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Generative Adversarial Networks
checkpoint_prefixg_AB = os.path.join(checkpoint_dirg_AB, "ckpt_
{epoch}")
checkpoint_dirg_BA = './training_checkpointsg_BA'
checkpoint_prefixg_BA = os.path.join(checkpoint_dirg_BA, "ckpt_
{epoch}")
Let us now combine it all and train the network for 50 epochs. Please remember that
in the paper, the test network was trained for 200 epochs, so our results will not be
that good:
def train(trainA_, trainB_, epochs):
for epoch in range(epochs):
start = time.time()
for batch_i, (imgs_A, imgs_B) in enumerate(zip(trainA_,
trainB_)):
dA_loss, dB_loss, g_loss = train_batch(imgs_A, imgs_B)
if batch_i % 1000 == 0:
test_imgA = next(iter(test_A))
test_imgB = next(iter(test_B))
print ('Time taken for epoch {} batch index {} is {}
seconds\n'.format(epoch, batch_i, time.time()-start))
print("discriminator A: ", dA_loss.numpy())
print("discriminator B: ", dB_loss.numpy())
print("generator: {}\n".format(g_loss))
fig, axs = plt.subplots(2, 2, figsize=(6, 3),
sharey=True, sharex=True)
gen_outputA = generator_AB(test_imgA, training=False)
gen_outputB = generator_BA(test_imgB, training=False)
axs[0,0].imshow(test_imgA[0]*0.5 + 0.5)
axs[0,0].set_title("Generator A Input")
axs[0,1].imshow(gen_outputA[0]*0.5 + 0.5)
axs[0,1].set_title("Generator A Output")
axs[1,0].imshow(test_imgB[0]*0.5 + 0.5)
axs[1,0].set_title("Generator B Input")
axs[1,1].imshow(gen_outputB[0]*0.5 + 0.5)
axs[1,1].set_title("Generator B Output")
plt.show()
discriminator_A.save_weights(checkpoint_prefixd_A.
format(epoch=epoch))
discriminator_B.save_weights(checkpoint_prefixd_B.
format(epoch=epoch))
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