当我尝试运行我的深度神经网络时出现 ValueError答案

【问题标题】：ValueError when I try to run my deep neural network当我尝试运行我的深度神经网络时出现 ValueError
【发布时间】：2018-11-09 03:30:36
【问题描述】：

我正在学习本教程：

Training and Testing on our Data for Deep Learning

代码是：

import tensorflow as tf
#from tensorflow.examples.tutorials.mnist import input_data
import pickle
import numpy as np

train_x,train_y,test_x,test_y = create_feature_sets_and_labels('pos.txt','neg.txt')

n_nodes_hl1 = 1500
n_nodes_hl2 = 1500
n_nodes_hl3 = 1500

n_classes = 2
batch_size = 100
hm_epochs = 10

x = tf.placeholder('float')
y = tf.placeholder('float')

hidden_1_layer = {'f_fum':n_nodes_hl1,
                  'weight':tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])),
                  'bias':tf.Variable(tf.random_normal([n_nodes_hl1]))}

hidden_2_layer = {'f_fum':n_nodes_hl2,
                  'weight':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
                  'bias':tf.Variable(tf.random_normal([n_nodes_hl2]))}

hidden_3_layer = {'f_fum':n_nodes_hl3,
                  'weight':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
                  'bias':tf.Variable(tf.random_normal([n_nodes_hl3]))}

output_layer = {'f_fum':None,
                'weight':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
                'bias':tf.Variable(tf.random_normal([n_classes])),}


# Nothing changes
def neural_network_model(data):

    l1 = tf.add(tf.matmul(data,hidden_1_layer['weight']), hidden_1_layer['bias'])
    l1 = tf.nn.relu(l1)

    l2 = tf.add(tf.matmul(l1,hidden_2_layer['weight']), hidden_2_layer['bias'])
    l2 = tf.nn.relu(l2)

    l3 = tf.add(tf.matmul(l2,hidden_3_layer['weight']), hidden_3_layer['bias'])
    l3 = tf.nn.relu(l3)

    output = tf.matmul(l3,output_layer['weight']) + output_layer['bias']

    return output

def train_neural_network(x):
    prediction = neural_network_model(x)
    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels=prediction,logits=y) )
    optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)

    with tf.Session() as sess:
        sess.run(tf.initialize_all_variables())

        for epoch in range(hm_epochs):
            epoch_loss = 0
            i=0
            while i < len(train_x):
                start = i
                end = i+batch_size
                batch_x = np.array(train_x[start:end])
                batch_y = np.array(train_y[start:end])

                _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
                epoch_loss += c
                i+=batch_size

            print('Epoch', epoch+1, 'completed out of',hm_epochs,'loss:',epoch_loss)
        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))

        print('Accuracy:',accuracy.eval({x:test_x, y:test_y}))

train_neural_network(x)

不幸的是，它给了我以下错误：

ValueError: 没有为任何变量提供梯度，检查你的图表中不支持梯度的操作，在变量 ["", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""] 和 loss Tensor("Mean:0", dtype=float32)。

如果您能帮助解决这个问题，我将不胜感激。

【问题讨论】：

您可以从pythonprogramming.net/… 轻松找到 pos.txt 和 neg.txt。请下载。

标签： python tensorflow deep-learning

【解决方案1】：

没有亲自尝试过：你需要改变

cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels=prediction,logits=y) )

到

cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction) )

TF 文档指出：“反向传播只会发生在 logits 中。要计算允许反向传播到 logits 和标签中的交叉熵损失，请参阅 tf.nn.softmax_cross_entropy_with_logits_v2。”

【讨论】：