Is there a way to make the numbers in a numpy array randomly positive or negative?

Question

I'm making a neural network, and when assigning random weight values using np.random.rand(797, 600) for example, they all turn out positive (from 0 to 1). This is fine normally, but I have up to 800 nodes which means that by the end of initial forward propagation if all the weights are positive the sigmoided output is always 1, just because the sum of all values adds up so fast with so many synapses and nodes.

To solve this problem, I wanted to make a function that would randomly multiply each weight by 1 or -1. Then, with a random number of positive and negative numbers, the outputs would be closer to 0 and the sigmoid function would return an actual prediction that wasn't 1 all the time. Here are the two methods I have tried to do this, and neither of them worked.

# method 1

import random as rand
import numpy as np


def random_positive_or_negative(value):
    return rand.choice([1, -1]) * value

example_weights = np.random.rand(4, 4)
print(random_positive_or_negative(example_weights))

prints either something like this:

[[0.89098337 0.82291754 0.7730489  0.371631  ]
 [0.22790221 0.19964653 0.94609767 0.57070762]
 [0.35840034 0.06689964 0.71565062 0.43360395]
 [0.57860037 0.11338668 0.338402   0.30737682]]

or like this:

[[-0.79750561 -0.94206793 -0.389792   -0.18541991]
 [-0.36132547 -0.66040689 -0.06270979 -0.90775857]
 [-0.22350726 -0.21148559 -0.78874412 -0.9702534 ]
 [-0.74124928 -0.31675956 -0.97471565 -0.18389436]]

expected output something like this:

[[0.2158195  0.16492544 0.25672823 -0.5392236 ]
 [-0.54530676 0.98215902 -0.14348151 0.02629328]
 [-0.8642513  -0.71726141 -0.15890395 -0.08488439]
 [0.54413198 -0.69790104 0.05317512 -0.06144755]]

# method 2

import random as rand
import numpy as np


def random_positive_or_negative(value):
    return (i * rand.choice([-1, 1]) for i in value)

example_weights = np.random.rand(4, 4)
print(random_positive_or_negative(example_weights))

prints this:

. at 0x114c474a0>

expected output something like this:

[[0.2158195  0.16492544 0.25672823 -0.5392236 ]
 [-0.54530676 0.98215902 -0.14348151 0.02629328]
 [-0.8642513  -0.71726141 -0.15890395 -0.08488439]
 [0.54413198 -0.69790104 0.05317512 -0.06144755]]

user_7896849 · Answer

You can create a matrix that is filled by random numbers sampled from universe [-1, 1] and multiply it with the random weights. See the code below

import random as rand
import numpy as np


def random_positive_or_negative(value):
    return np.matmul(value, np.random.choice(np.array([-1, 1]), value.shape))

example_weights = np.random.rand(4, 4)
print(random_positive_or_negative(example_weights))


[[-0.7193314  -0.1604493  -0.47038437 -0.34173619]
 [ 0.44388733 -0.55476039 -1.24586476 -0.77014132]
 [-0.05796445 -1.72406933 -1.5756221  -0.18125272]
 [ 0.15338058 -0.56916866 -1.5706919  -0.01815559]]

user_6075996 · Answer

Your first method chooses one number, 1 or -1, and multiplies the whole argument array by that number. Your second method uses a generator expression, so it will return a generator. If you don't understand this, you should read about generators first.

There is no need to multiply any values by 1, since that does nothing. Instead, pick random indices and multiply them by -1. Something like:

 n = example_weights.size
 inds = np.random.choice(n, n, replace=False)
 example_weights.flat[inds] *= -1

user_14399833 · Answer

There are a few ways to do it, but in my opinion the sleekest was proposed by @xdurch0: to use np.random.uniform(-1., 1., size)

Here is how the code would work:

import numpy as np

example_weights = np.random.uniform(-1., 1., [4, 4])
print(example_weights)

prints something like:

[[-0.91852112 -0.77686616 -0.41495832  0.78950649]
 [-0.7493404  -0.73794508  0.54622202 -0.89033855]
 [ 0.31196172  0.06584705 -0.88698673 -0.24149299]
 [-0.89654412  0.45450007 -0.40640681  0.81490564]]

Is there a way to make the numbers in a numpy array randomly positive or negative?

3 Answers