ValueError：无法为形状为（（？，2）'的Tensor'labels：0'输入形状（1，1，2）的值

Question

我对python和机器学习相对较新。 当我运行此代码时，它将出现此值错误。 不幸的是，我看不到能够调整输入形状以匹配两个形状。 我试图将标签的形状更改为（None，None，2），但是当我运行它时，火车成本上升为'nan'。 我究竟做错了什么？ 我该如何解决？ 任何帮助都感激不尽。

import csv  
import tensorflow as tf  
import numpy as np    
import pandas as pd   
import urllib.request as request  
import matplotlib.pyplot as plt

train_data = pd.read_csv("C:/Python35/train_data.csv", sep=',', header = None)  
test_data = pd.read_csv("C:/Python35/test_data.csv", sep=',', header = None)  

X_train = np.asarray(train_data)  
X_test = np.asarray(test_data)  

train_label = pd.read_csv("C:/Python35/train_label.csv", sep=',', header = None)  
test_label = pd.read_csv("C:/Python35/test_label.csv", sep=',', header = None)  

y_train = np.asarray(train_label)  
y_test = np.asarray(test_label)  

labels_train = (np.arange(2) == y_train[:,None]).astype(np.float32)  
labels_test = (np.arange(2) == y_test[:,None]).astype(np.float32)  

inputs = tf.placeholder(tf.float32, shape=(None, X_train.shape[1]), name='inputs')
label = tf.placeholder(tf.float32, shape=(None, 2), name='labels')

hid1_size = 128   
w1 = tf.Variable(tf.random_normal([hid1_size, X_train.shape[1]], stddev=0.01), name='w1')  
b1 = tf.Variable(tf.constant(0.1, shape=(hid1_size, 1)), name='b1')  
y1 = tf.nn.dropout(tf.nn.relu(tf.add(tf.matmul(w1, tf.transpose(inputs)), b1)),  keep_prob=0.5)

hid2_size = 256  
w2 = tf.Variable(tf.random_normal([hid2_size, hid1_size], stddev=0.01), name='w2')  
b2 = tf.Variable(tf.constant(0.1, shape=(hid2_size, 1)), name='b2')  
y2 = tf.nn.dropout(tf.nn.relu(tf.add(tf.matmul(w2, y1), b2)), keep_prob=0.5)  

wo = tf.Variable(tf.random_normal([2, hid2_size], stddev=0.01), name='wo')  
bo = tf.Variable(tf.random_normal([2, 1]), name='bo')  
yo = tf.transpose(tf.add(tf.matmul(wo, y2), bo))  

lr = tf.placeholder(tf.float32, shape=(), name='learning_rate')  
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=yo, labels=label))  
optimizer = tf.train.GradientDescentOptimizer(lr).minimize(loss)  

pred = tf.nn.softmax(yo)  
pred_label = tf.argmax(pred, 1)  
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(label, 1))  
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))  

init = tf.global_variables_initializer()  

config = tf.ConfigProto()  
config.gpu_options.allow_growth = True  

sess = tf.InteractiveSession(config=config)  
sess.run(init)  

for learning_rate in [0.05, 0.01]:  
    for epoch in range(50):  
        avg_cost = 0.0  
        for i in range(X_train.shape[0]):  
            _, c = sess.run([optimizer, loss], feed_dict={lr:learning_rate,  
                                                          inputs: X_train[i, None],  
                                                          label: labels_train[i, None]})  
            avg_cost += c  
        avg_cost /= X_train.shape[0]  
        if epoch % 10 == 0:  
            print("Epoch: {:3d}    Train Cost: {:.4f}".format(epoch, avg_cost))  

acc_train = accuracy.eval(feed_dict={inputs: X_train, label: labels_train})
print("Train accuracy: {:3.2f}%".format(acc_train*100.0))

acc_test = accuracy.eval(feed_dict={inputs: X_test, label: labels_test})
print("Test accuracy:  {:3.2f}%".format(acc_test*100.0))

sess.close()

Answer 1

错误的意思是，虽然labels占位符需要一个形状为(None, 2)的数组，但您正在传递的形状为(1, 1, 2)

这是在你运行你的对话和线路进行（ sess.run()更具体的feed_dict你在哪里传递labels_train[i, None]占位符的labels 。

从您的代码中，我无法分辨出您到底想使用标签做什么，但是简单的重塑就可以解决问题：

_, c = sess.run([optimizer, loss], feed_dict={lr:learning_rate,  
                                              inputs: X_train[i, None],  
                                              label: labels_train[i, None].reshape(-1, 2)}) 

这远不是最佳解决问题的方法； 将其视为一种解决方法，直到您更好地熟悉python和tensorflow。