Python 维数错误的Keras多类模型

Keras新手，尝试从中重新实现以下二值图像分类示例：

对我来说，它适用于二进制分类。 将其重建为3级分类，我得到以下尺寸不匹配错误：

     60         epochs=50,
     61         validation_data=validation_generator,
---> 62         validation_steps=250 // batch_size)
ValueError: Error when checking target: expected activation_50 to have shape (None, 1) but got array with shape (16, 3)

这是我当前的实现：

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
K.set_image_dim_ordering('th')
batch_size = 16

# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)

# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1./255)

# this is a generator that will read pictures found in
# subfolers of 'data/train', and indefinitely generate
# batches of augmented image data
train_generator = train_datagen.flow_from_directory(
        'F://train_data//',  # this is the target directory
        target_size=(150, 150),  # all images will be resized to 150x150
        batch_size=batch_size,
        class_mode='categorical')  # since we use binary_crossentropy loss, we need binary labels

# this is a similar generator, for validation data
validation_generator = test_datagen.flow_from_directory(
        'F://validation_data//',
        target_size=(150, 150),
        batch_size=batch_size,
        class_mode='categorical')

model = Sequential()

model.add(Conv2D(32, (3, 3), input_shape=(3, 150, 150)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_first"))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_first"))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_first"))

model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('softmax')) # instead of sigmoid

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

# another loss: sparse_categorical_crossentropy

model.fit_generator(
        train_generator,
        steps_per_epoch=1800 // batch_size,
        epochs=50,
        validation_data=validation_generator,
        validation_steps=250 // batch_size)

到目前为止，我已经将输出层的激活功能从sigmoid更改为softmax。将class_模式从二进制更改为分类。似乎找不到问题

此外，我知道有关StackOverflow的类似问题：

---

但没有一个解决方案对我有帮助

您需要将最终的

densed

层更改为

model.add（densed（3））

。Softmax激活希望

密集

层中的

单元

与类数匹配

---

另外，如果您要使用

loss='sparse'

，请记住将

class\u模式更改为
'sparse'
。您当前的设置，
class\u mode='classifical'
，应该与
loss='classifical\u crossentropy'
一起使用。您需要将最终的
密集层更改为
模型。添加（密集（3））
。Softmax激活希望
密集
层中的
单元
与类数匹配

另外，如果您要使用
loss='sparse'
，请记住将
class\u模式更改为
'sparse'
。您当前的设置，
class\u mode='classifical'
，应该与
loss='classifical\u crossentropy'
一起使用