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#!/usr/bin/env python |
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# -*- coding: utf-8 -*- |
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from deepy.networks import NeuralNetwork |
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class ComputationalGraph(NeuralNetwork): |
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""" |
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A class for defining computational graphs. |
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This class can be used to design very complex models, such as Neural Turing Machine. |
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""" |
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def __init__(self, input_dim=0, model=None, input_tensor=None, monitors=None, |
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cost=None, output=None, outputs=None, blocks=None, input_vars=None, target_vars=None): |
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""" |
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Create a basic network. |
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Parameters: |
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input_dim - dimension of input variable |
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model - a short hand to specify the model |
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config - network configuration |
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input_tensor - specify the tensor of input if it's special |
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""" |
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from deepy.core.neural_var import NeuralVariable |
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from deepy.core.tensor_conversion import convert_to_theano_var |
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from theano.sandbox.cuda import CudaNdarraySharedVariable |
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super(ComputationalGraph, self).__init__(input_dim, input_tensor=input_tensor) |
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if model: |
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self.stack(model) |
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if cost: |
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self.stack(cost) |
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if output: |
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if cost: |
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self._test_output = output.tensor |
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else: |
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self.stack(output) |
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if blocks: |
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self.register(*blocks) |
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if input_vars: |
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self.input_variables = [t.tensor for t in input_vars] |
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if target_vars: |
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self.target_variables = [t.tensor for t in target_vars] |
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if outputs: |
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if not output and not cost: |
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self._test_output = None |
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self._test_outputs, _, _ = convert_to_theano_var(outputs) |
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if monitors: |
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if type(monitors) == dict: |
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monitors = monitors.items() |
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for monitor in monitors: |
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if type(monitor) != tuple: |
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raise Exception("monitors shall be tuples of (name, var).") |
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name, var = monitor |
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if isinstance(var, NeuralVariable): |
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var = var.tensor |
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if isinstance(var, CudaNdarraySharedVariable): |
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var *= 1.0 # Avoid CudaNdarray |
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self.training_monitors.append((name, var)) |
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self.testing_monitors.append((name, var)) |
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@property |
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def cost(self): |
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return self.output |
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@property |
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def test_cost(self): |
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return self.test_output |
zomux /
deepy
