Model: “functional_3”
Layer (type) Output Shape Param #
input_2 (InputLayer) [(None, 64, 64, 3)] 0
conv2d_4 (Conv2D) (None, 64, 64, 8) 392
re_lu_4 (ReLU) (None, 64, 64, 8) 0
max_pooling2d_4 (MaxPooling2 (None, 8, 8, 8) 0
conv2d_5 (Conv2D) (None, 8, 8, 16) 528
re_lu_5 (ReLU) (None, 8, 8, 16) 0
max_pooling2d_5 (MaxPooling2 (None, 2, 2, 16) 0
flatten_3 (Flatten) (None, 64) 0
dense_3 (Dense) (None, 6) 390
I do not understand why Conv2D have 528 parameters? The number of parameters should be (2* 2* 16 * 3)+16 = 208 right?
