def test_backward(self):
x = ad.variable([1, 2, 3, 4, 5, 6])
y = ad.map_fn(lambda x: x * x, x)
self.numeric_gradient_check(y, {}, [x])
x = ad.variable([1, 2, 3])
y = ad.variable([-1, 1, -1])
z = ad.map_fn(lambda x: x[0] * x[1], (x, y))
self.numeric_gradient_check(z, {}, [x, y])
x = ad.variable([1, 2, 3])
y = ad.map_fn(lambda x: (x, -x), x)
z = y[0] * y[1]
self.numeric_gradient_check(z, {}, [x])
def call(self, inputs, **kwargs):
padded = ad.pad(inputs, ((0, ), (self.pad_width[0], ),
(self.pad_width[1], ), (0, )))
batch_size = ad.shape(inputs)[0]
reshaped = ad.map_fn(lambda i: self.call_batch(padded, i),
ad.arange(batch_size))
y = ad.dot(reshaped, self.w)
if self.use_bias:
y += self.b
if self.activation is not None:
y = self.activation(y)
return y
def test_forward(self):
x = ad.variable([1, 2, 3, 4, 5, 6])
y = ad.map_fn(lambda x: x * x, x)
actual = y.forward()
expect = np.array([1, 4, 9, 16, 25, 36])
self.assertEqual(expect.shape, y.shape)
self.assertTrue(np.allclose(expect, actual), (expect, actual))
x = ad.variable([1, 2, 3])
y = ad.variable([-1, 1, -1])
z = ad.map_fn(lambda x: x[0] * x[1], (x, y))
actual = z.forward()
expect = np.array([-1, 2, -3])
self.assertEqual(expect.shape, z.shape)
self.assertTrue(np.allclose(expect, actual), (expect, actual))
x = ad.variable([1, 2, 3])
y = ad.map_fn(lambda x: (x, -x), x)
actual = (y[0].forward(), y[1].forward())
expect = (np.array([1, 2, 3]), np.array([-1, -2, -3]))
for i in range(2):
self.assertEqual(expect[i].shape, y[i].shape)
self.assertTrue(np.allclose(expect[i], actual[i]),
(i, expect[i], actual[i]))
def call_batch(self, padded: ad.Operation, i: int):
height = ad.shape(padded)[1]
new_height = (height -
self.dilated_kernel_size[0]) // self.strides[0] + 1
return ad.map_fn(lambda r: self.call_row(padded, i, r),
ad.arange(new_height))
def call_row(self, padded: ad.Operation, i: int, r: int):
width = ad.shape(padded)[2]
new_width = (width -
self.dilated_kernel_size[1]) // self.strides[1] + 1
return ad.map_fn(lambda c: self.call_column(padded, i, r, c),
ad.arange(new_width))