Difference between SparseTensor and SparseTensorValue

Question

What is the difference between SparseTensor and SparseTensorValue? Is there anything I should keep in mind if I want to build the sparse tensor based on fed indices and values? I could only find a few toy examples.

Answer 1

It depends on where you define your Sparse Tensor.

If you would like to define the tensor outside the graph, e.g. define the sparse tensor for later data feed, use SparseTensorValue. In contrast, if the sparse tensor is defined in graph, use SparseTensor

Sample code for tf.SparseTensorValue:

x_sp = tf.sparse_placeholder(dtype=tf.float32)
W = tf.Variable(tf.random_normal([6, 6]))
y = tf.sparse_tensor_dense_matmul(sp_a=x_sp, b=W)

init = tf.global_variables_initializer()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.2)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
sess.run(init)

stv = tf.SparseTensorValue(indices=[[0, 0], [1, 2]], values=[1.1, 1.2], 
dense_shape=[2,6])
result = sess.run(y,feed_dict={x_sp:stv})

print(result)

Sample code for tf.SparseTensor:

indices_i = tf.placeholder(dtype=tf.int64, shape=[2, 2])
values_i = tf.placeholder(dtype=tf.float32, shape=[2])
dense_shape_i = tf.placeholder(dtype=tf.int64, shape=[2])
st = tf.SparseTensor(indices=indices_i, values=values_i, dense_shape=dense_shape_i)

W = tf.Variable(tf.random_normal([6, 6]))
y = tf.sparse_tensor_dense_matmul(sp_a=st, b=W)

init = tf.global_variables_initializer()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.2)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
sess.run(init)

result = sess.run(y,feed_dict={indices_i:[[0, 0], [1, 2]], values_i:[1.1, 1.2], dense_shape_i:[2,6]})

print(result)

Hope this help~