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test_activations.py
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test_activations.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import unittest
import tensorflow as tf
import numpy as np
import tensorlayer as tl
from tests.utils import CustomTestCase
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
class Test_Leaky_ReLUs(CustomTestCase):
@classmethod
def setUpClass(cls):
cls.alpha = 0.2
cls.vmin = 0
cls.vmax = 10
@classmethod
def tearDownClass(cls):
pass
def test_lrelu(self):
for i in range(-5, 15):
if i > 0:
good_output = i
else:
good_output = self.alpha * i
computed_output = tl.act.leaky_relu(float(i), alpha=self.alpha)
self.assertAlmostEqual(computed_output.numpy(), good_output, places=5)
net = tl.layers.Input([10, 2])
net = tl.layers.Dense(n_units=100, act=lambda x: tl.act.lrelu(x, 0.2), name='dense')(net)
print(net)
def test_lrelu6(self):
for i in range(-5, 15):
if i < 0:
good_output = self.alpha * i
else:
good_output = min(6, i)
computed_output = tl.act.leaky_relu6(float(i), alpha=self.alpha)
self.assertAlmostEqual(computed_output.numpy(), good_output, places=5)
net = tl.layers.Input([10, 2])
net = tl.layers.Dense(n_units=100, act=lambda x: tl.act.leaky_relu6(x, 0.2), name='dense')(net)
print(net)
def test_ltrelu6(self):
for i in range(-5, 15):
if i < 0:
good_output = self.alpha * i
elif i < 6:
good_output = i
else:
good_output = 6 + (self.alpha * (i - 6))
computed_output = tl.act.leaky_twice_relu6(float(i), alpha_low=self.alpha, alpha_high=self.alpha)
self.assertAlmostEqual(computed_output.numpy(), good_output, places=5)
net = tl.layers.Input([10, 200])
net = tl.layers.Dense(n_units=100, act=lambda x: tl.act.leaky_twice_relu6(x, 0.2, 0.2), name='dense')(net)
print(net)
def test_ramp(self):
for i in range(-5, 15):
if i < self.vmin:
good_output = self.vmin
elif i > self.vmax:
good_output = self.vmax
else:
good_output = i
computed_output = tl.act.ramp(float(i), v_min=self.vmin, v_max=self.vmax)
self.assertAlmostEqual(computed_output.numpy(), good_output, places=5)
def test_sign(self):
for i in range(-5, 15):
if i < 0:
good_output = -1
elif i == 0:
good_output = 0
else:
good_output = 1
computed_output = tl.act.sign(float(i))
self.assertAlmostEqual(computed_output.numpy(), good_output, places=5)
def test_swish(self):
import numpy as np
for i in range(-5, 15):
good_output = i / (1 + np.math.exp(-i))
computed_output = tl.act.swish(float(i))
self.assertAlmostEqual(computed_output.numpy(), good_output, places=5)
def test_mish(self):
for i in range(-5, 15):
good_output = i * np.tanh(np.math.log(1 + np.math.exp(i)))
computed_output = tl.act.mish(float(i))
self.assertAlmostEqual(computed_output.numpy(), good_output, places=5)
if __name__ == '__main__':
unittest.main()