feed_dict={
isTraining: True,
batchSizeTensor: batchSize
})
sess.run(G_optimizer,
feed_dict={
isTraining: True,
batchSizeTensor: batchSize
})
# Save summary
'''if globalStep % 20 == 0:
summary = sess.run(merged_summ, feed_dict={ isTraining: True, batchSizeTensor : batchSize })
summary_writer.add_summary(summary, globalStep)'''
# Save checkpoints and images
if globalStep % 100 == 0:
save_path = saver.save(
sess, base_path + "checkpoints/model-" + str(globalStep) +
".ckpt")
G_output = sess.run(G_z,
feed_dict={
isTraining: False,
batchSizeTensor: 5
})
util.saveImages(base_path + "images/out-" + str(globalStep),
G_output)
globalStep += 1
except tf.errors.OutOfRangeError:
pass
noise = util.sample_noise([batchSize, Z_dim])
# Train discriminator (more at the beginning)
D_iterations = 30 if (GStep < 5 or GStep % 500 == 0) else num_D
for _ in range(D_iterations):
_, summary = sess.run([D_optimizer, merged_summary],
feed_dict={
isTraining: True,
Z: noise
})
summary_writer.add_summary(summary, GStep)
# Train Generator
sess.run(G_optimizer, feed_dict={isTraining: True, Z: noise})
# Save checkpoint and generated images at this step
if GStep % 1000 == 0:
saver.save(
sess,
basePath + "checkpoints/ckpt-ResWDCGAN-" + str(GStep))
output = sess.run(G_z,
feed_dict={
isTraining: False,
Z: util.sample_noise([10, Z_dim])
})
util.saveImages(
basePath + "images/out-ResWDCGAN-" + str(GStep), output)
GStep += 1
except tf.errors.OutOfRangeError:
pass
AEStep = 0
sess.run(iterator.initializer)
try:
while True:
noise = util.sample_noise([batchSize, Z_dim])
# Train Critic
_, summary = sess.run([C_optimizer, AE_merged_summary],
feed_dict={
isTraining: True,
Z: noise
})
AE_summary_writer.add_summary(summary, AEStep)
# Train AE only
sess.run(AE_optimizer, feed_dict={isTraining: True, Z: noise})
if AEStep % 750 == 0:
saver.save(sess,
basePath + "checkpoints/ckpt-AE-" + str(AEStep))
AE_output = sess.run(G_AE,
feed_dict={
isTraining: False,
Z: util.sample_noise([4, Z_dim])
})
util.saveImages(basePath + "images/out-AE-" + str(AEStep),
AE_output)
AEStep += 1
except tf.errors.OutOfRangeError:
pass
Z = tf.placeholder(tf.float32, [None, Z_dim])
isTraining = tf.placeholder(dtype=tf.bool)
G_z = network.generator(Z, isTraining)
G_AE, _ = UNET_GAN.getAutoencoder(G_z, isTraining)
with tf.Session() as sess:
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
saver.restore(sess, basePath + "checkpoints/ckpt-AE-101250")
#saver.restore(sess, basePath + "checkpoints-mel/ckpt-AE-88500")
images, fullImg = [], []
for j in range(50):
UNET_output, G_output = sess.run([G_AE, G_z],
feed_dict={
isTraining: False,
Z: util.sample_noise([1, Z_dim])
})
images.append(G_output[0])
fullImg.append(UNET_output[0])
images = np.array(images)
fullImg = np.array(fullImg)
print(images.shape)
print(fullImg.shape)
util.saveImages(basePath + "generated/TEST-" + str(0), images)
util.saveImages(basePath + "generated/TEST-AE-" + str(0), fullImg)