Keras多类模型错了尺寸

新来Keras，试图重新实现从这个以下这些二进制图像分类例如：https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html Keras多类模型错了尺寸

它适用于二元分类我。重建它的3类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，我已经改变了输出层的激活功能从sigmoid到softmax。将class_mode从二进制更改为分类。似乎无法找到问题。

而且，我知道类似的问题在计算器上： Multi-Output Multi-Class Keras Model

Train multi-class image classifier in Keras

Multi-class classification using keras

但没有解决方案的帮助了我。

来源

2017-09-01 ivan_bilan

您需要将最终的Dense图层更改为model.add(Dense(3))。 Softmax激活期望Dense图层中的units与类的数量相匹配。

此外，如果您打算使用loss='sparse_categorical_crossentropy'，请记得将class_mode更改为'sparse'。您当前的设置class_mode='categorical'应与loss='categorical_crossentropy'一起使用。

来源

2017-09-01 08:00:32

Keras多类模型错了尺寸

回答

相关问题