TensorFlow: varscope.reuse_variables()

Question

How do I reuse variables in TensorFlow? I want to reuse the tf.contrib.layers.linear

with tf.variable_scope("root") as varscope:
    inputs_1 = tf.constant(0.5, shape=[2, 3, 4])
    inputs_2 = tf.constant(0.5, shape=[2, 3, 4])
    outputs_1 = tf.contrib.layers.linear(inputs_1, 5)
    varscope.reuse_variables()
    outputs_2 = tf.contrib.layers.linear(inputs_2, 5)

But it gives me the following result

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-51-a40b9ec68e25> in <module>()
      5     outputs_1 = tf.contrib.layers.linear(inputs_1, 5)
      6     varscope.reuse_variables()
----> 7     outputs_2 = tf.contrib.layers.linear(inputs_2, 5)
...
ValueError: Variable root/fully_connected_1/weights does not exist, or was not created with tf.get_variable(). Did you mean to set reuse=None in VarScope?

Answer 1

The problem is tf.contrib.layers.linear automatically creates a new set of linear layers with its own scope. When calling scope.reuse() there's nothing to be reused because those are new variables.

Try to do something like this instead

def function():
  with tf.variable_scope("root") as varscope:
    inputs = tf.constant(0.5, shape=[2, 3, 4])
    outputs = tf.contrib.layers.linear(inputs, 5)
    return outputs

result_1 = function()
tf.get_variable_scope().reuse_variables()
result_2 = function()

sess = tf.InteractiveSession()
sess.run(tf.initialize_all_variables())
a = sess.run(result_1)
b = sess.run(result_2)
np.all(a == b) # ==> True