Its A Basic Image Classifier using Keras we can the structure i use is similar which is available in keras documentation
conv2d_33 (Conv2D) (None, 148, 148, 32) 896
activation_31 (Activation) (None, 148, 148, 32) 0
max_pooling2d_29 (MaxPooling (None, 49, 49, 32) 0
conv2d_34 (Conv2D) (None, 47, 47, 32) 9248
activation_32 (Activation) (None, 47, 47, 32) 0
max_pooling2d_30 (MaxPooling (None, 23, 23, 32) 0
conv2d_35 (Conv2D) (None, 21, 21, 64) 18496
activation_33 (Activation) (None, 21, 21, 64) 0
max_pooling2d_31 (MaxPooling (None, 10, 10, 64) 0
flatten_3 (Flatten) (None, 6400) 0
dense_2 (Dense) (None, 64) 409664
activation_34 (Activation) (None, 64) 0
dropout_1 (Dropout) (None, 64) 0
dense_3 (Dense) (None, 1) 65
Total params: 438,369
Trainable params: 438,369
Non-trainable params: 0
Image -> 150*150*3 { height -> 150, width->150, depth or RGB -> 3}
than i apply filter 32 3*3 => (148,148,32)
where 32 is stack of filter basically we found 148 unique position while doing convolution
=>(I-F)/(S+1)
=>total number of observation = (3*3*3)*32 -> 864
it is basically moving the filter over the image with some stride
we do dot product of w and x value which lie in the filter and sum it up which give us a single
value this process is called convolution
its basically (w.transpose().x +bais)