Source code for chainer.links.connection.highway

from chainer.functions.activation import relu
from chainer.functions.activation import sigmoid
from chainer import link
from chainer.links.connection import linear


[docs]class Highway(link.Chain): """Highway module. In highway network, two gates are added to the ordinal non-linear transformation (:math:`H(x) = activate(W_h x + b_h)`). One gate is the transform gate :math:`T(x) = \\sigma(W_t x + b_t)`, and the other is the carry gate :math:`C(x)`. For simplicity, the author defined :math:`C = 1 - T`. Highway module returns :math:`y` defined as .. math:: y = activate(W_h x + b_h) \\odot \\sigma(W_t x + b_t) + x \\odot(1 - \\sigma(W_t x + b_t)) The output array has the same spatial size as the input. In order to satisfy this, :math:`W_h` and :math:`W_t` must be square matrices. Args: in_out_size (int): Dimension of input and output vectors. nobias (bool): If ``True``, then this function does not use the bias. activate: Activation function of plain array. :math:`tanh` is also available. init_Wh (2-D array): Initial weight value of plain array. If ``None``, then this function uses Gaussian distribution scaled by ``w_scale`` to initialize :math:`W_h`. May also be a callable that takes ``numpy.ndarray`` or``cupy.ndarray`` and edits its value. init_bh (1-D array): Initial bias value of plain array. If ``None``, then this function uses zero vector to initialize :math:`b_h`. May also be a callable that takes ``numpy.ndarray`` or ``cupy.ndarray`` and edits its value. init_Wt (2-D array): Initial weight value of transform array. If ``None``, then this function uses Gaussian distribution scaled by ``w_scale`` to initialize :math:`W_t`. May also be a callable that takes ``numpy.ndarray`` or ``cupy.ndarray`` and edits its value. init_bt (1-D array): Initial bias value of transform array. Default value is -1 vector. May also be a callable that takes ``numpy.ndarray`` or ``cupy.ndarray`` and edits its value. Negative value is recommended by the author of the paper. (e.g. -1, -3, ...). See: `Highway Networks <https://arxiv.org/abs/1505.00387>`_. """ def __init__(self, in_out_size, nobias=False, activate=relu.relu, init_Wh=None, init_Wt=None, init_bh=None, init_bt=-1): super(Highway, self).__init__( plain=linear.Linear(in_out_size, in_out_size, nobias=nobias, initialW=init_Wh, initial_bias=init_bh), transform=linear.Linear(in_out_size, in_out_size, nobias=nobias, initialW=init_Wt, initial_bias=init_bt) ) self.activate = activate def __call__(self, x): """Computes the output of the Highway module. Args: x (~chainer.Variable): Input variable. Returns: Variable: Output variable. Its array has the same spatial size and the same minibatch size as the input array. """ out_plain = self.activate(self.plain(x)) out_transform = sigmoid.sigmoid(self.transform(x)) y = out_plain * out_transform + x * (1 - out_transform) return y