Python ndarray.zeros_like方法代码示例

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import zeros_like [as 别名]
def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
        if ReluOp.guided_backprop:
            # Get output and gradients of output
            y = out_data[0]
            dy = out_grad[0]
            # Zero out the negatives in the gradients of the output
            dy_positives = nd.maximum(dy, nd.zeros_like(dy))
            # What output values were greater than 0?
            y_ones = y.__gt__(0)
            # Mask out the values for which at least one of dy or y is negative
            dx = dy_positives * y_ones
            self.assign(in_grad[0], req[0], dx)
        else:
            # Regular backward for ReLU
            x = in_data[0]
            x_gt_zero = x.__gt__(0)
            dx = out_grad[0] * x_gt_zero
            self.assign(in_grad[0], req[0], dx) 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import zeros_like [as 别名]
def test_out_grads():
    x = nd.ones((3, 5))
    dx = nd.zeros_like(x)
    mark_variables([x], [dx])
    da = None
    db = nd.array([1,2,3,4,5])
    dc = nd.array([5,4,3,2,1])

    with record():
        a, b, c = nd.split(x, axis=0, num_outputs=3, squeeze_axis=True)
        backward([a, b, c], [da, db, dc])

    assert (dx.asnumpy() == np.array(
        [[1,1,1,1,1],
         [1,2,3,4,5],
         [5,4,3,2,1]])).all() 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import zeros_like [as 别名]
def test_out_grads():
    x = nd.ones((3, 5))
    dx = nd.zeros_like(x)
    mark_variables([x], [dx])
    da = None
    db = nd.array([1,2,3,4,5])
    dc = nd.array([5,4,3,2,1])

    with train_section():
        a, b, c = nd.split(x, axis=0, num_outputs=3, squeeze_axis=True)
        backward([a, b, c], [da, db, dc])

    assert (dx.asnumpy() == np.array(
        [[1,1,1,1,1],
         [1,2,3,4,5],
         [5,4,3,2,1]])).all() 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import zeros_like [as 别名]
def forward(self, is_train, req, in_data, out_data, aux):
        x = in_data[0]
        y = nd.maximum(x, nd.zeros_like(x))
        self.assign(out_data[0], req[0], y) 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import zeros_like [as 别名]
def grad_and_loss(func, argnum=None):
    """Return function that computes both gradient of arguments and loss value.

    Parameters
    ----------
    func: a python function
        The forward (loss) function.
    argnum: an int or a list of int
        The index of argument to calculate gradient for.

    Returns
    -------
    grad_and_loss_func: a python function
        A function that would compute both the gradient of arguments and loss value.
    """
    @functools.wraps(func)
    def wrapped(*args):
        """Wrapped function."""
        variables = args
        if argnum is not None:
            argnum_ = argnum if isinstance(argnum, list) else [argnum]
            variables = [args[i] for i in argnum_]
        for x in variables:
            assert isinstance(x, NDArray), "type of autograd input should NDArray."
        grads = [zeros_like(x) for x in variables]
        mark_variables(variables, grads)
        with record():
            outputs = func(*args)
        backward([outputs] if isinstance(outputs, NDArray) else outputs)
        return grads, outputs
    return wrapped 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import zeros_like [as 别名]
def test_detach_updated_grad():
    x = nd.ones((2, 2))
    dx = nd.zeros_like(x)
    y = nd.ones_like(x)
    dy = nd.zeros_like(x)
    mark_variables([x, y], [dx, dy])
    assert x._fresh_grad == False
    assert y._fresh_grad == False

    with record():
        x2 = x + 2
        y2  = x2 + y
        y2.backward()
    assert (dx.asnumpy() == 1).all()
    assert x._fresh_grad == True
    assert y._fresh_grad == True

    dx[:] = 0
    x._fresh_grad = False
    y._fresh_grad = False
    assert x._fresh_grad == False
    assert y._fresh_grad == False
    with record():
        x2 = x + 2
        x2 = x2.detach()
        y2  = x2 + y
        y2.backward()
    assert (dx.asnumpy() == 0).all()
    assert y._fresh_grad == True
    assert x._fresh_grad == False 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import zeros_like [as 别名]
def test_detach_updated_grad():
    x = nd.ones((2, 2))
    dx = nd.zeros_like(x)
    y = nd.ones_like(x)
    dy = nd.zeros_like(x)
    mark_variables([x, y], [dx, dy])
    assert x._fresh_grad == False
    assert y._fresh_grad == False

    with train_section():
        x2 = x + 2
        y2  = x2 + y
        y2.backward()
    assert (dx.asnumpy() == 1).all()
    assert x._fresh_grad == True
    assert y._fresh_grad == True

    dx[:] = 0
    x._fresh_grad = False
    y._fresh_grad = False
    assert x._fresh_grad == False
    assert y._fresh_grad == False
    with train_section():
        x2 = x + 2
        x2 = x2.detach()
        y2  = x2 + y
        y2.backward()
    assert (dx.asnumpy() == 0).all()
    assert y._fresh_grad == True
    assert x._fresh_grad == False 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import zeros_like [as 别名]
def kv_push(self, key, value):
      #if value.context!=mx.cpu():
      #  value = value.as_in_context(mx.cpu())
      if not key in self._kvinit:
        self._distkv.init(key, nd.zeros_like(value))
        self._kvinit[key] = 1
      self._distkv.push(key, value)

    #get fc1 and partial fc7 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import zeros_like [as 别名]
def zeros_like(input):
    return nd.zeros_like(input)