Tensorflowで実行間にLSTM RNN状態を保存する

Question

Tensorflowでの実行の間にLSTM状態を保存する最善の方法は何ですか？予測段階では、次のタイムステップの入力は以前のタイムステップの出力に依存するため、一度に1タイムステップずつデータを渡す必要があります。

私はこの投稿からの提案を使用しました：Tensorflow, best way to save state in RNNs?とオプティマイザを実行せずに何度も同じ入力を繰り返してテストしました。私が正しく理解していれば、出力が毎回変わると状態が保存されますが、それが同じであればそれは変わりません。その結果、最初に状態を保存した後も同じ状態を維持していました。

pieces = data_generator.load_pieces(5) 

batches = 100 
sizes = [126, 122] 
steps = 128 
layers = 2 

x = tf.placeholder(tf.float32, shape=[batches, steps, sizes[0]]) 
y_ = tf.placeholder(tf.float32, shape=[batches, steps, sizes[1]]) 

W = tf.Variable(tf.random_normal([sizes[0], sizes[1]])) 
b = tf.Variable(tf.random_normal([sizes[1]])) 

layer = tf.nn.rnn_cell.BasicLSTMCell(sizes[0], forget_bias=0.0) 
lstm = tf.nn.rnn_cell.MultiRNNCell([layer] * layers) 

# ~~~~~ code from linked post ~~~~~ 
def get_state_variables(batch_size, cell): 
    # For each layer, get the initial state and make a variable out of it 
    # to enable updating its value. 
    state_variables = [] 
    for state_c, state_h in cell.zero_state(batch_size, tf.float32): 
     state_variables.append(tf.nn.rnn_cell.LSTMStateTuple(
      tf.Variable(state_c, trainable=False), 
      tf.Variable(state_h, trainable=False))) 
    # Return as a tuple, so that it can be fed to dynamic_rnn as an initial state 
    return tuple(state_variables) 

states = get_state_variables(batches, lstm) 

outputs, new_states = tf.nn.dynamic_rnn(lstm, x, initial_state=states, dtype=tf.float32) 

def get_state_update_op(state_variables, new_states): 
    # Add an operation to update the train states with the last state tensors 
    update_ops = [] 
    for state_variable, new_state in zip(state_variables, new_states): 
     # Assign the new state to the state variables on this layer 
     update_ops.extend([state_variable[0].assign(new_state[0]), 
          state_variable[1].assign(new_state[1])]) 
    # Return a tuple in order to combine all update_ops into a single operation. 
    # The tuple's actual value should not be used. 
    return tf.tuple(update_ops) 

update_op = get_state_update_op(states, new_states) 
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

output = tf.reshape(outputs, [-1, sizes[0]]) 
y = tf.nn.sigmoid(tf.matmul(output, W) + b) 
y = tf.reshape(y, [-1, steps, sizes[1]]) 

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), [1, 2])) 
# train_step = tf.train.AdadeltaOptimizer().minimize(cross_entropy) 

sess = tf.InteractiveSession() 
sess.run(tf.global_variables_initializer()) 
batch_x, batch_y = data_generator.get_batch(pieces) 
for i in range(500): 
    error, _ = sess.run([cross_entropy, update_op], feed_dict={x: batch_x, y_: batch_y}) 
    print str(i) + ': ' + str(error) 

ここでエラーが時間をかけてだ：

• 0：419.861
• 1：419.756
• 2：419.756
• 3：419.756

  は、ここに私のコードです。 ..

Answer 1

私は数日前に試したthis answerをお勧めします。それはうまくいく。ところで

、falseからstate_is_tupleを設定する方法の回避があります：

class CustomLSTMCell(tf.contrib.rnn.LSTMCell): 
    def __init__(self, *args, **kwargs): 
     # kwargs['state_is_tuple'] = False # force the use of a concatenated state. 
     returns = super(CustomLSTMCell, self).__init__(
      *args, **kwargs) # create an lstm cell 
     # change the output size to the state size 
     self._output_size = np.sum(self._state_size) 
     return returns 

    def __call__(self, inputs, state): 
     output, next_state = super(
      CustomLSTMCell, self).__call__(inputs, state) 
     # return two copies of the state, instead of the output and the state 
     return tf.reshape(next_state, shape=[1, -1]), next_state