tf.sparse_placeholder(dtype, shape=None, name=None)

tf.sparse_placeholder(dtype, shape=None, name=None)

See the guide: Inputs and Readers > Placeholders

Inserts a placeholder for a sparse tensor that will be always fed.

Important: This sparse tensor will produce an error if evaluated. Its value must be fed using the feed_dict optional argument to Session.run(), Tensor.eval(), or Operation.run().

For example:

x = tf.sparse_placeholder(tf.float32)
y = tf.sparse_reduce_sum(x)

with tf.Session() as sess:
  print(sess.run(y))  # ERROR: will fail because x was not fed.

  indices = np.array([[3, 2, 0], [4, 5, 1]], dtype=np.int64)
  values = np.array([1.0, 2.0], dtype=np.float32)
  shape = np.array([7, 9, 2], dtype=np.int64)
  print(sess.run(y, feed_dict={
    x: tf.SparseTensorValue(indices, values, shape)}))  # Will succeed.
  print(sess.run(y, feed_dict={
    x: (indices, values, shape)}))  # Will succeed.

  sp = tf.SparseTensor(indices=indices, values=values, dense_shape=shape)
  sp_value = sp.eval(session)
  print(sess.run(y, feed_dict={x: sp_value}))  # Will succeed.

Args:

• dtype: The type of values elements in the tensor to be fed.
• shape: The shape of the tensor to be fed (optional). If the shape is not specified, you can feed a sparse tensor of any shape.
• name: A name for prefixing the operations (optional).

Returns:

A SparseTensor that may be used as a handle for feeding a value, but not evaluated directly.

Defined in tensorflow/python/ops/array_ops.py.