szha commented on a change in pull request #9662: Gluon PReLU, ELU, SELU, Swish URL: https://github.com/apache/incubator-mxnet/pull/9662#discussion_r166086915
########## File path: python/mxnet/gluon/nn/activations.py ########## @@ -0,0 +1,215 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +# coding: utf-8 +# pylint: disable= arguments-differ +"""Basic neural network layers.""" +__all__ = ['Activation', 'LeakyReLU', 'PReLU', 'ELU', 'SELU', 'Swish'] + +from ... import initializer +from ..block import HybridBlock + + +class Activation(HybridBlock): + r"""Applies an activation function to input. + + Parameters + ---------- + activation : str + Name of activation function to use. + See :func:`~mxnet.ndarray.Activation` for available choices. + + + Inputs: + - **data**: input tensor with arbitrary shape. + + Outputs: + - **out**: output tensor with the same shape as `data`. + """ + def __init__(self, activation, **kwargs): + self._act_type = activation + super(Activation, self).__init__(**kwargs) + + def _alias(self): + return self._act_type + + def hybrid_forward(self, F, x): + return F.Activation(x, act_type=self._act_type, name='fwd') + + def __repr__(self): + s = '{name}({_act_type})' + return s.format(name=self.__class__.__name__, + **self.__dict__) + + +class LeakyReLU(HybridBlock): + r"""Leaky version of a Rectified Linear Unit. + + It allows a small gradient when the unit is not active + + .. math:: + + f\left(x\right) = \left\{ + \begin{array}{lr} + \alpha x & : x \lt 0 \\ + x & : x \geq 0 \\ + \end{array} + \right.\\ + + Parameters + ---------- + alpha : float + slope coefficient for the negative half axis. Must be >= 0. + + + Inputs: + - **data**: input tensor with arbitrary shape. + + Outputs: + - **out**: output tensor with the same shape as `data`. + """ + def __init__(self, alpha, **kwargs): + assert alpha >= 0, "Slope coefficient for LeakyReLU must be no less than 0." + super(LeakyReLU, self).__init__(**kwargs) + self._alpha = alpha + + def hybrid_forward(self, F, x): + return F.LeakyReLU(x, act_type='leaky', slope=self._alpha, name='fwd') + + def __repr__(self): + s = '{name}({alpha})' + return s.format(name=self.__class__.__name__, + alpha=self._alpha) + + +class PReLU(HybridBlock): + r"""Parametric leaky version of a Rectified Linear Unit. + <https://arxiv.org/abs/1502.01852>`_ paper. + + It learns a gradient when the unit is not active + + .. math:: + + f\left(x\right) = \left\{ + \begin{array}{lr} + \alpha x & : x \lt 0 \\ + x & : x \geq 0 \\ + \end{array} + \right.\\ + + where alpha is a learned parameter. + + Parameters + ---------- + alpha_initializer : Initializer + Initializer for the `embeddings` matrix. + + + Inputs: + - **data**: input tensor with arbitrary shape. + + Outputs: + - **out**: output tensor with the same shape as `data`. + """ + def __init__(self, alpha_initializer=initializer.Constant(0.25), *args): + super(PReLU, self).__init__(*args) + with self.name_scope(): + self.alpha = self.params.get('alpha', shape=(1,), init=alpha_initializer) + + def hybrid_forward(self, F, x, alpha): + return F.LeakyReLU(x, gamma=alpha, act_type='prelu', name='fwd') + + def __repr__(self): + s = '{name}' Review comment: Yes ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
