tf.sparse.expand_dims  |  TensorFlow v2.16.1 (original) (raw)

tf.sparse.expand_dims

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Returns a tensor with an length 1 axis inserted at index axis.

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tf.compat.v1.sparse.expand_dims

tf.sparse.expand_dims(
    sp_input, axis=None, name=None
)

Given a tensor input, this operation inserts a dimension of length 1 at the dimension index axis of input's shape. The dimension index follows python indexing rules: It's zero-based, a negative index it is counted backward from the end.

This operation is useful to:

• Add an outer "batch" dimension to a single element.
• Align axes for broadcasting.
• To add an inner vector length axis to a tensor of scalars.

For example:

If you have a sparse tensor with shape [height, width, depth]:

sp = tf.sparse.SparseTensor(indices=[[3,4,1]], values=[7,], dense_shape=[10,10,3])

You can add an outer batch axis by passing axis=0:

tf.sparse.expand_dims(sp, axis=0).shape.as_list() [1, 10, 10, 3]

The new axis location matches Python list.insert(axis, 1):

tf.sparse.expand_dims(sp, axis=1).shape.as_list() [10, 1, 10, 3]

Following standard python indexing rules, a negative axis counts from the end so axis=-1 adds an inner most dimension:

tf.sparse.expand_dims(sp, axis=-1).shape.as_list() [10, 10, 3, 1]

sp.shape.as_list() [10, 10, 3] tf.sparse.expand_dims(sp).shape.as_list() [10, 10, 3, 1]

This operation requires that axis is a valid index for input.shape, following python indexing rules:

-1-tf.rank(input) <= axis <= tf.rank(input)

This operation is related to:

• tf.expand_dims, which provides this functionality for dense tensors.
• tf.squeeze, which removes dimensions of size 1, from dense tensors.
• tf.sparse.reshape, which provides more flexible reshaping capability.