当运行下面的代码时，我得到一个错误，说有形状不匹配，在谷歌上搜索后，我发现这通常是由于当目标不是单码编码时，使用cross_entropy作为损失函数引起的。由于我的编码不是一次性编码，因此我使用了 sparse_categorical_crossentropy ，但是错误仍然存​​在。

这是我收到的错误：

Train on 60000 samples
Epoch 1/100
   32/60000 [..............................] - eta: 1:18
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-123-4ce1d4d047d4> in <module>()
----> 1 model.fit(x_train,y_train,batch_size=32,epochs=100,verbose=1)

25 frames
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_ops.py in sparse_softmax_cross_entropy_with_logits(_sentinel,labels,logits,name)
   3391                        "should equal the shape of logits except for the last "
   3392                        "dimension (received %s)." % (labels_static_shape,-> 3393                                                      logits.get_shape()))
   3394     # Check if no reshapes are required.
   3395     if logits.get_shape().ndims == 2:

ValueError: Shape mismatch: The shape of labels (received (32,)) should equal the shape of logits except 
for the last dimension (received (5408,10)).

这是我的代码：

import numpy as np
import tensorflow as tf
from tensorflow.keras.datasets import fashion_mnist

(x_train,y_train),(x_test,y_test) = fashion_mnist.load_data()

x_train = x_train / 255.0
x_test = x_test / 255.0
x_train = np.reshape(x_train,(-1,28,1))

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv2D(filters=64,kernel_size=3,strides=2,input_shape=[28,1],activation='relu'))
model.add(tf.keras.layers.Dense(units = 10,activation='softmax'))

model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['sparse_categorical_accuracy'])

model.fit(x_train,verbose=1)