How to compute number of weights of CNN? - tensorflow

Question

How can we compute number of weights considering a convolutional neural network that is used to classify images into two classes :
INPUT: 100x100 gray-scale images.
LAYER 1: Convolutional layer with 60 7x7 convolutional filters (stride=1, valid
padding).
LAYER 2: Convolutional layer with 100 5x5 convolutional filters (stride=1, valid
padding).
LAYER 3: A max pooling layer that down-samples Layer 2 by a factor of 4 (e.g., from 500x500 to 250x250)
LAYER 4: Dense layer with 250 units
LAYER 5: Dense layer with 200 units
LAYER 6: Single output unit
Assume the existence of biases in each layer. Moreover, pooling layer has a weight (similar to AlexNet)
How many weights does this network have?
Some Keras code
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Conv2D, MaxPooling2D
model = Sequential()
# Layer 1
model.add(Conv2D(60, (7, 7), input_shape = (100, 100, 1), padding="same", activation="relu"))
# Layer 2
model.add(Conv2D(100, (5, 5), padding="same", activation="relu"))
# Layer 3
model.add(MaxPooling2D(pool_size=(2, 2)))
# Layer 4
model.add(Dense(250))
# Layer 5
model.add(Dense(200))
model.summary()

Answer 1

TL;DR - For TensorFlow + Keras
Use Sequential.summary - Link to documentation.
Example usage:
from tensorflow.keras.models import *
model = Sequential([
# Your architecture here
]);
model.summary()
The output for your architecture is:
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 94, 94, 60) 3000
_________________________________________________________________
conv2d_1 (Conv2D) (None, 90, 90, 100) 150100
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 45, 45, 100) 0
_________________________________________________________________
flatten (Flatten) (None, 202500) 0
_________________________________________________________________
dense (Dense) (None, 250) 50625250
_________________________________________________________________
dense_1 (Dense) (None, 200) 50200
_________________________________________________________________
dense_2 (Dense) (None, 1) 201
=================================================================
Total params: 50,828,751
Trainable params: 50,828,751
Non-trainable params: 0
_________________________________________________________________
That's 50,828,751 parameters.
Explanation
Number of weights in a 2D Convolutional layer
For a 2D Convolutional layer having
num_filters filters,
a filter size of filter_size * filter_size * num_channels,
and a bias parameter per filter
The number of weights is: (num_filters * filter_size * filter_size * num_channels) + num_filters
E.g.: LAYER 1 in your neural network has
60 filters
and a filter size of 7 * 7 * 1. (Notice that the number of channels (1) comes from the input image.)
The number of weights in it is: (60 * 7 * 7 * 1) + 60, which is 3000.
Number of weights in a Dense layer
For a Dense layer having
num_units neurons,
num_inputs neurons in the layer prior to it,
and a bias parameter per neuron
The number of weights is: (num_units * num_inputs) + num_units
E.g. LAYER 5 in your neural network has
200 neurons
and the layer prior to it - LAYER 4 - has 250 neurons.
The number of weights in it is 200 * 250, which is 50200.