updating keras

doc/src/week41/week41.do.txt

@@ -2018,15 +2018,16 @@ plt.show()
 !split
 ===== Importing Keras and Tensorflow =====
 !bc pycod
+from tensorflow.keras import datasets, layers, models
 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 tensorflow.keras import Conv2D
+#from tensorflow.keras import MaxPooling2D
+#from tensorflow.keras import Flatten
 
 from sklearn.model_selection import train_test_split
 
@@ -2045,20 +2046,18 @@ X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=t
 ===== Running with Keras =====
 
 !bc pycod
-
-
 def create_convolutional_neural_network_keras(input_shape, receptive_field,
                                               n_filters, n_neurons_connected, n_categories,
                                               eta, lmbd):
     model = Sequential()
-    model.add(Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding='same',
-              activation='relu', kernel_regularizer=l2(lmbd)))
-    model.add(MaxPooling2D(pool_size=(2, 2)))
-    model.add(Flatten())
-    model.add(Dense(n_neurons_connected, activation='relu', kernel_regularizer=l2(lmbd)))
-    model.add(Dense(n_categories, activation='softmax', kernel_regularizer=l2(lmbd)))
+    model.add(layers.Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding='same',
+              activation='relu', kernel_regularizer=regularizers.l2(lmbd)))
+    model.add(layers.MaxPooling2D(pool_size=(2, 2)))
+    model.add(layers.Flatten())
+    model.add(layers.Dense(n_neurons_connected, activation='relu', kernel_regularizer=regularizers.l2(lmbd)))
+    model.add(layers.Dense(n_categories, activation='softmax', kernel_regularizer=regularizers.l2(lmbd)))
     
-    sgd = SGD(lr=eta)
+    sgd = optimizers.SGD(lr=eta)
     model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
     
     return model