Class Dense

tensorlayer/layers/dense/base_dense.py:19–111

The :class:`Dense` class is a fully connected layer. Parameters ---------- n_units : int The number of units of this layer. act : activation function The activation function of this layer. W_init : initializer The initializer for the weight matrix. b_

class Dense(Layer):
    """The :class:`Dense` class is a fully connected layer.

    Parameters
    ----------
    n_units : int
        The number of units of this layer.
    act : activation function
        The activation function of this layer.
    W_init : initializer
        The initializer for the weight matrix.
    b_init : initializer or None
        The initializer for the bias vector. If None, skip biases.
    in_channels: int
        The number of channels of the previous layer.
        If None, it will be automatically detected when the layer is forwarded for the first time.
    name : None or str
        A unique layer name. If None, a unique name will be automatically generated.

    Examples
    --------
    With TensorLayer

    >>> net = tl.layers.Input([100, 50], name='input')
    >>> dense = tl.layers.Dense(n_units=800, act=tf.nn.relu, in_channels=50, name='dense_1')
    >>> print(dense)
    Dense(n_units=800, relu, in_channels='50', name='dense_1')
    >>> tensor = tl.layers.Dense(n_units=800, act=tf.nn.relu, name='dense_2')(net)
    >>> print(tensor)
    tf.Tensor([...], shape=(100, 800), dtype=float32)

    Notes
    -----
    If the layer input has more than two axes, it needs to be flatten by using :class:`Flatten`.

    """

    def __init__(
        self,
        n_units,
        act=None,
        W_init=tl.initializers.truncated_normal(stddev=0.05),
        b_init=tl.initializers.constant(value=0.0),
        in_channels=None,
        name=None,  # 'dense',
    ):

        super(Dense, self).__init__(name, act=act)

        self.n_units = n_units
        self.W_init = W_init
        self.b_init = b_init
        self.in_channels = in_channels

        if self.in_channels is not None:
            self.build(self.in_channels)
            self._built = True