解释这段反向传播代码每行的含义并详细阐述它是如何达成反向传播效果的 def backward(y_pred, y_true, cache): x, z1, a1, z2, a2, z3 = cache delta3 = y_pred - y_true dW3 = np.outer(a2, delta3) db3 = delta3 delta2 = np.dot(delta3, W3.T) * dtanh(z2) dW2 = np.outer(a1, delta2) db2 = delta2 delta1 = np.dot(delta2, W2.T) * dtanh(z1) dW1 = np.outer(x, delta1) db1 = delta1 return dW1, db1, dW2, db2, dW3, db3

请帮我评估一下，我一共有9000行训练数据，代码如下：def get_data(train_df): train_df = train_df[['user_id', 'behavior_type']] train_df=pd.pivot_table(train_df,index=['user_id'],columns=['behavior_type'],aggfunc={'behavior_type':'count'}) train_df.fillna(0,inplace=True) train_df=train_df.reset_index(drop=True) train_df.columns=train_df.columns.droplevel(0) x_train=train_df.iloc[:,:3] y_train=train_df.iloc[:,-1] type=torch.float32 x_train=torch.tensor(x_train.values,dtype=type) y_train=torch.tensor(y_train.values,dtype=type) print(x_train) print(y_train) return x_train ,y_train x_train,y_train=get_data(train_df) x_test,y_test=get_data(test_df) print(x_test) #创建模型 class Order_pre(nn.Module): def init(self): super(Order_pre, self).init() self.ln1=nn.LayerNorm(3) self.fc1=nn.Linear(3,6) self.fc2 = nn.Linear(6, 12) self.fc3 = nn.Linear(12, 24) self.fc4 = nn.Linear(24, 1) def forward(self,x): x=self.ln1(x) x=self.fc1(x) x = nn.functional.relu(x) x = self.fc2(x) x = nn.functional.relu(x) x = self.fc3(x) x = nn.functional.relu(x) x = self.fc4(x) return x #定义模型、损失函数和优化器 model=Order_pre() loss_fn=nn.MSELoss() optimizer=torch.optim.SGD(model.parameters(),lr=1) #开始跑数据 for epoch in range(1,50): #预测值 y_pred=model(x_train) #损失值 loss=loss_fn(y_pred,y_train) #反向传播 optimizer.zero_grad() loss.backward() optimizer.step() print('epoch',epoch,'loss',loss) # 开始预测y值 y_test_pred=model(x_test) y_test_pred=y_test_pred.detach().numpy() y_test=y_test.detach().numpy() y_test_pred=pd.DataFrame(y_test_pred) y_test=pd.DataFrame(y_test) dfy=pd.concat([y_test,y_test_pred],axis=1) print(dfy) dfy.to_csv('resulty.csv')

根据你提供的代码，我认为你的模型可能存在一些问题。具体来说： 1. 数据处理部分：你的代码中首先对数据进行了透视操作，然后将数据分成了x_train和y_train两部分。x_train中只包含了前三列数据，而y_train中只...

这段反向传播代码出现报错请解释报错原因并修改代码 def backward(y_pred, y_true, cache): x, z1, a1, z2, a2, z3 = cache delta3 = y_pred - y_true dW3 = np.outer(a2, delta3) db3 = delta3 delta2 = np.dot(delta3, W3.T) * dtanh(z2) dW2 = np.outer(a1, delta2) db2 = delta2 delta1 = np.dot(delta2, W2.T) * dtanh(z1) dW1 = np.outer(x, delta1) db1 = delta1 return dW1, db1, dW2, db2, dW3, db3 报错如下：Traceback (most recent call last): File "/Users/tiger/Desktop/英才计划/bp神经网络/交叉熵bp模型的副本.py", line 92, in <module> W1, b1, W2, b2, W3, b3 = momentum_bp(X_train, y_train) File "/Users/tiger/Desktop/英才计划/bp神经网络/交叉熵bp模型的副本.py", line 72, in momentum_bp dW1, db1, dW2, db2, dW3, db3 = backward(y_pred, y, cache) File "/Users/tiger/Desktop/英才计划/bp神经网络/交叉熵bp模型的副本.py", line 56, in backward delta2 = np.dot(delta3.reshape(1, -1), W3.T) * dtanh(z2) File "<__array_function__ internals>", line 200, in dot ValueError: shapes (1,133225) and (1,3) not aligned: 133225 (dim 1) != 1 (dim 0)

def backward(y_pred, y_true, cache): x, z1, a1, z2, a2, z3 = cache delta3 = y_pred - y_true dW3 = np.outer(a2, delta3) db3 = delta3 delta2 = np.dot(delta3, W3.T).T * dtanh(z2) dW2 = np.outer(a1,...

def get_data(train_df): train_df = train_df[['user_id', 'behavior_type']] train_df=pd.pivot_table(train_df,index=['user_id'],columns=['behavior_type'],aggfunc={'behavior_type':'count'}) train_df.fillna(0,inplace=True) train_df=train_df.reset_index(drop=True) train_df.columns=train_df.columns.droplevel(0) x_train=train_df.iloc[:,:3] y_train=train_df.iloc[:,-1] type=torch.float32 x_train=torch.tensor(x_train.values,dtype=type) y_train=torch.tensor(y_train.values,dtype=type) print(x_train) print(y_train) return x_train ,y_train x_train,y_train=get_data(train_df) x_test,y_test=get_data(test_df) print(x_test) #创建模型 class Order_pre(nn.Module): def init(self): super(Order_pre, self).init() self.ln1=nn.LayerNorm(3) self.fc1=nn.Linear(3,6) self.fc2 = nn.Linear(6, 12) self.fc3 = nn.Linear(12, 24) self.dropout=nn.Dropout(0.5) self.fc4 = nn.Linear(24, 48) self.fc5 = nn.Linear(48, 96) self.fc6 = nn.Linear(96, 1) def forward(self,x): x=self.ln1(x) x=self.fc1(x) x = nn.functional.relu(x) x = self.fc2(x) x = nn.functional.relu(x) x = self.fc3(x) x = self.dropout(x) x = nn.functional.relu(x) x = self.fc4(x) x = nn.functional.relu(x) x = self.fc5(x) x = nn.functional.relu(x) x = self.fc6(x) return x #定义模型、损失函数和优化器 model=Order_pre() loss_fn=nn.MSELoss() optimizer=torch.optim.SGD(model.parameters(),lr=0.05) #开始跑数据 for epoch in range(1,50): #预测值 y_pred=model(x_train) #损失值 loss=loss_fn(y_pred,y_train) #反向传播 optimizer.zero_grad() loss.backward() optimizer.step() print('epoch',epoch,'loss',loss) # 开始预测y值 y_test_pred=model(x_test) y_test_pred=y_test_pred.detach().numpy() y_test=y_test.detach().numpy() y_test_pred=pd.DataFrame(y_test_pred) y_test=pd.DataFrame(y_test) dfy=pd.concat([y_test,y_test_pred],axis=1) print(dfy) dfy.to_csv('resulty.csv') 如果我想要使用学习率调度器应该怎么操作

如果您想使用学习率调度器，可以先创建一个学习率调度器对象，例如StepLR或ReduceLROnPlateau，然后在每个epoch或一定的步骤之后，通过调用学习率调度器对象的step方法来更新优化器的学习率。例如，对于StepLR，可以...

请解释这段代码：import torch # import matplotlib.pyplot as plt import numpy as np # def gradient(x,y): def sigmoid(x): return 1/(1 + 2.71828**x) x_data = torch.Tensor([[1.0], [2.0], [3.0], [4.0]]) y_data = torch.Tensor([[0.], [0.], [1.], [1.]]) class Model(torch.nn.Module): def init(self): super(Model, self).init() self.linear = torch.nn.Linear(1, 1) def forward(self, x): y_pred = sigmoid(self.linear(x)) return y_pred model = Model() criterion = torch.nn.MSELoss(size_average = False) optimizer = torch.optim.SGD(model.parameters(), lr = 1) for epoch in range(1000): y_pred = model(x_data) loss = criterion(y_pred, y_data) print(epoch, loss.item()) optimizer.zero_grad() loss.backward() optimizer.step() hour_var = torch.Tensor([[1.0]]) print("predict 1 hours", 1.0, model(hour_var).item() > 0.5) hour_var = torch.Tensor([[7.0]]) print("predict 7 hours", 7.0, model(hour_var).item() > 0.5)

这段代码实现了一个简单的逻辑回归模型，用于二分类的任务。下面是代码的解释： 1. 引入所需的库： - torch：PyTorch库，用于构建...以上就是这段代码的解释，它实现了一个简单的逻辑回归模型，并进行了训练和预测。

#模型 class Wine_net(nn.Module): def init(self): super(Wine_net, self).init() self.ln1=nn.LayerNorm(11) self.fc1=nn.Linear(11,22) self.fc2=nn.Linear(22,44) self.fc3=nn.Linear(44,1) def forward(self,x): x=self.ln1(x) x=self.fc1(x) x=nn.functional.relu(x) x=self.fc2(x) x=nn.functional.relu(x) x = self.fc3(x) x = nn.functional.softmax(x,dim=1) return x # 读取数据 df = pd.read_csv('winequality.csv') df1=df.drop('quality',axis=1) df2=df['quality'] train_x=torch.tensor(df1.values, dtype=torch.float32) train_y=torch.tensor(df2.values,dtype=torch.float32) train_y = train_y.unsqueeze(1) # 定义模型、损失函数和优化器 model=Wine_net() loss_fn=nn.MSELoss() optimizer =torch.optim.SGD(model.parameters(), lr=0.1) # 训练模型 for epoch in range(10): # 前向传播 print(epoch) y_pred = model(train_x) print(y_pred) print(epoch,'预测') # 计算损失 loss = loss_fn(y_pred, train_y) # 反向传播 optimizer.zero_grad() loss.backward() optimizer.step() print(loss)

这段代码实现了一个简单的神经网络模型（Wine_net），使用了MSE损失函数和SGD优化器来训练模型，训练数据是来自winequality.csv文件中的数据。在模型定义中，Wine_net继承了nn.Module类，并在构造函数中定义了网络...

解释这段话class GRUModel(nn.Module): def init(self, input_size, hidden_size, output_size, num_layers, dropout=0.5): super(GRUModel, self).init() self.hidden_size = hidden_size self.num_layers = num_layers self.gru = nn.GRU(input_size, hidden_size, num_layers, batch_first=True, dropout=dropout) self.attention = Attention(hidden_size) self.fc = nn.Linear(hidden_size, output_size) self.fc1=nn.Linear(hidden_size,256) self.fc2=nn.Linear(256,1)#这两句是加的 self.dropout = nn.Dropout(dropout) def forward(self, x): h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size) out, hidden = self.gru(x, h0) out, attention_weights = self.attention(hidden[-1], out) out = self.dropout(out) out = self.fc(out) return out def fit(epoch, model, trainloader, testloader): total = 0 running_loss = 0 train_bar = tqdm(train_dl) # 形成进度条（自己加的） model.train() #告诉模型处于训练状态，dropout层发挥作用 for x, y in trainloader: if torch.cuda.is_available(): x, y = x.to('cuda'), y.to('cuda') y_pred = model(x) #y的预测值 loss = loss_fn(y_pred, y) #计算损失，将预测值与真实值传进去，自动计算 optimizer.zero_grad() #将之前的梯度清零 loss.backward() #根据损失计算梯度，进行一次反向传播。 optimizer.step() #根据梯度进行优化 with torch.no_grad(): total += y.size(0) running_loss += loss.item() #计算所有批次的损失之和 exp_lr_scheduler.step() epoch_loss = running_loss / len(trainloader.dataset) test_total = 0 test_running_loss = 0 model.eval() #告诉模型处于预测状态，dropout层不发挥作用 with torch.no_grad(): for x, y in testloader: if torch.cuda.is_available(): x, y = x.to('cuda'), y.to('cuda') y_pred = model(x) loss = loss_fn(y_pred, y) test_total += y.size(0) test_running_loss += loss.item() epoch_test_loss = test_running_loss / len(testloader.dataset) print('epoch: ', epoch, #迭代次数 'loss： ', round(epoch_loss, 6), #保留小数点3位数 'test_loss： ', round(epoch_test_loss, 4) ) return epoch_loss,epoch_test_loss

在这个上下文中，遍历测试数据加载器中的每个批次，同样将输入数据和标签移动到GPU，并通过模型进行前向传播计算得到预测值y_pred，并计算损失值。最后，计算训练集和测试集上的平均损失，并打印出当前迭代次数、...

#模型 class Wine_net(nn.Module): def init(self): super(Wine_net, self).init() # self.ln1=nn.LayerNorm(11) self.fc1=nn.Linear(11,22) self.fc2=nn.Linear(22,44) self.fc3=nn.Linear(44,1) def forward(self,x): # x=self.ln1(x) x=self.fc1(x) x=nn.functional.relu(x) x=self.fc2(x) x=nn.functional.relu(x) x = self.fc3(x) x = nn.functional.softmax(x,dim=1) return x # 读取数据 df = pd.read_csv('winequality.csv') df1=df.drop('quality',axis=1) df2=df['quality'] # 将数据集分成10份 skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42) # 遍历每一份数据，并训练模型 for train_index, test_index in skf.split(df1, df2): train_x, test_x = df1.iloc[train_index], df1.iloc[test_index] train_y, test_y = df2.iloc[train_index], df2.iloc[test_index] train_x = torch.tensor(train_x.values, dtype=torch.float32) train_y = torch.tensor(train_y.values, dtype=torch.float32) train_y = train_y.unsqueeze(1) # 定义模型、损失函数和优化器 model = Wine_net() loss_fn = nn.MSELoss() optimizer = torch.optim.SGD(model.parameters(), lr=1) print(train_x) for epoch in range(1,10): # 前向传播 y_pred = model(train_x) # 计算损失 loss = loss_fn(y_pred, train_y) # 反向传播 optimizer.zero_grad() loss.backward() optimizer.step() # 打印损失 print('Epoch:',epoch, 'Train Loss:', loss.item())

这段代码实现了一个简单的分类模型，使用 Wine 数据集进行训练。首先将数据集分成 10 份，然后遍历每一份数据，并使用该份数据进行训练。每一份数据都被分为训练集和测试集，其中训练集用于训练模型，测试集用于评估...

import torch import matplotlib.pyplot as plt x_data=torch.Tensor([[1.0],[2.0],[3.0]]) y_data=torch.Tensor([[2.0],[4.0],[6.0]]) list1=[] list2=[] class LinearModel(torch.nn.Module): def init(self): super(LinearModel,self).init() self.linear=torch.nn.Linear(1,1) def forward(self,x): y_pred=self.linear(x) return y_pred model = LinearModel() criterion = torch.nn.MSELoss(size_average=False) optimizer = torch.optim.SGD(model.parameters(), lr=0.01) for epoch in range(100): y_pred=model(x_data) loss=criterion(y_pred,y_data) print(epoch,loss.item()) list1.append(loss.item()) list2.append(epoch) optimizer.zero_grad() loss.backward() optimizer.step() print('w=',model.linear.weight.item()) print('b=',model.linear.bias.item()) fig=plt.figure() ax=fig.add_subplot(111) ax.set(title='Adagrad',ylabel='loss',xlabel='time') ax.plot(list1,list2) x_test=torch.Tensor([[4.0]]) y_test=model(x_test) print('y_pred=',y_test.data) plt.show()基于这段代码可以解决什么样的实际问题，并且详细说明如何实现。

在每次训练过程中，我们先通过模型的前向传播计算预测值y_pred，然后计算损失值loss，最后反向传播并更新模型参数。最后，我们使用训练好的模型对新的数据进行预测，即输入一个特征x_test，输出该特征对应的预测...

import torch import matplotlib.pyplot as plt x_data=torch.Tensor([[1.0],[2.0],[3.0]]) y_data=torch.Tensor([[2.0],[4.0],[6.0]]) list1=[] list2=[] class LinearModel(torch.nn.Module): def init(self): super(LinearModel,self).init() self.linear=torch.nn.Linear(1,1) def forward(self,x): y_pred=self.linear(x) return y_pred model = LinearModel() criterion = torch.nn.MSELoss(size_average=False) optimizer = torch.optim.SGD(model.parameters(), lr=0.01) for epoch in range(100): y_pred=model(x_data) loss=criterion(y_pred,y_data) print(epoch,loss.item()) list1.append(loss.item()) list2.append(epoch) optimizer.zero_grad() loss.backward() optimizer.step() print('w=',model.linear.weight.item()) print('b=',model.linear.bias.item()) fig=plt.figure() ax=fig.add_subplot(111) ax.set(title='Adagrad',ylabel='loss',xlabel='time') ax.plot(list1,list2) x_test=torch.Tensor([[4.0]]) y_test=model(x_test) print('y_pred=',y_test.data) plt.show()给我讲一下这个代码

这是一个使用PyTorch实现的线性回归模型的代码。首先，定义了输入数据x_data和对应的输出数据y_data。然后，定义了一个继承自torch.nn.Module的线性模型LinearModel，其中包括一个线性层self.linear。forward函数...

import torch import matplotlib.pyplot as plt x_data=torch.Tensor([[1.0],[2.0],[3.0]]) y_data=torch.Tensor([[2.0],[4.0],[6.0]]) list1=[] list2=[] class LinearModel(torch.nn.Module): def init(self): super(LinearModel,self).init() self.linear=torch.nn.Linear(1,1) def forward(self,x): y_pred=self.linear(x) return y_pred model = LinearModel() criterion = torch.nn.MSELoss(size_average=False) optimizer = torch.optim.SGD(model.parameters(), lr=0.01) for epoch in range(100): y_pred=model(x_data) loss=criterion(y_pred,y_data) print(epoch,loss.item()) list1.append(loss.item()) list2.append(epoch) optimizer.zero_grad() loss.backward() optimizer.step() print('w=',model.linear.weight.item()) print('b=',model.linear.bias.item()) fig=plt.figure() ax=fig.add_subplot(111) ax.set(title='Adagrad',ylabel='loss',xlabel='time') ax.plot(list1,list2) x_test=torch.Tensor([[4.0]]) y_test=model(x_test) print('y_pred=',y_test.data) plt.show()用这段代码解决一个实际问题

这段代码可以用来解决一个简单的线性回归问题，即预测一个输出值Y与一个特征X的关系。因此，我们可以使用这段代码来解决一些简单的实际问题，例如：假设我们有一个销售数据集，其中包含每个销售员的工作年限和销售...

class Wine_net(nn.Module): def int(self): super(Wine_net, self).int() self.ln1=nn.LayerNorm(11) self.fc1=nn.Linear(11,22) self.fc2=nn.Linear(22,44) self.fc3=nn.Linear(44,1) def forward(self,x): x=self.ln1(x) x=self.fc1(x) x=nn.functional.relu(x) x=self.fc2(x) x=nn.functional.relu(x) x = self.fc3(x) x = nn.functional.softmax(x) return x # 读取数据 df = pd.read_csv('winequality.csv') df1=df.drop('quality',axis=1) df2=df['quality'] train_x=torch.tensor(df1.values, dtype=torch.float32) train_y=torch.tensor(df2.values,dtype=torch.float32) # 定义模型、损失函数和优化器 model=Wine_net() loss_fn=nn.MSELoss() optimizer =torch.optim.SGD(model.parameters(), lr=0.0001) # 训练模型 for epoch in range(10): # 前向传播 y_pred = model(train_x) # 计算损失 loss = loss_fn(y_pred, train_y) # 反向传播 optimizer.zero_grad() loss.backward() optimizer.step()

因此，你需要修改代码中的这一行： def __int__(self): 为： def __init__(self): 除此之外，还有一个小问题，就是在 Wine_net 类的 forward 方法中，最后一层应该使用 nn.functional.sigmoid ...

import numpy import numpy as np import matplotlib.pyplot as plt import math import torch from torch import nn from torch.utils.data import DataLoader, Dataset import os os.environ['KMP_DUPLICATE_LIB_OK']='True' dataset = [] for data in np.arange(0, 3, .01): data = math.sin(data * math.pi) dataset.append(data) dataset = np.array(dataset) dataset = dataset.astype('float32') max_value = np.max(dataset) min_value = np.min(dataset) scalar = max_value - min_value print(scalar) dataset = list(map(lambda x: x / scalar, dataset)) def create_dataset(dataset, look_back=3): dataX, dataY = [], [] for i in range(len(dataset) - look_back): a = dataset[i:(i + look_back)] dataX.append(a) dataY.append(dataset[i + look_back]) return np.array(dataX), np.array(dataY) data_X, data_Y = create_dataset(dataset) train_X, train_Y = data_X[:int(0.8 * len(data_X))], data_Y[:int(0.8 * len(data_Y))] test_X, test_Y = data_Y[int(0.8 * len(data_X)):], data_Y[int(0.8 * len(data_Y)):] train_X = train_X.reshape(-1, 1, 3).astype('float32') train_Y = train_Y.reshape(-1, 1, 3).astype('float32') test_X = test_X.reshape(-1, 1, 3).astype('float32') train_X = torch.from_numpy(train_X) train_Y = torch.from_numpy(train_Y) test_X = torch.from_numpy(test_X) class RNN(nn.Module): def init(self, input_size, hidden_size, output_size=1, num_layer=2): super(RNN, self).init() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layer = num_layer self.rnn = nn.RNN(input_size, hidden_size, batch_first=True) self.linear = nn.Linear(hidden_size, output_size) def forward(self, x): out, h = self.rnn(x) out = self.linear(out[0]) return out net = RNN(3, 20) criterion = nn.MSELoss(reduction='mean') optimizer = torch.optim.Adam(net.parameters(), lr=1e-2) train_loss = [] test_loss = [] for e in range(1000): pred = net(train_X) loss = criterion(pred, train_Y) optimizer.zero_grad() # 反向传播 loss.backward() optimizer.step() if (e + 1) % 100 == 0: print('Epoch:{},loss:{:.10f}'.format(e + 1, loss.data.item())) train_loss.append(loss.item()) plt.plot(train_loss, label='train_loss') plt.legend() plt.show()请适当修改代码，并写出预测值和真实值的代码

test_X, test_Y = data_Y[int(0.8 * len(data_X)):], data_Y[int(0.8 * len(data_Y)):] train_X = train_X.reshape(-1, 1, 3).astype('float32') train_Y = train_Y.reshape(-1, 1, 1).astype('float32') test_X...

class process: def init(self, model=1): self.model = model def train_one_epoch(self, data_loader, net, loss_func, optimizer): net.train() floss = 0 for i, (x_batch, y_batch) in enumerate(data_loader()): out = net(x_batch) loss = loss_func(out, y_batch) # avloss = paddle.mean(loss) floss = floss + loss # _, pred = paddle.max(out, 1) # pred_train.extend(pred.data.tolist()) # act_train.extend(y_batch.data.tolist()) #tolist() 返回列表或者数字 optimizer.clear_grad() loss.backward() optimizer.step() # floss = floss/i+1 return floss解释一下

这段代码定义了一个名为 "process" 的类，其中包含了一个初始化函数 "__...需要注意的是，这段代码中有些注释被注释掉了，这是因为这些注释是对代码中一些未使用的变量的解释或者是对代码中一些未实现的功能的注释。

import torch import matplotlib.pyplot as plt x_data=torch.Tensor([[1.0],[2.0],[3.0]]) y_data=torch.Tensor([[2.0],[4.0],[6.0]]) list1=[] list2=[] class LinearModel(torch.nn.Module): def init(self): super(LinearModel,self).init() self.linear=torch.nn.Linear(1,1) def forward(self,x): y_pred=self.linear(x) return y_pred model = LinearModel() criterion = torch.nn.MSELoss(size_average=False) optimizer = torch.optim.SGD(model.parameters(), lr=0.01) for epoch in range(100): y_pred=model(x_data) loss=criterion(y_pred,y_data) print(epoch,loss.item()) list1.append(loss.item()) list2.append(epoch) optimizer.zero_grad() loss.backward() optimizer.step() print('w=',model.linear.weight.item()) print('b=',model.linear.bias.item()) fig=plt.figure() ax=fig.add_subplot(111) ax.set(title='Adagrad',ylabel='loss',xlabel='time') ax.plot(list1,list2) x_test=torch.Tensor([[4.0]]) y_test=model(x_test) print('y_pred=',y_test.data) plt.show()给我具体讲一讲这个代码

然后，使用优化器进行反向传播和权重更新。 6. 输出模型参数：输出模型的权重和偏置。 7. 绘制损失曲线：使用matplotlib库绘制损失随时间的变化曲线。 8. 测试模型：使用训练好的模型进行一次测试，并输出预测值...

from sklearn.tree import DecisionTreeRegressor model2 = DecisionTreeRegressor(random_state=0) model2.fit(x_train, Y_train) y_pred2=model2.predict(x_test) assessing(Y_test,y_pred2)，对以上代码调用的模型换成PyTorch模型

同时，在训练模型时需要将inputs和labels都转换为Tensor并调用backward()方法进行反向传播，最后使用optimizer.step()方法更新模型参数。在预测时需要使用torch.no_grad()上下文管理器来关闭梯度计算，以加快...

import numpy as np import matplotlib.pyplot as plt import math import torch from torch import nn import pdb from torch.autograd import Variable import os os.environ['KMP_DUPLICATE_LIB_OK']='True' dataset = [] for data in np.arange(0, 3, .01): data = math.sin(data * math.pi) dataset.append(data) dataset = np.array(dataset) dataset = dataset.astype('float32') max_value = np.max(dataset) min_value = np.min(dataset) scalar = max_value - min_value dataset = list(map(lambda x: x / scalar, dataset)) def create_dataset(dataset, look_back=3): dataX, dataY = [], [] for i in range(len(dataset) - look_back): a = dataset[i:(i + look_back)] dataX.append(a) dataY.append(dataset[i + look_back]) return np.array(dataX), np.array(dataY) data_X, data_Y = create_dataset(dataset) # 对训练集测试集划分,划分比例0.8 train_X, train_Y = data_X[:int(0.8 * len(data_X))], data_Y[:int(0.8 * len(data_Y))] test_X, test_Y = data_Y[int(0.8 * len(data_X)):], data_Y[int(0.8 * len(data_Y)):] train_X = train_X.reshape(-1, 1, 3).astype('float32') train_Y = train_Y.reshape(-1, 1, 3).astype('float32') test_X = test_X.reshape(-1, 1, 3).astype('float32') class RNN(nn.Module): def init(self, input_size, hidden_size, output_size=1, num_layer=2): super(RNN, self).init() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layer = num_layer self.rnn = nn.RNN(input_size, hidden_size, batch_first=True) self.linear = nn.Linear(hidden_size, output_size) def forward(self, x): # 补充forward函数 out, h = self.rnn(x) out = self.linear(out[0]) # print("output的形状", out.shape) return out net = RNN(3, 20) criterion = nn.MSELoss(reduction='mean') optimizer = torch.optim.Adam(net.parameters(), lr=1e-2) train_loss = [] test_loss = [] for e in range(1000): pred = net(train_X) loss = criterion(pred, train_Y) optimizer.zero_grad() # 反向传播 loss.backward() optimizer.step() if (e + 1) % 100 == 0: print('Epoch:{},loss:{:.10f}'.format(e + 1, loss.data.item())) train_loss.append(loss.item()) plt.plot(train_loss, label='train_loss') plt.legend() plt.show()画出预测值真实值图

dataY_plot = data_Y.reshape(-1) data_predict_plot = data_predict.reshape(-1) plt.plot(dataY_plot, label='real') plt.plot(data_predict_plot, label='predict') plt.legend() plt.show()

反向传播代码

这段代码展示了如何定义模型、执行正向传播并调用.backward()方法来进行反向传播以计算梯度[^3]。 #### 使用TensorFlow实现反向传播同样地，在TensorFlow里也可以方便地完成同样的工作。这里给出一段基于...

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from sklearn.tree import DecisionTreeRegressor model2 = DecisionTreeRegressor(random_state=0) model2.fit(x_train, Y_train) y_pred2=model2.predict(x_test) assessing(Y_test,y_pred2)，对以上代码调用的模型换成PyTorch模型

反向传播代码

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