Why is my input and output shape from keras conv2d the same dimensions?

Question

I'm trying to rebuild someone else's.network with this shape:

My (image) data going into the.network has this shape:

print(X_train[0].shape)
print(len(X_train))
print(len(y_train))

(150,150,3)
2160
2160

I can write and get a neural.network to run no problem:

model = Sequential()
model.add(Input(shape=(150,150,3)))
model.add(Conv2D(32, kernel_size=3,strides=(1, 1),activation='relu', padding='same', dilation_rate=1))
model.add(MaxPooling2D(pool_size=(2, 2)))

But then when I view the plot, it looks like this:

Can someone explain to me why my output of the Conv2D layer does not decrease from 150 to 148, as expected? (Presumably then, the 'wrong' numbers in the max_pooling layers are a consequence of this, so I only need to focus on understanding the discrepancy in the Conv2D layer).

Answer 1

A possible solution is to use padding=valid in the Conv2D layer:

model = Sequential()
model.add(Input(shape=(150,150,3)))
model.add(Conv2D(32, kernel_size=3, strides=(1, 1), activation='relu', padding='valid', dilation_rate=1))
model.add(MaxPooling2D(pool_size=(2, 2)))

The resulting summary:

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 148, 148, 32)      896       
                                                                 
 max_pooling2d (MaxPooling2D)  (None, 74, 74, 32)       0         

=================================================================

Answer 2

You use padding='same so you dont "loose" any values on the side

This has a good gif on different padding strategies