Question

我正在尝试使用TensorFlow编写一个简单的程序来预测序列中的下一个数字。

我在TensorFlow方面没有经验，所以不是从头开始，而是从本指南开始：http://monik.in/a-noobs-guide-to-implementing-rnn-lstm-using-tensorflow/

然而，与上面链接中的实现相反，我不希望将问题视为分类问题 - 我只有n个可能的结果 - 而只是计算序列的单个值。

我尝试修改代码以适应我的问题：

import numpy as np
import random
from random import shuffle
import tensorflow as tf

NUM_EXAMPLES = 10000

train_input = ['{0:020b}'.format(i) for i in range(2**20)]
shuffle(train_input)
train_input = [map(int,i) for i in train_input]
ti  = []
for i in train_input:
    temp_list = []
    for j in i:
            temp_list.append([j])
    ti.append(np.array(temp_list))
train_input = ti

train_output = []
for i in train_input:
    count = 0
    for j in i:
        if j[0] == 1:
            count+=1
    #temp_list = ([0]*21)
    #temp_list[count]=1
    #train_output.append(temp_list)
    train_output.append(count)

test_input = train_input[NUM_EXAMPLES:]
test_output = train_output[NUM_EXAMPLES:]
train_input = train_input[:NUM_EXAMPLES]
train_output = train_output[:NUM_EXAMPLES]

print "test and training data loaded"


target = tf.placeholder(tf.float32, [None, 1])
data = tf.placeholder(tf.float32, [None, 20,1]) #Number of examples, number of input, dimension of each input
#target = tf.placeholder(tf.float32, [None, 1])

#print('target shape: ', target.get_shape())
#print('shape[0]', target.get_shape()[1])
#print('int(shape) ', int(target.get_shape()[1]))

num_hidden = 24
cell = tf.nn.rnn_cell.LSTMCell(num_hidden)
val, _ = tf.nn.dynamic_rnn(cell, data, dtype=tf.float32)
val = tf.transpose(val, [1, 0, 2])

print('val shape, ', val.get_shape())

last = tf.gather(val, int(val.get_shape()[0]) - 1)

weight = tf.Variable(tf.truncated_normal([num_hidden, int(target.get_shape()[1])]))
bias = tf.Variable(tf.constant(0.1, shape=[target.get_shape()[1]]))

#prediction = tf.nn.softmax(tf.matmul(last, weight) + bias)
prediction = tf.matmul(last, weight) + bias

cross_entropy = -tf.reduce_sum(target - prediction)
optimizer = tf.train.AdamOptimizer()
minimize = optimizer.minimize(cross_entropy)

mistakes = tf.not_equal(tf.argmax(target, 1), tf.argmax(prediction, 1))
error = tf.reduce_mean(tf.cast(mistakes, tf.float32))

init_op = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init_op)

batch_size = 100
no_of_batches = int(len(train_input)) / batch_size
epoch = 500

for i in range(epoch):
    ptr = 0
    for j in range(no_of_batches):
        inp, out = train_input[ptr:ptr+batch_size], train_output[ptr:ptr+batch_size]
        ptr+=batch_size
        sess.run(minimize,{data: inp, target: out})
    print "Epoch ",str(i)

incorrect = sess.run(error,{data: test_input, target: test_output})

#print sess.run(prediction,{data: [[[1],[0],[0],[1],[1],[0],[1],[1],[1],[0],[1],[0],[0],[1],[1],[0],[1],[1],[1],[0]]]})
#print('Epoch {:2d} error {:3.1f}%'.format(i + 1, 100 * incorrect))

sess.close()

它仍然在进行中，因为输入是伪造的以及交叉熵计算。

但是，我的主要问题是代码根本无法编译。

我收到此错误：

ValueError：无法为Tensor提供shape（100，）的值 u'Placeholder：0'，其形状为'（？，1）'

数字100来自“batch_size”，（？，1）来自我的预测是一维数字的事实。但是，我不知道我的代码中的问题在哪里？

任何人都可以帮助我获得相匹配的尺寸吗？

Answer 1

此错误表示您的targets占位符正在输入错误形状的内容。为了解决这个问题，我认为你应该重塑像test_output.reshape([-1, 1])

这样的东西

Answer 2

要修复占位符形状，请将代码更改为

for i in range(epoch):
    ptr = 0
    for j in range(no_of_batches):
        inp = train_input[ptr:ptr+batch_size]
        out = train_output[ptr:ptr+batch_size]
        ptr+=batch_size
        out = np.reshape(out, (100,1))   #reshape
        sess.run(minimize,{data: inp, target: out})
    print ("Epoch ",str(i))
test_output = np.reshape(test_output, (1038576,1))   #reshape
incorrect = sess.run(error,{data: test_input, target: test_output})

预测模式中的下一个数字

2 个答案: