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

    dtype, shape=None, name=None

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

For example:

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

with tf.compat.v1.Session() as sess:
  print(  # 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(, feed_dict={
    x: tf.compat.v1.SparseTensorValue(indices, values, shape)}))  # Will
  print(, 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(, feed_dict={x: sp_value}))  # Will succeed.

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


  • 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).


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


  • RuntimeError: if eager execution is enabled