Question

我已经构建了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, batch_size, learning_rate,
                     training_seq_len, vocab_size, infer=True)

上面的代码给我一个错误

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

如果我将reuse = True设置为如下面的代码块所示

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

我收到了不同的错误

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)

Answer 1

似乎不是：

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, batch_size, learning_rate,
                     training_seq_len, vocab_size, infer_sample=True)

这解决了问题

# Define LSTM Model
lstm_model = LSTM_Model(rnn_size, batch_size, learning_rate,
                        training_seq_len, vocab_size)

# Tell TensorFlow we are reusing the scope for the testing
with tf.variable_scope(tf.get_variable_scope(), reuse=True):
    test_lstm_model = LSTM_Model(rnn_size, batch_size, learning_rate,
                                 training_seq_len, vocab_size, infer_sample=True)

Answer 2

如果您使用一个变量两次（或更多次），则应首次使用with tf.variable_scope('scope_name', reuse=False):，然后使用with tf.variable_scope('scope_name', reuse=True):。

或者您可以使用方法tf.variable_scope.reuse_variables()

with tf.variable_scope("foo") as scope:
    v = tf.get_variable("v", [1])
    scope.reuse_variables()
    v1 = tf.get_variable("v", [1])

上面代码中的

v和v1是相同的变量。

Tensorflow变量重用

2 个答案: