layers.py 文件源码

def __init__(self, name, x, y, n_in, n_out):
        self.x= x
        self.name = name
        # weight matrix W (n_in, n_out)
        self.W = theano.shared(
                value=np.zeros((n_in, n_out), dtype=theano.config.floatX),
                name='W',
                borrow=True)
        # bias vector b (n_out, )
        self.b = theano.shared(
                value=np.zeros((n_out,), dtype=theano.config.floatX),
                name='b',
                borrow=True)
        # p(y|x, w, b)
        self.p_y_given_x = T.nnet.softmax(T.dot(x, self.W) + self.b)
        self.y_pred = T.argmax(self.p_y_given_x, axis=1)
        self.negative_log_likelihood = -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])
        self.errors = T.mean(T.neq(self.y_pred, y))
        # params
        self.params = [self.W, self.b]