add self_attention for yelp classification example.

reproduction/LSTM+self_attention_sentiment_analysis/config.cfg

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+[train]
+epochs = 30
+batch_size = 32
+pickle_path = "./save/"
+validate = true
+save_best_dev = true
+model_saved_path = "./save/"
+rnn_hidden_units = 300
+word_emb_dim = 300
+use_crf = true
+use_cuda = false
+loss_func = "cross_entropy"
+num_classes = 5

reproduction/LSTM+self_attention_sentiment_analysis/main.py

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+
+import os
+
+import torch.nn.functional as F
+
+from fastNLP.loader.dataset_loader import ClassDatasetLoader as Dataset_loader
+from fastNLP.loader.embed_loader import EmbedLoader as EmbedLoader
+from fastNLP.loader.config_loader import ConfigSection
+from fastNLP.loader.config_loader import ConfigLoader
+
+from fastNLP.models.base_model import BaseModel
+
+from fastNLP.core.preprocess import ClassPreprocess as Preprocess
+from fastNLP.core.trainer   import ClassificationTrainer
+
+from fastNLP.modules.encoder.embedding import Embedding as Embedding
+from fastNLP.modules.encoder.lstm import Lstm
+from fastNLP.modules.aggregation.self_attention import SelfAttention
+from fastNLP.modules.decoder.MLP import MLP
+
+
+train_data_path =  'small_train_data.txt'
+dev_data_path = 'small_dev_data.txt'
+# emb_path = 'glove.txt'
+
+lstm_hidden_size = 300
+embeding_size = 300
+attention_unit = 350
+attention_hops = 10
+class_num = 5
+nfc = 3000
+### data load  ###
+train_dataset = Dataset_loader(train_data_path)
+train_data = train_dataset.load()
+
+dev_args = Dataset_loader(dev_data_path)
+dev_data = dev_args.load()
+
+######  preprocess ####
+preprocess = Preprocess()
+word2index, label2index = preprocess.build_dict(train_data)
+train_data, dev_data = preprocess.run(train_data, dev_data)
+
+
+
+# emb = EmbedLoader(emb_path)
+# embedding = emb.load_embedding(emb_dim= embeding_size , emb_file= emb_path ,word_dict= word2index)
+### construct vocab ###
+
+class SELF_ATTENTION_YELP_CLASSIFICATION(BaseModel):
+    def __init__(self, args=None):
+        super(SELF_ATTENTION_YELP_CLASSIFICATION,self).__init__()
+        self.embedding = Embedding(len(word2index) ,embeding_size , init_emb= None )
+        self.lstm = Lstm(input_size = embeding_size,hidden_size = lstm_hidden_size ,bidirectional = True)
+        self.attention = SelfAttention(lstm_hidden_size * 2 ,dim =attention_unit ,num_vec=attention_hops)
+        self.mlp = MLP(size_layer=[lstm_hidden_size * 2*attention_hops ,nfc ,class_num ] ,num_class=class_num  ,)
+    def forward(self,x):
+        x_emb = self.embedding(x)
+        output = self.lstm(x_emb)
+        after_attention, penalty = self.attention(output,x)
+        after_attention =after_attention.view(after_attention.size(0),-1)
+        output = self.mlp(after_attention)
+        return output
+
+    def loss(self, predict, ground_truth):
+        print("predict:%s; g:%s" % (str(predict.size()), str(ground_truth.size())))
+        print(ground_truth)
+        return F.cross_entropy(predict, ground_truth)
+
+train_args = ConfigSection()
+ConfigLoader("good path").load_config('config.cfg',{"train": train_args})
+train_args['vocab'] = len(word2index)
+
+
+trainer = ClassificationTrainer(**train_args.data)
+
+# for k in train_args.__dict__.keys():
+#     print(k, train_args[k])
+model = SELF_ATTENTION_YELP_CLASSIFICATION(train_args)
+trainer.train(model,train_data , dev_data)