|
1
|
|
|
#!/usr/bin/env python |
|
2
|
|
|
# -*- coding: utf-8 -*- |
|
3
|
|
|
|
|
4
|
|
|
from layer import NeuralLayer |
|
5
|
|
|
from deepy.utils.decorations import neural_computation |
|
6
|
|
|
|
|
7
|
|
|
|
|
8
|
|
|
class NeuralVariable(NeuralLayer): |
|
9
|
|
|
""" |
|
10
|
|
|
Create a constant layer with tensors. |
|
11
|
|
|
""" |
|
12
|
|
|
|
|
13
|
|
|
def __init__(self, tensor, test_tensor=None, dim=0): |
|
14
|
|
|
""" |
|
15
|
|
|
Create a tensor layer. |
|
16
|
|
|
""" |
|
17
|
|
|
super(NeuralVariable, self).__init__("const") |
|
18
|
|
|
self.output_dim = dim |
|
19
|
|
|
self.tensor = tensor |
|
20
|
|
|
self.test_tensor = tensor if not test_tensor else test_tensor |
|
21
|
|
|
self.initialize(0) |
|
22
|
|
|
|
|
23
|
|
|
def __getitem__(self, index): |
|
24
|
|
|
@neural_computation |
|
25
|
|
|
def getitem_wrapper(t, index): |
|
26
|
|
|
if type(index) == list: |
|
27
|
|
|
index = tuple(index) |
|
28
|
|
|
return t.__getitem__(index) |
|
29
|
|
|
return getitem_wrapper(self, index) |
|
30
|
|
|
|
|
31
|
|
|
def apply(self, func, dim=None): |
|
32
|
|
|
""" |
|
33
|
|
|
Apply a function to tensors. |
|
34
|
|
|
""" |
|
35
|
|
|
output_dim = dim if dim else self.output_dim |
|
36
|
|
|
return NeuralVariable(func(self.tensor), func(self.test_tensor), output_dim) |
|
37
|
|
|
|
|
38
|
|
|
def compute_tensor(self, x): |
|
39
|
|
|
return self.tensor |
|
40
|
|
|
|
|
41
|
|
|
def compute_test_tesnor(self, x): |
|
42
|
|
|
return self.test_tensor |
|
43
|
|
|
|
|
44
|
|
|
def set_test_value(self, value): |
|
45
|
|
|
self.tensor.tag.test_value = value |
|
46
|
|
|
|
|
47
|
|
|
def dim(self): |
|
48
|
|
|
return self.output_dim |
|
49
|
|
|
|
|
50
|
|
|
def shape(self, dim_index): |
|
51
|
|
|
return NeuralVariable(self.tensor.shape[dim_index], self.test_tensor.shape[dim_index]) |
|
52
|
|
|
|
zomux /
deepy
