Hands-On Deep Learning Algorithms with Python

Generating handwritten digits

Start training and generate the image. For every 100 iterations, we print the image generated by the generator:

onehot = np.eye(10)for epoch in range(num_epochs):    for i in range(0, data.train.num_examples // batch_size):

Sample the images:

        x_batch, _ = data.train.next_batch(batch_size)        x_batch = np.reshape(x_batch, (batch_size, 28, 28, 1))

Sample the value of c:

        c_ = np.random.randint(low=0, high=10, size=(batch_size,))        c_one_hot = onehot[c_]

Sample noise z:

        z_batch = np.random.uniform(low=-1.0, high=1.0, size=(batch_size,64))

Optimize the loss of the generator and the discriminator:

        feed_dict={x: x_batch, c: c_one_hot, z: z_batch, is_train: True}        _ = session.run(D_optimizer, feed_dict=feed_dict) _ = session.run(G_optimizer, ...