评估回归神经网络模型的准确性

Question

我是机器学习的新手，我为回归输出创建了一个神经网络。我有大约 95000 个训练示例和大约 24000 个测试示例。我想知道如何评估我的模型并获得训练和测试错误？如何知道这个回归模型的准确性？我的 Y 变量值范围在 100-200 之间，X 在数据集中有 9 个输入特征。

这是我的代码：

import pandas as pd
from keras.layers import Dense, Activation,Dropout
from keras.models import Sequential
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from matplotlib import pyplot

# Importing the dataset
# Importing the dataset
dataset = pd.read_csv('data2csv.csv')

X = dataset.iloc[:,1:10].values
y = dataset.iloc[:, :1].values

# Splitting the dataset into the Training set and Test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size = 0.2, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# Initialising the ANN
model = Sequential()

# Adding the input layer and the first hidden layer

model.add(Dense(10, activation = 'relu', input_dim = 9))

# Adding the second hidden layer
model.add(Dense(units = 5, activation = 'sigmoid'))
model.add(Dropout(0.2))

# Adding the third hidden layer
model.add(Dense(units = 5, activation = 'relu'))
model.add(Dropout(0.2))

model.add(Dense(units = 5, activation = 'relu'))
model.add(Dense(units = 5, activation = 'relu'))

# Adding the output layer

model.add(Dense(units = 1))

#model.add(Dense(1))
# Compiling the ANN
model.compile(optimizer = 'adam', loss = 'mean_squared_error',metrics=['mae','mse','mape','cosine'])

# Fitting the ANN to the Training set
history=model.fit(X_train, y_train,validation_data=(X_val, y_val) ,batch_size = 1000, epochs = 100)
test_loss = model.evaluate(X_test,y_test)

loss = history.history['loss']
acc = history.history['mean_absolute_error']
val_loss = history.history['val_loss']
val_acc = history.history['val_mean_absolute_error']
mape_loss=history.history['mean_absolute_percentage_error']
cosine_los=history.history['cosine_proximity']
pyplot.plot(history.history['mean_squared_error'])
pyplot.plot(history.history['mean_absolute_error'])
pyplot.plot(history.history['mean_absolute_percentage_error'])
pyplot.plot(history.history['cosine_proximity'])
pyplot.show()
epochs = range(1, len(loss)+1)
plt.plot(epochs, loss, 'ro', label='Training loss')
plt.legend()
plt.show()

y_pred = model.predict(X_test)

plt.plot(y_test, color = 'red', label = 'Real data')
plt.plot(y_pred, color = 'blue', label = 'Predicted data')
plt.title('Prediction')
plt.legend()
plt.show()

[]

model.evaluate 后我的测试损失。注意这里有5个损失函数，如代码所示。

1) 84.69654303799824 2) 7.030169963975834 3) 84.69654303799824 4) 5.241855282313331 5) -0.9999999996023872

Answer 1

要评估您的模型，您可以使用evaluate 方法：

test_loss = model.evaluate(X_test, y_test)

它返回使用您在训练期间使用的相同损失函数计算的给定测试数据的损失（即mean_squared_error）。 此外，如果您想在每个 epoch 结束时获得训练损失，您可以使用 History object which is returned by fit 方法：

history = model.fit(...)
loss = history.history['loss']

loss 是一个列表，其中包含每个 epoch 结束时的训练损失值。如果您在训练模型时使用了验证数据（即model.fit(..., validation_data=(X_val, y_val)）或使用了任何其他指标，如mean_absolute_error（即model.compile(..., metrics=['mae'])），您还可以访问它们的值：

acc = history.history['mae']
val_loss = history.history['val_loss']
val_acc = history.history['val_mae']

---

奖励：绘制训练损失曲线：

epochs = range(1, len(loss)+1)
plt.plot(epochs, loss, 'ro', label='Training loss')
plt.legend()
plt.show()

Answer 2

在训练时显示验证损失：

model.fit(X_train, y_train, batch_size = 1000, epochs = 100, validation_data = (y_train,y_test))

我认为您不能通过绘图轻松获得准确性，因为您的输入是 9 维的，您可以绘制每个特征的预测 y，只需关闭连接点的线，即 plt.plot(x,y ,'k.') 注意 'k' 所以没有行，但我不确定这是否有用。