tf.compat.v1.sparse_placeholder

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

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.sparse.placeholder

tf.compat.v1.sparse_placeholder(
    dtype, shape=None, name=None
)

Key Point: 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.compat.v1.sparse.placeholder(tf.float32)
y = tf.sparse.reduce_sum(x)

with tf.compat.v1.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.compat.v1.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=sess)
  print(sess.run(y, feed_dict={x: sp_value}))  # Will succeed.

@compatibility{eager} Placeholders are not compatible with eager execution.

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.

Raises
RuntimeError	if eager execution is enabled