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run.py

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+#coding: UTF-8
+import time
+import mindspore
+import numpy as np
+from train_eval import train, init_network
+from importlib import import_module
+import mindspore.ops as ops 
+import argparse
+
+parser = argparse.ArgumentParser(description='Chinese Text Classification')
+parser.add_argument('--model', type=str, required=True, help='choose a model: TextCNN, TextRNN, FastText, TextRCNN, TextRNN_Att, DPCNN, Transformer')
+parser.add_argument('--embedding', default='pre_trained', type=str, help='random or pre_trained')
+parser.add_argument('--word', default=False, type=bool, help='True for word, False for char')
+args = parser.parse_args()
+
+if __name__ == '__main__':
+    dataset = 'THUCNews'  # 数据集
+
+    # 搜狗新闻:embedding_SougouNews.npz, 腾讯:embedding_Tencent.npz, 随机初始化:random
+    embedding = 'embedding_SougouNews.npz'
+    if args.embedding == 'random':
+        embedding = 'random'
+    model_name = args.model  # 'TextRCNN'  # TextCNN, TextRNN, FastText, TextRCNN, TextRNN_Att, DPCNN, Transformer
+    if model_name == 'FastText':
+        from utils_fasttext import build_dataset, build_iterator, get_time_dif
+        embedding = 'random'
+    else:
+        from utils import build_dataset, build_iterator, get_time_dif
+
+    x = import_module('models.' + model_name)
+    config = x.Config(dataset, embedding)
+    np.random.seed(1)
+    mindspore.set_seed(1)
+
+    start_time = time.time()
+    print("Loading data...")
+    vocab, train_data, dev_data, test_data = build_dataset(config, args.word)
+    train_iter = build_iterator(train_data, config)
+    dev_iter = build_iterator(dev_data, config)
+    test_iter = build_iterator(test_data, config)
+    time_dif = get_time_dif(start_time)
+    print("Time usage:", time_dif)
+
+    # train
+    config.n_vocab = len(vocab)
+    model = x.Model(config).to(config.device)
+    if model_name != 'Transformer':
+        init_network(model)
+    print(model.parameters)
+    train(config, model, train_iter, dev_iter, test_iter)

train_eval.py

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+#coding: UTF-8
+import numpy as np
+import mindspore
+import mindspore.nn as nn
+import mindspore.ops as ops
+from sklearn import metrics 
+import time
+from utils import get_time_dif
+from mindspore.common.initializer import initializer, XavierNormal, Normal, HeNormal, Constant
+from mindspore.train import Model, CheckpointConfig, ModelCheckpoint, LossMonitor
+
+
+
+# 权重初始化，默认xavier
+def init_network(model, method='xavier', exclude='embedding', seed=123):
+    for name, w in model.named_parameters():
+        if exclude not in name:
+            if 'weight' in name:
+                if method == 'xavier':
+                    XavierNormal(w)
+                elif method == 'kaiming':
+                    HeNormal(w)
+                else:
+                    Normal(w)
+            elif 'bias' in name:
+                Constant(w, 0)
+            else:
+                pass
+
+
+def train(config, model, train_iter, dev_iter, test_iter):
+    start_time = time.time()
+    model.train()
+    optimizer = nn.Adam(model.parameters(), lr=config.learning_rate)
+
+    # 学习率指数衰减，每次epoch：学习率 = gamma * 学习率
+    # scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
+    total_batch = 0  # 记录进行到多少batch
+    dev_best_loss = float('inf')
+    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):
+        print('Epoch [{}/{}]'.format(epoch + 1, config.num_epochs))
+        # scheduler.step() # 学习率衰减
+        for i, (trains, labels) in enumerate(train_iter):
+            outputs = model(trains)
+            model.zero_grad()
+            loss = ops.cross_entropy(outputs, labels)
+            grads = mindspore.grad(loss)
+            optimizer(grads)
+            if total_batch % 100 == 0:
+                # 每多少轮输出在训练集和验证集上的效果
+                true = labels.data.cpu()
+                predic = ops.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
+                    mindspore.save_checkpoint(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))
+                model.train()
+            total_batch += 1
+            if total_batch - last_improve > config.require_improvement:
+                # 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)
+
+
+def test(config, model, test_iter):
+    # test
+    param_dict = mindspore.load_checkpoint(config.save_path)
+    mindspore.load_param_into_net(model, param_dict)
+    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)
+
+
+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 ops.stop_gradient():
+        for texts, labels in data_iter:
+            outputs = model(texts)
+            loss = ops.cross_entropy(outputs, labels)
+            loss_total += loss
+            labels = labels.data.cpu().numpy()
+            predic = ops.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)

utils.py

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+#coding: UTF-8
+import os
+import mindspore
+import numpy as np
+import pickle as pkl
+from tqdm import tqdm
+import time
+from datetime import timedelta
+from mindspore import Tensor
+import mindspore.nn as nn
+
+
+
+MAX_VOCAB_SIZE = 10000  # 词表长度限制
+UNK, PAD = '<UNK>', '<PAD>'  # 未知字，padding符号
+
+
+def build_vocab(file_path, tokenizer, max_size, min_freq):
+    vocab_dic = {}
+    with open(file_path, 'r', encoding='UTF-8') as f:
+        for line in tqdm(f):
+            lin = line.strip()
+            if not lin:
+                continue
+            content = lin.split('\t')[0]
+            for word in tokenizer(content):
+                vocab_dic[word] = vocab_dic.get(word, 0) + 1
+        vocab_list = sorted([_ for _ in vocab_dic.items() if _[1] >= min_freq], key=lambda x: x[1], reverse=True)[:max_size]
+        vocab_dic = {word_count[0]: idx for idx, word_count in enumerate(vocab_list)}
+        vocab_dic.update({UNK: len(vocab_dic), PAD: len(vocab_dic) + 1})
+    return vocab_dic
+
+
+def build_dataset(config, ues_word):
+    if ues_word:
+        tokenizer = lambda x: x.split(' ')  # 以空格隔开，word-level
+    else:
+        tokenizer = lambda x: [y for y in x]  # char-level
+    if os.path.exists(config.vocab_path):
+        vocab = pkl.load(open(config.vocab_path, 'rb'))
+    else:
+        vocab = build_vocab(config.train_path, tokenizer=tokenizer, max_size=MAX_VOCAB_SIZE, min_freq=1)
+        pkl.dump(vocab, open(config.vocab_path, 'wb'))
+    print(f"Vocab size: {len(vocab)}")
+
+    def load_dataset(path, pad_size=32):
+        contents = []
+        with open(path, 'r', encoding='UTF-8') as f:
+            for line in tqdm(f):
+                lin = line.strip()
+                if not lin:
+                    continue
+                content, label = lin.split('\t')
+                words_line = []
+                token = tokenizer(content)
+                seq_len = len(token)
+                if pad_size:
+                    if len(token) < pad_size:
+                        token.extend([PAD] * (pad_size - len(token)))
+                    else:
+                        token = token[:pad_size]
+                        seq_len = pad_size
+                # word to id
+                for word in token:
+                    words_line.append(vocab.get(word, vocab.get(UNK)))
+                contents.append((words_line, int(label), seq_len))
+        return contents  # [([...], 0), ([...], 1), ...]
+    train = load_dataset(config.train_path, config.pad_size)
+    dev = load_dataset(config.dev_path, config.pad_size)
+    test = load_dataset(config.test_path, config.pad_size)
+    return vocab, train, dev, test
+
+class DatasetIterater(object):
+    def __init__(self, batches, batch_size, device):
+        self.batch_size = batch_size
+        self.batches = batches
+        self.n_batches = len(batches) // batch_size
+        self.residue = False  # 记录batch数量是否为整数
+        if len(batches) % self.n_batches != 0:
+            self.residue = True
+        self.index = 0
+        self.device = device
+
+    def _to_tensor(self, datas):
+        x = Tensor.long([_[0] for _ in datas]).to(self.device)
+        y = Tensor.long([_[1] for _ in datas]).to(self.device)
+
+        # pad前的长度(超过pad_size的设为pad_size)
+        seq_len = Tensor.long([_[2] for _ in datas]).to(self.device)
+        return (x, seq_len), y
+
+    def __next__(self):
+        if self.residue and self.index == self.n_batches:
+            batches = self.batches[self.index * self.batch_size: len(self.batches)]
+            self.index += 1
+            batches = self._to_tensor(batches)
+            return batches
+
+        elif self.index >= self.n_batches:
+            self.index = 0
+            raise StopIteration
+        else:
+            batches = self.batches[self.index * self.batch_size: (self.index + 1) * self.batch_size]
+            self.index += 1
+            batches = self._to_tensor(batches)
+            return batches
+
+    def __iter__(self):
+        return self
+
+    def __len__(self):
+        if self.residue:
+            return self.n_batches + 1
+        else:
+            return self.n_batches
+
+def build_iterator(dataset, config):
+    iter = DatasetIterater(dataset, config.batch_size, config.device)
+    return iter
+
+
+def get_time_dif(start_time):
+    """获取已使用时间"""
+    end_time = time.time()
+    time_dif = end_time - start_time
+    return timedelta(seconds=int(round(time_dif)))
+
+
+if __name__ == "__main__":
+    '''提取预训练词向量'''
+    # 下面的目录、文件名按需更改。
+    train_dir = "./THUCNews/data/train.txt"
+    vocab_dir = "./THUCNews/data/vocab.pkl"
+    pretrain_dir = "./THUCNews/data/sgns.sogou.char"
+    emb_dim = 300
+    filename_trimmed_dir = "./THUCNews/data/embedding_SougouNews"
+    if os.path.exists(vocab_dir):
+        word_to_id = pkl.load(open(vocab_dir, 'rb'))
+    else:
+        # tokenizer = lambda x: x.split(' ')  # 以词为单位构建词表(数据集中词之间以空格隔开)
+        tokenizer = lambda x: [y for y in x]  # 以字为单位构建词表
+        word_to_id = build_vocab(train_dir, tokenizer=tokenizer, max_size=MAX_VOCAB_SIZE, min_freq=1)
+        pkl.dump(word_to_id, open(vocab_dir, 'wb'))
+
+    embeddings = np.random.rand(len(word_to_id), emb_dim)
+    f = open(pretrain_dir, "r", encoding='UTF-8')
+    for i, line in enumerate(f.readlines()):
+        # if i == 0:  # 若第一行是标题，则跳过
+        #     continue
+        lin = line.strip().split(" ")
+        if lin[0] in word_to_id:
+            idx = word_to_id[lin[0]]
+            emb = [float(x) for x in lin[1:301]]
+            embeddings[idx] = np.asarray(emb, dtype='float32')
+    f.close()
+    np.savez_compressed(filename_trimmed_dir, embeddings=embeddings)