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)