Question

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渐变，检查图表中不支持渐变的操作，变量之间[＆＃34;＆＃34;，＆＃34;＆＃34;，＆＃34;＆＃ 34;，＆＃34;＆＃34;，＆＃34;＆＃34;，＆＃34;＆＃34;，＆＃34;＆＃34;，＆＃34;＆＃34;，＆＃ 34;＆＃34;，＆＃34;＆＃34;，＆＃34;＆＃34;，＆＃34;＆＃34;，＆＃34;＆＃34;，＆＃34;＆＃34 ;，＆＃34;＆＃34;，＆＃34;＆＃34;]和丢失张量（＆＃34;平均值：0＆＃34;，dtype = float32）。

如果您能帮忙解决这个问题，我将不胜感激。

Answer 1

我自己没有尝试过：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：＆＃34; 反向传播只会发生在logits中。要计算允许反向传播到logits和标签的交叉熵损失，请参阅tf.nn.softmax_cross_entropy_with_logits_v2。＆＃34;

我尝试运行深层神经网络时的ValueError

1 个答案: