transfer.py

expert_conv = VGG16(weights = 'imagenet', include_top = False,input_shape=(64,64,3))

for layer in expert_conv.layers:
      trainable = True
      layer.trainable = trainable

expert_model = Sequential()
expert_model.add(Reshape((64,64,3)))
expert_model.add(expert_conv)
expert_model.add(GlobalAveragePooling2D())

expert_model.add(Dense(128, activation = 'relu'))
expert_model.add(Dropout(0.3))
expert_model.add(Dense(64, activation = 'relu'))
expert_model.add(Dense(1, activation = "sigmoid"))

opt = keras.optimizers.SGD(lr=1e-4, momentum=0.95)

expert_model.compile(loss = "binary_crossentropy", optimizer = opt, \
                     metrics=['accuracy'])
history =expert_model.fit(train_data,train_labels,validation_data=\
                          (test_data,test_labels),epochs = 3)
plot_acc(history)
plot_loss(history)