model.py 文件源码

def __init__(self, x, y, n_x, n_y):
        # initialize with 0 the weights as a matrix of shape (n_in, n_out)
        self.w = theano.shared(
            value=numpy.zeros((n_x, n_y), dtype=theano.config.floatX),
            name='w',
            borrow=True
        )
        # initialize the biases b as a vector of n_out 0s
        self.b = theano.shared(
            value=numpy.zeros((n_y,), dtype=theano.config.floatX),
            name='b',
            borrow=True
        )
        self.params = [self.w, self.b]
        # save x, y
        self.x = x
        self.y = y
        # calculate
        p_y_given_x = T.nnet.softmax(T.dot(self.x, self.w) + self.b)
        # probability is maximal
        y_pred = T.argmax(p_y_given_x, axis=1)
        # error
        self.error = T.mean(T.neq(y_pred, self.y))
        # cost
        self.cost = -T.mean(T.log(p_y_given_x)[T.arange(self.y.shape[0]), self.y])