init.py

import mxnet as mx
import numpy as np
import random

from collections import namedtuple


def run(seed: int, init_method: str):

    mx.random.seed(seed)

    # module

    Batch = namedtuple('Batch', ['data'])
    data = mx.sym.Variable('data')
    out  = mx.sym.FullyConnected(data, name='out', num_hidden=6)
    mod = mx.mod.Module(symbol=out, label_names=None)
    mod.bind(data_shapes=[('data', (1, 2))])

    # initializer

    if init_method == "normal":
        init = mx.init.Normal(0.005)
    elif init_method == "xavier":
        init = mx.init.Xavier("uniform", "avg", 3.0)

    mod.init_params(init)

    for dictionary in mod.get_params():
        for key in dictionary:
            if key == "out_weight":
                return dictionary[key].asnumpy()

    return None

seed = 1
results = []

init_method = "normal"

for _ in range(100):
    results.append(run(seed, init_method))

first = results[0]

for result in results[1:]:
    assert np.allclose(first, result)

results = []

init_method = "xavier"

for _ in range(100):
    results.append(run(seed, init_method))

first = results[0]

for result in results[1:]:
    assert np.allclose(first, result)