def func(self, place):
# the shape of input variable should be clearly specified, not inlcude -1.
shape = [2, 3, 4, 5]
eps = 0.005
dtype = np.float64
x = layers.data('x', shape, False, dtype)
y = layers.data('y', shape, False, dtype)
x.persistable = True
y.persistable = True
out = layers.elementwise_mul(x, y)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
y_arr = np.random.uniform(-1, 1, shape).astype(dtype)
gradient_checker.triple_grad_check([x, y],
out,
x_init=[x_arr, y_arr],
place=place,
eps=eps)
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
gradient_checker.triple_grad_check_for_dygraph(self.multiply_wrapper,
[x, y],
out,
x_init=[x_arr, y_arr],
place=place)
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
def func(self, place):
shape = [2, 3, 7, 9]
eps = 0.0005
dtype = np.float64
x = layers.data('x', shape, False, dtype=dtype)
x.persistable = True
y = layers.sigmoid(x)
x_arr = np.random.random(shape).astype(dtype)
x_arr[np.abs(x_arr) < 0.005] = 0.002
gradient_checker.triple_grad_check(
[x], y, x_init=x_arr, place=place, eps=eps)
def func(self, place):
shape = [2, 3, 7, 9]
eps = 0.0005
dtype = np.float64
x = layers.data('x', shape, False, dtype=dtype)
x.persistable = True
y = layers.tanh(x)
x_arr = np.random.random(shape).astype(dtype)
x_arr[np.abs(x_arr) < 0.005] = 0.002
gradient_checker.triple_grad_check(
[x], y, x_init=x_arr, place=place, eps=eps)
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
gradient_checker.triple_grad_check_for_dygraph(
self.tanh_wrapper, [x], y, x_init=x_arr, place=place)
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
def func(self, place):
# the shape of input variable should be clearly specified, not inlcude -1.
shape = [2, 3, 4, 5]
eps = 0.005
dtype = np.float64
x = layers.data('x', shape, False, dtype)
y = layers.data('y', shape[:-1], False, dtype)
x.persistable = True
y.persistable = True
out = layers.elementwise_add(x, y, axis=0)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
y_arr = np.random.uniform(-1, 1, shape[:-1]).astype(dtype)
gradient_checker.triple_grad_check([x, y],
out,
x_init=[x_arr, y_arr],
place=place,
eps=eps)
def func(self, place):
eps = 0.005
dtype = np.float64
typename = "float64"
x = paddle.static.create_parameter(dtype=typename,
shape=self.x_shape,
name='x')
y = paddle.static.create_parameter(dtype=typename,
shape=self.y_shape,
name='y')
out = paddle.matmul(x, y, self.transpose_x, self.transpose_y, name='out')
np.random.seed(2021)
x_arr = np.random.uniform(-1, 1, self.x_shape).astype(dtype)
y_arr = np.random.uniform(-1, 1, self.y_shape).astype(dtype)
gradient_checker.triple_grad_check([x, y],
out,
x_init=[x_arr, y_arr],
place=place,
eps=eps)