TensorFlow:varscope.reuse_variables()

Question

如何在 TensorFlow 中重用变量？我想重用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)

但它给了我以下结果

---------------------------------------------------------------------------
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

问题是 tf.contrib.layers.linear 自动创建一组具有自己范围的新线性图层。当调用scope.reuse()时，没有什么可以重用的，因为这些是新变量。

尝试做这样的事情

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