def get_model(name): #INPUT name: name of the wanted model #OUPUT model: wanted model if name == "tree": model = ExtraTreesRegressor(n_estimators=50) elif name == "line": model = LinearRegression() elif name == "NN": model = Sequential() model.add(Dense(200, input_dim=3)) model.add(Dense(1)) model.compile(optimizer="SGD", loss='mean_squared_error') return model
test_size=0.3, random_state=0) #print(X_train.shape, X_test.shape) #print(y_train.shape,y_test.shape) ##ANN ##create ANN model import keras from keras.models import Sequential from keras.layers import Lambda, Dense, ReLU, LeakyReLU, Dropout model = Sequential() #input layer: model.add( Dense(128, kernel_initializer='normal', input_dim=X_train.shape[1], activation='relu')) ##Hidden Layer: model.add(Dense(256, kernel_initializer='normal', activation='relu')) model.add(Dense(256, kernel_initializer='normal', activation='relu')) model.add(Dense(256, kernel_initializer='normal', activation='relu')) #Output layer model.add(Dense(1, kernel_initializer='normal', activation='linear')) #Compile the model model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mean_absolute_error'])
msg = "%s: %f (%f)" % (name, score, mae) print(msg) model1 = ExtraTreesRegressor() model1.fit(X_Train, Y_Train) model_GT_T_D1 = 'finalized_model_GT_T_D1.sav' pickle.dump(model1, open(model_GT_T_D1, 'wb')) # Split Data to Train and Test X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size=0.3) # create model model = Sequential() model.add( Dense(6, input_dim=15, kernel_initializer='random_uniform', activation='relu')) model.add(Dropout(0.2)) model.add( Dense(4, kernel_initializer='random_uniform', activation='relu', kernel_constraint=maxnorm(3))) model.add(Dropout(0.2)) model.add(Dense(2, kernel_initializer='random_uniform', activation='relu')) model.add(Dense(1, kernel_initializer='random_uniform', activation='relu')) # Compile model model.compile(loss='mean_absolute_error', optimizer='adam')