updating keras

doc/src/week41/week41.do.txt

@@ -1116,23 +1116,19 @@ conda activate tf-gpu
   
 Keras is a high level "neural network":"https://en.wikipedia.org/wiki/Application_programming_interface"
 that supports Tensorflow, CTNK and Theano as backends.  
-If you have Tensorflow installed Keras is available through the *tf.keras* module.  
 If you have Anaconda installed you may run the following command
 !bc pycod
 conda install keras
 !ec
-  
-Alternatively, if you have Tensorflow or one of the other supported backends install you may use the pip package manager: 
-
-!bc pycod
-pip install keras
-!ec
-or look up the "instructions here":"https://keras.io/".
+You can look up the "instructions here":"https://keras.io/" for more information.
 
+We will to a large extent use _keras_ in this course. 
 
 !split
 ===== Collect and pre-process data =====
 
+Let us look again at the MINST data set.
+
 !bc pycod
 # import necessary packages
 import numpy as np
@@ -1203,6 +1199,13 @@ X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=t
 
 !bc pycod
 
+epochs = 100
+batch_size = 100
+n_neurons_layer1 = 100
+n_neurons_layer2 = 50
+n_categories = 10
+eta_vals = np.logspace(-5, 1, 7)
+lmbd_vals = np.logspace(-5, 1, 7)
 def create_neural_network_keras(n_neurons_layer1, n_neurons_layer2, n_categories, eta, lmbd):
     model = Sequential()
     model.add(Dense(n_neurons_layer1, activation='sigmoid', kernel_regularizer=regularizers.l2(lmbd)))
@@ -2005,7 +2008,16 @@ plt.show()
 !split
 ===== Importing Keras and Tensorflow =====
 !bc pycod
-from keras.utils import to_categorical
+from tensorflow.keras.layers import Input
+from tensorflow.keras.models import Sequential      #This allows appending layers to existing models
+from tensorflow.keras.layers import Dense           #This allows defining the characteristics of a particular layer
+from tensorflow.keras import optimizers             #This allows using whichever optimiser we want (sgd,adam,RMSprop)
+from tensorflow.keras import regularizers           #This allows using whichever regularizer we want (l1,l2,l1_l2)
+from tensorflow.keras.utils import to_categorical   #This allows using categorical cross entropy as the cost function
+from tensorflow.keras import Conv2D
+from tensorflow.keras import MaxPooling2D
+from tensorflow.keras import Flatten
+
 from sklearn.model_selection import train_test_split
 
 # representation of labels
@@ -2023,13 +2035,7 @@ X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=t
 ===== Running with Keras =====
 
 !bc pycod
-from keras.models import Sequential
-from keras.layers.convolutional import Conv2D
-from keras.layers.convolutional import MaxPooling2D
-from keras.layers import Flatten
-from keras.layers import Dense
-from keras.regularizers import l2
-from keras.optimizers import SGD
+
 
 def create_convolutional_neural_network_keras(input_shape, receptive_field,
                                               n_filters, n_neurons_connected, n_categories,