One dimensional convolutions
A convolution of an one-dimensional array with a kernel comprises of taking the kernel, sliding it along the array, multiplying it with the items in the array that overlap with the kernel in that location and summing this product.
This exercise is part of the course
Image Modeling with Keras
Exercise instructions
Multiply each window in the input array with the kernel and sum the multiplied result and allocate the result into the correct entry in the output array (conv).
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
array = np.array([1, 0, 1, 0, 1, 0, 1, 0, 1, 0])
kernel = np.array([1, -1, 0])
conv = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
# Output array
for ii in range(8):
conv[ii] = (____ * array[____:____+____]).sum()
# Print conv
print(conv)