Remove print statement, code style
@@ -40,9 +40,9 @@ Restrictions:
resolutions = [y]
for level in range(levels):
level_filters = filters if isinstance(filters, int) else filters[level]
- y = conv_layer(resolutions[0], level_filters, 2, strides=2, activation=tf.nn.relu, padding='valid', name='downconv_%d' % level, trainable=trainable, reuse=reuse)
+ y = conv_layer(resolutions[0], level_filters, 2, strides=2, activation=tf.nn.relu, padding='valid', name='down_convolution_%d' % level, trainable=trainable, reuse=reuse)
for i in range(blocks_per_level):
- y = residual_block(y, level_filters, name="downrb_%d_%d" % (level, i), training=training, trainable=trainable, reuse=reuse)
+ y = residual_block(y, level_filters, name='down_res_block_%d_%d' % (level, i), training=training, trainable=trainable, reuse=reuse)
resolutions.insert(0, y)
y = resolutions.pop(0)
@@ -60,11 +60,10 @@ Restrictions:
y = y.padded([[0, 1], [0, 1]])
if resolutions:
level_filters = filters if isinstance(filters, int) else reversed(filters)[level]
- y = conv_layer(y, level_filters, kernel_size=2, activation=tf.nn.relu, padding="valid", name="upconv_%d" % level, trainable=trainable, reuse=reuse)
+ y = conv_layer(y, level_filters, kernel_size=2, activation=tf.nn.relu, padding='valid', name='up_convolution_%d' % level, trainable=trainable, reuse=reuse)
for i in range(blocks_per_level):
- y = residual_block(y, level_filters, name="uprb_%d_%d" % (level, i), training=training, trainable=trainable, reuse=reuse)
+ y = residual_block(y, level_filters, name='up_res_block_%d_%d' % (level, i), training=training, trainable=trainable, reuse=reuse)
else: # Last iteration
- y = conv_layer(y, output_field.component_count, kernel_size=2, activation=None, padding='valid', name="upconv_%d" % level, trainable=trainable, reuse=reuse)
- print(y)
+ y = conv_layer(y, output_field.component_count, kernel_size=2, activation=None, padding='valid', name='up_convolution_%d' % level, trainable=trainable, reuse=reuse)
result = y.at(output_field)
return result
