The function definition named "sign" in activation.py may have a error.

[edwardzcl]

In the file named ''tensorlayer\activation.py'',there is a function called "sign". The code called "gradient_override_map({"sign": "QuantizeGrad"}):" which i have bolded may be a small error. It should use majuscule there, like "gradient_override_map({"Sign": "QuantizeGrad"}):",then it will work well.

please refer to https://github.com/tensorlayer/tensorlayer/blob/master/tensorlayer/layers/activation.py

@tf.RegisterGradient("QuantizeGrad")
def _sign_grad(unused_op, grad):
    return tf.clip_by_value(grad, -1, 1)

def sign(x):
    """Sign function.
    Clip and binarize tensor using the straight through estimator (STE) for the gradient, usually be used for
    quantizing values in `Binarized Neural Networks`: https://arxiv.org/abs/1602.02830.
    Parameters
    ----------
    x : Tensor
        input.
    Examples
    --------
    >>> net = tl.layers.DenseLayer(net, 100, act=lambda x : tl.act.lrelu(x, 0.2), name='dense')
    Returns
    -------
    Tensor
        A ``Tensor`` in the same type as ``x``.
    References
    ----------
    - `Rectifier Nonlinearities Improve Neural Network Acoustic Models, Maas et al. (2013)`
       http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf
    - `BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1, Courbariaux et al. (2016)`
       https://arxiv.org/abs/1602.02830
    """
    **with tf.get_default_graph().gradient_override_map({"sign": "QuantizeGrad"}):
        return tf.sign(x, name='sign')**

[wagamamaz]

Thanks, as @zsdonghao changed sign to `Sign, I close this issue now. Feel free to reopen if you have any other problems.