Machine learning Keras imageGenerator异常:生成器的输出应为元组(x,y,sample_weight)或(x,y)。发现:无
我目前正在尝试使用我自己生成的数据集来遵循这个示例。后端使用Theano运行。目录结构完全相同:Machine learning Keras imageGenerator异常:生成器的输出应为元组(x,y,sample_weight)或(x,y)。发现:无,machine-learning,computer-vision,theano,keras,Machine Learning,Computer Vision,Theano,Keras,我目前正在尝试使用我自己生成的数据集来遵循这个示例。后端使用Theano运行。目录结构完全相同: image_sets/ dogs/ dog001.jpg dog002.jpg ... cats/ cat001.jpg cat002.jpg ... validation/ dogs/ dog001.jpg dog002.jpg .
image_sets/
dogs/
dog001.jpg
dog002.jpg
...
cats/
cat001.jpg
cat002.jpg
...
validation/
dogs/
dog001.jpg
dog002.jpg
...
cats/
cat001.jpg
这是我的keras卷积神经网络代码
img_width, img_height = 150, 150
img_width, img_height = 150, 150
train_data_dir = './image_sets'
validation_data_dir = './validation'
nb_train_samples = 267
print nb_train_samples
#number of validation images I have
nb_validation_samples = 2002
print nb_validation_samples
nb_epoch = 50
# from keras import backend as K
# K.set_image_dim_ordering('th')
model = Sequential()
model.add(Convolution2D(32, 3, 3, input_shape=(3,img_width, img_height)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=32,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=32,
class_mode='binary')
model.fit_generator(
train_generator,
samples_per_epoch=nb_train_samples,
nb_epoch=nb_epoch,
validation_data=validation_generator,
nb_val_samples=nb_validation_samples)
model.save_weights('first_try.h5')
您的生成器应该是python生成器。 你可以读更多
简单解释一下,生成器允许您从调用的函数中生成一系列值,而无需清理其变量(例如
return
语句)。我在运行代码时遇到了相同的问题,但我使用tensorflow作为后端。我的问题是我在旧版本的keras上运行它
由升级到keras 2.0
pip安装——升级keras
然后更新fit_生成器
功能,如下所示-
model.fit_generator(generator=train_generator,
steps_per_epoch=2048 // 16,
epochs=20,
validation_data=validation_generator,
validation_steps=832//16)
这里,16是您的批量大小
您可以通过fchollet找到完整的更新代码: