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Merge pull request #12 from chummm/master
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Modify test_activations, remove whitespace, and begin Python3 support
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Ragav Venkatesan committed Feb 9, 2017
2 parents fd2c5dd + ae007af commit d2748e6
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155 changes: 100 additions & 55 deletions tests/test_activations.py
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Original file line number Diff line number Diff line change
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import pytest
"""
test_activations.py - Unit tests for YANN core activation functions
"""

import unittest
import numpy as np
import theano
import theano.tensor as T
import yann.core.activations as A

class TestActivations:
@classmethod
def setup_class(self):
self.theano_input = T.matrix()
self.numpy_input = np.asarray(np.random.uniform(-4, 4, (5,5)),dtype=theano.config.floatX)
# Create some 5X5 matrix randomly

def Elu(self, x, alpha = 1): return np.where(x > 0, x, alpha * (np.exp(x) - 1))
def Abs(self, x): return np.abs(x)
def ReLU(self, x): return x * (x > 0)
def Sigmoid(self, x): return 1 / (1 + np.exp(-x))
def Tanh(self, x): return np.tanh(x)
def Softmax(self, x, temp = 1):
if temp != 1:
expo = np.exp(x / float(temp))
return expo / expo.sum(axis = 1, keepdims=True)
else:
return np.exp(x) / np.exp(x).sum(axis = 1, keepdims=True)
def Squared(self, x): return x**2
class TestActivations(unittest.TestCase):
"""
expected_array values are precomputed using a Python interpreter, numpy_input,
and the corresponding activation function.
"""

def setUp(self):
"""
numpy_input is hardcoded so we can test against known result values for
different activation functions
"""
self.theano_input = T.matrix()
self.numpy_input = np.array([[-1,2,-3,4,5],
[-1,2,-3,4,5],
[-1,2,-3,4,5],
[-1,2,-3,4,5],
[-1,2,-3,4,5]],
dtype=theano.config.floatX)

def test_abs(self):
def test_abs(self):
expected_array = np.array([[ 1., 2., 3., 4., 5.],
[ 1., 2., 3., 4., 5.],
[ 1., 2., 3., 4., 5.],
[ 1., 2., 3., 4., 5.],
[ 1., 2., 3., 4., 5.]])
theano_result = A.Abs(self.theano_input).eval({self.theano_input: self.numpy_input})
np_result = self.Abs(self.numpy_input)
assert theano_result.shape == np_result.shape
assert np.allclose(theano_result, np_result)
self.assertEqual(theano_result.shape, expected_array.shape)
self.assertTrue(np.allclose(theano_result, expected_array))

def test_relu(self):
def test_relu(self):
expected_array = np.array([[-0., 2., -0., 4., 5.],
[-0., 2., -0., 4., 5.],
[-0., 2., -0., 4., 5.],
[-0., 2., -0., 4., 5.],
[-0., 2., -0., 4., 5.]])
theano_result = A.ReLU(self.theano_input).eval({self.theano_input: self.numpy_input})
np_result = self.ReLU(self.numpy_input)
assert theano_result.shape == np_result.shape
assert np.allclose(theano_result, np_result)
self.assertEqual(theano_result.shape, expected_array.shape)
self.assertTrue(np.allclose(theano_result, expected_array))

def test_elu(self):
alpha = np.asarray(np.random.random(1),dtype = theano.config.floatX)
def test_elu(self):
expected_array = np.array([[-0.63212056, 2., -0.95021293, 4., 5.],
[-0.63212056, 2., -0.95021293, 4., 5.],
[-0.63212056, 2., -0.95021293, 4., 5.],
[-0.63212056, 2., -0.95021293, 4., 5.],
[-0.63212056, 2., -0.95021293, 4., 5.]])
alpha = 1
theano_result = A.Elu(self.theano_input,alpha).eval({self.theano_input: self.numpy_input})
np_result = self.Elu(self.numpy_input,alpha)
assert theano_result.shape == np_result.shape
assert np.allclose(theano_result, np_result)
self.assertEqual(theano_result.shape, expected_array.shape)
self.assertTrue(np.allclose(theano_result, expected_array))

def test_sigmoid(self):
def test_sigmoid(self):
expected_array = np.array([[ 0.26894142, 0.88079708, 0.04742587, 0.98201379, 0.99330715],
[ 0.26894142, 0.88079708, 0.04742587, 0.98201379, 0.99330715],
[ 0.26894142, 0.88079708, 0.04742587, 0.98201379, 0.99330715],
[ 0.26894142, 0.88079708, 0.04742587, 0.98201379, 0.99330715],
[ 0.26894142, 0.88079708, 0.04742587, 0.98201379, 0.99330715]])
theano_result = A.Sigmoid(self.theano_input).eval({self.theano_input: self.numpy_input})
np_result = self.Sigmoid(self.numpy_input)
assert theano_result.shape == np_result.shape
assert np.allclose(theano_result, np_result)
self.assertEqual(theano_result.shape, expected_array.shape)
self.assertTrue(np.allclose(theano_result, expected_array))

def test_tanh(self):
def test_tanh(self):
expected_array = np.array([[-0.76159416, 0.96402758, -0.99505475, 0.9993293 , 0.9999092 ],
[-0.76159416, 0.96402758, -0.99505475, 0.9993293 , 0.9999092 ],
[-0.76159416, 0.96402758, -0.99505475, 0.9993293 , 0.9999092 ],
[-0.76159416, 0.96402758, -0.99505475, 0.9993293 , 0.9999092 ],
[-0.76159416, 0.96402758, -0.99505475, 0.9993293 , 0.9999092 ]])
theano_result = A.Tanh(self.theano_input).eval({self.theano_input: self.numpy_input})
np_result = self.Tanh(self.numpy_input)
assert theano_result.shape == np_result.shape
assert np.allclose(theano_result, np_result)
self.assertEqual(theano_result.shape, expected_array.shape)
self.assertTrue(np.allclose(theano_result, expected_array))

def test_softmax(self):
def test_softmax(self):
expected_array = np.array([[ 1.74500937e-03, 3.50494502e-02, 2.36161338e-04,
2.58982354e-01, 7.03987026e-01],
[ 1.74500937e-03, 3.50494502e-02, 2.36161338e-04,
2.58982354e-01, 7.03987026e-01],
[ 1.74500937e-03, 3.50494502e-02, 2.36161338e-04,
2.58982354e-01, 7.03987026e-01],
[ 1.74500937e-03, 3.50494502e-02, 2.36161338e-04,
2.58982354e-01, 7.03987026e-01],
[ 1.74500937e-03, 3.50494502e-02, 2.36161338e-04,
2.58982354e-01, 7.03987026e-01]])
theano_result = A.Softmax(self.theano_input).eval({self.theano_input: self.numpy_input})
np_result = self.Softmax(self.numpy_input)
assert theano_result.shape == np_result.shape
assert np.allclose(theano_result, np_result)
self.assertEqual(theano_result.shape, expected_array.shape)
self.assertTrue(np.allclose(theano_result, expected_array))

def test_temperature_softmax(self):
t = np.asarray(np.random.uniform(2, 10, 1),dtype = theano.config.floatX)
theano_result = A.Softmax(self.theano_input,
temp = t ).eval({self.theano_input: self.numpy_input})
np_result = self.Softmax(self.numpy_input, temp = t)
assert theano_result.shape == np_result.shape
assert np.allclose(theano_result, np_result)
def test_temperature_softmax(self):
"""
Utilized np.asarray(np.random.uniform(2, 10, 1), dtype = tehano.config.floatX)
to hardcode a value for t.
"""
t = np.array([7.94705237])
expected_array = np.array([[ 0.1381257 , 0.20147445, 0.10739337, 0.25912957, 0.29387691],
[ 0.1381257 , 0.20147445, 0.10739337, 0.25912957, 0.29387691],
[ 0.1381257 , 0.20147445, 0.10739337, 0.25912957, 0.29387691],
[ 0.1381257 , 0.20147445, 0.10739337, 0.25912957, 0.29387691],
[ 0.1381257 , 0.20147445, 0.10739337, 0.25912957, 0.29387691]])
theano_result = A.Softmax(self.theano_input,
temp=t).eval({self.theano_input: self.numpy_input})
self.assertEqual(theano_result.shape, expected_array.shape)
self.assertTrue(np.allclose(theano_result, expected_array))

def test_squared(self):
def test_squared(self):
expected_array = np.array([[1., 4., 9., 16., 25.],
[1., 4., 9., 16., 25.],
[1., 4., 9., 16., 25.],
[1., 4., 9., 16., 25.],
[1., 4., 9., 16., 25.]])
theano_result = A.Squared(self.theano_input).eval({self.theano_input: self.numpy_input})
np_result = self.Squared(self.numpy_input)
assert theano_result.shape == np_result.shape
assert np.allclose(theano_result, np_result)
self.assertEqual(theano_result.shape, expected_array.shape)
self.assertTrue(np.allclose(theano_result, expected_array))

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