layer.py 文件源码

def __init__(self, input, n_in, n_out, W=None, b=None,
                 activation=T.tanh, rng=None):
        """
        Typical hidden layer of an MLP: units are fully connected and have
        tangente hyperbolic activation function. Weight matrix (W) is of shape
        (n_in, n_out) and the bias vector (b) is of shape (nout,).

        Hidden unit activation is given by: tanh(dot(input, w)+ b)

        :type rng: numpy.random.RandomState
        :param rng: a random number generator used to initiaze the weights.

        :type input: theano.tensor.dmatrix
        :param input: a symbolic tensor of shape (n_examples, n_in)

        :type n_in: int
        :param n_in: dimension of the input

        :type n_out: int
        :param n_out: number of hidden units

        :type activation: theano.Op or function
        :param activation:  Non linearity to be applied in the hidden layer.
        """
        if rng is None:
            rng = np.random.RandomState()

        super(HiddenLayer, self).__init__(
            input, n_in, n_out, activation=activation, rng=rng)
        self.reset_layer()

        if W is not None:
            self.W = W

        if b is not None:
            self.b = b

        self.params = [self.W, self.b]
        self.setup_outputs(input)