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Build and Fit the Model B

  • Let us define the model for the classification of data set B that we have created previously.

    Later, let us also examine the classification of B set by using the trained weights of model A.

  • 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 >>,
  • 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

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

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