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
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'])
print(model.summary())
##Now train the model using fit method
result = model.fit(X_train,
y_train,
validation_split=0.3,
batch_size=10,
epochs=100)
#model evaluation
prediction = model.predict(X_test)
sns.distplot(y_test.values.reshape(-1, 1) - prediction)
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')
# Fit the model
model.fit(X_Train, Y_Train, epochs=100, batch_size=10)
# Evaluate the model
scores = model.evaluate(X_Test, Y_Test)
print("score: %.2f%%" % (100 - scores))
model_GT_C_D = 'finalized_model_GT_C_D.sav'
pickle.dump(model, open(model_GT_C_D, 'wb'))
# Split Data to Train and Test
X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y2, test_size=0.3)
# create model
