neural_net.py 文件源码

def make_nn_funs(layer_sizes, L2_reg):
    parser = WeightsParser()
    for i, shape in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):
        parser.add_weights(('weights', i), shape)
        parser.add_weights(('biases', i), (1, shape[1]))

    def predictions(W_vect, X):
        cur_units = X
        for i in range(len(layer_sizes) - 1):
            cur_W = parser.get(W_vect, ('weights', i))
            cur_B = parser.get(W_vect, ('biases', i))
            cur_units = np.tanh(np.dot(cur_units, cur_W) + cur_B)
        return cur_units - logsumexp(cur_units, axis=1)

    def loss(W_vect, X, T):
        log_prior = -L2_reg * np.dot(W_vect, W_vect)
        log_lik = np.sum(predictions(W_vect, X) * T) 
        return - log_prior - log_lik

    def frac_err(W_vect, X, T):
        return np.mean(np.argmax(T, axis=1) != np.argmax(pred_fun(W_vect, X), axis=1))

    return parser.N, predictions, loss, frac_err