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Let us first see how many trainable parameters are there for

`model_B`

we trained previously.Then we shall create a new model

`model_B_on_A`

which has the pre-trained parameters of`model_A`

but customized final dense layer with only 1 neuron.Finally, we shall compare the performance of both the models -

`model_B`

and`model_B_on_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))`

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