python – Tensorflow变量重用

概述我已经建立了我的LSTM模型.理想情况下,我希望稍后使用重用变量来定义测试LSTM模型. with tf.variable_scope('lstm_model') as scope: # Define LSTM Model lstm_model = LSTM_Model(rnn_size, batch_size, learning_rate, 我已经建立了我的LSTM模型.理想情况下,我希望稍后使用重用变量来定义测试LSTM模型.

with tf.variable_scope('lstm_model') as scope:    # define LSTM Model    lstm_model = LSTM_Model(rnn_size,batch_size,learning_rate,training_seq_len,vocab_size)    scope.reuse_variables()    test_lstm_model = LSTM_Model(rnn_size,vocab_size,infer=True)

上面的代码给了我一个错误

Variable lstm_model/lstm_vars/W already exists,disallowed. DID you mean to set reuse=True in VarScope?

如果我设置了reuse = True,如下面的代码块所示

with tf.variable_scope('lstm_model',reuse=True) as scope:

我得到了一个不同的错误

Variable lstm_model/lstm_model/lstm_vars/W/Adam/ does not exist,or was not created with tf.get_variable(). DID you mean to set reuse=None in VarScope?

作为参考,我在下面附上了相关的型号代码. LSTM模型中的相应部分,我有权重

with tf.variable_scope('lstm_vars'):    # softmax Output Weights    W = tf.get_variable('W',[self.rnn_size,self.vocab_size],tf.float32,tf.random_normal_initializer())

我有Adam优化器的相应部分：

optimizer = tf.train.AdamOptimizer(self.learning_rate)

解决方法 它似乎不是：

with tf.variable_scope('lstm_model') as scope:    # define LSTM Model    lstm_model = LSTM_Model(rnn_size,vocab_size)    scope.reuse_variables()        test_lstm_model = LSTM_Model(rnn_size,infer_sample=True)

这解决了这个问题

# define LSTM Modellstm_model = LSTM_Model(rnn_size,vocab_size)# Tell TensorFlow we are reusing the scope for the testingwith tf.variable_scope(tf.get_variable_scope(),reuse=True):    test_lstm_model = LSTM_Model(rnn_size,infer_sample=True)

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