def test(config, model, test_iter): # test model.load_state_dict(torch.load(config.save_path)) model.eval() start_time = time.time() test_acc, test_loss, test_report, test_confusion = evaluate(config, model, test_iter, test=True) msg = 'Test Loss: {0:>5.2}, Test Acc: {1:>6.2%}' print(msg.format(test_loss, test_acc)) print("Precision, Recall and F1-Score...") print(test_report) print("Confusion Matrix...") print(test_confusion) time_dif = get_time_dif(start_time) print("Time usage:", time_dif)怎么实现的

if config.adaptive_lr: scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=config.decay_rate) # 调整学习率指数衰减，每次epoch：学习率 = gamma * 学习率 scheduler.step(train_loss) if total_batch % 10 == 0: # 每多少轮输出在训练集和验证集上的效果 if 'multi' in config.classify_type and 'level3' in config.classify_type: # 多标签分类 print('Epoch [{0:>3}/{1:>3}/{2:>5}]'.format(epoch + 1, config.num_epochs, total_batch), end=' ') print(' | loss: {:.4f}'.format(train_loss.item()), end='') result_train = evaluate(outputs, labels, config.classify_type) print(' | time: {:.4f}s'.format(get_time_dif(t))) # 验证集和训练集的准确率 vali_loss, result_vali = vali_test(config, model, vali_iter, LOSS) # 验证 test_loss, result_test = vali_test(config, model, test_iter, LOSS) # 测试

然后，调用 vali_test 函数对验证集和测试集进行评估，计算验证集和测试集的损失值和评估结果。整个代码段的作用是在每个epoch的训练过程中，根据需要输出训练集和验证集的效果，并进行学习率调整。同时，还会...

else: # 单标签分类 print('Epoch [{0:>3}/{1:>3}/{2:>5}]'.format(epoch + 1, config.num_epochs, total_batch), end=' ') print(' | loss: {:.4f}'.format(train_loss.item()), end='') result_train = evaluate(outputs, labels, config.classify_type) print(' | time: {:.4f}s'.format(get_time_dif(t))) # 验证集和训练集的准确率 vali_loss, result_vali = vali_test(config, model, vali_iter, LOSS) # 验证 test_loss, result_test = vali_test(config, model, test_iter, LOSS) # 测试

分别传入配置文件config、模型对象model、验证集迭代器vali_iter和测试集迭代器test_iter，以及损失函数LOSS。返回验证集和测试集的损失值和评估结果。最后，将验证集和测试集的损失值和评估结果打印...

def train(config, model, train_iter, vali_iter, test_iter, K_on, fine_tune): start_time = time.time() if fine_tune: # 只优化最后的分类层 optimizer = torch.optim.Adam(model.fc.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay) else: optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay) best_pred = 0 # 记录验证集最优的结果 total_batch = 0 # 记录进行到多少batch last_improve = 0 # 记录上次验证集loss下降的batch数 flag = False # 记录是否很久没有效果提升 for epoch in range(config.num_epochs): for i, (trains, labels) in enumerate(train_iter): # 在不同的epoch中，batch的取法是不同的 t = time.time() model.train() # 训练 LOSS = margin_loss if ('multi' in config.classify_type) and ('level3' in config.classify_type) else nll_loss outputs = model(trains) optimizer.zero_grad() train_loss = LOSS(outputs, labels) train_loss.backward() optimizer.step()

函数接受配置文件 config、模型对象 model、训练数据迭代器 train_iter、验证数据迭代器 vali_iter、测试数据迭代器 test_iter、K_on和fine_tune作为输入。首先，根据是否进行fine-tune操作，选择...

model.to(config.device) print(model.parameters) print("模型参数数量：" + str(len(list(model.parameters())))) # 输出参数数量 print("模型的训练参数：" + str([i.size() for i in model.parameters()])) # 输出参数 vali_acc, test_acc, vali_pre_ma, test_pre_ma, \ vali_rec_ma, test_rec_ma, vali_F1_ma, test_F1_ma \ = train(config, model, train_iter, dev_iter, test_iter, K_on, args.fine_tune) print('vali accuracy:{:.4f}, test accuracy:{:.4f}'.format(vali_acc, test_acc)) print('vali pre_ma:{:.4f}, test pre_ma:{:.4f}'.format(vali_pre_ma, test_pre_ma)) print('vali rec_ma:{:.4f}, test rec_ma:{:.4f}'.format(vali_rec_ma, test_rec_ma)) print('vali F1_ma:{:.4f}, test F1_ma:{:.4f}'.format(vali_F1_ma, test_F1_ma))

首先，model.to(config.device)将模型转移到指定的设备上（例如GPU）。然后，model.parameters打印出模型的参数。len(list(model.parameters()))输出参数的数量。[i.size() for i in model.parameters()]...

def vali_test(config, model, data_iter, LOSS): t = time.time() model.eval() loss_total = 0 output_all = torch.FloatTensor([]).to(config.device) labels_all = torch.FloatTensor([]).to(config.device) with torch.no_grad(): for texts, labels in data_iter: outputs = model(texts) loss = LOSS(outputs, labels) loss_total += loss labels_all = torch.cat((labels_all, labels), 0) output_all = torch.cat((output_all, outputs), 0) loss = loss_total / len(data_iter) print(' ' * 22 + ' | loss: {:.4f}'.format(loss.item()), end='') output_result = evaluate(output_all, labels_all, config.classify_type) print(' | time: {:.4f}s'.format(get_time_dif(t))) return loss.item(), output_result

函数接受配置文件 config、模型对象 model、数据迭代器 data_iter 和损失函数 LOSS 作为输入。首先，将模型设置为评估模式，通过调用 model.eval() 来实现。接下来，定义一些变量用于记录评估过程的信息...

train_iter = build_iterator(X_train, config) # train dev_iter = build_iterator(X_valid, config) # dev test_iter = build_iterator(X_test, config) # test time_dif = get_time_dif(start_time) print("加载数据时间:", time_dif) print('训练模型...') config.n_vocab = len(vocab) model = x.Model(config).to(config.device)

- build_iterator(X_test, config)：该函数用于构建测试集的迭代器，输入参数X_test是测试集数据。 2. 使用get_time_dif函数计算加载数据所花费的时间，并打印出来。 3. 打印"训练模型..."的提示信息。 4....

for epoch in range(config.num_epochs): print('Epoch [{}/{}]'.format(epoch + 1, config.num_epochs)) for i, (trains, labels) in enumerate(train_iter): outputs = model(trains) model.zero_grad() loss = F.cross_entropy(outputs, labels) loss.backward() optimizer.step() if total_batch % 1 == 0: # 每多少轮输出在训练集和验证集上的效果 true = labels.data.cpu() predic = torch.max(outputs.data, 1)[1].cpu() train_acc = metrics.accuracy_score(true, predic) dev_acc, dev_loss = evaluate(config, model, dev_iter) if dev_loss < dev_best_loss: dev_best_loss = dev_loss torch.save(model.state_dict(), config.save_path) improve = '*' last_improve = total_batch else: improve = '' time_dif = get_time_dif(start_time) msg = 'Iter: {0:>6}, Train Loss: {1:>5.2}, Train Acc: {2:>6.2%}, Val Loss: {3:>5.2}, Val Acc: {4:>6.2%}, Time: {5} {6}' print(msg.format(total_batch, loss.item(), train_acc, dev_loss, dev_acc, time_dif, improve)) text = msg.format(total_batch, loss.item(), train_acc, dev_loss, dev_acc, time_dif, improve) with open(f"{config.model_name}_result.txt", mode="a+", encoding="utf8") as f: f.write(text + "\n") model.train() total_batch += 1 if (total_batch - last_improve > config.require_improvement) or total_batch == 188: # 验证集loss超过1000batch没下降，结束训练 print("No optimization for a long time, auto-stopping...") flag = True break if flag: break test(config, model, test_iter)

这是一个训练模型的代码片段，包括了模型的训练和验证过程。首先进入外层循环，遍历所有epoch，每个epoch都输出当前训练的进度。然后进入内层循环，遍历...最后调用了一个test函数，用于对测试集进行测试并输出结果。

def train(config, model, train_iter, dev_iter, test_iter):

其中，config是一个包含模型超参数的配置对象，model是要训练的TextLSTM模型实例，train_iter、dev_iter和test_iter是分别表示训练集、验证集和测试集的迭代器对象。训练函数的主要流程如下： 1. 定义优化器和...

import numpy as np from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt # 加载 iris 数据 iris = load_iris() # 只选取两个特征和两个类别进行二分类 X = iris.data[(iris.target==0)|(iris.target==1), :2] y = iris.target[(iris.target==0)|(iris.target==1)] # 将标签转化为 0 和 1 y[y==0] = -1 # 将数据集分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 实现逻辑回归算法 class LogisticRegression: def init(self, lr=0.01, num_iter=100000, fit_intercept=True, verbose=False): self.lr = lr self.num_iter = num_iter self.fit_intercept = fit_intercept self.verbose = verbose def __add_intercept(self, X): intercept = np.ones((X.shape[0], 1)) return np.concatenate((intercept, X), axis=1) def sigmoid(self, z): return 1 / (1 + np.exp(-z)) def loss(self, h, y): return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean() def fit(self, X, y): if self.fit_intercept: X = self.__add_intercept(X) # 初始化参数 self.theta = np.zeros(X.shape[1]) for i in range(self.num_iter): # 计算梯度 z = np.dot(X, self.theta) h = self.sigmoid(z) gradient = np.dot(X.T, (h - y)) / y.size # 更新参数 self.theta -= self.lr * gradient # 打印损失函数 if self.verbose and i % 10000 == 0: z = np.dot(X, self.theta) h = self.sigmoid(z) loss = self.__loss(h, y) print(f"Loss: {loss} \t") def predict_prob(self, X): if self.fit_intercept: X = self.__add_intercept(X) return self.__sigmoid(np.dot(X, self.theta)) def predict(self, X, threshold=0.5): return self.predict_prob(X) >= threshold # 训练模型 model = LogisticRegressio

y_pred = model.predict(X_test) # 计算准确率 accuracy = np.sum(y_pred == y_test) / y_test.shape[0] print(f"Accuracy: {accuracy}") # 可视化 plt.scatter(X_test[:, 0], X_test[:, 1], c=y_pred) plt....

import torch from IPython import display from d2l import torch as d2l import torch.utils.data.dataloader from matplotlib import pyplot as plt # import pylab batch_size=64 train_iter,test_iter=d2l.load_data_fashion_mnist(batch_size) #训练集和测试集的迭代器 num_inputs=784 num_outputs=10 w=torch.normal(0,0.1,size=(num_inputs,num_outputs),requires_grad=True)#定义权重，高斯初始化参数 b=torch.zeros(num_outputs,requires_grad=True)#偏移量，需计算梯度 # x=torch.tensor([[1.0,2.0,3.0],[4.0,5.0,6.0]]) # x.sum(0,keepdim=True) # x.sum(1,keepdim=True) # 给定一个对所有元素求和的矩阵X def softmax(x): x_exp=torch.exp(x)#对每个元素做指数计算 partition=x_exp.sum(1,keepdim=True)#按照维度为一把每一行求和 return x_exp/partition#应用广播机制，消除量纲 # softmax实现 def net(x): return softmax(torch.matmul(x.reshape((-1,w.shape[0])),w)+b)#-1是自动计算的意思，w。shape0是批量大小 #把wx+b放入spftmax里面 y=torch.tensor([0,2]) y_hat=torch.tensor([[0.1,0.3,0.6],[0.3,0.2,0.5]]) y_hat[[0,1],y] def cross_entropy(y_hat,y):#交叉熵损失函数 return -torch.log(y_hat[range(len(y_hat)),y]) cross_entropy(y_hat,y) def accuracy(y_hat,y):#正确率 if len(y_hat.shape)>1 and y_hat.shape[1]>1: y_hat=y_hat.argmax(axis=1) cmp=y_hat.type(y.dtype)==y return float(cmp.type(y.dtype).sum()) # accuracy(y_hat,y)/len(y) def evaluate_accuracy(net,data_iter): if isinstance(net,torch.nn.Module): net.eval() metric=Accumulator(2) for x,y in data_iter: metric.add(accuracy(net(x),y),y.numel()) return metric[0]/metric[1] class Accumulator: def init(self,n): self.data=[0.0]n def add(self,args): self.data=[a+float(b) for a,b in zip(self.data,args)] def reset(self): self.data = [0.0]len(self.data) def getitem(self,idx): return self.data[idx] def train_epoch_ch3(net,train_iter,loss,updater): if isinstance(net,torch.nn.Module): net.train() metric=Accumulator(3)#长度为3的迭代器累积信息 for x ,y in train_iter: Y_hat=net(x) l=loss(Y_hat,y) if isinstance(updater,torch.optim.Optimizer): updater.zero_grad() l.backward() updater.step() metric.add(float(l)len(y), accuracy(Y_hat,y), y.size().numel())#样本数、正确率等放进累加器 else: l.sum().backward() updater(x.shape[0]) metric.add(float(l.sum()),accuracy(Y_hat,y),y.size().numel()) return metric[0]/metric[2],metric[1]/metric[2] # 可视化 class Animator: def init(self,xlabel=None,ylabel=None,legend=None,xlim=None, ylim=None,xscale='linear',yscale='linear', fmts=('_','m--','g-.','r:'),nrows=1,ncols=1, figsize=(3.5,2.5)): if legend is None: legend=[] d2l.use_svg_display() self.fig,self.axes=d2l.plt.subplots(nrows,ncols,figsize=figsize) if nrows*ncols==1: self.axes=[self.axes,] self.config_axes=lambda:d2l.set_axes(self.axes[0],xlabel,ylabel,xlim,ylim,xscale,yscale,legend) self.x,self.y,self.fmts=None,None,fmts def add(self,x,y): if not hasattr(y,'len'): y=[y] n=len(y) # 训练函数 def train_ch3(net,train_iter,test_iter,loss,num_epochs,updater): animator=Animator(xlabel='epoch',xlim=[1,num_epochs],ylim=[0.3],legend=['train loss','train acc','test acc']) for epoch in range(num_epochs):#训练开始 train_metrics=train_epoch_ch3(net,train_iter,loss,updater) test_acc=evaluate_accuracy(net,test_iter) animator.add(epoch+1,train_metrics+(test_acc,)) train_loss,train_acc=train_metrics lr=0.1 def updater(batch_size): return d2l.sgd([w,b],lr,batch_size) num_epochs=10 train_ch3(net,train_iter,test_iter,cross_entropy,num_epochs,updater) d2l.plt.show()

train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size) # 获取训练/测试迭代器 - **作用**：导入PyTorch等必要库，加载Fashion MNIST数据集。 - **关键点**： - batch_size=64：每批加载64张图片...

def evaluate(config, model, data_iter, test=False): model.eval() loss_total = 0 predict_all = np.array([], dtype=int) labels_all = np.array([], dtype=int) with torch.no_grad(): for texts, labels in data_iter: outputs = model(texts) loss = F.cross_entropy(outputs, labels) loss_total += loss labels = labels.data.cpu().numpy() predic = torch.max(outputs.data, 1)[1].cpu().numpy() labels_all = np.append(labels_all, labels) predict_all = np.append(predict_all, predic) acc = metrics.accuracy_score(labels_all, predict_all) if test: report = metrics.classification_report(labels_all, predict_all, target_names=config.class_list, digits=4) confusion = metrics.confusion_matrix(labels_all, predict_all) return acc, loss_total / len(data_iter), report, confusion return acc, loss_total / len(data_iter)

这是一个用于模型评估的函数，输入参数包括配置文件config、模型model、数据迭代器data_iter以及一个布尔值test，表示是否进行测试。函数首先将模型设为评估模式（eval()），然后在数据迭代器上进行循环，对每个文本...

in_features = train_features.shape[1] def train(model, train_features, train_labels, test_features, test_labels, num_epochs, learning_rate, weight_decay, batch_size): train_ls, test_ls = [], [] theta = np.zeros((in_features, 1)) best_theta = np.zeros((in_features, 1)) best_loss = np.inf for epoch in range(num_epochs): train_iter = data_iter(batch_size, train_features, train_labels) for X, y in train_iter: theta=gradientDescent(X, y, theta, learning_rate, weight_decay) train_ls.append(log_rmse(model, train_features, train_labels, theta, len(train_labels)))帮我加个注释

def train(model, train_features, train_labels, test_features, test_labels, num_epochs, learning_rate, weight_decay, batch_size): # 初始化训练误差和测试误差列表 train_ls, test_ls = [], [] # ...

from sklearn.linear_model import LogisticRegression def digit_predict(train_image, train_label, test_image): ''' 实现功能：训练模型并输出预测结果 :param train_image: 训练样本图像，类型为ndarray,shape为[-1, 8, 8] :param train_label: 训练样本标签，类型为ndarray :param test_image: 测试样本图像，类型为ndarry :return: test_image对应的预测标签 ''' #******* Begin # # 1. 将图像数据展平为一维向量 train_X = train_image.reshape(train_image.shape[0], -1) test_X = test_image.reshape(test_image.shape[0], -1) # 2. 创建并训练逻辑回归模型 model = LogisticRegression(max_iter=1000, solver='liblinear') model.fit(train_X, train_label) # 3. 预测测试集 predictions = model.predict(test_X) return predictions #* End ********#

x_train, x_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.3, random_state=42) # 数据标准化 scaler = StandardScaler() x_train = scaler.fit_transform(x_train) x_test =...

import os import matplotlib.pyplot as plt import numpy as np from torchvision import transforms, models, datasets import json import torch from torchvision import models # 定义与训练时一致的模型架构 model = models.resnet18() # 如果使用其他预训练模型，请替换此处 [^3] # 将模型设置为评估模式 model.eval() # 加载保存的模型参数 state_dict = torch.load('best.pt') # 假设 best.pt 是模型权重文件路径 model.load_state_dict(state_dict) # 加载权重到模型中 batch_size = 8 # 定义批次大小 # 数据读取与预处理操作 data_dir = './flower_data/' # 数据集目录 valid_dir = data_dir + '/valid' # 验证集目录 # 加载数据集并进行预处理 # 使用 ImageFolder 从指定目录加载数据 image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['valid']} # 创建数据加载器以便批量处理数据 dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True) for x in ['valid']} dataset_sizes = {x: len(image_datasets[x]) for x in ['valid']} # 记录数据集的大小 #class_names = image_datasets['train'].classes # 获取类别名称 # 读取标签的实际名字 with open('cat_to_name.json', 'r') as f: cat_to_name = json.load(f) # 从 JSON 文件中加载类别名称 ''' 验证模式 ''' # 从验证集获取一个批次的数据 dataiter = iter(dataloaders['valid']) # 创建数据迭代器以便获取数据 images, labels = next(dataiter) # 获取一个批次的图像和标签 #model.eval() # 将模型设置为评估模式，停用 dropout 和 batch normalization 上述代码存在Traceback (most recent call last): File "D:\PycharmProjects\FinancialDataAnalysis\花卉识别\预测.py", line 28, in <module> model.load_state_dict(state_dict) # 加载权重到模型中 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\PycharmProjects\FinancialDataAnalysis\venv\Lib\site-packages\torch\nn\modules\module.py", line 2189, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for ResNet: 问题，怎么解决

model.load_state_dict(filtered_checkpoint, strict=False) 通过这种方式，即使存在一些未完全匹配的层，也不会中断整个过程[^1]。 #### 方法三：初始化缺失参数并保留已有权重对于新增加或者改变后的层，...

return data, label def len(self): return len(self.data)train_dataset = MyDataset(train, y[:split_boundary].values, time_steps, output_steps, target_index)test_ds = MyDataset(test, y[split_boundary:].values, time_steps, output_steps, target_index)class MyLSTMModel(nn.Module): def init(self): super(MyLSTMModel, self).init() self.rnn = nn.LSTM(input_dim, 16, 1, batch_first=True) self.flatten = nn.Flatten() self.fc1 = nn.Linear(16 * time_steps, 120) self.relu = nn.PReLU() self.fc2 = nn.Linear(120, output_steps) def forward(self, input): out, (h, c) = self.rnn(input) out = self.flatten(out) out = self.fc1(out) out = self.relu(out) out = self.fc2(out) return outepoch_num = 50batch_size = 128learning_rate = 0.001def train(): print('训练开始') model = MyLSTMModel() model.train() opt = optim.Adam(model.parameters(), lr=learning_rate) mse_loss = nn.MSELoss() data_reader = DataLoader(train_dataset, batch_size=batch_size, drop_last=True) history_loss = [] iter_epoch = [] for epoch in range(epoch_num): for data, label in data_reader: # 验证数据和标签的形状是否满足期望，如果不满足，则跳过这个批次 if data.shape[0] != batch_size or label.shape[0] != batch_size: continue train_ds = data.float() train_lb = label.float() out = model(train_ds) avg_loss = mse_loss(out, train_lb) avg_loss.backward() opt.step() opt.zero_grad() print('epoch {}, loss {}'.format(epoch, avg_loss.item())) iter_epoch.append(epoch) history_loss.append(avg_loss.item()) plt.plot(iter_epoch, history_loss, label='loss') plt.legend() plt.xlabel('iters') plt.ylabel('Loss') plt.show() torch.save(model.state_dict(), 'model_1')train()param_dict = torch.load('model_1')model = MyLSTMModel()model.load_state_dict(param_dict)model.eval()data_reader1 = DataLoader(test_ds, batch_size=batch_size, drop_last=True)res = []res1 = []# 在模型预测时，label 的处理for data, label in data_reader1: data = data.float() label = label.float() out = model(data) res.extend(out.detach().numpy().reshape(data.shape[0]).tolist()) res1.extend(label.numpy().tolist()) # 由于预测一步，所以无需 reshape，直接转为 list 即可title = "t321"plt.title(title, fontsize=24)plt.xlabel("time", fontsize=14)plt.ylabel("irr", fontsize=14)plt.plot(res, color='g', label='predict')plt.plot(res1, color='red', label='real')plt.legend()plt.grid()plt.show()的运算过程

这段代码是用 PyTorch 实现的一个 LSTM 模型，用于时间序列预测。首先定义了一个自定义的数据集类 MyDataset，用于加载训练数据和测试数据。然后定义了一个包含 LSTM 层的 MyLSTMModel 模型，并在 train 函数中使用 ...

model = TextLSTM.Model(config).to(config.device) init_network(model) print(model.parameters) train(config, model, train_iter, dev_iter, test_iter)

这段代码用于构建一个基于 LSTM 的文本分类模型，并在指定的设备上进行训练。...然后，调用 train 函数进行模型训练，其中 train_iter、dev_iter 和 test_iter 分别是训练集、开发集和测试集的迭代器。

def train(train_features, test_features, train_labels, test_labels, num_epochs=400): loss = nn.MSELoss(reduction='none') input_shape = train_features.shape[-1] # 不设置偏置，因为我们已经在多项式中实现了它 net = nn.Sequential(nn.Linear(input_shape, 1, bias=False)) batch_size = min(10, train_labels.shape[0]) train_iter = d2l.load_array((train_features, train_labels.reshape(-1,1)), batch_size) test_iter = d2l.load_array((test_features, test_labels.reshape(-1,1)), batch_size, is_train=False) trainer = torch.optim.SGD(net.parameters(), lr=0.01) animator = d2l.Animator(xlabel='epoch', ylabel='loss', yscale='log', xlim=[1, num_epochs], ylim=[1e-3, 1e2], legend=['train', 'test']) for epoch in range(num_epochs): d2l.train_epoch_ch3(net, train_iter, loss, trainer) if epoch == 0 or (epoch + 1) % 20 == 0: animator.add(epoch + 1, (evaluate_loss(net, train_iter, loss), evaluate_loss(net, test_iter, loss))) print('weight:', net[0].weight.data.numpy())

- test_features: 测试数据的特征，类型为numpy.ndarray - train_labels: 训练数据的标签，类型为numpy.ndarray - test_labels: 测试数据的标签，类型为numpy.ndarray - num_epochs: 训练轮数，默认为400 该函数的...

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def train(config, model, train_iter, dev_iter, test_iter):

model = TextLSTM.Model(config).to(config.device) init_network(model) print(model.parameters) train(config, model, train_iter, dev_iter, test_iter)

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