#!\/usr\/bin\/env python3
\n# -*- coding: utf-8 -*-
\n„““
\nCreated on Wed Mar 3 10:58:27 2021
\nKeras Modell Zeitreihe ml 5 Versicherung
\n@author: guttmann
\n„““
\nimport pandas as pd
\ndf = pd.read_csv(„insurance.csv“)
\nprint(df.head)
\nX=df[[‚age‘,’bmi‘,’children‘]].values
\ny=df[[‚charges‘]].values
\n#…………Normierung
\nfrom sklearn.preprocessing import StandardScaler
\nscaler_X=StandardScaler()
\nX=scaler_X.fit_transform(X)
\nscaler_y=StandardScaler()
\ny=scaler_y.fit_transform(y)
\n#…………….Modell
\nfrom tensorflow.keras.models import Sequential
\nfrom tensorflow.keras.layers import Dense
\nmodel = Sequential()
\nmodel.add(Dense(units=1, input_shape=(3,)))
\nmodel.summary()
\nmodel.compile(loss=’mse‘,metrics=[‚accuracy‘],optimizer=’sgd‘)
\nhistory=model.fit(X,y, epochs=5,batch_size=32)
\n#…………..Modell speichern
\nmodel.save(„ML5.h5“)
\n#…………..Evaluation
\nfrom sklearn.metrics import r2_score
\ny_pred=model.predict(X)
\nr2=r2_score(y,y_pred)
\nprint(‚Evaluation : ‚,’r2={:.3f}‘.format(r2))
\nprint(‚Gewichte :‘,model.get_layer(index=0).get_weights())
\n#…………….Prediction
\nX_pred=[[40.,20.1,1.]] #40 jahre, bmi 20.2, 1 kind
\nX_pred=scaler_X.transform(X_pred)
\ny_pred=model.predict(X_pred)
\ny_pred=scaler_y.inverse_transform(y_pred)
\nprint (‚loss : ‚,history.history[‚loss‘])
\nprint (‚accuracy : ‚,history.history[‚accuracy‘])
\nprint (‚keys : ‚,history.history.keys())
\nprint (‚Vorhersage : ‚,y_pred)<\/p>\n
#…………..Funktion plotten
\nimport matplotlib.pyplot as plt
\nplt.plot(history.history[‚loss‘])
\nplt.plot(history.history[‚accuracy‘]),plt.legend([‚train‘, ‚test‘], loc=’upper right‘)
\nplt.title(‚model accuracy‘)
\nplt.ylabel(‚accuracy‘),plt.xlabel(‚epoch‘)
\nplt.show()
\n# summarize history for loss
\nplt.plot(history.history[‚loss‘])
\nplt.title(‚model loss‘),plt.legend([‚train‘, ‚test‘], loc=’upper right‘)
\nplt.ylabel(‚loss‘),plt.xlabel(‚epoch‘)
\nplt.show()<\/p>\n","protected":false},"excerpt":{"rendered":"
#!\/usr\/bin\/env python3 # -*- coding: utf-8 -*- „““ Created on Wed Mar 3 10:58:27 2021 Keras Modell Zeitreihe ml 5 Versicherung @author: guttmann „““ import pandas as pd df = pd.read_csv(„insurance.csv“) print(df.head) X=df[[‚age‘,’bmi‘,’children‘]].values y=df[[‚charges‘]].values #…………Normierung from sklearn.preprocessing import StandardScaler scaler_X=StandardScaler() X=scaler_X.fit_transform(X) scaler_y=StandardScaler() y=scaler_y.fit_transform(y) #…………….Modell from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense model = Sequential()… ML5<\/span> weiterlesen<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-175","page","type-page","status-publish","hentry","entry"],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.medialearn.de\/index.php\/wp-json\/wp\/v2\/pages\/175","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.medialearn.de\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.medialearn.de\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.medialearn.de\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.medialearn.de\/index.php\/wp-json\/wp\/v2\/comments?post=175"}],"version-history":[{"count":1,"href":"https:\/\/www.medialearn.de\/index.php\/wp-json\/wp\/v2\/pages\/175\/revisions"}],"predecessor-version":[{"id":176,"href":"https:\/\/www.medialearn.de\/index.php\/wp-json\/wp\/v2\/pages\/175\/revisions\/176"}],"wp:attachment":[{"href":"https:\/\/www.medialearn.de\/index.php\/wp-json\/wp\/v2\/media?parent=175"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}