def test_softmax():
def softmax(values):
e = np.max(values, axis=-1, keepdims=True)
s = np.exp(values - e)
return s / np.sum(s)
activation_test(activations.softmax, softmax)
# Testing cases of 3D input
activation_test(activations.softmax,
softmax,
np_input=np.linspace(-1, 1, 5)[None, None, :])
with pytest.raises(KError):
# softmax accepts only 2D or 3D
x = B.placeholder(ndim=1)
B.function([x], [activations.softmax(x)])
def regularizer_test(reg, np_fn, np_input=np_input, debug=False):
ndim = len(np_input.shape)
x = B.placeholder(ndim=ndim)
f = B.function([x], [reg(x)])
if debug:
print("Expected Output Shape:\n{}".format(np_fn(np_input)))
print("Output:\n{}".format(f([np_input])[0]))
assert_allclose(f([np_input])[0], np_fn(np_input), rtol=1e-05)
def activation_test(k_fn, np_fn, np_input=np_input, debug=False):
ndim = len(np_input.shape)
x = B.placeholder(ndim=ndim)
f = B.function([x], [k_fn(x)])
if debug:
print("Expected Output Shape:\n{}".format(np_fn(np_input)))
print("Output:\n{}".format(f([np_input])[0]))
assert_allclose(f([np_input])[0],
np_fn(np_input).astype(B._FLOATX),
rtol=float_test_rtol)