Mindspore框架循环神经网络RNN模型实现情感分类

Mindspore框架循环神经网络RNN模型实现情感分类|（一）IMDB影评数据集准备
Mindspore框架循环神经网络RNN模型实现情感分类|（二）预训练词向量
Mindspore框架循环神经网络RNN模型实现情感分类|（三）RNN模型构建
Mindspore框架循环神经网络RNN模型实现情感分类|（四）损失函数与优化器
Mindspore框架循环神经网络RNN模型实现情感分类|（五）模型训练
Mindspore框架循环神经网络RNN模型实现情感分类|（六）模型加载和推理（情感分类模型资源下载）
Mindspore框架循环神经网络RNN模型实现情感分类|（七）模型导出ONNX与应用部署

一、模型资源下载

1. RNN升级版LSTM模型：本项目训练好的情感分类模型-下载训练好的IMDB分类模型。

二、模型加载与推理

class RNN(nn.Cell):def __init__(self, embeddings, hidden_dim, output_dim, n_layers,bidirectional, pad_idx):super().__init__()vocab_size, embedding_dim = embeddings.shapeself.embedding = nn.Embedding(vocab_size, embedding_dim, embedding_table=ms.Tensor(embeddings),padding_idx=pad_idx)self.rnn = nn.LSTM(embedding_dim,hidden_dim,num_layers=n_layers,bidirectional=bidirectional,batch_first=True)weight_init = HeUniform(math.sqrt(5))bias_init = Uniform(1 / math.sqrt(hidden_dim * 2))self.fc = nn.Dense(hidden_dim * 2, output_dim, weight_init=weight_init, bias_init=bias_init)def construct(self, inputs):embedded = self.embedding(inputs)_, (hidden, _) = self.rnn(embedded)hidden = ops.concat((hidden[-2, :, :], hidden[-1, :, :]), axis=1)output = self.fc(hidden)return output

编写预测接口：test_interface

def predict_sentiment(model, vocab, sentence):score_map = {1: "Positive",0: "Negative"}model.set_train(False)tokenized = sentence.lower().split()indexed = vocab.tokens_to_ids(tokenized)tensor = ms.Tensor(indexed, ms.int32)tensor = tensor.expand_dims(0)prediction = model(tensor)return score_map[int(np.round(ops.sigmoid(prediction).asnumpy()))]def test_interface():# train()score_map = {1: "Positive",0: "Negative"}ckpt_file_name = './IMDB/IMDB/sentiment-analysis.ckpt'# 预训练词向量表glove_path = r"./IMDB/IMDB/glove.6B.zip"vocab, embeddings = load_glove(glove_path)  # 预定义词向量表hidden_size = 256output_size = 1num_layers = 2bidirectional = Truepad_idx = vocab.tokens_to_ids('<pad>')model = RNN(embeddings, hidden_size, output_size, num_layers, bidirectional, pad_idx)param_dict = ms.load_checkpoint(ckpt_file_name)ms.load_param_into_net(model, param_dict)# 预测while True:try:print("go on!")sentence = input("请输入：")res = predict_sentiment(model, vocab, sentence)print("用户输入的内容为：", sentence, "评价结果是：", res)except:breakdef load_glove(glove_path):glove_100d_path = os.path.join(cache_dir, 'glove.6B.100d.txt')  # 保存数据词典if not os.path.exists(glove_100d_path):glove_zip = zipfile.ZipFile(glove_path)glove_zip.extractall(cache_dir)embeddings = []tokens = []with open(glove_100d_path, encoding='utf-8') as gf:for glove in gf:word, embedding = glove.split(maxsplit=1)tokens.append(word)embeddings.append(np.fromstring(embedding, dtype=np.float32, sep=' '))# 添加 <unk>, <pad> 两个特殊占位符对应的embeddingembeddings.append(np.random.rand(100))embeddings.append(np.zeros((100,), np.float32))vocab = ds.text.Vocab.from_list(tokens, special_tokens=["<unk>", "<pad>"], special_first=False)embeddings = np.array(embeddings).astype(np.float32)return vocab, embeddings

预测推理：

import mindspore as ms
import mindspore.nn as nn
import mindspore.ops as ops
import os
import zipfile
import numpy as nptest_interface()

预测结果。