将数组打包到张量的下三角 [英] Packing array into lower triangular of a tensor

问题描述

我想将形状为(..., n * (n - 1) / 2)的数组压缩到形状为(..., n, n)的张量的下三角部分，其中...表示任意形状.在numpy中，我将其实现为

I would like to pack an array of shape (..., n * (n - 1) / 2) into the lower triangular part of a tensor with shape (..., n, n) where ... denotes an arbitrary shape. In numpy, I would implement it as

import numpy as np

# Create the array to store data in
arbitrary_shape = (10, 11, 12)
n = 5
target = np.zeros(arbitrary_shape + (n, n))
# Create the source array
source = np.random.normal(0, 1, arbitrary_shape + (n * (n - 1) / 2,))
# Create indices and set values
u, v = np.tril_indices(n, -1)
target[..., u, v] = source
# Check that everything went ok
print target[0, 0, 0]

到目前为止，使用transpose，reshape和scatter_update的组合，我已经能够在张量流中实现类似的功能，但是感觉很笨拙.

So far, I've been able to achieve something similar in tensorflow using a combination of transpose, reshape and scatter_update but it feels clumsy.

import tensorflow as tf

# Create the source array
source = np.random.normal(0, 1, (n * (n - 1) / 2,) + arbitrary_shape)

sess = tf.InteractiveSession()

# Create a flattened representation
target = tf.Variable(np.zeros((n * n,) + arbitrary_shape))
# Assign the values
target = tf.scatter_update(target, u * n + v, source)
# Reorder the axes and reshape into a square matrix along the last dimension
target = tf.transpose(target, (1, 2, 3, 0))
target = tf.reshape(target, arbitrary_shape + (n, n))

# Initialise variables and check results
sess.run(tf.initialize_all_variables())
print target.eval()[0, 0, 0]

sess.close()

有没有更好的方法来实现这一目标?

Is there a better way to achieve this?

推荐答案

我意识到这有点晚了，但是我一直在尝试加载一个较低的三角矩阵，并且我使用sparse_to_dense使其工作:

I realise this is a bit late, but I've been attempting to load a lower triangular matrix, and I got it working using sparse_to_dense:

import tensorflow as tf
import numpy as np

session = tf.InteractiveSession()

n = 4 # Number of dimensions of matrix

# Get pairs of indices of positions
indices = list(zip(*np.tril_indices(n)))
indices = tf.constant([list(i) for i in indices], dtype=tf.int64)

# Test values to load into matrix
test = tf.constant(np.random.normal(0, 1, int(n*(n+1)/2)), dtype=tf.float64)

# Can pass in list of values and indices to tf.sparse_to_dense 
# and it will return a dense matrix
dense = tf.sparse_to_dense(sparse_indices=indices, output_shape=[n, n], \
                           sparse_values=test, default_value=0, \
                           validate_indices=True)

sess.close()

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