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@maxpumperla
maxpumperla committed Dec 12, 2015
1 parent 19fadb5 commit 671751f
Showing 1 changed file with 122 additions and 118 deletions.
240 changes: 122 additions & 118 deletions tests/keras/layers/test_advanced_activations.py
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import unittest
import pytest
from numpy.testing import assert_allclose
import numpy as np
from keras import backend as K
import pytest


def get_standard_values():
'''
Expand All @@ -15,142 +15,146 @@ def get_standard_values():
return np.array([[0, 0.1, 0.5, 0.9, 1.0, 10, 1e2, 0.01]], dtype=K.floatx())


class TestAdvancedActivations(unittest.TestCase):
def test_leaky_relu(self):
np.random.seed(1337)
from keras.layers.advanced_activations import LeakyReLU
inp = get_standard_values()
for alpha in [0., .5, -1.]:
layer = LeakyReLU(alpha=alpha)
layer.input = K.variable(inp)
for train in [True, False]:
outp = K.eval(layer.get_output(train))
assert_allclose(outp, inp)
def test_leaky_relu():
np.random.seed(1337)
from keras.layers.advanced_activations import LeakyReLU
inp = get_standard_values()
for alpha in [0., .5, -1.]:
layer = LeakyReLU(alpha=alpha)
layer.input = K.variable(inp)
for train in [True, False]:
outp = K.eval(layer.get_output(train))
assert_allclose(outp, inp)

layer.input = K.variable(-inp)
for train in [True, False]:
outp = K.eval(layer.get_output(train))
assert_allclose(outp, -inp*alpha)
layer.input = K.variable(-inp)
for train in [True, False]:
outp = K.eval(layer.get_output(train))
assert_allclose(outp, -inp*alpha)

config = layer.get_config()
assert config['alpha'] == alpha
config = layer.get_config()
assert config['alpha'] == alpha

def test_prelu(self):
from keras.layers.advanced_activations import PReLU
np.random.seed(1337)
inp = get_standard_values()

for train in [True, False]:
# test with custom weights
alphas = np.random.random(inp.shape)
layer = PReLU(weights=alphas, input_shape=inp.flatten().shape)
# calling build here causes an error, unclear if this is a bug
# layer.build()
def test_prelu():
from keras.layers.advanced_activations import PReLU
np.random.seed(1337)
inp = get_standard_values()

layer.input = K.variable(inp)
outp = K.eval(layer.get_output(train))
assert_allclose(inp, outp)
for train in [True, False]:
# test with custom weights
alphas = np.random.random(inp.shape)
layer = PReLU(weights=alphas, input_shape=inp.flatten().shape)
# calling build here causes an error, unclear if this is a bug
# layer.build()

layer.input = K.variable(-inp)
outp = K.eval(layer.get_output(train))
assert_allclose(-alphas*inp, outp)
layer.input = K.variable(inp)
outp = K.eval(layer.get_output(train))
assert_allclose(inp, outp)

# test with default weights
layer = PReLU(input_shape=inp.flatten().shape)
# layer.build()
layer.input = K.variable(inp)
outp = K.eval(layer.get_output(train))
assert_allclose(inp, outp)
layer.input = K.variable(-inp)
outp = K.eval(layer.get_output(train))
assert_allclose(-alphas*inp, outp)

layer.input = K.variable(-inp)
outp = K.eval(layer.get_output(train))
# test with default weights
layer = PReLU(input_shape=inp.flatten().shape)
# layer.build()
layer.input = K.variable(inp)
outp = K.eval(layer.get_output(train))
assert_allclose(inp, outp)

assert_allclose(0., alphas*outp)
layer.input = K.variable(-inp)
outp = K.eval(layer.get_output(train))

layer.get_config()
assert_allclose(0., alphas*outp)

def test_elu(self):
from keras.layers.advanced_activations import ELU
np.random.seed(1337)
inp = get_standard_values()
for alpha in [0.1, .5, -1., 1.]:
layer = ELU(alpha=alpha)
layer.input = K.variable(inp)
for train in [True, False]:
outp = K.eval(layer.get_output(train))
assert_allclose(outp, inp, rtol=1e-3)
layer.get_config()

layer.input = K.variable(-inp)
for train in [True, False]:
outp = K.eval(layer.get_output(train))
assert_allclose(outp, alpha*(np.exp(-inp)-1.), rtol=1e-3)

config = layer.get_config()
assert config['alpha'] == alpha

@pytest.mark.skipif(K._BACKEND=='tensorflow', reason="currently not working with TensorFlow")
def test_parametric_softplus(self):
from keras.layers.advanced_activations import ParametricSoftplus
np.random.seed(1337)
inp = np.vstack((get_standard_values(), -get_standard_values()))
# large values cause overflow in exp
inp = inp[:-2]
for alpha in [.5, -1., 1., 5]:
for beta in [.5, -1., 1., 2]:
layer = ParametricSoftplus(alpha_init=alpha,
beta_init=beta,
input_shape=inp.shape)
layer.input = K.variable(inp)
layer.build()
for train in [True, False]:
outp = K.eval(layer.get_output(train))
assert_allclose(outp, alpha*np.log(1.+np.exp(beta*inp)),
atol=1e-3)

config = layer.get_config()
assert config['alpha_init'] == alpha
assert config['beta_init'] == beta

@pytest.mark.skipif(K._BACKEND=='tensorflow', reason="currently not working with TensorFlow")
def test_thresholded_linear(self):
from keras.layers.advanced_activations import ThresholdedLinear
np.random.seed(1337)
inp = get_standard_values()
for theta in [0., .5, 1.]:
layer = ThresholdedLinear(theta=theta)
layer.input = K.variable(inp)
for train in [True, False]:
outp = K.eval(layer.get_output(train))
assert_allclose(outp, inp*(np.abs(inp) >= theta))
def test_elu():
from keras.layers.advanced_activations import ELU
np.random.seed(1337)
inp = get_standard_values()
for alpha in [0.1, .5, -1., 1.]:
layer = ELU(alpha=alpha)
layer.input = K.variable(inp)
for train in [True, False]:
outp = K.eval(layer.get_output(train))
assert_allclose(outp, inp, rtol=1e-3)

layer.input = K.variable(-inp)
layer.input = K.variable(-inp)
for train in [True, False]:
outp = K.eval(layer.get_output(train))
assert_allclose(outp, alpha*(np.exp(-inp)-1.), rtol=1e-3)

config = layer.get_config()
assert config['alpha'] == alpha


@pytest.mark.skipif(K._BACKEND == 'tensorflow', reason="currently not working with TensorFlow")
def test_parametric_softplus():
from keras.layers.advanced_activations import ParametricSoftplus
np.random.seed(1337)
inp = np.vstack((get_standard_values(), -get_standard_values()))
# large values cause overflow in exp
inp = inp[:-2]
for alpha in [.5, -1., 1., 5]:
for beta in [.5, -1., 1., 2]:
layer = ParametricSoftplus(alpha_init=alpha,
beta_init=beta,
input_shape=inp.shape)
layer.input = K.variable(inp)
layer.build()
for train in [True, False]:
outp = K.eval(layer.get_output(train))
assert_allclose(outp, -inp*(np.abs(inp) >= theta))
assert_allclose(outp, alpha*np.log(1.+np.exp(beta*inp)),
atol=1e-3)

config = layer.get_config()
assert config['theta'] == theta

@pytest.mark.skipif(K._BACKEND=='tensorflow', reason="currently not working with TensorFlow")
def test_thresholded_relu(self):
from keras.layers.advanced_activations import ThresholdedReLU
np.random.seed(1337)
inp = get_standard_values()
for theta in [-1, 0., .5, 1.]:
layer = ThresholdedReLU(theta=theta)
layer.input = K.variable(inp)
for train in [True, False]:
outp = K.eval(layer.get_output(train))
assert_allclose(outp, inp*(inp > theta))
assert config['alpha_init'] == alpha
assert config['beta_init'] == beta


@pytest.mark.skipif(K._BACKEND == 'tensorflow', reason="currently not working with TensorFlow")
def test_thresholded_linear():
from keras.layers.advanced_activations import ThresholdedLinear
np.random.seed(1337)
inp = get_standard_values()
for theta in [0., .5, 1.]:
layer = ThresholdedLinear(theta=theta)
layer.input = K.variable(inp)
for train in [True, False]:
outp = K.eval(layer.get_output(train))
assert_allclose(outp, inp*(np.abs(inp) >= theta))

layer.input = K.variable(-inp)
for train in [True, False]:
outp = K.eval(layer.get_output(train))
assert_allclose(outp, -inp*(-inp > theta))
layer.input = K.variable(-inp)
for train in [True, False]:
outp = K.eval(layer.get_output(train))
assert_allclose(outp, -inp*(np.abs(inp) >= theta))

config = layer.get_config()
assert config['theta'] == theta
config = layer.get_config()
assert config['theta'] == theta


@pytest.mark.skipif(K._BACKEND == 'tensorflow', reason="currently not working with TensorFlow")
def test_thresholded_relu():
from keras.layers.advanced_activations import ThresholdedReLU
np.random.seed(1337)
inp = get_standard_values()
for theta in [-1, 0., .5, 1.]:
layer = ThresholdedReLU(theta=theta)
layer.input = K.variable(inp)
for train in [True, False]:
outp = K.eval(layer.get_output(train))
assert_allclose(outp, inp*(inp > theta))

layer.input = K.variable(-inp)
for train in [True, False]:
outp = K.eval(layer.get_output(train))
assert_allclose(outp, -inp*(-inp > theta))

config = layer.get_config()
assert config['theta'] == theta


if __name__ == '__main__':
unittest.main()
pytest.main([__file__])

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