超参数调整 (Keras) 神经网络回归
Hyperparameter Tuning (Keras) a Neural Network Regression
我们在 Python 中开发了一个人工神经网络，在这方面，我们希望使用 GridSearchCV 调整超参数以找到最佳的超参数。我们的 ANN 的目标是根据其他相关特征来预测温度，到目前为止，这是对神经网络性能的评估：
Coefficient of Determination (R2) Root Mean Square Error (RMSE) Mean Squared Error (MSE) Mean Absolute Percent Error (MAPE) Mean Absolute Error (MAE) Mean Bias Error (MBE)
0.9808840288506496 0.7527763482280911 0.5666722304516204 0.09142692180578049 0.588041786518511 -0.07293321963266877
到目前为止，我们对如何正确使用 GridSearchCV 毫无头绪，因此我们寻求帮助以找到满足我们目标的解决方案。我们有一个可能有效的函数，但无法将其正确应用到我们的代码中。
这是超参数调整函数（GridSearchCV）：
def hyperparameterTuning():
# Listing all the parameters to try
Parameter_Trials = {'batch_size': [10, 20, 30],
'epochs': [10, 20],
'Optimizer_trial': ['adam', 'rmsprop']
}
# Creating the regression ANN model
RegModel = KerasRegressor(make_regression_ann, verbose=0)
# Creating the Grid search space
grid_search = GridSearchCV(estimator=RegModel,
param_grid=Parameter_Trials,
scoring=None,
cv=5)
# Running Grid Search for different paramenters
grid_search.fit(X, y, verbose=1)
print('### Printing Best parameters ###')
grid_search.best_params_
我们的主要功能：
if __name__ == '__main__':
print('--------------')
dataframe = pd.read_csv("/.../file.csv")
# Splitting data into training and tesing data
X_train, X_test, y_train, y_test, PredictorScalerFit, TargetVarScalerFit = splitData(dataframe=dataframe)
# Making the Regression Artificial Neural Network (ANN)
ann = ANN(X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test, PredictorScalerFit=PredictorScalerFit, TargetVarScalerFit=TargetVarScalerFit)
# Evaluation of the performance of the Aritifical Neural Network (ANN)
eval = evaluation(y_test_orig=ann['temp'], y_test_pred=ann['Predicted_temp'])
我们将数据拆分为训练数据和测试数据的函数：
def splitData(dataframe):
X = dataframe[Predictors].values
y = dataframe[TargetVariable].values
### Sandardization of data ###
PredictorScaler = StandardScaler()
TargetVarScaler = StandardScaler()
# Storing the fit object for later reference
PredictorScalerFit = PredictorScaler.fit(X)
TargetVarScalerFit = TargetVarScaler.fit(y)
# Generating the standardized values of X and y
X = PredictorScalerFit.transform(X)
y = TargetVarScalerFit.transform(y)
# Split the data into training and testing set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
return X_train, X_test, y_train, y_test, PredictorScalerFit, TargetVarScalerFit
我们拟合模型和利用人工神经网络 (ANN) 的函数
def ANN(X_train, y_train, X_test, y_test, TargetVarScalerFit, PredictorScalerFit):
model = make_regression_ann()
# Fitting the ANN to the Training set
model.fit(X_train, y_train, batch_size=5, epochs=100, verbose=1)
# Generating Predictions on testing data
Predictions = model.predict(X_test)
# Scaling the predicted temp data back to original price scale
Predictions = TargetVarScalerFit.inverse_transform(Predictions)
# Scaling the y_test temp data back to original temp scale
y_test_orig = TargetVarScalerFit.inverse_transform(y_test)
# Scaling the test data back to original scale
Test_Data = PredictorScalerFit.inverse_transform(X_test)
TestingData = pd.DataFrame(data=Test_Data, columns=Predictors)
TestingData['temp'] = y_test_orig
TestingData['Predicted_temp'] = Predictions
TestingData.head()
# Computing the absolute percent error
APE = 100 * (abs(TestingData['temp'] - TestingData['Predicted_temp']) / TestingData['temp'])
TestingData['APE'] = APE
# ...
TestingData = TestingData.round(2)
TestingData.to_csv("TestingData.csv")
return TestingData
我们制作ANN模型的函数
def make_regression_ann():
# create ANN model
model = Sequential()
# Defining the Input layer and FIRST hidden layer, both are same!
model.add(Dense(units=8, input_dim=7, kernel_initializer='normal', activation='sigmoid'))
# Defining the Second layer of the model
# after the first layer we don't have to specify input_dim as keras configure it automatically
model.add(Dense(units=6, kernel_initializer='normal', activation='sigmoid'))
# The output neuron is a single fully connected node
# Since we will be predicting a single number
model.add(Dense(1, kernel_initializer='normal'))
# Compiling the model
model.compile(loss='mean_squared_error', optimizer='adam')
return model
我们评估ANN性能的函数
def evaluation(y_test_orig, y_test_pred):
# Computing the Mean Absolute Percent Error
MAPE = mean_absolute_percentage_error(y_test_orig, y_test_pred)
# Computing R2 Score
r2 = r2_score(y_test_orig, y_test_pred)
# Computing Mean Square Error (MSE)
MSE = mean_squared_error(y_test_orig, y_test_pred)
# Computing Root Mean Square Error (RMSE)
RMSE = mean_squared_error(y_test_orig, y_test_pred, squared=False)
# Computing Mean Absolute Error (MAE)
MAE = mean_absolute_error(y_test_orig, y_test_pred)
# Computing Mean Bias Error (MBE)
MBE = np.mean(y_test_pred - y_test_orig) # here we calculate MBE
print('--------------')
print('The Coefficient of Determination (R2) of ANN model is:', r2)
print("The Root Mean Squared Error (RMSE) of ANN model is:", RMSE)
print("The Mean Squared Error (MSE) of ANN model is:", MSE)
print('The Mean Absolute Percent Error (MAPE) of ANN model is:', MAPE)
print("The Mean Absolute Error (MAE) of ANN model is:", MAE)
print("The Mean Bias Error (MBE) of ANN model is:", MBE)
print('--------------')
eval_list = [r2, RMSE, MSE, MAPE, MAE, MBE]
columns = ['Coefficient of Determination (R2)', 'Root Mean Square Error (RMSE)', 'Mean Squared Error (MSE)',
'Mean Absolute Percent Error (MAPE)', 'Mean Absolute Error (MAE)', 'Mean Bias Error (MBE)']
dataframe = pd.DataFrame([eval_list], columns=columns)
return dataframe
我最近成功使用 GridSearchCV 的方法是：
tuned_parameters2 = {'C': [1,10,100,10000], 'max_iter':[5000,10000,50000]}
model2 = GridSearchCV(svm.LinearSVC(), tuned_parameters2)
model2.fit(features, y_train)
所以将字典与超参数分开，然后将您的模型分配给 GridSearchCV(make_regression_ann, the_hyperparam_dict)。然后用数据拟合。
在你的情况下，这种方法需要更多的重构。将 ANN 提供给 GridSearchCV 是否更好由您决定。
如果您更新 make_regression_ann 函数以包含您想要优化的任何超参数作为输入，您的代码应该可以工作，但拟合参数除外。
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import make_regression
def make_regression_ann(initializer='uniform', activation='relu', optimizer='adam', loss='mse'):
model = Sequential()
model.add(Dense(units=8, input_dim=7, kernel_initializer=initializer, activation=activation))
model.add(Dense(units=6, kernel_initializer=initializer, activation=activation))
model.add(Dense(1, kernel_initializer=initializer))
model.compile(loss=loss, optimizer=optimizer)
return model
param_grid = {
'initializer': ['normal', 'uniform'],
'activation': ['relu', 'sigmoid'],
'optimizer': ['adam', 'rmsprop'],
'loss': ['mse', 'mae'],
'batch_size': [32, 64],
'epochs': [5, 10],
}
grid_search = GridSearchCV(
estimator=KerasRegressor(make_regression_ann, verbose=0),
param_grid=param_grid,
scoring='neg_mean_absolute_percentage_error',
cv=3,
)
X, y = make_regression(n_features=7, n_samples=100, random_state=42)
grid_search.fit(X, y, verbose=1)
grid_search.best_params_
# {'activation': 'sigmoid',
# 'batch_size': 32,
# 'epochs': 10,
# 'initializer': 'normal',
# 'loss': 'mae',
# 'optimizer': 'adam'}
我们在 Python 中开发了一个人工神经网络，在这方面，我们希望使用 GridSearchCV 调整超参数以找到最佳的超参数。我们的 ANN 的目标是根据其他相关特征来预测温度，到目前为止，这是对神经网络性能的评估：
Coefficient of Determination (R2) Root Mean Square Error (RMSE) Mean Squared Error (MSE) Mean Absolute Percent Error (MAPE) Mean Absolute Error (MAE) Mean Bias Error (MBE)
0.9808840288506496 0.7527763482280911 0.5666722304516204 0.09142692180578049 0.588041786518511 -0.07293321963266877
到目前为止，我们对如何正确使用 GridSearchCV 毫无头绪，因此我们寻求帮助以找到满足我们目标的解决方案。我们有一个可能有效的函数，但无法将其正确应用到我们的代码中。
这是超参数调整函数（GridSearchCV）：
def hyperparameterTuning():
# Listing all the parameters to try
Parameter_Trials = {'batch_size': [10, 20, 30],
'epochs': [10, 20],
'Optimizer_trial': ['adam', 'rmsprop']
}
# Creating the regression ANN model
RegModel = KerasRegressor(make_regression_ann, verbose=0)
# Creating the Grid search space
grid_search = GridSearchCV(estimator=RegModel,
param_grid=Parameter_Trials,
scoring=None,
cv=5)
# Running Grid Search for different paramenters
grid_search.fit(X, y, verbose=1)
print('### Printing Best parameters ###')
grid_search.best_params_
我们的主要功能：
if __name__ == '__main__':
print('--------------')
dataframe = pd.read_csv("/.../file.csv")
# Splitting data into training and tesing data
X_train, X_test, y_train, y_test, PredictorScalerFit, TargetVarScalerFit = splitData(dataframe=dataframe)
# Making the Regression Artificial Neural Network (ANN)
ann = ANN(X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test, PredictorScalerFit=PredictorScalerFit, TargetVarScalerFit=TargetVarScalerFit)
# Evaluation of the performance of the Aritifical Neural Network (ANN)
eval = evaluation(y_test_orig=ann['temp'], y_test_pred=ann['Predicted_temp'])
我们将数据拆分为训练数据和测试数据的函数：
def splitData(dataframe):
X = dataframe[Predictors].values
y = dataframe[TargetVariable].values
### Sandardization of data ###
PredictorScaler = StandardScaler()
TargetVarScaler = StandardScaler()
# Storing the fit object for later reference
PredictorScalerFit = PredictorScaler.fit(X)
TargetVarScalerFit = TargetVarScaler.fit(y)
# Generating the standardized values of X and y
X = PredictorScalerFit.transform(X)
y = TargetVarScalerFit.transform(y)
# Split the data into training and testing set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
return X_train, X_test, y_train, y_test, PredictorScalerFit, TargetVarScalerFit
我们拟合模型和利用人工神经网络 (ANN) 的函数
def ANN(X_train, y_train, X_test, y_test, TargetVarScalerFit, PredictorScalerFit):
model = make_regression_ann()
# Fitting the ANN to the Training set
model.fit(X_train, y_train, batch_size=5, epochs=100, verbose=1)
# Generating Predictions on testing data
Predictions = model.predict(X_test)
# Scaling the predicted temp data back to original price scale
Predictions = TargetVarScalerFit.inverse_transform(Predictions)
# Scaling the y_test temp data back to original temp scale
y_test_orig = TargetVarScalerFit.inverse_transform(y_test)
# Scaling the test data back to original scale
Test_Data = PredictorScalerFit.inverse_transform(X_test)
TestingData = pd.DataFrame(data=Test_Data, columns=Predictors)
TestingData['temp'] = y_test_orig
TestingData['Predicted_temp'] = Predictions
TestingData.head()
# Computing the absolute percent error
APE = 100 * (abs(TestingData['temp'] - TestingData['Predicted_temp']) / TestingData['temp'])
TestingData['APE'] = APE
# ...
TestingData = TestingData.round(2)
TestingData.to_csv("TestingData.csv")
return TestingData
我们制作ANN模型的函数
def make_regression_ann():
# create ANN model
model = Sequential()
# Defining the Input layer and FIRST hidden layer, both are same!
model.add(Dense(units=8, input_dim=7, kernel_initializer='normal', activation='sigmoid'))
# Defining the Second layer of the model
# after the first layer we don't have to specify input_dim as keras configure it automatically
model.add(Dense(units=6, kernel_initializer='normal', activation='sigmoid'))
# The output neuron is a single fully connected node
# Since we will be predicting a single number
model.add(Dense(1, kernel_initializer='normal'))
# Compiling the model
model.compile(loss='mean_squared_error', optimizer='adam')
return model
我们评估ANN性能的函数
def evaluation(y_test_orig, y_test_pred):
# Computing the Mean Absolute Percent Error
MAPE = mean_absolute_percentage_error(y_test_orig, y_test_pred)
# Computing R2 Score
r2 = r2_score(y_test_orig, y_test_pred)
# Computing Mean Square Error (MSE)
MSE = mean_squared_error(y_test_orig, y_test_pred)
# Computing Root Mean Square Error (RMSE)
RMSE = mean_squared_error(y_test_orig, y_test_pred, squared=False)
# Computing Mean Absolute Error (MAE)
MAE = mean_absolute_error(y_test_orig, y_test_pred)
# Computing Mean Bias Error (MBE)
MBE = np.mean(y_test_pred - y_test_orig) # here we calculate MBE
print('--------------')
print('The Coefficient of Determination (R2) of ANN model is:', r2)
print("The Root Mean Squared Error (RMSE) of ANN model is:", RMSE)
print("The Mean Squared Error (MSE) of ANN model is:", MSE)
print('The Mean Absolute Percent Error (MAPE) of ANN model is:', MAPE)
print("The Mean Absolute Error (MAE) of ANN model is:", MAE)
print("The Mean Bias Error (MBE) of ANN model is:", MBE)
print('--------------')
eval_list = [r2, RMSE, MSE, MAPE, MAE, MBE]
columns = ['Coefficient of Determination (R2)', 'Root Mean Square Error (RMSE)', 'Mean Squared Error (MSE)',
'Mean Absolute Percent Error (MAPE)', 'Mean Absolute Error (MAE)', 'Mean Bias Error (MBE)']
dataframe = pd.DataFrame([eval_list], columns=columns)
return dataframe
我最近成功使用 GridSearchCV 的方法是：
tuned_parameters2 = {'C': [1,10,100,10000], 'max_iter':[5000,10000,50000]}
model2 = GridSearchCV(svm.LinearSVC(), tuned_parameters2)
model2.fit(features, y_train)
所以将字典与超参数分开，然后将您的模型分配给 GridSearchCV(make_regression_ann, the_hyperparam_dict)。然后用数据拟合。
在你的情况下，这种方法需要更多的重构。将 ANN 提供给 GridSearchCV 是否更好由您决定。
如果您更新 make_regression_ann 函数以包含您想要优化的任何超参数作为输入，您的代码应该可以工作，但拟合参数除外。
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import make_regression
def make_regression_ann(initializer='uniform', activation='relu', optimizer='adam', loss='mse'):
model = Sequential()
model.add(Dense(units=8, input_dim=7, kernel_initializer=initializer, activation=activation))
model.add(Dense(units=6, kernel_initializer=initializer, activation=activation))
model.add(Dense(1, kernel_initializer=initializer))
model.compile(loss=loss, optimizer=optimizer)
return model
param_grid = {
'initializer': ['normal', 'uniform'],
'activation': ['relu', 'sigmoid'],
'optimizer': ['adam', 'rmsprop'],
'loss': ['mse', 'mae'],
'batch_size': [32, 64],
'epochs': [5, 10],
}
grid_search = GridSearchCV(
estimator=KerasRegressor(make_regression_ann, verbose=0),
param_grid=param_grid,
scoring='neg_mean_absolute_percentage_error',
cv=3,
)
X, y = make_regression(n_features=7, n_samples=100, random_state=42)
grid_search.fit(X, y, verbose=1)
grid_search.best_params_
# {'activation': 'sigmoid',
# 'batch_size': 32,
# 'epochs': 10,
# 'initializer': 'normal',
# 'loss': 'mae',
# 'optimizer': 'adam'}