Class Concat

tensorlayer/layers/merge.py:15–72

A layer that concats multiple tensors according to given axis. Parameters ---------- concat_dim : int The dimension to concatenate. name : None or str A unique layer name. Examples ---------- >>> class CustomModel(tl.models.Model): >>> def __init

class Concat(Layer):
    """A layer that concats multiple tensors according to given axis.

    Parameters
    ----------
    concat_dim : int
        The dimension to concatenate.
    name : None or str
        A unique layer name.

    Examples
    ----------
    >>> class CustomModel(tl.models.Model):
    >>>     def __init__(self):
    >>>         super(CustomModel, self).__init__(name="custom")
    >>>         self.dense1 = tl.layers.Dense(in_channels=20, n_units=10, act=tf.nn.relu, name='relu1_1')
    >>>         self.dense2 = tl.layers.Dense(in_channels=20, n_units=10, act=tf.nn.relu, name='relu2_1')
    >>>         self.concat = tl.layers.Concat(concat_dim=1, name='concat_layer')

    >>>     def forward(self, inputs):
    >>>         d1 = self.dense1(inputs)
    >>>         d2 = self.dense2(inputs)
    >>>         outputs = self.concat([d1, d2])
    >>>         return outputs

    """

    def __init__(
        self,
        concat_dim=-1,
        name=None,  #'concat',
    ):

        super(Concat, self).__init__(name)
        self.concat_dim = concat_dim

        self.build(None)
        self._built = True

        logging.info("Concat %s: concat_dim: %d" % (self.name, concat_dim))

    def __repr__(self):
        s = ('{classname}(concat_dim={concat_dim})')
        return s.format(classname=self.__class__.__name__, **self.__dict__)

    def build(self, inputs_shape):
        pass

    # @tf.function
    def forward(self, inputs):
        """

        prev_layer : list of :class:`Layer`
            List of layers to concatenate.
        """
        outputs = tf.concat(inputs, self.concat_dim, name=self.name)

        return outputs