layer.py 文件源码

def bn_forward(X, gamma, beta, cache, momentum=.9, train=True):
    running_mean, running_var = cache

    if train:
        mu = np.mean(X, axis=0)
        var = np.var(X, axis=0)

        X_norm = (X - mu) / np.sqrt(var + c.eps)
        out = gamma * X_norm + beta

        cache = (X, X_norm, mu, var, gamma, beta)

        running_mean = util.exp_running_avg(running_mean, mu, momentum)
        running_var = util.exp_running_avg(running_var, var, momentum)
    else:
        X_norm = (X - running_mean) / np.sqrt(running_var + c.eps)
        out = gamma * X_norm + beta
        cache = None

    return out, cache, running_mean, running_var