def join(axis, *tensors_list):
"""Convenience function to concatenate along the given axis.
Parameters
----------
axis : int
The axis to concatenate.
tensor_list : list of Tensor
The inputs to concatenate.
Returns
-------
Tensor
The output tensor.
"""
return ops.Concat(list(tensors_list), axis=axis)
def concat(values, axis, name=None):
"""
Concatenates tensors along one dimension.
Concatenates the list of tensors `values` along dimension `axis`. If
`values[i].shape = [D0, D1, ... Daxis(i), ...Dn]`, the concatenated
result has shape
[D0, D1, ... Raxis, ...Dn]
where
Raxis = sum(Daxis(i))
That is, the data from the input tensors is joined along the `axis`
dimension.
The number of dimensions of the input tensors must match, and all dimensions
except `axis` must be equal.
For example:
```python
t1 = [[1, 2, 3], [4, 5, 6]]
t2 = [[7, 8, 9], [10, 11, 12]]
tf.concat([t1, t2], 0) ==> [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]
tf.concat([t1, t2], 1) ==> [[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]]
# tensor t3 with shape [2, 3]
# tensor t4 with shape [2, 3]
tf.shape(tf.concat([t3, t4], 0)) ==> [4, 3]
tf.shape(tf.concat([t3, t4], 1)) ==> [2, 6]
```
Args:
values: A list of `Tensor` objects or a single `Tensor`.
axis: 0-D `int32` `Tensor`. Dimension along which to concatenate.
name: A name for the operation (optional).
Returns:
A `Tensor` resulting from concatenation of the input tensors.
"""
return ops.Concat(values, axis=axis, name=name)
def concatenate(tensor_list, axis=0):
"""Concatenate the inputs along the given axis.
All dimensions except specific ``axis`` should be same.
Parameters
----------
tensor_list : list of Tensor
The inputs to concatenate.
axis : int
The axis to concatenate.
Returns
-------
Tensor
The output tensor.
"""
return ops.Concat(tensor_list, axis=axis)
def concat(values, axis, name=None):
return ops.Concat(values, axis=axis, name=name)
def LayerSetup(self, bottom):
return _ops.Concat(bottom, **self.arguments)
def Setup(self, bottom):
super(ConcatLayer, self).Setup(bottom)
return ops.Concat(bottom, **self._param)
