July 2019
Intermediate to advanced
512 pages
19h 39m
English
Content preview from Hands-On Deep Learning Algorithms with Python
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Optimizing the loss
As we saw in vanilla GANs, we collect the parameters of the discriminator and generator as 
and 
respectively:
training_vars = tf.trainable_variables()theta_D = [var for var in training_vars if 'dis' in var.name]theta_G = [var for var in training_vars if 'gen' in var.name]
Optimize the loss using the Adam optimizer:
d_optimizer = tf.train.AdamOptimizer(learning_rate).minimize(D_loss, var_list=theta_D)g_optimizer = tf.train.AdamOptimizer(learning_rate).minimize(G_loss, var_list=theta_G)
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