Creating new model based on existing model A

See the model_B summary using summary() on model_B.

model_B.<<your code comes here >>

We see that there are 275,801 trainable parameters for model_B.

Now, before creating model_B_on_A(a model based on pre-trained layers of model_A), we shall clone the model_A and set its trained weights so that when you train model_B_on_A, it will not affect model_A.

We could copy the model_A architechture using keras.models.clone_model.

• Create model_A_clone which is the copy of model_A.

  model_A_clone = keras.models.clone_model(model_A)
  

• Get the weights of model_A using get_weights(), and set the model parameters for model_A_clone using set_weights().

  model_A_clone.<< your code comes here >>(model_A.get_weights())
  

Now, create a new model model_B_on_A, based on existing layers of model_A.

<< your code comes here >> = keras.models.Sequential(model_A.layers[:-1])

Add the final dense layer with 1 neuron to the model_B_on_A. Set the activation to "sigmoid", as this is a binary classification problem.

model_B_on_A.add(keras.layers.Dense(1, activation=<< your code comes here >>))

Set all the layers, except the last layer, of model_B_on_A to be non-trainable.

for layer in model_B_on_A.layers[:-1]:
    layer.trainable = False

Now check the number of trainable parameters of model_B_on_A.

model_B_on_A.summary()

We observe there are only 51 parameters to train in model_B_on_A, while there are as many as 275,801 trainable parameters for model_B.

Compile the model model_B_on_A by using model.compile.

• Set loss="binary_crossentropy".

• Set optimizer=keras.optimizers.SGD(lr=1e-3)

  model_B_on_A.compile(loss=<< your code comes here >>,
           optimizer=<< your code comes here >>,
           metrics=["accuracy"])
  

Now train the model_B_on_A uaing model.fit.

history = model_B_on_A.fit(X_train_B, y_train_B, epochs=5,
                   validation_data=(X_valid_B, y_valid_B))