Lambda =  1e-05
    -Accuracy score on test set:  0.975
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  0.001
    -Lambda =  0.0001
    -Accuracy score on test set:  0.9777777777777777
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  0.001
    -Lambda =  0.001
    -Accuracy score on test set:  0.9805555555555555
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  0.001
    -Lambda =  0.01
    -Accuracy score on test set:  0.9861111111111112
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  0.001
    -Lambda =  0.1
    -Accuracy score on test set:  0.9805555555555555
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  0.001
    -Lambda =  1.0
    -Accuracy score on test set:  0.9777777777777777
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  0.001
    -Lambda =  10.0
    -Accuracy score on test set:  0.9444444444444444
    -
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
    -  warnings.warn(
    -
    -
    -
    Learning rate  =  0.01
    -Lambda =  1e-05
    -Accuracy score on test set:  0.9861111111111112
    -
    -
    -
    Learning rate  =  0.01
    -Lambda =  0.0001
    -Accuracy score on test set:  0.9888888888888889
    -
    -
    -
    Learning rate  =  0.01
    -Lambda =  0.001
    -Accuracy score on test set:  0.9888888888888889
    -
    -
    -
    Learning rate  =  0.01
    -Lambda =  0.01
    -Accuracy score on test set:  0.9861111111111112
    -
    -
    -
    Learning rate  =  0.01
    -Lambda =  0.1
    -Accuracy score on test set:  0.9888888888888889
    -
    -
    -
    Learning rate  =  0.01
    -Lambda =  1.0
    -Accuracy score on test set:  0.9722222222222222
    -
    -Learning rate  =  0.01
    -Lambda =  10.0
    -Accuracy score on test set:  0.9527777777777777
    -
    -
    -
    Learning rate  =  0.1
    -Lambda =  1e-05
    -Accuracy score on test set:  0.9027777777777778
    -
    -
    -
    Learning rate  =  0.1
    -Lambda =  0.0001
    -Accuracy score on test set:  0.8583333333333333
    -
    -
    -
    Learning rate  =  0.1
    -Lambda =  0.001
    -Accuracy score on test set:  0.8722222222222222
    -
    -
    -
    Learning rate  =  0.1
    -Lambda =  0.01
    -Accuracy score on test set:  0.9055555555555556
    -
    -
    -
    Learning rate  =  0.1
    -Lambda =  0.1
    -Accuracy score on test set:  0.8805555555555555
    -
    -
    -
    Learning rate  =  0.1
    -Lambda =  1.0
    -Accuracy score on test set:  0.8722222222222222
    -
    -
    -
    Learning rate  =  0.1
    -Lambda =  10.0
    -Accuracy score on test set:  0.8666666666666667
    -
    -
    -
    Learning rate  =  1.0
    -Lambda =  1e-05
    -Accuracy score on test set:  0.08611111111111111
    -
    -Learning rate  =  1.0
    -Lambda =  0.0001
    -Accuracy score on test set:  0.10555555555555556
    -
    -
    -
    Learning rate  =  1.0
    -Lambda =  0.001
    -Accuracy score on test set:  0.10555555555555556
    -
    -Learning rate  =  1.0
    -Lambda =  0.01
    -Accuracy score on test set:  0.17777777777777778
    -
    -
    -
    Learning rate  =  1.0
    -Lambda =  0.1
    -Accuracy score on test set:  0.08333333333333333
    -
    -Learning rate  =  1.0
    -Lambda =  1.0
    -Accuracy score on test set:  0.08888888888888889
    -
    -
    -
    Learning rate  =  1.0
    -Lambda =  10.0
    -Accuracy score on test set:  0.09444444444444444
    -
    -Learning rate  =  10.0
    -Lambda =  1e-05
    -Accuracy score on test set:  0.17222222222222222
    -
    -
    -
    Learning rate  =  10.0
    -Lambda =  0.0001
    -Accuracy score on test set:  0.11666666666666667
    -
    -Learning rate  =  10.0
    -Lambda =  0.001
    -Accuracy score on test set:  0.10555555555555556
    -
    -
    -
    Learning rate  =  10.0
    -Lambda =  0.01
    -Accuracy score on test set:  0.1388888888888889
    -
    -Learning rate  =  10.0
    -Lambda =  0.1
    -Accuracy score on test set:  0.11388888888888889
    -
    -
    -
    Learning rate  =  10.0
    -Lambda =  1.0
    -Accuracy score on test set:  0.10555555555555556
    -
    -Learning rate  =  10.0
    -Lambda =  10.0
    -Accuracy score on test set:  0.09444444444444444
    -
    -
    -
    @@ -2412,10 +1922,6 @@ Accuracy score on test set: 0.09444444444444444
    -
    -_images/chapter10_63_0.png -_images/chapter10_63_1.png -
    @@ -2454,14 +1960,6 @@ and/or if you use anaconda, just write (or install from the gra
    -
    -
      Cell In[12], line 1
    -    conda create -n tf tensorflow
    -          ^
    -SyntaxError: invalid syntax
    -
    -
    -

    To install the current release of GPU TensorFlow

    diff --git a/doc/LectureNotes/_build/html/chapter11.html b/doc/LectureNotes/_build/html/chapter11.html index bb831fd2d..832b3261a 100644 --- a/doc/LectureNotes/_build/html/chapter11.html +++ b/doc/LectureNotes/_build/html/chapter11.html @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +
  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • @@ -2662,6 +2672,43 @@ Using TensorFlow results in a much better execution time. Try it!

    19 x = tuple(args[i] for i in argnum) ---> 20 return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs) +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:60, in jacobian(fun, x) + 50 @unary_to_nary + 51 def jacobian(fun, x): + 52 """ + 53 Returns a function which computes the Jacobian of `fun` with respect to + 54 positional argument number `argnum`, which must be a scalar or array. Unlike + (...) + 58 (out1, out2, ...) then the Jacobian has shape (out1, out2, ..., in1, in2, ...). + 59 """ +---> 60 vjp, ans = _make_vjp(fun, x) + 61 ans_vspace = vspace(ans) + 62 jacobian_shape = ans_vspace.shape + vspace(x).shape + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10, in make_vjp(fun, x) + 8 def make_vjp(fun, x): + 9 start_node = VJPNode.new_root() +---> 10 end_value, end_node = trace(start_node, fun, x) + 11 if end_node is None: + 12 def vjp(g): return vspace(x).zeros() + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10, in trace(start_node, fun, x) + 8 with trace_stack.new_trace() as t: + 9 start_box = new_box(x, t, start_node) +---> 10 end_box = fun(start_box) + 11 if isbox(end_box) and end_box._trace == start_box._trace: + 12 return end_box._value, end_box._node + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f(x) + 13 else: + 14 subargs = subvals(args, zip(argnum, x)) +---> 15 return fun(*subargs, **kwargs) + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f(*args, **kwargs) + 18 else: + 19 x = tuple(args[i] for i in argnum) +---> 20 return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs) + File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:64, in jacobian(fun, x) 62 jacobian_shape = ans_vspace.shape + vspace(x).shape 63 grads = map(vjp, ans_vspace.standard_basis()) @@ -2695,43 +2742,58 @@ Using TensorFlow results in a much better execution time. Try it!

    22 for parent, ingrad in zip(node.parents, ingrads): 23 outgrads[parent] = add_outgrads(outgrads.get(parent), ingrad) -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:78, in defvjp.<locals>.vjp_argnums.<locals>.<lambda>(g) - 76 vjp_0 = vjp_0_fun(ans, *args, **kwargs) - 77 vjp_1 = vjp_1_fun(ans, *args, **kwargs) ----> 78 return lambda g: (vjp_0(g), vjp_1(g)) - 79 else: - 80 vjps = [vjps_dict[argnum](ans, *args, **kwargs) for argnum in argnums] +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:67, in defvjp.<locals>.vjp_argnums.<locals>.<lambda>(g) + 64 raise NotImplementedError( + 65 "VJP of {} wrt argnum 0 not defined".format(fun.__name__)) + 66 vjp = vjpfun(ans, *args, **kwargs) +---> 67 return lambda g: (vjp(g),) + 68 elif L == 2: + 69 argnum_0, argnum_1 = argnums -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:660, in unbroadcast_f.<locals>.<lambda>(g) - 658 def unbroadcast_f(target, f): - 659 target_meta = anp.metadata(target) ---> 660 return lambda g: unbroadcast(f(g), target_meta) +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:423, in matmul_vjp_1.<locals>.<lambda>(g) + 421 A_ndim = anp.ndim(A) + 422 B_meta = anp.metadata(B) +--> 423 return lambda g: matmul_adjoint_1(A, g, A_ndim, B_meta) -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:34, in <lambda>(g) - 30 # ----- Binary ufuncs ----- - 32 defvjp(anp.add, lambda ans, x, y : unbroadcast_f(x, lambda g: g), - 33 lambda ans, x, y : unbroadcast_f(y, lambda g: g)) ----> 34 defvjp(anp.multiply, lambda ans, x, y : unbroadcast_f(x, lambda g: y * g), - 35 lambda ans, x, y : unbroadcast_f(y, lambda g: x * g)) - 36 defvjp(anp.subtract, lambda ans, x, y : unbroadcast_f(x, lambda g: g), - 37 lambda ans, x, y : unbroadcast_f(y, lambda g: -g)) - 38 defvjp(anp.divide, lambda ans, x, y : unbroadcast_f(x, lambda g: g / y), - 39 lambda ans, x, y : unbroadcast_f(y, lambda g: - g * x / y**2)) +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:413, in matmul_adjoint_1(A, G, A_ndim, B_meta) + 411 if B_is_vec: + 412 result = anp.squeeze(result, anp.ndim(G) - 1) +--> 413 return unbroadcast(result, B_meta) -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_boxes.py:27, in ArrayBox.__mul__(self, other) ----> 27 def __mul__(self, other): return anp.multiply(self, other) +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:653, in unbroadcast(x, target_meta, broadcast_idx) + 651 for axis, size in enumerate(target_shape): + 652 if size == 1: +--> 653 x = anp.sum(x, axis=axis, keepdims=True) + 654 if anp.iscomplexobj(x) and not target_iscomplex: + 655 x = anp.real(x) -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:44, in primitive.<locals>.f_wrapped(*args, **kwargs) - 42 parents = tuple(box._node for _ , box in boxed_args) - 43 argnums = tuple(argnum for argnum, _ in boxed_args) ----> 44 ans = f_wrapped(*argvals, **kwargs) - 45 node = node_constructor(ans, f_wrapped, argvals, kwargs, argnums, parents) - 46 return new_box(ans, trace, node) - -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:48, in primitive.<locals>.f_wrapped(*args, **kwargs) +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:45, in primitive.<locals>.f_wrapped(*args, **kwargs) + 43 argnums = tuple(argnum for argnum, _ in boxed_args) + 44 ans = f_wrapped(*argvals, **kwargs) +---> 45 node = node_constructor(ans, f_wrapped, argvals, kwargs, argnums, parents) 46 return new_box(ans, trace, node) 47 else: ----> 48 return f_raw(*args, **kwargs) + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:36, in VJPNode.__init__(self, value, fun, args, kwargs, parent_argnums, parents) + 33 fun_name = getattr(fun, '__name__', fun) + 34 raise NotImplementedError("VJP of {} wrt argnums {} not defined" + 35 .format(fun_name, parent_argnums)) +---> 36 self.vjp = vjpmaker(parent_argnums, value, args, kwargs) + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:66, in defvjp.<locals>.vjp_argnums(argnums, ans, args, kwargs) + 63 except KeyError: + 64 raise NotImplementedError( + 65 "VJP of {} wrt argnum 0 not defined".format(fun.__name__)) +---> 66 vjp = vjpfun(ans, *args, **kwargs) + 67 return lambda g: (vjp(g),) + 68 elif L == 2: + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:297, in grad_np_sum(ans, x, axis, keepdims, dtype) + 294 return lambda g: anp.sum(g, axis=broadcast_axes, keepdims=True) + 295 defvjp(anp.broadcast_to, grad_broadcast_to) +--> 297 def grad_np_sum(ans, x, axis=None, keepdims=False, dtype=None): + 298 shape, dtype = anp.shape(x), anp.result_type(x) + 299 return lambda g: repeat_to_match_shape(g, shape, dtype, axis, keepdims)[0] KeyboardInterrupt:
    diff --git a/doc/LectureNotes/_build/html/chapter12.html b/doc/LectureNotes/_build/html/chapter12.html index 29a64c88f..ef4815806 100644 --- a/doc/LectureNotes/_build/html/chapter12.html +++ b/doc/LectureNotes/_build/html/chapter12.html @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +
  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • @@ -1235,12 +1245,12 @@ labels = (n_inputs) = (1797,) 2 from tensorflow.keras.layers import Input 3 from tensorflow.keras.models import Sequential #This allows appending layers to existing models -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/__init__.py:443 - 441 _plugin_dir = _os.path.join(_s, 'tensorflow-plugins') - 442 if _os.path.exists(_plugin_dir): ---> 443 _ll.load_library(_plugin_dir) - 444 # Load Pluggable Device Library - 445 _ll.load_pluggable_device_library(_plugin_dir) +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/__init__.py:440 + 438 _plugin_dir = _os.path.join(_s, 'tensorflow-plugins') + 439 if _os.path.exists(_plugin_dir): +--> 440 _ll.load_library(_plugin_dir) + 441 # Load Pluggable Device Library + 442 _ll.load_pluggable_device_library(_plugin_dir) File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/framework/load_library.py:151, in load_library(library_location) 148 kernel_libraries = [library_location] diff --git a/doc/LectureNotes/_build/html/chapter13.html b/doc/LectureNotes/_build/html/chapter13.html index 3828cc5ae..c80a1f73e 100644 --- a/doc/LectureNotes/_build/html/chapter13.html +++ b/doc/LectureNotes/_build/html/chapter13.html @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +

  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • @@ -647,12 +657,12 @@ systems such as automatic translation and speech-to-text.

    8 from tensorflow.keras import datasets, layers, models 9 from tensorflow.keras.layers import Input -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/__init__.py:443 - 441 _plugin_dir = _os.path.join(_s, 'tensorflow-plugins') - 442 if _os.path.exists(_plugin_dir): ---> 443 _ll.load_library(_plugin_dir) - 444 # Load Pluggable Device Library - 445 _ll.load_pluggable_device_library(_plugin_dir) +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/__init__.py:440 + 438 _plugin_dir = _os.path.join(_s, 'tensorflow-plugins') + 439 if _os.path.exists(_plugin_dir): +--> 440 _ll.load_library(_plugin_dir) + 441 # Load Pluggable Device Library + 442 _ll.load_pluggable_device_library(_plugin_dir) File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/framework/load_library.py:151, in load_library(library_location) 148 kernel_libraries = [library_location] diff --git a/doc/LectureNotes/_build/html/chapter2.html b/doc/LectureNotes/_build/html/chapter2.html index cbd7b7d74..dbaa20168 100644 --- a/doc/LectureNotes/_build/html/chapter2.html +++ b/doc/LectureNotes/_build/html/chapter2.html @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +
  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • @@ -1305,10 +1315,10 @@ covariance matrix through the np.linalg.eig() function.

    -
    -0.009134699065945493
    -4.0244965108017645
    -[[0.85613835 2.50655379]
    - [2.50655379 8.3404509 ]]
    +
    0.04718566894028431
    +4.11080997912276
    +[[ 1.10517643  3.48455788]
    + [ 3.48455788 12.00216162]]
     
    @@ -1345,10 +1355,10 @@ a more brute force way. Here we scale the mean values for each column of the des
    -
    0.07971187802560528
    -1.800782161095708
    -[[1.         0.59411814]
    - [0.59411814 1.        ]]
    +
    0.07836997022107646
    +1.1378267322316808
    +[[1.         0.63980097]
    + [0.63980097 1.        ]]
     
    @@ -1378,30 +1388,30 @@ this matrix we easily see that it is a positive definite matrix.

    -
    [[ 0.81395716  1.89155934]
    - [-1.34726166 -4.13453411]
    - [-0.46229544 -2.34061974]
    - [ 0.24429334  1.4051634 ]
    - [ 0.41971814  1.6405671 ]
    - [ 2.02456235  5.03973227]
    - [-1.97311824 -4.72521196]
    - [ 0.10738656  0.24578123]
    - [-0.52702419 -2.34023682]
    - [ 0.69978197  3.31779928]]
    +
    [[ 1.34931214  3.06139439]
    + [-0.44476964 -2.60794187]
    + [ 0.02225493  0.16388664]
    + [-1.91193672 -3.82324216]
    + [-0.2044881  -1.56027537]
    + [-1.15572395 -3.25982474]
    + [ 0.94217756  1.49888671]
    + [ 0.28472162  2.92474572]
    + [ 2.38943     7.14118216]
    + [-1.27097785 -3.5388115 ]]
               0         1
    -0  0.813957  1.891559
    -1 -1.347262 -4.134534
    -2 -0.462295 -2.340620
    -3  0.244293  1.405163
    -4  0.419718  1.640567
    -5  2.024562  5.039732
    -6 -1.973118 -4.725212
    -7  0.107387  0.245781
    -8 -0.527024 -2.340237
    -9  0.699782  3.317799
    +0  1.349312  3.061394
    +1 -0.444770 -2.607942
    +2  0.022255  0.163887
    +3 -1.911937 -3.823242
    +4 -0.204488 -1.560275
    +5 -1.155724 -3.259825
    +6  0.942178  1.498887
    +7  0.284722  2.924746
    +8  2.389430  7.141182
    +9 -1.270978 -3.538811
               0         1
    -0  1.000000  0.969413
    -1  0.969413  1.000000
    +0  1.000000  0.950873
    +1  0.950873  1.000000
     
    @@ -1458,37 +1468,37 @@ this matrix we easily see that it is a positive definite matrix.

         0         1         2         3         4         5         6         7   \
     0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   
    -1   0.0  0.092746  0.090302  0.090561  0.088058  0.085463  0.081530  0.078898   
    -2   0.0  0.090302  0.088694  0.089052  0.086797  0.084423  0.080286  0.077782   
    -3   0.0  0.090561  0.089052  0.095106  0.092533  0.089858  0.089404  0.086489   
    -4   0.0  0.088058  0.086797  0.092533  0.090114  0.087585  0.086913  0.084127   
    -5   0.0  0.085463  0.084423  0.089858  0.087585  0.085197  0.084340  0.081681   
    -6   0.0  0.081530  0.080286  0.089404  0.086913  0.084340  0.086471  0.083596   
    -7   0.0  0.078898  0.077782  0.086489  0.084127  0.081681  0.083596  0.080849   
    -8   0.0  0.076334  0.075334  0.083645  0.081405  0.079080  0.080793  0.078170   
    -9   0.0  0.073841  0.072946  0.080875  0.078753  0.076543  0.078067  0.075562   
    -10  0.0  0.072973  0.071789  0.082361  0.079982  0.077541  0.081296  0.078544   
    -11  0.0  0.070485  0.069391  0.079518  0.077254  0.074928  0.078454  0.075823   
    -12  0.0  0.068088  0.067077  0.076777  0.074622  0.072404  0.075712  0.073198   
    -13  0.0  0.065778  0.064845  0.074134  0.072083  0.069970  0.073071  0.070667   
    -14  0.0  0.063554  0.062693  0.071588  0.069637  0.067623  0.070527  0.068229   
    +1   0.0  0.074334  0.080585  0.077061  0.078751  0.080220  0.070657  0.071406   
    +2   0.0  0.080585  0.088425  0.082009  0.084289  0.086338  0.074009  0.075052   
    +3   0.0  0.077061  0.082009  0.085147  0.086339  0.087297  0.081324  0.081796   
    +4   0.0  0.078751  0.084289  0.086339  0.087789  0.089007  0.081926  0.082537   
    +5   0.0  0.080220  0.086338  0.087297  0.089007  0.090492  0.082307  0.083061   
    +6   0.0  0.070657  0.074009  0.081324  0.081926  0.082307  0.079874  0.080032   
    +7   0.0  0.071406  0.075052  0.081796  0.082537  0.083061  0.080032  0.080271   
    +8   0.0  0.072148  0.076101  0.082240  0.083128  0.083801  0.080150  0.080474   
    +9   0.0  0.072902  0.077180  0.082670  0.083714  0.084548  0.080237  0.080651   
    +10  0.0  0.063646  0.065859  0.075320  0.075498  0.075472  0.075478  0.075409   
    +11  0.0  0.064071  0.066452  0.075576  0.075838  0.075897  0.075542  0.075525   
    +12  0.0  0.064514  0.067074  0.075838  0.076189  0.076340  0.075602  0.075639   
    +13  0.0  0.064980  0.067731  0.076108  0.076555  0.076803  0.075658  0.075753   
    +14  0.0  0.065472  0.068429  0.076389  0.076938  0.077292  0.075711  0.075868   
     
               8         9         10        11        12        13        14  
     0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
    -1   0.076334  0.073841  0.072973  0.070485  0.068088  0.065778  0.063554  
    -2   0.075334  0.072946  0.071789  0.069391  0.067077  0.064845  0.062693  
    -3   0.083645  0.080875  0.082361  0.079518  0.076777  0.074134  0.071588  
    -4   0.081405  0.078753  0.079982  0.077254  0.074622  0.072083  0.069637  
    -5   0.079080  0.076543  0.077541  0.074928  0.072404  0.069970  0.067623  
    -6   0.080793  0.078067  0.081296  0.078454  0.075712  0.073071  0.070527  
    -7   0.078170  0.075562  0.078544  0.075823  0.073198  0.070667  0.068229  
    -8   0.075609  0.073115  0.075864  0.073260  0.070747  0.068324  0.065989  
    -9   0.073115  0.070730  0.073260  0.070769  0.068364  0.066044  0.063809  
    -10  0.075864  0.073260  0.077608  0.074866  0.072223  0.069676  0.067224  
    -11  0.073260  0.070769  0.074866  0.072241  0.069711  0.067273  0.064924  
    -12  0.070747  0.068364  0.072223  0.069711  0.067288  0.064954  0.062705  
    -13  0.068324  0.066044  0.069676  0.067273  0.064954  0.062719  0.060565  
    -14  0.065989  0.063809  0.067224  0.064924  0.062705  0.060565  0.058503  
    +1   0.072148  0.072902  0.063646  0.064071  0.064514  0.064980  0.065472  
    +2   0.076101  0.077180  0.065859  0.066452  0.067074  0.067731  0.068429  
    +3   0.082240  0.082670  0.075320  0.075576  0.075838  0.076108  0.076389  
    +4   0.083128  0.083714  0.075498  0.075838  0.076189  0.076555  0.076938  
    +5   0.083801  0.084548  0.075472  0.075897  0.076340  0.076803  0.077292  
    +6   0.080150  0.080237  0.075478  0.075542  0.075602  0.075658  0.075711  
    +7   0.080474  0.080651  0.075409  0.075525  0.075639  0.075753  0.075868  
    +8   0.080766  0.081038  0.075293  0.075463  0.075634  0.075809  0.075988  
    +9   0.081038  0.081411  0.075136  0.075363  0.075595  0.075834  0.076082  
    +10  0.075293  0.075136  0.072406  0.072329  0.072240  0.072140  0.072028  
    +11  0.075463  0.075363  0.072329  0.072286  0.072234  0.072173  0.072101  
    +12  0.075634  0.075595  0.072240  0.072234  0.072220  0.072199  0.072171  
    +13  0.075809  0.075834  0.072140  0.072173  0.072199  0.072221  0.072238  
    +14  0.075988  0.076082  0.072028  0.072101  0.072171  0.072238  0.072303  
     
    @@ -1969,11 +1979,13 @@ We select values of the hyperparameter
    [2. 2.]
    -Training MSE for OLS
    +
    +
    +
    Training MSE for OLS
     3.0
     
    -_images/chapter2_252_1.png +_images/chapter2_252_2.png

    We see here that we reach a plateau for the Ridge results. Writing out the coefficients \(\boldsymbol{\beta}\), we observe that they are getting smaller and smaller and our error stabilizes since the predicted values of \(\tilde{\boldsymbol{y}}\) approach zero.

    diff --git a/doc/LectureNotes/_build/html/chapter3.html b/doc/LectureNotes/_build/html/chapter3.html index 67d3871ce..1bb44cff4 100644 --- a/doc/LectureNotes/_build/html/chapter3.html +++ b/doc/LectureNotes/_build/html/chapter3.html @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +
  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • @@ -859,10 +869,10 @@ number \(i\) is left out. Usin

    -
    Runtime: 0.146141 sec
    +
    Runtime: 0.154751 sec
     Jackknife Statistics :
     original           bias      std. error
    - 100.139        100.129        0.148776
    + 99.9896        99.9796        0.149524
     
    @@ -1081,7 +1091,7 @@ theorem.

    Bootstrap Statistics :
     original           bias      std. error
    -  99.989  15.1792        99.9878        0.152149
    + 100.307  14.9693        100.309        0.149416
     
    @@ -1288,7 +1298,9 @@ Error: 0.08426840630693411 Bias^2: 0.0796891867672603 Var: 0.004579219539673834 0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413 -Polynomial degree: 2 +
    +
    +
    Polynomial degree: 2
     Error: 0.10398646080125035
     Bias^2: 0.1007711427354898
     Var: 0.0032153180657605116
    @@ -1315,7 +1327,9 @@ Error: 0.037813671417389005
     Bias^2: 0.033657685071527665
     Var: 0.00415598634586135
     0.037813671417389005 >= 0.033657685071527665 + 0.00415598634586135 = 0.03781367141738902
    -Polynomial degree: 7
    +
    +
    +
    Polynomial degree: 7
     Error: 0.02760977349102253
     Bias^2: 0.022999498260366312
     Var: 0.004610275230656212
    @@ -1342,21 +1356,21 @@ Error: 0.07160048164233104
     Bias^2: 0.014436800088904942
     Var: 0.05716368155342608
     0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102
    -Polynomial degree: 12
    +
    +
    +
    Polynomial degree: 12
     Error: 0.11547777218872497
     Bias^2: 0.01628578269596628
     Var: 0.09919198949275869
     0.11547777218872497 >= 0.01628578269596628 + 0.09919198949275869 = 0.11547777218872497
    -
    -
    -
    Polynomial degree: 13
    +Polynomial degree: 13
     Error: 0.22842468702219465
     Bias^2: 0.01975416527185249
     Var: 0.20867052175034223
     0.22842468702219465 >= 0.01975416527185249 + 0.20867052175034223 = 0.2284246870221947
     
    -_images/chapter3_66_4.png +_images/chapter3_66_6.png

    The bias-variance tradeoff summarizes the fundamental tension in @@ -1647,12 +1661,12 @@ Mean squared error on test data: 877.21517262 Degree of polynomial: 23 Mean squared error on training data: 0.00085892 Mean squared error on test data: 5567.04664255 -Degree of polynomial: 24 -Mean squared error on training data: 0.00084707 -Mean squared error on test data: 1325.26124692 -

    Degree of polynomial:  25
    +
    Degree of polynomial:  24
    +Mean squared error on training data: 0.00084707
    +Mean squared error on test data: 1325.26124692
    +Degree of polynomial:  25
     Mean squared error on training data: 0.00079125
     Mean squared error on test data: 129012.83870189
     Degree of polynomial:  26
    @@ -1661,19 +1675,19 @@ Mean squared error on test data: 18388.59354079
     Degree of polynomial:  27
     Mean squared error on training data: 0.00069123
     Mean squared error on test data: 2351.97979891
    -Degree of polynomial:  28
    -Mean squared error on training data: 0.00062592
    -Mean squared error on test data: 3983.63037846
     
    -
    Degree of polynomial:  29
    +
    Degree of polynomial:  28
    +Mean squared error on training data: 0.00062592
    +Mean squared error on test data: 3983.63037846
    +Degree of polynomial:  29
     Mean squared error on training data: 0.00060704
     Mean squared error on test data: 3262.26814548
     
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
       plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
       plt.plot(polynomial, np.log10(testerror), label='Test Error')
     
    @@ -1907,7 +1921,7 @@ cross-validation (LOOCV).

    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
       plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
     
    @@ -2796,7 +2810,7 @@ linear system as an equation would reduce this down to
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
       cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
     
    @@ -2940,7 +2954,7 @@ with the form utilized in linear regression, viz.

    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
       cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
     
    @@ -2980,7 +2994,7 @@ cost function is given by

    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
       cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
     
    @@ -3015,7 +3029,7 @@ cost function is given by

    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
       cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
     
    @@ -3068,43 +3082,43 @@ constant as opposed to ridge and OLS. We get a sparse solution with
    -
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    +
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    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.924e+00, tolerance: 1.797e+00
       model = cd_fast.enet_coordinate_descent(
     
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    diff --git a/doc/LectureNotes/_build/html/chapter4.html b/doc/LectureNotes/_build/html/chapter4.html
    index 3e81bb26c..1abcd6de0 100644
    --- a/doc/LectureNotes/_build/html/chapter4.html
    +++ b/doc/LectureNotes/_build/html/chapter4.html
    @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output"
        Week 41 Neural networks and constructing a neural network code
       
      
    + 
  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • diff --git a/doc/LectureNotes/_build/html/chapter5.html b/doc/LectureNotes/_build/html/chapter5.html index 394c0c69a..1fac69acc 100644 --- a/doc/LectureNotes/_build/html/chapter5.html +++ b/doc/LectureNotes/_build/html/chapter5.html @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +

  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • diff --git a/doc/LectureNotes/_build/html/chapter6.html b/doc/LectureNotes/_build/html/chapter6.html index 9c904f401..5e85b0cbf 100644 --- a/doc/LectureNotes/_build/html/chapter6.html +++ b/doc/LectureNotes/_build/html/chapter6.html @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +

  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • @@ -787,9 +797,9 @@ predicting the target features of query instances is as follows:

    2nd degree coefficients:
    -zero power:  -5.030164788184997
    -first power:  0.001268544905013411
    -second power:  -5.234332597247826e-05
    +zero power:  -0.22158725223474995
    +first power:  0.24121476598003452
    +second power:  -0.0009532583857747255
     
    _images/chapter6_1_1.png diff --git a/doc/LectureNotes/_build/html/chapter7.html b/doc/LectureNotes/_build/html/chapter7.html index 1e6480c23..2b7035e16 100644 --- a/doc/LectureNotes/_build/html/chapter7.html +++ b/doc/LectureNotes/_build/html/chapter7.html @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +
  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • diff --git a/doc/LectureNotes/_build/html/chapter8.html b/doc/LectureNotes/_build/html/chapter8.html index d85525649..a38643236 100644 --- a/doc/LectureNotes/_build/html/chapter8.html +++ b/doc/LectureNotes/_build/html/chapter8.html @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +

  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • @@ -741,10 +751,10 @@ covariance matrix through the np.linalg.eig() function.

    -
    -0.012423940191689783
    -4.101008878523571
    -[[0.89527291 2.65532045]
    - [2.65532045 8.81987609]]
    +
    0.1001408041761458
    +4.2807716628772665
    +[[ 1.15654145  3.54867722]
    + [ 3.54867722 11.70485195]]
     
    @@ -784,10 +794,10 @@ a more brute force way. Here we scale the mean values for each column of the des
    -
    0.08705631913312815
    -1.7026908764394864
    -[[1.         0.65870313]
    - [0.65870313 1.        ]]
    +
    0.09543871010617433
    +1.6888043337746685
    +[[1.        0.7167077]
    + [0.7167077 1.       ]]
     
    @@ -816,30 +826,30 @@ this matrix we easily see that it is a positive definite matrix.

    -
    [[-1.7755649  -4.56778296]
    - [-0.81015037 -2.80072356]
    - [ 0.73628249  1.95206335]
    - [ 0.97366347  1.61130099]
    - [ 0.7271324   1.97965627]
    - [ 0.36881837  0.56037913]
    - [-1.33163086 -2.59391196]
    - [-0.68953877 -1.58298728]
    - [ 0.19982428 -1.08010965]
    - [ 1.60116388  6.52211567]]
    +
    [[-0.20575734  0.01384583]
    + [-0.89876098 -3.04065686]
    + [-0.76289128 -3.17080691]
    + [-0.0334136   0.16124569]
    + [ 2.73970542  9.28885103]
    + [ 0.75413023  2.98474769]
    + [-1.87894459 -5.48121459]
    + [-1.26814205 -2.4848097 ]
    + [ 0.18114057 -0.9889962 ]
    + [ 1.37293361  2.71779401]]
               0         1
    -0 -1.775565 -4.567783
    -1 -0.810150 -2.800724
    -2  0.736282  1.952063
    -3  0.973663  1.611301
    -4  0.727132  1.979656
    -5  0.368818  0.560379
    -6 -1.331631 -2.593912
    -7 -0.689539 -1.582987
    -8  0.199824 -1.080110
    -9  1.601164  6.522116
    -         0        1
    -0  1.00000  0.94335
    -1  0.94335  1.00000
    +0 -0.205757  0.013846
    +1 -0.898761 -3.040657
    +2 -0.762891 -3.170807
    +3 -0.033414  0.161246
    +4  2.739705  9.288851
    +5  0.754130  2.984748
    +6 -1.878945 -5.481215
    +7 -1.268142 -2.484810
    +8  0.181141 -0.988996
    +9  1.372934  2.717794
    +          0         1
    +0  1.000000  0.970965
    +1  0.970965  1.000000
     
    @@ -896,37 +906,37 @@ this matrix we easily see that it is a positive definite matrix.

         0         1         2         3         4         5         6         7   \
     0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   
    -1   0.0  0.088650  0.084209  0.092689  0.091653  0.090476  0.085764  0.085378   
    -2   0.0  0.084209  0.080559  0.088599  0.087876  0.087020  0.082478  0.082249   
    -3   0.0  0.092689  0.088599  0.102209  0.101292  0.100209  0.097667  0.097380   
    -4   0.0  0.091653  0.087876  0.101292  0.100523  0.099588  0.097017  0.096811   
    -5   0.0  0.090476  0.087020  0.100209  0.099588  0.098803  0.096203  0.096078   
    -6   0.0  0.085764  0.082478  0.097667  0.097017  0.096203  0.095425  0.095278   
    -7   0.0  0.085378  0.082249  0.097380  0.096811  0.096078  0.095278  0.095178   
    -8   0.0  0.084976  0.082002  0.097060  0.096570  0.095915  0.095090  0.095037   
    -9   0.0  0.084550  0.081730  0.096700  0.096287  0.095710  0.094857  0.094849   
    -10  0.0  0.077672  0.075070  0.090429  0.090002  0.089421  0.089826  0.089786   
    -11  0.0  0.077490  0.074976  0.090319  0.089940  0.089408  0.089800  0.089790   
    -12  0.0  0.077310  0.074882  0.090204  0.089872  0.089386  0.089763  0.089783   
    -13  0.0  0.077131  0.074787  0.090081  0.089795  0.089354  0.089714  0.089763   
    -14  0.0  0.076951  0.074688  0.089949  0.089708  0.089311  0.089653  0.089730   
    +1   0.0  0.090565  0.089065  0.093226  0.091674  0.090154  0.086287  0.084847   
    +2   0.0  0.089065  0.087946  0.092421  0.091068  0.089735  0.085988  0.084674   
    +3   0.0  0.093226  0.092421  0.102147  0.100816  0.099494  0.098144  0.096731   
    +4   0.0  0.091674  0.091068  0.100816  0.099622  0.098431  0.097115  0.095803   
    +5   0.0  0.090154  0.089735  0.099494  0.098431  0.097365  0.096077  0.094862   
    +6   0.0  0.086287  0.085988  0.098144  0.097115  0.096077  0.096630  0.095395   
    +7   0.0  0.084847  0.084674  0.096731  0.095803  0.094862  0.095395  0.094243   
    +8   0.0  0.083459  0.083405  0.095358  0.094527  0.093680  0.094189  0.093115   
    +9   0.0  0.082121  0.082180  0.094027  0.093288  0.092530  0.093011  0.092013   
    +10  0.0  0.078708  0.078711  0.091732  0.090935  0.090118  0.091871  0.090804   
    +11  0.0  0.077431  0.077523  0.090387  0.089668  0.088926  0.090626  0.089626   
    +12  0.0  0.076203  0.076378  0.089086  0.088441  0.087772  0.089417  0.088481   
    +13  0.0  0.075021  0.075274  0.087828  0.087255  0.086655  0.088242  0.087369   
    +14  0.0  0.073883  0.074212  0.086611  0.086107  0.085573  0.087102  0.086289   
     
               8         9         10        11        12        13        14  
     0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
    -1   0.084976  0.084550  0.077672  0.077490  0.077310  0.077131  0.076951  
    -2   0.082002  0.081730  0.075070  0.074976  0.074882  0.074787  0.074688  
    -3   0.097060  0.096700  0.090429  0.090319  0.090204  0.090081  0.089949  
    -4   0.096570  0.096287  0.090002  0.089940  0.089872  0.089795  0.089708  
    -5   0.095915  0.095710  0.089421  0.089408  0.089386  0.089354  0.089311  
    -6   0.095090  0.094857  0.089826  0.089800  0.089763  0.089714  0.089653  
    -7   0.095037  0.094849  0.089786  0.089790  0.089783  0.089763  0.089730  
    -8   0.094941  0.094797  0.089704  0.089738  0.089760  0.089769  0.089763  
    -9   0.094797  0.094697  0.089576  0.089639  0.089690  0.089726  0.089748  
    -10  0.089704  0.089576  0.085655  0.085690  0.085712  0.085722  0.085716  
    -11  0.089738  0.089639  0.085690  0.085747  0.085790  0.085819  0.085833  
    -12  0.089760  0.089690  0.085712  0.085790  0.085853  0.085902  0.085935  
    -13  0.089769  0.089726  0.085722  0.085819  0.085902  0.085970  0.086021  
    -14  0.089763  0.089748  0.085716  0.085833  0.085935  0.086021  0.086092  
    +1   0.083459  0.082121  0.078708  0.077431  0.076203  0.075021  0.073883  
    +2   0.083405  0.082180  0.078711  0.077523  0.076378  0.075274  0.074212  
    +3   0.095358  0.094027  0.091732  0.090387  0.089086  0.087828  0.086611  
    +4   0.094527  0.093288  0.090935  0.089668  0.088441  0.087255  0.086107  
    +5   0.093680  0.092530  0.090118  0.088926  0.087772  0.086655  0.085573  
    +6   0.094189  0.093011  0.091871  0.090626  0.089417  0.088242  0.087102  
    +7   0.093115  0.092013  0.090804  0.089626  0.088481  0.087369  0.086289  
    +8   0.092064  0.091034  0.089755  0.088642  0.087560  0.086508  0.085486  
    +9   0.091034  0.090075  0.088726  0.087675  0.086653  0.085659  0.084694  
    +10  0.089755  0.088726  0.088455  0.087327  0.086227  0.085155  0.084112  
    +11  0.088642  0.087675  0.087327  0.086256  0.085212  0.084195  0.083203  
    +12  0.087560  0.086653  0.086227  0.085212  0.084222  0.083257  0.082316  
    +13  0.086508  0.085659  0.085155  0.084195  0.083257  0.082342  0.081450  
    +14  0.085486  0.084694  0.084112  0.083203  0.082316  0.081450  0.080605  
     
    @@ -1115,10 +1125,12 @@ We can write our own code or simply use either the functionaly of numpy<
              0         1
    -0  3.914672  1.954823
    -1  1.954823  1.963858
    -[[3.91467223 1.95482298]
    - [1.95482298 1.96385798]]
    +0  4.114499  2.071143
    +1  2.071143  2.061388
    +
    +
    +
    [[4.11449851 2.07114326]
    + [2.07114326 2.0613875 ]]
     
    @@ -1145,8 +1157,8 @@ Our own code here is not very elegant and asks for obvious improvements. It is t
    Centered covariance using own code
    -[[3.91467223 1.95482298]
    - [1.95482298 1.96385798]]
    +[[4.11449851 2.07114326]
    + [2.07114326 2.0613875 ]]
     
    _images/chapter8_65_1.png @@ -1206,16 +1218,16 @@ questions.

    Eigenvalues of Covariance matrix
    -5.123928000581825
    -0.7546022135629743
    +5.399533503407795
    +0.776352510835556
     First eigenvector
    -[0.85043503 0.5260801 ]
    +[0.84973247 0.52721412]
     Second eigenvector
    -[-0.5260801   0.85043503]
    +[-0.52721412  0.84973247]
     
    Eigenvector of largest eigenvalue
    -[0.85043503 0.5260801 ]
    +[-0.84973247 -0.52721412]
     
    diff --git a/doc/LectureNotes/_build/html/chapter9.html b/doc/LectureNotes/_build/html/chapter9.html index 82abdae5b..b577b1a11 100644 --- a/doc/LectureNotes/_build/html/chapter9.html +++ b/doc/LectureNotes/_build/html/chapter9.html @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +
  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • diff --git a/doc/LectureNotes/_build/html/chapteroptimization.html b/doc/LectureNotes/_build/html/chapteroptimization.html index 637c870f3..838dfe57c 100644 --- a/doc/LectureNotes/_build/html/chapteroptimization.html +++ b/doc/LectureNotes/_build/html/chapteroptimization.html @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +

  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • diff --git a/doc/LectureNotes/_build/html/clustering.html b/doc/LectureNotes/_build/html/clustering.html index ee79e09c1..44e1749ec 100644 --- a/doc/LectureNotes/_build/html/clustering.html +++ b/doc/LectureNotes/_build/html/clustering.html @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +

  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • @@ -600,12 +610,12 @@ Gaussian distribution.

    6 from matplotlib import image 7 import matplotlib.pyplot as plt -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/__init__.py:443 - 441 _plugin_dir = _os.path.join(_s, 'tensorflow-plugins') - 442 if _os.path.exists(_plugin_dir): ---> 443 _ll.load_library(_plugin_dir) - 444 # Load Pluggable Device Library - 445 _ll.load_pluggable_device_library(_plugin_dir) +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/__init__.py:440 + 438 _plugin_dir = _os.path.join(_s, 'tensorflow-plugins') + 439 if _os.path.exists(_plugin_dir): +--> 440 _ll.load_library(_plugin_dir) + 441 # Load Pluggable Device Library + 442 _ll.load_pluggable_device_library(_plugin_dir) File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/framework/load_library.py:151, in load_library(library_location) 148 kernel_libraries = [library_location] diff --git a/doc/LectureNotes/_build/html/exercisesweek34.html b/doc/LectureNotes/_build/html/exercisesweek34.html index ea7980312..c620cebd0 100644 --- a/doc/LectureNotes/_build/html/exercisesweek34.html +++ b/doc/LectureNotes/_build/html/exercisesweek34.html @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +
  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • diff --git a/doc/LectureNotes/_build/html/exercisesweek35.html b/doc/LectureNotes/_build/html/exercisesweek35.html index 2f0422528..552519e48 100644 --- a/doc/LectureNotes/_build/html/exercisesweek35.html +++ b/doc/LectureNotes/_build/html/exercisesweek35.html @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +

  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • diff --git a/doc/LectureNotes/_build/html/exercisesweek36.html b/doc/LectureNotes/_build/html/exercisesweek36.html index b2187bd49..8f3f40509 100644 --- a/doc/LectureNotes/_build/html/exercisesweek36.html +++ b/doc/LectureNotes/_build/html/exercisesweek36.html @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +

  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • diff --git a/doc/LectureNotes/_build/html/exercisesweek37.html b/doc/LectureNotes/_build/html/exercisesweek37.html index f6a46f580..b9223bc23 100644 --- a/doc/LectureNotes/_build/html/exercisesweek37.html +++ b/doc/LectureNotes/_build/html/exercisesweek37.html @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +

  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • diff --git a/doc/LectureNotes/_build/html/exercisesweek38.html b/doc/LectureNotes/_build/html/exercisesweek38.html index 8bea48d90..80ddafe25 100644 --- a/doc/LectureNotes/_build/html/exercisesweek38.html +++ b/doc/LectureNotes/_build/html/exercisesweek38.html @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +

  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • diff --git a/doc/LectureNotes/_build/html/exercisesweek39.html b/doc/LectureNotes/_build/html/exercisesweek39.html index 74bb4cb9b..fa3740090 100644 --- a/doc/LectureNotes/_build/html/exercisesweek39.html +++ b/doc/LectureNotes/_build/html/exercisesweek39.html @@ -321,6 +321,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +

  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • diff --git a/doc/LectureNotes/_build/html/exercisesweek41.html b/doc/LectureNotes/_build/html/exercisesweek41.html index 6ae56b051..39e5531ee 100644 --- a/doc/LectureNotes/_build/html/exercisesweek41.html +++ b/doc/LectureNotes/_build/html/exercisesweek41.html @@ -804,15 +804,15 @@ regression.

    Own inversion
    -[[4.04881585]
    - [2.96745096]]
    -Eigenvalues of Hessian Matrix:[0.37175588 4.15690073]
    +[[4.1729993]
    + [3.0170097]]
    +Eigenvalues of Hessian Matrix:[0.28192769 4.68753434]
     theta from own gd
    -[[4.04881585]
    - [2.96745096]]
    +[[4.1729993]
    + [3.0170097]]
     theta from own sdg
    -[[4.04015455]
    - [2.95745651]]
    +[[4.14043884]
    + [2.99071523]]
     
    _images/exercisesweek41_5_1.png @@ -934,14 +934,14 @@ first example shows results with ordinary leats squares.

    Own inversion
    -[[3.37007195]
    - [3.48441798]]
    -Eigenvalues of Hessian Matrix:[0.32411274 4.30450049]
    +[[4.03696458]
    + [3.0324793 ]]
    +Eigenvalues of Hessian Matrix:[0.30959659 4.4150026 ]
     
    theta from own gd
    -[[3.37007195]
    - [3.48441798]]
    +[[4.03696458]
    + [3.0324793 ]]
     
    _images/exercisesweek41_16_2.png @@ -1012,73 +1012,73 @@ Eigenvalues of Hessian Matrix:[0.32411274 4.30450049]
    Own inversion
     [[4.]
      [3.]]
    -Eigenvalues of Hessian Matrix:[0.27227895 4.125757  ]
    -0 [-11.75978215] [-12.89770892]
    -1 [-0.06807123] [0.06136823]
    -2 [-0.06357887] [0.05731824]
    -3 [-0.05938299] [0.05353553]
    -4 [-0.05546402] [0.05000245]
    -5 [-0.05180367] [0.04670255]
    -6 [-0.04838489] [0.04362042]
    -7 [-0.04519174] [0.04074169]
    -8 [-0.04220931] [0.03805295]
    -9 [-0.03942371] [0.03554165]
    -10 [-0.03682195] [0.03319608]
    -11 [-0.03439189] [0.03100531]
    -12 [-0.0321222] [0.02895911]
    -13 [-0.0300023] [0.02704796]
    -14 [-0.0280223] [0.02526293]
    -15 [-0.02617297] [0.02359571]
    -16 [-0.02444569] [0.02203851]
    -17 [-0.0228324] [0.02058408]
    -18 [-0.02132557] [0.01922564]
    -19 [-0.01991819] [0.01795684]
    -20 [-0.0186037] [0.01677178]
    -21 [-0.01737595] [0.01566493]
    -22 [-0.01622922] [0.01463112]
    -23 [-0.01515818] [0.01366555]
    -24 [-0.01415781] [0.01276369]
    -25 [-0.01322347] [0.01192135]
    -26 [-0.01235079] [0.0111346]
    -27 [-0.0115357] [0.01039977]
    -28 [-0.0107744] [0.00971344]
    -29 [-0.01006335] [0.0090724]
    +Eigenvalues of Hessian Matrix:[0.23469347 4.90407685]
    +0 [-18.02987043] [-22.42501752]
    +1 [-0.32329897] [0.25206371]
    +2 [-0.30782691] [0.24000075]
    +3 [-0.2930953] [0.22851508]
    +4 [-0.27906869] [0.21757908]
    +5 [-0.26571335] [0.20716644]
    +6 [-0.25299716] [0.19725211]
    +7 [-0.24088952] [0.18781225]
    +8 [-0.22936132] [0.17882416]
    +9 [-0.21838482] [0.17026621]
    +10 [-0.20793362] [0.16211781]
    +11 [-0.19798258] [0.15435937]
    +12 [-0.18850776] [0.14697222]
    +13 [-0.17948638] [0.1399386]
    +14 [-0.17089674] [0.13324158]
    +15 [-0.16271817] [0.12686507]
    +16 [-0.15493099] [0.12079371]
    +17 [-0.14751649] [0.11501291]
    +18 [-0.14045682] [0.10950876]
    +19 [-0.13373501] [0.10426802]
    +20 [-0.12733488] [0.09927808]
    +21 [-0.12124104] [0.09452695]
    +22 [-0.11543883] [0.09000319]
    +23 [-0.10991429] [0.08569593]
    +24 [-0.10465415] [0.08159479]
    +25 [-0.09964573] [0.07768993]
    +26 [-0.094877] [0.07397193]
    +27 [-0.09033649] [0.07043187]
    +28 [-0.08601328] [0.06706123]
    +29 [-0.08189696] [0.06385189]
     theta from own gd
    -[[3.96547946]
    - [3.03112129]]
    -0 [-0.00939922] [0.00847367]
    -1 [-0.00877892] [0.00791445]
    -2 [-0.00801346] [0.00722437]
    -3 [-0.00725498] [0.00654058]
    -4 [-0.00654864] [0.00590379]
    -5 [-0.00590456] [0.00532314]
    -6 [-0.00532167] [0.00479764]
    -7 [-0.0047956] [0.00432337]
    -8 [-0.00432129] [0.00389577]
    -9 [-0.00389382] [0.00351039]
    -10 [-0.0035086] [0.00316311]
    -11 [-0.00316149] [0.00285017]
    -12 [-0.00284871] [0.0025682]
    -13 [-0.00256688] [0.00231412]
    -14 [-0.00231293] [0.00208517]
    -15 [-0.0020841] [0.00187888]
    -16 [-0.00187791] [0.00169299]
    -17 [-0.00169212] [0.0015255]
    -18 [-0.00152471] [0.00137458]
    -19 [-0.00137387] [0.00123858]
    -20 [-0.00123795] [0.00111605]
    -21 [-0.00111547] [0.00100563]
    -22 [-0.00100511] [0.00090614]
    -23 [-0.00090567] [0.00081649]
    -24 [-0.00081607] [0.00073571]
    -25 [-0.00073534] [0.00066293]
    -26 [-0.00066259] [0.00059734]
    -27 [-0.00059703] [0.00053824]
    -28 [-0.00053797] [0.00048499]
    -29 [-0.00048474] [0.00043701]
    +[[3.66774691]
    + [3.25904489]]
    +0 [-0.07797763] [0.06079614]
    +1 [-0.07424587] [0.05788664]
    +2 [-0.06957317] [0.05424351]
    +3 [-0.06484181] [0.05055465]
    +4 [-0.06031928] [0.04702861]
    +5 [-0.05607583] [0.04372016]
    +6 [-0.05211919] [0.04063532]
    +7 [-0.04843794] [0.03776519]
    +8 [-0.04501548] [0.03509683]
    +9 [-0.04183444] [0.0326167]
    +10 [-0.03887807] [0.03031173]
    +11 [-0.03613058] [0.02816961]
    +12 [-0.03357723] [0.02617887]
    +13 [-0.03120433] [0.02432881]
    +14 [-0.02899912] [0.02260949]
    +15 [-0.02694975] [0.02101168]
    +16 [-0.02504521] [0.01952679]
    +17 [-0.02327527] [0.01814683]
    +18 [-0.0216304] [0.01686439]
    +19 [-0.02010178] [0.01567258]
    +20 [-0.01868119] [0.014565]
    +21 [-0.01736099] [0.01353569]
    +22 [-0.01613409] [0.01257912]
    +23 [-0.01499389] [0.01169016]
    +24 [-0.01393427] [0.01086401]
    +25 [-0.01294954] [0.01009625]
    +26 [-0.01203439] [0.00938275]
    +27 [-0.01118392] [0.00871967]
    +28 [-0.01039355] [0.00810345]
    +29 [-0.00965904] [0.00753078]
     theta from own gd wth momentum
    -[[3.99839581]
    - [3.00144622]]
    +[[3.96175251]
    + [3.02982009]]
     
    @@ -1131,17 +1131,17 @@ theta from own gd wth momentum
    Own inversion
    -[[3.81818338]
    - [3.12840176]]
    -Eigenvalues of Hessian Matrix:[0.26024513 4.64291046]
    -0 [-16.3824865] [-20.20867716]
    -1 [9.71640303e-15] [1.05750493e-14]
    -2 [-2.05998413e-17] [-2.11570114e-16]
    -3 [6.96057795e-17] [1.90885733e-16]
    -4 [6.96057795e-17] [1.90885733e-16]
    +[[4.15451852]
    + [2.83230774]]
    +Eigenvalues of Hessian Matrix:[0.30616802 4.24299211]
    +0 [-10.57502449] [-11.57610367]
    +1 [-4.47905601e-15] [-4.07372439e-16]
    +2 [-6.9388939e-16] [-7.21432413e-16]
    +3 [-6.9388939e-16] [-7.21432413e-16]
    +4 [-6.9388939e-16] [-7.21432413e-16]
     beta from own Newton code
    -[[3.81818338]
    - [3.12840176]]
    +[[4.15451852]
    + [2.83230774]]
     
    @@ -1230,18 +1230,20 @@ beta from own Newton code
    Own inversion
    -[[3.6955259]
    - [3.2809076]]
    -Eigenvalues of Hessian Matrix:[0.31381731 4.50516278]
    -theta from own gd
    -[[3.6955259]
    - [3.2809076]]
    +[[4.04601419]
    + [3.12204312]]
    +Eigenvalues of Hessian Matrix:[0.33604208 4.45709724]
     
    -_images/exercisesweek41_22_1.png +
    theta from own gd
    +[[4.04601419]
    + [3.12204312]]
    +
    +
    +_images/exercisesweek41_22_2.png
    theta from own sdg
    -[[3.6805151 ]
    - [3.33045013]]
    +[[4.02781444]
    + [3.13976073]]
     
    @@ -1323,15 +1325,15 @@ theta from own gd
    Own inversion
    -[[3.98751068]
    - [2.92901115]]
    -Eigenvalues of Hessian Matrix:[0.28244905 4.61501312]
    +[[3.96417888]
    + [3.06634473]]
    +Eigenvalues of Hessian Matrix:[0.32962444 4.18715465]
     theta from own gd
    -[[3.98556236]
    - [2.93059014]]
    +[[3.9639885]
    + [3.0665111]]
     theta from own sdg with momentum
    -[[4.01133846]
    - [2.92452609]]
    +[[4.00842216]
    + [3.14285244]]
     
    @@ -1406,9 +1408,9 @@ theta from own sdg with momentum
    theta from own AdaGrad
    -[[1.99974365]
    - [3.0013839 ]
    - [3.99861193]]
    +[[1.99969895]
    + [3.00167058]
    + [3.99835872]]
     
    @@ -1490,9 +1492,9 @@ theta from own sdg with momentum
    theta from own RMSprop
    -[[1.99757634]
    - [2.9983289 ]
    - [3.99759503]]
    +[[1.99852187]
    + [3.03868311]
    + [3.95744254]]
     
    @@ -1578,9 +1580,9 @@ theta from own sdg with momentum
    theta from own ADAM
    -[[1.99993596]
    - [3.00035483]
    - [3.99963172]]
    +[[1.99996471]
    + [3.00026784]
    + [3.99973141]]
     
    @@ -1653,7 +1655,7 @@ It provides composable transformations of Python+NumPy programs: differentiate, return asarray(x, dtype=self.dtype)
    -
    [<matplotlib.lines.Line2D at 0x1360ef5e0>]
    +
    [<matplotlib.lines.Line2D at 0x11892e7f0>]
     
    _images/exercisesweek41_39_2.png @@ -1688,7 +1690,7 @@ It provides composable transformations of Python+NumPy programs: differentiate,
    -
    <matplotlib.collections.PathCollection at 0x135f8b1f0>
    +
    <matplotlib.collections.PathCollection at 0x118995f70>
     
    _images/exercisesweek41_41_1.png diff --git a/doc/LectureNotes/_build/html/exercisesweek42.html b/doc/LectureNotes/_build/html/exercisesweek42.html index f044e2489..f4d0fc025 100644 --- a/doc/LectureNotes/_build/html/exercisesweek42.html +++ b/doc/LectureNotes/_build/html/exercisesweek42.html @@ -445,13 +445,23 @@ const thebe_selector_output = ".output, .cell_output"
  • - - Exercise 4 + + Exercise 4 - Custom activation for each layer
  • - - Exercise 5 (Very optional and very hard :) + + Exercise 5 - Processing multiple inputs at once + +
  • +
  • + + Exercise 6 - Predicting on real data + +
  • +
  • + + Exercise 6 - Training on real data
  • @@ -500,13 +510,23 @@ const thebe_selector_output = ".output, .cell_output"
  • - - Exercise 4 + + Exercise 4 - Custom activation for each layer
  • - - Exercise 5 (Very optional and very hard :) + + Exercise 5 - Processing multiple inputs at once + +
  • +
  • + + Exercise 6 - Predicting on real data + +
  • +
  • + + Exercise 6 - Training on real data
  • @@ -523,17 +543,43 @@ doconce format html exercisesweek41.do.txt -->

    Exercises week 42

    -

    October 14-18, 2024

    +

    October 11-18, 2024

    Date: Deadline is Friday October 18 at midnight

    Overarching aims of the exercises this week

    The aim of the exercises this week is to get started with implementing a neural network. There are a lot of technical and finicky parts of implementing a neutal network, so take your time.

    -

    This week, you will implement only the feed-forward pass. Next week, you will implement backpropagation. We recommend that you do the exercises this week by editing and running this notebook file, as it includes several checks along the way that you have implemented the pieces of the feed-forward pass correctly. If you have trouble running a notebook, or importing pytorch, you can run this notebook in google colab instead: (LINK TO COLAB), though we recommend that you set up VSCode and your python environment to run code like this locally.

    +

    This week, you will implement only the feed-forward pass and updating the network parameters with simple gradient descent, the gradient will be computed using autograd using code we provide. Next week, you will implement backpropagation. We recommend that you do the exercises this week by editing and running this notebook file, as it includes some checks along the way that you have implemented the pieces of the feed-forward pass correctly, and running small parts of the code at a time will be important for understanding the methods.

    +

    If you have trouble running a notebook, you can run this notebook in google colab instead (https://colab.research.google.com/drive/1OCQm1tlTWB6hZSf9I7gGUgW9M8SbVeQu#offline=true&sandboxMode=true), an updated link will be provided on the course discord (you can also send an email to k.h.fredly@fys.uio.no if you encounter any trouble), though we recommend that you set up VSCode and your python environment to run code like this locally.

    -
    import autograd.numpy as np
    -from autograd import grad
    +
    import autograd.numpy as np  # We need to use this numpy wrapper to make automatic differentiation work later
    +from sklearn import datasets
    +import matplotlib.pyplot as plt
    +from sklearn.metrics import accuracy_score
    +
    +
    +# Defining some activation functions
    +def ReLU(z):
    +    return np.where(z > 0, z, 0)
    +
    +
    +def sigmoid(z):
    +    return 1 / (1 + np.exp(-z))
    +
    +
    +def softmax(z):
    +    """Compute softmax values for each set of scores in the rows of the matrix z.
    +    Used with batched input data."""
    +    e_z = np.exp(z - np.max(z, axis=0))
    +    return e_z / np.sum(e_z, axis=1)[:, np.newaxis]
    +
    +
    +def softmax_vec(z):
    +    """Compute softmax values for each set of scores in the vector z.
    +    Use this function when you use the activation function on one vector at a time"""
    +    e_z = np.exp(z - np.max(z))
    +    return e_z / np.sum(e_z)
     
    @@ -541,47 +587,43 @@ doconce format html exercisesweek41.do.txt -->

    Exercise 1

    -

    Complete the following parts to compute the activation of the first layer.

    +

    In this exercise you will compute the activation of the first layer. You only need to change the code in the cells right below an exercise, the rest works out of the box. Feel free to make changes and see how stuff works though!

    np.random.seed(2024)
     
    -
    -def ReLU(z):
    -    return np.where(z > 0, z, 0)
    -
    -
    -x = np.random.randn(2)  # network input
    +x = np.random.randn(2)  # network input. This is a single input with two features
     W1 = np.random.randn(4, 2)  # first layer weights
     
    -

    a) Define the bias of the first layer, b1with the correct shape

    +

    a) Given the shape of the first layer weight matrix, what is the input shape of the neural network? What is the output shape of the first layer?

    +

    b) Define the bias of the first layer, b1with the correct shape. (Run the next cell right after the previous to get the random generated values to line up with the test solution below)

    -
    b1 = np.random.randn(4)
    +
    b1 = ...
     
    -

    b) Compute the intermediary z1 for the first layer

    +

    c) Compute the intermediary z1 for the first layer

    -
    z1 = W1 @ x + b1
    +
    z1 = ...
     
    -

    c) Compute the activation a1 for the first layer using the ReLU activation function defined earlier.

    +

    d) Compute the activation a1 for the first layer using the ReLU activation function defined earlier.

    -
    a1 = ReLU(z1)
    +
    a1 = ...
     
    -

    Confirm that you got the correct activation with the test below.

    +

    Confirm that you got the correct activation with the test below. Make sure that you define b1 with the randn function right after you define W1.

    sol1 = np.array([0.60610368, 4.0076268, 0.0, 0.56469864])
    @@ -591,7 +633,41 @@ doconce format html exercisesweek41.do.txt  -->
     
    -
    True
    +
    ---------------------------------------------------------------------------
    +TypeError                                 Traceback (most recent call last)
    +Cell In[6], line 3
    +      1 sol1 = np.array([0.60610368, 4.0076268, 0.0, 0.56469864])
    +----> 3 print(np.allclose(a1, sol1))
    +
    +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:48, in primitive.<locals>.f_wrapped(*args, **kwargs)
    +     46     return new_box(ans, trace, node)
    +     47 else:
    +---> 48     return f_raw(*args, **kwargs)
    +
    +File <__array_function__ internals>:180, in allclose(*args, **kwargs)
    +
    +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/numeric.py:2251, in allclose(a, b, rtol, atol, equal_nan)
    +   2180 @array_function_dispatch(_allclose_dispatcher)
    +   2181 def allclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False):
    +   2182     """
    +   2183     Returns True if two arrays are element-wise equal within a tolerance.
    +   2184 
    +   (...)
    +   2249 
    +   2250     """
    +-> 2251     res = all(isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan))
    +   2252     return bool(res)
    +
    +File <__array_function__ internals>:180, in isclose(*args, **kwargs)
    +
    +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/numeric.py:2358, in isclose(a, b, rtol, atol, equal_nan)
    +   2355     dt = multiarray.result_type(y, 1.)
    +   2356     y = asanyarray(y, dtype=dt)
    +-> 2358 xfin = isfinite(x)
    +   2359 yfin = isfinite(y)
    +   2360 if all(xfin) and all(yfin):
    +
    +TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
     
    @@ -599,21 +675,22 @@ doconce format html exercisesweek41.do.txt -->

    Exercise 2

    -

    Compute the activation of the second layer with an output of length 8 and ReLU activation.

    -

    a) Define the weight and bias of the second layer with the right shapes.

    +

    Now we will add a layer to the network with an output of length 8 and ReLU activation.

    +

    a) What is the input of the second layer? What is its shape?

    +

    b) Define the weight and bias of the second layer with the right shapes.

    -
    W2 = np.random.randn(8, 4)
    -b2 = np.random.randn(8)
    +
    W2 = ...
    +b2 = ...
     
    -

    b) Compute intermediary z2 and activation a2 for the second layer.

    +

    c) Compute the intermediary z2 and activation a2 for the second layer.

    -
    z2 = W2 @ a1
    -a2 = ReLU(z2)
    +
    z2 = ...
    +a2 = ...
     
    @@ -621,12 +698,9 @@ doconce format html exercisesweek41.do.txt -->

    Confirm that you got the correct activation shape with the test below.

    -
    print(a2.shape == (8,))
    -
    -
    -
    -
    -
    True
    +
    print(
    +    np.allclose(np.exp(len(a2)), 2980.9579870417283)
    +)  # This should evaluate to True if a2 has the correct shape :)
     
    @@ -638,65 +712,115 @@ doconce format html exercisesweek41.do.txt -->

    a) Complete the function below so that it returns a list layers of weight and bias tuples (W, b) for each layer, in order, with the correct shapes that we can use later as our network parameters.

    -
    def create_layers(network_input_size, output_sizes):
    +
    def create_layers(network_input_size, layer_output_sizes):
         layers = []
     
         i_size = network_input_size
    -    for output_size in output_sizes:
    -        W = np.random.rand(output_size, i_size)
    -        b = np.random.rand(output_size)
    +    for layer_output_size in layer_output_sizes:
    +        W = ...
    +        b = ...
             layers.append((W, b))
     
    -        i_size = output_size
    +        i_size = layer_output_size
         return layers
     
    -

    b) Comple the function below so that it evaluates the intermediate z and activation a for each layer, and returns the final activation a. This is the complete feed-forward pass, a full neural network!

    +

    b) Comple the function below so that it evaluates the intermediary z and activation a for each layer, with ReLU actication, and returns the final activation a. This is the complete feed-forward pass, a full neural network!

    -
    def feed_forward(layers, input):
    +
    def feed_forward_all_relu(layers, input):
         a = input
         for W, b in layers:
    -        z = W @ a + b
    -        a = ReLU(z)
    +        z = ...
    +        a = ...
         return a
     

    c) Create a network with input size 8 and layers with output sizes 10, 16, 6, 2. Evaluate it and make sure that you get the correct size vectors along the way.

    -
    -
    -

    Exercise 4

    -

    So far, every layer has used the same activation, ReLU. We often want to use other types of activation however, so we need to update our code to support multiple types of activation. Make sure that you have completed every previous exercise before trying this one.

    -

    a) Make the create_layers function also accept a list of activation functions, which is used to add activation functions to each of the tuples in layers. Make new functions to not mess with the old ones.

    -
    def create_layers_4(network_input_size, output_sizes, activation_funcs):
    +
    input_size = ...
    +layer_output_sizes = [...]
    +
    +x = np.random.rand(input_size)
    +layers = ...
    +predict = ...
    +print(predict)
    +
    +
    +
    +
    +

    d) Why is a neural network with no activation functions always mathematically equivelent to a neural network with only one layer?

    +
    +
    +

    Exercise 4 - Custom activation for each layer

    +

    So far, every layer has used the same activation, ReLU. We often want to use other types of activation however, so we need to update our code to support multiple types of activation functions. Make sure that you have completed every previous exercise before trying this one.

    +

    a) Complete the feed_forward function which accepts a list of activation functions as an argument, and which evaluates these activation functions at each layer.

    +
    +
    +
    def feed_forward(input, layers, activations):
    +    a = input
    +    for (W, b), activation in zip(layers, activations):
    +        z = ...
    +        a = ...
    +    return a
    +
    +
    +
    +
    +

    b) Make a list with three activation functions(don’t call them yet! you can make a list with function names as elements, and then call these elements of the list later), two ReLU and one sigmoid. (If you add other functions than the ones defined at the start of the notebook, make sure everything is defined using autograd’s numpy wrapper, like above, since we want to use automatic differentiation on all of these functions later.)

    +

    Then evaluate a network with three layers and these activation functions.

    +
    +
    +
    network_input_size = ...
    +layer_output_sizes = [...]
    +activations = [...]
    +layers = ...
    +
    +x = np.random.randn(network_input_size)
    +feed_forward(x, layers, activations)
    +
    +
    +
    +
    +
    +
    +

    Exercise 5 - Processing multiple inputs at once

    +

    So far, the feed forward function has taken one input vector as an input. This vector then undergoes a linear transformation and then an element-wise non-linear operation for each layer. This approach of sending one vector in at a time is great for interpreting how the network transforms data with its linear and non-linear operations, but not the best for numerical efficiency. Now, we want to be able to send many inputs through the network at once. This will make the code a bit harder to understand, but it will make it faster, and more compact. It will be worth the trouble.

    +

    To process multiple inputs at once, while still performing the same operations, you will only need to flip a couple things around.

    +

    a) Complete the function create_layers_batch so that the weight matrix is the transpose of what it was when you only sent in one input at a time.

    +
    +
    +
    def create_layers_batch(network_input_size, layer_output_sizes):
         layers = []
     
         i_size = network_input_size
    -    for output_size, activation in zip(output_sizes, activation_funcs):
    -        W = np.random.rand(output_size, i_size)
    -        b = np.random.rand(output_size)
    -        layers.append((W, b, activation))
    +    for layer_output_size in layer_output_sizes:
    +        W = ...
    +        b = ...
    +        layers.append((W, b))
     
    -        i_size = output_size
    +        i_size = layer_output_size
         return layers
     
    -

    b) Update the feed_forward function to support this change.

    +

    b) Make a matrix of inputs with the shape (number of features, number of inputs), you choose the number of inputs and features per input. Then complete the function feed_forward_batch so that you can process this matrix of inputs with only one matrix multiplication and one broadcasted vector addition per layer. (Hint: You will only need to swap two variable around from your previous implementation, but remember to test that you get the same results for equivelent inputs!)

    -
    def feed_forward_4(layers, input):
    -    a = input
    -    for W, b, activation in layers:
    -        z = W @ a + b
    -        a = activation(z)
    +
    inputs = np.random.rand(1000, 4)
    +
    +
    +def feed_forward_batch(inputs, layers, activations):
    +    a = inputs
    +    for (W, b), activation in zip(layers, activations):
    +        z = ...
    +        a = ...
         return a
     
    @@ -705,28 +829,26 @@ doconce format html exercisesweek41.do.txt -->

    c) Create and evaluate a neural network with 4 inputs and layers with output sizes 12, 10, 3 and activations ReLU, ReLU, softmax.

    -
    from scipy.special import softmax
    -
    -network_input_size = 4
    -output_sizes = [12, 10, 3]
    -activation_funcs = [ReLU, ReLU, softmax]
    -layers = create_layers_4(network_input_size, output_sizes, activation_funcs)
    +
    network_input_size = ...
    +layer_output_sizes = [...]
    +activations = [...]
    +layers = create_layers_batch(network_input_size, layer_output_sizes)
     
     x = np.random.randn(network_input_size)
    -predict = feed_forward_4(layers, x)
    +feed_forward_batch(inputs, layers, activations)
     
    -

    The final exercise will hopefully be very simple if everything has worked so far. You will evaluate your neural network on the iris data set (https://scikit-learn.org/1.5/auto_examples/datasets/plot_iris_dataset.html).

    -

    This dataset contains data on 150 flowers of 3 different types which can be separated pretty well using the four features given for each flower, which includes the width and length of their leaves. You are not expected to do any training of the network or actual classification, unless you feel like it, in that case you can do exercise 5.

    +

    You should use this batched approach moving forward, as it will lead to much more compact code. However, remember that each input is still treated separately, and that you will need to keep in mind the transposed weight matrix and other details when implementing backpropagation.

    +
    +
    +

    Exercise 6 - Predicting on real data

    +

    You will now evaluate your neural network on the iris data set (https://scikit-learn.org/1.5/auto_examples/datasets/plot_iris_dataset.html).

    +

    This dataset contains data on 150 flowers of 3 different types which can be separated pretty well using the four features given for each flower, which includes the width and length of their leaves. You are will later train your network to actually make good predictions.

    -
    # Loading and plotting iris dataset
    -from sklearn import datasets
    -import matplotlib.pyplot as plt
    -
    -iris = datasets.load_iris()
    +
    iris = datasets.load_iris()
     
     _, ax = plt.subplots()
     scatter = ax.scatter(iris.data[:, 0], iris.data[:, 1], c=iris.target)
    @@ -737,29 +859,114 @@ doconce format html exercisesweek41.do.txt  -->
     
    -
    -_images/exercisesweek42_34_0.png
    -
    -

    c) Loop over the iris dataset(iris.data) and evaluate the network for each data point.

    -
    # No need to change this cell! Just make sure it works!
    -for x in iris.data:
    -    prediction = feed_forward_4(layers, x)
    +
    inputs = iris.data
    +
    +# Since each prediction is a vector with a score for each of the three types of flowers,
    +# we need to make each target a vector with a 1 for the correct flower and a 0 for the others.
    +targets = np.zeros((len(iris.data), 3))
    +for i, t in enumerate(iris.target):
    +    targets[i, t] = 1
    +
    +
    +def accuracy(predictions, targets):
    +    one_hot_predictions = np.zeros(predictions.shape)
    +
    +    for i, prediction in enumerate(predictions):
    +        one_hot_predictions[i, np.argmax(prediction)] = 1
    +    return accuracy_score(one_hot_predictions, targets)
    +
    +
    +
    +
    +

    a) What should the input size for the network be with this dataset? What should the output shape of the last layer be?

    +

    b) Create a network with two hidden layers, the first with sigmoid activation and the last with softmax, the first layer should have 8 “nodes”, the second has the number of nodes you found in exercise a). Softmax returns a “probability distribution”, in the sense that the numbers in the output are positive and add up to 1 and, their magnitude are in some sense relative to their magnitude before going through the softmax function. Remember to use the batched version of the create_layers and feed forward functions.

    +
    +
    +
    ...
    +layers = ...
    +
    +
    +
    +
    +

    c) Evaluate your model on the entire iris dataset! For later purposes, we will split the data into train and test sets, and compute gradients on smaller batches of the training data. But for now, evaluate the network on the whole thing at once.

    +
    +
    +
    predictions = feed_forward_batch(inputs, layers, activations)
    +
    +
    +
    +
    +

    d) Compute the accuracy of your model using the accuracy function defined above. Recreate your model a couple times and see how the accuracy changes.

    +
    +
    +
    print(accuracy(predictions, targets))
     
    -
    -

    Exercise 5 (Very optional and very hard :)

    -

    a) Make the iris target values into one-hot vectors.

    -

    b) Define the cross-entropy loss function to evaluate the performance of your network on the data set.

    -

    c) Use the autograd package to take the gradient of the cross entropy wrt. the weights and biases of the network.

    -

    d) Use gradient descent of some sort to optimize the parameters.

    -

    e) Evaluate the accuracy of the network.

    -

    e) Show off how you did in a group session!

    +
    +

    Exercise 6 - Training on real data

    +

    To be able to actually do anything useful with your neural network, you need to train it. For this, we need a cost function and a way to take the gradient of the cost function wrt. the network parameters. The following exercises guide you through taking the gradient using autograd, and updating the network parameters using the gradient. Feel free to implement gradient methods like ADAM if you finish everything.

    +

    The cross-entropy loss function can evaluate performance on classification tasks. It sees if your prediction is “most certain” on the correct target.

    +
    +
    +
    from autograd import grad
    +
    +
    +def cost(input, layers, activations, target):
    +    predict = feed_forward_batch(input, layers, activations)
    +    return cross_entropy(predict, target)
    +
    +
    +def cross_entropy(predict, target):
    +    return np.sum(-target * np.log(predict))
    +
    +
    +gradient_func = grad(
    +    cross_entropy, 1
    +)  # Taking the gradient wrt. the second input to the cost function
    +
    +
    +
    +
    +

    a) What shape should the gradient of the cost function wrt. weights and biases be?

    +

    b) Use the gradient_func function to take the gradient of the cross entropy wrt. the weights and biases of the network. Check the shapes of what’s inside. What does the grad func from autograd actually do?

    +
    +
    +
    layers_grad = gradient_func(inputs, layers, activations, targets)  # Don't change this
    +
    +
    +
    +
    +

    c) Finish the train_network function.

    +
    +
    +
    def train_network(
    +    inputs, layers, activations, targets, learning_rate=0.001, epochs=100
    +):
    +    for i in range(epochs):
    +        layers_grad = gradient_func(inputs, layers, activations, targets)
    +        for (W, b), (W_g, b_g) in zip(layers, layers_grad):
    +            W -= ...
    +            b -= ...
    +
    +
    +
    +
    +

    e) What do we call the gradient method used above?

    +

    d) Train your network and see how the accuracy changes! Make a plot if you want.

    +
    +
    +
    ...
    +
    +
    +
    +
    +

    e) How high of an accuracy is it possible to acheive with a neural network on this dataset, if we use the whole thing as training data?

    - + @@ -323,6 +323,16 @@ const thebe_selector_output = ".output, .cell_output" Week 41 Neural networks and constructing a neural network code +
  • + + Exercises week 42 + +
  • +
  • + + Week 42 Constructing a Neural Network code with examples + +
  • @@ -3610,10 +3620,10 @@ features).

    Exercises week 41

    - +

    next

    -

    Project 1 on Machine Learning, deadline October 7 (midnight), 2024

    +

    Exercises week 42

    diff --git a/doc/LectureNotes/_build/html/week42.html b/doc/LectureNotes/_build/html/week42.html index f8178314f..3a6eaabc7 100644 --- a/doc/LectureNotes/_build/html/week42.html +++ b/doc/LectureNotes/_build/html/week42.html @@ -3378,7 +3378,7 @@ the Hadamard product, meaning element-wise multiplication.

    Old accuracy on training data: 0.1440501043841336
     
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
     
    @@ -3714,7 +3714,7 @@ Lambda = 10.0 Accuracy score on test set: 0.19166666666666668
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
     
    @@ -3723,7 +3723,7 @@ Lambda = 1e-05 Accuracy score on test set: 0.10555555555555556
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
     
    @@ -3732,7 +3732,7 @@ Lambda = 0.0001 Accuracy score on test set: 0.08611111111111111
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
     
    @@ -3741,7 +3741,7 @@ Lambda = 0.001 Accuracy score on test set: 0.10555555555555556
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
     
    @@ -3750,7 +3750,7 @@ Lambda = 0.01 Accuracy score on test set: 0.08888888888888889
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
     
    @@ -3759,7 +3759,7 @@ Lambda = 0.1 Accuracy score on test set: 0.08611111111111111
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
     
    @@ -3768,7 +3768,7 @@ Lambda = 1.0 Accuracy score on test set: 0.08888888888888889
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
     
    @@ -3777,11 +3777,11 @@ Lambda = 10.0 Accuracy score on test set: 0.09166666666666666
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
       exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
       self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
     
    @@ -3790,11 +3790,11 @@ Lambda = 1e-05 Accuracy score on test set: 0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
       exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
       self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
     
    @@ -3803,11 +3803,11 @@ Lambda = 0.0001 Accuracy score on test set: 0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
       exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
       self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
     
    @@ -3816,11 +3816,11 @@ Lambda = 0.001 Accuracy score on test set: 0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
       exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
       self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
     
    @@ -3829,11 +3829,11 @@ Lambda = 0.01 Accuracy score on test set: 0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
       exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
       self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
     
    @@ -3842,7 +3842,7 @@ Lambda = 0.1 Accuracy score on test set: 0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
     
    @@ -3851,11 +3851,11 @@ Lambda = 1.0 Accuracy score on test set: 0.10555555555555556
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
       exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
       self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
     
    @@ -3864,11 +3864,11 @@ Lambda = 10.0 Accuracy score on test set: 0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
       exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
       self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
     
    @@ -3877,11 +3877,11 @@ Lambda = 1e-05 Accuracy score on test set: 0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
       exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
       self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
     
    @@ -3890,11 +3890,11 @@ Lambda = 0.0001 Accuracy score on test set: 0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
       exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
       self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
     
    @@ -3903,11 +3903,11 @@ Lambda = 0.001 Accuracy score on test set: 0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
       exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
       self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
     
    @@ -3916,11 +3916,11 @@ Lambda = 0.01 Accuracy score on test set: 0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
       exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
       self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
     
    @@ -3929,11 +3929,11 @@ Lambda = 0.1 Accuracy score on test set: 0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
       exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
       self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
     
    @@ -3942,11 +3942,11 @@ Lambda = 1.0 Accuracy score on test set: 0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:43: RuntimeWarning: overflow encountered in exp
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
       exp_term = np.exp(self.z_o)
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
       self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
     
    @@ -3999,15 +3999,15 @@ Accuracy score on test set: 0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
    -/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_55841/953065564.py:4: RuntimeWarning: overflow encountered in exp
    +/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
       return 1/(1 + np.exp(-x))
     
    @@ -4324,18 +4324,17 @@ Accuracy score on test set: 0.10555555555555556 Learning rate = 1.0 Lambda = 0.01 Accuracy score on test set: 0.17777777777777778 -
    -
    -
    Learning rate  =  1.0
    -Lambda =  0.1
    -Accuracy score on test set:  0.08333333333333333
     
     Learning rate  =  1.0
    -Lambda =  1.0
    -Accuracy score on test set:  0.08888888888888889
    +Lambda =  0.1
    +Accuracy score on test set:  0.08333333333333333
     
    Learning rate  =  1.0
    +Lambda =  1.0
    +Accuracy score on test set:  0.08888888888888889
    +
    +Learning rate  =  1.0
     Lambda =  10.0
     Accuracy score on test set:  0.09444444444444444
     
    @@ -4355,9 +4354,8 @@ Accuracy score on test set:  0.10555555555555556
     Learning rate  =  10.0
     Lambda =  0.01
     Accuracy score on test set:  0.1388888888888889
    -
    -
    -
    Learning rate  =  10.0
    +
    +Learning rate  =  10.0
     Lambda =  0.1
     Accuracy score on test set:  0.11388888888888889
     
    diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb index 7759019af..d4be4ab07 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb @@ -343,7 +343,7 @@ "outputs": [ { "data": { - "image/png": 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gyfbBBx9Uo0aNtHDhQj3//PPq2rWrnn76ad1zzz1q2rRpWD//8ssvV8eOHTV37lxdf/31Onr0qFq3bq2+ffuW1ZupX7++Bg4cqL///e/asWOHTp48qY4dO+quu+7SnXfeKUk67bTT1LRpU82dO1c//vij6tWrpzPOOEOLFi3S1KlTQ/78Vq1a6aOPPtKsWbM0a9YsFRYWqkuXLpo7d65uu+22Gv9dAbtReRiwmReq/8Ksi9O1a1fNnj1bd999t93DARIWMzYAEKFPP/1US5Ys0eDBg5WamqqvvvpKc+fOVWpqqq655hq7hwckNAIbAIhQo0aNtHHjRj333HM6cuSI0tLSNGLECD344IMht3wDiA+WogAAgGfYXqDvvvvuk8/nC/gIVqwMAACgOo5YiurRo0dAp9ryZbsBAADC5YjApm7duszSAACAWnNEYGPV7UhJSdHAgQP10EMPqUuXLkHvW1RUFFBmvbS0VIcPH1aLFi0iLrMOAADsYRiGjh49qvbt24dV2DJcticPv/322/r55591+umna9++fXrggQf05Zdf6vPPPw/aI+e+++6LWcEuAAAQX/n5+UpPT4/a97M9sKno2LFjOuWUU3TnnXcGrX5ZccamoKBAHTt2VH5+vlJTU+M5VAAAUEOFhYXKyMgoK5kQLY5YiiqvUaNG6tWrV8j+MCkpKUE776amphLYAADgMtFOI7F9u3dFRUVF+uKLL4J2WwYAAKiK7YHNHXfcobVr1yovL0/r16/X5MmTVVhYWGXzNgAAgGBsX4ratWuXLr30Uh08eFCtWrXSoEGDtG7dOnXq1MnuoQEAAJexPbBZunSp3UMAAAAeYftSFAAAQLQQ2AAAAM8gsAEAAJ5BYAMAADyDwAYAAHgGgQ0AAPAMAhsAAOAZBDYAAMAzCGwAAIBnENgAAADPILABAACeQWADAAA8g8AGAAB4BoENAADwDAIbAADgGQQ2AADAMwhsAACAZxDYAAAAzyCwAQAAnkFgAwAAPIPABgAAeAaBDQAA8AwCGwAA4BkENgAAwDMIbAAAgGcQ2AAAAM8gsAEAAJ5BYAMAADyDwAYAAHgGgQ0AAPAMAhsAAOAZBDYAAMAzCGwAAIBnENgAAADPILABAACeQWADAAA8g8AGAAB4BoENAADwDAIbAADgGQQ2AADAMwhsAACAZxDYAAAAzyCwAQAAnkFgAwAAPIPABgAAeAaBDQAA8AwCGwAA4BkENgAAwDMIbAAAgGcQ2AAAAM8gsAEAAJ5BYAMAADyDwAYAAHgGgQ0AAPAMAhsAAOAZBDYAAMAzCGwAAIBnENgAAADPILABAACeQWADAAA8g8AGAAB4BoENAADwDAIbAADgGY4KbLKzs+Xz+TRjxgy7hwIAAFzIMYHNhg0b9Kc//Um9e/e2eygAAMClHBHY/PTTT7rsssv05z//Wc2aNbN7OAAAwKUcEdhMmzZN48aN0+jRo6u9b1FRkQoLCwM+AAAAJKmu3QNYunSpNm/erA0bNoR1/+zsbM2ZMyfGowIAAG5k64xNfn6+pk+frhdeeEH169cP62tmzZqlgoKCso/8/PwYjxIAALiFzzAMw64f/tprr2nixIlKSkoqO1ZSUiKfz6c6deqoqKgo4LZgCgsLlZaWpoKCAqWmpsZ6yAAAIApidf22dSlq1KhR2rZtW8Cxq6++Wl27dtVdd91VbVADAABQnq2BTZMmTdSzZ8+AY40aNVKLFi0qHQcAAKiOI3ZFAQAARIPtu6IqWrNmjd1DAAAALsWMDQAA8AwCGwAA4BkENgAAwDMIbAAAgGcQ2AAAAM8gsAEAAJ5BYAMAADyDwAYAAHgGgQ0AAPAMAhsAAOAZBDYAAMAzCGwAAIBnENgAAADPILABAACeQWADAAA8g8AGAAB4BoENAADwDAIbAADgGQQ2AADAMwhsAACAZxDYAAAAzyCwAQAAnkFgAwAAPIPABgAAeAaBDQAA8AwCGwAA4BkENgAAwDMIbAAAgGcQ2AAAAM+oa/cAAACAjUpKpNxcac8eqV07aehQKSnJ7lHVGIENAACJKidHmj5d2rXLfyw9XXrySWnSJPvGVQssRQEAkIhycqTJkwODGknavds8npNjz7hqicAGAIBEU1JiztQYRuXbrGMzZpj3cxkCGwAAEk1ubuWZmvIMQ8rPN+/nMgQ2AAAkmj17ons/ByGwAQAg0bRrF937OQiBDQAAiWboUHP3k88X/HafT8rIMO/nMgQ2AAAkmqQkc0u3VDm4sT6fN8+V9WwIbAAASESTJkkvvyx16BB4PD3dPO7SOjYU6AMAIFFNmiRNmEDlYQAA4BFJSdKIEXaPImpYigIAAJ5BYAMAADyDwAYAAHgGgQ0AAPAMAhsAAOAZBDYAAMAzCGwAAIBnENgAAADPILABAACeQWADAAA8g5YKALytpMRTfXAAVI3ABoB35eRI06dLu3b5j6WnS08+6drOxQCqxlIUAG/KyZEmTw4MaiRp927zeE6OPeMCEFMENgC8p6TEnKkxjMq3WcdmzDDvB8BTCGwAeE9ubuWZmvIMQ8rPN+8HwFMIbAB4z5490b0fANcgsAHgPe3aRfd+AFyDwAaA9wwdau5+8vmC3+7zSRkZ5v0AeAqBDQDvSUoyt3RLlYMb6/N586hnA3gQgQ0Ab5o0SXr5ZalDh8Dj6enmcerYAJ5EgT4A3jVpkjRhApWHgQRCYAPA25KSpBEj7B4FgDixfSnq6aefVu/evZWamqrU1FRlZWXp7bfftntYAADAhWyfsUlPT9fDDz+sU089VZL017/+VRMmTNCWLVvUo0cPm0cHAEAU0ZQ15nyGEazmuL2aN2+uRx99VNdcc0219y0sLFRaWpoKCgqUmpoah9EBAFADNGUNEKvrt+1LUeWVlJRo6dKlOnbsmLKysoLep6ioSIWFhQEfAAA4Gk1Z48YRgc22bdvUuHFjpaSk6IYbbtCrr76q7t27B71vdna20tLSyj4yMjLiPFoAiEBJibRmjbRkifkvjTcTD01Z48oRS1EnTpzQzp07deTIEb3yyit69tlntXbt2qDBTVFRkYqKiso+LywsVEZGhj1LUayVAs7klOcmSw+QzIB25Mjq77d6dULt4IvVUpTtycOSVK9evbLk4f79+2vDhg168skn9cwzz1S6b0pKilJSUuI9xMp4wQKcySnPTWvpoeJ7R2vpgSKBiYOmrHHliKWoigzDCJiVcRzWSgFncspzk6UHlEdT1riyPbC5++67lZubqx07dmjbtm265557tGbNGl122WV2Dy04XrAAZ3LSczM3t3JwVXE8+fnm/eB9TmrKmgA5X7YHNvv27dMVV1yhM844Q6NGjdL69ev1zjvvaMyYMXYPLThesABnctJzk6UHlOeUpqw5OVJmppnvM2WK+W9mpudWGWzPsXnuuefsHkJkeMECnMlJz02WHlCR1ZQ1WP7XvHnh51vVNDE+gXK+bA9sXIcXLMCZnPTctJYedu8OvjTm85m3x2PpAc5R26asNU2Mr26Z1uczl2knTPDEzl5HbPeujbhXHi4pMafuqnvBysvzxAMEcA2nPTetd8hS4HispQcPvUNGHISacQnn8eTQ7eYJUXnYFZyyVgogkNOem9bSQ4cOgcfT0wlqEJnaJsY7aZk2DghsaoIXLMCZnPbcnDRJ2rHDfCe8eLH5b14erxGITG0T4520TBsH5NjUVG3XSgHEhtOem0lJCVVNFjFQ2xmXBMv5IrCpDV6wAGfiuQkvqe2Mi7VMO3myGcQEy/nyUAoFS1EAAHfzetG5aBT4c9oybQwxYwMAcC+n9AaLpWjNuDhtmTZG2O4NAHCn2myBdqNgQVxGRmQF/hwkVtdvAhsAgPtYdYtC7Rbyak2xmlYedqBYXb9ZigIAuE8kW6C9lEjulsR4GwMwAhsAgPskWNE5V7E574ldUQAA90mwonOuYeU9VZxNs5ptxqGTOIENAMB9orEFGtFV29YPUUJgAwCx5PUaK3ZxWm8w1L71Q5QQ2ABwFzcFCjk55s6dkSOlKVPMfzMz4zIdnxASqOicKzgk74nkYQDu4aZibKFqrFi5Blx4oyNBis65gkPynqhjA8Ad3FSMLVFrrCCxWY/76ppt/vtxH6vrN0tRAJzPIUmJYXNIrgEQVw7JeyKwAeB8bgsUHJJrAMSdA/KeyLEB4HxuCxQckmsA2MLmvCcCGwDO57ZAwaqxUl2ugZNqrHioBxEcwMbWDyxFAXA+txVjc0iuQdjYll41N5UYAIENABdwW6AgOSLXICwOKIHvaAR9oTk04GO7NwD3CFbHJiPDDGqcEihU5OQlHralV81NJQbiLQo1pWJ1/SawAeAuTg4U3GbNGnMGojqrV9uWL2Ebgr7QohTwOaKOTX5+ftR+MADUiJWUeOml5r+JdlGJJrftNosnt5UYiBcX1JSKKLDp2rWrfve73+nYsWOxGg8QXw5dIwbiwm27zaKtquc/QV9wLgj4Igpsli9frvfee0+nnXaann/++ViNCYgPkgKR6Ny22yyaqnv+J3rQF4oLAr6IApvBgwdr/fr1evjhh3XvvffqzDPP1Jo1a2I0NCCG2AkCuHO3WTSE8/xP5KCvKi4I+Gq03fvKK6/U119/rQsuuEDjxo3TxIkT9e2330Z7bEBsuGCNGIgbt2xLj5Zwn/9SYgZ91XFBwFfjOjaGYWjs2LG67rrr9MYbb6hnz566/fbbdfTo0WiOD4g+F6wRA3E1aZK0Y4e5+2nxYvPfvDzvBTVSZM//RAv6wuGCWb6IWiosXLhQGzZs0IYNG/TFF18oKSlJvXv31rRp09S3b1+9+OKL6t69u1599VX1798/VmMGascFa8RA3NlYAj+uIn3+29z3yJGsgC9YHRsH1JSKKLB58MEHNWjQIE2dOlWDBg1S//79lZKSUnb7r3/9az300EO66qqr9Nlnn0V9sEBUuGCNGECM1OT5nyhBXyQcHPBFvUDfvn371L59e5XEKT+BAn2ImFV4q7oGhYlYeAvwOp7/juGIAn3haN26tVatWhXtbwtEjwvWiAHECM9/z4t6YOPz+TR8+PBof1sgukgKBBIXz39Po1cUEht9h4DExfPfVrG6fkeUPAx4DkmBQOLi+e9JUV+KAgAAsAuBDQAA8AyWogAAQPTYnLtEYAMAAKIjJyd4ReInn4zbbjOWogAAQO2F0zU9DghsAABA7YTbNT0OXQkIbAAAQO1E0jU9xghsAABA7UTaNT2GCGwAAEDt1KRreowQ2AAAgNoZOtTc/VSxsajF55MyMsz7xRiBDQB3KimR1qyRliwx/41DUiKAEBzUNZ3ABoD75ORImZnSyJHSlCnmv5mZcdtOCiAIh3RNp7s3AHexamVUfOmy3hXG8QUUQBBhVh6O1fWbwAaAe5SUmDMzobaV+nzmu8O8vLiWcAcQuVhdv1mKAuAeDqqVAeDfHJbvRq8oAO7hoFoZAOSI3lAVMWMDwD0cVCsDSHgO6Q1VEYENAPdwUK0MIKE5qDdURQQ2ANzDQbUygITm4Hw3AhsA7uKQWhlAQnNwvhvJwwDcZ9IkacKEsGplAIgBB+e7EdgAcKekJGnECLtHASQmK99t9+7geTZWTSkb8t1YigIAAJFxcL4bgQ0AAIicQ/PdbA9ssrOzdfbZZ6tJkyZq3bq1LrroIn311Vd2DwsAAFRn0iRpxw5p9Wpp8WLz37w8W5P4bc+xWbt2raZNm6azzz5bxcXFuueeezR27Fht375djRo1snt4AACgKg7Ld3NcE8wDBw6odevWWrt2rYYNG1bt/WmCCQCA+8Tq+m37jE1FBQUFkqTmzZsHvb2oqEhFRUVlnxcWFsZlXADgKSUlbJeHJ9meY1OeYRi67bbbdM4556hnz55B75Odna20tLSyj4yMjDiPEgBcLidHysyURo6Upkwx/83MtK23DxBNjlqKmjZtmt566y198MEHSk9PD3qfYDM2GRkZLEUBQDisxoUVX/qtLbpUb0acxGopyjGBzW9+8xu99tprev/999W5c+ewv44cGwAIU0mJOTMTqsePVVQtL49lKcRcrK7fti9FGYahm2++WTk5OVq1alVEQQ2QUEpKpDVrpCVLzH9t6JoLl3Nw40IgWmxPHp42bZoWL16s119/XU2aNNHevXslSWlpaWrQoIHNowMcIidHmj498KKUnm5W/mTZAOFycONCIFpsn7F5+umnVVBQoBEjRqhdu3ZlH8uWLbN7aIAzWDkRFd9p795tHifhE+FycONCRFkCz/A6JsempsixgaeRE4Fosh5P1TUu5PHkbNVt1XfJDK9nc2wAVIGcCESTgxsXIkzVbdV3ywxvYaH09tsx+dYENoCTkROBaHNo40KEobqg5aWXzJmaYLNx1rEZM+xZlioqktaule69Vxo8WGreXPrv/47Jj7I9eRhAFcLNddi+3VxHp3psbHmlWu+kSdKECd74XRJFSUnVQYvPJ02bJh04EPp7lJ/hjXVvp9JS6Z//lFaskFaulN5/X/r558D7dOkiff991H80OTaAk1WXE1GRA9fRPcMleQvwqDVrzGWnaFi8WLr00uh8r/Ly8sxAZsUKadUq6eDBwNtbt5ZGj5ZGjZJGjVJhs2aJ0SsKQDlWTsTkyeY7suqCG2tKmiWF6ApVrZe/N+IlmsvN0dr1duCAGcCsXGkGM3l5gbc3biwNH+4PZnr2DMztilGvR2ZsADcINlsQCjtbooudaXCCcGdsWrUyZ0pisevt2DFzGctaXtq6NfD2unWlrCwziBk9WhowQEpODvntEqa7N4AgyudErFwpPfBA6PvGcx09EUSyM42/N2Jl6FAzKKluq/7jj0sXX1x5hrcmu96Ki6UNG/zLSx9/LJ08GXif3r39MzLDhpmzNDYjsAHcIinJvHAm4k4pO5N2E/HvDeepalm6fNBi7XoLlg9m3R6KYZgbEaylpTVrpKNHA+/TqZMZyIweLZ17rpk34zAENoDbJFr1WLuTdhPt7w3nCjdoiWTXW36+GchYwcy/2xqVad68LNlXo0ebO5kq1kByGHJsALdJpOqxoZJ2rRfWeCTtJtLfG+5QmxnMf/3LnImxlpe+/jrw9gYNzO9nLS/17SvViU3Ju1hdvwlsADeyLvhS8ClpL+zScVLSbiL8veFNx49LH37on5HZtMmsMWOpU8dM8rVmZLKypJSUuAyN5GEAfrVZR3cLJyXtJsLfG95QUiJt2eLfufTBB2ZwU163bv4ZmeHDpaZNbRlqrBDYAG5jTUMXFUmLFpnH9u/3XvVYpyXtUq0XTmQY0jff+GdkVq82l5vKa98+MOG3YjuNWLEp6Z/ABnCTqhJpvbbV2IlJu9bONMBOe/cGJvzm5wfenpZm1ryxlpfOOCP+Cb82Jv2TYwO4hRMSaeOJpF3AdPSo2UDSSvj9/PPA2+vVk4YM8S8v9etnFsuzS5ivVSQPh0Bgg4TgpETaeCJpF4noxAlp3Tr/jMz69YEduX0+6cwz/ctLQ4ZIDRvaN97yInitKjx2jORhIGE5KZE2nkjaRSIoLZW2bfMn/K5dW7kT9qmn+peWRo6UWrSwZ6zVieS16qyzYjIEAhvADZyWSBtPJO3Ci/Ly/DMyK1cG74RtBTKjRpkVf93AAa9VBDaAG4SbILt9u1l8y2sXfpJ24XYHDwZ2wv7++8DbGzXyd8IePbpyJ2y3cEDSPzk2gBtUl0hbUTxbDljs7OcEOM2xY2YNGWtGZsuWwNvr1pUGDQrshF2vnj1jjaYIkv7JsQESWVUN8ILZvdu8b7ySa+3u5wTYzeqEbc3IfPRR5U7YvXr5Z2SGDpWaNLFnrLEUbrPOGL7pYcYGcJNgAUQo8doplWjb0AHJfLx/8YV/RmbNGqmwMPA+GRnmktIpp5gJvxMmJM4sZrDXqoyMgKR/tnuHQGCDhGMt+axcKT3wQPX3X706dvkpiboNHYlp167AhN+KCbDNm5uVfUeNMp8bDz+c2LOY1SxP0ysKgMlKpHXA7oOE3YaOxHDkSGAn7K++Cry9fn1/J+zRo/2dsEPNYsZ7idhuNiX9E9gAbuWA3QeOCK6AaDl+3MyNsWZkNm6s3An77LMDO2HXrx/4PUpKzCWYYIshhmHOYs6YkVjLUnFGYAO41dCh5tR2dbsPhg6N3RicEFwBNVVSIm3d6p+RCdYJu2tX/4xMOJ2wmcW0HYEN4FYO2H3giOAKCJdhSN9+65+RWbUqeCfs8oXxIu2EzSym7QhsADezu+WAE4IroCr79gUm/O7cGXh7ampgJ+yuXWtXGI9ZTNuxKwrwAruL44WxtROIi6NHpfff9y8vffZZ4O316kmDB/uXl6LdCZuu9GFju3cIBDaAQ9gdXCExnThhdr+2ZmTWrzeL5VmsTtjWjMw558S+E7aXutLH8HnNdm+v4MUfXkU/J8RDaak5C2PNyLz/vtm+oLxTTvHnyIwcKbVsGd8x2r1EHC0urSjOjE08ufRBAgC22rEjME/mwIHA21u1Ckz4zcy0Y5SVlX8j27q1eWz/fne8qY1DRXGWokJwTWBD2XkAXhOrGeiDB82K2VYg8913gbdbnbCtYKZnT7PGjFO57U1tuBXFv/3WrPtTw/NPYBOCKwIbys4D8JpoXqx//tnfCXvFCrO2TPlLU1KS2QnbmpEZONA9nbDd+KZ2zRpzCa86LVuaQaglwvNPYBOCKwKbcB8ksezpAwDRUtuLdXGxWdXXmpH56CMzCbi8nj39O5eGDXNmJ+zqZqzc+qZ2yRJpypTIvy7CYI3kYTejYBMAr6hJywDDkL780j8jE6oT9pgx5ozMuedKbdvG+jepnXBmrNxahbimNXaqaxlRMRDs06fWQw2GwCYeKNgEwCvCvVjn5Ei//OIPZiq+cWvWzAxgrOWlU0+tXWG8eAq3yaVb39RWV1G8KqGCtWCBYPv2URluRQQ28UDZeQBeEe5F+OKLAz+3OmFbCb99+0a+/OKEchmRzFi59U1tVRXFw1X+cRIqEPzxx9qNMwQCm3ig7DwArwj3IuzzmZ2wrRmZwYMrd8KOhFN2FkWyvOTmN7WhavG0alV5u30w1uOkqkAwRhy8P85jrAdJxYZq6enOzIoHgPJKSqRNm6R166SUlKrv27KlefFbv1568EFzyam2Qc3kyZUDCmvpJyen5t87UpEsL1lvaqXKy2xueFM7aZJZQ2j1amnxYvPfXbvM61aoZUOfz8yXsoK16gLBGGDGJp4mTTKnJ+2eSgWQGGqzdGMYZv2Y8p2wDx+u+musi90zz0gtWtRu7JaaJCvHUqTLS26vQhysongkKxA25A+x3RsAvKgmSzf79pkBjBXM/PBD4O1NmpilK0aPNlsb/OEPsW986rRyGTVtcnnihLRggRksnnKKdNNN7qnFE0y4jW+rOH+FktIk6thURGADABWEW2fG6oRttSvYti3w/snJ0pAh/oTf/v0DO2HHI5k33JoqixdLl14a3Z8dSqRNLp2SHxRt4Zz/KgJBApsQCGwAoJwTJ8xcvvIVYStKTZV69arcCVsyO2FbCb/nnGO2L7CT02ZsLOHOWLix8nC0hQgECWxCILCJEydsswRQtZwc6YYbwtu1YunSxV/h145O2NWp6dJPvMbmxcrDsRAkECzs0EFpu3cT2FREYBMHXp1GBbwk1MxAKNdeK919t9S5c2zHFQ2RLv04hVNnm+xSIRAs7NNHac2b01IBcRZuhU23YiYKbnfokJkfc801kdUKuewydwQ1Uvx3FkXrdcEJlYed9BpXcYdVxbYaUUJgg9Ccts0y2piJghtZnbCthN8tWyILaJxcFK4q8SqXEc3XBbsrDyfoaxxLUQjNy9OoJPTBLYqLzcJ41hbsDz+s3Ak7PT38Img+H4/vUKL9umBnfpALXuNidf0msEFoTtxmGQ0k9MHJrE7Y1ozMmjVSQUHgfTIy/DuXzj1X+uqr8N6EtGolLVxo+wUtqqK11BKr1wU78oOc/BpX7nwVpqYqbfx4cmwQR3ZPo8ZKJL1e3DYTBXfavdsMZKxgpmJzwGbN/IXxRo+u3Am7devquzG3amU+7t1cFK6iaC61xOp1wY7Kw059jQt2vmKAwAahubmBW1WckNCHxFZQYM7EWMtLX3wReHtKSmAn7DPPrPqddTiNdhcu9F5QE82NDbF8XYh3Ox0nvsZFumuvFghsEJpXu5J7dSYqGCftiEhkRUXSxx+bgcyKFdKGDWZLAkudOlK/foGdsBs0iOxnuL0nUSRisbEh1q8LwXouxYrTXuPi3OGbHBtUL9wKm27h5IJf0ZSgOyIcobRU2rrVv7SUmyv98kvgfc44wz8jM2KEudwUDYkQzMZiY4OXXhec9ruEOF+xqjzMjA2q57Wu5F6diSrP6fWHvHbxNQzp++/9MzKrV5v1Zcpr29Y/IzNqlPnmIBbiOTNgl1gstXjpdcFpv0u8l/UNlysoKDAkGQUFBXYPBW7zyiuGkZ5uGObT3vzIyDCPu1lxceXfq/yHz2f+nsXF9owv2N89Pd19f/d9+wxjyRLDuOYaw+jUqfLfuUkTw7jgAsN48knD+PxzwygttXvE3rF6dejHd/mP1asj/95eel1wyu8S4nwVSDG5frMUhcTmtZkDydn1h1xQWyOkn34K7IT9z38G3p6cbObGWMtLZ58d2Akb0RPrpRYvvS444XcJcb5YigJiwa5p+1i+2DhxR4TkvkrWJ09Kn3ziX15at65yJ+y+fc06Mm3amNupO3d27kXQCRe4aIn1UouXlvOc8LtUdb5igMAGiLdYJ/U6bUeExam1Ncr//M8+88/IrF1rztKU16WLf0Zm5EhzrPFM0K5pcOLFRPJE2gXmBaHOVwywFAXEUzyWYpy2I8LixErWO3f6Z2RWrZL27Qu8vWVLf7LvqFFmYGOJ97JaTYMTNy//hcNLM1GJIA6VhwlsgHiJZ5lzO8q4V8cJuT+HD5vf3wpmvv028PaGDaVhw/y7l3r3NmvMVBTvkvU1DU6cXFofCY9eUSEQ2MA14n1hd1r9ITtmkn75JbAT9ubNgT87KUkaONC/vDRoUHjVeeN5LmsTnDghmARCiNX1mxwbIF7indTrtPpD8aitUVLi74S9YoX00Udm1d/yevTwz8gMHy7V5AU1nueyNrlJTk0kB2LI9sDm/fff16OPPqpNmzZpz549evXVV3XRRRfZPSznY13ZfexI6nXCjojyop3waRhmZ2trRmb16sqdsNPT/c0jzz03On/feJ7L2gQnTk0kB2LI9sDm2LFj6tOnj66++mr96le/sns47uDFHQ6JwKtNRSNV25mkH3/0BzIrV5p/z/KaNg3shH3aaYGdsKMhnueyNsEJjzkkIEfl2Ph8vohnbBIux8brOxy8zolJvU5XUGBuvbaWl4J1wj7nHP/y0llnxWf2Ml7nsra5STzm4FDk2PxbUVGRisqtmRcWFto4mjhzW4EzVEbtjepZnbCtWZkNG8zHvsXn83fCHj26Zp2wo6G6czlhgpm8W9vl4trmJrnhMcfSOqLIdTM29913n+bMmVPpeELM2LDDwTt4IfcrLZU+/dQ/IxOsE/bpp/tnZEaMkJo3t2WoQQU7l6+/Hv3l4trucnPqY46l9YSVENu9wwlsgs3YZGRkJEZg48QCZ0BNlO+EvWpV5U7Ybdr4Z2Ri2Qk7FmK5XOzU4KSmWFpPaCxF/VtKSopSUlLsHoY92OGAmnDCxXD/fjOAsZaXduwIvL1JE3PrtRXMdO8e/YTfeIh0uTjSc+O0XW61wdI6YsR1gU1CY4cDImXXNP9PP5kXbGvn0qefBt6enCxlZflnZM4+2zzmZiUl0lNPhV9z5vDhxF6CcXrvMLiW7YHNTz/9pG/LlTXPy8vT1q1b1bx5c3Xs2NHGkTlQPAqcwTtCTfPv3m0ej+Y0v9UJ25qRWbfOPFZenz7+GZmhQ6VGjQJvd8LMUk0FCyCr8vrr5nM5HufGqSgeiFgxbLZ69WpDUqWPqVOnhvX1BQUFhiSjoKAgtgN1kldeMYz0dMMwXxbNj4wM8zgSU3GxYaxebRiLF5v/FhVVfoyU//D5zMdMcXHNfl5pqWFs22YYTzxhGOPHG0bjxpV/RmamYfy//2cYS5caxv79VX+/YI/p9HR3PKZfecX8e4b6Wwf7aNkydufGLVavDu9vtXq13SNFjMTq+u2o5OGaSLg6NhY3v7tFdAWbLWjZUjp4sPqvjWQH3c6dgYXxKnbCbtHC33OpYifs6sbv1gTS6vo4VeTzmefmwIHq7+v13Y1O7UKPuCF5GIG8lESImgsVFIQT1EhVT/NbnbCtYOabbwJvb9jQDKit5aVQnbCr4vYE0uryRMqzArXLLjOXjKvj9SUYNy6t84bSFQhsALeqKigIV/kddL/8In34oX9GZtOmyp2wBwzwz8gMGmRW/a0NtyeQRhJ8WAXxmjcPL7BJhN2NbigeaKHejmsQ2ABuFclsQUU+n9ShgxmYZGebwcyHH1buhN29u39GpqadsKvi9gTScIOPJ56QfvMb/xZvdjf6Oa0LfTDxTMRHrRHYAG5Vm4u9YZhLTYMHBx7v0CGwE3b79rUbY3XcXpsp3BIMVlAjuXMJJtacvLTu9uXSBBThgjgAxwj3Yt+kSfDjP/8spaVJEydK//u/0pdfmss+ixZJl18e+6BG8gcGoYrx+Xxm1WGnzl5YQYpU+XeoKkixlmA6dAg8np7Ou3+niWS5FI7AjA3gVtXNFliOHvX/PznZ7IQ9ZoyZJ9Ovn73vMr0we1HTPBE3LMHA/culCYjABnCrpCTpD3+Q/vu/q75f//7+bdhDhtjTCbsqbkogDaWmQYqTl2BgcvtyaQKijg3gJlYnbGsLdm6uuaRUUePG0g03SLNmOasTdlXYSgsnot5OzFDHBkhUVifslSvNRpIVa9S0aSONHCl17Gi+wPbq5c6ggNkLOJEXlksTDIEN4DQHDpgBjBXM5OUF3t64sRkAWMtLPXpE1gmbmREgMl5YLk0gBDaA3Y4d83fCXrEieCfsQYP8hfEGDKh5J2yKjAE1Q7K3a5BjA8TbyZPShg3+PJmPPw7eCduakRk61JylqS0392QCooUZS8eI1fWbwAaINcOQtm/3Ly2tWRO4BVuSOnXyb8E+91ypdevojqG6Zo2JkADJBQ3MWDoKycOAm+TnB3bC3rs38PYWLcwApnwn7EjyZCLl9p5MtcUFDbRFSBgENkA0/OtfgZ2wv/468PYGDaRhw/zLS336RN4JuzYSucgYFzTQFiGhENgANXH8uL8T9ooV0ubNZo0ZS506gZ2ws7Jq3wm7NhK1yBgXNEjMWCYYAhsgHCUlZvBizch88EHwTtjWjMzw4WYfJqcIt1mjU3sy1RQXNEiJPWOZgAhsgGAMQ/rmm8DCeEeOBN7H6oQ9apT5EY+mkTWVqEXGuKBBStwZywRFYANY9u71z8isWFH5nX5amlnh1wpmzjgjtgm/0ZaIRca4oEFK3BnLBMV2bySuwkJp7Vp/MPP554G316tnNo0cPdr8OOssqa4H3gsk0rZn+vzEn1MfX1YSuRR8xpIk8rhjuzdQWydOSOvW+WdkPvnEfBG2+Hxm8FK+E3bDhvaNN1YSqSdToi7B2cXJ2+oTccYyQTFjA+8qLZX++U//jMz771fuhH3qqf4ZmREjzPoybuHUd8ZOFOyCm5HBBS2a3FLZOpGfNw773ak8HAKBDQLk5fkTfleurNwJu3Vr/4zMqFFmxV83cvI7Y6dy2Iu6p1DZ2vkc+JpBYBMCgU2CO3jQ3wl7xYrKnbAbNTJnYqxApmdPdyX8BuOWd8ZIHGvWmIn11Vm9OnGWQZ3Eoa8Z5NgAkr8TtrW8tHVr4O116/o7YY8eXbtO2E5EwTk4EdvqnSsBXzMIbOBsxcVmJ2xreemjjyp3wu7d27+8NGxYdDphOxUF5+BEbKt3rgR8zSCwgbMYhvTFF/6lpbVrzW3Z5XXq5J+RiUUnbCfjnTGciDoxzpWArxkENrDfrl2BCb8Vn2DNm/s7YY8eHftO2E7GO2N3SLREZbbVO1cCvmaQPIz4+9e/zGRDK5j56qvA2xs0MC8E1vJS377x7YTtZBSccz4H7j6JG7bVO4+DXzPYFRUCgY0LHD9u5sZYy0ubNlXuhH322f4ZGbs7YTsdFVSdy6G7T+Iq0War3MChrxkENiEQ2DhQSYm0ZYt/RuaDD8zgprxu3QI7YTdtastQXYt3xs5DLRc4mQNfMwhsQiCwcQDDkL791j8js3q1udxUXvv2gQm/HTrYM1Yv4Z2xs1DLBU7nsNcM6thUJzfX3D3jgJOVEKxO2FY9mfz8wNvT0vyF8UaPdl8nbDdIpJ5PbpCAu0/gMgnymuGdwGb8eP//EyVRL56OHjW3XlvLS599Fni71QnbWl7q188bnbCBcCXg7hPAibyzFCWpbCIrkRL1YuXECWn9+sBO2MXF/tt9PunMM/2tCs45x5udsIFwOXj3CTzCYUtJtcVSVCScUCbabQ/A0lJp2zb/jMz775vtC8o79VT/jMzIke7qhA3EGrVcEEuJXEYgQt6csSnPjkQ9tzwAd+zwz8isWiUdOBB4e6tW/hmZUaPMd6MAqubA3SdwOY+WEWBXVAjVBjaLF0uXXhq/ATn5AXjwoBnoWcHM998H3t6okbn1unwnbArjAZFz24wtnMvDZQRYiqqpeCbqOa2L6s8/my+u1vLSli2Bt1udsK3lpQEDzCRgALWTILtPEAcJ2MSytrwb2NjRdM3uB2BxsbRxo39G5uOPzSTg8nr18s/IDBsmNWkS/XEAAKKDMgIR82ZgY1eiXrwfgOU7Ya9caRYIq9gJu2PHwMJ4bdpE52eHwhQ8AEQPZQQi5s3AJj3dnkS9eDwAd+0KLIwXqhO2tbx0yinxK4znlqRpAHCLoUPN19HqygjEc3XC4byTPPzmm0q1u/JwLOpYHDkS2An7yy8Db69f3/x9reWlvn3t+d2dnDQNAG7m0CaWtcWuqBAc1yuqtg9AqxO2NSOzcWPwTtjWjExWlhnc2MnDWfsA4AgeLCNAYBOC4wIbKbIHYEmJtHWrf0YmN7dyJ+yuXf0zMiNGOK8TNs3/ACD2PJbDyHZvN5k0ydzSHewBaHXCtmZkVq2q3Am7aVNzVmbKFGnMGOd3wiZrHwBijzICYUmMwMaOKLf8A3DfPmnZMn8ws3Nn4H1TU83u1998Y+bUHDkiLV9u7nhKTXX+NCNZ+wAAh/D+UpQdO3WOHjV7LVnLS9u2Bd5er540eLB/eSk/X7rkEvcm3tL8DwAQIXJsQqjyDxOvnTpWJ2xrRmb9+uCdsK2E3/KdsL2SeOvRrH0AQGwQ2IQQ8g8Ty4ChtFT67DP/jMzatZU7YZ9ySmAn7JYtg38vuxNvo7lM58GsfSAkjyVyAvFG8nCkot3eYMeOwITf/fsDb2/Vyt8Fe9QoqXPn8MZpZ+JttJfpqkqaBryEYpSAY3k3sKltwHDokBnAWMHMd98F3t6okdlryWpXUNNO2HYl3oZaptu92zxe06UjsvbhdbF67gCICu8uRUW6xPPzz9IHHwR2wi7/p0lKCuyEPXBgdDph25F465W8HiDeeO4AUcNSVKTC6a/RqpW5bDJnjlntt2In7J49Azthx6IAYFKSOX09ebI5pmCJt9Fu5ml3F3LArXjuAI7n3cCmqoBBMj/fv1+6917/sYyMwE7YbdvGZ6yTJpnT18HW7GOReEtBPaBmeO4AjufdwEYyl4tuvll67jlzqamiZs0CO2Gfemr8OmFXFM/EWwrqATXDcwdwPG/l2BiGvxP2ihWVO2EnJ0u9epmzOGPH2tcJ224U1ANqhucOEDXk2FTn3HPNhN+KnbD79/fPyAwebH8nbCewI68H8AKeO4DjeWfGRlKqZPZcKt8Ju1kzewdot6qKiFFQD6gZnjtArVF5OISyP8zTTyt1/HhzGhimcIqIUT0VqBmeO0CtENiEEKs/jOvFq08WAAA1EKvrdw1K5cLxSkrMmZpgMat1bMYM836omZISM1F9yRLzX/6WAOAIjghsFixYoM6dO6t+/frq16+fcnNz7R6Su0VSRAyRy8kxd8aMHClNmWL+m5lpHgcA2Mr2wGbZsmWaMWOG7rnnHm3ZskVDhw7Vf/7nf2rnzp12D829KCIWWm1nWqwlvoqBo9UniOAGAGxle2Dz+OOP65prrtG1116rbt26ad68ecrIyNDTTz9t99DciyJiwdV2poUlPgBwPFsDmxMnTmjTpk0aO3ZswPGxY8fqo48+smlUDlGbmQWrT1aoKso+n7k1dejQaIzUHaIx08ISHwA4nq0F+g4ePKiSkhK1adMm4HibNm20d+/eoF9TVFSkoqKiss8LCgokmdnVnvHGG9Jdd0k//ug/1r699Mgj0oUXhvc9srOlK64IfpthSA89JB07VvuxukFJifSb31Q903LLLeYMTlXbdb/7Lryf99130llnRT5OAEgg1nU72puzHVF52FdhZsEwjErHLNnZ2ZozZ06l4xkZGTEZm2P8+GPoQKUmovm9vGD3bql58+h8r2uvNT8AANU6dOiQ0tLSovb9bA1sWrZsqaSkpEqzM/v37680i2OZNWuWbrvttrLPjxw5ok6dOmnnzp1R/cMgcoWFhcrIyFB+fj41hRyA8+EcnAvn4Fw4R0FBgTp27Kjm0XpT+W+2Bjb16tVTv379tHz5ck2cOLHs+PLlyzVhwoSgX5OSkqKUlJRKx9PS0niQOkRqairnwkE4H87BuXAOzoVz1KkT3XRf25eibrvtNl1xxRXq37+/srKy9Kc//Uk7d+7UDTfcYPfQAACAy9ge2FxyySU6dOiQ7r//fu3Zs0c9e/bUP/7xD3Xq1MnuoQEAAJexPbCRpJtuukk33XRTjb42JSVFs2fPDro8hfjiXDgL58M5OBfOwblwjlidC9c3wQQAALDYXnkYAAAgWghsAACAZxDYAAAAzyCwAQAAnuGKwGbBggXq3Lmz6tevr379+im3miaDa9euVb9+/VS/fn116dJFCxcujNNIvS+Sc5GTk6MxY8aoVatWSk1NVVZWlt599904jtbbIn1eWD788EPVrVtXffv2je0AE0yk56OoqEj33HOPOnXqpJSUFJ1yyin6y1/+EqfReluk5+LFF19Unz591LBhQ7Vr105XX321Dh06FKfRetf777+vCy64QO3bt5fP59Nrr71W7ddE5fptONzSpUuN5ORk489//rOxfft2Y/r06UajRo2MH374Iej9v//+e6Nhw4bG9OnTje3btxt//vOfjeTkZOPll1+O88i9J9JzMX36dOORRx4xPvnkE+Prr782Zs2aZSQnJxubN2+O88i9J9JzYTly5IjRpUsXY+zYsUafPn3iM9gEUJPzceGFFxoDBw40li9fbuTl5Rnr1683PvzwwziO2psiPRe5ublGnTp1jCeffNL4/vvvjdzcXKNHjx7GRRddFOeRe88//vEP45577jFeeeUVQ5Lx6quvVnn/aF2/HR/YDBgwwLjhhhsCjnXt2tWYOXNm0PvfeeedRteuXQOOXX/99cagQYNiNsZEEem5CKZ79+7GnDlzoj20hFPTc3HJJZcYv/3tb43Zs2cT2ERRpOfj7bffNtLS0oxDhw7FY3gJJdJz8eijjxpdunQJOPbHP/7RSE9Pj9kYE1E4gU20rt+OXoo6ceKENm3apLFjxwYcHzt2rD766KOgX/Pxxx9Xuv95552njRs36uTJkzEbq9fV5FxUVFpaqqNHj0a94Vmiqem5eP755/Xdd99p9uzZsR5iQqnJ+XjjjTfUv39/zZ07Vx06dNDpp5+uO+64Q7/88ks8huxZNTkXgwcP1q5du/SPf/xDhmFo3759evnllzVu3Lh4DBnlROv67YjKw6EcPHhQJSUllTp9t2nTplJHcMvevXuD3r+4uFgHDx5Uu3btYjZeL6vJuajoscce07Fjx3TxxRfHYogJoybn4ptvvtHMmTOVm5urunUd/bR3nZqcj++//14ffPCB6tevr1dffVUHDx7UTTfdpMOHD5NnUws1OReDBw/Wiy++qEsuuUTHjx9XcXGxLrzwQj311FPxGDLKidb129EzNhafzxfwuWEYlY5Vd/9gxxG5SM+FZcmSJbrvvvu0bNkytW7dOlbDSyjhnouSkhJNmTJFc+bM0emnnx6v4SWcSJ4bpaWl8vl8evHFFzVgwACdf/75evzxx7Vo0SJmbaIgknOxfft23XLLLbr33nu1adMmvfPOO8rLy6MRs02icf129Fu3li1bKikpqVKkvX///kpRnaVt27ZB71+3bl21aNEiZmP1upqcC8uyZct0zTXX6KWXXtLo0aNjOcyEEOm5OHr0qDZu3KgtW7bo5ptvlmReWA3DUN26dfXee+/p3HPPjcvYvagmz4127dqpQ4cOSktLKzvWrVs3GYahXbt26bTTTovpmL2qJuciOztbQ4YM0f/8z/9Iknr37q1GjRpp6NCheuCBB5jlj6NoXb8dPWNTr1499evXT8uXLw84vnz5cg0ePDjo12RlZVW6/3vvvaf+/fsrOTk5ZmP1upqcC8mcqbnqqqu0ePFi1qyjJNJzkZqaqm3btmnr1q1lHzfccIPOOOMMbd26VQMHDozX0D2pJs+NIUOG6Mcff9RPP/1Uduzrr79WnTp1lJ6eHtPxellNzsXPP/+sOnUCL4VJSUmS/LMFiI+oXb8jSjW2gbV177nnnjO2b99uzJgxw2jUqJGxY8cOwzAMY+bMmcYVV1xRdn9ru9itt95qbN++3XjuuefY7h0lkZ6LxYsXG3Xr1jXmz59v7Nmzp+zjyJEjdv0KnhHpuaiIXVHRFen5OHr0qJGenm5MnjzZ+Pzzz421a9cap512mnHttdfa9St4RqTn4vnnnzfq1q1rLFiwwPjuu++MDz74wOjfv78xYMAAu34Fzzh69KixZcsWY8uWLYYk4/HHHze2bNlStvU+Vtdvxwc2hmEY8+fPNzp16mTUq1fPOOuss4y1a9eW3TZ16lRj+PDhAfdfs2aNceaZZxr16tUzMjMzjaeffjrOI/auSM7F8OHDDUmVPqZOnRr/gXtQpM+L8ghsoi/S8/HFF18Yo0ePNho0aGCkp6cbt912m/Hzzz/HedTeFOm5+OMf/2h0797daNCggdGuXTvjsssuM3bt2hXnUXvP6tWrq7wGxOr67TMM5toAAIA3ODrHBgAAIBIENgAAwDMIbAAAgGcQ2AAAAM8gsAEAAJ5BYAMAADyDwAYAAHgGgQ0AAPAMAhsAAOAZBDYAAMAzCGwAOM6SJUtUv3597d69u+zYtddeq969e6ugoMDGkQFwOnpFAXAcwzDUt29fDR06VP/7v/+rOXPm6Nlnn9W6devUoUMHu4cHwMHq2j0AAKjI5/PpwQcf1OTJk9W+fXs9+eSTys3NJagBUC1mbAA41llnnaXPP/9c7733noYPH273cAC4ADk2ABzp3Xff1ZdffqmSkhK1adPG7uEAcAlmbAA4zubNmzVixAjNnz9fS5cuVcOGDfXSSy/ZPSwALkCODQBH2bFjh8aNG6eZM2fqiiuuUPfu3XX22Wdr06ZN6tevn93DA+BwzNgAcIzDhw9ryJAhGjZsmJ555pmy4xMmTFBRUZHeeecdG0cHwA0IbAAAgGeQPAwAADyDwAYAAHgGgQ0AAPAMAhsAAOAZBDYAAMAzCGwAAIBnENgAAADPILABAACeQWADAAA8g8AGAAB4BoENAADwDAIbAADgGf8fhxvmCVJdzuQAAAAASUVORK5CYII=", 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", 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    " ] @@ -515,7 +515,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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    " ] @@ -583,18 +583,18 @@ "output_type": "stream", "text": [ "The intercept alpha: \n", - " [1.94485679]\n", + " [2.07549007]\n", "Coefficient beta : \n", - " [[5.13059483]]\n", - "Mean squared error: 0.32\n", - "Variance score: 0.87\n", + " [[5.14029264]]\n", + "Mean squared error: 0.23\n", + "Variance score: 0.89\n", "Mean squared log error: 0.01\n", - "Mean absolute error: 0.45\n" + "Mean absolute error: 0.38\n" ] }, { "data": { - "image/png": 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", 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", 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    " ] @@ -822,7 +822,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
    " ] @@ -838,7 +838,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.004999999999999996\n" + "0.004999999999999987\n" ] } ], @@ -5124,7 +5124,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.15" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb index 70350cab6..ee16dc42e 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb @@ -1077,7 +1077,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1655,7 +1655,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1673,7 +1673,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1691,7 +1691,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1709,7 +1709,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1727,7 +1727,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1745,7 +1745,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1763,322 +1763,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.09166666666666666\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.10555555555555556\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 11\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, lmbd \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(lmbd_vals):\n\u001b[1;32m 9\u001b[0m dnn \u001b[38;5;241m=\u001b[39m NeuralNetwork(X_train, Y_train_onehot, eta\u001b[38;5;241m=\u001b[39meta, lmbd\u001b[38;5;241m=\u001b[39mlmbd, epochs\u001b[38;5;241m=\u001b[39mepochs, batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m 10\u001b[0m n_hidden_neurons\u001b[38;5;241m=\u001b[39mn_hidden_neurons, n_categories\u001b[38;5;241m=\u001b[39mn_categories)\n\u001b[0;32m---> 11\u001b[0m \u001b[43mdnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 13\u001b[0m DNN_numpy[i][j] \u001b[38;5;241m=\u001b[39m dnn\n\u001b[1;32m 15\u001b[0m test_predict \u001b[38;5;241m=\u001b[39m dnn\u001b[38;5;241m.\u001b[39mpredict(X_test)\n", + "Cell \u001b[0;32mIn[6], line 98\u001b[0m, in \u001b[0;36mNeuralNetwork.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX_data_full[chosen_datapoints]\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mY_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mY_data_full[chosen_datapoints]\n\u001b[0;32m---> 98\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeed_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 99\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbackpropagation()\n", + "Cell \u001b[0;32mIn[6], line 38\u001b[0m, in \u001b[0;36mNeuralNetwork.feed_forward\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfeed_forward\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 37\u001b[0m \u001b[38;5;66;03m# feed-forward for training\u001b[39;00m\n\u001b[0;32m---> 38\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_h \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatmul\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mX_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhidden_weights\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhidden_bias\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ma_h \u001b[38;5;241m=\u001b[39m sigmoid(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_h)\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_o \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmatmul(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ma_h, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_weights) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_bias\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], @@ -2123,52 +1822,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n" - ] - }, - { - "data": { - "image/png": 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7xKc/6fNZ3xnat37TGpq2dKj6hL6mlIPHcz336cbx8qvsm2efx299WSdK2jFu10BPRoSpVkBlnTpxRitiNujzeWsM7Vs/qJqmffa0+twzVSl/ncz1XJuOjRT+1J2qUfcfOnE0Tau//FWfz1ujC5lZ1+G3KLpuvb2hnhx6t2rVr6JTx89oxWc/6/M5/zG0b/3G1TVt4WD1uWuSUg6dyPVcjXqV1Oe5zmrWKkAXMrO0PW6v5k38SocPHC/g1UqWkDb11WPgxc+/s/p60UbFfLC2wO1Ll/bQI0/cprD7glWpSgUdSU7Vf77ZqpgP1unChZJ1zl6NoroKVseOHS/7/OrVq/MdP3z4sKTsJOJ/Va5cWUlJSXm2r1Spko4cOaKsrCx5eHhIktLS0nTq1CkdO3Ysz/YFIQExQaOWAXrl02f0w5I4ffT6UjVp1UA9RneRrZRN0W+tuOy+dRvX0PjoCJUuk/c/VfsHb9VLHzylrz9Yo49eX6qKlXz1xAsPatKyEXqmw6vKTL9wvX6lIiWoWQ29MjVca2K366M536txcC31fPpOlbLZ9FkBb8JPDe+ksM7B+jTyB+2K/0v1G1bVE/1DVdn/Bk2b8GWubcv7eumVqeHyr3aDCb9N0dSoRT29Mn+Qfli6UR+9vkxNWtVXj5e6yFaqlKKnGTiHFwxW6TIeeZ5r/8CteilqgL7+cI0+en1Z9jn8/AOatGS4nuk4ocScw41uraOxkX31w/Jf9fHkFWrcop56jLxXpWw2Rc/MWwL/X3UbVdO4D/rne3xv+IeP/Cr76r1xS5SweV+u51JPnHHlr1DkNbq5lsbOekI/rNymj6fHqvEtddTj2TCVKmVT9Hv/d9l96zb017g5PfI9xs3b1Nfod7rph5Xb9MFb36rOTVXUc8g/VaFiOb372lfX6bcpeho1r62xc3rqhxVb9PG0b9X41jrqMaxT9jn87veX3bduYFWNe79Pvsf3H1UraGrMIB3ce0SThi6Q3auMegy9W6992E9P3ztVGSXkPaIgQc1q6pVp4Vqzaoc+mv29Gt9cSz0H/v35F/VDvvs8NaKTwu67WZ++v0a7dhxS/cBqemLAHdmff68uM/k3gNUuXjxut9tzjXt6eurUqVN5tu/cubPmzJmj119/XcOGDVNWVpbGjRsnm82W7zUjBSEBMUH3Uffrz20HNPnpKEnSptU75FHGQ/9+9h4tnh2rjPOZefYpXcZDD/S/U0++8GC+z0vS4yPu04ZVWzVj+PycsQO7D2v66pfU6u5mWvfl5uvzCxUx3fqG6s9dhzV57FJJ0saf96h0aQ/9u0dbLVrwc54PqPK+Xur8cIgiZ3ynhfN/kiT9FrdXktTv2TBFzfxOp06elSS1ub2hnh7RSd7euf/HLGm6j7xff24/oMkD/z6Hv794DnfS4ncvcw73u1NPPv+gMs7n/6b0+PDO2hC7TTNGfJozduCPJE2PfUmt/tlM674qIefwkE76M/6QpgzJPg6b1iSodBkP/WtgRy2e93/KSC/g+PZsrydG3Fvge0RA4xqSpJ++2ZrnW+WSptvAO/Xn70ma8vwXkqRN63ardOlS+lffUC3+cF2+f8iWLuOhB7q10ROD7yrwD92wh27VkaRTmvzc53I4nPr15z90g5+PHurRVnMnfa2sfCqq7qhbRJj+/P0vTRkRLUna9MNOlS7toX8N6KDFUT8UfHyfbKsnhtxd4Dn8RMQ/de5Mul58cq7S/94m+cBxjX2vpxo0rakdG/dev1+qGOjW/w79ufOwJo9ZLEna+PMf2Z9/Pdtp0ac/5fP5563Oj4Qocvp3WvjJj5L+5/NvyD8VNSM25/MPxUtBFY4r8fLykpR9LcjFf0tSenq6vL2982xfu3ZtzZgxQ2PGjNGnn34qLy8vPfHEE2rSpIl8fHwMz8s1INdZGXtpNW17k35cnvsPqXVfblLZ8l5q0qZBvvu1CGuqbqPuV/RbKxQ1blGe5202mzb/X7xWfJT7G46DfyRLkqrWqeyi36BoK1PGQ81uraN1//k91/ja7+NVtpynmtxcK88+ZX289PXijfpl7c5c44f2Z5cO/atXlCSV8/HUy2/+W1s37dOLEfPzvE5J8d9z+Ndc4+u+2qSyPl5q0rqAc/iupuo28j5FT1uhqPGL8zxvs9m0eU28Vnx86TmcIkmqWqeSi36Doq2M3UPNWtfXj99szTW+7uvfso9vy3r57teiQ5C6DemkmBmxipqY/zft9RpX1+lTZ0t88lGmjIeataynH7/bkWt83art2e8Tt9bJd78Wt9+kbgPvVMzc/1PU1PzbDcvYPXT+XIYc/7MMTerJMypjLy3vcp4u+x2KsjJ2DzVrFaAfV23PNb7um63Z53CLAs7h0EB1GxymmNmrFTU5/0rqbXc31bdfxOUkH5K0e/tBdW87ocQnHwV+/q3ekX1eN6+dZ5+yPp76etFG/fJDQq7xQ/uPSpL8a1S8fgG7iSzZiuTjal1svUpJSck1npKSIn9//3z3CQ0N1Zo1a7R27Vr98ssvGj58uA4cOKA6deoYnpcE5Drzr/MP2T3L6NCe5Fzjf/2Z/R+6ekCVfPfb9es+9Qh+XtFvrcj3GzSn06l5L3+hX1bmvh6h7f3NJUmJvx9yRfhFnn/1irLbS+ckDxf99XdvcI1aN+bZJ/mvk5o5aYUOJl5yLUKHQGVmZuW8Vvr5TPV/dLamjFum1BL8jZB/7YLO4SOSrnAON39B0dNWKCufvmKn06l5YxbmPYfv+/scTvjLFeEXef61/qEynqV16O/jedFfidl/EFSvl38itmvLfvW4bbyiZ8YW+C17QFB1pZ06q9Hv9dLC7RO1+PdJen7mk6qYzzUh7sy/pp/K2Evr0L5L3if+/n+9ep1/5Lvfrm2H1CNssqLf+z9lZeV/jL9a8Iuq1b5Rj/Rur3LlvRTYrKa6PNFWG9YkKO1U4dbFL678a96YfXz3XnoO/3186xZ0fA+oR+jrin73+3zP4So1KsrH11vJh45r4CsPKSbuFS3b8bpemdtLlare4PLfo7jJ+fxLLOTn3xtf5/38uzNImZkX8rwW3F9gYKB8fHy0fv36nLHU1FTFx8crJCQkz/abNm1S9+7dlZGRoUqVKsnLy0sbNmzQiRMndNtttxme1/IWrAsXLmjVqlXauHGj/vrrL2VkZMjb21v+/v4KCQlRWFiYSpe2PMyr5lOhrCTp7OnzucbPpmX/XLZ83vKWJB1LOlnouarVq6y+47pq92+Jivtu+5V3cAM+5bPLhWfPpOcaP3s2++eyBr+BbNehkTreG6yl0euV9vd/qwsXHHnepEuiK5/DXnn2kaRjh08Weq5q9Sqr7ytdtXtLyTmHy/lmvwdcPJ4XnU37+xz2KeD4Juftzb1UvaDq+of/Dfrms1+0JHKNatWvou7D7tGbnz+jZ+6ZovRzxvt1i7NyOe8TlxzjM9m/f1mf/N8njqWkXvG1t274Uwsj16rviHvUd8Q9kqQ/4g9p0siYawm5WPnvOXzJ+/CZK53Dlz++Ffyy2zl6j7xXO7ce0KShC1ThRh/1GnGPJn36lJ7uPFXp5/Jv3SoJCv78+/u8Nvr5d2cjdby3mZZ+9t/PP5Qcdrtd3bt315QpU+Tn56fq1atr8uTJ8vf3V1hYmLKysnT8+HGVL19eXl5eCggI0O7du/X666+rT58+OnDggEaNGqXHHntMNWvWNDyvpX/Z79+/X/369VNycrKCgoJUuXJlVahQQenp6fr999+1aNEizZgxQ++//76qVatmZahXzVYquyzmdOZ/lxinwzX9wTVv8tfri4cpM/2CJvScU+B87sZmu3h883/eYeA4tLuzkZ579WFt27xPUTONrTpUklz5HHbNuVazgb9eXzRUmRmZmtCr5JzDpf4+vgWdxEbO4YK8NeIzZaZnas+O7Irojg1/KnHXYU1d/KzueqSFvp7/41W/dnHy32Oc//OOaziHB7/SRWEP3aIF736v337ZI/8aFdX9mbs0YW4vvdA7MlfrkLsqZbvCOXyVx7fM3xelnzyWpgkDP855T0hKPKppCwfrzgdv1croX67qtd3Bld6bDX3+dQzScxMe0bbNiYqacfkFL5DN4Syaq2Bdi4iICF24cEGjR4/W+fPn1aJFC0VGRsput+vgwYPq2LGjJk6cqIcfflg33HCD5s6dq4kTJ+r+++9XxYoV9dhjj+npp58u1JyWJiDjxo1TjRo1tHDhQpUvXz7P86mpqRo6dKjGjx+vOXPmWBDhtTvzdwn+0krHxW+EzqRee4m+WbuGevnjp3Uu7bxeeHiakv/u5SwJzlz8Fv6Sb3rKls3++dJv5C718OOt1TciTFs379Mrw6OVWcKWzTTizKns9rNLKx055/BpF53DHz6lc2npeqHrNCXvLzmVp7S/3wMu/Zb44rfyZ1Ov/hvJS1e+kqT4jXuVduqc6gYVzy91rsbFb3XzvE+Uy15c4uzpy79PFOTGyr7q1DVEMXPX6JMZ2V9ebIvbq13bDmnOl8/qnw/fqq8WuP8fyGmnCziH/z7el1ZPjbr4zX7cmoRcf2Qn/LZfp0+dVUCjknMO5+fMxfPa59LPv7/P6yt9/nVro77P/lNbN+3TK8M+4/OvBPPw8NDIkSM1cuTIPM/VqFFDO3fmvmY2ODhY0dHR1zSnpQnIpk2bFBMTk2/yIWXfCGXkyJHq1q2byZG5zl97U5R1IUvVLunjrlYv+yLx/TvzrrFcGHc80lLDZ/XSoT3JGv2vd3T0r5J1selfB48r64JD1Wr45RqvVjP758RLepL/18AR9+jBR1tqTex2TR67lDffAvy178jf53DuhQ0untMuOYdn9Mw+h//9jo5eRfthcZaUeFRZF7JU9ZLrEKrVzv55/+7DV/W65Xy91bZTMyX8uk/7d+e+fqe03UOpx0vOMrxJ+49nH+PauXviq/3dI79/T0p+u11RpaoVVKpUKcX/mphrPPGPZJ06cUa16+d/fZS7SUo8lv/x/fvn/X8k57fblV93/zFlZTlUxp73T5XSpT2Uns/qcCXJXwdPZL8317jkuF/8/Puz4PN64Mh79eBjrbRm1XZNHrOYzz+YztKL0H19ffNcdX+pv/76K9eyYMVNZvoFbftpt9red0uu8XYP3KrTJ89o5+arX8WjxV1NNPLd3vp9wx4N6zSpxCUfkpSZkaVtvyaqbYfAXOPt7wzS6dRz2rkj/4vxew3qqAcfbalFn/6s119cxJvvZWSmX9C2n3erbefmucbb3e+ic3hWL/0et0fD7n2zxCUf0t/Hd/0ete0UnGu8XeebdfrUWe38bf9Vve6FjAsaNKGr/jXwrlzjbf7ZVF7edm395Y+rjrm4ycy4oG0b96ntXY1zjbf7ZxOdPnVOO7cduKrXvZjYNL5kFa3qdf6hChXL6fDBknGjvMyMC9oWt1dt726aa7xdp2bZ5/CWqzuHz5/N0I6Ne9X2n01Vxv7fe4Tc3Ka+vMt5akdcyV4FKzPjQvbn352Nco2379j48p9/z9ylBx9rpUXzf9LrL3zB518hWb3alatXwbKKpRWQrl276oUXXlBERIRatWqlqlWrym63KyMjQ8nJydqwYYOmTJmirl27WhnmNfts6teauGSoXvpggL799EcFtQxQ18H/VNS4Rco4n6my5b1Uq2E1Je1N0aljaYZes4xnaQ15p4fOpp3XZ299rVoNc9/B8uhfJ0pMQrIg6ge9MetJvTSxq7796jcFNauprk/cpsgZ3ykj/YLKlrOrVt1KSjp4QqdOnlW9m6ro30+21c74Q/rhux0KbFI91+vt33sk5+JUZPvsra81cdFQvRQ5QN8u+FFBLeqp6zP/VNT4xdnnsI+XajWsqqR9Rwp3Dr/95N/n8ArVuin3cn9H/zpRYhKS6Bmxen3B03rx3Z5aFbNejW6to0cGdFDUxK+UkZ6psj6eqtXAX0mJR3XKYOUi/XymFs75Xo8PuVsnj5zWpjW/q05gNXUf2knrV+/Qb+t2XeffqmiJfu8/ej2yt16cFq5Vizep0c219Ejv9op669u/3yc8VSugspIOHNcpgzdpPHXijJZ+8pO69movSfr1pz9UudoN6jbwTiX/dULfLIy7nr9SkRI96zu9/nF/vTiju1Z9EadGt9TRI/1CFfXmiuzj6+OpWvWrKGn/McPnsCR9MGWl3vz0KY1/v48Wvb9GN/yjvHqPulcJvyXql9U7rvwCbm5B5A96Y/aTemnSv/Xtss0KCq6lrk/epsjp3/33vK5bSUkHj//9+eevf/doq507DumH2B0KbFIj1+tlf/5dXUsiUBiWJiCDBw9WqVKlNGnSJJ09m3eZ03Llyqlbt2569tlnLYjOdbasTdCEHnP0xPMPaMwnA3Us6aTeH7tQi2dlX/BVv1ktvfnVSE0d9IFiP/vJ0GsGtayvG/9ehnDi4mF5np8/6UvNn1Qy7sK7ZeM+vfrc53qi/x0aO/lRHTtyWu9Pj9WiT3+WJNVvWFWT3+upKeOWKnb5FrXr0EilStnUMKi63vmgb57XGzngQ23dnJhnvCTbsnanJvScoyeee0BjPn46+xx+ZZEWz/77HA6upTeXjdDUZz5QbPTPhl4zqGWAbvS/QZI0cdHQPM/Pf/MrzX+zhJzDP+3WawM+UPdh92jMvD46mnxSka99qcXz/k+SFNCkpt78/BlNHbZA3y3cYPh150/7RieOnlbnJ9rq/p7tdPrEWa1Y8JPmF3BPC3e2Zf2feu3ZBer+TEeNmdFdR5NTFTnlGy3+cJ0kKSComt78qJ+mvrhQ3y01fgPM9yev1NHDp3Tvo630SM92On7ktDb/uFsfvROrtGu4fqe42fLLHr026BN1f/afGjOnp44ePqXISV9rcWT2fX4CGlfXm58+ramjYvTd4o2GXzfh10Q9122OegzvpJdmPan0c5n6+bvten/i8mtaPMBdbInbq1dHxeiJAR00dmq4jqWk6v13YrXo75vs1g+sqslze2nKK0sU+9VvandnI5UqVUoNG1fXOx/1y/N6I/t/oK2b9pn8W6AksjmLwFIzmZmZ+v3335WcnKxz587Jy8tL/v7+CgwMzHNr+GvRyS/v/2xwHWdAjStvhGti21sy7u9iFVvZslaH4P58OMbXXUbJvjbienNU4By+3r7dNM7qEAoUt7+O1SHkq0WtfVaHUChF4gYbZcqUUbNmzawOAwAAAMB1xp3QAQAAAJimSFRAAAAAgKLOHW9EaAUqIAAAAABMQwICAAAAwDS0YAEAAAAGFMeb/hVFVEAAAAAAmIYEBAAAAIBpaMECAAAADMhy8t29K3AUAQAAAJiGBAQAAACAaWjBAgAAAAxw8N29S3AUAQAAAJiGBAQAAACAaWjBAgAAAAzgRoSuQQUEAAAAgGlIQAAAAACYhhYsAAAAwABuROgaHEUAAAAApiEBAQAAAGAaWrAAAAAAAxysguUSVEAAAAAAmIYEBAAAAIBpaMECAAAADMjiu3uX4CgCAAAAMA0JCAAAAADT0IIFAAAAGMCNCF2DowgAAADANCQgAAAAAExDCxYAAABggIPv7l2CowgAAADANCQgAAAAAExDCxYAAABgQJbTZnUIboEKCAAAAADTkIAAAAAAMA0JCAAAAADTcA0IAAAAYEAW3927BEcRAAAAgGlIQAAAAACYhhYsAAAAwACHk+/uXYGjCAAAAMA0JCAAAAAATEMLFgAAAGAAq2C5BkcRAAAAgGlIQAAAAACYhhYsAAAAwIAsp83qENwCFRAAAAAApiEBAQAAAGAaWrAAAAAAAxx8d+8SJSoBSXg10OoQ3FqpiulWh+D2nM66Vofg1krbs6wOwe1dOOxtdQhuz+HtsDoEtzakbazVIQDFHmkcAAAAANOUqAoIAAAAcLWynHx37wocRQAAAACmIQEBAAAAYBpasAAAAAADHOJGhK5ABQQAAACAaUhAAAAAAJiGFiwAAADAAFbBcg2OIgAAAADTkIAAAAAAMA0tWAAAAIABWXx37xIcRQAAAACmIQEBAAAAYBpasAAAAAADHE5uROgKVEAAAAAAmIYEBAAAAIBpaMECAAAADGAVLNfgKAIAAAAwDQkIAAAAANPQggUAAAAY4HDy3b0rcBQBAACAEsrhcGj69Olq3769goOD1bt3byUmJha4/ZEjRzRs2DC1atVKrVq10rPPPqvDhw8Xak4SEAAAAKCEmj17tqKjozVhwgTFxMTIZrOpX79+ysjIyHf7oUOHKikpSR988IE++OADHT58WAMHDizUnCQgAAAAgAFZshXJx9XKyMhQVFSUBg8erNDQUAUGBmratGlKTk5WbGxsnu1TU1MVFxenfv36KSgoSEFBQerfv7927NihEydOGJ6XBAQAAAAogRISEnTmzBm1bt06Z8zX11dBQUGKi4vLs72np6fKli2rpUuXKi0tTWlpaVq2bJnq1KmjChUqGJ6Xi9ABAACAEujitRtVq1bNNV65cmUlJSXl2d7T01Ovvfaaxo8fr5CQENlsNlWqVEnz589XqVLG6xokIAAAAIABRXUVrI4dO172+dWrV+c7fu7cOUmS3W7PNe7p6alTp07l2d7pdGrnzp1q3ry5+vbtq6ysLE2bNk2DBg3SZ599Jh8fH0PxkoAAAAAAJZCXl5ek7GtBLv5bktLT0+Xt7Z1n+6+//loLFizQf/7zn5xkY86cOerQoYMWLVqkHj16GJqXBAQAAAAoxgqqcFzJxdarlJQU1apVK2c8JSVFgYGBebbftGmT6tatm6vSUaFCBdWtW1f79u0zPG/RrCMBAAAARYzVq125ehWswMBA+fj4aP369Tljqampio+PV0hISJ7tq1atqsTERKWnp+eMnTt3TgcPHlTt2rUNz0sCAgAAAJRAdrtd3bt315QpU7R69WolJCRo6NCh8vf3V1hYmLKysnTkyBGdP39ektSlSxdJ0pAhQ5SQkJCzvd1u18MPP2x4XhIQAAAAoISKiIhQ165dNXr0aIWHh8vDw0ORkZGy2+1KSkpSu3bttGLFCknZq2MtWLBATqdTPXr0UK9evVSmTBl99tln8vX1NTynzel0Oq/XL1TU1Jk11eoQ3FqpiulX3gjXxOm8+jIrrqy0PcvqENzehcN5L2qEazm8HVaH4NaGtM17cza41tBGq6wOoUCv7bjP6hDy9VLj5VaHUChUQAAAAACYhgQEAAAAgGlYhhcAAAAwIKuI3oiwuOEoAgAAADANCQgAAAAA09CCBQAAABjguIab/uG/qIAAAAAAMA0JCAAAAADT0IIFAAAAGMAqWK7BUQQAAABgGhIQAAAAAKahBQsAAAAwwOFkFSxXoAICAAAAwDQkIAAAAABMQwsWAAAAYEAW3927BEcRAAAAgGlIQAAAAACYhhYsAAAAwABWwXINKiAAAAAATEMCAgAAAMA0tGABAAAABjj47t4lOIoAAAAATEMCAgAAAMA0lrdgPfHEE7LZjK0o8PHHH1/naAAAAID8ZbEKlktYnoC0adNGM2bMUL169dSsWTOrwwEAAABwHVmegAwcOFBly5bV9OnT9d5776lGjRpWhwQAAADgOikS14D07NlTt9xyi95++22rQwEAAADy5XDaiuSjuLG8AnLRa6+9pvj4eKvDAAAAAHAdFZkEpEqVKqpSpYrVYQAAAAC4jopMAgIAAAAUZQ5nkbh6odjjKAIAAAAwDQkIAAAAANPQggUAAAAYkKXit+JUUUQFBAAAAIBpSEAAAAAAmIYWLAAAAMCA4njTv6KICggAAAAA05CAAAAAADANLVgAAACAAdyI0DU4igAAAABMQwICAAAAwDS0YAEAAAAGOLgRoUtQAQEAAABgGhIQAAAAAKahBQsAAAAwIIsbEboEFRAAAAAApiEBAQAAAGAaWrAAAAAAA7gRoWtwFAEAAACYhgQEAAAAgGlowQIAAAAMcLAKlktQAQEAAABgGhIQAAAAAKahBQsAAAAwwCFasFyBCggAAAAA05CAAAAAADANLVgAAACAAayC5RpUQAAAAACYhgQEAAAAgGlowQIAAAAMcDj57t4VOIoAAAAATEMCAgAAAMA0tGABAAAABrAKlmtQAQEAAABgGhIQAAAAAKahBQsAAAAwwCFasFyBCggAAAAA05CAAAAAADANLVgAAACAAayC5RpUQAAAAACYhgQEAAAAgGlowQIAAAAMoAXLNaiAAAAAADANCQgAAAAA09CCBQAAABhAC5ZrUAEBAAAAYBoSEAAAAKCEcjgcmj59utq3b6/g4GD17t1biYmJ+W47Y8YMNWzYMN/HCy+8YHhOm9PpdLrqFyjqlv15s9UhANfEr1Sa1SG4tUx5WB2C28ty8r3X9dbRO8vqENzaMccZq0Nwe5WqHbI6hAJ1/iHC6hDy9fXt069635kzZ2rBggWaOHGiqlSposmTJ+vAgQNavny57HZ7rm3PnDmjs2fP5hpbuHCh5syZo5iYGAUGBhqak08CAAAAoATKyMhQVFSUBg8erNDQUAUGBmratGlKTk5WbGxsnu3LlSunSpUq5TzOnTun9957T88//7zh5EMiAQEAAABKpISEBJ05c0atW7fOGfP19VVQUJDi4uKuuP8bb7yhBg0a6NFHHy3UvKyCBQAAABjgUNFcBatjx46XfX716tX5jh8+fFiSVLVq1VzjlStXVlJS0mVfc9u2bVq9erU++ugjlSpVuJoGFRAAAACgBDp37pwk5bnWw9PTU+np6Zfd98MPP1RwcHCu6olRVEAAAACAYqygCseVeHl5Scq+FuTivyUpPT1d3t7eBe539uxZxcbGauzYsVc1LwkIAAAAYIC73YjwYutVSkqKatWqlTOekpJy2YvK165dK4fDobCwsKualxYsAAAAoAQKDAyUj4+P1q9fnzOWmpqq+Ph4hYSEFLjfpk2b1LhxY/n6+l7VvFRAAAAAgBLIbrere/fumjJlivz8/FS9enVNnjxZ/v7+CgsLU1ZWlo4fP67y5cvnatFKSEjQTTfddNXzUgEBAAAADHA4bUXycS0iIiLUtWtXjR49WuHh4fLw8FBkZKTsdruSkpLUrl07rVixItc+R48e1Q033HDVc3IndKAY4U7o1xd3Qr/+uBP69ced0K8v7oR+/RXlO6GH/d9Qq0PIV+wd06wOoVD4JAAAAABgGq4BAQAAAAxwt1WwrEIFBAAAAIBpSEAAAAAAmIYWLAAAAMAAWrBcgwoIAAAAANOQgAAAAAAwDS1YAAAAgAFOWrBcggoIAAAAANOQgAAAAAAwDS1YAAAAgAEO0YLlClRAAAAAAJiGBAQAAACAaWjBAgAAAAzgRoSuQQUEAAAAgGlIQAAAAACYhhYsAAAAwABuROgaVEAAAAAAmIYEBAAAAIBpaMECAAAADGAVLNegAgIAAADANCQgAAAAAExDCxYAAABgAKtguQYVEAAAAACmIQEBAAAAYBpasAAAAAADWAXLNaiAAAAAADANCQgAAAAA09CCBQAAABjgdFodgXugAgIAAADANCQgAAAAAExDCxYAAABggEOsguUKVEAAAAAAmIYEBAAAAIBpaMECAAAADHByI0KXoAICAAAAwDQkIAAAAABMQwsWAAAAYICDFiyXoAICAAAAwDQkIAAAAABMQwsWAAAAYIDTaXUE7oEKCAAAAADTWJ6A7N27VzNmzNCECRO0Zs2aPM+npaXphRdesCAyAAAAAK5maQKyadMmPfTQQ1q+fLl++OEHPfXUUxo8eLAyMjJytjl//ryWLl1qXZAAAACAsm9EWBQfxY2lCcjUqVPVtWtXffvtt1q1apXeeust/fjjj3rqqaeUmZlpZWgAAAAArgNLE5CdO3eqe/fuOT/fc889mjdvnn799VeNGjXKwsgAAAAAXA+WJiA+Pj46ceJErrFbb71VkydP1rfffquJEydaFBkAAACQm9WtVrRguUBoaKjGjx+vLVu25Gq5uuuuu/Tiiy/qo48+0vjx4y2MEAAAAIArWZqADB8+XBUrVtRjjz2mn3/+Oddz3bt315gxY/T9999bFB0AAAAAV7P0RoQVKlRQVFSU9u/fr4oVK+Z5/vHHH1ebNm20atUqC6IDAAAA/stRDNudiqIicSf0WrVqFfhc3bp1NWDAABOjAQAAAHC9WH4jQgAAAAAlR5GogAAAAABFndNpdQTugQoIAAAAANOQgAAAAAAwDS1YAAAAgAHF8aZ/RREVEAAAAACmIQEBAAAAYBpasAAAAAADaMFyDSogAAAAAExDAgIAAADANLRgAQAAAAZwH0LXoAICAAAAwDQkIAAAAABMQwsWAAAAYACrYLkGFRAAAAAApiEBAQAAAGAaWrAAAAAAI1gGyyWogAAAAAAwDQkIAAAAANPQggUAAAAYwCpYrkEFBAAAAIBpSEAAAAAAmIYWLAAAAMAAJ6tguQQVEAAAAACmIQEBAAAAYBoSEAAAAMAAp9NWJB/XwuFwaPr06Wrfvr2Cg4PVu3dvJSYmFrh9Zmampk6dqvbt2+vmm29W9+7d9fvvvxdqThIQAAAAoISaPXu2oqOjNWHCBMXExMhms6lfv37KyMjId/tXXnlFCxcu1KuvvqpFixbphhtuUL9+/XT69GnDc5KAAAAAACVQRkaGoqKiNHjwYIWGhiowMFDTpk1TcnKyYmNj82x/4MABLVy4UBMnTtQdd9yhgIAAvf7667Lb7dq+fbvheVkFCwAAADDCzW5EmJCQoDNnzqh169Y5Y76+vgoKClJcXJw6d+6ca/t169bJ19dXt99+e67tv//++0LNSwICAAAAFGMdO3a87POrV6/Od/zw4cOSpKpVq+Yar1y5spKSkvJsv2/fPtWsWVOrVq3S3LlzlZycrKCgID3//PMKCAgwHC8tWAAAAEAJdO7cOUmS3W7PNe7p6an09PQ826elpWn//v2aPXu2hg0bpnfffVelS5fW448/rmPHjhmelwoIAAAAYEBRvRFhQRWOK/Hy8pKUfS3IxX9LUnp6ury9vfNsX6ZMGZ0+fVrTpk3LqXhMmzZNoaGhWrJkifr27WtoXiogAAAAQAl0sfUqJSUl13hKSor8/f3zbO/v76/SpUvnarfy8vJSzZo1dfDgQcPzkoAAAAAAJVBgYKB8fHy0fv36nLHU1FTFx8crJCQkz/YhISG6cOGCtm3bljN2/vx5HThwQLVr1zY8Ly1YAAAAgBFFtAXratntdnXv3l1TpkyRn5+fqlevrsmTJ8vf319hYWHKysrS8ePHVb58eXl5eSkkJES33XabnnvuOY0fP1433HCDpk+fLg8PDz344IOG56UCAgAAAJRQERER6tq1q0aPHq3w8HB5eHgoMjJSdrtdSUlJateunVasWJGz/YwZM9SyZUs988wz6tq1q9LS0vTxxx/Lz8/P8Jw2p7OoXk7jesv+vNnqEIBr4lcqzeoQ3FqmPKwOwe1lOfne63rr6J1ldQhu7ZjjjNUhuL1K1Q5ZHUKB6i143eoQ8vXn4y9aHUKh0IIFAAAAGOB0sxsRWoWvogAAAACYhgQEAAAAgGlowQIAAACMKDFXTl9fVEAAAAAAmIYEBAAAAIBpaMECAAAADGAVLNegAgIAAADANCQgAAAAAExDCxYAAABgBKtguQQVEAAAAACmKVEVkE7eqVaH4NZSnelWh+D2fG2eVofg1tKc560Owe1VLFXW6hDc3qjk5laH4Na2P3mT1SG4vW+2WB0BrrcSlYAAAAAAV49VsFyBFiwAAAAApiEBAQAAAGAaWrAAAAAAI1gFyyWogAAAAAAwDQkIAAAAANPQggUAAAAYQQuWS1ABAQAAAGAaEhAAAAAApqEFCwAAADDCyY0IXYEKCAAAAADTkIAAAAAAMA0tWAAAAIABTlbBcgkqIAAAAABMQwICAAAAwDS0YAEAAABG0ILlElRAAAAAAJiGBAQAAACAaWjBAgAAAIzgRoQuQQUEAAAAgGlIQAAAAACYhhYsAAAAwAAbq2C5BBUQAAAAAKYhAQEAAABgGlqwAAAAACNowXIJKiAAAAAATEMCAgAAAMA0tGABAAAARnAjQpegAgIAAADANCQgAAAAAExDCxYAAABgBKtguQQVEAAAAACmIQEBAAAAYBpasAAAAAAjaMFyCSogAAAAAExDAgIAAADANLRgAQAAAEbQguUSVEAAAAAAmIYEBAAAAIBpaMECAAAAjHDarI7ALVABAQAAAGAaEhAAAAAApqEFCwAAADDAxipYLkEFBAAAAIBpSEAAAAAAmIYWLAAAAMAIWrBcggoIAAAAANOQgAAAAAAwDQkIAAAAANOQgAAAAAAwTaEuQj9y5IhmzZqlAwcO6B//+IcaNWqkJk2aqHHjxvL29r5eMQIAAABwE4VKQF588UWtW7dODRo00MGDB/XVV1/J6XSqVKlSqlevnpo0aaKmTZuqadOmCgwMVJkyZa5X3AAAAICpuBGhaxQqAfn11181cuRI9e7dW5J09uxZ7dixQ9u2bdO2bdsUFxenJUuWSJLsdru2bt16xddMT0/X7t27Vb9+fXl5een333/X/PnzlZycrAYNGqhHjx7y9/e/il8NAAAAQFFTqATE09NTQUFBOT+XLVtWLVq0UIsWLXLGTp48qa1bt2r79u1XfL09e/aoZ8+eOnLkiKpVq6YJEyZo4MCBqlGjhgICAvTdd99p8eLFWrBggQICAgoTKgAAAIAiqFAXod91112Kj4+/7DY33HCDbr/9dg0cOPCKr/fmm2+qefPmWrp0qW699VY9/fTTuv/++/XVV1/pnXfe0cqVK9W2bVtNnDixMGECAAAArue0Fc1HMVOoBOSRRx7RypUr9ccff7hk8g0bNmjIkCEKDAzUc889p/T0dIWHh8tmyz6QpUuX1lNPPaVNmza5ZD4AAAAA1ipUC9a///1v2Ww2/etf/1KnTp3Uvn17NW7cWLVr176qyb28vHT+/HlJ0j/+8Q/9+9//lqenZ65tUlNTVb58+at6fQAAAABFS6ESkAkTJuj333/Xjh07tHLlSi1ZskQ2m03lypVTUFCQmjRpolGjRhl+vXbt2unVV1/VhAkTFBAQoPHjx+c853Q6tWHDBo0bN0533XVXYcIEAAAAXI9VsFyiUC1YXbt21csvv6zo6Ght3rxZX331lSZOnKguXbrowoULio6OLtTkL7zwgrKysjR79uw8z61YsUI9evRQ9erVNWzYsEK9LgAAAICiqVAVkP9VqlQpNWjQQA0aNFCXLl0kZVctCsPPz0+ff/65Tp48mee5Nm3aaOnSpQoMDLzaEAEAAAAUMVedgOTn4sXjhXXDDTfkGfPz85Ofn981RgQAAAC4CC1YLlGoFiwAAAAAuBYkIAAAAABM49IWLAAAAMBd2WjBcgkqIAAAAEAJ5XA4NH36dLVv317BwcHq3bu3EhMTC9x+yZIlatiwYZ7H5fa5FBUQAAAAoISaPXu2oqOjNXHiRFWpUkWTJ09Wv379tHz5ctnt9jzb79y5Uy1bttRbb72Va7wwi0dRAQEAAACMcBbRx1XKyMhQVFSUBg8erNDQUAUGBmratGlKTk5WbGxsvvvs2rVLgYGBqlSpUq6Hh4eH4XlJQAAAAIASKCEhQWfOnFHr1q1zxnx9fRUUFKS4uLh899m5c6fq169/TfPSggUAAAAUYx07drzs86tXr853/PDhw5KkqlWr5hqvXLmykpKS8mx//PhxHT16VHFxcfrkk0908uRJBQcHa8SIEapbt67heKmAAAAAAEZY3Wrl4hasc+fOSVKeaz08PT2Vnp6eZ/tdu3ZJkjw8PDRp0iRNmzZNZ8+e1eOPP66jR48anpcKCAAAAFCMFVThuBIvLy9J2deCXPy3JKWnp8vb2zvP9q1bt9aGDRtUoUKFnLFZs2apQ4cOWrx4sfr3729oXiogAAAAQAl0sfUqJSUl13hKSor8/f3z3ed/kw9JKlu2rGrUqKHk5GTD85KAAAAAAAbYnEXzcbUCAwPl4+Oj9evX54ylpqYqPj5eISEhebZfsGCBWrVqpfPnz+eMpaWlad++fYW6MJ0EBAAAACiB7Ha7unfvrilTpmj16tVKSEjQ0KFD5e/vr7CwMGVlZenIkSM5CUeHDh3kdDo1atQo7d69W9u2bdPgwYPl5+enhx56yPC8JCAAAABACRUREaGuXbtq9OjRCg8Pl4eHhyIjI2W325WUlKR27dppxYoVkrJbtj766COdOXNG4eHh6tmzp8qXL6+PP/441zUkV2JzOp3XULgpXtKT6lkdgltLdeZdLQGu5WvztDoEt5bmzLA6BLdXsVRZq0Nwe6OSm1sdglvb/uRNVofg9r7Z8qrVIRSo/pvTrA4hX3+MGmp1CIVCBQQAAACAaUhAAAAAAJiG+4AAAAAARpSYCxeuLyogAAAAAExDAgIAAADANLRgAQAAAAZcy03/8F9UQAAAAACYhgQEAAAAgGlowQIAAACMoAXLJaiAAAAAADANCQgAAAAA09CCBQAAABjAKliuQQUEAAAAgGlIQAAAAACYhhYsAAAAwAhasFyCCggAAAAA05CAAAAAADANLVgAAACAEbRguQQVEAAAAACmIQEBAAAAYBpasAAAAAADuBGha1ABAQAAAGAaEhAAAAAApiEBAQAAAGAaEhAAAAAApiEBAQAAAGAaVsECAAAAjGAVLJegAgIAAADANCQgAAAAAExDCxYAAABgADcidA0qIAAAAABMQwICAAAAwDS0YAEAAABG0ILlEiUqAXmwYajVIbg1x7lzVofg9kp5e1sdgltznE+3OgT353RYHYH7s9HccD3ZSv1pdQhAsce7FAAAAADTlKgKCAAAAHDVaMFyCSogAAAAAExDAgIAAADANLRgAQAAAAZwI0LXoAICAAAAwDQkIAAAAABMQwsWAAAAYAQtWC5BBQQAAACAaUhAAAAAAJiGFiwAAADAAFbBcg0qIAAAAABMQwICAAAAwDS0YAEAAABG0ILlElRAAAAAAJiGBAQAAACAaWjBAgAAAIygBcslqIAAAAAAMA0JCAAAAADT0IIFAAAAGMCNCF2DCggAAAAA05CAAAAAADANLVgAAACAEbRguQQVEAAAAACmIQEBAAAAYBpasAAAAAAjaMFyCSogAAAAAExDAgIAAADANLRgAQAAAAZwI0LXoAICAAAAwDQkIAAAAABMQwsWAAAAYAQtWC5BBQQAAACAaUhAAAAAAJiGFiwAAADAAFbBcg0qIAAAAABMQwICAAAAwDS0YAEAAABG0ILlElRAAAAAAJiGBAQAAACAaUhAAAAAAJiGBAQAAAAwwllEH9fA4XBo+vTpat++vYKDg9W7d28lJiYa2verr75Sw4YNdfDgwULNSQICAAAAlFCzZ89WdHS0JkyYoJiYGNlsNvXr108ZGRmX3e/QoUMaN27cVc1JAgIAAACUQBkZGYqKitLgwYMVGhqqwMBATZs2TcnJyYqNjS1wP4fDoZEjR6px48ZXNS8JCAAAAGCArYg+rlZCQoLOnDmj1q1b54z5+voqKChIcXFxBe43Z84cZWZmasCAAVc1L/cBAQAAAEqgw4cPS5KqVq2aa7xy5cpKSkrKd5+tW7cqKipKCxcuVHJy8lXNSwICAAAAFGMdO3a87POrV6/Od/zcuXOSJLvdnmvc09NTp06dyrP92bNnNWLECI0YMUJ16tQhAQEAAACuKze7E7qXl5ek7GtBLv5bktLT0+Xt7Z1n+wkTJqhOnTp67LHHrmleEhAAAACgGCuownElF1uvUlJSVKtWrZzxlJQUBQYG5tl+0aJFstvtat68uSQpKytLknTffffpgQce0Pjx4w3NSwICAAAAlECBgYHy8fHR+vXrcxKQ1NRUxcfHq3v37nm2X7VqVa6ft2zZopEjR2ru3LkKCAgwPC8JCAAAAGCAzc1asOx2u7p3764pU6bIz89P1atX1+TJk+Xv76+wsDBlZWXp+PHjKl++vLy8vFS7du1c+1+8iL1atWq68cYbDc/LMrwAAABACRUREaGuXbtq9OjRCg8Pl4eHhyIjI2W325WUlKR27dppxYoVLp3T5nQ6i2Qud//992vu3Ll5lgW7Fp18e7nstZCX4++VFHD9lMrngjC4juN8utUhuD+nw+oI3J+N7xavJ1upa7nrAoz49vynVodQoOAh06wOIV9b3h5qdQiFYmkL1tKlSwt8LjExUStXrpSfn58kqUuXLuYEBQAAAOSnSH5tX/xYmoCMGzdO58+flyTlV4h58803JUk2m40EBAAAAHADliYgixcv1ogRI1S+fHlNmjRJVapUyXmuefPm+vLLL1WzZk0LIwQAAADgSpY2itatW1cxMTFq1qyZHnzwQZdf4AIAAAC4jLOIPooZy69UK126tIYNG6YZM2ZoypQpGj58uE6fPm11WAAAAACuA8sTkItatGiRc1H6fffdp8zMTGsDAgAAAOByRepGhL6+vpo6daqWLl2qxYsXy9PT0+qQAAAAAEnudyNCqxSpBOSiLl26sOoVAAAA4IaKTAsWAAAAAPdXJCsgAAAAQJFDC5ZLUAEBAAAAYBoSEAAAAACmoQULAAAAMIBVsFyDCggAAAAA05CAAAAAADANLVgAAACAEbRguQQVEAAAAACmIQEBAAAAYBpasAAAAAADWAXLNaiAAAAAADANCQgAAAAA09CCBQAAABhBC5ZLUAEBAAAAYBoSEAAAAACmoQULAAAAMIIWLJegAgIAAADANCQgAAAAAExDCxYAAABgADcidA0qIAAAAABMQwICAAAAwDS0YAEAAABG0ILlElRAAAAAAJiGBAQAAACAaWjBAgAAAAywOenBcgUqIAAAAABMQwICAAAAwDS0YAEAAABG0IHlElRAAAAAAJiGBAQAAACAaWjBAgAAAAyw0YLlElRAAAAAAJiGBAQAAACAaWjBAgAAAIygBcslqIAAAAAAMA0JCAAAAADT0IIFAAAAGMAqWK5BBQQAAACAaUhAAAAAAJiGFiwAAADACFqwXIIKCAAAAADTkIAAAAAAMA0tWAAAAIABrILlGlRAAAAAAJiGBAQAAACAaWjBAgAAAIygBcslqIAAAAAAME2JqoBkpaVZHQJwTTiHAVyJh4+31SG4Nd6HgWtXohIQAAAA4GqxCpZr0IIFAAAAwDQkIAAAAABMQwsWAAAAYISTHixXoAICAAAAwDQkIAAAAABMQwsWAAAAYACrYLkGFRAAAAAApiEBAQAAAGAaWrAAAAAAI2jBcgkqIAAAAABMQwICAAAAwDS0YAEAAAAG2BxWR+AeqIAAAAAAMA0JCAAAAADT0IIFAAAAGMEqWC5BBQQAAACAaUhAAAAAAJiGFiwAAADAABstWC5BBQQAAAAooRwOh6ZPn6727dsrODhYvXv3VmJiYoHbb9++XT169FDz5s3VunVrjRkzRqmpqYWakwQEAAAAKKFmz56t6OhoTZgwQTExMbLZbOrXr58yMjLybJuSkqJevXqpVq1aWrJkiWbPnq3NmzfrueeeK9ScJCAAAACAEU5n0XxcpYyMDEVFRWnw4MEKDQ1VYGCgpk2bpuTkZMXGxubZ/tChQ2rfvr3Gjh2rOnXq6JZbbtG//vUv/fzzz4WalwQEAAAAKIESEhJ05swZtW7dOmfM19dXQUFBiouLy7N98+bN9dZbb6l06ezLyP/44w8tWbJEbdu2LdS8XIQOAAAAFGMdO3a87POrV6/Od/zw4cOSpKpVq+Yar1y5spKSki77mnfffbf27dun6tWra/bs2YWIlgoIAAAAYIjNWTQfV+vcuXOSJLvdnmvc09NT6enpl913ypQpmj9/vipVqqQnn3xSZ86cMTwvFRAAAACgGCuownElXl5ekrKvBbn4b0lKT0+Xt7f3Zfdt2rSpJGnGjBkKDQ1VbGysunTpYmheKiAAAABACXSx9SolJSXXeEpKivz9/fNsv2fPHq1ZsybXWOXKlVWhQgUlJycbnpcEBAAAADDCWUQfVykwMFA+Pj5av359zlhqaqri4+MVEhKSZ/u1a9fq2WefVVpaWs7Y/v37deLECQUEBBielwQEAAAAKIHsdru6d++uKVOmaPXq1UpISNDQoUPl7++vsLAwZWVl6ciRIzp//rwk6cEHH1T58uU1cuRI7d69Wxs3blRERISaNWumDh06GJ6XBAQAAAAooSIiItS1a1eNHj1a4eHh8vDwUGRkpOx2u5KSktSuXTutWLFCklSxYkV9/PHHcjgcCg8P16BBgxQUFKTIyEh5eHgYntPmdF7D3UuKmbBS/7I6BAAArisPHx+rQ3BrWf/TeoLrI9bxhdUhFKj9Q1OsDiFfa5eMsDqEQqECAgAAAMA0JCAAAAAATMN9QAAAAAAjSs6VC9cVFRAAAAAApiEBAQAAAGAaWrAAAAAAA2x0YLkEFRAAAAAApiEBAQAAAGAaWrAAAAAAI2jBcgkqIAAAAABMQwICAAAAwDS0YAEAAAAGsAqWa1ABAQAAAGAaEhAAAAAApqEFCwAAADDCQQ+WK1ABAQAAAGAaEhAAAAAApqEFCwAAADCCDiyXoAICAAAAwDQkIAAAAABMQwsWAAAAYAA3InQNSysgCxcuVEZGRq6xX375Rf3799cDDzyg4cOH648//rAoOgAAAACuZmkC8vLLL+v06dM5P69bt069evWSw+FQu3btdOTIET3yyCPavHmzhVECAAAAcBVLW7Ccztx1rNmzZ+vJJ5/UCy+8kDM2ceJETZkyRQsWLDA7PAAAAOC/nPRguUKRugg9MTFRDz74YK6xRx99VPHx8RZFBAAAAMCVLE1AbDZbrp/r1Kmjs2fP5ho7ceKEypcvb2ZYAAAAAK4Ty1uwOnbsqLp16yogIEB2u12TJ0/W/PnzVaZMGW3evFnjxo1TaGiolWECAAAArILlIpYmIN9//7127typXbt2aefOnTpy5Ij27dunrKwslSlTRn369FHDhg01fPhwK8MEAAAA4CKWJiDVqlVTtWrV1KFDh5yxzMxMlSlTRpIUHR2tm266KU+rFgAAAIDiqcjdiPBi8iFJDRs2tDASAAAA4H/QguUSRWoVLAAAAADujQQEAAAAgGmKXAsWAAAAUBTZuBGhS1ABAQAAAGAaEhAAAAAApqEFCwAAADDCYXUA7oEKCAAAAADTkIAAAAAAMA0tWAAAAIABrILlGlRAAAAAAJiGBAQAAACAaWjBAgAAAIygA8slqIAAAAAAMA0JCAAAAADT0IIFAAAAGMEqWC5BBQQAAACAaUhAAAAAAJiGFiwAAADAABsdWC5BBQQAAACAaUhAAAAAAJiGFiwAAADACFbBcgkqIAAAAABMQwICAAAAwDS0YAEAAAAG2BxWR+AeqIAAAAAAMA0JCAAAAADT0IIFAAAAGMEqWC5BBQQAAACAaUhAAAAAAJiGFiwAAADACDqwXIIKCAAAAADTkIAAAAAAMA0tWAAAAIABNlbBcgkqIAAAAABMQwICAAAAwDS0YAEAAABG0ILlElRAAAAAAJiGBAQAAACAaWjBAgAAAIxwWB2Ae6ACAgAAAMA0JCAAAAAATEMLFgAAAGAANyJ0DSogAAAAAExDAgIAAADANLRgAQAAAEbQguUSVEAAAAAAmIYEBAAAACihHA6Hpk+frvbt2ys4OFi9e/dWYmJigdvv3r1b/fv3V6tWrdSmTRtFRETor7/+KtScJCAAAACAEU5n0Xxcg9mzZys6OloTJkxQTEyMbDab+vXrp4yMjDzbnjhxQr169VK5cuU0f/58zZs3TydOnFDfvn2Vnp5ueE6uAQEAwI1kpaVZHQKAYiIjI0NRUVEaOXKkQkNDJUnTpk1T+/btFRsbq86dO+fa/rvvvtO5c+f0xhtvyNPTU5I0efJkhYaGavPmzWrTpo2heamAAAAAACVQQkKCzpw5o9atW+eM+fr6KigoSHFxcXm2b9OmjWbNmpWTfPyvU6dOGZ6XCggAAABghMPqAPLXsWPHyz6/evXqfMcPHz4sSapatWqu8cqVKyspKSnP9jVq1FCNGjVyjb333nvy9PRUixYtDMdLBQQAAAAogc6dOydJstvtucY9PT0NXdPx8ccfa8GCBRo2bJhuvPFGw/NSAQEAAACKsYIqHFfi5eUlKftakIv/lqT09HR5e3sXuJ/T6dQ777yjd999VwMGDFDPnj0LNS8JCAAAAGCAzc1uRHix9SolJUW1atXKGU9JSVFgYGC++2RmZuqFF17Q8uXLNWrUKPXp06fQ89KCBQAAAJRAgYGB8vHx0fr163PGUlNTFR8fr5CQkHz3GTVqlL755htNnTr1qpIPiQoIAAAAUCLZ7XZ1795dU6ZMkZ+fn6pXr67JkyfL399fYWFhysrK0vHjx1W+fHl5eXlp8eLFWrFihUaNGqWWLVvqyJEjOa91cRsjqIAAAAAARlh9w8HrcCPCiIgIde3aVaNHj1Z4eLg8PDwUGRkpu92upKQktWvXTitWrJAkLV++XJL05ptvql27drkeF7cxwuZ0ulkz22WElfqX1SEAAADgMmIdX1gdQoE6Bb9sdQj5+mbLq1aHUChUQAAAAACYhmtAAAAAACNKTuPQdUUFBAAAAIBpSEAAAAAAmIYWLAAAAMAIWrBcggoIAAAAANOQgAAAAAAwDS1YAAAAgBEOqwNwD1RAAAAAAJiGBAQAAACAaWjBAgAAAAywsQqWS1ABAQAAAGAaEhAAAAAApqEFCwAAADCCFiyXoAICAAAAwDQkIAAAAABMQwsWAAAAYISDFixXoAICAAAAwDQkIAAAAABMQwsWAAAAYASrYLkEFRAAAAAApiEBAQAAAGAaWrAAAAAAI2jBcgkqIAAAAABMQwICAAAAwDS0YAEAAABG0ILlElRAAAAAAJiGBAQAAACAaWjBAgAAAIxw0ILlClRAAAAAAJiGBAQAAACAaWjBAgAAAIxwOqyOwC1QAQEAAABgGhIQAAAAAKahBQsAAAAwghsRugQVEAAAAACmIQEBAAAAYBpasAAAAAAjuBGhS1ABAQAAAGAaEhAAAAAApqEFCwAAADCCVbBcggoIAAAAANOQgAAAAAAwjeUtWFu2bNH69evVv39/SdIvv/yiDz/8UAcPHlStWrXUu3dvhYSEWBwlAAAASjxasFzC0grIN998o/DwcG3YsEGS9J///Ee9evWS0+lUaGioMjMz1aNHD/3nP/+xMkwAAAAALmJpBWTmzJl65plnNHDgQEnSu+++q6eeekrPPvtszjbvvvuupk+frg4dOlgVJgAAAAAXsbQCsn//ft1///05Px88eFB33313rm3uu+8+7dmzx+zQAAAAgNyczqL5KGYsTUBq1qypNWvW5PzcqFEjJSQk5Npm69atqlKlitmhAQAAALgOLG3B6tevn1566SUdPnxY9913nwYOHKjnn39e6enpatCggbZs2aJZs2bpmWeesTJMAAAAAC5iaQLSpUsX2Ww2TZ8+Xe+//75sNpucTqfGjh0rSSpXrpz69u2rnj17WhkmAAAAIDkcVkfgFixfhvfBBx/Ugw8+qD///FP79u1TWlqaypQpI39/fwUFBcnT09PqEAEAAAC4iOUJyEX16tVTvXr1rA4DAAAAwHVUZBIQAAAAoEgrhitOFUWWroIFAAAAoGQhAQEAAABgGlqwAAAAACNowXIJKiAAAAAATEMCAgAAAMA0tGABAAAARjhowXIFKiAAAAAATEMCAgAAAMA0tGABAAAABjidDqtDcAtUQAAAAACYhgQEAAAAgGlowQIAAACMYBUsl6ACAgAAAMA0JCAAAAAATEMLFgAAAGCEkxYsV6ACAgAAAMA0JCAAAAAATEMLFgAAAGCEgxsRugIVEAAAAACmIQEBAAAAYBpasAAAAAAjWAXLJaiAAAAAADANCQgAAABQQjkcDk2fPl3t27dXcHCwevfurcTEREP79enTRzNmzCj0nCQgAAAAgAFOh6NIPq7F7NmzFR0drQkTJigmJkY2m039+vVTRkZGgfucP39eI0eO1Lp1665qThIQAAAAoATKyMhQVFSUBg8erNDQUAUGBmratGlKTk5WbGxsvvts3rxZDz30kLZs2SJfX9+rmpcEBAAAACiBEhISdObMGbVu3TpnzNfXV0FBQYqLi8t3n7Vr1yosLExLly5V+fLlr2peVsECAAAAjHCzVbAOHz4sSapatWqu8cqVKyspKSnffZ599tlrnpcEBAAAACjGOnbseNnnV69ene/4uXPnJEl2uz3XuKenp06dOuWa4PJBCxYAAABQAnl5eUlSngvO09PT5e3tfd3mpQICAAAAGOEomi1YBVU4ruRi61VKSopq1aqVM56SkqLAwECXxJYfKiAAAABACRQYGCgfHx+tX78+Zyw1NVXx8fEKCQm5bvNSAQEAAABKILvdru7du2vKlCny8/NT9erVNXnyZPn7+yssLExZWVk6fvy4ypcvn9Ou5QokIAAAAIARzmu76V9RFBERoQsXLmj06NE6f/68WrRoocjISNntdh08eFAdO3bUxIkT9fDDD7tsTpvT6WbriV1GWKl/WR0CAAAALiPW8YXVIRTobq9uVoeQr2/Pf2p1CIXCNSAAAAAATEMLFgAAAGCAs4iuglXcUAEBAAAAYBoSEAAAAACmoQULAAAAMMINV8GyAhUQAAAAAKYhAQEAAABgGlqwAAAAAANYBcs1qIAAAAAAMA0JCAAAAADT0IIFAAAAGMEqWC5BBQQAAACAaUhAAAAAAJjG5nQ6uZwfAAAAgCmogAAAAAAwDQkIAAAAANOQgAAAAAAwDQkIAAAAANOQgAAAAAAwDQkIAAAAANOQgAAAAAAwDQkIAAAAANOQgAAAAAAwDQkIAAAAANOQgAAAAAAwDQkIAAAAANOQgAAAAAAwDQlIEeNwODR9+nS1b99ewcHB6t27txITE60Oy23Nnj1bTzzxhNVhuJWTJ09qzJgxuv3223XLLbcoPDxcGzdutDost3Ls2DGNHDlSrVu3VvPmzdW/f3/98ccfVofllvbu3avmzZtr8eLFVofiVg4dOqSGDRvmeXzxxRdWh+ZWli5dqnvvvVdNmzZV586dtXLlSqtDAiSRgBQ5s2f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    " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter10_59_2.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# visual representation of grid search\n", "# uses seaborn heatmap, you can also do this with matplotlib imshow\n", @@ -2235,626 +1889,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.18333333333333332\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.18611111111111112\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 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"Lambda = 0.1\n", - "Accuracy score on test set: 0.23333333333333334\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.12777777777777777\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.1527777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.9111111111111111\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.8888888888888888\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.8722222222222222\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.8305555555555556\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.8888888888888888\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.8805555555555555\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.8944444444444445\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.975\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.9777777777777777\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.9805555555555555\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.9861111111111112\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.9805555555555555\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.9777777777777777\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.9444444444444444\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.01\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.9861111111111112\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.01\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.9888888888888889\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.01\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.9888888888888889\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.01\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.9861111111111112\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.01\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.9888888888888889\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.01\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.9722222222222222\n", - "\n", - "Learning rate = 0.01\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.9527777777777777\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.9027777777777778\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.8583333333333333\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.8722222222222222\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.9055555555555556\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.8805555555555555\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.8722222222222222\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.8666666666666667\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.08611111111111111\n", - "\n", - "Learning rate = 1.0\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.10555555555555556\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.10555555555555556\n", - "\n", - "Learning rate = 1.0\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.17777777777777778\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.08333333333333333\n", - "\n", - "Learning rate = 1.0\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.08888888888888889\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.09444444444444444\n", - "\n", - "Learning rate = 10.0\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.17222222222222222\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.11666666666666667\n", - "\n", - "Learning rate = 10.0\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.10555555555555556\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.1388888888888889\n", - "\n", - "Learning rate = 10.0\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.11388888888888889\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.10555555555555556\n", - "\n", - "Learning rate = 10.0\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.09444444444444444\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.neural_network import MLPClassifier\n", "# store models for later use\n", @@ -2892,36 +1927,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter10_63_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# optional\n", "# visual representation of grid search\n", @@ -3011,16 +2017,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (2259440937.py, line 1)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m Cell \u001b[0;32mIn[12], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m conda create -n tf tensorflow\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" - ] - } - ], + "outputs": [], "source": [ "conda create -n tf tensorflow\n", "conda activate tf" @@ -3797,7 +2794,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.15" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb index edd0b9ddb..a20b59798 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb @@ -2989,17 +2989,24 @@ "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 19\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:81\u001b[0m, in \u001b[0;36mhessian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 78\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m 79\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhessian\u001b[39m(fun, x):\n\u001b[1;32m 80\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturns a function that computes the exact Hessian.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 81\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mjacobian\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjacobian\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 19\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:60\u001b[0m, in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mjacobian\u001b[39m(fun, x):\n\u001b[1;32m 52\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;124;03m Returns a function which computes the Jacobian of `fun` with respect to\u001b[39;00m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;124;03m positional argument number `argnum`, which must be a scalar or array. Unlike\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;124;03m (out1, out2, ...) then the Jacobian has shape (out1, out2, ..., in1, in2, ...).\u001b[39;00m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 60\u001b[0m vjp, ans \u001b[38;5;241m=\u001b[39m \u001b[43m_make_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 61\u001b[0m ans_vspace \u001b[38;5;241m=\u001b[39m vspace(ans)\n\u001b[1;32m 62\u001b[0m jacobian_shape \u001b[38;5;241m=\u001b[39m ans_vspace\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m+\u001b[39m vspace(x)\u001b[38;5;241m.\u001b[39mshape\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10\u001b[0m, in \u001b[0;36mmake_vjp\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_vjp\u001b[39m(fun, x):\n\u001b[1;32m 9\u001b[0m start_node \u001b[38;5;241m=\u001b[39m VJPNode\u001b[38;5;241m.\u001b[39mnew_root()\n\u001b[0;32m---> 10\u001b[0m end_value, end_node \u001b[38;5;241m=\u001b[39m \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m end_node \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m vspace(x)\u001b[38;5;241m.\u001b[39mzeros()\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10\u001b[0m, in \u001b[0;36mtrace\u001b[0;34m(start_node, fun, x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trace_stack\u001b[38;5;241m.\u001b[39mnew_trace() \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m 9\u001b[0m start_box \u001b[38;5;241m=\u001b[39m new_box(x, t, start_node)\n\u001b[0;32m---> 10\u001b[0m end_box \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_box\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m isbox(end_box) \u001b[38;5;129;01mand\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_trace \u001b[38;5;241m==\u001b[39m start_box\u001b[38;5;241m.\u001b[39m_trace:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_value, end_box\u001b[38;5;241m.\u001b[39m_node\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f..unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 14\u001b[0m subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 19\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:64\u001b[0m, in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 62\u001b[0m jacobian_shape \u001b[38;5;241m=\u001b[39m ans_vspace\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m+\u001b[39m vspace(x)\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m 63\u001b[0m grads \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m(vjp, ans_vspace\u001b[38;5;241m.\u001b[39mstandard_basis())\n\u001b[0;32m---> 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mreshape(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m)\u001b[49m, jacobian_shape)\n", "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88\u001b[0m, in \u001b[0;36mstack\u001b[0;34m(arrays, axis)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstack\u001b[39m(arrays, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m 84\u001b[0m \u001b[38;5;66;03m# this code is basically copied from numpy/core/shape_base.py's stack\u001b[39;00m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;66;03m# we need it here because we want to re-implement stack in terms of the\u001b[39;00m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;66;03m# primitives defined in this file\u001b[39;00m\n\u001b[0;32m---> 88\u001b[0m arrays \u001b[38;5;241m=\u001b[39m [array(arr) \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m arrays:\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneed at least one array to stack\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstack\u001b[39m(arrays, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m 84\u001b[0m \u001b[38;5;66;03m# this code is basically copied from numpy/core/shape_base.py's stack\u001b[39;00m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;66;03m# we need it here because we want to re-implement stack in terms of the\u001b[39;00m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;66;03m# primitives defined in this file\u001b[39;00m\n\u001b[0;32m---> 88\u001b[0m arrays \u001b[38;5;241m=\u001b[39m [array(arr) \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m arrays:\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneed at least one array to stack\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:14\u001b[0m, in \u001b[0;36mmake_vjp..vjp\u001b[0;34m(g)\u001b[0m\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbackward_pass\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_node\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:21\u001b[0m, in \u001b[0;36mbackward_pass\u001b[0;34m(g, end_node)\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m toposort(end_node):\n\u001b[1;32m 20\u001b[0m outgrad \u001b[38;5;241m=\u001b[39m outgrads\u001b[38;5;241m.\u001b[39mpop(node)\n\u001b[0;32m---> 21\u001b[0m ingrads \u001b[38;5;241m=\u001b[39m \u001b[43mnode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutgrad\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m parent, ingrad \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(node\u001b[38;5;241m.\u001b[39mparents, ingrads):\n\u001b[1;32m 23\u001b[0m outgrads[parent] \u001b[38;5;241m=\u001b[39m add_outgrads(outgrads\u001b[38;5;241m.\u001b[39mget(parent), ingrad)\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:78\u001b[0m, in \u001b[0;36mdefvjp..vjp_argnums..\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m 76\u001b[0m vjp_0 \u001b[38;5;241m=\u001b[39m vjp_0_fun(ans, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 77\u001b[0m vjp_1 \u001b[38;5;241m=\u001b[39m vjp_1_fun(ans, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 78\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: (\u001b[43mvjp_0\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m)\u001b[49m, vjp_1(g))\n\u001b[1;32m 79\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 80\u001b[0m vjps \u001b[38;5;241m=\u001b[39m [vjps_dict[argnum](ans, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;28;01mfor\u001b[39;00m argnum \u001b[38;5;129;01min\u001b[39;00m argnums]\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:660\u001b[0m, in \u001b[0;36munbroadcast_f..\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m 658\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21munbroadcast_f\u001b[39m(target, f):\n\u001b[1;32m 659\u001b[0m target_meta \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mmetadata(target)\n\u001b[0;32m--> 660\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: unbroadcast(\u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m)\u001b[49m, target_meta)\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:34\u001b[0m, in \u001b[0;36m\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;66;03m# ----- Binary ufuncs -----\u001b[39;00m\n\u001b[1;32m 32\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39madd, \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g),\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: g))\n\u001b[0;32m---> 34\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mmultiply, \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[43my\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mg\u001b[49m),\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: x \u001b[38;5;241m*\u001b[39m g))\n\u001b[1;32m 36\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39msubtract, \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g),\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[38;5;241m-\u001b[39mg))\n\u001b[1;32m 38\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mdivide, \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m/\u001b[39m y),\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[38;5;241m-\u001b[39m g \u001b[38;5;241m*\u001b[39m x \u001b[38;5;241m/\u001b[39m y\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m))\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_boxes.py:27\u001b[0m, in \u001b[0;36mArrayBox.__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[0;32m---> 27\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__mul__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43manp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmultiply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:44\u001b[0m, in \u001b[0;36mprimitive..f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 42\u001b[0m parents \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(box\u001b[38;5;241m.\u001b[39m_node \u001b[38;5;28;01mfor\u001b[39;00m _ , box \u001b[38;5;129;01min\u001b[39;00m boxed_args)\n\u001b[1;32m 43\u001b[0m argnums \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(argnum \u001b[38;5;28;01mfor\u001b[39;00m argnum, _ \u001b[38;5;129;01min\u001b[39;00m boxed_args)\n\u001b[0;32m---> 44\u001b[0m ans \u001b[38;5;241m=\u001b[39m \u001b[43mf_wrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margvals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 45\u001b[0m node \u001b[38;5;241m=\u001b[39m node_constructor(ans, f_wrapped, argvals, kwargs, argnums, parents)\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_box(ans, trace, node)\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:48\u001b[0m, in \u001b[0;36mprimitive..f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_box(ans, trace, node)\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf_raw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:67\u001b[0m, in \u001b[0;36mdefvjp..vjp_argnums..\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m 65\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnum 0 not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(fun\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m))\n\u001b[1;32m 66\u001b[0m vjp \u001b[38;5;241m=\u001b[39m vjpfun(ans, \u001b[38;5;241m*\u001b[39margs, 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\u001b[43mmatmul_adjoint_1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mA_ndim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mB_meta\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:413\u001b[0m, in \u001b[0;36mmatmul_adjoint_1\u001b[0;34m(A, G, A_ndim, B_meta)\u001b[0m\n\u001b[1;32m 411\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m B_is_vec:\n\u001b[1;32m 412\u001b[0m result \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39msqueeze(result, anp\u001b[38;5;241m.\u001b[39mndim(G) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m--> 413\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munbroadcast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mB_meta\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:653\u001b[0m, in \u001b[0;36munbroadcast\u001b[0;34m(x, target_meta, broadcast_idx)\u001b[0m\n\u001b[1;32m 651\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, size \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(target_shape):\n\u001b[1;32m 652\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m size \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 653\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43manp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msum\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 654\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m anp\u001b[38;5;241m.\u001b[39miscomplexobj(x) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m target_iscomplex:\n\u001b[1;32m 655\u001b[0m x \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mreal(x)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:45\u001b[0m, in \u001b[0;36mprimitive..f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 43\u001b[0m argnums \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(argnum \u001b[38;5;28;01mfor\u001b[39;00m argnum, _ \u001b[38;5;129;01min\u001b[39;00m boxed_args)\n\u001b[1;32m 44\u001b[0m ans \u001b[38;5;241m=\u001b[39m f_wrapped(\u001b[38;5;241m*\u001b[39margvals, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 45\u001b[0m node \u001b[38;5;241m=\u001b[39m \u001b[43mnode_constructor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mans\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf_wrapped\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margvals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margnums\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparents\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_box(ans, trace, node)\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:36\u001b[0m, in \u001b[0;36mVJPNode.__init__\u001b[0;34m(self, value, fun, args, kwargs, parent_argnums, parents)\u001b[0m\n\u001b[1;32m 33\u001b[0m fun_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(fun, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__name__\u001b[39m\u001b[38;5;124m'\u001b[39m, fun)\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnums \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;241m.\u001b[39mformat(fun_name, parent_argnums))\n\u001b[0;32m---> 36\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvjp \u001b[38;5;241m=\u001b[39m \u001b[43mvjpmaker\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparent_argnums\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:66\u001b[0m, in \u001b[0;36mdefvjp..vjp_argnums\u001b[0;34m(argnums, ans, args, kwargs)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m 65\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnum 0 not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(fun\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m))\n\u001b[0;32m---> 66\u001b[0m vjp \u001b[38;5;241m=\u001b[39m \u001b[43mvjpfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mans\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: (vjp(g),)\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m L \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:297\u001b[0m, in \u001b[0;36mgrad_np_sum\u001b[0;34m(ans, x, axis, keepdims, dtype)\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: anp\u001b[38;5;241m.\u001b[39msum(g, axis\u001b[38;5;241m=\u001b[39mbroadcast_axes, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 295\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mbroadcast_to, grad_broadcast_to)\n\u001b[0;32m--> 297\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgrad_np_sum\u001b[39m(ans, x, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 298\u001b[0m shape, dtype \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mshape(x), anp\u001b[38;5;241m.\u001b[39mresult_type(x)\n\u001b[1;32m 299\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: repeat_to_match_shape(g, shape, dtype, axis, keepdims)[\u001b[38;5;241m0\u001b[39m]\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } @@ -3638,7 +3645,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.15" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb index 251cb920c..0323d3ca5 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb @@ -1305,7 +1305,7 @@ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNotFoundError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m datasets, layers, models\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Input\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Sequential \u001b[38;5;66;03m#This allows appending layers to existing models\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/__init__.py:443\u001b[0m\n\u001b[1;32m 441\u001b[0m _plugin_dir \u001b[38;5;241m=\u001b[39m _os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(_s, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtensorflow-plugins\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(_plugin_dir):\n\u001b[0;32m--> 443\u001b[0m \u001b[43m_ll\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_library\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_plugin_dir\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 444\u001b[0m \u001b[38;5;66;03m# Load Pluggable Device Library\u001b[39;00m\n\u001b[1;32m 445\u001b[0m _ll\u001b[38;5;241m.\u001b[39mload_pluggable_device_library(_plugin_dir)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/__init__.py:440\u001b[0m\n\u001b[1;32m 438\u001b[0m _plugin_dir \u001b[38;5;241m=\u001b[39m _os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(_s, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtensorflow-plugins\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 439\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(_plugin_dir):\n\u001b[0;32m--> 440\u001b[0m \u001b[43m_ll\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_library\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_plugin_dir\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 441\u001b[0m \u001b[38;5;66;03m# Load Pluggable Device Library\u001b[39;00m\n\u001b[1;32m 442\u001b[0m _ll\u001b[38;5;241m.\u001b[39mload_pluggable_device_library(_plugin_dir)\n", "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/framework/load_library.py:151\u001b[0m, in \u001b[0;36mload_library\u001b[0;34m(library_location)\u001b[0m\n\u001b[1;32m 148\u001b[0m kernel_libraries \u001b[38;5;241m=\u001b[39m [library_location]\n\u001b[1;32m 150\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m lib \u001b[38;5;129;01min\u001b[39;00m kernel_libraries:\n\u001b[0;32m--> 151\u001b[0m \u001b[43mpy_tf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTF_LoadLibrary\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlib\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 153\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 154\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\n\u001b[1;32m 155\u001b[0m errno\u001b[38;5;241m.\u001b[39mENOENT,\n\u001b[1;32m 156\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe file or folder to load kernel libraries from does not exist.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 157\u001b[0m library_location)\n", "\u001b[0;31mNotFoundError\u001b[0m: dlopen(/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): symbol not found in flat namespace '_TF_GetInputPropertiesList'" ] @@ -1649,7 +1649,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.15" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb index 5d9486227..07ba8933b 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb @@ -67,7 +67,7 @@ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNotFoundError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[1], line 7\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m datasets, layers, models\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Input\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/__init__.py:443\u001b[0m\n\u001b[1;32m 441\u001b[0m _plugin_dir \u001b[38;5;241m=\u001b[39m _os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(_s, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtensorflow-plugins\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(_plugin_dir):\n\u001b[0;32m--> 443\u001b[0m \u001b[43m_ll\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_library\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_plugin_dir\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 444\u001b[0m \u001b[38;5;66;03m# Load Pluggable Device Library\u001b[39;00m\n\u001b[1;32m 445\u001b[0m _ll\u001b[38;5;241m.\u001b[39mload_pluggable_device_library(_plugin_dir)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/__init__.py:440\u001b[0m\n\u001b[1;32m 438\u001b[0m _plugin_dir \u001b[38;5;241m=\u001b[39m _os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(_s, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtensorflow-plugins\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 439\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(_plugin_dir):\n\u001b[0;32m--> 440\u001b[0m \u001b[43m_ll\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_library\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_plugin_dir\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 441\u001b[0m \u001b[38;5;66;03m# Load Pluggable Device Library\u001b[39;00m\n\u001b[1;32m 442\u001b[0m _ll\u001b[38;5;241m.\u001b[39mload_pluggable_device_library(_plugin_dir)\n", "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/framework/load_library.py:151\u001b[0m, in \u001b[0;36mload_library\u001b[0;34m(library_location)\u001b[0m\n\u001b[1;32m 148\u001b[0m kernel_libraries \u001b[38;5;241m=\u001b[39m [library_location]\n\u001b[1;32m 150\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m lib \u001b[38;5;129;01min\u001b[39;00m kernel_libraries:\n\u001b[0;32m--> 151\u001b[0m \u001b[43mpy_tf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTF_LoadLibrary\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlib\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 153\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 154\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\n\u001b[1;32m 155\u001b[0m errno\u001b[38;5;241m.\u001b[39mENOENT,\n\u001b[1;32m 156\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe file or folder to load kernel libraries from does not exist.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 157\u001b[0m library_location)\n", "\u001b[0;31mNotFoundError\u001b[0m: dlopen(/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): symbol not found in flat namespace '_TF_GetInputPropertiesList'" ] @@ -1892,7 +1892,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.15" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1_17_0.png b/doc/LectureNotes/_build/jupyter_execute/chapter1_17_0.png index 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"stream", "text": [ - "Runtime: 0.146141 sec\n", + "Runtime: 0.154751 sec\n", "Jackknife Statistics :\n", "original bias std. error\n", - " 100.139 100.129 0.148776\n" + " 99.9896 99.9796 0.149524\n" ] } ], @@ -917,7 +917,7 @@ "text": [ "Bootstrap Statistics :\n", "original bias std. error\n", - " 99.989 15.1792 99.9878 0.152149\n" + " 100.307 14.9693 100.309 0.149416\n" ] } ], @@ -975,7 +975,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
    " ] @@ -1277,7 +1277,13 @@ "Error: 0.08426840630693411\n", "Bias^2: 0.0796891867672603\n", "Var: 0.004579219539673834\n", - "0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n", + "0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Polynomial degree: 2\n", "Error: 0.10398646080125035\n", "Bias^2: 0.1007711427354898\n", @@ -1308,7 +1314,13 @@ "Error: 0.037813671417389005\n", "Bias^2: 0.033657685071527665\n", "Var: 0.00415598634586135\n", - "0.037813671417389005 >= 0.033657685071527665 + 0.00415598634586135 = 0.03781367141738902\n", + "0.037813671417389005 >= 0.033657685071527665 + 0.00415598634586135 = 0.03781367141738902\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Polynomial degree: 7\n", "Error: 0.02760977349102253\n", "Bias^2: 0.022999498260366312\n", @@ -1339,18 +1351,18 @@ "Error: 0.07160048164233104\n", "Bias^2: 0.014436800088904942\n", "Var: 0.05716368155342608\n", - "0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102\n", - "Polynomial degree: 12\n", - "Error: 0.11547777218872497\n", - "Bias^2: 0.01628578269596628\n", - "Var: 0.09919198949275869\n", - "0.11547777218872497 >= 0.01628578269596628 + 0.09919198949275869 = 0.11547777218872497\n" + "0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "Polynomial degree: 12\n", + "Error: 0.11547777218872497\n", + "Bias^2: 0.01628578269596628\n", + "Var: 0.09919198949275869\n", + "0.11547777218872497 >= 0.01628578269596628 + 0.09919198949275869 = 0.11547777218872497\n", "Polynomial degree: 13\n", "Error: 0.22842468702219465\n", "Bias^2: 0.01975416527185249\n", @@ -1367,7 +1379,7 @@ }, "metadata": { "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_66_4.png" + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_66_6.png" } }, "output_type": "display_data" @@ -1694,16 +1706,16 @@ "Mean squared error on test data: 877.21517262\n", "Degree of polynomial: 23\n", "Mean squared error on training data: 0.00085892\n", - "Mean squared error on test data: 5567.04664255\n", - "Degree of polynomial: 24\n", - "Mean squared error on training data: 0.00084707\n", - "Mean squared error on test data: 1325.26124692\n" + "Mean squared error on test data: 5567.04664255\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "Degree of polynomial: 24\n", + "Mean squared error on training data: 0.00084707\n", + "Mean squared error on test data: 1325.26124692\n", "Degree of polynomial: 25\n", "Mean squared error on training data: 0.00079125\n", "Mean squared error on test data: 129012.83870189\n", @@ -1712,16 +1724,16 @@ "Mean squared error on test data: 18388.59354079\n", "Degree of polynomial: 27\n", "Mean squared error on training data: 0.00069123\n", - "Mean squared error on test data: 2351.97979891\n", - "Degree of polynomial: 28\n", - "Mean squared error on training data: 0.00062592\n", - "Mean squared error on test data: 3983.63037846\n" + "Mean squared error on test data: 2351.97979891\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "Degree of polynomial: 28\n", + "Mean squared error on training data: 0.00062592\n", + "Mean squared error on test data: 3983.63037846\n", "Degree of polynomial: 29\n", "Mean squared error on training data: 0.00060704\n", "Mean squared error on test data: 3262.26814548\n" @@ -1731,9 +1743,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n", " plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n", " plt.plot(polynomial, np.log10(testerror), label='Test Error')\n" ] }, @@ -2075,7 +2087,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n", " plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n" ] }, @@ -3749,7 +3761,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", " cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n" ] }, @@ -4059,7 +4071,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", " cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n" ] }, @@ -4136,7 +4148,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", " cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n" ] }, @@ -4221,7 +4233,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", " cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n" ] }, @@ -4282,7 +4294,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 0/10 [00:00s`cXsw-e)P+&kH5Nstys5S(GCEPlrn%FXTk zzi!}i^|0d>tZS$OFF|)xH1dQ%a4q3~5Xz;>9KideERf!W4+k)GGi6Nq{@7`oBE%Bbbd`lIvzMzGd)Y2w=#Ep@+=a{ z_=(#loAL5;P?wG1jjHYD$e&kTW~XmjJnjCp3^M*56S-VuNM%$)#K*@^N#P-+3WdR7 zim8T+c;II(OLti)6e=Itj}0!96O#ympKNsh=f!>*FNi5SCue_-n6HJM9a@$uvnUR@ zT`!zbX=rpbvaF0{WF&V{$l9ZS6x_11vB4yY3a(es(a|AbQwaF2NirKO&j0vR%bU(jf{fwj4LB9(b{c3c-MNNBFVW-veEQ_%=%{I_wVj+->UYHu2M6f zyPsOH4lgY&t;9YC|I9BeJRW_lm+?Oz@x0O7N+ZbCRQ>;)&j0IN|NmU%fTGdTS2lKc zS6=?;GQSalDc00&MFH!^V+6RshMH@AqlbR~*WbiFrl+UpW8-iE*N4h$o9V!hMZIhP 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-2017,7 +2017,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.15" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb index d35a3759b..1f64a36af 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb @@ -86,14 +86,14 @@ "output_type": "stream", "text": [ "2nd degree coefficients:\n", - "zero power: -5.030164788184997\n", - "first power: 0.001268544905013411\n", - "second power: -5.234332597247826e-05\n" + "zero power: -0.22158725223474995\n", + "first power: 0.24121476598003452\n", + "second power: -0.0009532583857747255\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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    " ] @@ -107,7 +107,7 @@ }, { "data": { - "image/png": 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0.089790 0.089783 0.089763 0.089730 \n", - "8 0.094941 0.094797 0.089704 0.089738 0.089760 0.089769 0.089763 \n", - "9 0.094797 0.094697 0.089576 0.089639 0.089690 0.089726 0.089748 \n", - "10 0.089704 0.089576 0.085655 0.085690 0.085712 0.085722 0.085716 \n", - "11 0.089738 0.089639 0.085690 0.085747 0.085790 0.085819 0.085833 \n", - "12 0.089760 0.089690 0.085712 0.085790 0.085853 0.085902 0.085935 \n", - "13 0.089769 0.089726 0.085722 0.085819 0.085902 0.085970 0.086021 \n", - "14 0.089763 0.089748 0.085716 0.085833 0.085935 0.086021 0.086092 \n" + "1 0.083459 0.082121 0.078708 0.077431 0.076203 0.075021 0.073883 \n", + "2 0.083405 0.082180 0.078711 0.077523 0.076378 0.075274 0.074212 \n", + "3 0.095358 0.094027 0.091732 0.090387 0.089086 0.087828 0.086611 \n", + "4 0.094527 0.093288 0.090935 0.089668 0.088441 0.087255 0.086107 \n", + "5 0.093680 0.092530 0.090118 0.088926 0.087772 0.086655 0.085573 \n", + "6 0.094189 0.093011 0.091871 0.090626 0.089417 0.088242 0.087102 \n", + "7 0.093115 0.092013 0.090804 0.089626 0.088481 0.087369 0.086289 \n", + "8 0.092064 0.091034 0.089755 0.088642 0.087560 0.086508 0.085486 \n", + "9 0.091034 0.090075 0.088726 0.087675 0.086653 0.085659 0.084694 \n", + "10 0.089755 0.088726 0.088455 0.087327 0.086227 0.085155 0.084112 \n", + "11 0.088642 0.087675 0.087327 0.086256 0.085212 0.084195 0.083203 \n", + "12 0.087560 0.086653 0.086227 0.085212 0.084222 0.083257 0.082316 \n", + "13 0.086508 0.085659 0.085155 0.084195 0.083257 0.082342 0.081450 \n", + "14 0.085486 0.084694 0.084112 0.083203 0.082316 0.081450 0.080605 \n" ] } ], @@ -916,10 +916,17 @@ "output_type": "stream", "text": [ " 0 1\n", - "0 3.914672 1.954823\n", - "1 1.954823 1.963858\n", - "[[3.91467223 1.95482298]\n", - " [1.95482298 1.96385798]]\n" + "0 4.114499 2.071143\n", + "1 2.071143 2.061388" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[[4.11449851 2.07114326]\n", + " [2.07114326 2.0613875 ]]\n" ] } ], @@ -949,13 +956,13 @@ "output_type": "stream", "text": [ "Centered covariance using own code\n", - "[[3.91467223 1.95482298]\n", - " [1.95482298 1.96385798]]\n" + "[[4.11449851 2.07114326]\n", + " [2.07114326 2.0613875 ]]\n" ] }, { "data": { - "image/png": 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", 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", 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    " ] @@ -1044,12 +1051,12 @@ "output_type": "stream", "text": [ "Eigenvalues of Covariance matrix\n", - "5.123928000581825\n", - "0.7546022135629743\n", + "5.399533503407795\n", + "0.776352510835556\n", "First eigenvector\n", - "[0.85043503 0.5260801 ]\n", + "[0.84973247 0.52721412]\n", "Second eigenvector\n", - "[-0.5260801 0.85043503]\n" + "[-0.52721412 0.84973247]\n" ] }, { @@ -1057,7 +1064,7 @@ "output_type": "stream", "text": [ "Eigenvector of largest eigenvalue\n", - "[0.85043503 0.5260801 ]\n" + "[-0.84973247 -0.52721412]\n" ] } ], @@ -1725,7 +1732,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.15" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png index 96f17541210f14e0f0c4eb73f2aa7ebaa3026dd5..ec7c0c4d0deadb08ac14140b7b3764e6a0f6340e 100644 GIT binary patch literal 41116 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a/doc/LectureNotes/_build/jupyter_execute/chapter9.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter9.ipynb index 6898ccd5e..b620c56fd 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter9.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter9.ipynb @@ -1345,7 +1345,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.15" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb index 89cf87cd9..b29e2fb8f 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb @@ -4038,7 +4038,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.15" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/clustering.ipynb b/doc/LectureNotes/_build/jupyter_execute/clustering.ipynb index 7a2e19d41..b1b822104 100644 --- a/doc/LectureNotes/_build/jupyter_execute/clustering.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/clustering.ipynb @@ -290,7 +290,7 @@ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNotFoundError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtime\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m image\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/__init__.py:443\u001b[0m\n\u001b[1;32m 441\u001b[0m _plugin_dir \u001b[38;5;241m=\u001b[39m _os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(_s, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtensorflow-plugins\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(_plugin_dir):\n\u001b[0;32m--> 443\u001b[0m \u001b[43m_ll\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_library\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_plugin_dir\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 444\u001b[0m \u001b[38;5;66;03m# Load Pluggable Device Library\u001b[39;00m\n\u001b[1;32m 445\u001b[0m _ll\u001b[38;5;241m.\u001b[39mload_pluggable_device_library(_plugin_dir)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/__init__.py:440\u001b[0m\n\u001b[1;32m 438\u001b[0m _plugin_dir \u001b[38;5;241m=\u001b[39m _os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(_s, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtensorflow-plugins\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 439\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(_plugin_dir):\n\u001b[0;32m--> 440\u001b[0m \u001b[43m_ll\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_library\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_plugin_dir\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 441\u001b[0m \u001b[38;5;66;03m# Load Pluggable Device Library\u001b[39;00m\n\u001b[1;32m 442\u001b[0m _ll\u001b[38;5;241m.\u001b[39mload_pluggable_device_library(_plugin_dir)\n", "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/framework/load_library.py:151\u001b[0m, in \u001b[0;36mload_library\u001b[0;34m(library_location)\u001b[0m\n\u001b[1;32m 148\u001b[0m kernel_libraries \u001b[38;5;241m=\u001b[39m [library_location]\n\u001b[1;32m 150\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m lib \u001b[38;5;129;01min\u001b[39;00m kernel_libraries:\n\u001b[0;32m--> 151\u001b[0m \u001b[43mpy_tf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTF_LoadLibrary\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlib\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 153\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 154\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\n\u001b[1;32m 155\u001b[0m errno\u001b[38;5;241m.\u001b[39mENOENT,\n\u001b[1;32m 156\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe file or folder to load kernel libraries from does not exist.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 157\u001b[0m library_location)\n", "\u001b[0;31mNotFoundError\u001b[0m: dlopen(/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): symbol not found in flat namespace '_TF_GetInputPropertiesList'" ] @@ -668,7 +668,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.15" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek34.ipynb b/doc/LectureNotes/_build/jupyter_execute/exercisesweek34.ipynb index cdf78568e..2c59e68d2 100644 --- a/doc/LectureNotes/_build/jupyter_execute/exercisesweek34.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek34.ipynb @@ -308,7 +308,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.15" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek35.ipynb b/doc/LectureNotes/_build/jupyter_execute/exercisesweek35.ipynb index a8308aef9..d099b3087 100644 --- a/doc/LectureNotes/_build/jupyter_execute/exercisesweek35.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek35.ipynb @@ -405,7 +405,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.15" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb index ea16c3166..0e1d2dc6e 100644 --- a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb @@ -103,20 +103,20 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[4.04881585]\n", - " [2.96745096]]\n", - "Eigenvalues of Hessian Matrix:[0.37175588 4.15690073]\n", + "[[4.1729993]\n", + " [3.0170097]]\n", + "Eigenvalues of Hessian Matrix:[0.28192769 4.68753434]\n", "theta from own gd\n", - "[[4.04881585]\n", - " [2.96745096]]\n", + "[[4.1729993]\n", + " [3.0170097]]\n", "theta from own sdg\n", - "[[4.04015455]\n", - " [2.95745651]]\n" + "[[4.14043884]\n", + " [2.99071523]]\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
    " ] @@ -338,9 +338,9 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[3.37007195]\n", - " [3.48441798]]\n", - "Eigenvalues of Hessian Matrix:[0.32411274 4.30450049]\n" + "[[4.03696458]\n", + " [3.0324793 ]]\n", + "Eigenvalues of Hessian Matrix:[0.30959659 4.4150026 ]\n" ] }, { @@ -348,13 +348,13 @@ "output_type": "stream", "text": [ "theta from own gd\n", - "[[3.37007195]\n", - " [3.48441798]]\n" + "[[4.03696458]\n", + " [3.0324793 ]]\n" ] }, { "data": { - "image/png": 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", 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", 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    " ] @@ -440,73 +440,73 @@ "Own inversion\n", "[[4.]\n", " [3.]]\n", - "Eigenvalues of Hessian Matrix:[0.27227895 4.125757 ]\n", - "0 [-11.75978215] [-12.89770892]\n", - "1 [-0.06807123] [0.06136823]\n", - "2 [-0.06357887] [0.05731824]\n", - "3 [-0.05938299] [0.05353553]\n", - "4 [-0.05546402] [0.05000245]\n", - "5 [-0.05180367] [0.04670255]\n", - "6 [-0.04838489] [0.04362042]\n", - "7 [-0.04519174] [0.04074169]\n", - "8 [-0.04220931] [0.03805295]\n", - "9 [-0.03942371] [0.03554165]\n", - "10 [-0.03682195] [0.03319608]\n", - "11 [-0.03439189] [0.03100531]\n", - "12 [-0.0321222] [0.02895911]\n", - "13 [-0.0300023] [0.02704796]\n", - "14 [-0.0280223] [0.02526293]\n", - "15 [-0.02617297] [0.02359571]\n", - "16 [-0.02444569] [0.02203851]\n", - "17 [-0.0228324] [0.02058408]\n", - "18 [-0.02132557] [0.01922564]\n", - "19 [-0.01991819] [0.01795684]\n", - "20 [-0.0186037] [0.01677178]\n", - "21 [-0.01737595] [0.01566493]\n", - "22 [-0.01622922] [0.01463112]\n", - "23 [-0.01515818] [0.01366555]\n", - "24 [-0.01415781] [0.01276369]\n", - "25 [-0.01322347] [0.01192135]\n", - "26 [-0.01235079] [0.0111346]\n", - "27 [-0.0115357] [0.01039977]\n", - "28 [-0.0107744] [0.00971344]\n", - "29 [-0.01006335] [0.0090724]\n", + "Eigenvalues of Hessian Matrix:[0.23469347 4.90407685]\n", + "0 [-18.02987043] [-22.42501752]\n", + "1 [-0.32329897] [0.25206371]\n", + "2 [-0.30782691] [0.24000075]\n", + "3 [-0.2930953] [0.22851508]\n", + "4 [-0.27906869] [0.21757908]\n", + "5 [-0.26571335] [0.20716644]\n", + "6 [-0.25299716] [0.19725211]\n", + "7 [-0.24088952] [0.18781225]\n", + "8 [-0.22936132] [0.17882416]\n", + "9 [-0.21838482] [0.17026621]\n", + "10 [-0.20793362] [0.16211781]\n", + "11 [-0.19798258] [0.15435937]\n", + "12 [-0.18850776] [0.14697222]\n", + "13 [-0.17948638] [0.1399386]\n", + "14 [-0.17089674] [0.13324158]\n", + "15 [-0.16271817] [0.12686507]\n", + "16 [-0.15493099] [0.12079371]\n", + "17 [-0.14751649] [0.11501291]\n", + "18 [-0.14045682] [0.10950876]\n", + "19 [-0.13373501] [0.10426802]\n", + "20 [-0.12733488] [0.09927808]\n", + "21 [-0.12124104] [0.09452695]\n", + "22 [-0.11543883] [0.09000319]\n", + "23 [-0.10991429] [0.08569593]\n", + "24 [-0.10465415] [0.08159479]\n", + "25 [-0.09964573] [0.07768993]\n", + "26 [-0.094877] [0.07397193]\n", + "27 [-0.09033649] [0.07043187]\n", + "28 [-0.08601328] [0.06706123]\n", + "29 [-0.08189696] [0.06385189]\n", "theta from own gd\n", - "[[3.96547946]\n", - " [3.03112129]]\n", - "0 [-0.00939922] [0.00847367]\n", - "1 [-0.00877892] [0.00791445]\n", - "2 [-0.00801346] [0.00722437]\n", - "3 [-0.00725498] [0.00654058]\n", - "4 [-0.00654864] [0.00590379]\n", - "5 [-0.00590456] [0.00532314]\n", - "6 [-0.00532167] [0.00479764]\n", - "7 [-0.0047956] [0.00432337]\n", - "8 [-0.00432129] [0.00389577]\n", - "9 [-0.00389382] [0.00351039]\n", - "10 [-0.0035086] [0.00316311]\n", - "11 [-0.00316149] [0.00285017]\n", - "12 [-0.00284871] [0.0025682]\n", - "13 [-0.00256688] [0.00231412]\n", - "14 [-0.00231293] [0.00208517]\n", - "15 [-0.0020841] [0.00187888]\n", - "16 [-0.00187791] [0.00169299]\n", - "17 [-0.00169212] [0.0015255]\n", - "18 [-0.00152471] [0.00137458]\n", - "19 [-0.00137387] [0.00123858]\n", - "20 [-0.00123795] [0.00111605]\n", - "21 [-0.00111547] [0.00100563]\n", - "22 [-0.00100511] [0.00090614]\n", - "23 [-0.00090567] [0.00081649]\n", - "24 [-0.00081607] [0.00073571]\n", - "25 [-0.00073534] [0.00066293]\n", - "26 [-0.00066259] [0.00059734]\n", - "27 [-0.00059703] [0.00053824]\n", - "28 [-0.00053797] [0.00048499]\n", - "29 [-0.00048474] [0.00043701]\n", + "[[3.66774691]\n", + " [3.25904489]]\n", + "0 [-0.07797763] [0.06079614]\n", + "1 [-0.07424587] [0.05788664]\n", + "2 [-0.06957317] [0.05424351]\n", + "3 [-0.06484181] [0.05055465]\n", + "4 [-0.06031928] [0.04702861]\n", + "5 [-0.05607583] [0.04372016]\n", + "6 [-0.05211919] [0.04063532]\n", + "7 [-0.04843794] [0.03776519]\n", + "8 [-0.04501548] [0.03509683]\n", + "9 [-0.04183444] [0.0326167]\n", + "10 [-0.03887807] [0.03031173]\n", + "11 [-0.03613058] [0.02816961]\n", + "12 [-0.03357723] [0.02617887]\n", + "13 [-0.03120433] [0.02432881]\n", + "14 [-0.02899912] [0.02260949]\n", + "15 [-0.02694975] [0.02101168]\n", + "16 [-0.02504521] [0.01952679]\n", + "17 [-0.02327527] [0.01814683]\n", + "18 [-0.0216304] [0.01686439]\n", + "19 [-0.02010178] [0.01567258]\n", + "20 [-0.01868119] [0.014565]\n", + "21 [-0.01736099] [0.01353569]\n", + "22 [-0.01613409] [0.01257912]\n", + "23 [-0.01499389] [0.01169016]\n", + "24 [-0.01393427] [0.01086401]\n", + "25 [-0.01294954] [0.01009625]\n", + "26 [-0.01203439] [0.00938275]\n", + "27 [-0.01118392] [0.00871967]\n", + "28 [-0.01039355] [0.00810345]\n", + "29 [-0.00965904] [0.00753078]\n", "theta from own gd wth momentum\n", - "[[3.99839581]\n", - " [3.00144622]]\n" + "[[3.96175251]\n", + " [3.02982009]]\n" ] } ], @@ -585,17 +585,17 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[3.81818338]\n", - " [3.12840176]]\n", - "Eigenvalues of Hessian Matrix:[0.26024513 4.64291046]\n", - "0 [-16.3824865] [-20.20867716]\n", - "1 [9.71640303e-15] [1.05750493e-14]\n", - "2 [-2.05998413e-17] [-2.11570114e-16]\n", - "3 [6.96057795e-17] [1.90885733e-16]\n", - "4 [6.96057795e-17] [1.90885733e-16]\n", + "[[4.15451852]\n", + " [2.83230774]]\n", + "Eigenvalues of Hessian Matrix:[0.30616802 4.24299211]\n", + "0 [-10.57502449] [-11.57610367]\n", + "1 [-4.47905601e-15] [-4.07372439e-16]\n", + "2 [-6.9388939e-16] [-7.21432413e-16]\n", + "3 [-6.9388939e-16] [-7.21432413e-16]\n", + "4 [-6.9388939e-16] [-7.21432413e-16]\n", "beta from own Newton code\n", - "[[3.81818338]\n", - " [3.12840176]]\n" + "[[4.15451852]\n", + " [2.83230774]]\n" ] } ], @@ -660,24 +660,30 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[3.6955259]\n", - " [3.2809076]]\n", - "Eigenvalues of Hessian Matrix:[0.31381731 4.50516278]\n", + "[[4.04601419]\n", + " [3.12204312]]\n", + "Eigenvalues of Hessian Matrix:[0.33604208 4.45709724]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "theta from own gd\n", - "[[3.6955259]\n", - " [3.2809076]]\n" + "[[4.04601419]\n", + " [3.12204312]]\n" ] }, { "data": { - "image/png": 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", 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6Ca1bt0aNGjVQv359PPbYYzh79qzvGkxERER+xefBUElJCdq2bYtFixZV2Xfjxg3s3LkTf/3rX7Fz505kZWXhyJEjGDFihA9aSkRERP5IJYQQvm6EgUqlwooVK5Cenm7xmO3bt6NLly44efIkGjRoYNd5i4uLERkZiaKiIkRERLiptURERORJ3rp/B3nszB5SVFQElUqFWrVqWTymrKwMZWVlxsfFxcVeaBkRERHJkc+HyRxRWlqKGTNmYMyYMVYjxHnz5iEyMtL4k5iY6MVWEhERkZzIJhi6ffs2Hn74Yeh0Onz00UdWj505cyaKioqMP/n5+V5qJREREcmNLIbJbt++jVGjRiEvLw/r1q2zOW4YGhqK0NBQL7WOiIiI5EzywZAhEDp69ChycnIQHR3t6yYRERGRH/F5MHT9+nUcO3bM+DgvLw+7d+9GVFQU6tevj5EjR2Lnzp1YtWoVtFotCgoKAABRUVEICQnxVbOJiIjIT/h8an1ubi769u1bZfu4ceMwe/ZsJCUlmX1eTk4O0tLS7HoNTq0nIiKSH8VMrU9LS4O1eExCZZCIiIjID8lmNhkRERGRJzAYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJStCBfN4CIiEgWtFpg40bg3DkgLg5ITQUCA33dKnIDBkNERES2ZGUBzz0HnD59d1tCAvD++0BGhm/axODMbRgMERERWZOVBYwcCQhhuv3MGf32b77xfkAkxeDMnQyB3vHjXnk5lRCVP13/U1xcjMjISBQVFSEiIsLXzSEiIrnQaoFGjUyDjopUKn0QkpfnvV4ZS8GZSqX/7YvgzJ0qBHrFACIBj9+/mUBNRERkycaNlgMhQB+Q5Ofrj/MGrVYfKJjrxzBsmzJFf5wcGQI9a++5BzAYIiIisuTcOfce5yqpBWf20mqB3Fxg2TL9b3PBmrVAz8OYM0RERGRJXJx7j3OV1IIze9ib32Qr0PMg9gwRERFZkpqqv3Eb8nEqU6mAxET9cd4gteDMFkvDXobk86ysu9t8GMAxGCIiIrIkMFDfgwFUDYgMjxcu9F7ytNSCM2sczW/yYQDHYIiIiMiajAz9DK34eNPtCQnen7klteDMGkfzm2wFeh7EYIiIiMiWjAzgxAkgJwfIzNT/zsvzzRR2KQVn1jia32Qt0PMwJlATERHZIzAQSEvzdSv0MjIAtVraFaidyW8yBHqVE649jEUXiYiIyP0MBSvPnDGfN2StYOWdCtTFx48jcvx4Fl0kIiKyyp4aNkrly/fGlfwmQy/cgw96soVGHCYjIiL5qLw46cWLwPPP++8aXa6Qwvplloa9EhL0gZBEPiMOkxERkTyYu7mb4y9rdLnC0+uXVQ5KbeUrOXr8Hd66fzMYIiIi6bN0c7fEFwuoSoWnF5f1Yo+Tt+7fzBkiIiJpc2bNKqmu0eUNrq5fZi3PyJGK0jLCYIiIiKTNlTWrpLRGl7e4sn5ZVpa+V6lvX2DMGP3vRo302x2tKC0jDIaIiEjaXAlopLJGlzc5u36ZrV6fuXNd63GSMM4mIyIi93MyYdYsZwIaQ16MFNbo8jbDsha26vtUfG9s9fqoVHenydviht644+tOQvN+HpbneidMYTBERETu5e4EW1s398qktkaXtxnq+4wcqX8vKr5nlt4be/KMLl+27/WdCF6FTmDHFweRvbgQmt/isb+sKYCGAIodPpczOExGRETu44kEW0fXrJLaGl2+4Oj6Zfb25kRFWf4MVCogMdHu3rhb12/hp3k7MLH1eiQGF6DzuGTM3ZSG/WVNEYhy3Ft7J94a/ot97XIRp9YTEZF7+GJKd2IisGABULeudNfo8iV7hytzc/XJ0raEhwPXrlnucbIRhBadKsIP7/wOjQb4/lQrFCPSuK8GrmNI/D6oh+tw37RkRDWprZw6Qxs2bMDbb7+NHTt24Ny5c1ixYgXS09ON+4UQmDNnDhYvXowrV66ga9eu+PDDD9GqVSu7X4PBEBGRF9h7Q83JcX7BU3fmItFdttYRqyw6Grh06e7jxESLFaVPbz+HlQuOQPNzDeRcaoPbCDHuiw0ohLrZIagfroZ7n2uNsFphJs/11v3b5zlDJSUlaNu2LZ544gk88MADVfbPnz8f7777LpYuXYpmzZrhjTfewIABA3D48GGEh4f7oMVERGSWK1O67SWlleP9ibU8o8pUKqBaNeDnn4HCwipBqdAJ/K45Bs1HZ5C9OQa/3UgGcDePqEXIcaR3yIf6qTro8ngyAoJ6e/jibPN5MDRkyBAMGTLE7D4hBBYuXIhXXnkFGXeizf/85z+IjY1FZmYmnn76aW82lYiIrHF2SjdJgyHP6C9/AS5csHycEPqhysBAYPRoAID2lhabPtoDzX+uQLMnCcfLmwJoCgBQQYfu4fuh7nUZ6skN0HxIEwBNPH89DvB5MGRNXl4eCgoKMHDgQOO20NBQ9OnTB5s3b7YYDJWVlaGsrMz4uLjYO9noRESK5syUbpKWjAzg5k3g0UdtHlp25AR+WL0Nmm9uYdXxlrgo2hr3haIUA2L2Qj24DMOnt0BsShtPttplkg6GCgoKAACxsbEm22NjY3Hy5EmLz5s3bx7mzJnj0bYREVElzkzp9gZ/zzNy9/VVnoFmwYin6+EndDU+rq26guFJv0P9QBAGTk1BzXpdnG+Dl8liar2q0jQ+IUSVbRXNnDkTRUVFxp/8/HxPN5GIiADHp3R7mrXlJfyBueuLiQFef935ZTEMPXwW7rM6qHAKifgZA9AoKB/PtVuPnPd2o/BGOP5zvBcy5ndDzXo1nb8mH5B0MFSvXj0Ad3uIDAoLC6v0FlUUGhqKiIgIkx8iIvKSjAzgxAn9rLHMTP3vvDzfBEJ+uKiokaXru3wZmDULiI116hp1QoWj6hcghIAOpgGR4fHWlPHY+dUx/FGWgIW7+iBtSjsEhUl6sMkqSQdDSUlJqFevHtasWWPcduvWLaxfvx49evTwYcuIiMgqw6yv0aP1v30xNOani4oCsH59BpcuAQ88YFdAdOv6Laye+xueabUBiaHn0ezDKXgAy3EGpj18upg4BCz/BqP2vYa2o5pDFWBHEUwZ8HkYd/36dRw7dsz4OC8vD7t370ZUVBQaNGiAKVOm4M0330TTpk3RtGlTvPnmm6hevTrGjBnjw1YTEZGk2bO8hGFRUTlO1bd1fRVNmQKo1VUC0qJTRfj+7TsFEPNTcA2djPtq4hqCEuLwy7D/w7DexQjHNSAuDkH+lm91h8+Dod9++w19KxTpmjp1KgBg3LhxWLp0KV588UXcvHkTEyZMMBZd/Omnn1hjiIiILPNGzSNfcqTdFYK+/G1nsXLBUWjW1kTO5TYox91RlnoB56FuflhfAHFKG4RGdPdAwyHJhHafB0NpaWmwVgRbpVJh9uzZmD17tvcaRURE8ubvNY8cbHfWxLV480QMdtxIBlDfuL1lyHGkd9QXQOw8LhkBQZbzcd3C3Yv4uonPl+PwBi7HQUSkMLaWl3B1nTRPsqfnxNY6cJWkIQfrkQYVdOgRvh/pvS9D/VwjNB3QyP1ts8SQ8F3587Cyrpm37t+STqAmIiJyirWV7n1Z88gWe0sBGK7PSpkZQD/7Kx8JqBUTgn+P24hz+y7hl+I2mLYqzfFAyJUyBRJPaGcwRERE/klqNY9scbAUQGHzVKxNex3FqkiYowOggkDdz95C9vkeeGppKmJT6nqlbVU4ktDuAxwmIyIi/ybBhN0qbA173RnWO7o4B5pF+cjeEIXN11IgEIAAaPEy5mIq3kNtXL37HCsryXuibVaHHJct0/cm2ZKZaVzvDFDQqvVEREQeJYeV7u3sOfnTkHysR5pxc8fqB6DuVoj0CQ+j1oiXgU2/uD/oc0eZAokntDMYIiIi8jU7p8onIB/9o3Ygvd91jHihKRK7JgNIvnuAPUGfoz1l7ihTIPFFfBkMERGR98lh6MqLrqvCYc9qXh8vq42aD3d0/oWcmdrujl4dqS7iewcTqImIyLv8ffFUO+VvO4tFD67HgOgdiB7dH/lIqLIWmJFKBSQmouaDQ5x/QWeToG0s3Gpom81eHQkntDOBmoiIvMeJWjP+QugE9mUdRfZHZ6HZGoudN1ua7H826EMsLJ8EQAUVzPScuPLeuJoEbfjcAPO9Oo60zYFeQW/dvxkMERH5AzkMO7ljVpLMlJeW45eP9yP7P0XQ7G+ME+WJxn0q6NAzYh/Se1+BekoS7unX0PwwljtmheXm6nvgbMnJsZx35Km2WcHZZEREZB+JLnFQhb8vnnpHSWEJVr+zD5rl5ViVl4zLop1xXxhuYmC9vVAPvoVh01ogplVb0ydnZOgXVbWnArW3k6DtbZsMMRgiIpIzS8NOhjwQKQ07+fHiqef3X8C3bx+C5sdQ/FzYGqXoZtwXrbqE4U0OQD0yBAOeT0GNmK7WT2arFICvkqDtaZtMcZiMiEiu5Dbs5I6hGgk5sjoPmg9OIntjFLbcKYBo0DjoJNRt8pD+eG30+FMrBIW5qe/B2Zwrma7VxmEyIiKyTm7DThKvNWOLrlyHX5cegObTi8jemYhDt5oASDLu71T9ANTdC5E+MR6t1PdAFdDQ8RexNvxla30vlUq/vpdaXTWgsTa13fD88eMdb6+fYDBERCRXcht2sqfWzPjxwP/+B8TE6B8XFvo0N6X0ainWvb8Pmi9vYuWR5ijQpRj3BeE27o3eA3X/Eox4oRkSOt8pgKjVAhvWO55XY2v4y9Xg1zC1vfJrGMyaBfzrX9LLNfMGoQBFRUUCgCgqKvJ1U4iI3CcnRwj9LdD6T06Or1tqavlyIRISTNsYHa3/sXQNCQn653nB5T+uiC+e+UWMjN8saqLYpBnhKBIPJW4SyyZvEldPXrXv2uxp+/LlQqhUVa9bpdL/LF8uRGamfZ93Zqb11yovF2LOHPPPrfh6EuCt+zdzhoiI5EqmeSAATIeDjh7V90pY4+E6RKe2nIFmwTFkrwvHhiutUY5g4774gHMY0fIo0h+pgbTJrRFSM8T8SVzN57GV+7VkCdC/v+2LsZVzJaNcM9YZciMGQ0QkWa7WB3JnMTxfsHVjrsiNN2mhE9jz9RFoPj4HzbZY7KpUADEl9CjUnc5A/acYdHykBQKCbCzY4EqAYW9i+c8/A48/7nrwK6NEdiZQExH5O3fUB7KUB5KQ4NFieG5jKw+mIhcTwstLy7Hxo33I/m8xNPub4KS2OYDmAIAAaNEzYj/S065A/VwSmtzbFEBT+0/uSj6PvTldhYXuWd9LbrlmXsBgiIjIF9xZH0jOxfCcueE68JzrBdexesE+aLK0WJXXCldEe+O+ariBgfX2QT3kFoZNb4m6LdtaOZOb2mTuOEdqAKWluR78uqvmkB/hMBkRkbfJKGfD4+wdsqnIxvBNwd5CfPvOIWhWh+HnwjYoQ5hxXx3VRQy/56C+AOLU1qhep7pz7a7MlaEnZ3K/XBlelVGuGXOG3IjBEBFJioxyNjzO1o25Iis36cM//IHsD05B80s0tl5vZVIAsUnQSaS3zYP68dro8ecUBIZ44AbvaoDh7dwvmeSaeev+bSMjjIiI3I45G3cZag8Bd2/E5lTKidGV67Bl8T7M6JaLFqF/oMV9jTHjxzRsud4aAgHoXON3vNE/F/uzj+FoWQO881saUie19UwgZOs67MnnMeR+xcebbk9I8Exg4u3Xkzj2DBEReZuve4akuMK9uWTyihITcWvufKw53gSar25i5ZEWOK+LMe4Oxi3cG70X6gE3MOKFpojv5KN8F1dXdvf2Z+OO1/NgmzlM5kYMhohIUnyZsyHlFe4r3lTvVKC+vi8Pm9fdxL93tMf3Z9uhBDWNh0egCEMb7odarcLgF1ohskGkr1puSorBpqd4+N8TgyE3YjBERJLji5wNR4oC+vCGfnLTaWjePY7sdRHYcLU1tBUmPscHnIM6+QjSH6mJPpOsFEAkz3O2yKQDGAy5EYMhIpIkV4dUHOHIDDaNxqu9R0InsPurw9B8UgDNr/Ww+2YLk/2tw45A3emsvgDioy2hCrCSW0Te4aUZkQyG3IjBEBFJlrd6YOzNU5ozB5g926N/7QPA7Ru3seHDfdB8cQ2a35vglDbBuC8AWqRG7oM67SrUUxqjcVoDl1+P3MxLeW+sQE1EpASBgd6ZPm/vzLT33zefxySEPiCaMkVf4NGJgO3a2WtYvWA/srN0+O5kK1wVHYz7quEGBsXtxf1DyqDucxWRwTfuBIfxVs7oR+SWZ+RnMyIZDBERKYG91YQvX7a8z4nlMAr2FmLl/EPQ/FQNP19og1vobtxXR3URI+45CPWDIej/fGtU33BWPzz3mQSTuz1JykntlvhZFWsOkxERyY0zvQj2zGCLigIuXbL9+pmZwOjRFncf+v5OAcRN0dh6vbXJvnuCTyC97Qmon4hC9/Gt7tb9cXcyrlx6WryQhOwRXpoR6bX7t1CAoqIiAUAUFRX5uilERK5ZvlyIhAQh9Lcg/U9Cgn67Pc9VqfQ/FZ9v2DZnjul2Sz85OSanLS8rF5s+3iumd84RzYL/qHJ4lxr7xJsDc8TvmqNCp9VVbVd5edVrqty+xET9cZ5+j1xVXq5/fzIz9b+ttdnd1+1ttv49ueH99tb9m8EQEZEU2HMTNdx8zN007b35mAsUEhP12w03Z3OvUenmfOPSDfHtX7eJ8c3XixhVoclhwSgTg+v8Kv45er04s+Oc7Tbl5NgXhL36qu0Awx3vkbMcDcLsve5KwaekWPv35AYMhtyIwRARSZo9N1F39iJYC7ys/LWvU6lETv83REb9LaIGrpnsjsRVMabhL+KrKZtEUb6D/6/NzLQvKLAVYPiyp8WZIMze687MdH973cmR3jAHeev+zZwhIpIvueSFWGNvzog3l/Awk9B7XhWLieIfWI4HjdsSAs8iPfko1I+Go/eEFOcLIDq6cr2lfBpfLXPibM0dXy/LIgOcWk9EZI0cZ+BUptXqr8Hc36RCmE5l99JUZqET2HWzJVYmLUX++TO4cTsY5xCHjSIVOgSiTdhhqDufQ/rTsWg/ugVUAfVdej0A+iA2IcG+leuBqu+NIcDw1XTvjRstB0KAvr3mZuHZum5DEJWa6t72UhUMhohIfiz1ppw5o98u1Rk4lTlyE/XgVObbN25j/aI7BRAP3IN8bUsALQHoCyD2rrUXC9J+gfr5xkjq3RxAc4dfwyrDiu8jR+oDAHsDosoBhq+mezsbhFm7bntWuie3YTBERPLiSG+K1G8ijtxER41yay/CtbPX8OM7+gKI358yLYBYHSUYFLcP6cPKMXR6MqKbtjd/EncOU2Zk6INYayvXm1PxPfRVT4srQZil605I8MyyLGSeRzOS3OD27dvilVdeEY0aNRJhYWEiKSlJzJkzR2i1WrvPwQRqIj/iDzNwDBy9FhenMp/dVSA+HrNeDK7zqwhBqckpYlSF4qlmG8S3f90mbly6Ybvtnpq+bkjGffVV5z5nL0z3NttmO2fh2bxuDyQhyxlnk93xxhtviOjoaLFq1SqRl5cnvv76a1GzZk2xcOFCu8/BYIjIj8hxBo6lG50zN1EHpjLrtDpx4Ntj4s2BOaJrjX1VTt80+A8xvXOO+OWjPaK8zIGbrzemr7sSYHh4urdZvgjCFICzye4YNmwYYmNj8emnnxq3PfDAA6hevTo+//xzu87B2WREfkRuM3BsJXob8p8A8zkj5vKfrAxPaW9psfXT35H92WVo9jTE0dtJJk/tWmM/0ntdhHpSIloMagjVpl+cq2Tt4dXKATj33lRsp7dnGpr7rBMTOdzlAlagvmPevHmiYcOG4vDhw0IIIXbv3i1iYmJEpgN/9bFniMiPuGNIwlvs7UFxsSfjxqUbYuWr28STTTeIupUKIIagVAyp+6v45JH14uyuAtO2OTPM5e1hSl/08riCw11uxZ6hO4QQePnll/HWW28hMDAQWq0Wc+fOxcyZMy0+p6ysDGVlZcbHxcXFSExMZM8QKYM/1N6xxVaPwezZQNOmvr1+R3tQHPzcLh29jFXzD0DzXRBWn2uNG6hh3BeJIgxrtB/q+wMweFoKwuuHmz7ZlfWwli0DxoyxcfGwuX6ZQ5Twb5rMYs/QHcuWLRMJCQli2bJlYu/eveK///2viIqKEkuXLrX4nFmzZgkAVX7YM0R+z5drMnmbuWuNjtb/SOH6PdCDcjznpHhXnSP6RO4SASg37SwJPC0mt8kVP8/fIW6V3LJ8ElerNPtTAjtJHnuG7khMTMSMGTMwceJE47Y33ngDX3zxBQ4dOmT2OewZIkWS6+rXrqjYY3D0KDBrVtVjfHX9buhBETqBnZmHoFl8Htnb62NfaTOT/W3DDkPd5RzS/1IP7R5qDlWAyvbruZpz5aXVyokAVqA2unHjBgICAky2BQYGQqfTWXxOaGgoQkNDPd00Iunwp9o7jggM1N+wDTdoczx9/ZaGcJysPXPr+i19AcTM69AcaIrTFQogBqIcvWvtg7pvMdRTm6BRLycKILpapdmeQoHjxwP/+x+HtEg2JB8MDR8+HHPnzkWDBg3QqlUr7Nq1C++++y6efPJJXzeNSDqcXQ7AX/jq+q3NFFOr7S4AWHy6GD+8vR8ajcD3J1NQhI7Gw2rgOgbV34/04eUYOr0VoppYKIBYmZuDNBOWCgVGRel/V+yhk9sSKaRMHh2Ec4Pi4mLx3HPPiQYNGoiwsDDRuHFj8corr4iysjK7z8HZZOT35Fh7x518cf32zBSzsQL8j73+JgZFbxfBKDPZHaMqFOObrxerZv0qbl656VzbLOWOuXM2XsWZU3PmWD4f6+yQk5gz5EasM0R+T261d9zN29fvyEwxjaZKD0qBqh4miA+xAnd7S5oF5yG9/UmkPxWNrk+2QkBQgLkz22ZP7hjgfP0ec+x9P44dAzZv5qwwspu37t8Mhoj8gdKTWr19/XYGX9qffsbmw3WwcslFnN1zATqt0K8AD/0K8N1q7kN6r0tQT26AFvc1dr1dLgZpThcItDcYrVsXuHDh7mMOoZENTKAmIvspffVrb1+/nUnIEwYew2L0Mz4ORSn61d2HjwdvwvBpLVCvTWv3tMfAkdypjAx9XpM76vfYm5RdMRAC9MHryJH+OdORZMWhYCg/Px+JiYmeagsRuULpq1978/rtTEI+jOaIxkU8X28Z+ne4jNZPdEb1+we5FpRZK0Do6Ewxw2w8V9mblF2ZP890JFlxaJisRo0amDp1KmbMmIEaNWrYfoJEcJiMFEXp1Xq9cf1aLcrjEhF44RzMVfbRQYWi4Lo4/fALSFn3AVRnztzd6crQkK11znyVO2ZrmNIe/prPRi7x1v3boQy9NWvW4KeffkLTpk2xZMkST7WJiFxh+Gt/9Gj9byUFQoDHrl/oBH777wG82isXrWv8gVEXFkFABV2lcEhABZUKqD1lHFp/McM0EALuDg1lZTnWAENidOVhsIrnS03VB0cqcyEa9NsTE/XHuZNhmNLwGs6wt1eLyAMcCoZ69OiBbdu24e9//ztee+01tG/fHrm5uR5qGhGRb926fgs/zduBia3XIzG4AJ3HJWPupjTsL2uKlRiBOTXmo6RaHZPnqBIToPrqK30FaktFMAH90JBWa19DbBXVNJwPsByUeDp3zDBMGR9vur1uXfue7+xQG5EbOD2b7ObNm5g3bx4WLFiAgQMH4u2338Y999zj7va5BYfJiBTMwWGzolNF+OGd36HRAN+faoViRBr31cB1DInfB/VwHe6bloyoJrXNn3/jRvcOVzk6/GVuOM3ZmWKOqvx+9OgBNGmi3JmO5BLJzyYTQmDgwIG4du0aPvjgA/zwww+YOHEiZs+ejfDwcNsnIPIHSs/PkTpbOTZ3nN5+DisXHIHm5xrIudQGt9HDuC82oBAjmh1C+sPVcO9zrRFWq7vpa5hLQnZ1yQtXj3PnTDFHmXs/lDzTkWTBoWDo448/xvbt27F9+3YcPHgQgYGBaNOmDSZOnIh27drh//7v/5CcnIwVK1agU6dOnmozkTTYeaMlH7FUfPDMGYiRI5E//QN8vjMF2Ztj8NuNZAB3h2lahByHun0+1E8aCiD2tu81DcHxgQP2He+OpTEsHeeumWLuoPSZjiR5Dg2TJSYmolu3bsafTp06VVkQ9c0330RmZib279/v9sY6i8Nk5HZKXCFeTmwUH9RBhdNIQBLyoEMgVNChe/h+qHtdhnpyAzQf2NDxXhVzwbEljg4N+UtRTfakkoNkW4H6/PnzqF+/PrT2JgZ6AYMhcitHqvzyf/S+YWeOzcxaH6HJiBQMn94CsSl3En2d6fGzFByb42zAbHgNwD1LaBDJgCSn1tsjJiYG69atc/dpiaTDkSq/5BitVh/ILFum/+3EH1UXDl5E7uwcu46d91EtjP9PqmkgZGv6urk2W5rpZU5CgnOBi6XZWs6ej4iM3L4ch0qlQp8+fdx9WiLpcHfSK+m5kIN1bO1JaBbmQbOhNjYVpyAVfZGL122/ZsUcG1vT1y1VSrYVHBtEROiv0ZXaR75MjCbyY1ybjMhR7k56dYS/5lxYSXY2t3aVrlyH3z4/CM2/L0CzIx6/lzUF0NC4/3pYHRSJuogouwgVrOTYpKbefU/XrrW/x69iYrK9QW9xsf6zcvXzklJiNJGfYDBE5ChDlV9byazurvLrzdlr3gy67OyRudVvCHIW/Y7szBtYeagpzupaGQ8LRDnSau+F+t5rGPF8EzTsmQJkfXwnx8bKdG5zK7fbUjn4cSToZW8hkSQxGCJylC9WiHew58Tl1/JmyQA7c7DSa63DDxhq3FwT1zAkYb++AOL0Vqid1MH0ebamcwP2Jz1XVDn4SU0F6tQBLl50/LnkG/7aw0pOc/tsMinibDLyCG9V+fXm7DVflAxYtgwYM8bmYaORidyAe6Fufhjqh6vh3iltEBoRavN5Zm98gPX31Bxr7/PXXwOjRll/fmIiZxhKAeuDyYpsp9ZLEYMh8hhv/IXprZXIfVQyQKzLgarfvTaPOzDtM7SYNw4BQW6YBGvve2pgTzD44ovA229bfj5nfPke64PJjuSX4yAieCeZ1Vuz1+wtGZCbq79uFwLA8tJybPpkPzT/vYqVexohBwmIxxkEWEl2Tv77Y0Cgm6qBOPpe2VMpef58oEsXYMIE4MKFu9u9tSYYWefsbEFSBAZDRFLnrdlr9gYIo0YBly/ffWznEENJYQl+WrAPmuXlWPVHS1wS7Yz7puFtfInREABM1lr3VA6Wve/Vq68C/frZH/CNHAncfz/zUaTIkfpgnK2nOAyGiKTOW7PX7A0QKgZCgNUk7sLfL+Dbtw9B82MI1pxvg1J0M+6LUl3GsKQDSB8ZhIEvDIfql+XeW7vK3vd09mzHAxlOfZcm1gcjKxgMEUmdt2av2QoQLKk0xHB0XT40759A9oYobL6WAoG7QVpS0CmoW+dB/Vgkev0lBUFhve6ex5sFBX0xI5B8y5f1wUjymEBNJBfemL1maf0rOz0e/AX+c/sRk20dqh1EevfzUD9TH60zmkIVoLLwbB/w1oxA8j1/WexWYTibzI3sejNZd4LkwBv/Ts0FCNHRwKVLNp86Gpn4BiORFrUX6f2uY8QLTZHYtb572+duzryn/P+FPHGxW9lhMORGNt9M1p0gufH0zbjy+bVaoH9/m0/7Wf0BOr3/GGo1jHRfW6SG/7+QN/YGygqDITey+may7gTJjRdvxvnbzkLzzlF8u7Ya/n0lA/E4a3b6u1CpoFLCEAP/f+Ef2LMnGwyG3Mjim+mjInNETvPwzVjoBPZlHUX2R2eh2RqLnTdbGvfdjyx8g5FQQZif/u7vgQD/f0Hkdd4KhtxUwUymHKk7QeRrtorGAfoZXVqtQ6ctLy1H7sLdmNJ+PRqHnkbbB5thVk4adt5sCRV06BWxB28PzcVbP3VAwPJv9D1AFSUk+H8gBPD/F0R+TNlT6+VSd4JdugS4tWhcSWEJVr9zpwBiXjIuVyiAGIabGFhvL9SDb2HYtBaIadW2wjMbeW/6u9TI5f8XROQwZQdDcqg7wWRNMnDxZnx+TwF+e/F/OLC1CGuKu2At+kMHfRATrbqEYY0PIv3BYAx4PgU1YrpaPr9SiwrK4f8XROQU5gxJue4EkzWpIicWbD2yOg+aD07iyrqdeKb0PSTiblB9DnH4MekvaPK8Gj3+1ApBYcr+28gmqf//gsgPMWfIGwxVaIG7AYaBr6vQeig/hGTMUCG68r9VA5UKIiERW4/UxszuuWgZehzNBydhy/eX8UbpNMTDdIitnqoAT5yYjd5xRxG09Rdg2TJ9wMV/U+ZJ+f8XROQSZfcMGXiz7oS9+T9O9AKQAlgoGmf4r/GqT/GZeNK4PQSlOK1qiDqiEBbrPgcGmgZAHIa1jnVqiLyGU+vdSDIVqB3J/1m2DBgzxvY5MzOB0aPd206Stqws6CZORkDBWeOmU0jEFCzECmQgHMW4L3E/1GpgeM/LqDl6uGPn5zCsbZzUQOQV3gqGmCRg4OmkUEv5P5ZW/GayJlVyassZaBYcQ/a6RvjlynF0x1bE4RzOIQ7HVfdgePIf+HH0b0ib3BqhET30T1q2zPEXqrTwKm/yZig1iZzIT7FnyBucKdbGZE3FEzqBPV8fgebjc9Bsi8WuCgUQAaBV6FGkdzoD9Z9i0PGRFggIMpMCaO9wqyUchiUiH2LPkL/QaoF//MPx+jCGZM2RI/WBj7lFBb2RrMnhAK8qLy3Hxo/2Ifu/xdDsb4KT2uYAmgMAAqBFz4j9UPe+AvWUJNzTrymAptZPaEi6thRU28KaOUSkAAyGPMlcjpA1lW88GRn64TNzeUbeSNZkjSOvuF5wHasX7IMmS4tVea1wRbQ37quGGxhYbx/UQ25h2PSWqNuyrZUzmWEtqLYHh2GJSAE4TOYplnKErLE0JOGL3hnWOPKogr2F+PadQ9CsDsPPhW1QhjDjvjqqixjW5CDSHwzBgKmtUb1Odddf0FxgW3kWWUUchiUiCeBssgrOnDmDl156CT/88ANu3ryJZs2a4dNPP0XHjh3ter7XgyFbOUKVSe3GwwUpPeLwqqPY8fp3OLy3DBvKOmMD+hgrQDcJOgl12zykP14bPf6cgsAQD7yvlYPqCxeAhx7S7zM3DMuAl4h8jDlDd1y5cgU9e/ZE37598cMPPyAmJgbHjx9HrVq1fN00y2ytIVWRFIu1uXENLCXTleuw7bPfofnsEkp2HMSL5W9iTIXCh+dVsdjYegJazB6NVup7oApo6NkGmZsBFRjou2FYIiKJkHww9NZbbyExMRFLliwxbmvUqJHvGmQPR5JOpXjj4YKUTiu9Woq1C/dB89VNrDzSAud1rXE/svANJuJuaUS9WBRi5L7ZgEgBAmwkQntKRoZyF14lIrpD8sHQypUrMWjQIDz44INYv3494uPjMWHCBPzpT3+y+JyysjKUlZUZHxcXF3ujqXfZm3T63nvA5MnSu/GwxpFDLh+/gu/e/h2aVYH48UxrlKCzcV8tXMa/Av8ClVZUrQAtlXo+vqiZw1mKRCQhks8ZCgvTJ5ZOnToVDz74IH799VdMmTIFn3zyCR577DGzz5k9ezbmzJlTZbvXc4bkWiNI7u33gpObTkPz7nFkr4vAhqutoUUQAqBFKjaileoAmja8heTxPdC3QxGC7xto+4RKqufDWYpEZCcmUN8REhKCTp06YfPmzcZtzz77LLZv344tW7aYfY65nqHExETfzCYD5JmcKvf2u5nQCez+6jA0nxRA82s97L7ZwmT/c0GLMCfgdUTeunB3Y0KC/j1cuND2CyhlWRXOUiQiB3DV+jvi4uKQnJxssq1ly5Y4deqUxeeEhoYiIiLC5MfrDDWC4uNNtyckyON/+HJvvxvcvnEba9/eiWfbrkejkDPoMKYF5qxPw+6bLRAALXpH7saCEbk4N/sTLNQ+axoIAfqeNXsCIUAZQ45arb5HyNzfX4ZtU6ZYnu5PROQhks8Z6tmzJw4fPmyy7ciRI2jY0MMzb9xB7smpnmi/xHNFrp29htUL9iM7S4fvTrbCVdHBuK8abmBQ3F6o7yvHsOktUad5uztDimMt3+BVKiAgwHY9n9RUz1yQlHCWIhFJlOSDoeeffx49evTAm2++iVGjRuHXX3/F4sWLsXjxYl83zT5yX9DRne2XaK5Iwd5CrJx/CJqfquHnC21wC92N++qoLmLEPQehfjAE/Z9vjep1upk+2Z4bvCEQ8uWyKlLAWYpEJFGSD4Y6d+6MFStWYObMmXj99deRlJSEhQsX4pFHHvF108gRlnJFzpzRb/fy0Nuh7/9A9genoNkUja3XWwOIMe5rEnQS6W3zkP5kFLqPb4XAECu9NvbeuKdM0V+jkuv5cJYiEUmU5BOo3cHnq9a7k8SHmcySQEVr7S0tti05gOxPL0GzuyGO3E4y2d+lxn6oe1yEemICkoc3gSqgykR48+xdFT4nR/9Zye2zcyfOUiQiB7ECNVVlzzCTVIKliu04f94nuSI3L9/UF0D8XylWHmmJQtHauC8Yt9Cvzh6oB9zEiGnNUL9DinMvYmtV+Io5QXIfMnWVYdHYBx6ouk9pQ4ZEJCkMhuTCnmEmQBo5OeaCNnu4IVfk0tHL+O7tA9B8F4TVZ1NQgi7GfZEown0N9yP9fhUGv5CCiITOVs5kJ2urwvMGb150NHDpkum2qChg8WLlDBkSkaQwGJIDW1OSVSrgz38GLl/2fU6OpaDNHk7miuRtyIfmvT+gyY3AxqutoUUv476EwLNQJx+FekxN9JnUGiE1ezr1GlYZyhBwjS/rrP3buHzZ++0hIrqDOUNyYG9eiiXeysWwlRtkiYPtEzqBXcsOQbP4PLJ/jcPe0uYm+1uHHUF657NQ/zkWHca0sD//x1XWhiilMnzpKxLIGyMi+WHOEN3l6vCRt+q32Jpmbo6dQ0m3b9zG+kX7oPniGjQH7kG+tiWAlgCgXwYjch/S+xZhxHNJaJzWDEAzpy7BJZZygiRaUsCrWGOIiCSMwZAcuGuqsafrtzhzfitDSdfOXsOP7+gLIH5/yrQAYnWUYFDcPqiHlmPotDsFEKXI0tDQ6dP6ROI5c4BXXvH/3hDWGCIiCWMwJAe2ZizZy9P1W+w9/3vvAbGxZoeLzu0+j5VvH0b2T9Ww7qJpAcS6qgsY0fQQ1KNC0f/51qgW1c3c2aXDWq6XwaxZ+sThDz7w714i1hgiIgljzpBcWFs4VQj9DB1zCdSGY7yZM+RAHRmhEzi46jg0H56GZlMdbCsxneLeNDgP6e1OQv1EFLo91QqBITLqQXEk10ul8u8131hjiIicwJwhb5FLYqutGUuA+endgP7x+PGeb6Od08y1WmDr4r3I/uwyNHsa4ujtewDcYzy0a439UPe8iPTJiWhxX2OoAkwLJMqGo0M+U6bo14KT4r8/V7EEARFJmLJ7huSY2GoteLNV38db12amHbr4BOzoPgkf7+mBb4+1wAVR17gvBGXoV3cv1ANuYvgLzVC/Qz3Pts9bAbAzswBzcvw7gdjcv9HERJYgICKzvNUzpNxgyFJiq+GvVLkOWWi1wNy5+lyUyrx5bVotipZ9hz2f7cSaPTFYePlRXMfdf8iRKMLQhvuRnhGAwdNSEF4/3LPtMfBmAGxraMiczExg9Gj3tkNq5NIbS0Q+x2DIjaq8mf5c88TH1/ZH7iloFv4BTW4tbCxqDR3uvkZi4Bmok48hfWw4ek9sjeDqwW5/fat8EQA7WoTS33uGiIgcwGDIjaq8mY4srim3G5OXr03oBHZm3imAuL0+9pWa1vdpG3YY6i7noP5zLNqP9mIBxMp8GSTaszyJnANwIiIPYQK1J/lzzRMvXNut67f0BRAzr0NzoClOVyiAGIhypNbah/S+xRgxpTGSejcH0Nzq+bzC3qJ///gHMHmyewOSjAx9YrSt4UsmEBMR+YQygyF/rnlib5sPHND3ItmZr1F8uhg/vL0fGo3A9ydTUISOxn3VUYLB9fchfXg57nshGdFN2zvZeA+yN/h7/nlgwQL35xAFBgKvvQakpHANMyIiiVHmMJk/1zxxNGnXSvLw2Z0FWPnOEWT/VB3rLrXBbYQY98WoLmBEs4NQjwpDvymtUS2qmhsvwgMcrfkDeC7RnAnERER2Yc6QG1mdTQaYr3ki19lkgOVrM6fC9Yr0+3HgW30BxOzNdbG9pJXJoc2C85De/iTUT0aj6xPJ0iyAaCnQcDRIlHNATETkJxgMuZFDdYb8peaJPUm7dwiocDW4DrqJLThS3sRkX7ea+6DueQnpzzZAi/sae6q17mFr2rwjQaKBHJPoiYj8BIMhN7L6ZvrzkIXh2tauBd54w+bhacjBFnRH/7p7oR54E8OnNUdcu1gvNNQN7J0270CQCEAZdX+IiCSKwZAb+cXaZK5YtgwYM8bmYdvum43kf031XgFEd3F02rxWq5819vzzts/NniEiIp/x1v07wGNnJp87vu4k3lXn4rk/ldh1fNfpfbwTCGm1+oTmZcv0v7Va185n77T5jRv1jwMD9dPnExLu9hxVplLph0xTU11rGxERSZ4yp9b7KaET2PHFQWQvLoTmt3jsL2sKoCECkIppmIN4nEEArMye88aN3xPLYThTW4kLhxIR0R3sGZK5W9dv4ad5OzCx9XokBheg87hkzN2Uhv1lTRGIcvSttQvvZfyC0DdeQ4AKVXtCvHnjN+T1VO7FOXNGvz0ry7nzOls3KiNDn0sUH2+6PSFB3rMJiYjIIcwZkqGiU0X44Z3fodEA359qhWJEGvfVwHUMjt+H9OE63DctGVFNat99oi9nz3lyOQxX60b5cxI9EZGMMYHajfwhGDq9/RxWLjgCzc81kFOpAGJsQCFGNDsE9UPV0G9Ka4TVCrN8Il/d+D29Zpo/140iIlIork2mBFYCE6ET+F1zDJqPziB7cwx+u5EM4O4wT/OQP5De/pS+AOKTrRAQ1Nu+1wwM1L+O4XU3bvROQOTpNdMMQ15c6oKIiBzEYMhXzAxZifgEHBj8PD7b3QHZe5LwR3lTAE0BACro0K3m71D3ugT1ZEMBRCeKIHoigdke3lgPzrAgKoe8iIjIARwm8wULBQJ10A/pjMQ3WIEMhKIU/WP2Qj2oFMOntUC9NjEeeV2vDCX583pwRETkEcwZciNJBUNaLbTxDRBw/izMVbjRQYUrgXWR+9xyDJreDjXr1XTb63osgdlezOvRY8I2EZFdWHTRzxxbexILhufi2YglCLQQCAFAAASitYV4YHi5+wIhwPHChJ7Aqez6gLBRI30y+Zgx+t+NGjlfVoCIiFymvJwhL/1VrivX4bfPD0Lz7wvQ7IjH73cKID4MDycSu3o+d79uZUrO67E0TGmos6SUgJCISGKUFQytXAnMnOmx5OFb128h5/29yM68gZWHmuKsrpVxXyDKkVZ7Lx5sfR7YYMfJXEkkduV87n5dcwIDlbfel1arT1w3NyothH6ocMoUfaCohMCQiEhClJUzBKDKiKOL+SpFp4rw/dt3CiDmp+BahVeoiWsYHL8f6SN0uG96K9ROquW7RGImMPuWp+ssERH5IdYZ8hYn/irP33YWKxcchWZtTeRcboNy9DDuqxdwHiOaHYb64Wq497nWCKvV3fTJvloTi2tx+ZZUhimJiKgKBkOAafKwmb/KhU5g/4qjyP7oLDRbY7DjRjKA+sb9LUKOI71DPtRP1UGXx5MREBRr/fV8VSCQhQl9R0rDlEREZILDZBV98QXwyCMAgPLScmz6ZD80/72K7L2NkVfewHiYCjp0D9+P9NTLUD/bEM0GJTnXMF9NsebUbu/jMCURkcNYZ8iN7A2GdFF1sL33C/jnvl5Y9UdLXBLRxn2hKMWAmL1QDy7D8OktEJtS1+PtJj/DOktERA5hMORGdgdDAACVsQJ0lOoyhiUdgPqBIAycmuLeuj/kWVLt/TK3HEpiIocpiYjMYDBkwbx58/Dyyy/jueeew8KFC+16jt3DZNAHREXBMdjz9+/Ra0JbBIUxrUp2fLX+mr2kGqgREUkMZ5OZsX37dixevBht2rTx2GsEAKh9uxBpHa4B5gIh3sikTQ6FDZVYZ4mISMJksxzH9evX8cgjj+Bf//oXateu7fkX1GiqbuNSCtJmq7AhoC+hoNV6tVlERCRtsgmGJk6ciKFDh6J///42jy0rK0NxcbHJj8P+7/9Mb5qGHofK63sZehwYEPmeFNZfIyIi2ZFFMPTll19i586dmDdvnl3Hz5s3D5GRkcafxMREx1/0woW7N032OMgDCxsSEZETJB8M5efn47nnnsMXX3yBsLAwu54zc+ZMFBUVGX/y8/Ode3HDTZM9DvLAwoZEROQEySdQ79ixA4WFhejYsaNxm1arxYYNG7Bo0SKUlZUhsFICc2hoKEJDQ11/ccNNU+k9DnJJGk9N1c8as1XYMDXV+20jIiLJknww1K9fP+zbt89k2xNPPIEWLVrgpZdeqhIIuUXlm6aSexykPk29Iq6/RkRETpD8MFl4eDhSUlJMfmrUqIHo6GikpKS4/wXN3TQNPQ6Gfeaek5jofz0OckwaN6y/Fh9vuj0hQRrT6omISHIkHwx5TGIiMH26/iZZkbmbpqHHAagaEPlrj4Ock8YzMoATJ4CcHCAzU/87L4+BEBERmSW7CtTOMFawXLUKEcXFpnkvjuTDKGkphdxcfR0lW3JyWECQiIg8ghWoPSE1Faj8ZjpSDTgjA1Cr5ZFM7CqlJ40TEZFiKCsYcgelLKWg5KRxIiJSFOXmDJF1Sk0aJyIixWEwZItWq8+fWbZM/1uKCcOeoMSkcSIiUiQGQ9YofWFWTlMnIiIFUNZsMkey0Q01diq/PYZeESUFA3KpQE1ERH7FW7PJGAyZo9Xqe4AsrUdmqFCdl8eggIiIyEO8FQxxmMwcLsxKRESkGAyGzGGNHSIiIsVgnSFzlFZjhzlBRESkYOwZMkdJNXaUPmOOiIgUj8GQOd6ssePLOkZyXJWeiIjIzRgMWeKNGju+7JWR86r0REREbsSp9bZ4Kp/G13WMuCo9ERFJHFetlwpPLMxqq1dGpdL3yqjVnktk5ow5IiIiABwm8w0p1DFS2ow5IiIiCxgM+YIUemWUNGOOiIjICgZDviCFXhmuSk9ERASAwZBvSKVXhqvSExERMYHaJwy9MiNH6gOfionU3u6VycjQJ2qzAjURESkUgyFfMfTKPPecaTJ1QoI+EPJmr4wnZswRERHJBIMhX2KvDBERkc8xGPI19soQERH5FBOoiYiISNEYDBEREZGiMRgiIiIiRWMwRERERIrGYIiIiIgUjcEQERERKRqDISIiIlI0BkNERESkaAyGiIiISNEYDBEREZGiMRgiIiIiRWMwRERERIrGYIiIiIgUTVmr1mu1QG4ucO4cEBcHpKbqV40nIiIixVJWMJSSApw9e/dxQgLw/vtARobv2kREREQ+Jflhsnnz5qFz584IDw9HTEwM0tPTcfjwYedOVjEQAoAzZ4CRI4GsLNcbSkRERLIk+WBo/fr1mDhxIrZu3Yo1a9agvLwcAwcORElJiesnF0L/e8oU/RAaERERKY5KCENEIA8XLlxATEwM1q9fj969e9v1nOLiYkRGRqIIQISlg3JygLQ0N7WSiIiIXGW8fxcVISLC4h3cZbLLGSoqKgIAREVFWTymrKwMZWVlxsfFxcW2T3zunMttIyIiIvmR/DBZRUIITJ06Fb169UJKSorF4+bNm4fIyEjjT2Jiou2Tx8W5saVEREQkF7IaJps4cSK+++47/PLLL0hISLB4nLmeocTERPPDZCqVflZZXh6n2RMREUkIh8kqmTx5MlauXIkNGzZYDYQAIDQ0FKGhobZPqlLpfy9cyECIiIhIoSQ/TCaEwKRJk5CVlYV169YhKSnJ+ZPVr2/6OCEB+OYb1hkiIiJSMMn3DE2cOBGZmZnQaDQIDw9HQUEBACAyMhLVqlVz7GT79wN79rACNRERERlJPmdIZRjKqmTJkiV4/PHH7TqHt8YciYiIyH2YM3SHxGM1IiIikjnJ5wwREREReRKDISIiIlI0BkNERESkaAyGiIiISNEYDBEREZGiMRgiIiIiRWMwRERERIrGYIiIiIgUjcEQERERKRqDISIiIlI0BkNERESkaAyGiIiISNEYDBEREZGiMRgiIiIiRWMwRERERIrGYIiIiIgUjcEQERERKRqDISIiIlI0BkNERESkaAyGiIiISNEYDBEREZGiMRgiIiIiRWMwRERERIrGYIiIiIgUjcEQERERKRqDISIiIlI0BkNERESkaAyGiIiISNEYDBEREZGiMRgiIiIiRWMwRERERIrGYIiIiIgUjcEQERERKRqDISIiIlI0BkNERESkaAyGiIiISNEYDBEREZGiMRgiIiIiRZNNMPTRRx8hKSkJYWFh6NixIzZu3OjrJhEREZEfkEUw9NVXX2HKlCl45ZVXsGvXLqSmpmLIkCE4deqUr5tGREREMqcSQghfN8KWrl27okOHDvjnP/9p3NayZUukp6dj3rx5Np9fXFyMyMhIFBUVISIiwpNNJSIiIjfx1v1b8j1Dt27dwo4dOzBw4ECT7QMHDsTmzZt91CoiIiLyF0G+boAtFy9ehFarRWxsrMn22NhYFBQUmH1OWVkZysrKjI+LiooA6CNMIiIikgfDfdvTg1iSD4YMVCqVyWMhRJVtBvPmzcOcOXOqbE9MTPRI24iIiMhzLl26hMjISI+dX/LBUJ06dRAYGFilF6iwsLBKb5HBzJkzMXXqVOPjq1evomHDhjh16pRH30ypKS4uRmJiIvLz8xWVK8Xr5nUrAa+b160ERUVFaNCgAaKiojz6OpIPhkJCQtCxY0esWbMG999/v3H7mjVroFarzT4nNDQUoaGhVbZHRkYq6h+RQUREBK9bQXjdysLrVhalXndAgGdTnCUfDAHA1KlTMXbsWHTq1Andu3fH4sWLcerUKfzlL3/xddOIiIhI5mQRDD300EO4dOkSXn/9dZw7dw4pKSn4/vvv0bBhQ183jYiIiGROFsEQAEyYMAETJkxw6rmhoaGYNWuW2aEzf8br5nUrAa+b160EvG7PXrcsii4SEREReYrkiy4SEREReRKDISIiIlI0BkNERESkaAyGiIiISNFkGQx99NFHSEpKQlhYGDp27IiNGzdaPX79+vXo2LEjwsLC0LhxY3z88cdVjlm+fDmSk5MRGhqK5ORkrFixwlPNd5oj152VlYUBAwagbt26iIiIQPfu3bF69WqTY5YuXQqVSlXlp7S01NOX4hBHrjs3N9fsNR06dMjkOH/7vB9//HGz192qVSvjMXL4vDds2IDhw4ejfv36UKlUyM7Otvkcf/h+O3rd/vL9dvS6/eX77eh1+8v3e968eejcuTPCw8MRExOD9PR0HD582ObzvPEdl10w9NVXX2HKlCl45ZVXsGvXLqSmpmLIkCE4deqU2ePz8vJw3333ITU1Fbt27cLLL7+MZ599FsuXLzces2XLFjz00EMYO3Ys9uzZg7Fjx2LUqFHYtm2bty7LJkeve8OGDRgwYAC+//577NixA3379sXw4cOxa9cuk+MiIiJw7tw5k5+wsDBvXJJdHL1ug8OHD5tcU9OmTY37/PHzfv/9902uNz8/H1FRUXjwwQdNjpP6511SUoK2bdti0aJFdh3vL99vR6/bX77fjl63gdy/345et798v9evX4+JEydi69atWLNmDcrLyzFw4ECUlJRYfI7XvuNCZrp06SL+8pe/mGxr0aKFmDFjhtnjX3zxRdGiRQuTbU8//bTo1q2b8fGoUaPE4MGDTY4ZNGiQePjhh93Uatc5et3mJCcnizlz5hgfL1myRERGRrqriR7h6HXn5OQIAOLKlSsWz6mEz3vFihVCpVKJEydOGLfJ4fOuCIBYsWKF1WP85ftdkT3XbY4cv98V2XPd/vL9rsiZz9sfvt9CCFFYWCgAiPXr11s8xlvfcVn1DN26dQs7duzAwIEDTbYPHDgQmzdvNvucLVu2VDl+0KBB+O2333D79m2rx1g6p7c5c92V6XQ6XLt2rcpid9evX0fDhg2RkJCAYcOGVfnL0pdcue727dsjLi4O/fr1Q05Ojsk+JXzen376Kfr371+lSruUP29n+MP32x3k+P12hZy/3+7gL9/voqIiALC6CKu3vuOyCoYuXrwIrVZbZbX62NjYKqvaGxQUFJg9vry8HBcvXrR6jKVzepsz113ZggULUFJSglGjRhm3tWjRAkuXLsXKlSuxbNkyhIWFoWfPnjh69Khb2+8sZ647Li4OixcvxvLly5GVlYXmzZujX79+2LBhg/EYf/+8z507hx9++AHjx4832S71z9sZ/vD9dgc5fr+d4Q/fb1f5y/dbCIGpU6eiV69eSElJsXict77jslmOoyKVSmXyWAhRZZut4ytvd/ScvuBsG5ctW4bZs2dDo9EgJibGuL1bt27o1q2b8XHPnj3RoUMH/OMf/8AHH3zgvoa7yJHrbt68OZo3b2583L17d+Tn5+Odd95B7969nTqnrzjbxqVLl6JWrVpIT0832S6Xz9tR/vL9dpbcv9+O8Kfvt7P85fs9adIk7N27F7/88ovNY73xHZdVz1CdOnUQGBhYJdorLCysEhUa1KtXz+zxQUFBiI6OtnqMpXN6mzPXbfDVV1/hqaeewv/+9z/079/f6rEBAQHo3LmzZP6ScOW6K+rWrZvJNfnz5y2EwGeffYaxY8ciJCTE6rFS+7yd4Q/fb1fI+fvtLnL7frvCX77fkydPxsqVK5GTk4OEhASrx3rrOy6rYCgkJAQdO3bEmjVrTLavWbMGPXr0MPuc7t27Vzn+p59+QqdOnRAcHGz1GEvn9DZnrhvQ/8X4+OOPIzMzE0OHDrX5OkII7N69G3FxcS632R2cve7Kdu3aZXJN/vp5A/rZGseOHcNTTz1l83Wk9nk7wx++386S+/fbXeT2/XaF3L/fQghMmjQJWVlZWLduHZKSkmw+x2vfcbtTrSXiyy+/FMHBweLTTz8VBw4cEFOmTBE1atQwZtXPmDFDjB071nj8H3/8IapXry6ef/55ceDAAfHpp5+K4OBg8c033xiP2bRpkwgMDBR///vfxcGDB8Xf//53ERQUJLZu3er167PE0evOzMwUQUFB4sMPPxTnzp0z/ly9etV4zOzZs8WPP/4ojh8/Lnbt2iWeeOIJERQUJLZt2+b167PE0et+7733xIoVK8SRI0fE/v37xYwZMwQAsXz5cuMx/vh5Gzz66KOia9euZs8ph8/72rVrYteuXWLXrl0CgHj33XfFrl27xMmTJ4UQ/vv9dvS6/eX77eh1+8v329HrNpD79/uZZ54RkZGRIjc31+Tf7Y0bN4zH+Oo7LrtgSAghPvzwQ9GwYUMREhIiOnToYDItb9y4caJPnz4mx+fm5or27duLkJAQ0ahRI/HPf/6zyjm//vpr0bx5cxEcHCxatGhh8uWSCkeuu0+fPgJAlZ9x48YZj5kyZYpo0KCBCAkJEXXr1hUDBw4Umzdv9uIV2ceR637rrbdEkyZNRFhYmKhdu7bo1auX+O6776qc098+byGEuHr1qqhWrZpYvHix2fPJ4fM2TJ229O/WX7/fjl63v3y/Hb1uf/l+O/Pv3B++3+auGYBYsmSJ8RhffcdVdxpIREREpEiyyhkiIiIicjcGQ0RERKRoDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaISJaWLVuGsLAwnDlzxrht/PjxaNOmDYqKinzYMiKSG65NRkSyJIRAu3btkJqaikWLFmHOnDn497//ja1btyI+Pt7XzSMiGQnydQOIiJyhUqkwd+5cjBw5EvXr18f777+PjRs3MhAiIoexZ4iIZK1Dhw74/fff8dNPP6FPnz6+bg4RyRBzhohItlavXo1Dhw5Bq9UiNjbW180hIplizxARydLOnTuRlpaGDz/8EF9++SWqV6+Or7/+2tfNIiIZYs4QEcnOiRMnMHToUMyYMQNjx45FcnIyOnfujB07dqBjx46+bh4RyQx7hohIVi5fvoyePXuid+/e+OSTT4zb1Wo1ysrK8OOPP/qwdUQkRwyGiIiISNGYQE1ERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJStP8HWw2lTEn901wAAAAASUVORK5CYII=", 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    " ] }, "metadata": { "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_22_1.png" + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_22_2.png" } }, "output_type": "display_data" @@ -687,8 +693,8 @@ "output_type": "stream", "text": [ "theta from own sdg\n", - "[[3.6805151 ]\n", - " [3.33045013]]\n" + "[[4.02781444]\n", + " [3.13976073]]\n" ] } ], @@ -787,15 +793,15 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[3.98751068]\n", - " [2.92901115]]\n", - "Eigenvalues of Hessian Matrix:[0.28244905 4.61501312]\n", + "[[3.96417888]\n", + " [3.06634473]]\n", + "Eigenvalues of Hessian Matrix:[0.32962444 4.18715465]\n", "theta from own gd\n", - "[[3.98556236]\n", - " [2.93059014]]\n", + "[[3.9639885]\n", + " [3.0665111]]\n", "theta from own sdg with momentum\n", - "[[4.01133846]\n", - " [2.92452609]]\n" + "[[4.00842216]\n", + " [3.14285244]]\n" ] } ], @@ -912,9 +918,9 @@ "output_type": "stream", "text": [ "theta from own AdaGrad\n", - "[[1.99974365]\n", - " [3.0013839 ]\n", - " [3.99861193]]\n" + "[[1.99969895]\n", + " [3.00167058]\n", + " [3.99835872]]\n" ] } ], @@ -1020,9 +1026,9 @@ "output_type": "stream", "text": [ "theta from own RMSprop\n", - "[[1.99757634]\n", - " [2.9983289 ]\n", - " [3.99759503]]\n" + "[[1.99852187]\n", + " [3.03868311]\n", + " [3.95744254]]\n" ] } ], @@ -1126,9 +1132,9 @@ "output_type": "stream", "text": [ "theta from own ADAM\n", - "[[1.99993596]\n", - " [3.00035483]\n", - " [3.99963172]]\n" + "[[1.99996471]\n", + " [3.00026784]\n", + " [3.99973141]]\n" ] } ], @@ -1256,7 +1262,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 11, @@ -1332,7 +1338,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -1394,7 +1400,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.15" } 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true + }, "source": [ "# Exercises week 42\n", "\n", - "**October 14-18, 2024**\n", + "**October 11-18, 2024**\n", "\n", "Date: **Deadline is Friday October 18 at midnight**\n" ] @@ -25,13 +29,17 @@ { "cell_type": "markdown", "id": "bb01f126", - "metadata": {}, + "metadata": { + "editable": true + }, "source": [ "# Overarching aims of the exercises this week\n", "\n", "The aim of the exercises this week is to get started with implementing a neural network. There are a lot of technical and finicky parts of implementing a neutal network, so take your time.\n", "\n", - "This week, you will implement only the feed-forward pass. Next week, you will implement backpropagation. We recommend that you do the exercises this week by editing and running this notebook file, as it includes several checks along the way that you have implemented the pieces of the feed-forward pass correctly. If you have trouble running a notebook, or importing pytorch, you can run this notebook in google colab instead: (LINK TO COLAB), though we recommend that you set up VSCode and your python environment to run code like this locally.\n" + "This week, you will implement only the feed-forward pass and updating the network parameters with simple gradient descent, the gradient will be computed using autograd using code we provide. Next week, you will implement backpropagation. We recommend that you do the exercises this week by editing and running this notebook file, as it includes some checks along the way that you have implemented the pieces of the feed-forward pass correctly, and running small parts of the code at a time will be important for understanding the methods.\n", + "\n", + "If you have trouble running a notebook, you can run this notebook in google colab instead (https://colab.research.google.com/drive/1OCQm1tlTWB6hZSf9I7gGUgW9M8SbVeQu#offline=true&sandboxMode=true), an updated link will be provided on the course discord (you can also send an email to k.h.fredly@fys.uio.no if you encounter any trouble), though we recommend that you set up VSCode and your python environment to run code like this locally.\n" ] }, { @@ -41,8 +49,33 @@ "metadata": {}, "outputs": [], "source": [ - "import autograd.numpy as np\n", - "from autograd import grad" + "import autograd.numpy as np # We need to use this numpy wrapper to make automatic differentiation work later\n", + "from sklearn import datasets\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "\n", + "# Defining some activation functions\n", + "def ReLU(z):\n", + " return np.where(z > 0, z, 0)\n", + "\n", + "\n", + "def sigmoid(z):\n", + " return 1 / (1 + np.exp(-z))\n", + "\n", + "\n", + "def softmax(z):\n", + " \"\"\"Compute softmax values for each set of scores in the rows of the matrix z.\n", + " Used with batched input data.\"\"\"\n", + " e_z = np.exp(z - np.max(z, axis=0))\n", + " return e_z / np.sum(e_z, axis=1)[:, np.newaxis]\n", + "\n", + "\n", + "def softmax_vec(z):\n", + " \"\"\"Compute softmax values for each set of scores in the vector z.\n", + " Use this function when you use the activation function on one vector at a time\"\"\"\n", + " e_z = np.exp(z - np.max(z))\n", + " return e_z / np.sum(e_z)" ] }, { @@ -52,7 +85,7 @@ "source": [ "# Exercise 1\n", "\n", - "Complete the following parts to compute the activation of the first layer.\n" + "In this exercise you will compute the activation of the first layer. You only need to change the code in the cells right below an exercise, the rest works out of the box. Feel free to make changes and see how stuff works though!\n" ] }, { @@ -64,21 +97,24 @@ "source": [ "np.random.seed(2024)\n", "\n", - "\n", - "def ReLU(z):\n", - " return np.where(z > 0, z, 0)\n", - "\n", - "\n", - "x = np.random.randn(2) # network input\n", + "x = np.random.randn(2) # network input. This is a single input with two features\n", "W1 = np.random.randn(4, 2) # first layer weights" ] }, + { + "cell_type": "markdown", + "id": "4ed2cf3d", + "metadata": {}, + "source": [ + "**a)** Given the shape of the first layer weight matrix, what is the input shape of the neural network? What is the output shape of the first layer?\n" + ] + }, { "cell_type": "markdown", "id": "edf7217b", "metadata": {}, "source": [ - "**a)** Define the bias of the first layer, `b1`with the correct shape\n" + "**b)** Define the bias of the first layer, `b1`with the correct shape. (Run the next cell right after the previous to get the random generated values to line up with the test solution below)\n" ] }, { @@ -88,7 +124,7 @@ "metadata": {}, "outputs": [], "source": [ - "b1 = np.random.randn(4)" + "b1 = ..." ] }, { @@ -96,7 +132,7 @@ "id": "09e8d453", "metadata": {}, "source": [ - "**b)** Compute the intermediary `z1` for the first layer\n" + "**c)** Compute the intermediary `z1` for the first layer\n" ] }, { @@ -106,7 +142,7 @@ "metadata": {}, "outputs": [], "source": [ - "z1 = W1 @ x + b1" + "z1 = ..." ] }, { @@ -114,7 +150,7 @@ "id": "6f71374e", "metadata": {}, "source": [ - "**c)** Compute the activation `a1` for the first layer using the ReLU activation function defined earlier.\n" + "**d)** Compute the activation `a1` for the first layer using the ReLU activation function defined earlier.\n" ] }, { @@ -124,7 +160,7 @@ "metadata": {}, "outputs": [], "source": [ - "a1 = ReLU(z1)" + "a1 = ..." ] }, { @@ -132,7 +168,7 @@ "id": "088710c0", "metadata": {}, "source": [ - "Confirm that you got the correct activation with the test below.\n" + "Confirm that you got the correct activation with the test below. Make sure that you define `b1` with the randn function right after you define `W1`.\n" ] }, { @@ -142,10 +178,19 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n" + "ename": "TypeError", + "evalue": "ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m sol1 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m0.60610368\u001b[39m, \u001b[38;5;241m4.0076268\u001b[39m, \u001b[38;5;241m0.0\u001b[39m, \u001b[38;5;241m0.56469864\u001b[39m])\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mallclose\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msol1\u001b[49m\u001b[43m)\u001b[49m)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:48\u001b[0m, in \u001b[0;36mprimitive..f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_box(ans, trace, node)\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf_raw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mallclose\u001b[0;34m(*args, **kwargs)\u001b[0m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/numeric.py:2251\u001b[0m, in \u001b[0;36mallclose\u001b[0;34m(a, b, rtol, atol, equal_nan)\u001b[0m\n\u001b[1;32m 2180\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_allclose_dispatcher)\n\u001b[1;32m 2181\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mallclose\u001b[39m(a, b, rtol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.e-5\u001b[39m, atol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.e-8\u001b[39m, equal_nan\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 2182\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2183\u001b[0m \u001b[38;5;124;03m Returns True if two arrays are element-wise equal within a tolerance.\u001b[39;00m\n\u001b[1;32m 2184\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2249\u001b[0m \n\u001b[1;32m 2250\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2251\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mall\u001b[39m(\u001b[43misclose\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrtol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrtol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43matol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43matol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mequal_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mequal_nan\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 2252\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mbool\u001b[39m(res)\n", + "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36misclose\u001b[0;34m(*args, **kwargs)\u001b[0m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/numeric.py:2358\u001b[0m, in \u001b[0;36misclose\u001b[0;34m(a, b, rtol, atol, equal_nan)\u001b[0m\n\u001b[1;32m 2355\u001b[0m dt \u001b[38;5;241m=\u001b[39m multiarray\u001b[38;5;241m.\u001b[39mresult_type(y, \u001b[38;5;241m1.\u001b[39m)\n\u001b[1;32m 2356\u001b[0m y \u001b[38;5;241m=\u001b[39m asanyarray(y, dtype\u001b[38;5;241m=\u001b[39mdt)\n\u001b[0;32m-> 2358\u001b[0m xfin \u001b[38;5;241m=\u001b[39m \u001b[43misfinite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2359\u001b[0m yfin \u001b[38;5;241m=\u001b[39m isfinite(y)\n\u001b[1;32m 2360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m(xfin) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mall\u001b[39m(yfin):\n", + "\u001b[0;31mTypeError\u001b[0m: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''" ] } ], @@ -162,20 +207,22 @@ "source": [ "# Exercise 2\n", "\n", - "Compute the activation of the second layer with an output of length 8 and ReLU activation.\n", + "Now we will add a layer to the network with an output of length 8 and ReLU activation.\n", "\n", - "**a)** Define the weight and bias of the second layer with the right shapes.\n" + "**a)** What is the input of the second layer? What is its shape?\n", + "\n", + "**b)** Define the weight and bias of the second layer with the right shapes.\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "00063acf", "metadata": {}, "outputs": [], "source": [ - "W2 = np.random.randn(8, 4)\n", - "b2 = np.random.randn(8)" + "W2 = ...\n", + "b2 = ..." ] }, { @@ -183,18 +230,18 @@ "id": "5bd7d84b", "metadata": {}, "source": [ - "**b)** Compute intermediary `z2` and activation `a2` for the second layer.\n" + "**c)** Compute the intermediary `z2` and activation `a2` for the second layer.\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "2fd0383d", "metadata": {}, "outputs": [], "source": [ - "z2 = W2 @ a1\n", - "a2 = ReLU(z2)" + "z2 = ...\n", + "a2 = ..." ] }, { @@ -207,20 +254,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "f7f2f8a1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n" - ] - } - ], + "outputs": [], "source": [ - "print(a2.shape == (8,))" + "print(\n", + " np.allclose(np.exp(len(a2)), 2980.9579870417283)\n", + ") # This should evaluate to True if a2 has the correct shape :)" ] }, { @@ -237,21 +278,21 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "c58f10f9", "metadata": {}, "outputs": [], "source": [ - "def create_layers(network_input_size, output_sizes):\n", + "def create_layers(network_input_size, layer_output_sizes):\n", " layers = []\n", "\n", " i_size = network_input_size\n", - " for output_size in output_sizes:\n", - " W = np.random.rand(output_size, i_size)\n", - " b = np.random.rand(output_size)\n", + " for layer_output_size in layer_output_sizes:\n", + " W = ...\n", + " b = ...\n", " layers.append((W, b))\n", "\n", - " i_size = output_size\n", + " i_size = layer_output_size\n", " return layers" ] }, @@ -260,21 +301,21 @@ "id": "bdc0cda2", "metadata": {}, "source": [ - "**b)** Comple the function below so that it evaluates the intermediate `z` and activation `a` for each layer, and returns the final activation `a`. This is the complete feed-forward pass, a full neural network!\n" + "**b)** Comple the function below so that it evaluates the intermediary `z` and activation `a` for each layer, with ReLU actication, and returns the final activation `a`. This is the complete feed-forward pass, a full neural network!\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "5262df05", "metadata": {}, "outputs": [], "source": [ - "def feed_forward(layers, input):\n", + "def feed_forward_all_relu(layers, input):\n", " a = input\n", " for W, b in layers:\n", - " z = W @ a + b\n", - " a = ReLU(z)\n", + " z = ...\n", + " a = ...\n", " return a" ] }, @@ -292,14 +333,30 @@ "id": "89a8f70d", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "input_size = ...\n", + "layer_output_sizes = [...]\n", + "\n", + "x = np.random.rand(input_size)\n", + "layers = ...\n", + "predict = ...\n", + "print(predict)" + ] + }, + { + "cell_type": "markdown", + "id": "0da7fd52", + "metadata": {}, + "source": [ + "**d)** Why is a neural network with no activation functions always mathematically equivelent to a neural network with only one layer?\n" + ] }, { "cell_type": "markdown", "id": "306d8b7c", "metadata": {}, "source": [ - "# Exercise 4\n" + "# Exercise 4 - Custom activation for each layer\n" ] }, { @@ -307,29 +364,7 @@ "id": "221c7b6c", "metadata": {}, "source": [ - "So far, every layer has used the same activation, ReLU. We often want to use other types of activation however, so we need to update our code to support multiple types of activation. Make sure that you have completed every previous exercise before trying this one.\n", - "\n", - "**a)** Make the `create_layers` function also accept a list of activation functions, which is used to add activation functions to each of the tuples in `layers`. Make new functions to not mess with the old ones.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "9df82312", - "metadata": {}, - "outputs": [], - "source": [ - "def create_layers_4(network_input_size, output_sizes, activation_funcs):\n", - " layers = []\n", - "\n", - " i_size = network_input_size\n", - " for output_size, activation in zip(output_sizes, activation_funcs):\n", - " W = np.random.rand(output_size, i_size)\n", - " b = np.random.rand(output_size)\n", - " layers.append((W, b, activation))\n", - "\n", - " i_size = output_size\n", - " return layers" + "So far, every layer has used the same activation, ReLU. We often want to use other types of activation however, so we need to update our code to support multiple types of activation functions. Make sure that you have completed every previous exercise before trying this one.\n" ] }, { @@ -337,21 +372,119 @@ "id": "10896d06", "metadata": {}, "source": [ - "**b)** Update the `feed_forward` function to support this change.\n" + "**a)** Complete the `feed_forward` function which accepts a list of activation functions as an argument, and which evaluates these activation functions at each layer.\n" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "de062369", "metadata": {}, "outputs": [], "source": [ - "def feed_forward_4(layers, input):\n", + "def feed_forward(input, layers, activations):\n", " a = input\n", - " for W, b, activation in layers:\n", - " z = W @ a + b\n", - " a = activation(z)\n", + " for (W, b), activation in zip(layers, activations):\n", + " z = ...\n", + " a = ...\n", + " return a" + ] + }, + { + "cell_type": "markdown", + "id": "8f7df363", + "metadata": {}, + "source": [ + "**b)** Make a list with three activation functions(don't call them yet! you can make a list with function names as elements, and then call these elements of the list later), two ReLU and one sigmoid. (If you add other functions than the ones defined at the start of the notebook, make sure everything is defined using autograd's numpy wrapper, like above, since we want to use automatic differentiation on all of these functions later.)\n", + "\n", + "Then evaluate a network with three layers and these activation functions.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "301b46dc", + "metadata": {}, + "outputs": [], + "source": [ + "network_input_size = ...\n", + "layer_output_sizes = [...]\n", + "activations = [...]\n", + "layers = ...\n", + "\n", + "x = np.random.randn(network_input_size)\n", + "feed_forward(x, layers, activations)" + ] + }, + { + "cell_type": "markdown", + "id": "a8d6c425", + "metadata": {}, + "source": [ + "# Exercise 5 - Processing multiple inputs at once\n" + ] + }, + { + "cell_type": "markdown", + "id": "0f4330a4", + "metadata": {}, + "source": [ + "So far, the feed forward function has taken one input vector as an input. This vector then undergoes a linear transformation and then an element-wise non-linear operation for each layer. This approach of sending one vector in at a time is great for interpreting how the network transforms data with its linear and non-linear operations, but not the best for numerical efficiency. Now, we want to be able to send many inputs through the network at once. This will make the code a bit harder to understand, but it will make it faster, and more compact. It will be worth the trouble.\n", + "\n", + "To process multiple inputs at once, while still performing the same operations, you will only need to flip a couple things around.\n" + ] + }, + { + "cell_type": "markdown", + "id": "17023bb7", + "metadata": {}, + "source": [ + "**a)** Complete the function `create_layers_batch` so that the weight matrix is the transpose of what it was when you only sent in one input at a time.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a241fd79", + "metadata": {}, + "outputs": [], + "source": [ + "def create_layers_batch(network_input_size, layer_output_sizes):\n", + " layers = []\n", + "\n", + " i_size = network_input_size\n", + " for layer_output_size in layer_output_sizes:\n", + " W = ...\n", + " b = ...\n", + " layers.append((W, b))\n", + "\n", + " i_size = layer_output_size\n", + " return layers" + ] + }, + { + "cell_type": "markdown", + "id": "a6349db6", + "metadata": {}, + "source": [ + "**b)** Make a matrix of inputs with the shape (number of features, number of inputs), you choose the number of inputs and features per input. Then complete the function `feed_forward_batch` so that you can process this matrix of inputs with only one matrix multiplication and one broadcasted vector addition per layer. (Hint: You will only need to swap two variable around from your previous implementation, but remember to test that you get the same results for equivelent inputs!)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "425f3bcc", + "metadata": {}, + "outputs": [], + "source": [ + "inputs = np.random.rand(1000, 4)\n", + "\n", + "\n", + "def feed_forward_batch(inputs, layers, activations):\n", + " a = inputs\n", + " for (W, b), activation in zip(layers, activations):\n", + " z = ...\n", + " a = ...\n", " return a" ] }, @@ -365,20 +498,34 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "ce6fcc2f", "metadata": {}, "outputs": [], "source": [ - "from scipy.special import softmax\n", - "\n", - "network_input_size = 4\n", - "output_sizes = [12, 10, 3]\n", - "activation_funcs = [ReLU, ReLU, softmax]\n", - "layers = create_layers_4(network_input_size, output_sizes, activation_funcs)\n", + "network_input_size = ...\n", + "layer_output_sizes = [...]\n", + "activations = [...]\n", + "layers = create_layers_batch(network_input_size, layer_output_sizes)\n", "\n", "x = np.random.randn(network_input_size)\n", - "predict = feed_forward_4(layers, x)" + "feed_forward_batch(inputs, layers, activations)" + ] + }, + { + "cell_type": "markdown", + "id": "87999271", + "metadata": {}, + "source": [ + "You should use this batched approach moving forward, as it will lead to much more compact code. However, remember that each input is still treated separately, and that you will need to keep in mind the transposed weight matrix and other details when implementing backpropagation.\n" + ] + }, + { + "cell_type": "markdown", + "id": "237eb782", + "metadata": {}, + "source": [ + "# Exercise 6 - Predicting on real data\n" ] }, { @@ -386,37 +533,18 @@ "id": "54d5fde7", "metadata": {}, "source": [ - "The final exercise will hopefully be very simple if everything has worked so far. You will evaluate your neural network on the iris data set (https://scikit-learn.org/1.5/auto_examples/datasets/plot_iris_dataset.html).\n", + "You will now evaluate your neural network on the iris data set (https://scikit-learn.org/1.5/auto_examples/datasets/plot_iris_dataset.html).\n", "\n", - "This dataset contains data on 150 flowers of 3 different types which can be separated pretty well using the four features given for each flower, which includes the width and length of their leaves. You are not expected to do any training of the network or actual classification, unless you feel like it, in that case you can do exercise 5.\n" + "This dataset contains data on 150 flowers of 3 different types which can be separated pretty well using the four features given for each flower, which includes the width and length of their leaves. You are will later train your network to actually make good predictions.\n" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "6bd4c148", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/exercisesweek42_34_0.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# Loading and plotting iris dataset\n", - "from sklearn import datasets\n", - "import matplotlib.pyplot as plt\n", - "\n", "iris = datasets.load_iris()\n", "\n", "_, ax = plt.subplots()\n", @@ -427,24 +555,91 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed3e2fc9", + "metadata": {}, + "outputs": [], + "source": [ + "inputs = iris.data\n", + "\n", + "# Since each prediction is a vector with a score for each of the three types of flowers,\n", + "# we need to make each target a vector with a 1 for the correct flower and a 0 for the others.\n", + "targets = np.zeros((len(iris.data), 3))\n", + "for i, t in enumerate(iris.target):\n", + " targets[i, t] = 1\n", + "\n", + "\n", + "def accuracy(predictions, targets):\n", + " one_hot_predictions = np.zeros(predictions.shape)\n", + "\n", + " for i, prediction in enumerate(predictions):\n", + " one_hot_predictions[i, np.argmax(prediction)] = 1\n", + " return accuracy_score(one_hot_predictions, targets)" + ] + }, { "cell_type": "markdown", - "id": "c528846f", + "id": "0362c4a9", "metadata": {}, "source": [ - "**c)** Loop over the iris dataset(`iris.data`) and evaluate the network for each data point.\n" + "**a)** What should the input size for the network be with this dataset? What should the output shape of the last layer be?\n" + ] + }, + { + "cell_type": "markdown", + "id": "bf62607e", + "metadata": {}, + "source": [ + "**b)** Create a network with two hidden layers, the first with sigmoid activation and the last with softmax, the first layer should have 8 \"nodes\", the second has the number of nodes you found in exercise a). Softmax returns a \"probability distribution\", in the sense that the numbers in the output are positive and add up to 1 and, their magnitude are in some sense relative to their magnitude before going through the softmax function. Remember to use the batched version of the create_layers and feed forward functions.\n" ] }, { "cell_type": "code", - "execution_count": 16, - "id": "2efc507d", + "execution_count": null, + "id": "5366d4ae", "metadata": {}, "outputs": [], "source": [ - "# No need to change this cell! Just make sure it works!\n", - "for x in iris.data:\n", - " prediction = feed_forward_4(layers, x)" + "...\n", + "layers = ..." + ] + }, + { + "cell_type": "markdown", + "id": "c528846f", + "metadata": {}, + "source": [ + "**c)** Evaluate your model on the entire iris dataset! For later purposes, we will split the data into train and test sets, and compute gradients on smaller batches of the training data. But for now, evaluate the network on the whole thing at once.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c783105", + "metadata": {}, + "outputs": [], + "source": [ + "predictions = feed_forward_batch(inputs, layers, activations)" + ] + }, + { + "cell_type": "markdown", + "id": "01a3caa8", + "metadata": {}, + "source": [ + "**d)** Compute the accuracy of your model using the accuracy function defined above. Recreate your model a couple times and see how the accuracy changes.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2612b82", + "metadata": {}, + "outputs": [], + "source": [ + "print(accuracy(predictions, targets))" ] }, { @@ -452,31 +647,126 @@ "id": "334560b6", "metadata": {}, "source": [ - "# Exercise 5 (Very optional and very hard :)\n" + "# Exercise 6 - Training on real data\n", + "\n", + "To be able to actually do anything useful with your neural network, you need to train it. For this, we need a cost function and a way to take the gradient of the cost function wrt. the network parameters. The following exercises guide you through taking the gradient using autograd, and updating the network parameters using the gradient. Feel free to implement gradient methods like ADAM if you finish everything.\n" ] }, { "cell_type": "markdown", - "id": "ea0a8fe0", + "id": "700cabe4", "metadata": {}, "source": [ - "**a)** Make the iris target values into one-hot vectors.\n", + "The cross-entropy loss function can evaluate performance on classification tasks. It sees if your prediction is \"most certain\" on the correct target.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56bef776", + "metadata": {}, + "outputs": [], + "source": [ + "from autograd import grad\n", "\n", - "**b)** Define the cross-entropy loss function to evaluate the performance of your network on the data set.\n", "\n", - "**c)** Use the autograd package to take the gradient of the cross entropy wrt. the weights and biases of the network.\n", + "def cost(input, layers, activations, target):\n", + " predict = feed_forward_batch(input, layers, activations)\n", + " return cross_entropy(predict, target)\n", "\n", - "**d)** Use gradient descent of some sort to optimize the parameters.\n", "\n", - "**e)** Evaluate the accuracy of the network.\n", + "def cross_entropy(predict, target):\n", + " return np.sum(-target * np.log(predict))\n", "\n", - "**e)** Show off how you did in a group session!\n" + "\n", + "gradient_func = grad(\n", + " cross_entropy, 1\n", + ") # Taking the gradient wrt. the second input to the cost function" + ] + }, + { + "cell_type": "markdown", + "id": "7b1b74bc", + "metadata": {}, + "source": [ + "**a)** What shape should the gradient of the cost function wrt. weights and biases be?\n", + "\n", + "**b)** Use the `gradient_func` function to take the gradient of the cross entropy wrt. the weights and biases of the network. Check the shapes of what's inside. What does the `grad` func from autograd actually do?\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "841c9e87", + "metadata": {}, + "outputs": [], + "source": [ + "layers_grad = gradient_func(inputs, layers, activations, targets) # Don't change this" + ] + }, + { + "cell_type": "markdown", + "id": "adc9e9be", + "metadata": {}, + "source": [ + "**c)** Finish the `train_network` function.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e4d38d3", + "metadata": {}, + "outputs": [], + "source": [ + "def train_network(\n", + " inputs, layers, activations, targets, learning_rate=0.001, epochs=100\n", + "):\n", + " for i in range(epochs):\n", + " layers_grad = gradient_func(inputs, layers, activations, targets)\n", + " for (W, b), (W_g, b_g) in zip(layers, layers_grad):\n", + " W -= ...\n", + " b -= ..." + ] + }, + { + "cell_type": "markdown", + "id": "2f65d663", + "metadata": {}, + "source": [ + "**e)** What do we call the gradient method used above?\n" + ] + }, + { + "cell_type": "markdown", + "id": "7059dd8c", + "metadata": {}, + "source": [ + "**d)** Train your network and see how the accuracy changes! Make a plot if you want.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5027c7a5", + "metadata": {}, + "outputs": [], + "source": [ + "..." + ] + }, + { + "cell_type": "markdown", + "id": "3bc77016", + "metadata": {}, + "source": [ + "**e)** How high of an accuracy is it possible to acheive with a neural network on this dataset, if we use the whole thing as training data?\n" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -490,7 +780,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.15" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek42.py b/doc/LectureNotes/_build/jupyter_execute/exercisesweek42.py index c6282b034..073fceec2 100644 --- a/doc/LectureNotes/_build/jupyter_execute/exercisesweek42.py +++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek42.py @@ -8,7 +8,7 @@ # # Exercises week 42 # -# **October 14-18, 2024** +# **October 11-18, 2024** # # Date: **Deadline is Friday October 18 at midnight** # @@ -17,19 +17,46 @@ # # The aim of the exercises this week is to get started with implementing a neural network. There are a lot of technical and finicky parts of implementing a neutal network, so take your time. # -# This week, you will implement only the feed-forward pass. Next week, you will implement backpropagation. We recommend that you do the exercises this week by editing and running this notebook file, as it includes several checks along the way that you have implemented the pieces of the feed-forward pass correctly. If you have trouble running a notebook, or importing pytorch, you can run this notebook in google colab instead: (LINK TO COLAB), though we recommend that you set up VSCode and your python environment to run code like this locally. +# This week, you will implement only the feed-forward pass and updating the network parameters with simple gradient descent, the gradient will be computed using autograd using code we provide. Next week, you will implement backpropagation. We recommend that you do the exercises this week by editing and running this notebook file, as it includes some checks along the way that you have implemented the pieces of the feed-forward pass correctly, and running small parts of the code at a time will be important for understanding the methods. +# +# If you have trouble running a notebook, you can run this notebook in google colab instead (https://colab.research.google.com/drive/1OCQm1tlTWB6hZSf9I7gGUgW9M8SbVeQu#offline=true&sandboxMode=true), an updated link will be provided on the course discord (you can also send an email to k.h.fredly@fys.uio.no if you encounter any trouble), though we recommend that you set up VSCode and your python environment to run code like this locally. # # In[1]: -import autograd.numpy as np -from autograd import grad +import autograd.numpy as np # We need to use this numpy wrapper to make automatic differentiation work later +from sklearn import datasets +import matplotlib.pyplot as plt +from sklearn.metrics import accuracy_score + + +# Defining some activation functions +def ReLU(z): + return np.where(z > 0, z, 0) + + +def sigmoid(z): + return 1 / (1 + np.exp(-z)) + + +def softmax(z): + """Compute softmax values for each set of scores in the rows of the matrix z. + Used with batched input data.""" + e_z = np.exp(z - np.max(z, axis=0)) + return e_z / np.sum(e_z, axis=1)[:, np.newaxis] + + +def softmax_vec(z): + """Compute softmax values for each set of scores in the vector z. + Use this function when you use the activation function on one vector at a time""" + e_z = np.exp(z - np.max(z)) + return e_z / np.sum(e_z) # # Exercise 1 # -# Complete the following parts to compute the activation of the first layer. +# In this exercise you will compute the activation of the first layer. You only need to change the code in the cells right below an exercise, the rest works out of the box. Feel free to make changes and see how stuff works though! # # In[2]: @@ -37,43 +64,41 @@ from autograd import grad np.random.seed(2024) - -def ReLU(z): - return np.where(z > 0, z, 0) - - -x = np.random.randn(2) # network input +x = np.random.randn(2) # network input. This is a single input with two features W1 = np.random.randn(4, 2) # first layer weights -# **a)** Define the bias of the first layer, `b1`with the correct shape +# **a)** Given the shape of the first layer weight matrix, what is the input shape of the neural network? What is the output shape of the first layer? +# + +# **b)** Define the bias of the first layer, `b1`with the correct shape. (Run the next cell right after the previous to get the random generated values to line up with the test solution below) # # In[3]: -b1 = np.random.randn(4) +b1 = ... -# **b)** Compute the intermediary `z1` for the first layer +# **c)** Compute the intermediary `z1` for the first layer # # In[4]: -z1 = W1 @ x + b1 +z1 = ... -# **c)** Compute the activation `a1` for the first layer using the ReLU activation function defined earlier. +# **d)** Compute the activation `a1` for the first layer using the ReLU activation function defined earlier. # # In[5]: -a1 = ReLU(z1) +a1 = ... -# Confirm that you got the correct activation with the test below. +# Confirm that you got the correct activation with the test below. Make sure that you define `b1` with the randn function right after you define `W1`. # # In[6]: @@ -86,35 +111,39 @@ print(np.allclose(a1, sol1)) # # Exercise 2 # -# Compute the activation of the second layer with an output of length 8 and ReLU activation. +# Now we will add a layer to the network with an output of length 8 and ReLU activation. # -# **a)** Define the weight and bias of the second layer with the right shapes. +# **a)** What is the input of the second layer? What is its shape? +# +# **b)** Define the weight and bias of the second layer with the right shapes. # -# In[7]: +# In[ ]: -W2 = np.random.randn(8, 4) -b2 = np.random.randn(8) +W2 = ... +b2 = ... -# **b)** Compute intermediary `z2` and activation `a2` for the second layer. +# **c)** Compute the intermediary `z2` and activation `a2` for the second layer. # -# In[8]: +# In[ ]: -z2 = W2 @ a1 -a2 = ReLU(z2) +z2 = ... +a2 = ... # Confirm that you got the correct activation shape with the test below. # -# In[9]: +# In[ ]: -print(a2.shape == (8,)) +print( + np.allclose(np.exp(len(a2)), 2980.9579870417283) +) # This should evaluate to True if a2 has the correct shape :) # # Exercise 3 @@ -124,33 +153,33 @@ print(a2.shape == (8,)) # **a)** Complete the function below so that it returns a list `layers` of weight and bias tuples `(W, b)` for each layer, in order, with the correct shapes that we can use later as our network parameters. # -# In[10]: +# In[ ]: -def create_layers(network_input_size, output_sizes): +def create_layers(network_input_size, layer_output_sizes): layers = [] i_size = network_input_size - for output_size in output_sizes: - W = np.random.rand(output_size, i_size) - b = np.random.rand(output_size) + for layer_output_size in layer_output_sizes: + W = ... + b = ... layers.append((W, b)) - i_size = output_size + i_size = layer_output_size return layers -# **b)** Comple the function below so that it evaluates the intermediate `z` and activation `a` for each layer, and returns the final activation `a`. This is the complete feed-forward pass, a full neural network! +# **b)** Comple the function below so that it evaluates the intermediary `z` and activation `a` for each layer, with ReLU actication, and returns the final activation `a`. This is the complete feed-forward pass, a full neural network! # -# In[11]: +# In[ ]: -def feed_forward(layers, input): +def feed_forward_all_relu(layers, input): a = input for W, b in layers: - z = W @ a + b - a = ReLU(z) + z = ... + a = ... return a @@ -160,75 +189,127 @@ def feed_forward(layers, input): # In[ ]: +input_size = ... +layer_output_sizes = [...] + +x = np.random.rand(input_size) +layers = ... +predict = ... +print(predict) - -# # Exercise 4 +# **d)** Why is a neural network with no activation functions always mathematically equivelent to a neural network with only one layer? # -# So far, every layer has used the same activation, ReLU. We often want to use other types of activation however, so we need to update our code to support multiple types of activation. Make sure that you have completed every previous exercise before trying this one. -# -# **a)** Make the `create_layers` function also accept a list of activation functions, which is used to add activation functions to each of the tuples in `layers`. Make new functions to not mess with the old ones. +# # Exercise 4 - Custom activation for each layer # -# In[12]: +# So far, every layer has used the same activation, ReLU. We often want to use other types of activation however, so we need to update our code to support multiple types of activation functions. Make sure that you have completed every previous exercise before trying this one. +# + +# **a)** Complete the `feed_forward` function which accepts a list of activation functions as an argument, and which evaluates these activation functions at each layer. +# + +# In[ ]: -def create_layers_4(network_input_size, output_sizes, activation_funcs): +def feed_forward(input, layers, activations): + a = input + for (W, b), activation in zip(layers, activations): + z = ... + a = ... + return a + + +# **b)** Make a list with three activation functions(don't call them yet! you can make a list with function names as elements, and then call these elements of the list later), two ReLU and one sigmoid. (If you add other functions than the ones defined at the start of the notebook, make sure everything is defined using autograd's numpy wrapper, like above, since we want to use automatic differentiation on all of these functions later.) +# +# Then evaluate a network with three layers and these activation functions. +# + +# In[ ]: + + +network_input_size = ... +layer_output_sizes = [...] +activations = [...] +layers = ... + +x = np.random.randn(network_input_size) +feed_forward(x, layers, activations) + + +# # Exercise 5 - Processing multiple inputs at once +# + +# So far, the feed forward function has taken one input vector as an input. This vector then undergoes a linear transformation and then an element-wise non-linear operation for each layer. This approach of sending one vector in at a time is great for interpreting how the network transforms data with its linear and non-linear operations, but not the best for numerical efficiency. Now, we want to be able to send many inputs through the network at once. This will make the code a bit harder to understand, but it will make it faster, and more compact. It will be worth the trouble. +# +# To process multiple inputs at once, while still performing the same operations, you will only need to flip a couple things around. +# + +# **a)** Complete the function `create_layers_batch` so that the weight matrix is the transpose of what it was when you only sent in one input at a time. +# + +# In[ ]: + + +def create_layers_batch(network_input_size, layer_output_sizes): layers = [] i_size = network_input_size - for output_size, activation in zip(output_sizes, activation_funcs): - W = np.random.rand(output_size, i_size) - b = np.random.rand(output_size) - layers.append((W, b, activation)) + for layer_output_size in layer_output_sizes: + W = ... + b = ... + layers.append((W, b)) - i_size = output_size + i_size = layer_output_size return layers -# **b)** Update the `feed_forward` function to support this change. +# **b)** Make a matrix of inputs with the shape (number of features, number of inputs), you choose the number of inputs and features per input. Then complete the function `feed_forward_batch` so that you can process this matrix of inputs with only one matrix multiplication and one broadcasted vector addition per layer. (Hint: You will only need to swap two variable around from your previous implementation, but remember to test that you get the same results for equivelent inputs!) # -# In[13]: +# In[ ]: -def feed_forward_4(layers, input): - a = input - for W, b, activation in layers: - z = W @ a + b - a = activation(z) +inputs = np.random.rand(1000, 4) + + +def feed_forward_batch(inputs, layers, activations): + a = inputs + for (W, b), activation in zip(layers, activations): + z = ... + a = ... return a # **c)** Create and evaluate a neural network with 4 inputs and layers with output sizes 12, 10, 3 and activations ReLU, ReLU, softmax. # -# In[14]: +# In[ ]: -from scipy.special import softmax - -network_input_size = 4 -output_sizes = [12, 10, 3] -activation_funcs = [ReLU, ReLU, softmax] -layers = create_layers_4(network_input_size, output_sizes, activation_funcs) +network_input_size = ... +layer_output_sizes = [...] +activations = [...] +layers = create_layers_batch(network_input_size, layer_output_sizes) x = np.random.randn(network_input_size) -predict = feed_forward_4(layers, x) +feed_forward_batch(inputs, layers, activations) -# The final exercise will hopefully be very simple if everything has worked so far. You will evaluate your neural network on the iris data set (https://scikit-learn.org/1.5/auto_examples/datasets/plot_iris_dataset.html). -# -# This dataset contains data on 150 flowers of 3 different types which can be separated pretty well using the four features given for each flower, which includes the width and length of their leaves. You are not expected to do any training of the network or actual classification, unless you feel like it, in that case you can do exercise 5. +# You should use this batched approach moving forward, as it will lead to much more compact code. However, remember that each input is still treated separately, and that you will need to keep in mind the transposed weight matrix and other details when implementing backpropagation. # -# In[15]: +# # Exercise 6 - Predicting on real data +# +# You will now evaluate your neural network on the iris data set (https://scikit-learn.org/1.5/auto_examples/datasets/plot_iris_dataset.html). +# +# This dataset contains data on 150 flowers of 3 different types which can be separated pretty well using the four features given for each flower, which includes the width and length of their leaves. You are will later train your network to actually make good predictions. +# + +# In[ ]: -# Loading and plotting iris dataset -from sklearn import datasets -import matplotlib.pyplot as plt iris = datasets.load_iris() @@ -240,29 +321,123 @@ _ = ax.legend( ) -# **c)** Loop over the iris dataset(`iris.data`) and evaluate the network for each data point. -# - -# In[16]: +# In[ ]: -# No need to change this cell! Just make sure it works! -for x in iris.data: - prediction = feed_forward_4(layers, x) +inputs = iris.data + +# Since each prediction is a vector with a score for each of the three types of flowers, +# we need to make each target a vector with a 1 for the correct flower and a 0 for the others. +targets = np.zeros((len(iris.data), 3)) +for i, t in enumerate(iris.target): + targets[i, t] = 1 -# # Exercise 5 (Very optional and very hard :) +def accuracy(predictions, targets): + one_hot_predictions = np.zeros(predictions.shape) + + for i, prediction in enumerate(predictions): + one_hot_predictions[i, np.argmax(prediction)] = 1 + return accuracy_score(one_hot_predictions, targets) + + +# **a)** What should the input size for the network be with this dataset? What should the output shape of the last layer be? # -# **a)** Make the iris target values into one-hot vectors. +# **b)** Create a network with two hidden layers, the first with sigmoid activation and the last with softmax, the first layer should have 8 "nodes", the second has the number of nodes you found in exercise a). Softmax returns a "probability distribution", in the sense that the numbers in the output are positive and add up to 1 and, their magnitude are in some sense relative to their magnitude before going through the softmax function. Remember to use the batched version of the create_layers and feed forward functions. # -# **b)** Define the cross-entropy loss function to evaluate the performance of your network on the data set. + +# In[ ]: + + +... +layers = ... + + +# **c)** Evaluate your model on the entire iris dataset! For later purposes, we will split the data into train and test sets, and compute gradients on smaller batches of the training data. But for now, evaluate the network on the whole thing at once. # -# **c)** Use the autograd package to take the gradient of the cross entropy wrt. the weights and biases of the network. + +# In[ ]: + + +predictions = feed_forward_batch(inputs, layers, activations) + + +# **d)** Compute the accuracy of your model using the accuracy function defined above. Recreate your model a couple times and see how the accuracy changes. # -# **d)** Use gradient descent of some sort to optimize the parameters. + +# In[ ]: + + +print(accuracy(predictions, targets)) + + +# # Exercise 6 - Training on real data # -# **e)** Evaluate the accuracy of the network. +# To be able to actually do anything useful with your neural network, you need to train it. For this, we need a cost function and a way to take the gradient of the cost function wrt. the network parameters. The following exercises guide you through taking the gradient using autograd, and updating the network parameters using the gradient. Feel free to implement gradient methods like ADAM if you finish everything. # -# **e)** Show off how you did in a group session! + +# The cross-entropy loss function can evaluate performance on classification tasks. It sees if your prediction is "most certain" on the correct target. +# + +# In[ ]: + + +from autograd import grad + + +def cost(input, layers, activations, target): + predict = feed_forward_batch(input, layers, activations) + return cross_entropy(predict, target) + + +def cross_entropy(predict, target): + return np.sum(-target * np.log(predict)) + + +gradient_func = grad( + cross_entropy, 1 +) # Taking the gradient wrt. the second input to the cost function + + +# **a)** What shape should the gradient of the cost function wrt. weights and biases be? +# +# **b)** Use the `gradient_func` function to take the gradient of the cross entropy wrt. the weights and biases of the network. Check the shapes of what's inside. What does the `grad` func from autograd actually do? +# + +# In[ ]: + + +layers_grad = gradient_func(inputs, layers, activations, targets) # Don't change this + + +# **c)** Finish the `train_network` function. +# + +# In[ ]: + + +def train_network( + inputs, layers, activations, targets, learning_rate=0.001, epochs=100 +): + for i in range(epochs): + layers_grad = gradient_func(inputs, layers, activations, targets) + for (W, b), (W_g, b_g) in zip(layers, layers_grad): + W -= ... + b -= ... + + +# **e)** What do we call the gradient method used above? +# + +# **d)** Train your network and see how the accuracy changes! Make a plot if you want. +# + +# In[ ]: + + +... + + +# **e)** How high of an accuracy is it possible to acheive with a neural network on this dataset, if we use the whole thing as training data? # diff --git a/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb b/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb index 1eb3ab202..98aa12925 100644 --- a/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb @@ -225,8 +225,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ 0.03563709 0.57852915 0.83220985 1.27108866 -0.3587467 -0.38713573\n", - " -0.09584387 0.5223261 1.7663967 0.94027059]\n" + "[ 1.34851141 0.11232457 -0.59560497 0.79558423 -0.74632311 0.97068015\n", + " 1.45921927 0.39225935 -0.87171492 0.44129766]\n" ] } ], @@ -662,26 +662,36 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[0.79010601 0.87637891 0.68824222 0.4636751 0.50100007 0.22715479\n", - " 0.17865868 0.90903158 0.5736973 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", 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", 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    " ] @@ -2764,12 +2764,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "-0.0370757046153366 1.001106660107757\n" + "0.03974553487733608 1.0433282860079154\n" ] }, { "data": { - "image/png": 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", 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birth'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Shire\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m'Date of Birth T.A.'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2990\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57294/1326197715.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m new_hobbit = {'First Name': [\"Peregrin\"],\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m'Last Name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Took\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m'Place of birth'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Shire\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m'Date of Birth T.A.'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2990\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 6200\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6201\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6202\u001b[0m ):\n\u001b[1;32m 6203\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6204\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'append'" ] @@ -5217,7 +5217,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.15" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb index 04e32da79..5376525c8 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb @@ -1533,7 +1533,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.9960887274532307\n" + "0.9953466931203151\n" ] } ], @@ -1564,7 +1564,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.010148621093080332\n" + "0.010175219431920396\n" ] } ], @@ -1599,23 +1599,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "[0.01006224 0.02667406 0.0272743 0.00100202 0.01480927 0.00410183\n", - " 0.02088307 0.02506901 0.00463124 0.00672505 0.0304378 0.00076521\n", - " 0.05364579 0.01232366 0.00969623 0.00677215 0.02239876 0.03858916\n", - " 0.00923474 0.00848739 0.01701429 0.07172932 0.08858739 0.04575775\n", - " 0.02988432 0.00988356 0.01546126 0.00033415 0.03201922 0.01425384\n", - " 0.00575681 0.01882218 0.05853984 0.00528252 0.03254614 0.03092729\n", - " 0.01094916 0.05696574 0.03412067 0.02444266 0.05749111 0.0689303\n", - " 0.00736737 0.02104639 0.00274212 0.00588836 0.05043958 0.01456865\n", - " 0.01813363 0.06019833 0.01078008 0.01059242 0.02369044 0.02692965\n", - " 0.00657601 0.01218403 0.03745816 0.05363722 0.00561212 0.03820746\n", - " 0.00988992 0.00774376 0.03426412 0.01323341 0.02387182 0.01151556\n", - " 0.01097287 0.07292363 0.02846506 0.04186033 0.00836649 0.00340452\n", - " 0.06200455 0.03246707 0.02987496 0.00355323 0.01740381 0.01196506\n", - " 0.02635861 0.07487128 0.08472879 0.0073544 0.01150437 0.00571884\n", - " 0.02025574 0.0014028 0.01512884 0.02146636 0.05097344 0.0284405\n", - " 0.06151386 0.00737863 0.04452918 0.03906948 0.01163942 0.07468007\n", - " 0.01647074 0.0096667 0.00369201 0.0168171 ]\n" + "[0.03699304 0.0218856 0.02021288 0.0334505 0.02960445 0.01369614\n", + " 0.00606112 0.01429853 0.01174937 0.02585059 0.02044223 0.00174884\n", + " 0.06133572 0.03159246 0.04435458 0.00125802 0.00100423 0.00230308\n", + " 0.01055384 0.02352843 0.06971862 0.01300036 0.00547332 0.05554795\n", + " 0.01142354 0.01181458 0.00148216 0.01419341 0.0305221 0.01272102\n", + " 0.01700746 0.01552039 0.01616657 0.10695771 0.00405576 0.02979087\n", + " 0.0529838 0.01420773 0.06220192 0.04104182 0.00653725 0.07170448\n", + " 0.00997215 0.02490769 0.02580654 0.01682317 0.03221473 0.01838531\n", + " 0.02028936 0.0064349 0.04323964 0.02705202 0.03692828 0.01975755\n", + " 0.05636265 0.02880987 0.05692207 0.04085864 0.01085261 0.01457889\n", + " 0.03418643 0.01919791 0.00101555 0.00636951 0.04217103 0.0476266\n", + " 0.01169528 0.04544327 0.00978267 0.04046984 0.0032882 0.02072876\n", + " 0.05526935 0.05461692 0.00877816 0.00724303 0.00045923 0.00059105\n", + " 0.00917881 0.04254787 0.08728977 0.04513394 0.01606644 0.08956994\n", + " 0.02687671 0.07449122 0.04497158 0.01713187 0.02553907 0.0397137\n", + " 0.03313193 0.00738299 0.01743124 0.02953975 0.01131825 0.0864086\n", + " 0.01925934 0.02439287 0.09331672 0.01721277]\n" ] } ], @@ -1669,15 +1669,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ 1.97802245 0.57331229 2.49761526 3.47609206 -1.5187643 ]\n", + "[ 1.94735263 0.70175778 2.99348646 1.86611894 -0.45576546]\n", "Training R2\n", - "0.995702810640425\n", + "0.9959836634296064\n", "Training MSE\n", - "0.007370297974992432\n", + "0.0085274606925055\n", "Test R2\n", - "0.9950019477819025\n", + "0.992232777849821\n", "Test MSE\n", - "0.009880124918446542\n" + "0.010638334964957053\n" ] } ], @@ -6363,7 +6363,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.15" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/week36.ipynb b/doc/LectureNotes/_build/jupyter_execute/week36.ipynb index ee58e1ea9..d8a5a5ee0 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week36.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week36.ipynb @@ -4581,7 +4581,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.15" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb index e4a4034f2..0e217531e 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb @@ -1671,7 +1671,7 @@ "text": [ "Bootstrap Statistics :\n", "original bias std. error\n", - " 100.257 14.853 100.257 0.149111\n" + " 100.091 15.1529 100.089 0.15052\n" ] } ], @@ -1737,7 +1737,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
    " ] @@ -2085,18 +2085,18 @@ "Error: 0.06547790180152355\n", "Bias^2: 0.06208238634231949\n", "Var: 0.0033955154592040936\n", - "0.06547790180152355 >= 0.06208238634231949 + 0.0033955154592040936 = 0.06547790180152359\n" + "0.06547790180152355 >= 0.06208238634231949 + 0.0033955154592040936 = 0.06547790180152359\n", + "Polynomial degree: 4\n", + "Error: 0.06844519414009445\n", + "Bias^2: 0.06453579006728324\n", + "Var: 0.003909404072811226\n", + "0.06844519414009445 >= 0.06453579006728324 + 0.003909404072811226 = 0.06844519414009446\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Polynomial degree: 4\n", - "Error: 0.06844519414009445\n", - "Bias^2: 0.06453579006728324\n", - "Var: 0.003909404072811226\n", - "0.06844519414009445 >= 0.06453579006728324 + 0.003909404072811226 = 0.06844519414009446\n", "Polynomial degree: 5\n", "Error: 0.05227921801205686\n", "Bias^2: 0.0481872773043029\n", @@ -2116,13 +2116,7 @@ "Error: 0.017355848195593347\n", "Bias^2: 0.010331721306655127\n", "Var: 0.007024126888938232\n", - "0.017355848195593347 >= 0.010331721306655127 + 0.007024126888938232 = 0.01735584819559336\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "0.017355848195593347 >= 0.010331721306655127 + 0.007024126888938232 = 0.01735584819559336\n", "Polynomial degree: 9\n", "Error: 0.02660572763718093\n", "Bias^2: 0.010018312644137363\n", @@ -2138,7 +2132,13 @@ "Error: 0.021592704588025025\n", "Bias^2: 0.010516485576645508\n", "Var: 0.011076219011379514\n", - "0.021592704588025025 >= 0.010516485576645508 + 0.011076219011379514 = 0.021592704588025022\n", + "0.021592704588025025 >= 0.010516485576645508 + 0.011076219011379514 = 0.021592704588025022\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Polynomial degree: 11\n", "Error: 0.07160048164233104\n", "Bias^2: 0.014436800088904942\n", @@ -2664,34 +2664,34 @@ "Mean squared error on test data: 1184.60929685\n", "Degree of polynomial: 23\n", "Mean squared error on training data: 0.00089193\n", - "Mean squared error on test data: 3892.17483760\n" + "Mean squared error on test data: 3892.17483760\n", + "Degree of polynomial: 24\n", + "Mean squared error on training data: 0.00083355\n", + "Mean squared error on test data: 1332.46736215\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Degree of polynomial: 24\n", - "Mean squared error on training data: 0.00083355\n", - "Mean squared error on test data: 1332.46736215\n", "Degree of polynomial: 25\n", "Mean squared error on training data: 0.00079904\n", "Mean squared error on test data: 7577.76690383\n", "Degree of polynomial: 26\n", "Mean squared error on training data: 0.00075590\n", - "Mean squared error on test data: 1079.36895644\n" + "Mean squared error on test data: 1079.36895644\n", + "Degree of polynomial: 27\n", + "Mean squared error on training data: 0.00068091\n", + "Mean squared error on test data: 3207.25343155\n", + "Degree of polynomial: 28\n", + "Mean squared error on training data: 0.00063362\n", + "Mean squared error on test data: 674.79633065\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Degree of polynomial: 27\n", - "Mean squared error on training data: 0.00068091\n", - "Mean squared error on test data: 3207.25343155\n", - "Degree of polynomial: 28\n", - "Mean squared error on training data: 0.00063362\n", - "Mean squared error on test data: 674.79633065\n", "Degree of polynomial: 29\n", "Mean squared error on training data: 0.00063866\n", "Mean squared error on test data: 3099.60342978\n" @@ -2701,9 +2701,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22575/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57311/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n", " plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22575/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57311/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n", " plt.plot(polynomial, np.log10(testerror), label='Test Error')\n" ] }, @@ -2838,7 +2838,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22575/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57311/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n", " plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n" ] }, @@ -3301,7 +3301,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.15" } }, "nbformat": 4, diff --git 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Matrix:[0.29950088 4.27458376]\n", - "[[3.66841959]\n", - " [3.26280614]]\n", - "[[3.66841959]\n", - " [3.26280614]]\n" + "Eigenvalues of Hessian Matrix:[0.33433563 4.2051784 ]\n", + "[[3.94121002]\n", + " [3.08191754]]\n", + "[[3.94121002]\n", + " [3.08191754]]\n" ] }, { "data": { - "image/png": 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", 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", 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    " ] @@ -2232,9 +2232,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[4.06267057]\n", - " [2.86868711]]\n", - "[4.05285677] [2.86799623]\n" + "[[3.91483251]\n", + " [3.09848937]]\n", + "[3.87616945] [3.07001431]\n" ] } ], @@ -2390,16 +2390,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Eigenvalues of Hessian Matrix:[0.24719968 4.31175179]\n", - "[[4.07235641]\n", - " [3.05476114]]\n", - "[[4.06908518]\n", - " [3.05761244]]\n" + "Eigenvalues of Hessian Matrix:[0.26638638 4.40906565]\n", + "[[4.26969649]\n", + " [2.78617455]]\n", + "[[4.26953857]\n", + " [2.78630865]]\n" ] }, { "data": { - "image/png": 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", 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    " ] @@ -4896,7 +4896,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "theta from own gd\n", + "theta from own gd" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", "[[4.0586484]\n", " [3.0718316]]\n" ] @@ -4910,7 +4917,7 @@ }, "metadata": { "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week39_269_2.png" + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week39_269_3.png" } }, "output_type": "display_data" @@ -5536,7 +5543,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.15" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/week39_153_1.png b/doc/LectureNotes/_build/jupyter_execute/week39_153_1.png index 717dbfc685f980ec6d68b0498a2346050b22bd27..6be50b53d3ca2cda1b25c31bd14115af09e22ecb 100644 GIT binary patch literal 23907 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100644 --- a/doc/LectureNotes/_build/jupyter_execute/week40.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week40.ipynb @@ -145,17 +145,17 @@ "output_type": "stream", "text": [ "Parameters for OLS using gradient descent\n", - "[[4.26604611]\n", - " [2.29234681]\n", - " [5.33329216]]\n", + "[[3.8184887 ]\n", + " [3.47851966]\n", + " [4.77551387]]\n", "Parameters for Ridge using gradient descent\n", - "[[3.6161104 ]\n", - " [3.78762558]\n", - " [4.65410649]]\n", + "[[3.92021197]\n", + " [3.11388017]\n", + " [4.9458396 ]]\n", "Parameters for Lasso using gradient descent\n", - "[[4.24335376]\n", - " [2.36219301]\n", - " [5.29669411]]\n" + "[[3.87323528]\n", + " [3.3008836 ]\n", + " [4.86284277]]\n" ] } ], @@ -234,11 +234,11 @@ "[[4.]\n", " [3.]\n", " [5.]]\n", - "0 [-26.28886314] [-34.64721597]\n", - "1 [-1.83231208e-13] [-1.80848093e-13]\n", - "2 [6.92779167e-16] [1.22835717e-15]\n", - "3 [-1.3500312e-15] [-1.8110093e-15]\n", - "4 [7.46069873e-16] [9.30442002e-16]\n", + "0 [-31.91417133] [-44.48017159]\n", + "1 [3.48805429e-13] [4.67477123e-13]\n", + "2 [6.75015599e-16] [1.30675215e-15]\n", + "3 [-1.17239551e-15] [-1.78477759e-15]\n", + "4 [6.75015599e-16] [1.30675215e-15]\n", "beta from own Newton code\n", "[[4.]\n", " [3.]\n", @@ -746,20 +746,20 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[4.36014743]\n", - " [2.76030841]]\n", - "Eigenvalues of Hessian Matrix:[0.36278226 3.73204357]\n", + "[[3.90300704]\n", + " [3.16913489]]\n", + "Eigenvalues of Hessian Matrix:[0.2964378 4.12443871]\n", "theta from own gd\n", - "[[4.36014743]\n", - " [2.76030841]]\n", + "[[3.90300704]\n", + " [3.16913489]]\n", "theta from own sdg\n", - "[[4.34582863]\n", - " [2.81684805]]\n" + "[[3.93272428]\n", + " [3.16328315]]\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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    " ] @@ -2082,17 +2082,17 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[3.85068028]\n", - " [3.09808149]]\n", - "Eigenvalues of Hessian Matrix:[0.31897935 3.95770298]\n", + "[[4.13791264]\n", + " [2.92552059]]\n", + "Eigenvalues of Hessian Matrix:[0.27874136 4.16226023]\n", "theta from own gd\n", - "[[3.85068028]\n", - " [3.09808149]]\n" + "[[4.13791264]\n", + " [2.92552059]]\n" ] }, { "data": { - "image/png": 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7SkpKMHnyZLRu3Ro1a9ZETEwMHnvsMZw8eVK+BhMREZFfkT0Yunr1Ktq2bYsFCxZUuu/atWvYtm0bXnvtNWzbtg3p6ek4ePAghgwZIkNLiYiIyB/phBBC7kaY6XQ6LF++HEOHDrW7z+bNm9GlSxccO3YMjRo1knTcwsJChIWFoaCgAHXq1PFQa4mIiMibfHX9DvLakb2koKAAOp0OdevWtbtPcXExiouLLbcLCwt90DIiIiJSI9mHyZxRVFSEKVOm4OGHH64yQpw1axbCwsIsP/Hx8T5sJREREamJaoKhkpISPPTQQzAajfjwww+r3Hfq1KkoKCiw/OTm5vqolURERKQ2qhgmKykpwfDhw5GTk4O1a9c6HDcMCQlBSEiIj1pHREREaqb4YMgcCB06dAiZmZmIiIiQu0lERETkR2QPhq5cuYLDhw9bbufk5GDHjh0IDw9HTEwMhg0bhm3btuG7775DWVkZ8vPzAQDh4eEIDg6Wq9lERETkJ2SfWp+VlYVevXpV2j5q1ChMnz4dCQkJNh+XmZmJlJQUSc/BqfVERETqo5mp9SkpKagqHlNQGSQiIiLyQ6qZTUZERETkDQyGiIiISNNkHyYjIiIimZSVAdnZwKlTQHQ0kJwMBAbK3SqfYzBERESkRenpwPPPAydO3NoWFwfMnw+kpsrXLhlwmIyIiEhr0tOBYcOsAyEAyMszbU9Pl6ddMmEwREREpCVlZaYeIVuztc3b0tJM+2kEgyEiIiItyc6u3CNUnhBAbq5pP41gMERERKQlp055dj8/wGCIiIhIS6KjPbufH2AwREREpCXJyaZZYzqd7ft1OiA+3rSfRjAYIiIi0pLAQNP0eaByQGS+PW+epuoNMRgiIiLSmtRU4JtvgNhY6+1xcabtGqszxKKLREREWpSaCuj1rEANBkNERETaFRgIpKTI3QrZcZiMiIiINI3BEBEREWkagyEiIiLSNAZDREREpGkMhoiIiEh5ysp8tj4agyEiIiJSlvR0oEkTYNAgnzwdp9YTERGRcqSnA8OGAUL47CnZM0RERETKUFYGPP+8TwMhgMEQERERKUV2NnDihM+flsNkRERE5H3mhOiqlv44dUqWpjEYIiIiIu9KTzcNf5Xv9YmLA+bPt14UNjra920Dh8mIiIjIm8wJ0RWHv/LyTNvT029tS042BUk6nU+byGCIiIiIvKOqhGjztrQ0036Aadhs/nzT/30YEDEYIiIiIu9wlBAtBJCba11cMTUV+OYbIDbW++27icEQEREReYfUhOiK+6WmAkePAt995/Em2cJgiIiIiLxDakK0rf0CA005RD7AYIiIiIi8w1FCtE4HxMf7LOixh8EQEREReUdVCdHm2/PmVa435GMMhoiIiMh77CVEx8WZtpevMyQTFl0kIiIi70pNBfR6xxWoZcJgiIiISG2kLG2hNIGBQEqK3K2wicEQERGRmkhd2oIkY84QERGRWjiztAVJxmCIiIhIDZxd2oIkkz0YWr9+PQYPHoyYmBjodDqsWLHC6n4hBKZPn46YmBhUr14dKSkp2LNnjzyNJSIikosrS1uQJLIHQ1evXkXbtm2xYMECm/fPnj0bc+fOxYIFC7B582Y0bNgQffr0weXLl33cUiIiIhm5urQFOSR7AnX//v3Rv39/m/cJITBv3jy88sorSL2ZFPbf//4XUVFRWLJkCZ599llfNpWIiEg+7ixtoTIHf8qB4b1jSM+u5pPnkz0YqkpOTg7y8/PRt29fy7aQkBD07NkTGzZssBsMFRcXo7i42HK7sLDQ620lIiLyKvPSFnl5tvOGdDrT/TIvbeEKY6kRmz7dg4xF52HYHo/9N5oCSADgm+u37MNkVcnPzwcAREVFWW2Pioqy3GfLrFmzEBYWZvmJj4/3ajuJiIi8TiVLW0h1/cJ1fPva73iqeTaig8+j219b4+3fUrD/RlMEoQR9wrfin0N/8UlbFB0MmekqvOlCiErbyps6dSoKCgosP7m5ud5uIhERkfepYGmLqpzddw6LnsjG0OjfEBEhMOTNLvj0YDLOiAYIQwFGNP4VS8dvwLlj17DqfEc889/uPmmXoofJGjZsCMDUQxRdbgz0zJkzlXqLygsJCUFISIjX20dERORzCl/aoqKDP+Ug4/1jMGTXw4bCJBhxaxivUeAJDEk8Av2jtdFjTBKCa3WTpY2KDoYSEhLQsGFDrF69Gu3btwcA3LhxA+vWrcPbb78tc+uIiIhkouClLYylRvz2nz0w/Kdi/o9J++r7oL/zNPR/jUbbB+6ALiBOvsbeJHswdOXKFRw+fNhyOycnBzt27EB4eDgaNWqEtLQ0vPXWW2jWrBmaNWuGt956CzVq1MDDDz8sY6uJiIh8RAXrkF2/cB0/v7sLhq+K8e2hFjgjWlvuC0IJeoXvxJC/XMGQibejUdeWAFrK11gbZA+GtmzZgl69elluT5w4EQAwatQoLF68GC+//DKuX7+OMWPG4OLFi7jzzjuxatUq1K5dW64mExER+UZ6OjBhgmkGmVlsLPDee7LnB53ddw4r5+yH4fsgrDrVGtfQxXJfHRRgQKM90OuB/i+2QlijjjK21DGdELbm5/mXwsJChIWFoaCgAHXq1JG7OURERI6lpwP332///mXLfBcQ3eydOrVqJ9ZnGvHhnh745XJbGHGrhyo+MA/6xMPl8n+C3X5aX12/GQwREREpTVkZEBUFnD9vf5+ICOD0aa8OmRlLjTg0bj6iFs1C3RtnLdvPoD7G4EP8WT0JQ7qchv7Zhmj3YHPoAuzP9HaFr67fsg+TERERUQVZWVUHQoDp/qws4J57PPrU1y9cx5p5u2D4qghlB4/gEzEJgHW/SSTO4WsMh27cS8Ds2R59fjkwGCIiIvIkTyQ8Z2VJ388DwdC5A+fx3T/3WeX/BKAMR/EIAGGzKKEOAP75T6BLF2DYMLfbICcGQ0RERJ6Sng48/7z16vJxcabK0QoriHho9VEY5h9FRnY9/FqYBCNuFTiMD8zDpEZfIz7nRBVHuGnMGOC++xQ3w80ZqqhATUREpHjp6aYekhMVAoi8PNP29HTpx5JaQ8iJWkPGUiM2fbIbU7tmITHkCO7o2wQvrUxBdqEpEbpd9f2Y1jML25bsx7EbMXh+pv3ixlbOnjX1hKkYe4aIiIjcVVZm6hGyNSdJCNPaYWlppsrRUnpQUlJMCdKOEqgdBEPl83++PdQCp41JlvuCUIKU8J3Q/+UKBqc1ReNuLQC0uPXgcis/OHTqlPR9FYjBEBGRt6igWJ4q2qgG2dmVe4TKEwLIzTXtJ6U3JzAQWLiw6qn1CxfafK/M+T8Z3wfhJxv1f/rH36r/U7dxFfV/kpOB+vWBc+cct9eZwEmBGAwREXmDGnJH1NBGtZDaM+JMD0pqqqmWkIT36PCaYzDMy4Fhve38nyGJh6F/uBZ6jmuN4Fp3S3v+wEDgww+B4cOr3i8uzhQ4qRjrDBEReZo5d6Tir1fdzRosSlhdXA1tVJOsLKDcagp2ZWY6v6aYjd47o9Dh98V7Yfj0HDK2x2Fv8e1WD2lXfT/0XfIx5OkotB/Rwr36Py+/bJo1Zk9EhKmXygufFxZd9CAGQ0TkM2VlQJMm9odMdDrTX9I5OfINR6mhjWpjfk3z8mznDXngNTXn/2R8XYRvDzZHvvFWgnMQStCz3i7o/3IZQ15oisbdPLz46TffAE8+CRQWVr7PiwE0gyEPYjBERD7jzR4CT1FDG9XI3NsGWAdEbgQL5w6cx8p39sGw0pz/U9NyX+X8nzB3z8A+mQJoVqAmIlKj8gtqVkXO2TfeyG+RmxISwVNTTQGPrRyfefMkB0Lm/J+M7Lr4paC1Vf5PXOBJ6BMPYciIWkgZ70T+j7s8nSCuMAyGiMh3lHDB8qb0dOCFF6TtK+fsG6nPrZYZQkpKBE9NNU2fd+Jzbiw14vfFe5Hxn3MwbDPn/zS23N829AD0XU5B/4w5/yfGBydSgT8G0OUwGCIi31DSBcsb7CUkV2QeTpBz9k1ysqkNjvJb1DBDyN7rbi50KEcieGCgw96RoktFpvo/X16/mf9jXf/HOv+nOYDm3m2zI/4WQFfAnCEi8j5/n7nkKJ+iPJ1OGefrhfwWn1NZIrg5/yfj+yD8dDIJV1HLcl9tFGJA/G4MGSww4OUk7+b/uMIHCeK2MIHagxgMEclIZRcsl0hNSG7QAPjoI+UEGbZ66+Ljncpv8RopQ6oqSAQ/vOYYMt7LgWGdOf/n1jnEBZ7EkJaHoH/YnP8TLEsbJZMhgGYCNRH5Bz9PvAQgPU/i3XflDzLKcyG/xSekDqkqMI/FWGrE5v+a6v8oNv/HVR5KEFciBkNE5F0KvGB5nNQ8idhY77bDFRLyW3zKmRwgheSxmPN/Mr68jm8P3oFTNvJ/hvQy5f806a6A/B93KDWAdhOHyYjIu1QwlOE2mfIp/I6zQ6oyvu7nD13Ayn/uNdX/sZH/0z9+N/SDBfq/2Ar1Eup69Lm1hMNkROQf/Gnmkj2BgaYhnGHDTOdjK59i3jwGQo44O6Tq49f9yNpjMMwvn/9jXf/HnP/Tc2wSQur4qP4PeQSDISLyLq0ECn6cT+EzrgypevF1N+f/ZPznHAxbY7GnuBkq5v8M6WzK/+nwsMryfwD/r/vlBAZDROR9WgkU/DSfwmecyQGqeCE/cgTYsMHt173oUhHWzt8FwxeV838CUYqe9XZC7w/5P/5e98tJzBkiIt/hX6JUFak5QHPnmip9e+hCrrn8HxXV/WKdIQ9iMEREpBKOatm8+CLwzjtuX8iPrL1Z/yerLn4pSEJZuYGS2IBTpvyfETWQMr41QuqEuHNGyqKyul8MhjyIwRARkYrYKwY5Zw4wcaJLF3JjqRFbPtsHwydny+X/3NIm9AD0Vvk/Ok+flTKobHYnZ5MREZE22cu9cnK2WeX8n1aWXcvn/wx+/jYk9FBx/o8ztFD3ywUMhoiISHlsFYOUeIFe/3oW5j8SejP/p7Nley1cRv+43dAPNmLAS61QL6GDBxusEgopVKk0DIaIiEgdJF6g/56ZgnW4C4Ct/J+u3myh8mmh7pcLGAwRkfs4S4x84eaFXOTlQWfjQm6EDicQh4KQSLzWJQtDnopEx0dbQhcgIYjSymdYK3W/nMRgiIjcw3olnqP0C7KM7TPn/xwXz+IZ8XcI6BCAWxdyI3TQQSDkjVex/dVEAInSD661z7BW6n45gbPJiMh1KqpXonhKvyDL0L4LRy5i5T/3wPBdIH7KS8IV1AYA3Id0vIfxiMPJWzvHx7t2IdfyZ1jpwTc4td6jGAwReYHK6pUomtIvyD5s359Zx2GY92eV9X+GPFQDvcYmIuSP3927kPMzrHgMhjyIwRCRF6isXoliKfGCXL7HIDISGDXKlHDrhfaZ6/9kfHoWhi2x2F2h/k/r0IPQdzoJ/dPm/B8P1v/hZ1jxWGeIiJSN9Uo8w9mV2r3N1nBYVVxoX3FhMdbO22mq/3OgGU5WqP/To+4u6HsVYkjabUjocQeAO5w/Dyn4GaabGAwRkWtYr8QzlHRBtjccJoWD9lXO/6lc/2fIICMGvJiI8KbtnX9+V/AzTDcxGCIi17BeiWco5YJcVmbqEXI1c8JG+/7MOo6M+TkwZNVB9qXWKEN3y30xAacwpIWp/k+vCTLV/9HaZ1gFCdNyYTBERK5Rcr0SNf3SV8oF2dFwnT3l2mcsNWLr/+2H4d9nyuX/NLLsas7/GfJUJDo+0gIBQTL3uCj5M+xpSp+tKDehAQUFBQKAKCgokLspRP5n2TIh4uKEMF1KTD/x8abtSmlPXJx87ZFi2TIhdDrTT/l2m7f5ou1Lllg/t5QfnU4YdTqx9cG3xbMt14mYgJNWdweiRPSqu028OzRLHMk85v1zcJXSPsOeZv582Xj/fPb5cpGvrt+Kn01WWlqK6dOn4//+7/+Qn5+P6OhojB49Gq+++ioCAgIkHYOzyYi8zNwTk5cHnD0LNGgAxMb6vkdG6VPUq2JvpXZfFcGTOrOqnHOBkXi+bC6W4BHLtlq4jHtjb67/9WIiwpvWq/xAJfbcKbFNnqDE2YpO8Nn126uhlge8+eabIiIiQnz33XciJydHfP3116JWrVpi3rx5ko/BniEiH5C7R6a0tPLzV/wrOD7etJ9SlZYKkZlp6qXJzPRtW82vn60eBEAYoROF1RuItBofiYfxmeiJTBGAUgEIERNwUvw1cZ34fsbvoqigqOrnkftz4ityvpflZWZK6+XLzJSnfQ746vqt+JyhjRs3Qq/XY+DAgQCAJk2aYOnSpdiyZYvMLSMiC3s9Mnl5pu3le2S89Re40qaou8LWSu2+fG47+TNGmHrWRl3/CMtheh9bhx7E1I7Zpvo/UvN/nPmcqJmS8nOUNFtRwaSNM8moe/fuWLNmDQ4ePAgA+OOPP/DLL79gwIABMreMiABUPQvJvC0tzbRferqpy75XL+Dhh03/Nmli2u4u/tJ3W3Hvgdj+4D9wMbC+1fYTiMNwfImLdRPw7tB1OJJ5HDuv34E3f0lB51GJCAiScClx5nOiZuaAr2Jgbg74PPFZd4ZSZisqnOJ7hiZPnoyCggK0aNECgYGBKCsrw8yZMzFixAi7jykuLkZxcbHldmFhoS+aSqRNUntkZs4Epk/3Xq8Af+m75MKRi/j+nb0wfBuAH/OScAUvIwCTkIxsJOBPNKp/Hc3uS8LCyW1s5/9IJfVz8v77wPjxisxfcchRwKfTmQI+vd5356eU2YpK59VBOA9YunSpiIuLE0uXLhU7d+4U//vf/0R4eLhYvHix3cdMmzZNAKj0w5whIi+QOgspPNy7+TwOcl5UkTPkKQ7yVf5cd1y8OzRL9Kq7TQSixOplig44JZ5tacr/uX7xuufa5MxsNbXmECk1P0cJsxVd5KucIcUHQ3FxcWLBggVW29544w3RvHlzu48pKioSBQUFlp/c3FwGQ0TeIvUC4IuLhIp/6XuMjQRlY1ycODR+nni1e6ZoHXqg0sueFHJQvNItU/y+eI8oKynzTruc+Zyo9f2SGvAtWeL7tqm0fAATqG+6du1apSn0gYGBMBqNdh8TEhKCkJAQbzeNiABp3fD16gEXLjg+lrv5PKmppuE2W8mrnpyirtRp2PYSlE+cQNP307APX2MXUhCIUiTX3YUhPQugT7sNt6U0A9DM5iE9xtHnpDy5hpTcpeSh2tRU02upxM+tEng11PKAUaNGidjYWMvU+vT0dFG/fn3x8ssvSz4Gp9YTeZmjHpkZM3w7fODNac1KnRpeWirKGsYIYxWvbykCRGbv18W5g+flaaO9z4mShpTcwaFaj+Mw2U2FhYXi+eefF40aNRKhoaHitttuE6+88oooLi6WfAwGQ0Q+UFU3vL9cJNyp5OulAC0nO1fMuy9LpNVcKD3AkDNws/U5UdqQkjs4VOtRrEDtQaxATeQjVQ0fmYdwAOthEjVUhwbcq+Trwbozwiiw9fN9MPz7DDK2RGNnUXMAwENYiqV4WNpBGjQA3n1XnirhgOm1fP994IUXHO+bmanculD2yF1N3I/46vrNYIiIfEfNFwmpy1VUvHh7YImQ4sJiZL63CxlfXEPGvmbIM97KOQlAGZLDdmFsUhYe+FVCcFGRXMUAzcGloynfCl0mwiGl5pWpDIMhD2IwRIqlxV+Yaj3npUtNhSIdWbIEMNdBc6M36WLOJXz/zz2m+j8nWuEybv3uqokruDd2F/SDyjBgUiIimoU7fi575OyZU3tvIXmdr67fTs0my83NRXx8vLfaQqQtSirZ70tyLjnhDldmCjm5RMjRX07AMPcIMrJqY93FNihDt1uHDcjH4DsOQv9Qdfzl+dYIrdvV+ljm5TTuv9+Jk4K8M7d8NfuPyAGneoZq1qyJiRMnYsqUKahZs6Y32+VR7BkixVHa6upq7a3xJVeGdST2Jn3d/FW8eexRS/6PWauQQ9B3zIP+qQboNLKltGUvvvkGeOgh15a1kCs/h58/ssNX12+n1iZbvXo1Vq1ahWbNmmHRokXeahORf1PaGk3ffGO6AHljvTBPKisz5e0sXWr619drWJl7XoBbQauZ+fa8edYX8UOHJB36gwP3YGdRcwSgDD3DdmCuPguH1xzD7qJmmPlrCro83kpaIASYguylS6XtW9GaNfK8vubewhEjTP8yECJfc2UK2n//+18RFxcn2rVrJzJVUAOCU+tJUZRUsv+ll6quAqyUacBKqu0jtZKvuZxAFe+xERDHESMeiMkW/30627P1f5ydwl7xRwm1k0jzFF9n6Nq1a+K1114TNWrUEEOHDhWHDh3yZLs8isEQKYpSSvZ/9ZXjNiih9o87tX28obRUiJ9/FuLVV00/P/9s8zU69Z6E1xcQN6a+5t22ZmYK8fnnQtSv71wwxLo4pAC+un47NUxWoUcJffv2xTPPPIOMjAwkJSVh0qRJuHz5sqc6rYj8kxJK9peVAWPGON7PnNwrF6UNKaanm4YQe/cG3nzT9DN6NGAwWOr//L1HFtpWP4AXJpRKOmS11i29117z8NMjjwAff2wazqs4xGePHK8vkUycCoY++ugjPPnkk2jTpg3CwsLQu3dv/Prrrxg7diw+/PBD7NixA4mJidiyZYu32kukfuY1muxdlHQ6U+2d5GTvtSE7Gzh3Ttq+7q4X5g5nZmN5mznpvUJ7xIkTEPffj2eqfYpOI1vijewU7CxqjtOIlHZcX61TZZ65FRsr/TG+fH2JZORUMDRz5kwUFhZi1KhRyMrKQkFBAX7//Xe89957eOKJJ7BmzRo899xzGD16tJeaS+QHXEnE9TRnAhw5FpU0k9pObwdsVfRQ6QAI6PCa8XXURgFSYzbhv0//gq/3tZE/6K0oNRU4etQ0a2zJEuDVV6U9Ts6AWMvknjSgJZ4ed8vPzxcBAQGePqxbmDNEiiQ1EdcbpCZxN2hwKx/Gm4ufuttOLyebS83/Kf72J+sHKn2dKoW8vmSDkiYNyEjxOUP2REZGYu3atZ4+LJH/qfhXemamqUaNL+oLmYfqHPnwQ1MPlTlXxtfT72UaUjTn/0zrmYV2TuT/BF8+b73B3tBUXJwyqisrYciWKrMzJIu8PNN2pZW98ANcjoNIq+wVfjR76SVg9mx5CkSWL8J36BAwbZrp+cq3wcPPf+PKDWS9vwuGJVeQsa8ZTpTFWO7rhTVYi96OD2KvaKGSiwpySQxlcWdBYD/Etck8iMEQ0U0VL8pnzwITJ1r/4m3QAPjgA+CBB+T5xWxrmZKICNO/58v1vHhggddLxwrw/ezdMHyrww+5SZXW/+oXsxv6gaUYkHYH6vfraL/6tLmNp0+r8wKl5gV0/Y2rCwL7KUWuTUZEKmZvLbR33wXq17fda+Hk2loeaaOtXqgLF0z/zpgBNGvmVu/KsV9PIOPdIzCsrY11F1ujtNz6Xw0DTmPwHQegf7A67klrjdC6d916oKN1v86fBwwGdQYPqammdcmU2nulJUqZNKAxDIaItMBekJGXBwwfbhoKMa+0Xp4nfjFLHSJyVFNIpwM++cTpXihhFNi+dD8MC0/D8Hs0/ihqDuBWvlRiyGHoO5yA/sn66DwqEQFBUbYPpNeben/On7d9v1yLnXqKWhfQ9TdKqEOmQQyGiPydlCDD3kXc3V/M9nqj5s+v3IPiwV6oyvk/LQGYihsGoAzdw3ZB3/MShkxIwO333A7gdoeniOxs+4GQk+0jssuc1O5oQWAmtXsUgyEif+dOkOHOL+aqeqOGDaucmOtmL9SlYwX44Z09MBiAH3JboRAdLffVwFX0i94F/cBSDHyxJeo3byftuSQ8r8v7yUHJidxkYq5DNmyY/UkD3q5DpkEMhoj8nTsXcVd/MbvSG+VCL1Tl/J+7LfdFBZzBkDv2Q/9gdfxlQhKqh99l62jSqX34wpleOpKXuSSDrfeLSe1ewdlkRP7OE7NTnJ1t5MpzmmeuVdELJeLisH3m9zB8cq5c/s8t5vyfIU/UR5fRiQgI8mApNQntU+yUZznKI5D72JPH2WREmuLNX3pnzzrex1FhPWdnG7nSG1VFL5QAACHwbN5r+PdjSZbtAShDtzq7oe9xEfo0J/J/XKHW4Qt3csZIXkxq9xkGQ0Su8lQA483hi7IyUx0hR+bOddx2Z34xuzqkdHN4wDhuPAJOnbRszkU80jAPy42pNvJ/2kp7Lk9Q4/CFr8sjEKkQgyEiV3gqgHE2ydhZji6EZvXru/4ctriQeH18Yx4Mcw7DsLYJsi8eQVdsQjRO4RSicVDXHIOaH8K3D/yOe9JaO5//48meN7XV5PFW4jeHcMiPMBgicpanAhhfDF/INQNKwpCSmPsudnx1CIaP82H4vSF2XG8B4NYaXmeC49G1A/D8k/XRZXQUAoJcTEz2Rs+bmoYvvJH4zWRs8jdeXQZWIbhqPXlMaWnllaQrrkYeHy9tRXdfrBgu96rkNlbevhYWJRY2el3EB56wakIASkVynR3inUGZ4uCqHM89f8UV45W0arwtpaWm92PJEtO/Uj5LVR3r55+FCA/3zGdWCHW+pqRavrp+czYZkTM8uW7Q0qWmFeAdWbLEdnVoKRQwA+rSnxewddIS7Fl/Hj9d6Igf0R9GmJ7LnP8zZEApBk5qgQYtPThcp8YFLz3Z42LrWBU5O5vMldeUw2nkBs4mI1IiTw47+aJujUwzoI5vzEPG3MMwrK2FrAttUIpxlvuiAs5gcLP90A8PdS3/Ryq1JQ57Mn/M3rEqcjbx29nXlMNppBIMhoic4ckAxldl930wA0oYBXZ8ecBu/k/L4CPQd8jFkMcjcOcTrRAQ1MPt53RITRWjPZk/VtWxzMLDga++MgUszgTCzrym3p4cQORBDIZIOdTQne7JAMaXvTZemAF148oNrFtwc/2vvbcjt6wFgBYAbtX/GZJ8Efrnm6BZn6YAmrp/Hs5QU8VoT/ZiSZlBeOGC6b139v2X+lpFRgKjR7O2EakGgyFSBrV0p3s6gPFl3RoPzIAqyCmX/3O+I36okP/TN3oX9Jb8Hx/W/7FFTQteerIXy5s9YlJfU0BdQ5SkeQyGSH5q6073dADji7o1bvS6mfN/zv/4O564Mh9/QR7+AmA8gJOIxoqYMYh/oi96v+DF/B9XqKlitCd7sbzZIyb1NT1zRtrxlDBESQRwaj3JzJNT1X3Nk1OgvcnG9HYRF2d3CrSxzCi2LdknpvXMFO2r7xWAEPdhmSiDTpRVeH+MaphObev84+OV1Wbz98DWlHVnvweePJY9jl5TuUs6kN/g1HoP4tR6BfPkVHWqTOICnSXXSkz5P59fRsbepjheFmfZNQg3kKdrhAbiNHS2nkOJU9QrUkM+mvm9Amz3uLgym8wTx7KnqtdUASUdyD9waj1pg5pm/KiNgxlKQqfDtVHP4em0Bvg+tw0K0MFyd3VcQ7/onRhybwnu63kBdUeftv88asj/cDdfyhfBlCeHX32Ri1bVa6qmIUoiMBgiualpxo/aOJhVpBMCNa+cwckrZShAGCJ1Z2/W/wmxzv9ZulTa8/lrwOrL5H5P5o/JvYaaGhe1Jc1iMETyUtOMH7WRGJw823QNZr1srv9j43XWcsAqR3K/J9c9k3sNNbkDMiKJmDNE8vNFfoOGmPN/9ny0Hs/nvOD4AY7ysbSa/6HG5TyI/Iyvrt8BXjsykVTm7vTYWOvtcXEMhCQqOF6AL5/fgIeb/IoGNa+hz+QOmJgzHrmIg9F22rPpYh4f77jXzZz/YX5MxWMA/pn/4UwhRCJSNQ6TkTKwO91pub+dRMacQzCsqYWsC61Rgrst95nzf/LbjEPcsqkA3Exi1WL+B5P7iTRDFcFQXl4eJk+ejB9++AHXr1/HHXfcgU8//RQdO3aUu2nkSXLnNyicMAr88fVBGD46hYzforDteksAMZb7WwQfwZB2udA/EYE7H09EYHAygGQgvZnnZii5G7CqYYq7mZZzpYg0RvE5QxcvXkT79u3Rq1cvPPfcc4iMjMSRI0fQpEkTNG0qba0j5gyRWpVcK8H6D27W/9nTFMfK1f/RwYi7a++GPvkC9BMa445+CfYPpIQgRC1LrphpNVeKSEF8df1WfDA0ZcoU/Prrr8h2Y1yewRCpSeGJQvzwz90wGAS+P5aEAoRZ7quOa+jbcBf0/W9g0Est0aBlfRlb6gSJxR8Vh8n9RLJiMHRTYmIi+vXrhxMnTmDdunWIjY3FmDFj8PTTT9t9THFxMYqLiy23CwsLER8fr+5gSAl/2ZPXmPN/MtbWROb5NihBsOW+BrqzGHz7fugfCEbvF1qjRv0aMrbUBWqflWWrRys+3n9zpYgUhMHQTaGhoQCAiRMn4oEHHsDvv/+OtLQ0fPzxx3jsscdsPmb69OmYMWNGpe2qDYbcHV5gIKU4wiiw8xtT/o9hkzn/55bmwX9C3+54ufwfFb9f/rDkCr9DRLJgMHRTcHAwOnXqhA0bNli2TZgwAZs3b8bGjRttPsaveobcHV5QW56GOxR+wZKa/zNkXCM073+bjC31sKVLgYcfdrzfkiXAiBHebw8RqQbXJrspOjoaiYmJVttatmyJZcuW2X1MSEgIQkJCvN0073OwthR0OiAtzTTDx9ZFX47quXJRaNBnzv/JMAh8f7wVLgnr9b/6NNwF/b03MOjFFohs1Ua2dnoVZ2URkcIpPhjq1q0bDhw4YLXt4MGDaNy4sUwt8iFnir5VHF5wN5BSE4UFfSc2n0LGnIMw/GzO/7lV/6dy/s+dPmuXbLjkChEpnOKDoRdeeAF333033nrrLQwfPhy///47Fi5ciIULF8rdNO9zp+ibO4GUp/hi2EoBQZ/t/J9bvRyV8380dtHnCuZEpHCKD4Y6d+6M5cuXY+rUqXj99deRkJCAefPm4ZFHHpG7ad7nzvCC3NVzfTVsJTXomz4duOeeW70PbgZp5vyfjP+7DMPupjhW1hxAcwCm/J+utXdD3/0C9OPN+T9+lAPkCi1WsCYi1VB8ArUnqLbOkDtF3+ScwePLmjJSk3PNIiJM/54/f2ubxCCt8EQhfpyzG4bl5vyfupb7Kuf/NHDiJDRE4UnuRKQsnE3mQaoNhgDXi77JVT3X1zVlpAZ9Vanitayc/+Nk/R9e/ImIXMZgyINUHQwBrhd9k6N6rq97pBwFfVLdDNLEkT+xc/kRZHx8CoZNkdh6zXom4x3VcqBvdwz6x8Nx15Otqq7/o9AZbkREasGp9XSLqwtkypGn4etcpaqSc51xM7doRGg6vjQOR9X5P1WsAWamsBluRERkH4MhtXB1RXdPrDRui73hHzlqytgL+lygM5YhFNfRt+FODOlnyv+JSnKy/o8CZrgREZF0HCYj51U1/KPXy7fStzlAW7MGePNNlw7xy8MfoMP80e6t/+UPy08QESmAr67fAV47Mvkn8/BPxR4Y8/CPwWAKioBbuUlm3q4pY+49mz7dlP9T8fmrIHQ6ID4e3f/3rPsLocpd1oCIiJzCYIikczT8A9wa/vnmGyA21nqfuDiv58qUXCvB2nk78WngMxACMMI6IBI3f6zodKa9PBWkcfkJIiJV0W7OEKc8Oy8rS3pVa2/lKtlQeKIQP83dA8NyI1Yea4VLoj2A9liJVngPExCHPMu+Ont1hjyZUM7lJ4iIVEWbwZBapzzLGcClpwNPPy1tX/Pwj6tJ3xLkbTmFjDmHYFhdA5nnW+MGulruq687h8G374N+WCzqjd8LHNhm/ZoB3n0dfbH8BIN5IiLPERpQUFAgAIiCggIhli0TQqcTwnSJuvWj05l+li2Tu7m2LVsmRFycdZvj4nzTXnuvmb2fzEyPN8FYZhR/fH1AvP6XTNGxxp5KT9ms2p/ixU6ZIvuDP0RpcanHn98ltt6z+Hj33zM5PwtERD5kdf32Im3NJrtwAXXatPFddWRP8eXyFhU5qihdsT0efP1KrpXgl492w/BZIQy7b8PR0vhbTwUj7qq1B/ru56Ef3wgtBthZ+0vuHhRPP7+cnwUiIh9jBWoPsryY332HOoMGOX5A+SnPSriY+nJ5i4qcWe5Cp3P7Ynz55GX8+M5uGJYb8f2xRFwU9Sz3heI6+kTttKz/FZXkYP0vtQ6H2iP3Z4GIyMdYgdob8vOl7WfOeVHCxVTqquzZ2d7Jz5E6/Ts8HPj3v116XRzl/wxqug/6YcHo80ISakbeaf1ge8GqP1aAlvuzQETkp7QVDDVsKG2/6GjlXEzlrlkjdfr3V18B99wjaVdhFNi9/BAM/zoJw8ZIbLmWCODW8zSrlgN9W9P6X12faoXAYDuzruwFq3PnAhMn+l8FaLk/C0REfkpbwdDddwMREdbTqiuKiDDt17SpMi6mcteskTpN3EFPRGlRKbI/3AXDZ4XI2J2AnNI7ANxhOkS5/J8hY+PRYsBt0AU4WP+rqmB1+PCqH6vWHhS5PwtERH5KW8GQVEoajpC7Zo0b08Qr5/+0t9wXiuvoHbkL+nuLMfilFohKai29TVKKP0qhth4UuT8LRER+SlvB0IYNVfcKAab7s7KkHc8XF1Nf1KxxxN5CqDaKFZ7clo+Mdw7CsLo61p5r4yD/p4tr7XEUrEqlth6UwEDTEKCtni9ffRaIiPyQtoIhqQnUUvnqYupEMOLVNtioKC10Adi97GCF/J9buVnm/J8ho+rh7meS7Of/OMPdIFStPSjp6aZcKFt8+VkgIvIz2gqGpCZQp6QAixaZhiNskeNi6sPlLey6WVHakv/zwi8283/urLUH+m7noR8nMf/HWc4EoXL1pnmavRwps7lzGQgREblIW3WGzEUXHeVczJkDPPec7SE1jRa3u3zyMn6asxuG5WVYebRVpfo/5vyfQZOao2GbSO82xlxvx9H7OHcu8MIL1r1p8fGm7fXrq2cpC9YXIiKNYp0hb5CSf/PQQ8CDD9r/Czw8HFi4UBOBkJT8nyGp1dB3UmvX839cITWPKjUVuO8+6960s2crB0hKL8SopIR+IiI/pK1gCKg6/8bck1BVZ1n16qbhKl/wcfXr8vV/MjY1wOarrVA+/+f2akehb3MU+tEu5P94+lyk5lGVXyw2Pd12oKv0QoysL0RE5FXaGiYr381m6+KcnS1t6Ynyy3V4i4+qX5cWlZrW//pfAQy7EpBT2shyn+38H53zT+LNc5EaZKl5qEnqkii++FwSEfkQh8m8rXyPgZlS/gL3cvXryvk/7Sz3haAIfSJ3Yki/Igx+sQUatnGi/o8t3q7kbet9tEXNQ02sL0RE5FXaDYZsUUKFX0cFBV2sfm3O/8n4uTrWnLXO/4nQnceg2/ZBf3+QZ/N/vHQuLlFKoOsKJdSaIiLyYwyGylPCX+Ae6sEQRoE9hsMwfJgHw8aq83+6PtUKQaHdPXcOZu6ci6dzjJQQ6LpDCbWmiIj8FIOh8pTwF7gbPRjl838ydjXBn6XNADSz3H9XrV0Ycvd56MfGoeWgptAFNPFMm51oo6T9vJFjpIRA111KqDVFROSHGAxVJPdf4E72YFw+eRmr3t0NQ3oZVuYk4kKF/J/ekTuh91T+j7Nc6Y3xVo6Rs4Guj2fySSY1R4qIiCTT7mwyR+S6GEooKFgaGY3/9PocK9bUupn/E2K5u3z+T58XklCrYS3vt9keqcURzTO4fDHjy1avU3y8daDro5l8RERUNV/NJmMwpETm3hHAKogw/U+H+/ENluPWRblp0DHo2+ZAP6oe7n66FYJCFdThZ+dcbFby9tUU8qoCXXs9UxqtPE5EJCdOrVcSX/cSpaaibMmXKHluPEIvnbZszkU80jAPy5GKO2vuhr7buXL5P4291x53ODPs6KsZX/aGmpQ0+42IiHyGwZAjPhwyuZJ/BT/N2XUz/+ceXBJ5SEY2onEK5xGB0Aa1MfjeEnww8TSi2yV59Lm9Smrir9wzvtRci4iIiFzGYKgq3i4YCODUjtPI+OcBZKyujjVnW6O4XP2fcN0FNE4Igv7+BPSdKCH/R6lJv4C0xF+5Z3ypuRYRERG5jMGQPV4aMhFGgb3fHoHhgxMwbKiP368mAYiy3F85/0di/R9/SPoNDATefRd44IHK9/mitIHcPVNERCQLJlDb48Fk3tKiUvz68W4Y/ncJGTsTcKTUOr+ncv6Pk+t/+UvSr62AzqzijC9vcHb2GxEReRUTqOXm5pCJOf8nI70M39mo/3NPg13Q972OwS82dy//x1+Sfu0FdGZz5ng/oFNC0U0iIvI5BkP2uDBkcmrHaXz7zgEYVtnO/xmUsNe0/tfEJNRq2Nkz7fSHpN+qAjrAFIhMmmQKhrwdiDia/abXm3oNlZiXRURELmEwZI+EZF4RF4e9F6Jh6Jvl2fwfZ/hD0q/SAjp7s98MhspFIdWWl0VERJUEyN0AZ82aNQs6nQ5paWnefSLzkAlwa4jkJgEdhBAYm/cKku5vjldWp9wMhIAuNXdjZp8s7F5xGIeKG2HOlhT0GN/We4UQ/SHpV4kBnXn224gRpn8NBtPwWcWgzTyzMD3dd20jIiKPUlUwtHnzZixcuBBt2rTxzRPeHDIxNoyx2pyLONyPZfiX8VmEoAgDGmzGx4+sx8ntp/HblST8bVUKWulvdz4R2hXJyUBsrP37dTpT8rGSFyBVekDnKC8LMOVllZX5tFlEROQZqgmGrly5gkceeQT//ve/Ua9ePa8/36kdp7Hw0fUY+Nd41Dp1ACnIxAgsQQoy0RFbUfu2SCx7aRPOnSrFyjOd8cznPRDdLsrxgT3NYACKimzfp5akX/OQpM5O8Ch3QOfMMB4REamOanKGxo4di4EDB6J379548803q9y3uLgYxcXFltuFhYUOj2+u/5Px4QkYfq2P3yrk/+QGJaBDG+Cvj9VFt2fruZf/46niiI5mYIWHAwsXKj+fRemzuJQ4jEdERB6jimDoiy++wLZt27B582ZJ+8+aNQszZsxwuF9pUSk2/HsPDP+9CMMfCThSejuA2y33d6m5G/q7TfV/Egd7aP0vV4oj2gqegKpnYAFA9eqmRGA1cGYNM19T+jAeERG5RfFFF3Nzc9GpUyesWrUKbdu2BQCkpKSgXbt2mDdvns3H2OoZio+PR0FBAQKuBWDV3N0wLCvFypyWOC8iLPuZ6/8M6XMdgyfdgZgODT17Mq4UR7QXPD39NDBtmuPndHeFd19T4pIiLMZIRCQLXxVdVHwwtGLFCtx3330ILHeRKSsrg06nQ0BAAIqLi63us8X8Yvatvwbrzt2NYoRa7gvXXcDAhL3Qpwai36TWjtf/cpX5gmov98TWBbWq4Enq27ZkiWlGFLnH/F4Atofx1FLlm4hIRRgM3XT58mUcO3bMatvjjz+OFi1aYPLkyUhKcly92fxiAgUA6uC2oGPQt8mB/rG66PZskvemvZfn7PIejoInqdTWM6RktnrpfLFMCBGRRnE5jptq165dKeCpWbMmIiIiJAVC5b2ako0HJ9xxc9q7B/J/bLE3zONsEq6jGUyOeHuFdy2yV4yRQ2NERKqm+GDIk14yJHs1sqwyOdrZJFxnZiYpcQaWvzIXYyQiIr+hymAoKyvLOwd2J3nXXn6PuULxV185XN7DqidHavA0Ywbw7397ZgaWEpOXiYiIvEzxOUOeIGnM0ZUp72ZSk6PnzAEefNC0zVESrjMzmAD3gxh3zp+IiMgLfJUzpJoK1F5l7tVxdd0pqRWKGzQwBTwVl8+Ii6s8G6mKtdEqDYNVXEerqkCorMyUzL10qenfsjL3z5+IiEjF2DPkypT3ipYuBR5+2HFDzNPcnRmO8uQMJlvHio01Ledx/rztx7CGjndxaJKIyC7OJvMVZ9adspc462xytDNJuJ6awVRVTlNVpJw/uYZDk0REisBgyBPrTpkXGpWaHO0sd2cwVbXqulRcd8uzHCXcs4gjEZHPMGfIE+tOOZPfIwd3axYBXHfLk6oKTs3b0tJM+xERkdcxGDL36lQMYsx0OlOOjqNeHfNCo1KSo33NnV4dqedP0jkzNEtERF7HYTJzr86wYe4XL1RqhWJXe3WU0KvljzwxNEtERB7DniHAs706zkxz9xUpvV8REaZ9ylNCr5Y/8sTQLBEReQyn1pfnz9Ocpay6rsReLX/kTEFNvv5EpGFctd6DfPViKh5XXVcOKcEp3xMi0jgGQx7EYKgcf+79UhsGp0REVWIw5EEMhkixGJwSEdnFCtREWuBuQU0iInIbZ5MRERGRprFniOTDISIiIlIABkMkDy5SSkRECsFhMvI987TyiktSmBcpTU+Xp11ERKRJDIbIt7hIKRERKQyDIXvKyoCsLGDpUtO/vDh7BhcpJSIihWHOkC3MZ/EeLlJKREQKw56hipjP4l1cpJSIiBSGwVB5zGfxvuRkUy+beQ2uinQ605IUycm+bRcREWkWg6HymM/ifYGBpuFGoHJAZL49bx7rDRERkc8wGCqP+Sy+kZpqWpU9NtZ6e1wcV2snIiKfYwJ1ecxn8Z3UVECvZwVqIiKSHYOh8sz5LHl5tvOGdDrT/cxn8QwuUkpERArAYbLymM9CRESkOQyGKmI+CxERkaZwmMwW5rMQERFphraCoexsoLBQWnDDfBYiIiJN0FYwNGjQrf9zeQ0iIiKClnOGuLwGERERQcvBEJfXICIiImg5GAK4vAYRERFpPBgy4/IaREREmsVgCODyGkRERBqmrdlkFXF5DSIiIs1TfM/QrFmz0LlzZ9SuXRuRkZEYOnQoDhw44P6BubwGERERQQXB0Lp16zB27Fhs2rQJq1evRmlpKfr27YurV6+6d2Aur0FEREQAdELYWp5duc6ePYvIyEisW7cOPXr0kPSYwsJChIWFoeC771BHagVqIiIikpXl+l1QgDp16njteVSXM1RQUAAACA8Pt7tPcXExiouLLbcLCwtN/0lOBrz4YhIREZH6KH6YrDwhBCZOnIju3bsjKSnJ7n6zZs1CWFiY5Sc+Pt6HrSQiIiI1UdUw2dixY7Fy5Ur88ssviIuLs7ufrZ6h+Ph4r3ezERERkedwmKyC8ePHIyMjA+vXr68yEAKAkJAQhISE+KhlREREpGaKD4aEEBg/fjyWL1+OrKwsJCQkyN0kIiIi8iOKD4bGjh2LJUuWwGAwoHbt2sjPzwcAhIWFoXr16jK3joiIiNRO8TlDOnNxxAoWLVqE0aNHSzqGr8YciYiIyHOYM3STwmM1IiIiUjlVTa0nIiIi8jQGQ0RERKRpDIaIiIhI0xgMERERkaYxGCIiIiJNYzBEREREmsZgiIiIiDSNwRARERFpGoMhIiIi0jQGQ0RERKRpDIaIiIhI0xgMERERkaYxGCIiIiJNYzBEREREmsZgiIiIiDSNwRARERFpGoMhIiIi0jQGQ0RERKRpDIaIiIhI0xgMERERkaYxGCIiIiJNYzBEREREmsZgiIiIiDSNwRARERFpGoMhIiIi0jQGQ0RERKRpDIaIiIhI0xgMERERkaYxGCIiIiJNYzBEREREmsZgiIiIiDSNwRARERFpGoMhIiIi0jQGQ0RERKRpDIaIiIhI0xgMERERkaYxGCIiIiJNYzBEREREmqaaYOjDDz9EQkICQkND0bFjR2RnZ8vdJCIiIvIDqgiGvvzyS6SlpeGVV17B9u3bkZycjP79++P48eNyN42IiIhUTieEEHI3wpE777wTHTp0wL/+9S/LtpYtW2Lo0KGYNWuWw8cXFhYiLCwMBQUFqFOnjjebSkRERB7iq+u34nuGbty4ga1bt6Jv375W2/v27YsNGzbI1CoiIiLyF0FyN8CRc+fOoaysDFFRUVbbo6KikJ+fb/MxxcXFKC4uttwuKCgAYIowiYiISB3M121vD2IpPhgy0+l0VreFEJW2mc2aNQszZsyotD0+Pt4rbSMiIiLvOX/+PMLCwrx2fMUHQ/Xr10dgYGClXqAzZ85U6i0ymzp1KiZOnGi5fenSJTRu3BjHjx/36oupNIWFhYiPj0dubq6mcqV43jxvLeB587y1oKCgAI0aNUJ4eLhXn0fxwVBwcDA6duyI1atX47777rNsX716NfR6vc3HhISEICQkpNL2sLAwTX2IzOrUqcPz1hCet7bwvLVFq+cdEODdFGfFB0MAMHHiRIwcORKdOnVC165dsXDhQhw/fhx//etf5W4aERERqZwqgqEHH3wQ58+fx+uvv45Tp04hKSkJ33//PRo3bix304iIiEjlVBEMAcCYMWMwZswYlx4bEhKCadOm2Rw682c8b563FvC8ed5awPP27nmrougiERERkbcovugiERERkTcxGCIiIiJNYzBEREREmsZgiIiIiDRNlcHQhx9+iISEBISGhqJjx47Izs6ucv9169ahY8eOCA0NxW233YaPPvqo0j7Lli1DYmIiQkJCkJiYiOXLl3ur+S5z5rzT09PRp08fNGjQAHXq1EHXrl3x008/We2zePFi6HS6Sj9FRUXePhWnOHPeWVlZNs9p//79Vvv52/s9evRom+fdqlUryz5qeL/Xr1+PwYMHIyYmBjqdDitWrHD4GH/4fjt73v7y/Xb2vP3l++3sefvL93vWrFno3LkzateujcjISAwdOhQHDhxw+DhffMdVFwx9+eWXSEtLwyuvvILt27cjOTkZ/fv3x/Hjx23un5OTgwEDBiA5ORnbt2/H3/72N0yYMAHLli2z7LNx40Y8+OCDGDlyJP744w+MHDkSw4cPx2+//ear03LI2fNev349+vTpg++//x5bt25Fr169MHjwYGzfvt1qvzp16uDUqVNWP6Ghob44JUmcPW+zAwcOWJ1Ts2bNLPf54/s9f/58q/PNzc1FeHg4HnjgAav9lP5+X716FW3btsWCBQsk7e8v329nz9tfvt/OnreZ2r/fzp63v3y/161bh7Fjx2LTpk1YvXo1SktL0bdvX1y9etXuY3z2HRcq06VLF/HXv/7ValuLFi3ElClTbO7/8ssvixYtWlhte/bZZ8Vdd91luT18+HBx7733Wu3Tr18/8dBDD3mo1e5z9rxtSUxMFDNmzLDcXrRokQgLC/NUE73C2fPOzMwUAMTFixftHlML7/fy5cuFTqcTR48etWxTw/tdHgCxfPnyKvfxl+93eVLO2xY1fr/Lk3Le/vL9Ls+V99sfvt9CCHHmzBkBQKxbt87uPr76jquqZ+jGjRvYunUr+vbta7W9b9++2LBhg83HbNy4sdL+/fr1w5YtW1BSUlLlPvaO6WuunHdFRqMRly9frrTY3ZUrV9C4cWPExcVh0KBBlf6ylJM7592+fXtER0fjnnvuQWZmptV9Wni/P/30U/Tu3btSlXYlv9+u8Ifvtyeo8fvtDjV/vz3BX77fBQUFAFDlIqy++o6rKhg6d+4cysrKKq1WHxUVVWlVe7P8/Hyb+5eWluLcuXNV7mPvmL7mynlXNGfOHFy9ehXDhw+3bGvRogUWL16MjIwMLF26FKGhoejWrRsOHTrk0fa7ypXzjo6OxsKFC7Fs2TKkp6ejefPmuOeee7B+/XrLPv7+fp86dQo//PADnnrqKavtSn+/XeEP329PUOP32xX+8P12l798v4UQmDhxIrp3746kpCS7+/nqO66a5TjK0+l0VreFEJW2Odq/4nZnjykHV9u4dOlSTJ8+HQaDAZGRkZbtd911F+666y7L7W7duqFDhw54//338d5773mu4W5y5rybN2+O5s2bW2537doVubm5eOedd9CjRw+XjikXV9u4ePFi1K1bF0OHDrXarpb321n+8v12ldq/387wp++3q/zl+z1u3Djs3LkTv/zyi8N9ffEdV1XPUP369REYGFgp2jtz5kylqNCsYcOGNvcPCgpCRERElfvYO6avuXLeZl9++SWefPJJfPXVV+jdu3eV+wYEBKBz586K+UvCnfMu76677rI6J39+v4UQ+M9//oORI0ciODi4yn2V9n67wh++3+5Q8/fbU9T2/XaHv3y/x48fj4yMDGRmZiIuLq7KfX31HVdVMBQcHIyOHTti9erVVttXr16Nu+++2+ZjunbtWmn/VatWoVOnTqhWrVqV+9g7pq+5ct6A6S/G0aNHY8mSJRg4cKDD5xFCYMeOHYiOjna7zZ7g6nlXtH37dqtz8tf3GzDN1jh8+DCefPJJh8+jtPfbFf7w/XaV2r/fnqK277c71P79FkJg3LhxSE9Px9q1a5GQkODwMT77jktOtVaIL774QlSrVk18+umnYu/evSItLU3UrFnTklU/ZcoUMXLkSMv+f/75p6hRo4Z44YUXxN69e8Wnn34qqlWrJr755hvLPr/++qsIDAwU//jHP8S+ffvEP/7xDxEUFCQ2bdrk8/Ozx9nzXrJkiQgKChIffPCBOHXqlOXn0qVLln2mT58ufvzxR3HkyBGxfft28fjjj4ugoCDx22+/+fz87HH2vN99912xfPlycfDgQbF7924xZcoUAUAsW7bMso8/vt9mjz76qLjzzjttHlMN7/fly5fF9u3bxfbt2wUAMXfuXLF9+3Zx7NgxIYT/fr+dPW9/+X47e97+8v129rzN1P79fu6550RYWJjIysqy+txeu3bNso9c33HVBUNCCPHBBx+Ixo0bi+DgYNGhQweraXmjRo0SPXv2tNo/KytLtG/fXgQHB4smTZqIf/3rX5WO+fXXX4vmzZuLatWqiRYtWlh9uZTCmfPu2bOnAFDpZ9SoUZZ90tLSRKNGjURwcLBo0KCB6Nu3r9iwYYMPz0gaZ8777bffFk2bNhWhoaGiXr16onv37mLlypWVjulv77cQQly6dElUr15dLFy40Obx1PB+m6dO2/vc+uv329nz9pfvt7Pn7S/fb1c+5/7w/bZ1zgDEokWLLPvI9R3X3WwgERERkSapKmeIiIiIyNMYDBEREZGmMRgiIiIiTWMwRERERJrGYIiIiIg0jcEQERERaRqDISIiItI0BkNERESkaQyGiIiISNMYDBEREZGmMRgiIlVaunQpQkNDkZeXZ9n21FNPoU2bNigoKJCxZUSkNlybjIhUSQiBdu3aITk5GQsWLMCMGTPwySefYNOmTYiNjZW7eUSkIkFyN4CIyBU6nQ4zZ87EsGHDEBMTg/nz5yM7O5uBEBE5jT1DRKRqHTp0wJ49e7Bq1Sr07NlT7uYQkQoxZ4iIVOunn37C/v37UVZWhqioKLmbQ0QqxZ4hIlKlbdu2ISUlBR988AG++OIL1KhRA19//bXczSIiFWLOEBGpztGjRzFw4EBMmTIFI0eORGJiIjp37oytW7eiY8eOcjePiFSGPUNEpCoXLlxAt27d0KNHD3z88ceW7Xq9HsXFxfjxxx9lbB0RqRGDISIiItI0JlATERGRpjEYIiIiIk1jMERERESaxmCIiIiINI3BEBEREWkagyEiIiLSNAZDREREpGkMhoiIiEjTGAwRERGRpjEYIiIiIk1jMERERESaxmCIiIiINO3/ASKzVmq8DVHYAAAAAElFTkSuQmCC", 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", 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    " ] @@ -2180,83 +2180,76 @@ "name": "stdout", "output_type": "stream", "text": [ - "Own inversion" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", + "Own inversion\n", "[[4.]\n", " [3.]]\n", - "Eigenvalues of Hessian Matrix:[0.35713539 3.88632765]\n", - "0 [-9.01615836] [-9.6932681]\n", - "1 [0.01454469] [-0.01357366]\n", - "2 [0.0132081] [-0.0123263]\n", - "3 [0.01199434] [-0.01119357]\n", - "4 [0.01089212] [-0.01016493]\n", - "5 [0.00989118] [-0.00923082]\n", - "6 [0.00898223] [-0.00838255]\n", - "7 [0.0081568] [-0.00761224]\n", - "8 [0.00740723] [-0.00691271]\n", - "9 [0.00672654] [-0.00627746]\n", - "10 [0.0061084] [-0.00570059]\n", - "11 [0.00554707] [-0.00517673]\n", - "12 [0.00503732] [-0.00470102]\n", - "13 [0.00457441] [-0.00426902]\n", - "14 [0.00415405] [-0.00387671]\n", - "15 [0.00377231] [-0.00352046]\n", - "16 [0.00342565] [-0.00319695]\n", - "17 [0.00311085] [-0.00290316]\n", - "18 [0.00282498] [-0.00263638]\n", - "19 [0.00256537] [-0.0023941]\n", - "20 [0.00232963] [-0.0021741]\n", - "21 [0.00211555] [-0.00197431]\n", - "22 [0.00192114] [-0.00179288]\n", - "23 [0.00174459] [-0.00162812]\n", - "24 [0.00158427] [-0.0014785]\n", - "25 [0.00143869] [-0.00134264]\n", - "26 [0.00130648] [-0.00121925]\n", - "27 [0.00118642] [-0.00110721]\n", - "28 [0.00107739] [-0.00100546]\n", - "29 [0.00097839] [-0.00091307]\n", + "Eigenvalues of Hessian Matrix:[0.32606365 3.80499859]\n", + "0 [-10.98955596] [-10.8332972]\n", + "1 [-0.26421616] [0.25444295]\n", + "2 [-0.24157456] [0.23263884]\n", + "3 [-0.22087319] [0.2127032]\n", + "4 [-0.20194579] [0.19447592]\n", + "5 [-0.18464035] [0.1778106]\n", + "6 [-0.16881787] [0.16257339]\n", + "7 [-0.15435127] [0.1486419]\n", + "8 [-0.14112437] [0.13590426]\n", + "9 [-0.12903093] [0.12425815]\n", + "10 [-0.11797382] [0.11361003]\n", + "11 [-0.10786423] [0.10387439]\n", + "12 [-0.09862097] [0.09497303]\n", + "13 [-0.09016979] [0.08683446]\n", + "14 [-0.08244283] [0.07939331]\n", + "15 [-0.07537801] [0.07258982]\n", + "16 [-0.06891861] [0.06636934]\n", + "17 [-0.06301273] [0.06068192]\n", + "18 [-0.05761295] [0.05548187]\n", + "19 [-0.05267589] [0.05072744]\n", + "20 [-0.04816191] [0.04638043]\n", + "21 [-0.04403475] [0.04240593]\n", + "22 [-0.04026126] [0.03877201]\n", + "23 [-0.03681113] [0.0354495]\n", + "24 [-0.03365665] [0.03241171]\n", + "25 [-0.0307725] [0.02963424]\n", + "26 [-0.02813549] [0.02709478]\n", + "27 [-0.02572446] [0.02477293]\n", + "28 [-0.02352005] [0.02265005]\n", + "29 [-0.02150453] [0.02070909]\n", "theta from own gd\n", - "[[4.00248779]\n", - " [2.9976783 ]]\n", - "0 [0.00088848] [-0.00082916]\n", - "1 [0.00080683] [-0.00075296]\n", - "2 [0.00070819] [-0.00066091]\n", - "3 [0.00061352] [-0.00057256]\n", - "4 [0.00052874] [-0.00049344]\n", - "5 [0.00045472] [-0.00042436]\n", - "6 [0.00039072] [-0.00036464]\n", - "7 [0.00033562] [-0.00031321]\n", - "8 [0.00028825] [-0.000269]\n", - "9 [0.00024755] [-0.00023102]\n", - "10 [0.00021259] [-0.0001984]\n", - "11 [0.00018256] [-0.00017038]\n", - "12 [0.00015678] [-0.00014631]\n", - "13 [0.00013464] [-0.00012565]\n", - "14 [0.00011562] [-0.0001079]\n", - "15 [9.92929225e-05] [-9.26639089e-05]\n", - "16 [8.52694246e-05] [-7.95766504e-05]\n", - "17 [7.32265127e-05] [-6.83377498e-05]\n", - "18 [6.28844641e-05] [-5.86861591e-05]\n", - "19 [5.40030606e-05] [-5.03976976e-05]\n", - "20 [4.63760101e-05] [-4.32798457e-05]\n", - "21 [3.98261559e-05] [-3.71672742e-05]\n", - "22 [3.42013616e-05] [-3.19180036e-05]\n", - "23 [2.93709777e-05] [-2.74101067e-05]\n", - "24 [2.52228066e-05] [-2.35388766e-05]\n", - "25 [2.1660497e-05] [-2.02143946e-05]\n", - "26 [1.86013055e-05] [-1.73594414e-05]\n", - "27 [1.59741748e-05] [-1.49077038e-05]\n", - "28 [1.37180834e-05] [-1.2802234e-05]\n", - "29 [1.17806281e-05] [-1.09941275e-05]\n", + "[[3.93969971]\n", + " [3.05806981]]\n", + "0 [-0.01966173] [0.01893445]\n", + "1 [-0.01797685] [0.0173119]\n", + "2 [-0.01593089] [0.01534161]\n", + "3 [-0.01395192] [0.01343585]\n", + "4 [-0.01216264] [0.01171276]\n", + "5 [-0.0105836] [0.01019212]\n", + "6 [-0.00920294] [0.00886253]\n", + "7 [-0.00800011] [0.00770419]\n", + "8 [-0.00695371] [0.00669649]\n", + "9 [-0.0060439] [0.00582034]\n", + "10 [-0.00525303] [0.00505872]\n", + "11 [-0.00456562] [0.00439674]\n", + "12 [-0.00396815] [0.00382137]\n", + "13 [-0.00344887] [0.0033213]\n", + "14 [-0.00299754] [0.00288666]\n", + "15 [-0.00260527] [0.0025089]\n", + "16 [-0.00226433] [0.00218058]\n", + "17 [-0.00196801] [0.00189522]\n", + "18 [-0.00171047] [0.0016472]\n", + "19 [-0.00148663] [0.00143164]\n", + "20 [-0.00129209] [0.00124429]\n", + "21 [-0.001123] [0.00108146]\n", + "22 [-0.00097604] [0.00093994]\n", + "23 [-0.00084831] [0.00081693]\n", + "24 [-0.0007373] [0.00071003]\n", + "25 [-0.00064081] [0.00061711]\n", + "26 [-0.00055695] [0.00053635]\n", + "27 [-0.00048407] [0.00046616]\n", + "28 [-0.00042072] [0.00040516]\n", + "29 [-0.00036566] [0.00035214]\n", "theta from own gd wth momentum\n", - "[[4.00002833]\n", - " [2.99997356]]\n" + "[[3.99902531]\n", + " [3.00093864]]\n" ] } ], @@ -2341,30 +2334,24 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[3.91650453]\n", - " [2.94495682]]\n", - "Eigenvalues of Hessian Matrix:[0.34862407 4.10453899]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "[[4.11762444]\n", + " [3.04098313]]\n", + "Eigenvalues of Hessian Matrix:[0.29738252 4.51279273]\n", "theta from own gd\n", - "[[3.91650453]\n", - " [2.94495682]]\n" + "[[4.11762444]\n", + " [3.04098313]]\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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- " [2.90281987]]\n" + "[[4.44845593]\n", + " [2.72577807]]\n" ] } ], @@ -2595,9 +2588,9 @@ "output_type": "stream", "text": [ "theta from own AdaGrad\n", - "[[1.99994537]\n", - " [3.00034209]\n", - " [3.99966798]]\n" + "[[2.00036797]\n", + " [2.99817613]\n", + " [4.00177485]]\n" ] } ], @@ -2696,9 +2689,9 @@ "output_type": "stream", "text": [ "theta from own RMSprop\n", - "[[1.99975636]\n", - " [3.00348281]\n", - " [3.99607299]]\n" + "[[2.00119865]\n", + " [3.01346635]\n", + " [3.99284588]]\n" ] } ], @@ -2793,9 +2786,9 @@ "output_type": "stream", "text": [ "theta from own ADAM\n", - "[[2.00002276]\n", - " [2.99985884]\n", - " [4.00009004]]\n" + "[[1.99997244]\n", + " [3.00018876]\n", + " [3.99983332]]\n" ] } ], @@ -2999,7 +2992,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 25, @@ -3080,7 +3073,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 26, @@ -4302,7 +4295,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": 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z_sA!Bu5A+L^OX)wV2qKMm}Aba5;ISZeMyO&y+(q|H~5f1Q}pUZ;7EQ&23CFmILrbk zWy<`f%*woK=hPXEXVlEqZ{NP%*pVxt4-5>rzk9s*4xkYUE>iSe+GXvnIqf4b7etrD zYDQZOeN__KhatP_&;W8$_qfZ44 zG+%o~?s{RGc7%Gk{67*mGMkZ|oC<>fn&g;I&CPoi4H#K#p0EYxO(~PsLpTp^-&>48 zG&+3|tFQf#p)T|HD@)wVE-lzCr<$8D%rj#}@^}Vu@$n3CaPJ#d=}>Q+i)r&!3CNc7D!rdSN9G3j*pO7IdNhlciO0g1CZq*ytR%H0tmVb?6FCpj93BECWyAS(wW-WHCmmP zj|sJ|LzYTYDcc%^0v8NUhebKD>p&17;>aD@nDx1j3@e0Rq`+B43ZmCr=@bJ@cFzHv zxl*h>VBVldDx4>64li_k9DA!D%ot\n", @@ -13,11 +15,13 @@ { "cell_type": "markdown", "id": "bcb25e64", - "metadata": {}, + "metadata": { + "editable": true + }, "source": [ "# Exercises week 42\n", "\n", - "**October 14-18, 2024**\n", + "**October 11-18, 2024**\n", "\n", "Date: **Deadline is Friday October 18 at midnight**\n" ] @@ -25,13 +29,17 @@ { "cell_type": "markdown", "id": "bb01f126", - "metadata": {}, + "metadata": { + "editable": true + }, "source": [ "# Overarching aims of the exercises this week\n", "\n", "The aim of the exercises this week is to get started with implementing a neural network. There are a lot of technical and finicky parts of implementing a neutal network, so take your time.\n", "\n", - "This week, you will implement only the feed-forward pass. Next week, you will implement backpropagation. We recommend that you do the exercises this week by editing and running this notebook file, as it includes several checks along the way that you have implemented the pieces of the feed-forward pass correctly. If you have trouble running a notebook, or importing pytorch, you can run this notebook in google colab instead: (LINK TO COLAB), though we recommend that you set up VSCode and your python environment to run code like this locally.\n" + "This week, you will implement only the feed-forward pass and updating the network parameters with simple gradient descent, the gradient will be computed using autograd using code we provide. Next week, you will implement backpropagation. We recommend that you do the exercises this week by editing and running this notebook file, as it includes some checks along the way that you have implemented the pieces of the feed-forward pass correctly, and running small parts of the code at a time will be important for understanding the methods.\n", + "\n", + "If you have trouble running a notebook, you can run this notebook in google colab instead (https://colab.research.google.com/drive/1OCQm1tlTWB6hZSf9I7gGUgW9M8SbVeQu#offline=true&sandboxMode=true), an updated link will be provided on the course discord (you can also send an email to k.h.fredly@fys.uio.no if you encounter any trouble), though we recommend that you set up VSCode and your python environment to run code like this locally.\n" ] }, { @@ -41,8 +49,33 @@ "metadata": {}, "outputs": [], "source": [ - "import autograd.numpy as np\n", - "from autograd import grad" + "import autograd.numpy as np # We need to use this numpy wrapper to make automatic differentiation work later\n", + "from sklearn import datasets\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "\n", + "# Defining some activation functions\n", + "def ReLU(z):\n", + " return np.where(z > 0, z, 0)\n", + "\n", + "\n", + "def sigmoid(z):\n", + " return 1 / (1 + np.exp(-z))\n", + "\n", + "\n", + "def softmax(z):\n", + " \"\"\"Compute softmax values for each set of scores in the rows of the matrix z.\n", + " Used with batched input data.\"\"\"\n", + " e_z = np.exp(z - np.max(z, axis=0))\n", + " return e_z / np.sum(e_z, axis=1)[:, np.newaxis]\n", + "\n", + "\n", + "def softmax_vec(z):\n", + " \"\"\"Compute softmax values for each set of scores in the vector z.\n", + " Use this function when you use the activation function on one vector at a time\"\"\"\n", + " e_z = np.exp(z - np.max(z))\n", + " return e_z / np.sum(e_z)" ] }, { @@ -52,7 +85,7 @@ "source": [ "# Exercise 1\n", "\n", - "Complete the following parts to compute the activation of the first layer.\n" + "In this exercise you will compute the activation of the first layer. You only need to change the code in the cells right below an exercise, the rest works out of the box. Feel free to make changes and see how stuff works though!\n" ] }, { @@ -64,21 +97,24 @@ "source": [ "np.random.seed(2024)\n", "\n", - "\n", - "def ReLU(z):\n", - " return np.where(z > 0, z, 0)\n", - "\n", - "\n", - "x = np.random.randn(2) # network input\n", + "x = np.random.randn(2) # network input. This is a single input with two features\n", "W1 = np.random.randn(4, 2) # first layer weights" ] }, + { + "cell_type": "markdown", + "id": "4ed2cf3d", + "metadata": {}, + "source": [ + "**a)** Given the shape of the first layer weight matrix, what is the input shape of the neural network? What is the output shape of the first layer?\n" + ] + }, { "cell_type": "markdown", "id": "edf7217b", "metadata": {}, "source": [ - "**a)** Define the bias of the first layer, `b1`with the correct shape\n" + "**b)** Define the bias of the first layer, `b1`with the correct shape. (Run the next cell right after the previous to get the random generated values to line up with the test solution below)\n" ] }, { @@ -88,7 +124,7 @@ "metadata": {}, "outputs": [], "source": [ - "b1 = np.random.randn(4)" + "b1 = ..." ] }, { @@ -96,7 +132,7 @@ "id": "09e8d453", "metadata": {}, "source": [ - "**b)** Compute the intermediary `z1` for the first layer\n" + "**c)** Compute the intermediary `z1` for the first layer\n" ] }, { @@ -106,7 +142,7 @@ "metadata": {}, "outputs": [], "source": [ - "z1 = W1 @ x + b1" + "z1 = ..." ] }, { @@ -114,7 +150,7 @@ "id": "6f71374e", "metadata": {}, "source": [ - "**c)** Compute the activation `a1` for the first layer using the ReLU activation function defined earlier.\n" + "**d)** Compute the activation `a1` for the first layer using the ReLU activation function defined earlier.\n" ] }, { @@ -124,7 +160,7 @@ "metadata": {}, "outputs": [], "source": [ - "a1 = ReLU(z1)" + "a1 = ..." ] }, { @@ -132,7 +168,7 @@ "id": "088710c0", "metadata": {}, "source": [ - "Confirm that you got the correct activation with the test below.\n" + "Confirm that you got the correct activation with the test below. Make sure that you define `b1` with the randn function right after you define `W1`.\n" ] }, { @@ -154,9 +190,11 @@ "source": [ "# Exercise 2\n", "\n", - "Compute the activation of the second layer with an output of length 8 and ReLU activation.\n", + "Now we will add a layer to the network with an output of length 8 and ReLU activation.\n", "\n", - "**a)** Define the weight and bias of the second layer with the right shapes.\n" + "**a)** What is the input of the second layer? What is its shape?\n", + "\n", + "**b)** Define the weight and bias of the second layer with the right shapes.\n" ] }, { @@ -166,8 +204,8 @@ "metadata": {}, "outputs": [], "source": [ - "W2 = np.random.randn(8, 4)\n", - "b2 = np.random.randn(8)" + "W2 = ...\n", + "b2 = ..." ] }, { @@ -175,7 +213,7 @@ "id": "5bd7d84b", "metadata": {}, "source": [ - "**b)** Compute intermediary `z2` and activation `a2` for the second layer.\n" + "**c)** Compute the intermediary `z2` and activation `a2` for the second layer.\n" ] }, { @@ -185,8 +223,8 @@ "metadata": {}, "outputs": [], "source": [ - "z2 = W2 @ a1\n", - "a2 = ReLU(z2)" + "z2 = ...\n", + "a2 = ..." ] }, { @@ -204,7 +242,9 @@ "metadata": {}, "outputs": [], "source": [ - "print(a2.shape == (8,))" + "print(\n", + " np.allclose(np.exp(len(a2)), 2980.9579870417283)\n", + ") # This should evaluate to True if a2 has the correct shape :)" ] }, { @@ -226,16 +266,16 @@ "metadata": {}, "outputs": [], "source": [ - "def create_layers(network_input_size, output_sizes):\n", + "def create_layers(network_input_size, layer_output_sizes):\n", " layers = []\n", "\n", " i_size = network_input_size\n", - " for output_size in output_sizes:\n", - " W = np.random.rand(output_size, i_size)\n", - " b = np.random.rand(output_size)\n", + " for layer_output_size in layer_output_sizes:\n", + " W = ...\n", + " b = ...\n", " layers.append((W, b))\n", "\n", - " i_size = output_size\n", + " i_size = layer_output_size\n", " return layers" ] }, @@ -244,7 +284,7 @@ "id": "bdc0cda2", "metadata": {}, "source": [ - "**b)** Comple the function below so that it evaluates the intermediate `z` and activation `a` for each layer, and returns the final activation `a`. This is the complete feed-forward pass, a full neural network!\n" + "**b)** Comple the function below so that it evaluates the intermediary `z` and activation `a` for each layer, with ReLU actication, and returns the final activation `a`. This is the complete feed-forward pass, a full neural network!\n" ] }, { @@ -254,11 +294,11 @@ "metadata": {}, "outputs": [], "source": [ - "def feed_forward(layers, input):\n", + "def feed_forward_all_relu(layers, input):\n", " a = input\n", " for W, b in layers:\n", - " z = W @ a + b\n", - " a = ReLU(z)\n", + " z = ...\n", + " a = ...\n", " return a" ] }, @@ -276,14 +316,30 @@ "id": "89a8f70d", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "input_size = ...\n", + "layer_output_sizes = [...]\n", + "\n", + "x = np.random.rand(input_size)\n", + "layers = ...\n", + "predict = ...\n", + "print(predict)" + ] + }, + { + "cell_type": "markdown", + "id": "0da7fd52", + "metadata": {}, + "source": [ + "**d)** Why is a neural network with no activation functions always mathematically equivelent to a neural network with only one layer?\n" + ] }, { "cell_type": "markdown", "id": "306d8b7c", "metadata": {}, "source": [ - "# Exercise 4\n" + "# Exercise 4 - Custom activation for each layer\n" ] }, { @@ -291,29 +347,7 @@ "id": "221c7b6c", "metadata": {}, "source": [ - "So far, every layer has used the same activation, ReLU. We often want to use other types of activation however, so we need to update our code to support multiple types of activation. Make sure that you have completed every previous exercise before trying this one.\n", - "\n", - "**a)** Make the `create_layers` function also accept a list of activation functions, which is used to add activation functions to each of the tuples in `layers`. Make new functions to not mess with the old ones.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9df82312", - "metadata": {}, - "outputs": [], - "source": [ - "def create_layers_4(network_input_size, output_sizes, activation_funcs):\n", - " layers = []\n", - "\n", - " i_size = network_input_size\n", - " for output_size, activation in zip(output_sizes, activation_funcs):\n", - " W = np.random.rand(output_size, i_size)\n", - " b = np.random.rand(output_size)\n", - " layers.append((W, b, activation))\n", - "\n", - " i_size = output_size\n", - " return layers" + "So far, every layer has used the same activation, ReLU. We often want to use other types of activation however, so we need to update our code to support multiple types of activation functions. Make sure that you have completed every previous exercise before trying this one.\n" ] }, { @@ -321,7 +355,7 @@ "id": "10896d06", "metadata": {}, "source": [ - "**b)** Update the `feed_forward` function to support this change.\n" + "**a)** Complete the `feed_forward` function which accepts a list of activation functions as an argument, and which evaluates these activation functions at each layer.\n" ] }, { @@ -331,11 +365,109 @@ "metadata": {}, "outputs": [], "source": [ - "def feed_forward_4(layers, input):\n", + "def feed_forward(input, layers, activations):\n", " a = input\n", - " for W, b, activation in layers:\n", - " z = W @ a + b\n", - " a = activation(z)\n", + " for (W, b), activation in zip(layers, activations):\n", + " z = ...\n", + " a = ...\n", + " return a" + ] + }, + { + "cell_type": "markdown", + "id": "8f7df363", + "metadata": {}, + "source": [ + "**b)** Make a list with three activation functions(don't call them yet! you can make a list with function names as elements, and then call these elements of the list later), two ReLU and one sigmoid. (If you add other functions than the ones defined at the start of the notebook, make sure everything is defined using autograd's numpy wrapper, like above, since we want to use automatic differentiation on all of these functions later.)\n", + "\n", + "Then evaluate a network with three layers and these activation functions.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "301b46dc", + "metadata": {}, + "outputs": [], + "source": [ + "network_input_size = ...\n", + "layer_output_sizes = [...]\n", + "activations = [...]\n", + "layers = ...\n", + "\n", + "x = np.random.randn(network_input_size)\n", + "feed_forward(x, layers, activations)" + ] + }, + { + "cell_type": "markdown", + "id": "a8d6c425", + "metadata": {}, + "source": [ + "# Exercise 5 - Processing multiple inputs at once\n" + ] + }, + { + "cell_type": "markdown", + "id": "0f4330a4", + "metadata": {}, + "source": [ + "So far, the feed forward function has taken one input vector as an input. This vector then undergoes a linear transformation and then an element-wise non-linear operation for each layer. This approach of sending one vector in at a time is great for interpreting how the network transforms data with its linear and non-linear operations, but not the best for numerical efficiency. Now, we want to be able to send many inputs through the network at once. This will make the code a bit harder to understand, but it will make it faster, and more compact. It will be worth the trouble.\n", + "\n", + "To process multiple inputs at once, while still performing the same operations, you will only need to flip a couple things around.\n" + ] + }, + { + "cell_type": "markdown", + "id": "17023bb7", + "metadata": {}, + "source": [ + "**a)** Complete the function `create_layers_batch` so that the weight matrix is the transpose of what it was when you only sent in one input at a time.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a241fd79", + "metadata": {}, + "outputs": [], + "source": [ + "def create_layers_batch(network_input_size, layer_output_sizes):\n", + " layers = []\n", + "\n", + " i_size = network_input_size\n", + " for layer_output_size in layer_output_sizes:\n", + " W = ...\n", + " b = ...\n", + " layers.append((W, b))\n", + "\n", + " i_size = layer_output_size\n", + " return layers" + ] + }, + { + "cell_type": "markdown", + "id": "a6349db6", + "metadata": {}, + "source": [ + "**b)** Make a matrix of inputs with the shape (number of features, number of inputs), you choose the number of inputs and features per input. Then complete the function `feed_forward_batch` so that you can process this matrix of inputs with only one matrix multiplication and one broadcasted vector addition per layer. (Hint: You will only need to swap two variable around from your previous implementation, but remember to test that you get the same results for equivelent inputs!)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "425f3bcc", + "metadata": {}, + "outputs": [], + "source": [ + "inputs = np.random.rand(1000, 4)\n", + "\n", + "\n", + "def feed_forward_batch(inputs, layers, activations):\n", + " a = inputs\n", + " for (W, b), activation in zip(layers, activations):\n", + " z = ...\n", + " a = ...\n", " return a" ] }, @@ -354,15 +486,29 @@ "metadata": {}, "outputs": [], "source": [ - "from scipy.special import softmax\n", - "\n", - "network_input_size = 4\n", - "output_sizes = [12, 10, 3]\n", - "activation_funcs = [ReLU, ReLU, softmax]\n", - "layers = create_layers_4(network_input_size, output_sizes, activation_funcs)\n", + "network_input_size = ...\n", + "layer_output_sizes = [...]\n", + "activations = [...]\n", + "layers = create_layers_batch(network_input_size, layer_output_sizes)\n", "\n", "x = np.random.randn(network_input_size)\n", - "predict = feed_forward_4(layers, x)" + "feed_forward_batch(inputs, layers, activations)" + ] + }, + { + "cell_type": "markdown", + "id": "87999271", + "metadata": {}, + "source": [ + "You should use this batched approach moving forward, as it will lead to much more compact code. However, remember that each input is still treated separately, and that you will need to keep in mind the transposed weight matrix and other details when implementing backpropagation.\n" + ] + }, + { + "cell_type": "markdown", + "id": "237eb782", + "metadata": {}, + "source": [ + "# Exercise 6 - Predicting on real data\n" ] }, { @@ -370,9 +516,9 @@ "id": "54d5fde7", "metadata": {}, "source": [ - "The final exercise will hopefully be very simple if everything has worked so far. You will evaluate your neural network on the iris data set (https://scikit-learn.org/1.5/auto_examples/datasets/plot_iris_dataset.html).\n", + "You will now evaluate your neural network on the iris data set (https://scikit-learn.org/1.5/auto_examples/datasets/plot_iris_dataset.html).\n", "\n", - "This dataset contains data on 150 flowers of 3 different types which can be separated pretty well using the four features given for each flower, which includes the width and length of their leaves. You are not expected to do any training of the network or actual classification, unless you feel like it, in that case you can do exercise 5.\n" + "This dataset contains data on 150 flowers of 3 different types which can be separated pretty well using the four features given for each flower, which includes the width and length of their leaves. You are will later train your network to actually make good predictions.\n" ] }, { @@ -382,10 +528,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Loading and plotting iris dataset\n", - "from sklearn import datasets\n", - "import matplotlib.pyplot as plt\n", - "\n", "iris = datasets.load_iris()\n", "\n", "_, ax = plt.subplots()\n", @@ -396,24 +538,91 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed3e2fc9", + "metadata": {}, + "outputs": [], + "source": [ + "inputs = iris.data\n", + "\n", + "# Since each prediction is a vector with a score for each of the three types of flowers,\n", + "# we need to make each target a vector with a 1 for the correct flower and a 0 for the others.\n", + "targets = np.zeros((len(iris.data), 3))\n", + "for i, t in enumerate(iris.target):\n", + " targets[i, t] = 1\n", + "\n", + "\n", + "def accuracy(predictions, targets):\n", + " one_hot_predictions = np.zeros(predictions.shape)\n", + "\n", + " for i, prediction in enumerate(predictions):\n", + " one_hot_predictions[i, np.argmax(prediction)] = 1\n", + " return accuracy_score(one_hot_predictions, targets)" + ] + }, { "cell_type": "markdown", - "id": "c528846f", + "id": "0362c4a9", "metadata": {}, "source": [ - "**c)** Loop over the iris dataset(`iris.data`) and evaluate the network for each data point.\n" + "**a)** What should the input size for the network be with this dataset? What should the output shape of the last layer be?\n" + ] + }, + { + "cell_type": "markdown", + "id": "bf62607e", + "metadata": {}, + "source": [ + "**b)** Create a network with two hidden layers, the first with sigmoid activation and the last with softmax, the first layer should have 8 \"nodes\", the second has the number of nodes you found in exercise a). Softmax returns a \"probability distribution\", in the sense that the numbers in the output are positive and add up to 1 and, their magnitude are in some sense relative to their magnitude before going through the softmax function. Remember to use the batched version of the create_layers and feed forward functions.\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "2efc507d", + "id": "5366d4ae", "metadata": {}, "outputs": [], "source": [ - "# No need to change this cell! Just make sure it works!\n", - "for x in iris.data:\n", - " prediction = feed_forward_4(layers, x)" + "...\n", + "layers = ..." + ] + }, + { + "cell_type": "markdown", + "id": "c528846f", + "metadata": {}, + "source": [ + "**c)** Evaluate your model on the entire iris dataset! For later purposes, we will split the data into train and test sets, and compute gradients on smaller batches of the training data. But for now, evaluate the network on the whole thing at once.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c783105", + "metadata": {}, + "outputs": [], + "source": [ + "predictions = feed_forward_batch(inputs, layers, activations)" + ] + }, + { + "cell_type": "markdown", + "id": "01a3caa8", + "metadata": {}, + "source": [ + "**d)** Compute the accuracy of your model using the accuracy function defined above. Recreate your model a couple times and see how the accuracy changes.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2612b82", + "metadata": {}, + "outputs": [], + "source": [ + "print(accuracy(predictions, targets))" ] }, { @@ -421,31 +630,126 @@ "id": "334560b6", "metadata": {}, "source": [ - "# Exercise 5 (Very optional and very hard :)\n" + "# Exercise 6 - Training on real data\n", + "\n", + "To be able to actually do anything useful with your neural network, you need to train it. For this, we need a cost function and a way to take the gradient of the cost function wrt. the network parameters. The following exercises guide you through taking the gradient using autograd, and updating the network parameters using the gradient. Feel free to implement gradient methods like ADAM if you finish everything.\n" ] }, { "cell_type": "markdown", - "id": "ea0a8fe0", + "id": "700cabe4", "metadata": {}, "source": [ - "**a)** Make the iris target values into one-hot vectors.\n", + "The cross-entropy loss function can evaluate performance on classification tasks. It sees if your prediction is \"most certain\" on the correct target.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56bef776", + "metadata": {}, + "outputs": [], + "source": [ + "from autograd import grad\n", "\n", - "**b)** Define the cross-entropy loss function to evaluate the performance of your network on the data set.\n", "\n", - "**c)** Use the autograd package to take the gradient of the cross entropy wrt. the weights and biases of the network.\n", + "def cost(input, layers, activations, target):\n", + " predict = feed_forward_batch(input, layers, activations)\n", + " return cross_entropy(predict, target)\n", "\n", - "**d)** Use gradient descent of some sort to optimize the parameters.\n", "\n", - "**e)** Evaluate the accuracy of the network.\n", + "def cross_entropy(predict, target):\n", + " return np.sum(-target * np.log(predict))\n", "\n", - "**e)** Show off how you did in a group session!\n" + "\n", + "gradient_func = grad(\n", + " cross_entropy, 1\n", + ") # Taking the gradient wrt. the second input to the cost function" + ] + }, + { + "cell_type": "markdown", + "id": "7b1b74bc", + "metadata": {}, + "source": [ + "**a)** What shape should the gradient of the cost function wrt. weights and biases be?\n", + "\n", + "**b)** Use the `gradient_func` function to take the gradient of the cross entropy wrt. the weights and biases of the network. Check the shapes of what's inside. What does the `grad` func from autograd actually do?\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "841c9e87", + "metadata": {}, + "outputs": [], + "source": [ + "layers_grad = gradient_func(inputs, layers, activations, targets) # Don't change this" + ] + }, + { + "cell_type": "markdown", + "id": "adc9e9be", + "metadata": {}, + "source": [ + "**c)** Finish the `train_network` function.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e4d38d3", + "metadata": {}, + "outputs": [], + "source": [ + "def train_network(\n", + " inputs, layers, activations, targets, learning_rate=0.001, epochs=100\n", + "):\n", + " for i in range(epochs):\n", + " layers_grad = gradient_func(inputs, layers, activations, targets)\n", + " for (W, b), (W_g, b_g) in zip(layers, layers_grad):\n", + " W -= ...\n", + " b -= ..." + ] + }, + { + "cell_type": "markdown", + "id": "2f65d663", + "metadata": {}, + "source": [ + "**e)** What do we call the gradient method used above?\n" + ] + }, + { + "cell_type": "markdown", + "id": "7059dd8c", + "metadata": {}, + "source": [ + "**d)** Train your network and see how the accuracy changes! Make a plot if you want.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5027c7a5", + "metadata": {}, + "outputs": [], + "source": [ + "..." + ] + }, + { + "cell_type": "markdown", + "id": "3bc77016", + "metadata": {}, + "source": [ + "**e)** How high of an accuracy is it possible to acheive with a neural network on this dataset, if we use the whole thing as training data?\n" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -459,7 +763,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/doc/LectureNotes/gaussian.pdf b/doc/LectureNotes/gaussian.pdf index 54868ad183cf9e7a92bc4dc004dcc70d4d01212b..61385cfd2f7f19f42e9e56bf59b987c7ec2a1ba6 100644 GIT binary patch delta 30 lcmX@!%y+z*uc3vpg=q`3a22_66d delta 30 lcmX@!%y+z*uc3vpg=q`3a2