执行ValueError时softmax_cross_entropy_with_logits

Question

我在跟踪tensorflow教程。最近出现了张量流更新，其中成本函数softmax_cross_entropy_with_logits()已被修改。因此，本教程中的代码给出了以下错误：

ValueError: Only call softmax_cross_entropy_with_logits with named arguments (labels=..., logits=..., ...)

这意味着什么以及如何纠正它？

以下是这段代码的全部内容：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)

n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500

n_classes = 10
batch_size = 100

x = tf.placeholder('float', [None, 784])
y = tf.placeholder('float')

def neural_network_model(data):
hidden_1_layer = {'weights':tf.Variable(tf.random_normal([784, n_nodes_hl1])),
                  'biases':tf.Variable(tf.random_normal([n_nodes_hl1]))}

hidden_2_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
                  'biases':tf.Variable(tf.random_normal([n_nodes_hl2]))}

hidden_3_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
                  'biases':tf.Variable(tf.random_normal([n_nodes_hl3]))}

output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
                'biases':tf.Variable(tf.random_normal([n_classes])),}


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

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

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

output = tf.matmul(l3,output_layer['weights']) + output_layer['biases']

return output

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

Answer 1

变化

tf.nn.softmax_cross_entropy_with_logits(prediction,y)

至

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

Answer 2

函数softmax_cross_entropy_with_logits已被废弃。新功能是softmax_cross_entropy_with_logits_v2

cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(pred, y))

文档

Answer 3

这是因为代码不支持Tensorflow v1.0，所以当您对以下代码行做细微更改时，它可以很好地工作：

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

使用

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