预测模式中的下一个数

Question

我想用TensorFlow编写一个简单的程序来预测序列中的下一个数。

我不是TensorFlow经历了从头开始，这样反而我开始用这个指南：http://monik.in/a-noobs-guide-to-implementing-rnn-lstm-using-tensorflow/

然而，在对比中的链接执行上面我不想把这个问题作为一个分类问题 - 我只有n个可能的结果 - 但是只是为一个序列计算单个值。

我试图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() 

据工作仍然在进行中，因为输入的是假的，以及交叉熵的计算。

但是，我的主要问题是，代码根本不编译。

我得到这个错误：

ValueError: Cannot feed value of shape (100,) for Tensor u'Placeholder:0', which has shape '(?, 1)'

数量100来自于“的batch_size”和来自于事实，我的预测是一张维数（1？）。但是，我不知道问题出在我的代码中？

任何人都可以帮助我得到尺寸匹配？

Answer 1

此错误意味着您的targets占位符被喂食的东西形状不正确。为了解决这个问题，我想你应该重塑类似test_output.reshape([-1, 1])

Answer 2

要解决的占位符形状，更改您的代码

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})