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我正在尝试将自己的 3D 数据提供给 LSTM。数据有：高度 = 365，宽度 = 310，时间 = 未知/不一致，由 0 和 1 组成，产生输出的每个数据块都被分隔到一个文件中。

import tensorflow as tf
import os
from tensorflow.contrib import rnn

filename = "C:/Kuliah/EmotionRecognition/Train1/D2N2Sur.txt"

hm_epochs = 10
n_classes = 12
n_chunk = 443
n_hidden = 500

data = tf.placeholder(tf.bool, name='data')
cat = tf.placeholder("float", [None, n_classes])

weights = {
    'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))
}
biases = {
    'out': tf.Variable(tf.random_normal([n_classes]))
}

def RNN(x, weights, biases):
    lstm_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)
    outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)
    return tf.matmul(outputs[-1], weights['out']) + biases['out']

pred = RNN(data, weights, biases)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=cat))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)

correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(cat, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

saver = tf.train.Saver()

temp = [[]]
d3 = [[]]
counter = 0
with tf.Session() as sess:
    #load
    #saver.restore(sess, "C:/Kuliah/EmotionRecognition/model.ckpt")
    sess.run(tf.global_variables_initializer())
    with open(filename) as inf:
        for line in inf:
            bla = list(line)
            bla.pop(len(bla) - 1)
            for index, item in enumerate(bla):
                if (item == '0'):
                    bla[index] = False
                else:
                    bla[index] = True
            temp.append(bla)
            counter += 1
            if counter%365==0: #height 365
                temp.pop(0)
                d3.append(temp)
                temp = [[]]
        temp.pop(0)
        d3.append(temp)

        batch_data = d3.reshape()
        sess.run(optimizer, feed_dict={data: d3, cat: 11})

        acc = sess.run(accuracy, feed_dict={data: d3, cat: 11})
        loss = sess.run(loss, feed_dict={data: d3, cat: 11})
        print(acc)
        print(loss)
        #save
        saver.save(sess, "C:/Kuliah/EmotionRecognition/model.ckpt")

这段代码给我一个错误：

Traceback (most recent call last):
  File "C:/Kuliah/EmotionRecognition/Main", line 31, in <module>
    pred = RNN(data, weights, biases)
  File "C:/Kuliah/EmotionRecognition/Main", line 28, in RNN
    outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)
  File "C:\Users\Anonymous\AppData\Roaming\Python\Python35\site-packages\tensorflow\python\ops\rnn.py", line 1119, in static_rnn
    raise TypeError("inputs must be a sequence")
TypeError: inputs must be a sequence
4

1 回答 1

7

当您调用pred = RNN(data, weights, biases)时，data参数应该是一个长度为您的 RNN 长度的序列。但在你的情况下，它是一个data = tf.placeholder(tf.bool, name='data').

你可以试试pred = RNN([data], weights, biases)。

查看方法的字符串文档：

输入：长度为 T 的输入列表，每个输入Tensor的形状 为 a [batch_size, input_size]，或此类元素的嵌套元组。

如果你的 RNN 的长度未知，你应该考虑使用tf.nn.dynamic_rnn.

于 2017-07-20T14:34:03.557 回答