是的，Thymen的回应帮助了我。谢谢。模型现在看起来是这样的：
我是数据科学的学生，我正在努力进行深度学习。我的模型如下：
Model: "sequential_32"
Layer (type) Output Shape Param #
=================================================================
conv2d_26 (Conv2D) (None, 27, 27, 32) 288
_________________________________________________________________
max_pooling2d_12 (MaxPooling (None, 13, 13, 32) 0
_________________________________________________________________
dense_13 (Dense) (None, 13, 13, 128) 4224
_________________________________________________________________
dropout_6 (Dropout) (None, 13, 13, 128) 0
_________________________________________________________________
dense_14 (Dense) (None, 13, 13, 10) 1290
=================================================================
Total params: 5,802
Trainable params: 5,802
Non-trainable params: 0
_________________________________________________________________
现在我正试图用以下代码将一些数据放入其中：
print(x_train.shape)
print(x_test.shape)
result = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
输出以下内容：
(60000, 28, 28, 1)
(10000, 28, 28, 1)
# some error codes followed by:
ValueError: Shapes (32, 10) and (32, 13, 13, 11) are incompatible
我觉得这确实是一个很容易纠正的错误，但我就是看不出来。欢迎任何帮助和/或解释！
Model: "sequential_35"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_29 (Conv2D) (None, 27, 27, 32) 160
_________________________________________________________________
max_pooling2d_15 (MaxPooling (None, 13, 13, 32) 0
_________________________________________________________________
flatten (Flatten) (None, 5408) 0
_________________________________________________________________
dense_17 (Dense) (None, 128) 692352
_________________________________________________________________
dropout_8 (Dropout) (None, 128) 0
_________________________________________________________________
dense_18 (Dense) (None, 10) 1290
=================================================================
Total params: 693,802
Trainable params: 693,802
Non-trainable params: 0
_________________________________________________________________