model.compile(loss=my_rmse, optimizer=optimizer)怎么添加R2
时间: 2024-02-01 17:13:01 浏览: 29
您可以尝试使用Keras的自定义评估指标来添加R2。以下是示例代码:
``` python
import keras.backend as K
def my_rmse(y_true, y_pred):
return K.sqrt(K.mean(K.square(y_pred - y_true)))
def r_square(y_true, y_pred):
SS_res = K.sum(K.square(y_true - y_pred))
SS_tot = K.sum(K.square(y_true - K.mean(y_true)))
return 1 - SS_res/(SS_tot + K.epsilon())
model.compile(loss=my_rmse, optimizer=optimizer, metrics=[r_square])
```
在上面的代码中,我们定义了两个自定义评估指标函数,一个是用于RMSE的`my_rmse`,另一个是用于R2的`r_square`。在`model.compile`中,我们将这两个评估指标都传递给了`metrics`参数。这样,在训练过程中,Keras将计算并记录这两个指标的值,并在每个时期结束时输出它们的平均值。
相关问题
train_X = train_x.reshape((train_x.shape[0], n_hours, n_features)) test_X = test_x.reshape((test_x.shape[0], n_hours, n_features)) model = Sequential() model.add(LSTM(20, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True, kernel_regularizer=regularizers.l2(0.005), recurrent_regularizer=regularizers.l2(0.005))) model.add(LSTM(20, kernel_regularizer=regularizers.l2(0.005), recurrent_regularizer=regularizers.l2(0.005))) model.add(Dense(1)) model.compile(loss='mae', optimizer='adam') history = model.fit(train_X, train_y, epochs=500, batch_size=2 ** 8, validation_data=(test_X, test_y)) plt.plot(history.history['loss'], label='train') plt.plot(history.history['val_loss'], label='test') plt.legend() plt.show() # make the prediction,为了在原始数据的维度上计算损失,需要将数据转化为原来的范围再计算损失 yHat = model.predict(test_X) y = model.predict(train_X) test_X = test_X.reshape((test_X.shape[0], n_hours * n_features))怎么求训练集rmse
可以使用以下代码计算训练集的 RMSE:
```
from sklearn.metrics import mean_squared_error
import numpy as np
# 将预测结果转换为原始范围
yHat = scaler.inverse_transform(yHat)
train_y = scaler.inverse_transform(train_y)
# 计算 RMSE
rmse = np.sqrt(mean_squared_error(train_y, y))
print('Train RMSE: %.3f' % rmse)
```
其中,`scaler` 是对数据进行标准化或归一化处理时使用的对象,需要根据具体情况进行定义和初始化。
import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import Dense, LSTM from sklearn.metrics import r2_score,median_absolute_error,mean_absolute_error # 读取数据 data = pd.read_csv(r'C:/Users/Ljimmy/Desktop/yyqc/peijian/销量数据rnn.csv') # 取出特征参数 X = data.iloc[:,2:].values # 数据归一化 scaler = MinMaxScaler(feature_range=(0, 1)) X[:, 0] = scaler.fit_transform(X[:, 0].reshape(-1, 1)).flatten() #X = scaler.fit_transform(X) #scaler.fit(X) #X = scaler.transform(X) # 划分训练集和测试集 train_size = int(len(X) * 0.8) test_size = len(X) - train_size train, test = X[0:train_size, :], X[train_size:len(X), :] # 转换为监督学习问题 def create_dataset(dataset, look_back=1): X, Y = [], [] for i in range(len(dataset) - look_back - 1): a = dataset[i:(i + look_back), :] X.append(a) Y.append(dataset[i + look_back, 0]) return np.array(X), np.array(Y) look_back = 12 X_train, Y_train = create_dataset(train, look_back) #Y_train = train[:, 2:] # 取第三列及以后的数据 X_test, Y_test = create_dataset(test, look_back) #Y_test = test[:, 2:] # 取第三列及以后的数据 # 转换为3D张量 X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) # 构建LSTM模型 model = Sequential() model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1))) model.add(LSTM(units=50)) model.add(Dense(units=1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(X_train, Y_train, epochs=5, batch_size=32) #model.fit(X_train, Y_train.reshape(Y_train.shape[0], 1), epochs=10, batch_size=32) # 预测下一个月的销量 last_month_sales = data.tail(12).iloc[:,2:].values #last_month_sales = data.tail(1)[:,2:].values last_month_sales = scaler.transform(last_month_sales) last_month_sales = np.reshape(last_month_sales, (1, look_back, 1)) next_month_sales = model.predict(last_month_sales) next_month_sales = scaler.inverse_transform(next_month_sales) print('Next month sales: %.0f' % next_month_sales[0][0]) # 计算RMSE误差 rmse = np.sqrt(np.mean((next_month_sales - last_month_sales) ** 2)) print('Test RMSE: %.3f' % rmse)IndexError Traceback (most recent call last) Cell In[1], line 36 33 X_test, Y_test = create_dataset(test, look_back) 34 #Y_test = test[:, 2:] # 取第三列及以后的数据 35 # 转换为3D张量 ---> 36 X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) 37 X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) 38 # 构建LSTM模型 IndexError: tuple index out of range代码修改
在代码中,X_train 和 X_test 的维度为 (样本数量,时间步长),需要将其转换为 (样本数量,时间步长,特征数量) 的形式。因此在创建数据集时,需要将数据 reshape 为 (样本数量,时间步长,1),即每个时间步长只有一个特征。代码如下:
```
def create_dataset(dataset, look_back=1):
X, Y = [], []
for i in range(len(dataset) - look_back - 1):
a = dataset[i:(i + look_back), :]
X.append(a.reshape(-1, 1))
Y.append(dataset[i + look_back, 0])
return np.array(X), np.array(Y)
```
在训练和测试数据集转换为3D张量时,需要保证第三个维度为特征数量。因此需要修改如下代码:
```
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
```
修改后的完整代码如下:
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM
from sklearn.metrics import r2_score,median_absolute_error,mean_absolute_error
# 读取数据
data = pd.read_csv(r'C:/Users/Ljimmy/Desktop/yyqc/peijian/销量数据rnn.csv')
# 取出特征参数
X = data.iloc[:,2:].values
# 数据归一化
scaler = MinMaxScaler(feature_range=(0, 1))
X = scaler.fit_transform(X)
# 划分训练集和测试集
train_size = int(len(X) * 0.8)
test_size = len(X) - train_size
train, test = X[0:train_size, :], X[train_size:len(X), :]
# 转换为监督学习问题
def create_dataset(dataset, look_back=1):
X, Y = [], []
for i in range(len(dataset) - look_back - 1):
a = dataset[i:(i + look_back), :]
X.append(a.reshape(-1, 1))
Y.append(dataset[i + look_back, 0])
return np.array(X), np.array(Y)
look_back = 12
X_train, Y_train = create_dataset(train, look_back)
X_test, Y_test = create_dataset(test, look_back)
# 转换为3D张量
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
# 构建LSTM模型
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
model.add(LSTM(units=50))
model.add(Dense(units=1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X_train, Y_train, epochs=5, batch_size=32)
# 预测下一个月的销量
last_month_sales = data.tail(12).iloc[:,2:].values
last_month_sales = scaler.transform(last_month_sales)
last_month_sales = np.reshape(last_month_sales, (1, look_back, 1))
next_month_sales = model.predict(last_month_sales)
next_month_sales = scaler.inverse_transform(next_month_sales)
print('Next month sales: %.0f' % next_month_sales[0][0])
# 计算RMSE误差
rmse = np.sqrt(np.mean((next_month_sales - last_month_sales) ** 2))
print('Test RMSE: %.3f' % rmse)
```