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]答案

【问题标题】：CNN model Categorical error: logits and labels must be broadcastable: logits_size=[32,10] labels_size=[32,13]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]
【发布时间】：2020-11-30 20:58:35
【问题描述】：

这里我尝试在图像分类上运行 CNN 模型。

这是批量大小和 13 个标签

Image batch shape:  (32, 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=(32,32,3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (5, 5), 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'])

从以下部分代码，错误来了：

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, attrs, 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

如何解决这个分类错误

【问题讨论】：

标签： python tensorflow keras conv-neural-network

【解决方案1】：

错误是由这一行引起的：

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

最后一层包含与数据集中的类别一样多的神经元，这一点很重要。我猜你有 13 个类别，所以应该是 13 个。你也可以使用

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

【讨论】：

谢谢@Nicolas Gervais，你帮我搞定了。我将其更改为存在的类别数量并且有效！