def autograd_assert(*args, **kwargs):
f = kwargs["func"]
grad_f = kwargs["grad_func"]
grad_func = grad_and_loss(f)
grad_vals, output = grad_func(*args)
res = f(*args)
assert same(output.asnumpy(), res.asnumpy())
grad_res = grad_f(*args)
assert len(grad_vals) == len(grad_res)
for a, b in zip(grad_vals, grad_res):
assert same(a.asnumpy(), b.asnumpy())
def autograd_assert(*args, **kwargs):
f = kwargs["func"]
grad_f = kwargs["grad_func"]
grad_func = grad_and_loss(f)
grad_vals, output = grad_func(*args)
res = f(*args)
assert same(output.asnumpy(), res.asnumpy())
grad_res = grad_f(*args)
assert len(grad_vals) == len(grad_res)
for a, b in zip(grad_vals, grad_res):
assert same(a.asnumpy(), b.asnumpy())
def autograd_assert(*args, **kwargs):
f = kwargs["func"]
grad_f = kwargs["grad_func"]
grad_func = grad_and_loss(f)
grad_vals, output = grad_func(*args)
res = f(*args)
assert output == res
grad_res = grad_f(*args)
assert len(grad_vals) == len(grad_res)
for a, b in zip(grad_vals, grad_res):
assert a == b
def autograd_assert(*args, **kwargs):
func = kwargs["func"]
grad_f = kwargs["grad_func"]
argnum = kwargs["argnum"] if 'argnum' in kwargs else None
grad_func = grad_and_loss(func, argnum)
grad_vals, output = grad_func(*args)
res = func(*args)
assert same(output.asnumpy(), res.asnumpy())
grad_res = grad_f(*args)
assert len(grad_vals) == len(grad_res)
for a, b in zip(grad_vals, grad_res):
assert same(a.asnumpy(), b.asnumpy())
def test_operator_with_state():
def f_fc(a, b, weight, bias):
x = a * b
fc = nd.FullyConnected(x, weight, bias, num_hidden=32)
return fc
a = nd.uniform(shape=(64, 50))
b = nd.uniform(shape=(64, 50))
weight = nd.uniform(shape=(32, 50))
bias = nd.uniform(shape=(32, ))
grad_func = grad_and_loss(f_fc)
grad_vals, outputs = grad_func(a, b, weight, bias)
def autograd_assert(*args, **kwargs):
func = kwargs["func"]
grad_f = kwargs["grad_func"]
argnum = kwargs["argnum"] if 'argnum' in kwargs else None
grad_func = grad_and_loss(func, argnum)
grad_vals, output = grad_func(*args)
res = func(*args)
assert same(output.asnumpy(), res.asnumpy())
grad_res = grad_f(*args)
assert len(grad_vals) == len(grad_res)
for a, b in zip(grad_vals, grad_res):
assert same(a.asnumpy(), b.asnumpy())
def test_operator_with_state():
def f_fc(a, b, weight, bias):
x = a*b
fc = nd.FullyConnected(
x, weight, bias, num_hidden=32)
return fc
a = nd.uniform(shape=(64, 50))
b = nd.uniform(shape=(64, 50))
weight = nd.uniform(shape=(32, 50))
bias = nd.uniform(shape=(32, ))
grad_func = grad_and_loss(f_fc)
grad_vals, outputs = grad_func(a, b, weight, bias)
