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updates to be compatible with tensorflow 1.1 #34

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updates to be compatible with tensorflow 1.1 #34

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2 changes: 1 addition & 1 deletion srez_main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -100,7 +100,7 @@ def setup_tensorflow():
random.seed(FLAGS.random_seed)
np.random.seed(FLAGS.random_seed)

summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph)
summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)

return sess, summary_writer

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14 changes: 7 additions & 7 deletions srez_model.py
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Expand Up @@ -326,7 +326,7 @@ def _discriminator_model(sess, features, disc_input):
mapsize = 3
layers = [64, 128, 256, 512]

old_vars = tf.all_variables()
old_vars = tf.global_variables()

model = Model('DIS', 2*disc_input - 1)

Expand All @@ -352,7 +352,7 @@ def _discriminator_model(sess, features, disc_input):
model.add_conv2d(1, mapsize=1, stride=1, stddev_factor=stddev_factor)
model.add_mean()

new_vars = tf.all_variables()
new_vars = tf.global_variables()
disc_vars = list(set(new_vars) - set(old_vars))

return model.get_output(), disc_vars
Expand All @@ -363,7 +363,7 @@ def _generator_model(sess, features, labels, channels):
mapsize = 3
res_units = [256, 128, 96]

old_vars = tf.all_variables()
old_vars = tf.global_variables()

# See Arxiv 1603.05027
model = Model('GEN', features)
Expand Down Expand Up @@ -396,7 +396,7 @@ def _generator_model(sess, features, labels, channels):
model.add_conv2d(channels, mapsize=1, stride=1, stddev_factor=1.)
model.add_sigmoid()

new_vars = tf.all_variables()
new_vars = tf.global_variables()
gene_vars = list(set(new_vars) - set(old_vars))

return model.get_output(), gene_vars
Expand Down Expand Up @@ -449,7 +449,7 @@ def _downscale(images, K):

def create_generator_loss(disc_output, gene_output, features):
# I.e. did we fool the discriminator?
cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(disc_output, tf.ones_like(disc_output))
cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_output, labels=tf.ones_like(disc_output))
gene_ce_loss = tf.reduce_mean(cross_entropy, name='gene_ce_loss')

# I.e. does the result look like the feature?
Expand All @@ -466,10 +466,10 @@ def create_generator_loss(disc_output, gene_output, features):

def create_discriminator_loss(disc_real_output, disc_fake_output):
# I.e. did we correctly identify the input as real or not?
cross_entropy_real = tf.nn.sigmoid_cross_entropy_with_logits(disc_real_output, tf.ones_like(disc_real_output))
cross_entropy_real = tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_real_output, labels=tf.ones_like(disc_real_output))
disc_real_loss = tf.reduce_mean(cross_entropy_real, name='disc_real_loss')

cross_entropy_fake = tf.nn.sigmoid_cross_entropy_with_logits(disc_fake_output, tf.zeros_like(disc_fake_output))
cross_entropy_fake = tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_fake_output, labels=tf.zeros_like(disc_fake_output))
disc_fake_loss = tf.reduce_mean(cross_entropy_fake, name='disc_fake_loss')

return disc_real_loss, disc_fake_loss
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8 changes: 4 additions & 4 deletions srez_train.py
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Expand Up @@ -19,10 +19,10 @@ def _summarize_progress(train_data, feature, label, gene_output, batch, suffix,

clipped = tf.maximum(tf.minimum(gene_output, 1.0), 0.0)

image = tf.concat(2, [nearest, bicubic, clipped, label])
image = tf.concat(axis=2, values=[nearest, bicubic, clipped, label])

image = image[0:max_samples,:,:,:]
image = tf.concat(0, [image[i,:,:,:] for i in range(max_samples)])
image = tf.concat(axis=0, values=[image[i,:,:,:] for i in range(max_samples)])
image = td.sess.run(image)

filename = 'batch%06d_%s.png' % (batch, suffix)
Expand Down Expand Up @@ -62,8 +62,8 @@ def _save_checkpoint(train_data, batch):
def train_model(train_data):
td = train_data

summaries = tf.merge_all_summaries()
td.sess.run(tf.initialize_all_variables())
summaries = tf.summary.merge_all()
td.sess.run(tf.global_variables_initializer())

lrval = FLAGS.learning_rate_start
start_time = time.time()
Expand Down