loss='mean_squared_error'
时间: 2024-05-22 19:15:38 浏览: 7
'Mean squared error' is a widely used loss function in machine learning that measures the difference between the predicted values and the actual values of the target variable. It is calculated as the average of the squared differences between the predicted and actual values, and is expressed as a single number. The goal of using this loss function is to minimize the difference between the predicted and actual values, which in turn improves the accuracy of the model.
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def mcp_loss(y_true, y_pred): """ 计算单调性约束损失函数在所有样本和时刻上的平均值 """ batch_size=64 num_timesteps=6 delta_p = tf.random.uniform(shape=(batch_size, num_timesteps, 15), minval=0, maxval=1) p = inputs[:, :, -15:] # 取原始输入数据的最后15列作为 p p_new = p + delta_p newinputs = tf.concat([inputs[:, :, 0:1], p_new], axis=-1) y_pred_ = model(newinputs) y_true_ = model(inputs) return tf.keras.losses.mean_squared_error(y_true_, y_pred_) def combined_loss(y_true, y_pred): """ 组合MSE损失函数和单调性约束损失函数的新损失函数 """ alpha = 0.5 # 可以根据需要进行调整 mse_loss = mean_squared_error(y_true, y_pred) mcp_loss_val = mcp_loss(y_true, y_pred) return alpha * mse_loss + (1 - alpha) * mcp_loss_val将以上代码修改为kera能识别的代码
下面是修改后的代码:
```python
import tensorflow as tf
from tensorflow.keras.losses import mean_squared_error
def mcp_loss(y_true, y_pred):
"""
计算单调性约束损失函数在所有样本和时刻上的平均值
"""
batch_size=64
num_timesteps=6
delta_p = tf.random.uniform(shape=(batch_size, num_timesteps, 15), minval=0, maxval=1)
p = inputs[:, :, -15:] # 取原始输入数据的最后15列作为 p
p_new = p + delta_p
newinputs = tf.concat([inputs[:, :, 0:1], p_new], axis=-1)
y_pred_ = model(newinputs)
y_true_ = model(inputs)
return mean_squared_error(y_true_, y_pred_)
def combined_loss(y_true, y_pred):
"""
组合MSE损失函数和单调性约束损失函数的新损失函数
"""
alpha = 0.5 # 可以根据需要进行调整
mse_loss = mean_squared_error(y_true, y_pred)
mcp_loss_val = mcp_loss(y_true, y_pred)
return alpha * mse_loss + (1 - alpha) * mcp_loss_val
```
loss='mean_squared_error'如何改为RMSE
在Keras中,可以使用`Root Mean Squared Error (RMSE)`作为损失函数,可以通过将`mean_squared_error`替换为`mean_squared_error`的平方根来实现。
具体代码如下:
```python
from keras.metrics import RootMeanSquaredError
model.compile(loss='mean_squared_error', optimizer='adam', metrics=[RootMeanSquaredError()])
```
在模型编译时,将损失函数设为`mean_squared_error`,并将指标(metrics)设为`RootMeanSquaredError()`,即可将损失函数改为RMSE。