ValueError podczas wykonywania softmax_cross_entropy_with_logits

Question

Podążam za tensorflow tutorial. Nastąpiła ostatnia aktualizacja przepływu tensora, w której zmodyfikowano funkcję kosztu softmax_cross_entropy_with_logits(). Stąd kod w samouczku daje następujący błąd:

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

Co to oznacza i jak go poprawienia?

Oto cały kod do tego punktu:

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

Zmień

tf.nn.softmax_cross_entropy_with_logits(prediction,y) 

do

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