Tensorboard and Dropout Layers

Question

I have a very basic query. I have made 4 almost identical(Difference being input shapes) CNN and have merged them while connecting to a Feed Forward Network of fully connected layers.

Code for the almost identical CNN(s):

model3 = Sequential()
model3.add(Convolution2D(32, (3, 3), activation='relu', padding='same', 
                                     input_shape=(batch_size[3], seq_len, channels)))
model3.add(MaxPooling2D(pool_size=(2, 2)))
model3.add(Dropout(0.1))
model3.add(Convolution2D(64, (3, 3), activation='relu', padding='same'))
model3.add(MaxPooling2D(pool_size=(2, 2)))
model3.add(Flatten())

But on tensorboard I see all the Dropout layers are interconnected, and Dropout1 is of different color than Dropout2,3,4,etc which all are the same color.

Answer 1

I know this is an old question but I had the same issue myself and just now I realized what's going on

Dropout is only applied if we're training the model. This should be deactivated by the time we're evaluating/predicting. For that purpose, keras creates a learning_phase placeholder, set to 1.0 if we're training the model. This placeholder is created inside the first Dropout layer you create and is shared across all of them. So that's what you're seeing there!