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"""
# -*- coding: utf-8 -*-
#
# Copyright 2021 Michael Büsch <m@bues.ch>
#
# Licensed under the Apache License version 2.0
# or the MIT license, at your option.
# SPDX-License-Identifier: Apache-2.0 OR MIT
#
"""
from mlplib.activation import *
from mlplib.backward import *
from mlplib.forward import *
from mlplib.gradient_check import *
from mlplib.init import *
from mlplib.loss import *
from mlplib.parameters import *
def test_gradient_check_ok():
seed(42)
inputs = 3
layout = (6, 15, 5, 2)
params = Parameters(
weights=init_layers_weights(inputs, layout),
biases=init_layers_biases(layout),
actvns=[
ReLU(),
ReLU(),
ReLU(),
Sigmoid(),
],
)
x = standard_normal((10, inputs))
y = standard_normal((10, layout[-1]))
gradients, _ = backward_prop(x, y, params, MSE())
ok = gradient_check(x, y, params, MSE(), gradients)
assert ok
def test_gradient_check_notok_weight():
seed(43)
inputs = 3
layout = (6, 15, 5, 2)
params = Parameters(
weights=init_layers_weights(inputs, layout),
biases=init_layers_biases(layout),
actvns=[
ReLU(),
ReLU(),
ReLU(),
Sigmoid(),
],
)
x = standard_normal((10, inputs))
y = standard_normal((10, layout[-1]))
gradients, _ = backward_prop(x, y, params, MSE())
# Manipulate one weight gradient.
gradients.dw[1][4][3] += 0.01
ok = gradient_check(x, y, params, MSE(), gradients)
assert not ok
def test_gradient_check_notok_bias():
seed(44)
inputs = 3
layout = (6, 15, 5, 2)
params = Parameters(
weights=init_layers_weights(inputs, layout),
biases=init_layers_biases(layout),
actvns=[
ReLU(),
ReLU(),
ReLU(),
Sigmoid(),
],
)
x = standard_normal((10, inputs))
y = standard_normal((10, layout[-1]))
gradients, _ = backward_prop(x, y, params, MSE())
# Manipulate one bias gradient.
gradients.db[1][0][3] += 0.01
ok = gradient_check(x, y, params, MSE(), gradients)
assert not ok
# vim: ts=4 sw=4 expandtab
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