Tensorflow/Keras Conv2D layers with padding='SAME' behave strangely

Question

My question:

A straightforward experiment that I conducted showed that using padding=\'SAME\' in a conv2d layer in Keras/TF is differen

Answer 1

padding='Same' in Keras means padding is added as required to make up for overlaps when the input size and kernel size do not perfectly fit.

Example of padding='Same':

# Importing dependency
import keras
from keras.models import Sequential
from keras.layers import Conv2D

# Create a sequential model
model = Sequential()

# Convolutional Layer
model.add(Conv2D(filters=24, input_shape=(5,5,1), kernel_size=(2,2), strides =(2,2) ,padding='Same'))

# Model Summary
model.summary()

Output of the code -

Model: "sequential_20"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_28 (Conv2D)           (None, 3, 3, 24)          120       
=================================================================
Total params: 120
Trainable params: 120
Non-trainable params: 0
_________________________________________________________________

Pictorial Representation: Below image shows how the padding for the input (input_shape=(5,5,1), kernel_size=(2,2), strides =(2,2)) when padding='Same'.

------------------------------------------------------------------------------------------------------------------

padding='Valid' in Keras means no padding is added.

Example of padding='Valid': Have used same input for Conv2D that we used above for padding = 'Same' .i.e. (input_shape=(5,5,1), kernel_size=(2,2), strides =(2,2))

# Importing dependency
import keras
from keras.models import Sequential
from keras.layers import Conv2D

# Create a sequential model
model = Sequential()

# Convolutional Layer
model.add(Conv2D(filters=24, input_shape=(5,5,1), kernel_size=(2,2), strides =(2,2) ,padding='Valid'))

# Model Summary
model.summary()

Output of the code -

Model: "sequential_21"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_29 (Conv2D)           (None, 2, 2, 24)          120       
=================================================================
Total params: 120
Trainable params: 120
Non-trainable params: 0
_________________________________________________________________

Pictorial Representation: Below image shows there is no padding added for the input (input_shape=(5,5,1), kernel_size=(2,2), strides =(2,2)) when padding='Valid'.

------------------------------------------------------------------------------------------------------------------

Now lets try same code that we used for padding='Valid' for the input (input_shape=(6,6,1), kernel_size=(2,2), strides =(2,2)). Here padding='Valid' should behave same as padding='Same'.

Code -

# Importing dependency
import keras
from keras.models import Sequential
from keras.layers import Conv2D

# Create a sequential model
model = Sequential()

# Convolutional Layer
model.add(Conv2D(filters=24, input_shape=(6,6,1), kernel_size=(2,2), strides =(2,2) ,padding='Valid'))

# Model Summary
model.summary()

Output of the code -

Model: "sequential_22"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_30 (Conv2D)           (None, 3, 3, 24)          120       
=================================================================
Total params: 120
Trainable params: 120
Non-trainable params: 0
_________________________________________________________________