评估回归神经网络模型的准确性

Question

我是机器学习的新手，并为回归输出创建了神经网络。我有〜95000个培训示例和〜24000个测试示例。我想知道如何评估我的模型并得到训练和测试错误？如何知道该回归模型的准确性？我的Y变量值范围在100-200之间，X在数据集中有9个输入要素。

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()

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模型评估后我的测试损失。请注意，如代码所示，这里有5个损失函数。

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

Answer 1

要评估模型，可以使用evaluate方法：

test_loss = model.evaluate(X_test, y_test)

它返回给定测试数据上的损耗，该测试数据是使用您在训练期间使用的相同损耗函数（即mean_squared_error）计算得出的。 此外，如果您想在每个时期结束时失去训练，可以通过History方法使用fit对象which is returned：

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

loss是一个列表，其中包含每个时期结束时训练的损失值。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)）或使用了其他任何指标（例如mean_absolute_error（即model.compile(..., metrics=['mae'])），则还可以访问其值：

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

---

奖金：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

要显示训练时的验证丢失：

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

我认为您无法通过绘制轻松获得准确性，因为您的输入是9维的，因此您可以绘制每个特征的预测y，只需关闭连接点的线即可，即plt.plot（x，y ，'k。'）注意'k'，所以没有一行，但是我不确定这是否有用。