mean_p = predict.mean(axis=0)是什么意思

这个代码行是在对预测结果进行操作。"predict" 是一个数组，可能是由模型预测得到的，"mean(axis=0)"则是对这个数组进行操作求平均值，其中的 "axis=0" 表示按照第一维 (即第一个轴，这通常是一个样本) 操作。这行代码的意思是取预测结果的每一个特征的平均值，返回一个包含每一个特征平均值的一维数组。

相关问题

下面的这段python代码，哪里有错误，修改一下：import numpy as np import matplotlib.pyplot as plt import pandas as pd import torch import torch.nn as nn from torch.autograd import Variable from sklearn.preprocessing import MinMaxScaler training_set = pd.read_csv('CX2-36_1971.csv') training_set = training_set.iloc[:, 1:2].values def sliding_windows(data, seq_length): x = [] y = [] for i in range(len(data) - seq_length): _x = data[i:(i + seq_length)] _y = data[i + seq_length] x.append(_x) y.append(_y) return np.array(x), np.array(y) sc = MinMaxScaler() training_data = sc.fit_transform(training_set) seq_length = 1 x, y = sliding_windows(training_data, seq_length) train_size = int(len(y) * 0.8) test_size = len(y) - train_size dataX = Variable(torch.Tensor(np.array(x))) dataY = Variable(torch.Tensor(np.array(y))) trainX = Variable(torch.Tensor(np.array(x[1:train_size]))) trainY = Variable(torch.Tensor(np.array(y[1:train_size]))) testX = Variable(torch.Tensor(np.array(x[train_size:len(x)]))) testY = Variable(torch.Tensor(np.array(y[train_size:len(y)]))) class LSTM(nn.Module): def __init__(self, num_classes, input_size, hidden_size, num_layers): super(LSTM, self).__init__() self.num_classes = num_classes self.num_layers = num_layers self.input_size = input_size self.hidden_size = hidden_size self.seq_length = seq_length self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, num_classes) def forward(self, x): h_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) c_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) # Propagate input through LSTM ula, (h_out, _) = self.lstm(x, (h_0, c_0)) h_out = h_out.view(-1, self.hidden_size) out = self.fc(h_out) return out num_epochs = 2000 learning_rate = 0.001 input_size = 1 hidden_size = 2 num_layers = 1 num_classes = 1 lstm = LSTM(num_classes, input_size, hidden_size, num_layers) criterion = torch.nn.MSELoss() # mean-squared error for regression optimizer = torch.optim.Adam(lstm.parameters(), lr=learning_rate) # optimizer = torch.optim.SGD(lstm.parameters(), lr=learning_rate) runn = 10 Y_predict = np.zeros((runn, len(dataY))) # Train the model for i in range(runn): print('Run: ' + str(i + 1)) for epoch in range(num_epochs): outputs = lstm(trainX) optimizer.zero_grad() # obtain the loss function loss = criterion(outputs, trainY) loss.backward() optimizer.step() if epoch % 100 == 0: print("Epoch: %d, loss: %1.5f" % (epoch, loss.item())) lstm.eval() train_predict = lstm(dataX) data_predict = train_predict.data.numpy() dataY_plot = dataY.data.numpy() data_predict = sc.inverse_transform(data_predict) dataY_plot = sc.inverse_transform(dataY_plot) Y_predict[i,:] = np.transpose(np.array(data_predict)) Y_Predict = np.mean(np.array(Y_predict)) Y_Predict_T = np.transpose(np.array(Y_Predict))

在这段代码中，可能存在以下错误：

1. 缺少必要的库或模块。

2. training_set 的文件路径是否正确。

3. training_set 的数据处理是否正确。

4. LSTM 模型的定义是否正确。

5. 训练和预测的代码是否正确。

下面是修改后的代码：

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import torch
import torch.nn as nn
from torch.autograd import Variable
from sklearn.preprocessing import MinMaxScaler

# 读取数据
training_set = pd.read_csv('CX2-36_1971.csv')
training_set = training_set.iloc[:, 1:2].values

# 定义数据处理函数
def sliding_windows(data, seq_length):
    x = []
    y = []
    
    for i in range(len(data) - seq_length):
        _x = data[i:(i + seq_length)]
        _y = data[i + seq_length]
        x.append(_x)
        y.append(_y)
    
    return np.array(x), np.array(y)

# 对数据进行归一化处理
sc = MinMaxScaler()
training_data = sc.fit_transform(training_set)

# 定义窗口长度
seq_length = 1

# 对数据进行窗口划分
x, y = sliding_windows(training_data, seq_length)

# 划分训练集和测试集
train_size = int(len(y) * 0.8)
test_size = len(y) - train_size

dataX = Variable(torch.Tensor(np.array(x)))
dataY = Variable(torch.Tensor(np.array(y)))

trainX = Variable(torch.Tensor(np.array(x[1:train_size])))
trainY = Variable(torch.Tensor(np.array(y[1:train_size])))

testX = Variable(torch.Tensor(np.array(x[train_size:len(x)])))
testY = Variable(torch.Tensor(np.array(y[train_size:len(y)])))

# 定义 LSTM 模型
class LSTM(nn.Module):
    def __init__(self, num_classes, input_size, hidden_size, num_layers):
        super(LSTM, self).__init__()
        
        self.num_classes = num_classes
        self.num_layers = num_layers
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.seq_length = seq_length
        
        self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size,
                            num_layers=num_layers, batch_first=True)
        
        self.fc = nn.Linear(hidden_size, num_classes)
    
    def forward(self, x):
        h_0 = Variable(torch.zeros(
            self.num_layers, x.size(0), self.hidden_size))
        
        c_0 = Variable(torch.zeros(
            self.num_layers, x.size(0), self.hidden_size))
        
        # Propagate input through LSTM
        ula, (h_out, _) = self.lstm(x, (h_0, c_0))
        
        h_out = h_out.view(-1, self.hidden_size)
        
        out = self.fc(h_out)
        
        return out

# 定义训练参数
num_epochs = 2000
learning_rate = 0.001
input_size = 1
hidden_size = 2
num_layers = 1
num_classes = 1

# 实例化 LSTM 模型
lstm = LSTM(num_classes, input_size, hidden_size, num_layers)

# 定义损失函数和优化器
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(lstm.parameters(), lr=learning_rate)

# 训练模型
runn = 10
Y_predict = np.zeros((runn, len(dataY)))

for i in range(runn):
    print('Run: ' + str(i + 1))
    for epoch in range(num_epochs):
        outputs = lstm(trainX)
        optimizer.zero_grad()
        loss = criterion(outputs, trainY)
        loss.backward()
        optimizer.step()
        
        if epoch % 100 == 0:
            print("Epoch: %d, loss: %1.5f" % (epoch, loss.item()))
    
    lstm.eval()
    train_predict = lstm(dataX)
    data_predict = train_predict.data.numpy()
    dataY_plot = dataY.data.numpy()
    
    # 对结果进行反归一化
    data_predict = sc.inverse_transform(data_predict)
    dataY_plot = sc.inverse_transform(dataY_plot)
    
    Y_predict[i,:] = np.transpose(np.array(data_predict))

Y_Predict = np.mean(Y_predict, axis=0)
Y_Predict_T = np.transpose(np.array(Y_Predict))

def mcp_loss(f_new): """ 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 num_features = 15 delta_p = tf.random.uniform(shape=(batch_size, num_timesteps, num_features), minval=0, maxval=1) p = inputs[:, :, -15:] p_new = p + delta_p newinputs = np.concatenate([train_ds[:, :, 0], p_new], axis=-1) f = ssmodel.predict(train_ds) f_new = ssmodel.predict(newinputs) # 预测 f_new delta_f = f - f_new relu = tf.nn.relu(delta_f) return tf.reduce_mean(tf.square(relu)) 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 """ def loss(y_true,y_pred): return alpha* mean_squared_error(y_true, y_pred) + (1-alpha) * mcp_loss() return loss将这段代码修改为keras的自定义损失函数的正确形式

可以将上述代码修改为 Keras 的自定义损失函数的正确形式，如下所示：

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
    """
    delta_p = tf.random.uniform(shape=(batch_size, num_timesteps, num_features), minval=0, maxval=1)
    p = y_true[:, :, -15:]
    p_new = p + delta_p
    newinputs = tf.concat([y_true[:, :, 0], p_new], axis=-1)
    f = ssmodel.predict(y_true)
    f_new = ssmodel.predict(newinputs)  # 预测 f_new
    delta_f = f - f_new
    relu = tf.nn.relu(delta_f)
    return tf.reduce_mean(tf.square(relu))

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

在上述代码中，`mcp_loss` 函数和 `combined_loss` 函数都采用了 Keras 的标准输入格式：`y_true` 和 `y_pred`，分别表示真实值和预测值。在 `mcp_loss` 函数中，我们使用 `tf.random.uniform` 函数生成一个与 `y_true` 相同形状的随机张量 `delta_p`，并使用它来计算新的输入 `newinputs` 和相应的预测值 `f_new`。然后，我们计算 `delta_f` 和 ReLU 函数，并将其平方并取平均值作为单调性约束损失函数的值。在 `combined_loss` 函数中，我们将 MSE 损失函数和单调性约束损失函数的值组合在一起，并返回它们的加权平均值作为最终的损失函数值。

请注意，上述代码可能需要根据您的具体情况进行适当修改，例如调整 `alpha` 或修改输入和输出的形状。