Keras多级模型，尺寸错误

新的Keras，尝试重新实现以下二进制图像分类示例：https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html

它适用于我的二进制分类 . 重建它为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)

这是我目前的实施：

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，我已将输出层的激活功能从sigmoid更改为softmax . 将class_mode从二进制更改为分类 . 似乎无法找到问题 .

另外，我知道StackOverflow上有类似的问题：Multi-Output Multi-Class Keras Model

Train multi-class image classifier in Keras

Multi-class classification using keras

但是这些解决方案都没有帮助我 .