CNN模型分类错误：logits和标签必须是可广播的：logits_size = [32,10] labels_size = [32,13]

如何解决CNN模型分类错误：logits和标签必须是可广播的：logits_size = [32,10] labels_size = [32,13]

在这里，我正在尝试对图像分类运行CNN模型。

这是批次大小和13个标签

Image batch shape:  (32,32,3)
Label batch shape:  (32,13)
['Watch_Back' 'Watch_Chargers' 'Watch_Earpods' 'Watch_Front'
 'Watch_Lifestyle' 'Watch_Others' 'Watch_Packages' 'Watch_Side'
 'Watch_Text' 'Watch_Tilted' 'Watch_With_Accessories'
 'Watch_With_Ear_Pods' 'Watch_With_People']

以下是cnn的模型

model = Sequential()
model.add(Conv2D(32,(5,5),activation='relu',input_shape=(32,3)))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(64,activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(1000,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(500,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(250,activation='relu'))
model.add(Dense(10,activation='softmax'))

model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])

从下面的代码部分中，出现错误：

steps_per_epoch = np.ceil(train_generator.samples/train_generator.batch_size)
val_steps_per_epoch = np.ceil(valid_generator.samples/valid_generator.batch_size)
hist = model.fit(
train_generator,epochs=10,verbose=1,steps_per_epoch=steps_per_epoch,validation_data=valid_generator,validation_steps=val_steps_per_epoch).history

以下是错误

Epoch 1/10
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-64-b89d5efc8aaf> in <module>()
      7 steps_per_epoch=steps_per_epoch,8 validation_data=valid_generator,----> 9 validation_steps=val_steps_per_epoch).history

8 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name,num_outputs,inputs,attrs,ctx,name)
     58     ctx.ensure_initialized()
     59     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle,device_name,op_name,---> 60                                         inputs,num_outputs)
     61   except core._NotOkStatusException as e:
     62     if name is not None:

InvalidArgumentError:  logits and labels must be broadcastable: logits_size=[32,10] labels_size=[32,13]
     [[node categorical_crossentropy/softmax_cross_entropy_with_logits (defined at <ipython-input-64-b89d5efc8aaf>:9) ]] [Op:__inference_train_function_6504]

Function call stack:
train_function

如何解决此类别错误

解决方法

该错误是由以下行引起的：

model.add(Dense(10,activation='softmax'))

重要的是，最后一层包含的神经元数量与数据集中类别的数量一样多。我猜您有13个类别，所以应该是13个。您也可以使用

model.add(Dense(len(train_generator.classes),activation='softmax'))