How many model parameters do we need to optimize for the following CNN model?

Question

We use the following convolutional neural network to classify a set of 32×32 greyscale images (so the input size will be 32$\\times$32$\\times$1):

1. Layer 1: convolutional layer with the ReLU nonlinear activation function, 100 5×5 filters with stride 1.

2. Layer 2: 2×2 max-pooling layer

3. Layer 3: convolutional layer with the ReLU nonlinear activation function, 50 3×3 filters with stride 1.

4. Layer 4: 2×2 max-pooling layer

5. Layer 5: fully-connected layer

6. Layer 6: classification layer

How many model parameters do we need to optimize in the first layer and in the second layer (assume the bias term is used)

Answer 1

You can use tensorflow to display the number of trainable parameters:

import tensorflow as tf
from tensorflow.keras import layers

def make_model():
    model = tf.keras.Sequential()
    model.add(layers.Conv2D(100, (5, 5), strides=1,input_shape=[32, 32, 1]))
    model.add(layers.LeakyReLU())
    model.add(layers.MaxPooling2D(pool_size=(2, 2)))

    return model

model=make_model()

model.summary()

This gives 2600 trainable params and 0 non-trainable params.