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#!/usr/bin/env python |
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# -*- coding: utf-8 -*- |
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from layer import NeuralLayer |
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from deepy.utils.decorations import neural_computation |
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class NeuralVariable(NeuralLayer): |
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""" |
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Create a constant layer with tensors. |
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""" |
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def __init__(self, tensor, test_tensor=None, dim=0): |
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""" |
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Create a tensor layer. |
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""" |
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super(NeuralVariable, self).__init__("const") |
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self.output_dim = dim |
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self.tensor = tensor |
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self.test_tensor = tensor if not test_tensor else test_tensor |
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self.initialize(0) |
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def __getitem__(self, index): |
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@neural_computation |
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def getitem_wrapper(t, index): |
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if type(index) == list: |
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index = tuple(index) |
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return t.__getitem__(index) |
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ret = getitem_wrapper(self, index) |
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ret.output_dim = self.dim() |
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return ret |
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def __call__(self, *args, **kwargs): |
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return NeuralVariable(self.tensor(*args, **kwargs), self.test_tensor(*args, **kwargs), dim=self.dim()) |
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def __getattr__(self, name): |
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return NeuralVariable(getattr(self.tensor, name), getattr(self.test_tensor, name), dim=self.dim()) |
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def apply(self, func, dim=None): |
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""" |
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Apply a function to tensors. |
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""" |
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output_dim = dim if dim else self.output_dim |
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return NeuralVariable(func(self.tensor), func(self.test_tensor), output_dim) |
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def compute_tensor(self, x): |
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return self.tensor |
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def compute_test_tesnor(self, x): |
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return self.test_tensor |
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def set_test_value(self, value): |
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self.tensor.tag.test_value = value |
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def dim(self): |
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return self.output_dim |
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def shape(self, dim_index): |
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return NeuralVariable(self.tensor.shape[dim_index], self.test_tensor.shape[dim_index]) |
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@property |
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def test_value(self): |
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if hasattr(self.tensot.tag, 'test_value'): |
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return self.tensor.tag.test_value |
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else: |
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return None |
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@property |
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def tv(self): |
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return self.test_value |
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@property |
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def ts(self): |
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if self.test_value: |
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return self.test_value.shape |
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else: |
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return None |
zomux /
deepy
