多尺度CNN网络Python Keras

我在Python Keras中创建了一个多尺度的CNN.网络架构类似于图表.在这里,相同的图像被馈送到具有不同架构的3个CNN.权重不共享.

我写的代码如下.问题是,当我运行这个,即使在train_dir网络中有10个图像需要大约40GB RAM,最后被操作系统杀死.这是"Out of memory ERROR".我在CPU上运行它.知道为什么在Keras发生这种情况？

我正在使用Theano-0.9.0.dev5 | Keras-1.2.1 | Python 2.7.12 | OSX Sierra 10.12.3(16D32)

## Multi scale CNN in Keras Python
## https://img.devbox.cn/3cccf/16086/243/6db233c03a276edb.png

#main CNN model - CNN1
main_model = Sequential()
main_model.add(Convolution2D(32, 3, 3, input_shape=(3, 224, 224)))
main_model.add(Activation('relu'))
main_model.add(MaxPooling2D(pool_size=(2, 2)))

main_model.add(Convolution2D(32, 3, 3))
main_model.add(Activation('relu'))
main_model.add(MaxPooling2D(pool_size=(2, 2)))

main_model.add(Convolution2D(64, 3, 3))
main_model.add(Activation('relu'))
main_model.add(MaxPooling2D(pool_size=(2, 2))) # the main_model so far outputs 3D feature maps (height, width, features)

main_model.add(Flatten())

#lower features model - CNN2
lower_model1 = Sequential()
lower_model1.add(Convolution2D(32, 3, 3, input_shape=(3, 224, 224)))
lower_model1.add(Activation('relu'))
lower_model1.add(MaxPooling2D(pool_size=(2, 2)))
lower_model1.add(Flatten())

#lower features model - CNN3
lower_model2 = Sequential()
lower_model2.add(Convolution2D(32, 3, 3, input_shape=(3, 224, 224)))
lower_model2.add(Activation('relu'))
lower_model2.add(MaxPooling2D(pool_size=(2, 2)))
lower_model2.add(Flatten())

#merged model
merged_model = Merge([main_model, lower_model1, lower_model2], mode='concat')

final_model = Sequential()                     
final_model.add(merged_model)                  
final_model.add(Dense(64))
final_model.add(Activation('relu'))
final_model.add(Dropout(0.5))      
final_model.add(Dense(1))
final_model.add(Activation('sigmoid'))
final_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

print 'About to start training merged CNN'
train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
train_generator = train_datagen.flow_from_directory(train_data_dir, target_size=(224, 224), batch_size=32, class_mode='binary')

test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(args.test_images, target_size=(224, 224), batch_size=32, class_mode='binary')

final_train_generator = zip(train_generator, train_generator, train_generator)
final_test_generator  = zip(test_generator, test_generator, test_generator)
final_model.fit_generator(final_train_generator, samples_per_epoch=nb_train_samples, nb_epoch=nb_epoch, validation_data=final_test_generator, nb_val_samples=nb_validation_samples)

展平中lower_model1和lower_model2之后的节点数量是 32 * 112 * 112 = 401 408.接下来是具有64个节点的完全连接的层,这给出了401 408 * 2 * 64 = 51 380 224参数,这是相当大的数字.我建议重新考虑喂给你的"低级"模特的图像的大小.你真的需要224 x 224大小吗？仔细查看附加的图表.在那里你看到第二个和第三个模型的第一步是子采样:8:1和4:1.这是您在实施中遗漏的步骤.

你main_model很好,因为那里有足够的最大池层,可以减少参数的数量.