Applied Neural Networks with TensorFlow 2 by Orhan Gazi Yalçın

Author:Orhan Gazi Yalçın
Language: eng
Format: epub
ISBN: 9781484265130
Publisher: Apress

You can obtain the following output in Figure 6-7 by running train_stats.

Figure 6-7train_stats DataFrame for Train Set Statistics

Now that we have the mean and standard deviation values for training set features, it is time to normalize the train and test sets. The custom normalizer(x) function can be used for train, test, and new observation sets.# Feature scaling with the mean

# and std. dev. values in train_stats

def normalizer(x):

return (x-train_stats['mean'])/train_stats['std']

train_x_scaled = normalizer(train_x)

test_x_scaled = normalizer(test_x)

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