Tensorflow: tf.matmul() gagal ketika matriks jarang, bahkan jika a_is_sparse diatur. Kesalahan samar.

tf.matmul(A, B, a_is_sparse=True) gagal pada a = ops.convert_to_tensor(a, name="a") dengan kesalahan samar.

Contoh kode:

import tensorflow as tf
import numpy as np

x = tf.sparse_placeholder(tf.float32)
y = tf.Variable(tf.random_uniform([9, 9], minval=0.0, maxval=1.0, dtype=tf.float32))

with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())
    indices = np.array([[3, 2], [4, 5]], dtype=np.int64)
    values = np.array([1.0, 2.0], dtype=np.float32)
    shape = np.array([9, 9], dtype=np.int64)
    _ = sess.run(tf.matmul(x, y), feed_dict={
        x: tf.SparseTensorValue(indices, values, shape)})

Kesalahan:
baris 12, di
_ = sess.run(tf.matmul(x, y, a_is_sparse=Benar), feed_dict={
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", baris 1189, di matmul
a = ops.convert_to_tensor(a, nama="a")
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", baris 620, di convert_to_tensor
ret = konversi_fungsi(nilai, dtype=dtype, nama=nama, as_ref=as_ref)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/constant_op.py", baris 179, di _constant_tensor_conversion_function
kembalikan konstanta(v, dtype=dtype, name=name)
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/constant_op.py", baris 162, secara konstan
tensor_util.make_tensor_proto(nilai, dtype=dtype, shape=shape))
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/tensor_util.py", baris 421, di make_tensor_proto
tensor_proto.string_val.extend([compat.as_bytes(x) untuk x dalam nilai_proto])
File "/usr/local/lib/python2.7/site-packages/tensorflow/python/util/compat.py", baris 44, di as_bytes
menaikkan TypeError('String biner atau unicode yang diharapkan, dapatkan %r' % bytes_or_text)
TypeError: String biner atau unicode yang diharapkan, dapatkan

Diperbaiki dengan mengonversi secara eksplisit dari tensor jarang ke tensor padat sebelum perkalian:

import tensorflow as tf
import numpy as np

x = tf.sparse_placeholder(tf.float32)
z = tf.sparse_tensor_to_dense(x)
y = tf.Variable(tf.random_uniform([9, 9], minval=0.0, maxval=1.0, dtype=tf.float32))

with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())
    indices = np.array([[3, 2], [4, 5]], dtype=np.int64)
    values = np.array([1.0, 2.0], dtype=np.float32)
    shape = np.array([9, 9], dtype=np.int64)
    _ = sess.run(tf.matmul(z, y), feed_dict={
        x: tf.SparseTensorValue(indices, values, shape)})