我是TensorFlow的新手，我正在尝试编写一种算法来对CIFAR-10数据集中的图像进行分类。我收到了这个错误：
%ALLUSERSPROFILE%\Application Data这是我的代码：
InvalidArgumentError (see above for traceback): logits and labels must be same size: logits_size=[10000,10] labels_size=[1,10000]
[[Node: SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](Reshape, Reshape_1)]]
我很确定这意味着第48行（如上所示）import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import cPickle
n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500
n_classes = 10
batch_size = 100
image_size = 32*32*3 # because 3 channels
x = tf.placeholder('float', shape=(None, image_size))
y = tf.placeholder('float')
def neural_network_model(data):
hidden_1_layer = {'weights':tf.Variable(tf.random_normal([image_size, 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(I am new to TensorFlow and tf.random_normal([n_nodes_hl3, n_classes])), 'biases':tf.Variable(tf.random_normal([n_classes]))}
# input_data * weights + biases
l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['biases'])
# activation function
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))//THIS IS LINE 48 WHERE THE ERROR OCCURS
#learning rate = 0.001
optimizer = tf.train.AdamOptimizer().minimize(cost)
hm_epochs = 10
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for epoch in range(hm_epochs):
epoch_loss = 0
for i in range(5):
with open('data_batch_'+str(i+1),'rb') as f:
train_data = cPickle.load(f)
print train_data
print prediction.get_shape()
#print len(y)
_, c = sess.run([optimizer, cost], feed_dict={x:train_data['data'],y:train_data['labels']})
epoch_loss += c
print 'Epoch ' + str(epoch) + ' completed out of ' + str(hm_epochs) + ' loss: ' + str(epoch_loss)
correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
with open('test_batch','rb') as f:
test_data = cPickle.load(f)
accuracy = accuracy.eval({x:test_data['data'],y:test_data['labels']})
print 'Accuracy: ' + str(accuracy)
train_neural_network(x)
和prediction形状不一样，但我不太了解TensorFlow，知道如何解决它。我甚至不太了解y的设置位置，我从教程中获取了大部分代码，并将其应用于不同的数据集。我该如何解决这个错误？
答案 0 :(得分：2)
tf.nn.softmax_cross_entropy_with_logits(logits, labels) op期望其logits和labels参数为具有相同形状的张量。此外，logits和labels参数应为包含batch_size行和num_classes列的二维张量（矩阵）。
从错误消息和logits的大小，我猜测batch_size是10000，num_classes是10.从labels的大小，我猜测您的标签被编码为整数列表，其中整数表示相应输入示例的类的索引。 （我预计这会是tf.int32值，而不是tf.float32，因为它似乎在您的程序中，但也许会有一些自动转换。）
在TensorFlow中，您可以使用tf.nn.sparse_softmax_cross_entropy_with_logits()来计算此表单中数据的交叉熵。在您的程序中，您可以通过将cost计算替换为：
cost = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
prediction, tf.squeeze(y)))
请注意，需要tf.squeeze()操作才能将y转换为长度为batch_size的向量（以便成为tf.nn.sparse_softmax_cross_entropy_with_logits()的有效参数。
答案 1 :(得分：1)
以下是支持TensorFlow版本1.0的代码的一些更新：
def train_neural_network(x): <br>
 prediction = neural_network_model(x) <br>
 # OLD VERSION: <br>
 cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) ) <br>
 # NEW:<br>
 cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y) )<br>
 optimizer = tf.train.AdamOptimizer().minimize(cost) <br>
 hm_epochs = 10 <br>
 with tf.Session() as sess: <br>
  #OLD: #sess.run(tf.initialize_all_variables()) <br>
  #NEW:<br>
  sess.run(tf.global_variables_initializer())