Naive Conv2D implementation

conv_2d.py

#from here https://gist.githubusercontent.com/anirudhshenoy/4d5e579d3585159f133ca7804cdca25a/raw/c7314527d27bc70ab38b061b5e33b086704252e0/conv_2d.py
def conv_2d(x, kernel, bias):
    kernel_shape = kernel.shape[0]
    
    # Assuming Padding = 0, stride = 1
    output_shape = x.shape[0] - kernel_shape + 1
    result = np.zeros((output_shape, output_shape))
    
    for row in range(x.shape[0] - 1):
        for col in range(x.shape[1] - 1):
            window = x[row: row + kernel_shape, col: col + kernel_shape]
            result[row, col] = np.sum(np.multiply(kernel,window))
    return result + bias