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#!/usr/bin/env python |
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# -*- coding: utf-8 -*- |
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from deepy.utils import AutoEncoderCost |
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import theano.tensor as T |
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from network import NeuralNetwork |
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class AutoEncoder(NeuralNetwork): |
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""" |
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Auto encoder. |
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Must call stack_encoding before stack_decoding. |
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Parameters: |
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rep_dim - dimension of representation |
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""" |
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def __init__(self, input_dim, rep_dim=None, config=None, input_tensor=None): |
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super(AutoEncoder, self).__init__(input_dim, config=config, input_tensor=input_tensor) |
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self.rep_dim = rep_dim |
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self.encoding_layes = [] |
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self.decoding_layers = [] |
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self.encoding_network = None |
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self.decoding_network = None |
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def _cost_func(self, y): |
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return AutoEncoderCost(self.input_variables[0], y).get() |
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@property |
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def cost(self): |
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return self._cost_func(self.output) |
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@property |
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def test_cost(self): |
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return self._cost_func(self.test_output) |
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def stack_encoders(self, *layers): |
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""" |
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Stack encoding layers, this must be done before stacking decoding layers. |
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""" |
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self.stack(*layers) |
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self.encoding_layes.extend(layers) |
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def stack_decoders(self, *layers): |
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""" |
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Stack decoding layers. |
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""" |
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self.stack(*layers) |
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self.decoding_layers.extend(layers) |
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def encode(self, x): |
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""" |
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Encode given input. |
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""" |
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if not self.encoding_network: |
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self.encoding_network = NeuralNetwork(self.input_dim, self.network_config, self.input_tensor) |
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for layer in self.encoding_layes: |
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self.encoding_network.stack_layer(layer, no_setup=True) |
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return self.encoding_network.compute(x) |
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def decode(self, x): |
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""" |
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Decode given representation. |
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""" |
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if not self.rep_dim: |
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raise Exception("rep_dim must be set to decode.") |
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if not self.decoding_network: |
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self.decoding_network = NeuralNetwork(self.rep_dim, self.network_config) |
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for layer in self.decoding_layers: |
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self.decoding_network.stack_layer(layer, no_setup=True) |
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return self.decoding_network.compute(x) |
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uaca /
deepy
