张量流变量初始化

Question

rnn_cell = tf.nn.rnn_cell.BasicLSTMCell(rnn_size)
state = rnn_cell.zero_state(batch_size, tf.float32)
init = tf.global_variables_initializer()
sess = tf.Session()
for i in range(len(x_data)):
    x = process_x(x_data[i])[:std_size]
    y = word[i][:std_size]
    x_split = tf.split(0, time_step_size, x)
    outputs, state = tf.nn.rnn(rnn_cell, x_split, state)
    prediction = tf.reshape(tf.concat(1, outputs), [-1, rnn_size])
    real = tf.reshape(y, [-1])
    ratio = tf.ones([time_step_size * batch_size])
    loss = tf.nn.seq2seq.sequence_loss_by_example([prediction], [real], [ratio])
    cost = tf.reduce_mean(loss)/batch_size
    train = tf.train.AdamOptimizer(0.01).minimize(cost)
    tf.global_variables_initializer().run(session=sess)
    step = 0
    print state
    while step < 1000:
        sess.run(train)
        step+=1
    result = sess.run(tf.arg_max(prediction, 1))
    print result, [t for t in result] == y
    tf.get_variable_scope().reuse_variables()

如果源代码如上，for循环的每个步骤中初始化rnn_cell和状态？
如果我想在其他训练案例中使用状态，那么我必须重用它。因此，rnn_cell和状态应该首先初始化，而不是在那之后初始化。
我无法想象这段代码是如何工作的。

Answer 1

我认为问题是您必须将计算图部分与会话运行部分分开。 你现在正在做的不是TensorFlow通常的工作方式。 也许试试这个：

rnn_cell = tf.nn.rnn_cell.BasicLSTMCell(rnn_size)
state = rnn_cell.zero_state(batch_size, tf.float32)
x_split = tf.split(0, time_step_size, x)
outputs, state = tf.nn.rnn(rnn_cell, x_split, state)
prediction = tf.reshape(tf.concat(1, outputs), [-1, rnn_size])
real = tf.reshape(y, [-1])
ratio = tf.ones([time_step_size * batch_size])
loss = tf.nn.seq2seq.sequence_loss_by_example([prediction], [real], [ratio])
cost = tf.reduce_mean(loss)/batch_size
train = tf.train.AdamOptimizer(0.01).minimize(cost)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
for i in range(len(x_data)):
    x = process_x(x_data[i])[:std_size]
    y = word[i][:std_size]
step = 0
while step < 1000:
    sess.run(train, feed_dict={x_split:x, real:y})
    step+=1
    result = sess.run(tf.arg_max(prediction, 1))
    print result, [t for t in result] == y

你的

代码可能有一些设计问题，但重点是将你的图形设计与你的"训练"分开。