deep learning - TensorFlow: varscope.reuse_variables() -


how reuse variables in tensorflow? want reuse tf.contrib.layers.linear

with tf.variable_scope("root") 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 gives me 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 not exist, or not created tf.get_variable(). did mean set reuse=none in varscope?

the problem tf.contrib.layers.linear automatically creates new set of linear layers own scope. when calling scope.reuse() there's nothing reused because new variables.

try instead

def function():   tf.variable_scope("root") 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()) = sess.run(result_1) b = sess.run(result_2) np.all(a == b) # ==> true

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