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Returns a tensor with a length 1 axis inserted at index axis. (deprecated arguments)
tf.compat.v1.expand_dims(
input, axis=None, name=None, dim=None
)
Used in the notebooks
| Used in the tutorials |
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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 single image of shape [height, width, channels]:
image = tf.zeros([10,10,3])
You can add an outer batch axis by passing axis=0:
tf.expand_dims(image, axis=0).shape.as_list()[1, 10, 10, 3]
The new axis location matches Python list.insert(axis, 1):
tf.expand_dims(image, 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.expand_dims(image, -1).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.squeeze, which removes dimensions of size 1.tf.reshape, which provides more flexible reshaping capability.tf.sparse.expand_dims, which provides this functionality fortf.SparseTensor
Args | |
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input
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A Tensor.
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axis
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0-D (scalar). Specifies the dimension index at which to expand the
shape of input. Must be in the range [-rank(input) - 1, rank(input)].
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name
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The name of the output Tensor (optional).
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dim
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0-D (scalar). Equivalent to axis, to be deprecated.
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Returns | |
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A Tensor with the same data as input, but its shape has an additional
dimension of size 1 added.
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Raises | |
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ValueError
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if either both or neither of dim and axis are specified.
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