Is it possible to implement switching between layers based on input in Keras?

Question

It is possible to implement a neural network with structure like this in Keras?

The idea is the following:

In the input the model receives an integer i (labeled red) and some other stuff v (in picture it is 0.12345). Next, there are several similar parallel layers. Depending on the value of i, v goes into the ith layer, ignoring other layers and then output of that layer goes into the output layer.

To put it in another words, all layers except of ith a being ignored.

You should add the specification of your special layer clearly, hope it will more helpful. — ashraful16, Aug 30 '20 at 23:06
Does this answer your question? [Combining the outputs of multiple models into one model](https://stackoverflow.com/questions/43150635/combining-the-outputs-of-multiple-models-into-one-model) — cddt, Aug 31 '20 at 01:36
I believe these previous questions can answer this one: https://stackoverflow.com/questions/43150635/combining-the-outputs-of-multiple-models-into-one-model and https://stackoverflow.com/questions/43151775/how-to-have-parallel-convolutional-layers-in-keras — cddt, Aug 31 '20 at 01:38
@ashraful I'm sorry for not formulating the question clearly enough. I've updated the question with more detailed description — kandi, Aug 31 '20 at 10:18
@cddt I'm afraid they are not relevant. I've updated my question with more detailed description to make it more clear — kandi, Aug 31 '20 at 10:19

score 0 · Answer 1 · answered Sep 07 '20 at 01:34

I think the easiest solution here, if I have understood your question correctly, is to separate your data sets based on your value of i.

So take your X_train and split it into X_train_1, X_train_2, etc. Likewise with your X_test, split it into X_test_1, X_test_2, etc.

from keras.models import Sequential, Model
from keras.layers import *
from keras.utils import plot_model

Then set up separate models:

model1 = Sequential()
model1.add(Conv2D(32, kernel_size=(3,3), activation="relu", input_shape=(24,24,3)))
model1.add(MaxPooling2D(pool_size=(2,2)))
model1.add(Flatten())
model1.add(Dropout(0.5))
model1.add(Dense(512, activation = "relu"))

model2 = Sequential()
model2.add(Conv2D(32, kernel_size=(3,3), activation="relu", input_shape=(24,24,3)))
model2.add(MaxPooling2D(pool_size=(2,2)))
model2.add(Flatten())
model2.add(Dropout(0.5))
model2.add(Dense(512, activation = "relu"))

You will want to use the functional API to combine them. I have used Concatenate(), other options are shown in the documentation here.

outputs = Concatenate()([model1.output,model2.output])
outputs = Dense(256, activation='relu')(outputs)
outputs = Dropout(.5)(outputs)
outputs = Dense(5, activation='softmax')(outputs)

Now configure your final model, specifying the inputs and outputs:

model = Model(inputs=[model1.inputs, model2.inputs], outputs=outputs)

Checking model.summary(), you can see how each layer is connected:

__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
conv2d_input (InputLayer)       [(None, 24, 24, 3)]  0                                            
__________________________________________________________________________________________________
conv2d_1_input (InputLayer)     [(None, 24, 24, 3)]  0                                            
__________________________________________________________________________________________________
conv2d (Conv2D)                 (None, 22, 22, 32)   896         conv2d_input[0][0]               
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 22, 22, 32)   896         conv2d_1_input[0][0]             
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D)    (None, 11, 11, 32)   0           conv2d[0][0]                     
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)  (None, 11, 11, 32)   0           conv2d_1[0][0]                   
__________________________________________________________________________________________________
flatten (Flatten)               (None, 3872)         0           max_pooling2d[0][0]              
__________________________________________________________________________________________________
flatten_1 (Flatten)             (None, 3872)         0           max_pooling2d_1[0][0]            
__________________________________________________________________________________________________
dropout (Dropout)               (None, 3872)         0           flatten[0][0]                    
__________________________________________________________________________________________________
dropout_1 (Dropout)             (None, 3872)         0           flatten_1[0][0]                  
__________________________________________________________________________________________________
dense (Dense)                   (None, 512)          1982976     dropout[0][0]                    
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 512)          1982976     dropout_1[0][0]                  
__________________________________________________________________________________________________
concatenate (Concatenate)       (None, 1024)         0           dense[0][0]                      
                                                                 dense_1[0][0]                    
__________________________________________________________________________________________________
dense_2 (Dense)                 (None, 256)          262400      concatenate[0][0]                
__________________________________________________________________________________________________
dropout_2 (Dropout)             (None, 256)          0           dense_2[0][0]                    
__________________________________________________________________________________________________
dense_3 (Dense)                 (None, 5)            1285        dropout_2[0][0]                  
==================================================================================================
Total params: 4,231,429
Trainable params: 4,231,429
Non-trainable params: 0
__________________________________________________________________________________________________

But it's easier to visualise the model with plot_model(model, to_file='image.png', show_shapes=True):

Then for training the model, you will need to feed in the different inputs, not forgetting your test (or validation) data:

model.fit([X_train_1, X_train_2], y_train, validation_data = ([X_test_1, X_test_2], y_val), ...)

NB: The sub-models (here model1, model2, etc.) don't have to have the same structure. They can have different sized layers, different numbers of layers, and different types of layers. This is also how you can include data sets with different types of features in your model.

Is it possible to implement switching between layers based on input in Keras?

1 Answers1