Build and Fit the Model B

Create a keras neural network as follows:

• Add keras.layers.Flatten to flatten the input image to the model.

• Add 5 dense layers with n_hidden number of neurons and selu activation function.

• Add a final dense layer with 1 neuron and softmax activation function(for classifying 2 classes of data).

  model_B = keras.models.Sequential()
  model_B.add(keras.layers.Flatten(input_shape=[28, 28]))
  for n_hidden in (300, 100, 50, 50, 50):
      model_B.add(keras.layers.Dense(n_hidden, activation="selu"))
  model_B.add(keras.layers.Dense(1, activation="softmax"))
  

Define the compiling part of the model:

• Set "binary_crossentropy" as loss, as this is binary classification among sandals and shirts.

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

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

Now train model_B on X_train_B and y_train_B for 5 epochs , and validation_data=(X_valid_B, y_valid_B) using model.fit.

history = << your code comes here >>(X_train_B, y_train_B, epochs=5,
            validation_data=(X_valid_B, y_valid_B))