normalization

问题 One common task in DL is that you normalize input samples to zero mean and unit variance. One can "manually" perform the normalization using code like this: mean = np.mean(X, axis = 0) std = np.std(X, axis = 0) X = [(x - mean)/std for x in X] However, then one must keep the mean and std values around, to normalize the testing data, in addition to the Keras model being trained. Since the mean and std are learnable parameters, perhaps Keras can learn them? Something like this: m = Sequential()

问题 One common task in DL is that you normalize input samples to zero mean and unit variance. One can "manually" perform the normalization using code like this: mean = np.mean(X, axis = 0) std = np.std(X, axis = 0) X = [(x - mean)/std for x in X] However, then one must keep the mean and std values around, to normalize the testing data, in addition to the Keras model being trained. Since the mean and std are learnable parameters, perhaps Keras can learn them? Something like this: m = Sequential()

问题 One common task in DL is that you normalize input samples to zero mean and unit variance. One can "manually" perform the normalization using code like this: mean = np.mean(X, axis = 0) std = np.std(X, axis = 0) X = [(x - mean)/std for x in X] However, then one must keep the mean and std values around, to normalize the testing data, in addition to the Keras model being trained. Since the mean and std are learnable parameters, perhaps Keras can learn them? Something like this: m = Sequential()

问题 I'm currently studying lighting in OpenGL, which utilizes a function in GLSL called normalize. According to OpenGL docs, it says that it "calculates the normalized product of two vectors". However, it still doesn't explain what "normalized" mean. I have tried look for what a normalized product is on Google, however I can't seem to find anything about it. Can anyone explain what normalizing means and provide a few example of a normalized value? 回答1: It's a mathematical term and this link

问题 I'm using the MinMaxScaler model in sklearn to normalize the features of a model. training_set = np.random.rand(4,4)*10 training_set [[ 6.01144787, 0.59753007, 2.0014852 , 3.45433657], [ 6.03041646, 5.15589559, 6.64992437, 2.63440202], [ 2.27733136, 9.29927394, 0.03718093, 7.7679183 ], [ 9.86934288, 7.59003904, 6.02363739, 2.78294206]] scaler = MinMaxScaler() scaler.fit(training_set) scaler.transform(training_set) [[ 0.49184811, 0. , 0.29704831, 0.15972182], [ 0.4943466 , 0.52384506, 1. , 0.

问题 I want to create a document term matrix. In my case it is not like documents x words but it is sentences x words so the sentences will act as the documents. I am using 'l2' normalization post doc-term matrix creation. The term count is important for me to create summarization using SVD in further steps. My query is which axis will be appropriate to apply 'l2' normalization. With sufficient research I understood: Axis=1 : Will give me the importance of the word in a sentence (column wise

问题 I want to create a document term matrix. In my case it is not like documents x words but it is sentences x words so the sentences will act as the documents. I am using 'l2' normalization post doc-term matrix creation. The term count is important for me to create summarization using SVD in further steps. My query is which axis will be appropriate to apply 'l2' normalization. With sufficient research I understood: Axis=1 : Will give me the importance of the word in a sentence (column wise

问题 I am doing auto encoder model.I have saved the model before which I scaled the data using min max scaler. X_train = df.values scaler = MinMaxScaler() X_train_scaled = scaler.fit_transform(X_train) After doing this I fitted the model and saved it as 'h5' file.Now when I give test data, after loading the saved model naturally it should be scaled as well. So when I load the model and scale it by using X_test_scaled = scaler.transform(X_test) It gives the error NotFittedError: This MinMaxScaler

问题 I am doing auto encoder model.I have saved the model before which I scaled the data using min max scaler. X_train = df.values scaler = MinMaxScaler() X_train_scaled = scaler.fit_transform(X_train) After doing this I fitted the model and saved it as 'h5' file.Now when I give test data, after loading the saved model naturally it should be scaled as well. So when I load the model and scale it by using X_test_scaled = scaler.transform(X_test) It gives the error NotFittedError: This MinMaxScaler

问题 I'm learning Python/Pandas with a DataFrame having the following structure: import pandas as pd df = pd.DataFrame({'key' : [111, 222, 333, 444, 555, 666, 777, 888, 999], 'score1' : [-1, 0, 2, -1, 7, 0, 15, 0, 1], 'score2' : [2, 2, -1, 10, 0, 5, -1, 1, 0]}) print(df) key score1 score2 0 111 -1 2 1 222 0 2 2 333 2 -1 3 444 -1 10 4 555 7 0 5 666 0 5 6 777 15 -1 7 888 0 1 8 999 1 0 The possible values for the score1 and score2 Series are -1 and all positive integers (including 0 ). My goal is to