Tensor Randomization

tensor_randomization_example.py

import numpy as np

# Data
tensor = np.array([
    [ 9.8348e+18,  4.5845e-41, -3.5873e-11],
    [ 3.0950e-41,  1.2141e-40,  3.8102e-38],
    [ 5.3741e-30,  4.8419e+30,  7.7765e+31],
    [ 4.7987e+30,  4.9796e-37,  2.1325e-41],
    [ 2.4230e+21,  1.6045e-37,  1.9106e-28]
])

# Setup
rate = 0.2

# Replace a random 20% of each array in the tensor with zeroes
keep_prob = 1 - rate 
ones = np.ones(tensor.shape, dtype=int)
mask = np.random.binomial(n=ones, p=keep_prob, size=ones.shape)
tensor * mask

# Mask a random 20% of each array in the tensor
ones = np.ones(tensor.shape, dtype=int)
mask = np.random.binomial(n=ones, p=rate, size=ones.shape)
np.ma.masked_array(tensor, mask)


# # Links re: how DL libraries do this for dropout layers
# https://stackoverflow.com/questions/54109617/implementing-dropout-from-scratch

# # Tensorflow internals
# https://github.com/tensorflow/tensorflow/blob/v2.3.0/tensorflow/python/ops/nn_ops.py#L5024
# https://github.com/tensorflow/tensorflow/blob/v2.3.0/tensorflow/python/framework/ops.py#L1480