def test_dsoftmax_matrix():
tensor_shape = [random.randint(2, 10) for _ in range(2)]
helpers.check_gradient(lambda X: calculate.softmax(X),
lambda X: calculate.dsoftmax(calculate.softmax(X)),
f_arg_tensor=numpy.random.random(tensor_shape),
f_shape='jac-stack')
def test_softmax_transfer():
assert list(calculate.softmax(numpy.array([1.0, 1.0]))) == [0.5, 0.5]
assert helpers.approx_equal(calculate.softmax(numpy.array([1.0, 0.0])),
[0.7310585, 0.2689414])
softmax_out = calculate.softmax(numpy.array([1.0, -1.0]))
assert softmax_out[0] > 0.5 and softmax_out[1] < 0.5
assert helpers.approx_equal(sum(softmax_out), 1.0)
def test_softmax_matrix():
assert helpers.approx_equal(
calculate.softmax(numpy.array([[1.0, 1.0], [1.0, 0.0]])),
[[0.5, 0.5], [0.7310585, 0.2689414]])
assert helpers.approx_equal(
calculate.softmax(numpy.array([[1.0, 0.0], [0.5, 0.5]])),
[[0.7310585, 0.2689414], [0.5, 0.5]])
shape = (random.randint(2, 10), random.randint(2, 10))
softmax_out = calculate.softmax(
numpy.sort(numpy.random.random(shape), axis=1))
assert (numpy.sort(softmax_out, axis=1) == softmax_out).all()
assert helpers.approx_equal(numpy.sum(softmax_out, axis=1),
numpy.ones(shape[0]))
def test_softmax_vector():
assert list(calculate.softmax(numpy.array([1.0, 1.0]))) == [0.5, 0.5]
assert helpers.approx_equal(calculate.softmax(numpy.array([1.0, 0.0])),
[0.7310585, 0.2689414])
softmax_out = calculate.softmax(numpy.array([1.0, -1.0]))
assert softmax_out[0] > 0.5 and softmax_out[1] < 0.5
assert helpers.approx_equal(sum(softmax_out), 1.0)
shape = random.randint(2, 10)
softmax_out = calculate.softmax(
numpy.array(sorted(numpy.random.random(shape))))
assert sorted(softmax_out) == list(softmax_out)
assert helpers.approx_equal(sum(softmax_out), 1.0)
def test_softmax_large_input():
"""Softmax includes an exponential, which can cause overflows.
Our softmax implementation should protect against overflow.
"""
assert list(calculate.softmax(numpy.array([-1000.0,
1000.0]))) == [0.0, 1.0]
def test_dsoftmax():
helpers.check_gradient(calculate.softmax,
lambda x: calculate.dsoftmax(calculate.softmax(x)),
f_shape='jac')
def __call__(self, input_vec):
return calculate.softmax(input_vec)
