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Mindspore框架循环神经网络RNN模型实现情感分类|#xff08;一#xff09;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|#xff08;二#xff09;预训练词向量 Mindspore框架循环神经网络RNN模型实现…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
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_tablems.Tensor(embeddings),padding_idxpad_idx)self.rnn nn.LSTM(embedding_dim,hidden_dim,num_layersn_layers,bidirectionalbidirectional,batch_firstTrue)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_initweight_init, bias_initbias_init)def construct(self, inputs):embedded self.embedding(inputs)_, (hidden, _) self.rnn(embedded)hidden ops.concat((hidden[-2, :, :], hidden[-1, :, :]), axis1)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.zipvocab, 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, encodingutf-8) as gf:for glove in gf:word, embedding glove.split(maxsplit1)tokens.append(word)embeddings.append(np.fromstring(embedding, dtypenp.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_firstFalse)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。
