Inconsistent behavior for tf.variable_scope #6189
Description
Problem:
Tensorflow doesn't place ops (e.g. mul) in pre-existing variable scopes (and automatically creates a new scope instead).
Minimal Reproducible Example
with tf.variable_scope('layer123'):
v = tf.get_variable('v', [], initializer=tf.constant_initializer(42., tf.float32))
w = v * 2
print(w.name) # Prints 'layer123/mul:0'However,
with tf.variable_scope('layer123'):
v = tf.get_variable('v', [], initializer=tf.constant_initializer(42., tf.float32))
with tf.variable_scope('layer123'):
w = v * 2
print(w.name) # Prints 'layer123_1/mul:0'Observe that for the latter, the op w is placed in a different variable scope, auto-named layer123_1.
I've tried the following, to the same effect:
with tf.variable_scope('layer123') as scope:
v = tf.get_variable('v', [], initializer=tf.constant_initializer(42., tf.float32))
with tf.variable_scope(scope):
w = v * 2
print(w.name) # Prints 'layer123_1/mul:0'with tf.variable_scope('layer123'):
v = tf.get_variable('v', [], initializer=tf.constant_initializer(42., tf.float32))
with tf.variable_scope('layer123', reuse=True):
w = v * 2
print(w.name) # Prints 'layer123_1/mul:0'VersionSpec
Tensorflow version: 0.11.0 (GPU)
OS: Ubuntu 14.04 (w/ CUDA 8)