Tensorflow，在RNN中保存状态的最佳方法？

Question

我目前在tensorflow中具有一系列链接在一​​起的RNN的以下代码。我不使用MultiRNN，因为稍后我将对每个图层的输出进行处理。

 for r in range(RNNS):
    with tf.variable_scope('recurent_%d' % r) as scope:
        state = [tf.zeros((BATCH_SIZE, sz)) for sz in rnn_func.state_size]
        time_outputs = [None] * TIME_STEPS

        for t in range(TIME_STEPS):
            rnn_input = getTimeStep(rnn_outputs[r - 1], t)
            time_outputs[t], state = rnn_func(rnn_input, state)
            time_outputs[t] = tf.reshape(time_outputs[t], (-1, 1, RNN_SIZE))
            scope.reuse_variables()
        rnn_outputs[r] = tf.concat(1, time_outputs)

目前，我有固定的时间步数。但是，我想将其更改为只有一个时间步长，但要记住批次之间的状态。因此，我需要为每个层创建一个状态变量，并将其分配给每个层的最终状态。这样的事情。

for r in range(RNNS):
    with tf.variable_scope('recurent_%d' % r) as scope:
        saved_state = tf.get_variable('saved_state', ...)
        rnn_outputs[r], state = rnn_func(rnn_outputs[r - 1], saved_state)
        saved_state = tf.assign(saved_state, state)

然后，对于每一层，我都需要评估sess.run函数中的保存状态以及调用训练函数。我需要为每个rnn层执行此操作。这似乎有点麻烦。我需要跟踪每个保存的状态并在运行中对其进行评估。同样，然后运行将需要将状态从我的GPU复制到主机内存，这会造成效率低下和不必要的情况。有更好的方法吗？

Answer 1

这是state_is_tuple=True通过定义状态变量来更新LSTM初始状态的代码。它还支持多层。

我们定义了两个函数-
一个用于获取具有初始零状态的状态变量，另一个用于返回操作的函数，可以传递给该函数以session.run用LSTM的最后一个隐藏状态更新状态变量。

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.contrib.rnn.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)


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)

我们可以用它来更新每批LSTM的状态。请注意，我tf.nn.dynamic_rnn用于展开：

data = tf.placeholder(tf.float32, (batch_size, max_length, frame_size))
cell_layer = tf.contrib.rnn.GRUCell(256)
cell = tf.contrib.rnn.MultiRNNCell([cell] * num_layers)

# For each layer, get the initial state. states will be a tuple of LSTMStateTuples.
states = get_state_variables(batch_size, cell)

# Unroll the LSTM
outputs, new_states = tf.nn.dynamic_rnn(cell, data, initial_state=states)

# Add an operation to update the train states with the last state tensors.
update_op = get_state_update_op(states, new_states)

sess = tf.Session()
sess.run(tf.global_variables_initializer())
sess.run([outputs, update_op], {data: ...})

该答案的主要区别在于，state_is_tuple=True使LSTM的状态成为包含两个变量（单元状态和隐藏状态）而不是单个变量的LSTMStateTuple。然后，使用多层可以使LSTM的状态成为LSTMStateTuples的元组-
每层一个。

重置为零

使用训练有素的模型进行预测/解码时，您可能需要将状态重置为零。然后，您可以使用此功能：

def get_state_reset_op(state_variables, cell, batch_size):
    # Return an operation to set each variable in a list of LSTMStateTuples to zero
    zero_states = cell.zero_state(batch_size, tf.float32)
    return get_state_update_op(state_variables, zero_states)

例如上面的例子：

reset_state_op = get_state_reset_op(state, cell, max_batch_size)
# Reset the state to zero before feeding input
sess.run([reset_state_op])
sess.run([outputs, update_op], {data: ...})