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Inserts a dimension of 1 into a tensor's shape. (deprecated arguments)
tf.compat.v1.expand_dims(
input, axis=None, name=None, dim=None
)
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.
Args | |
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input
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A Tensor.
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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)].
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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 | |
|---|---|
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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