Simple classifier for tabular data such as IRIS (model can be replaced)

Keras 2.2.4

Keras-applications 1.0.6

Keras-preprocessing 1.0.5

TensorFlow 1.11.0

NumPy 1.15.2

Pandas 0.23.4

Scikit-learn 0.20.0

Test success

1 #-*-coding:utf-8-*-2 ImportNumPy3 ImportPandas4  fromKeras.layers.coreImportdense, dropout, Activation5  fromKeras.modelsImportSequential6  fromKeras.utilsImportnp_utils7  fromKeras.utilsImportPlot_model8  fromSklearnImportUtils9  fromSklearn.model_selectionImportStratifiedshufflesplitTen  fromSklearn.preprocessingImportLabelencoder One  A  - defload_data (): -     " " the Get Data - : Return X_train, Y_train, X_test, Y_test, encoder: -     " " -     #Loading Data +Data_frame = Pandas.read_csv ("Iris.csv", header=None) -Data_set =data_frame.values +     #take all rows, from column NO. 0 to column 4th (not including column 4th) AX_data = data_set[:, 0:4].astype (float) at     #fetch All rows, 4th column -Y_data = data_set[:, 4] -     #Tag Encoding -Encoder =Labelencoder () -     #compiling a string into a 0,1,2,3 class -     #Encoder.classes_ with NPY can save the load Encoding rules (Np.save (' Encoder.npy ', Encoder.classes_), Encoder.classes_=np.load (' Encoder.npy ')) inEncoded_transform_y =encoder.fit_transform (y_data) -     #compiled 0,1,2,3 one_hot toY_data =np_utils.to_categorical (encoded_transform_y) +     #Disrupt data sets -X_data, Y_data =utils.shuffle (X_data, Y_data) the     #Slicing data sets *Train_idx, Test_idx =Next (ITER ( $Stratifiedshufflesplit (N_splits=1, test_size=0.2,Panax NotoginsengRandom_state=0). Split (X_data, Y_data ))) -X_train =X_data[train_idx] theY_train =Y_data[train_idx] +X_test =X_data[test_idx] AY_test =Y_data[test_idx] the     returnX_train, Y_train, X_test, y_test, encoder +  -  $ defCompile_model (): $     #Model -_model =Sequential () -_model.add (Dense, input_shape= (4,))) the_model.add (Activation ('Tanh')) -_model.add (Dropout (0.2))Wuyi_model.add (Dense (3)) the_model.add (Activation ('Softmax')) - _model.compile ( Wuloss="categorical_crossentropy", -Optimizer='Adam', Aboutmetrics=['accuracy']) $     #Create a model picture -Plot_model (_model, to_file='Model.png', show_shapes='True') -     return_model -  A  + defTrain_model (_model, _x_train, _y_train, _x_test, _y_test): the     #Training -History = _model.fit (_x_train, _y_train, epochs=100, batch_size=12, $Verbose=1, Validation_data=[_x_test, _y_test]) the     #Test Training Set theScore = _model.evaluate (_x_test, _y_test, verbose=1) the     Print('Test Score:', score[0]) the     Print('Test accuracy:', score[1]) -  in  the defTest (_model, _encoder, _x_test): the     #Check, return label Aboutresult =_model.predict (_x_test) theresult = Numpy.argmax (result, Axis=1) theresult =_encoder.inverse_transform (Result) the     Print(Result) +  -  the if __name__=='__main__':BayiX_train, Y_train, X_test, y_test, encoder =Load_data () theModel =Compile_model () the Train_model (model, X_train, Y_train, X_test, y_test) -Test (model, encoder, x_test)

Simple classifier for tabular data such as IRIS (model can be replaced)