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# tf.expand_dims

Inserts a dimension of 1 into a tensor's shape. (deprecated arguments)

Given a tensor `input`, this operation inserts a dimension of 1 at the dimension index `axis` of `input`'s shape. The dimension index `axis` starts at zero; if you specify a negative number for `axis` it is counted backward from the end.

This operation is useful if you want to add a batch dimension to a single element. For example, if you have a single image of shape ```[height, width, channels]```, you can make it a batch of 1 image with `expand_dims(image, 0)`, which will make the shape `[1, height, width, channels]`.

#### Other examples:

``````# 't' is a tensor of shape [2]
tf.shape(tf.expand_dims(t, 0))  # [1, 2]
tf.shape(tf.expand_dims(t, 1))  # [2, 1]
tf.shape(tf.expand_dims(t, -1))  # [2, 1]

# 't2' is a tensor of shape [2, 3, 5]
tf.shape(tf.expand_dims(t2, 0))  # [1, 2, 3, 5]
tf.shape(tf.expand_dims(t2, 2))  # [2, 3, 1, 5]
tf.shape(tf.expand_dims(t2, 3))  # [2, 3, 5, 1]
``````

This operation requires that:

`-1-input.dims() <= dim <= input.dims()`

This operation is related to `squeeze()`, which removes dimensions of size 1.

`input` A `Tensor`.
`axis` 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)]`.
`name` The name of the output `Tensor` (optional).
`dim` 0-D (scalar). Equivalent to `axis`, to be deprecated.

A `Tensor` with the same data as `input`, but its shape has an additional dimension of size 1 added.

`ValueError` if either both or neither of `dim` and `axis` are specified.

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