TensorFlow数据归一化 1. tf.nn.l2_normalize - l2_normalize(x, dim, epsilon=1e-12,name=None) - output = x / sqrt(max(sum(x**2), epsilon)) 2.使用scikit-learn进行归一化(**numpyarray**) ``` min_max_scaler = preprocessing.MinMaxScaler() standar_scaler = preprocessing.StandardScaler() feature_1_scaled = standar_scaler.fit_transform(feature_1) feature_3_scaled = min_max_scaler.fit_transform(feature_1) ``` 3. tensor与numpyarray相互转换 - tf.convert_to_tensor(img.eval()) - print(type(tf.Session().run(tf.constant([1,2,3])))) --*<class 'numpy.ndarray'>* 1 2 3 4 5 6 7 8 9 10 11 12 13 People typically use scikit-learn (StandardScaler) for standardizing data before they train their models on TensorFlow.
def normalize(train, test): mean, std = train.mean(), test.std() train = (train - mean) / std test = (test - mean) / std return train, test --------------------- 作者：zoray 来源： 原文：https://blog.csdn.net/zoray/article/details/74276570 版权声明：本文为博主原创文章，转载请附上博文链接！
