import math
import keras
import theano
import theano.tensor as T
import numpy
def list_assert_equal(a, b, round_to=7):
'''
This will do a pairwise, rounded equality test across two lists of
numbers.
'''
pairs = zip(a, b)
for i, j in pairs:
assert round(i, round_to) == round(j, round_to)
def get_standard_values():
'''
These are just a set of floats used for testing the activation
functions, and are useful in multiple tests.
'''
return [0,0.1,0.5,0.9,1.0]
def test_softmax():
from keras.activations import softmax as s
# Test using a reference implementation of softmax
def softmax(values):
m = max(values)
values = numpy.array(values)
e = numpy.exp(values - m)
dist = list(e / numpy.sum(e))
return dist
x = T.vector()
exp = s(x)
f = theano.function([x], exp)
test_values=get_standard_values()
result = f(test_values)
expected = softmax(test_values)
print(str(result))
print(str(expected))
list_assert_equal(result, expected)
def test_relu():
'''
Relu implementation doesn't depend on the value being
a theano variable. Testing ints, floats and theano tensors.
'''
from keras.activations import relu as r
assert r(5) == 5
assert r(-5) == 0
assert r(-0.1) == 0
assert r(0.1) == 0.1
x = T.vector()
exp = r(x)
f = theano.function([x], exp)
test_values = get_standard_values()
result = f(test_values)
list_assert_equal(result, test_values) # because no negatives in test values
def test_tanh():
from keras.activations import tanh as t
test_values = get_standard_values()
x = T.vector()
exp = t(x)
f = theano.function([x], exp)
result = f(test_values)
expected = [math.tanh(v) for v in test_values]
print(result)
print(expected)
list_assert_equal(result, expected)
def test_linear():
'''
This function does no input validation, it just returns the thing
that was passed in.
'''
from keras.activations import linear as l
xs = [1, 5, True, None, 'foo']
for x in xs:
assert x == l(x)
