Question

我有一个太长而不适合记忆的序列，但是初始状态非常关键所以我想把它作为变量进行训练。如何训练初始状态变量以在序列开始时传入，但是对于序列的其余部分继续使用输出状态？

这是我到目前为止所得到的：

    cell = tf.contrib.rnn.BasicLSTMCell(num_lstm_cells, state_is_tuple=True)

    init_vars = cell.zero_state(batch_size, tf.float32)
    init_c = tf.Variable(init_vars.c, trainable=True)
    init_h = tf.Variable(init_vars.h, trainable=True)
    init_state = tf.contrib.rnn.LSTMStateTuple(init_c, init_h)

    state_vars = cell.zero_state(batch_size, tf.float32)
    state_c = tf.Variable(state_vars.c, trainable=False)
    state_h = tf.Variable(state_vars.h, trainable=False)
    state = tf.contrib.rnn.LSTMStateTuple(state_c, state_h)

    layer = tf.nn.rnn_cell.DropoutWrapper(cell, output_keep_prob=0.7)
    val, new_state = tf.nn.dynamic_rnn(layer, lstm_input, initial_state=state, dtype=tf.float32)

    with tf.control_dependencies([state[0].assign(new_state[0]), state[1].assign(new_state[1])]):
        output = tf.identity(val)

    inititalise_c = tf.assign(state[0], init_state[0])
    inititalise_h = tf.assign(state[1], init_state[1])
    initialise_state = tf.group([inititalise_c, inititalise_h])

我的想法是我有一个可训练的初始状态变量（init_vars）和一个不可训练的状态（state_vars），我通过调用initialise_state op将初始状态分配给每个序列的开头。

我认为这不会起作用，因为init_state实际上并不是训练的一部分，它只是用于复制。我怎么能这样做？

编辑：我已经在测试中证实初始状态没有被训练并且保持全部为0

Answer 1

我最终通过在单独的变量范围内创建初始状态变量来解决这个问题。然后使用Optimizer.Minimize（）中的var_list可选参数，我可以指定在每个序列的开头训练初始状态。在初始状态训练之后，我会将其复制到这个单独的变量范围，并训练该序列的其余部分的图形。

    with tf.variable_scope("state"):
        state_c = tf.Variable(tf.random_uniform([batch_size, num_lstm_cells], 0, 1), trainable=True)
        state_h = tf.Variable(tf.random_uniform([batch_size, num_lstm_cells], 0, 1), trainable=True)
        state = tf.contrib.rnn.LSTMStateTuple(state_c, state_h)

    with tf.variable_scope("nn"):
        layer = tf.nn.rnn_cell.DropoutWrapper(cell, output_keep_prob=0.7)
        val, new_state = tf.nn.dynamic_rnn(layer, lstm_input, initial_state=state, dtype=tf.float32)

        logits = tf.layers.dense(val, units=5, activation=tf.nn.relu)
        losses = tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=targets)

    init_c = tf.Variable(tf.zeros([batch_size, num_lstm_cells]), trainable=False)
    init_h = tf.Variable(tf.zeros([batch_size, num_lstm_cells]), trainable=False)
    init_state = tf.contrib.rnn.LSTMStateTuple(init_c, init_h)

    restore_c = tf.assign(state[0], init_state[0])
    restore_h = tf.assign(state[1], init_state[1])
    restore_state = tf.group([restore_c, restore_h])

    save_c = tf.assign(init_state[0], state[0])
    save_h = tf.assign(init_state[1], state[1])
    save_state = tf.group([save_c, save_h])

    propagate_c = tf.assign(state[0], new_state[0])
    propagate_h = tf.assign(state[1], new_state[1])
    propagate_state = tf.group([propagate_c, propagate_h])

    nn_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "nn")
    state_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "state")

    total_loss = tf.reduce_mean(losses)

    train_nn_step = tf.train.AdamOptimizer().minimize(total_loss, var_list=nn_vars)
    train_nn_state_step = tf.train.AdamOptimizer().minimize(total_loss, var_list=[nn_vars, state_vars])

所以你通过调用开始一个序列：

sess.run(restore_state)将初始状态复制回图表
_, er = sess.run([train_nn_state_step, error])训练初始状态和nn
sess.run(save_state)保存初始状态
sess.run(propagate_state)将该州传播到下一个列车步骤

你通过调用来训练剩下的序列：

_, er = sess.run([train_nn_step, error])只是训练神经网络
sess.run(propagate_state)继续通过状态

Answer 2

如何在培训网络和初始状态之间切换？冻结模型，使初始状态可训练，训练一段时间。然后切换冻结。

Answer 3

我不确定你想做什么，但是，为什么不将new_state分配给另一个状态变量，如下所示，

batch_size = 10
num_lstm_cells = 20
num_times = 5
input_dims = 6

lstm_input = tf.random_normal([batch_size, num_times, input_dims],0.,1.0)

cell = tf.contrib.rnn.BasicLSTMCell(num_lstm_cells, state_is_tuple=True)

init_vars = cell.zero_state(batch_size, tf.float32)
init_c = tf.Variable(init_vars.c, trainable=True)
init_h = tf.Variable(init_vars.h, trainable=True)
init_state = tf.contrib.rnn.LSTMStateTuple(init_c, init_h)

state_vars = cell.zero_state(batch_size, tf.float32)
state_c = tf.Variable(state_vars.c, trainable=False)
state_h = tf.Variable(state_vars.h, trainable=False)
state = tf.contrib.rnn.LSTMStateTuple(state_c, state_h)

layer = tf.nn.rnn_cell.DropoutWrapper(cell, output_keep_prob=0.7)
val, new_state = tf.nn.dynamic_rnn(layer, lstm_input, initial_state=state, dtype=tf.float32)

trained_state_c = tf.assign(state[0], new_state[0])
trained_state_h = tf.assign(state[1], new_state[1])
trained_state = tf.contrib.rnn.LSTMStateTuple(trained_state_c, trained_state_h)

在TensorFlow中为LSTM创建可训练的初始状态

3 个答案: