I have question about loss function. something wrong, i think....? about capsnet-tensorflow

[hhjung1202]

im sorry i know that vj is like unit vector of 16 D

[naturomics]

You do not understand squashing correctly. Squashing ensures the length (euclidean norm) of its output vector is in [0, 1], not about the element in the vector. Have fun with the following code:

import numpy as np
from matplotlib import pyplot as plt

def squash(vector, axis=None):

norm = np.linalg.norm(vector, axis=axis, keepdims=True)`
norm_squared = np.square(norm)
scalar_factor = norm_squared / (1 + norm_squared)
return scalar_factor * (vector / norm)

#Create 10000 samples each with 3 elements
num_samples = 10000
c1 = np.random.uniform(size=(num_samples, 1))
c2 = np.random.normal(size=(num_samples, 1))
c3 = np.random.logistic(size=(num_samples, 1))
vectors = np.hstack((c1, c2, c3)) # [num_samples, 3]
squashed_vector = squash(vectors, axis=1)
length = np.sqrt(np.sum(np.square(squashed_vector), axis=1))
plt.plot(np.sort(length))

[parinaya-007]

Look "reduce_mean" function in utils.py carefully and also after this function is applied using axis=2, we are taking the mean of the sum of squared sum of vector so the range is not (-4, 4) it is (0, 1).