def grads(x, y):
return (
grad(f, 'x')(x, y),
grad(f, 'x')(x, y, dout=2),
grad(f, 0)(x, y),
grad(f, 'y')(x, y),
grad(f, 'x', 'y')(x, y),
grad(f, '*')(x, y),
grad(f, return_value=True)(x, y),
)
def test_grad_cast(x):
def f(x):
return scalar_cast(x, f16)
return grad(f)(x)
def test_grad(x, y):
def f(x, y):
return x * (y + x)
return grad(f)(x, y)
def f(xs, y):
return grad(g)(xs, y)
def gradbad7(x, y, z):
return grad(f, raturn_velue=True)(x, y)
def gradbad6(x, y, z):
return grad(f, 0, '*')(x, y)
def gradbad5(x, y, z):
return grad(f)(x=x, y=y)
def apple(x, y):
return grad(gadd)(x, y)
def gradbad3(x, y, z):
return grad(f, z)(x, y)
def gradbad2(x, y):
return grad(f, 'z')(x, y)
def gradbad(x, y):
return grad(f, (0, 1))(x, y)
def gradbad10(x, y):
return grad(partial(f, x))(y)
def gradbad9(x, y):
return grad(x)(y)
def gradbad8(x, y):
def klojure(q):
return q + y
return grad(klojure)(x)
def test_grad_reduce(xs, ys):
def f(xs, ys):
return array_reduce(scalar_add, xs * ys, ())
return grad(f)(xs, ys)
def gradbad4(x, y, z):
return grad(f, 2)(x, y)
def test_backward_infer(model, x, y):
return grad(cost)(model, x, y)
def peach(x, y):
return grad(scalar_mul)(x, y)
