如何在 TensorFlow 中重用变量？我想重复使用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?
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
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