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#!/usr/bin/env python |
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# -*- coding: utf-8 -*- |
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import theano.tensor as T |
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from . import NeuralLayer |
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from dense import Dense |
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class Maxout(NeuralLayer): |
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""" |
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Maxout activation unit. |
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- http://arxiv.org/pdf/1302.4389.pdf |
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""" |
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def __init__(self, output_dim=None, num_pieces=4, init=None, linear_transform=True): |
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""" |
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:param num_pieces: pieces of sub maps |
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""" |
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super(Maxout, self).__init__("maxout") |
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self.num_pieces = num_pieces |
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self.output_dim = output_dim |
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self.linear_transform = linear_transform |
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self.init = init |
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def prepare(self): |
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if self.output_dim is None: |
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self.output_dim = self.input_dim // self.num_pieces |
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if self.linear_transform: |
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self.transformer = Dense(self.output_dim * self.num_pieces).init(self.input_dim) |
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self.register(self.transformer) |
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def compute_tensor(self, x): |
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if self.linear_transform: |
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x = self.transformer.compute_tensor(x) |
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# x ~ batch, time, size / batch, size |
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new_shape = [x.shape[i] for i in range(x.ndim - 1)] + [self.output_dim, self.num_pieces] |
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# new_shape ~ batch, time, out_dim, pieces / batch, out_dim, pieces |
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output = T.max(x.reshape(new_shape, ndim=x.ndim + 1), axis=x.ndim) |
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return output |
zomux /
deepy
