def test_forward(self):
y = ad.while_loop(
cond=lambda inputs: ad.less(inputs[0], ad.constant(10)),
body=lambda inputs: [inputs[0] + 1],
loop_vars=[ad.variable(0.0)],
)
actual = y.forward()
expect = np.arange(1, 11)
self.assertEqual((None, ), y.shape)
self.assertTrue(np.allclose(expect, actual), (expect, actual))
y = ad.while_loop(
cond=lambda inputs: ad.less(inputs[0], ad.constant(5)),
body=lambda inputs: [inputs[0] + 1, (inputs[0] + 1) * inputs[1]],
loop_vars=[ad.variable(0.0), ad.variable(1.0)],
output_index=1,
)
actual = y.forward()
expect = np.array([1, 2, 6, 24, 120])
self.assertEqual((None, ), y.shape)
self.assertTrue(np.allclose(expect, actual), (expect, actual))
y = ad.while_loop(
cond=lambda inputs: ad.less(inputs[0], ad.constant(64)),
body=lambda inputs:
[inputs[0] * 2,
ad.dot(inputs[1], ad.variable([[1, 1], [1, 0]]))],
loop_vars=[ad.variable(1),
ad.variable([[1, 0], [0, 1]])],
output_index=1,
)
actual = y.forward()
expect = np.array([1, 2, 3, 5, 8, 13])
self.assertEqual((None, 2, 2), y.shape)
self.assertTrue(np.allclose(expect, actual[:, 0, 0]), (expect, actual))
def _gen_random_and_result(x_shape, y_shape, call_type=True):
x_val = np.random.random(x_shape)
y_val = np.random.random(y_shape)
x = ad.variable(x_val, name='X%s' % str(x_shape))
y = ad.variable(y_val, name='Y%s' % str(y_shape))
if call_type:
z = x < y
else:
z = ad.less(x, y)
expect = (x_val < y_val).astype(dtype=np.float64)
return z, [x, y], expect
def test_backward(self):
x = ad.variable([[1, 1], [1, 0]])
y = ad.while_loop(
cond=lambda inputs: ad.less(inputs[0], ad.constant(64)),
body=lambda inputs: [inputs[0] * 2,
ad.dot(inputs[1], x)],
loop_vars=[ad.variable(1),
ad.variable([[1, 0], [0, 1]])],
output_index=1,
)
self.numeric_gradient_check(y, {}, [x])
def call(self, inputs, **kwargs):
initial_val = ad.dot(
ad.zeros_like(inputs)[:, 0, :],
ad.zeros_like(self.wx[:, :self.units]))
outputs = ad.while_loop(
lambda body_inputs: ad.less(body_inputs[0],
ad.shape(inputs)[1]),
lambda x: self.step(inputs, x),
[ad.variable(0.0), initial_val, initial_val],
output_index=-1,
)
if self.return_sequences:
return outputs.transpose(axes=[1, 0, 2])
return outputs[-1]