def test_rowwise_add(self):
op = create_op("rowwise_add")
inputs = {
"X": np.random.uniform(0.1, 1, [5, 10]).astype("float32"),
"b": np.random.uniform(0.1, 1, [10]).astype("float32")
}
self.check_grad(op, inputs, set(["X", "b"]), "Out")
def test_grad(self):
op = create_op("sigmoid")
inputs = {"X": np.random.uniform(0.1, 1, [11, 17]).astype("float32")}
# compare gpu and cpu results for backward op.
# this test will be skiped if only compiling CPU version.
self.compare_grad(op, inputs)
# check gradients
self.check_grad(op, inputs, set("X"), "Y", max_relative_error=0.007)
def test_mul(self):
op = create_op("mul")
inputs = {
'X': np.random.random((32, 84)).astype("float32"),
'Y': np.random.random((84, 100)).astype("float32")
}
# mul op will enlarge the relative error
self.check_grad(
op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.5)
def test_check_grad(self):
op = create_op("onehot_cross_entropy")
batch_size = 30
class_num = 10
inputs = {
"X":
numpy.random.uniform(0.1, 1.0,
[batch_size, class_num]).astype("float32"),
"label": (class_num / 2) * numpy.ones(batch_size).astype("int32")
}
self.check_grad(op, inputs, set("X"), "Y")
def test_scatter_grad(self):
op = create_op("scatter")
# test data setup
ref_np = numpy.ones((3, 10)).astype("float32")
index_np = numpy.array([1, 2]).astype("int32")
updates_np = numpy.random.random((2, 10)).astype("float32")
output_np = numpy.copy(ref_np)
output_np[index_np] += updates_np
inputs = {'Ref': ref_np, 'Index': index_np, 'Updates': updates_np}
self.check_grad(op,
inputs,
set(["Updates", "Ref"]),
"Out",
in_place=True)
def test_softmax(self):
op = create_op("softmax")
inputs = {"X": np.random.uniform(0.1, 1, [10, 10]).astype("float32")}
self.check_grad(op, inputs, set("X"), "Y")
def test_gather_grad(self):
op = create_op("gather")
xnp = numpy.random.random((10, 20)).astype("float32")
inputs = {'X': xnp, 'Index': numpy.array([1, 3, 5]).astype("int32")}
self.check_grad(op, inputs, set("X"), "Out")
def test_normal(self):
op = create_op("mean")
inputs = {"X": np.random.random((10, 10)).astype("float32")}
self.check_grad(op, inputs, set("X"), "Out")