[python] Keras model.summary() result - Understanding the # of Parameters

For Dense Layers:

output_size * (input_size + 1) == number_parameters 

For Conv Layers:

output_channels * (input_channels * window_size + 1) == number_parameters

Consider following example,

model = Sequential([
Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
Conv2D(64, (3, 3), activation='relu'),
Conv2D(128, (3, 3), activation='relu'),
Dense(num_classes, activation='softmax')
])

model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 222, 222, 32)      896       
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 220, 220, 64)      18496     
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 218, 218, 128)     73856     
_________________________________________________________________
dense_9 (Dense)              (None, 218, 218, 10)      1290      
=================================================================

Calculating params,

assert 32 * (3 * (3*3) + 1) == 896
assert 64 * (32 * (3*3) + 1) == 18496
assert 128 * (64 * (3*3) + 1) == 73856
assert num_classes * (128 + 1) == 1290

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