在 keras 中连接四个 CNN 模型

Question

我想在 Keras 中将四个 CNN（pop1、pop2、pop3 和 pop4）合并为一个。我的目标是构建一个能够将图像分配给三种可能结果的分类器。以下是连接 CNN 的方法：

from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers import Concatenate
from keras.models import Model

# Start With Pop1 Branch ############################################################

classifier_pop1 = Sequential()
classifier_pop1.add(Conv2D(1, (3,3), input_shape = (4009, 36, 1), activation = 'relu'))
classifier_pop1.add(MaxPooling2D(pool_size = (3,3)))
classifier_pop1.add(Conv2D(1, (3,3), activation = 'relu'))
classifier_pop1.add(MaxPooling2D(pool_size = (3,3)))
classifier_pop1.add(Flatten())
classifier_pop1.add(Dense(units = 300, activation = 'relu'))
classifier_pop1.add(Dense(units = 24, activation = 'relu'))

# Start With Pop2 Branch ############################################################

classifier_pop2 = Sequential()
classifier_pop2.add(Conv2D(1, (3,3), input_shape = (4009, 36, 1), activation = 'relu'))
classifier_pop2.add(MaxPooling2D(pool_size = (3,3)))
classifier_pop2.add(Conv2D(1, (3,3), activation = 'relu'))
classifier_pop2.add(MaxPooling2D(pool_size = (3,3)))
classifier_pop2.add(Flatten())
classifier_pop2.add(Dense(units = 300, activation = 'relu'))
classifier_pop2.add(Dense(units = 24, activation = 'relu'))

# Start With Pop3 Branch ############################################################

classifier_pop3 = Sequential()
classifier_pop3.add(Conv2D(1, (3,3), input_shape = (4009, 32, 1), activation = 'relu'))
classifier_pop3.add(MaxPooling2D(pool_size = (3,3)))
classifier_pop3.add(Conv2D(1, (3,3), activation = 'relu'))
classifier_pop3.add(MaxPooling2D(pool_size = (3,3)))
classifier_pop3.add(Flatten())
classifier_pop3.add(Dense(units = 300, activation = 'relu'))
classifier_pop3.add(Dense(units = 24, activation = 'relu'))

# Start With Pop4 Branch ############################################################

classifier_pop4 = Sequential()
classifier_ pop4.add(Conv2D(1, (3,3), input_shape = (4009, 18, 1), activation = 'relu'))
classifier_ pop4.add(MaxPooling2D(pool_size = (3,3)))
classifier_ pop4.add(Conv2D(1, (3,3), activation = 'relu'))
classifier_ pop4.add(MaxPooling2D(pool_size = (3,3)))
classifier_ pop4.add(Flatten())
classifier_ pop4.add(Dense(units = 300, activation = 'relu'))
classifier_ pop4.add(Dense(units = 24, activation = 'relu'))

# Making The Combinition ##########################################################

model_concat = Concatenate()([classifier_pop1.output,classifier_pop2.output,classifier_pop3.output,classifier_ pop4.output])

model_concat = Dense(3, activation='softmax')(model_concat)

model = Model(inputs=[classifier_pop1.input,classifier_pop2.input,classifier_pop3.input,classifier_ pop4.input], outputs=model_concat)

Model_plot

## Compiling the model
model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])

我怀疑我的代码中的错误与以下代码有关。我有四个文件夹（pop1、pop2、pop3 和 pop4），其中包含三个子文件夹（Model1、Model2 和 Model3）。我不确定如何在这部分使用 imageGenerator。我刚刚关注了另一个帖子：Combining Two CNN's

##Image preprocessing

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set_pop1 = train_datagen.flow_from_directory('./Datasets/training_set/Pop1',
                                                 target_size = (4009, 36),
                                                 batch_size = 100,
                                                 class_mode = 'categorical',
                                                 color_mode='grayscale',
                                                 shuffle=True)

test_set_pop1 = test_datagen.flow_from_directory('./Datasets/test_set/Pop1',
                                            target_size = (4009, 36),
                                            batch_size = 100,
                                            class_mode = 'categorical',
                                            color_mode='grayscale',
                                            shuffle=False)

training_set_pop2 = train_datagen.flow_from_directory('./Datasets/training_set/Pop2',
                                                 target_size = (4009, 36),
                                                 batch_size = 100,
                                                 class_mode = 'categorical',
                                                 color_mode='grayscale',
                                                 shuffle=True)

test_set_pop2 = test_datagen.flow_from_directory('./Datasets/test_set/Pop2',
                                            target_size = (4009, 36),
                                            batch_size = 100,
                                            class_mode = 'categorical',
                                            color_mode='grayscale',
                                            shuffle=False)
                                            
training_set_pop3 = train_datagen.flow_from_directory('./Datasets/training_set/Pop3',
                                                 target_size = (4009, 32),
                                                 batch_size = 100,
                                                 class_mode = 'categorical',
                                                 color_mode='grayscale',
                                                 shuffle=True)

test_set_pop3 = test_datagen.flow_from_directory('./Datasets/test_set/Pop3',
                                            target_size = (4009, 32),
                                            batch_size = 100,
                                            class_mode = 'categorical',
                                            color_mode='grayscale',
                                            shuffle=False)

training_set_pop4 = train_datagen.flow_from_directory('./Datasets/training_set/Planiceps',
                                                 target_size = (4009, 18),
                                                 batch_size = 100,
                                                 class_mode = 'categorical',
                                                 color_mode='grayscale',
                                                 shuffle=True)

test_set_pop4 = test_datagen.flow_from_directory('./Datasets/test_set/Planiceps',
                                            target_size = (4009, 18),
                                            batch_size = 100,
                                            class_mode = 'categorical',
                                            color_mode='grayscale',
                                            shuffle=False) 

model.fit([training_set_pop1,training_set_pop2,training_set_pop3,training_set_pop4],
                        steps_per_epoch = int(2400/100),
                         epochs = 4,
                         validation_data = [test_set_pop1,test_set_pop2,test_set_pop3,test_set_pop4],
                         validation_steps = int(600/100))

然后，我得到了这个错误：

ValueError: Failed to find data adapter that can handle input: (<class 'list'> containing values of types {"<class 'keras_preprocessing.image.directory_iterator.DirectoryIterator'>"}), <class 'NoneType'>

Answer 1

也许您可以尝试以下方法，因为您无法将生成器作为列表传递：

model.fit(zip(training_set_pop1,training_set_pop2,training_set_pop3,training_set_pop4),
                        steps_per_epoch = int(2400/100),
                         epochs = 4,
                         validation_data = zip(test_set_pop1,test_set_pop2,test_set_pop3,test_set_pop4),
                         validation_steps = int(600/100))