Concatenate four CNN models in keras - keras

Question

I want to combine four CNN (pop1, pop2, pop3, and pop4) into one in Keras. My goal is to build a classifier able to assign an image to three possible outcomes. Here is how a concatenate the CNNs:
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'])
I suspect that the error in my code has to do with the below code. I have four folders (pop1, pop2, pop3, and pop4) that contain three subfolders inside (Model1, Model2, and Model3). I am not sure how to use imageGenerator in this part. I just followed this other post: 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))
Then, I got this error:
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

Maybe you could try the following, since you can't pass the generators as a list:
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))