what is the number of layers in EfficientNetB2?

Question

Knowing that the total number of layers in EfficientNet-B0 is 237 and in EfficientNet-B7 the total comes out to 813, what is the total number of layers in EfficientNetB2 ?

Answer 1

If you print len(model.layers) on EfficientNetB2 model with keras you will have 342 layers.

import tensorflow as tf 
from tensorflow.keras.applications import EfficientNetB2
model = EfficientNetB2(weights='imagenet')
print(len(model.layers))

You can do this with all other versions of EfficientNetBx if you wish.

But as Pradyut said here normally not all layers are taken into account when we count them:

While counting the number of layers in a Neural Network we usually only count convolutional layers and fully connected layers. Pooling Layer is taken together with the Convolutional Layer and counted as one layer and Dropout is a regularization technique so it will also not be counted as a separate layer.

For reference, the VGG16 mode is defined as a 16 layer model. Those 16 layers are only the Convolutional Layers and Fully Connected Dense Layers. If you count all the pooling and activation layers it will change to a 41 layer model, which it is not. Reference: VGG16, VGG16 Paper

So as per your code you have 3 layers (1 Convolutional Layer with 28 Neurons, 1 Fully Connected Layer with 128 Neurons and 1 Fully Connected Layer with 10 neurons)

As for making it a 10 layer network you can add more convolutional layers or dense layers before the output layers, but it won't be necessary for the MNIST dataset.

I hope I answered your question!