Now, it's time to build the second core piece of the GAN network, which is the discriminator. In previous implementations, we said that the discriminator will produce a binary output that represents whether the input image is from the real dataset (1) or it's generated by the generator (0). The scenario is different here, so the discriminator will now be a multi-class classifier.

Now, let's go ahead and build up the discriminator part of the architecture:

# Defining the discriminator part of the networkdef discriminator(input_x, reuse_vars=False, leaky_alpha=0.2, drop_out_rate=0., num_classes=10, size_mult=64):    with tf.variable_scope('discriminator', reuse=reuse_vars):        # defining a dropout layer        drop_out_output = tf.layers.dropout(input_x ...

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