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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:
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:
"binary_crossentropy" as loss, as this is binary classification among sandals and shirts.
keras.optimizers.SGD(lr=1e-3) as optimizer.
model_B.compile(loss= << your code comes here >>, optimizer= << your code comes here >>, metrics=["accuracy"])
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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