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_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_softmax(self):
from keras.activations import softmax as s
# Test using a reference implementation of softmax
def softmax(values):
m = np.max(values)
e = np.exp(values - m)
return e / np.sum(e)
x = K.placeholder(ndim=2)
exp = s(x)
f = K.function([x], [exp])
test_values = get_standard_values()
result = f([test_values])[0]
expected = softmax(test_values)
assert_allclose(result, expected, rtol=1e-05)
def test_softmax(self):
from keras.activations import softmax as s
# Test using a reference implementation of softmax
def softmax(values):
m = np.max(values)
e = np.exp(values - m)
return e / np.sum(e)
x = K.placeholder(ndim=2)
exp = s(x)
f = K.function([x], [exp])
test_values = get_standard_values()
result = f([test_values])[0]
expected = softmax(test_values)
assert_allclose(result, expected, rtol=1e-05)