In the following code, I want dense matrix B
to left multiply a sparse matrix A
, but I got errors.
import tensorflow as tf
import numpy as np
A = tf.sparse_placeholder(tf.float32)
B = tf.placeholder(tf.float32, shape=(5,5))
C = tf.matmul(B,A,a_is_sparse=False,b_is_sparse=True)
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})
The error message is as follows:
TypeError: Failed to convert object of type <class 'tensorflow.python.framework.sparse_tensor.SparseTensor'>
to Tensor. Contents: SparseTensor(indices=Tensor("Placeholder_4:0", shape=(?, ?), dtype=int64), values=Tensor("Placeholder_3:0", shape=(?,), dtype=float32), dense_shape=Tensor("Placeholder_2:0", shape=(?,), dtype=int64)).
Consider casting elements to a supported type.
What's wrong with my code?
I'm doing this following the documentation and it says we should use a_is_sparse
to denote whether the first matrix is sparse, and similarly with b_is_sparse
. Why is my code wrong?
As is suggested by vijay, I should use C = tf.matmul(B,tf.sparse_tensor_to_dense(A),a_is_sparse=False,b_is_sparse=True)
I tried this but I met with another error saying:
Caused by op u'SparseToDense', defined at:
File "a.py", line 19, in <module>
C = tf.matmul(B,tf.sparse_tensor_to_dense(A),a_is_sparse=False,b_is_sparse=True)
File "/home/mypath/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/sparse_ops.py", line 845, in sparse_tensor_to_dense
name=name)
File "/home/mypath/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/sparse_ops.py", line 710, in sparse_to_dense
name=name)
File "/home/mypath/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gen_sparse_ops.py", line 1094, in _sparse_to_dense
validate_indices=validate_indices, name=name)
File "/home/mypath/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
op_def=op_def)
File "/home/mypath/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/home/mypath/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1269, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): indices[1] = [1,2] is out of order
[[Node: SparseToDense = SparseToDense[T=DT_FLOAT, Tindices=DT_INT64, validate_indices=true, _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_Placeholder_4_0_2, _arg_Placeholder_2_0_0, _arg_Placeholder_3_0_1, SparseToDense/default_value)]]
Thank you all for helping me!
In tf.matmul
, flags a_is_sparse
and b_is_sparse
do not indicate the operands being SparseTensors
, but instead, they are algorithmic hints to invoke more efficient ways to compute multiplication on two dense Tensors. In your code should be :
C = tf.matmul(B,tf.sparse_tensor_to_dense(A),a_is_sparse=False,b_is_sparse=True)
To matmul a SparseTensor
and a dense
Tensor, you can also use tf.sparse_tensor_dense_matmul()
instead.