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python - 在theano中创建共享行

转载 作者:太空狗 更新时间:2023-10-30 00:10:20 25 4
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我注意到在 theano 中,当一个人基于一维 numpy 数组创建一个共享变量时,它变成了一个向量,而不是一行:

import theano.tensor as T
import theano, numpy

shared_vector = theano.shared(numpy.zeros((10,)))
print(shared_vector.type)
# TensorType(float64, vector)
print(shared_vector.broadcastable)
# (False,)

1xN的矩阵也是一样,它变成了矩阵而不是行:

shared_vector = theano.shared(numpy.zeros((1,10,)))
print(shared_vector.type)
# TensorType(float64, matrix)
print(shared_vector.broadcastable)
# (False, False)

当我想将 M x N 矩阵添加到 1 X N 行向量时,这很麻烦,因为共享向量在第一维中不可广播。首先,这是行不通的:

row = T.row('row')
mat=T.matrix('matrix')
f=theano.function(
[],
mat + row,
givens={
mat: numpy.zeros((20,10), dtype=numpy.float32),
row: numpy.zeros((10,), dtype=numpy.float32)
},
on_unused_input='ignore'
)

出现错误:

TypeError: Cannot convert Type TensorType(float32, vector) (of Variable <TensorType(float32, vector)>) into Type TensorType(float32, row). You can try to manually convert <TensorType(float32, vector)> into a TensorType(float32, row).

好的,很清楚,我们不能将向量分配给行。不幸的是,这也不行:

row = T.matrix('row')
mat=T.matrix('matrix')
f=theano.function(
[],
mat + row,
givens={
mat: numpy.zeros((20,10), dtype=numpy.float32),
row: numpy.zeros((1,10,), dtype=numpy.float32)
},
on_unused_input='ignore'
)
f()

出现错误:

ValueError: Input dimension mis-match. (input[0].shape[0] = 20, input[1].shape[0] = 1)
Apply node that caused the error: Elemwise{add,no_inplace}(<TensorType(float32, matrix)>, <TensorType(float32, matrix)>)
Inputs types: [TensorType(float32, matrix), TensorType(float32, matrix)]
Inputs shapes: [(20, 10), (1, 10)]
Inputs strides: [(40, 4), (40, 4)]
Inputs values: ['not shown', 'not shown']

Backtrace when the node is created:
File "<ipython-input-55-0f03bee478ec>", line 5, in <module>
mat + row,

HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.

所以我们也不能只使用 1 x N 矩阵作为一行(因为 1 x N 矩阵的第一个维度是不可广播的)。

问题仍然存在,我们“能”做什么?我如何创建一个行类型的共享变量,这样我就可以使用矩阵行加法进行广播?

最佳答案

使用 reshape(1, N) 的替代方法是使用 dimshuffle('x', 0) 作为 described in the documentation .

下面是这两种方法的演示:

import numpy
import theano

x = theano.shared(numpy.arange(10))
print x
print x.dimshuffle('x', 0).type
print x.dimshuffle(0, 'x').type
print x.reshape((1, x.shape[0])).type
print x.reshape((x.shape[0], 1)).type

f = theano.function([], outputs=[x, x.dimshuffle('x', 0), x.reshape((1, x.shape[0]))])
theano.printing.debugprint(f)

这打印

<TensorType(int32, vector)>
TensorType(int32, row)
TensorType(int32, col)
TensorType(int32, row)
TensorType(int32, col)
DeepCopyOp [@A] '' 2
|<TensorType(int32, vector)> [@B]
DeepCopyOp [@C] '' 4
|InplaceDimShuffle{x,0} [@D] '' 1
|<TensorType(int32, vector)> [@B]
DeepCopyOp [@E] '' 6
|Reshape{2} [@F] '' 5
|<TensorType(int32, vector)> [@B]
|MakeVector{dtype='int64'} [@G] '' 3
|TensorConstant{1} [@H]
|Shape_i{0} [@I] '' 0
|<TensorType(int32, vector)> [@B]

证明 dimshuffle 可能更可取,因为它比 reshape 涉及的工作更少。

关于python - 在theano中创建共享行,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/33152078/

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