Python grad_and_loss Examples

Example #1

File: test_autograd.py Project: freddycct/mxnet

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())

Example #2

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())

Example #3

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

Example #4

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())

Example #5

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)

Example #6

File: test_autograd.py Project: Johnqczhang/mxnet

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())

Example #7

File: test_autograd.py Project: freddycct/mxnet

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)