CODEWITHSUNDEEP
pythontensorflowkerasneural-networkdeep-learning
Olca Orakcı
Olca Orakcı

Reputation: 382

Counting CNN layers

i created a CNN for a project i am involved with and i need to present it. The issue is, I am not sure about how to count the layers.

Here is my model: 

model = Sequential()
model.add(Conv2D(64,(3,3), input_shape = (40,40,2)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64,(3,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))


model.add(Dense(1600))
model.add(Reshape((40,40)))
model.add(Activation('sigmoid'))

model.compile(loss='MSE',
             optimizer='SGD',
             metrics=['MAE'])

len(model.layers) returned 12 :

So i used 1 input 10 hidden 1 output layers,

or

i need to count them as a group and say 1 input 2 hidden 1 output?

Upvotes: 6

Views: 4662

Answers (1)

Ranmal Dewage
Ranmal Dewage

Reputation: 466

When calculating the depth of a CNN network, we only consider the layers that have tunable/trainable weights/parameters. In CNN only Convolutional Layers and Fully Connected Layers will have trainable parameters. If you want to label layers consider only Convolutional, Full Connected and Output layers (Conv2D and Dense). Max Pooling Layers are generally take together with Convolutional Layers as one layer.

Upvotes: 10

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