结合两个CNN [英] Combining Two CNN's

问题描述

我想在Keras中将两个CNN合并为一个，我的意思是我希望神经网络拍摄两个图像并在单独的CNN中处理每个图像，然后将它们一起连接到平坦化层中并使用完全连接层做最后的工作，这是我所做的:

I Want to Combine Two CNN Into Just One In Keras, What I Mean Is that I Want The Neural Network To Take Two Images And Process Each One in Separate CNN, and Then Concatenate Them Together Into The Flattening Layer and Use Fully Connected Layer to Do The Last Work, Here What I Did:

# Start With First Branch ############################################################
branch_one = Sequential()

# Adding The Convolution
branch_one.add(Conv2D(32, (3,3),input_shape = (64,64,3) , activation = 'relu'))
branch_one.add(Conv2D(32, (3, 3), activation='relu'))

# Doing The Pooling Phase
branch_one.add(MaxPooling2D(pool_size=(2, 2)))
branch_one.add(Dropout(0.25))
branch_one.add(Flatten())

# Start With Second Branch ############################################################

branch_two = Sequential()

# Adding The Convolution
branch_two.add(Conv2D(32, (3,3),input_shape = (64,64,3) , activation = 'relu'))
branch_two.add(Conv2D(32, (3, 3), activation='relu'))

# Doing The Pooling Phase
branch_two.add(MaxPooling2D(pool_size=(2, 2)))
branch_two.add(Dropout(0.25))
branch_two.add(Flatten())

# Making The Combinition ##########################################################
final = Sequential()
final.add(Concatenate([branch_one, branch_two]))
final.add(Dense(units = 128, activation = "relu"))
final.add(Dense(units = 1, activation = "sigmoid"))

# Doing The Compilation
final.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
# Adding and Pushing The Images to CNN

# use ImageDataGenerator to preprocess the data

from keras.preprocessing.image import ImageDataGenerator

# augment the data that we have
train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)

# prepare training data
X1 = train_datagen.flow_from_directory('./ddsm1000_resized/images/train',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')

X2 = train_datagen.flow_from_directory('./ddsm1000_resized_canny/images/train',

                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')

# prepare test data
Y1 = test_datagen.flow_from_directory('./ddsm1000_resized/images/test',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'binary')
Y2 = test_datagen.flow_from_directory('./ddsm1000_resized_canny/images/test',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'binary')
final.fit_generator([X1, X2], steps_per_epoch = (8000 / 32), epochs = 1, validation_data = [Y1,Y2], validation_steps = 2000)

Keras告诉我

RuntimeError:您必须先编译模型，然后再使用它.

RuntimeError: You must compile your model before using it.

我认为那是CNN并没有输入数据的形状，那我该怎么办?谢谢

I Think That is The CNN Does not the shapes of input data, so what Can I Do Here ?? Thanks

推荐答案

进行如下更改:

from keras.layers import Merge
...
...

# Making The Combinition ##########################################################
final = Sequential()
final.add(Merge([branch_one, branch_two], mode = 'concat'))

...
...

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