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We shall now build the classification network, with a hidden layer(with 30 neurons and
selu activation function) and an output layer( with 1 neuron since we have to predict only one value).
We shall also specify the loss function we have defined - the
huber_fn and the optimizer to use, while compiling.
kernel_initializerdefines the way to set the initial random weights of Keras layers.
"selu" as the
"lecun_normal" as the
kernel_initializer for the following model.
model = keras.models.Sequential([ keras.layers.Dense(30, activation=<< your code comes here >>, kernel_initializer=<< your code comes here >>, input_shape=X_train.shape[1:]), keras.layers.Dense(1), ])
loss to be
huber_fn which is the custom loss function we defined
We will be using
"nadam" optimizer and
model.<< your code comes here >>(loss=<< your code comes here >>, optimizer="nadam", metrics=["mae"])
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