CODEWITHSUNDEEP
pythongraphtensorflowsparse-matrixautoencoder
user4706825
user4706825

Reputation:

Sparse Tensor (matrix) from a dense Tensor Tensorflow

I am creating a convolutional sparse autoencoder and I need to convert a 4D matrix full of values (whose shape is [samples, N, N, D]) into a sparse matrix.

For each sample, I have D NxN feature maps. I want to convert each NxN feature map to a sparse matrix, with the maximum value mapped to 1 and all the others to 0.

I do not want to do this at run time but during the Graph declaration (because I need to use the resulting sparse matrix as an input to other graph operations), but I do not understand how to get the indices to build the sparse matrix.

Upvotes: 10

Views: 7238

Answers (5)

hoefling
hoefling

Reputation: 66431

Tensorflow has tf.sparse.from_dense since 1.15. Example:

In [1]: import tensorflow as tf

In [2]: x = tf.eye(3) * 5

In [3]: x
Out[3]: 
<tf.Tensor: shape=(3, 3), dtype=float32, numpy=
array([[5., 0., 0.],
       [0., 5., 0.],
       [0., 0., 5.]], dtype=float32)>

Applying tf.sparse.from_dense:

In [4]: y = tf.sparse.from_dense(x)

In [5]: y.values
Out[5]: <tf.Tensor: shape=(3,), dtype=float32, numpy=array([5., 5., 5.], dtype=float32)>

In [6]: y.indices
Out[6]: 
<tf.Tensor: shape=(3, 2), dtype=int64, numpy=
array([[0, 0],
       [1, 1],
       [2, 2]])>

Verify identity by applying tf.sparse.to_dense:

In [7]: tf.sparse.to_dense(y) == x
Out[7]: 
<tf.Tensor: shape=(3, 3), dtype=bool, numpy=
array([[ True,  True,  True],
       [ True,  True,  True],
       [ True,  True,  True]])>

Upvotes: 1

RobR
RobR

Reputation: 2190

In TF 2.3 Tensorflow Probability has a function for this:

import tensorflow_probability as tfp

tfp.math.dense_to_sparse(x, ignore_value=None, name=None)

Upvotes: 0

borgr
borgr

Reputation: 25415

Note there is a built in function in the contrib (taken from )

Upvotes: 0

Alexey Antonenko
Alexey Antonenko

Reputation: 2627

Simple code to convert dense numpy array to tf.SparseTensor:

def denseNDArrayToSparseTensor(arr):
  idx  = np.where(arr != 0.0)
  return tf.SparseTensor(np.vstack(idx).T, arr[idx], arr.shape)

Upvotes: 3

javidcf
javidcf

Reputation: 59731

You can use tf.where and tf.gather_nd to do that:

import numpy as np
import tensorflow as tf

# Make a tensor from a constant
a = np.reshape(np.arange(24), (3, 4, 2))
a_t = tf.constant(a)
# Find indices where the tensor is not zero
idx = tf.where(tf.not_equal(a_t, 0))
# Make the sparse tensor
# Use tf.shape(a_t, out_type=tf.int64) instead of a_t.get_shape()
# if tensor shape is dynamic
sparse = tf.SparseTensor(idx, tf.gather_nd(a_t, idx), a_t.get_shape())
# Make a dense tensor back from the sparse one, only to check result is correct
dense = tf.sparse_tensor_to_dense(sparse)
# Check result
with tf.Session() as sess:
    b = sess.run(dense)
np.all(a == b)
>>> True

Upvotes: 17

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