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tensorflowsparse-matrixmultiplicationelementwise-operations

tensorflow: how to perform element-wise multiplication between two sparse matrix

I have two sparse matrices declared using the tf.sparse_placeholder. I need to perform the element-wise multiplication between the two matrices. But I cannot find such an implementation in tensorflow. The most related function is tf.sparse_tensor_dense_matmul, but this is a function performing matrix multiplication between one sparse matrix and one dense matrix.

What I hope to find is to performing element-wise multiplication between two sparse matrices. Is there any implementation of this in tensorflow?

I show the following example of performing multiplication between dense matrices. I'm looking forward to seeing a solution.

import tensorflow as tf
import numpy as np

# Element-wise multiplication, two dense matrices
A = tf.placeholder(tf.float32, shape=(100, 100))
B = tf.placeholder(tf.float32, shape=(100, 100))
C = tf.multiply(A, B)
sess = tf.InteractiveSession()
RandA = np.random.rand(100, 100)
RandB = np.random.rand(100, 100)
print sess.run(C, feed_dict={A: RandA, B: RandB})

# matrix multiplication, A is sparse and B is dense
A = tf.sparse_placeholder(tf.float32)
B = tf.placeholder(tf.float32, shape=(5,5))
C = tf.sparse_tensor_dense_matmul(A, B)
sess = tf.InteractiveSession()
indices = np.array([[3, 2], [1, 2]], dtype=np.int64)
values = np.array([1.0, 2.0], dtype=np.float32)
shape = np.array([5,5], dtype=np.int64)
Sparse_A = tf.SparseTensorValue(indices, values, shape)
RandB = np.ones((5, 5))
print sess.run(C, feed_dict={A: Sparse_A, B: RandB})

Thank you very much!!!

Solution

  • TensorFlow currently has no sparse-sparse element-wise multiplication operation.

    We don't plan to add support for this currently, but contributions are definitely welcome! Feel free to create a github issue here: https://github.com/tensorflow/tensorflow/issues/new and perhaps you or someone in the community can pick it up :)

    Thanks!