LSTM
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import os,sys
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os.chdir(sys.path[0])
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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from datasets import EnNews
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import tensorflow as tf
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from keras.backend.tensorflow_backend import set_session
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import numpy as np
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import random as rn
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np.random.seed(421)
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rn.seed(12345)
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import logging
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from keras import regularizers
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from keras.layers import Bidirectional, Dense, Dropout, Embedding, LSTM, TimeDistributed
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from keras.models import Sequential, load_model
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from datasets import *
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from eval import keras_metrics, metrics
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from nlp import tokenizer as tk
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from utils import info, preprocessing, postprocessing, plots
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# 记录配置
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logging.basicConfig(
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format='%(asctime)s\t%(levelname)s\t%(message)s',
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level=logging.DEBUG)
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info.log_versions()
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# 全局变量
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SAVE_MODEL = False
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MODEL_PATH = "models/bilstm.h5"
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SHOW_PLOTS = True
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# 数据集和超参数
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Dataset = EnNews
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rootpath = "/home/zhangxj/WorkFile/本科毕业设计"
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tokenizer = tk.tokenizers.nltk
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DATASET_FOLDER = rootpath+"/EnergyNews"
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MAX_DOCUMENT_LENGTH = 400
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MAX_VOCABULARY_SIZE = 20000
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EMBEDDINGS_SIZE = 50
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batch_size = 32
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epochs = 20
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KP_WEIGHT = 10
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STEM_MODE = metrics.stemMode.both
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STEM_TEST = False
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# 加载数据集
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logging.info("Loading dataset...")
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data = Dataset(DATASET_FOLDER)
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train_doc_str, train_answer_str = data.load_train()
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test_doc_str, test_answer_str = data.load_test()
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val_doc_str, val_answer_str = data.load_validation()
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train_doc, train_answer = tk.tokenize_set(train_doc_str, train_answer_str, tokenizer)
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test_doc, test_answer = tk.tokenize_set(test_doc_str, test_answer_str, tokenizer)
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val_doc, val_answer = tk.tokenize_set(val_doc_str, val_answer_str, tokenizer)
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# 完整性检查
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logging.info("Dataset loaded. Preprocessing data...")
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train_x, train_y, test_x, test_y, val_x, val_y, embedding_matrix = preprocessing. \
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prepare_sequential(train_doc, train_answer, test_doc, test_answer, val_doc, val_answer,
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max_document_length=MAX_DOCUMENT_LENGTH,
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max_vocabulary_size=MAX_VOCABULARY_SIZE,
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embeddings_size=EMBEDDINGS_SIZE,
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stem_test=STEM_TEST)
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# 权重训练示例：所有不是 kp的内容
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from sklearn.utils import class_weight
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train_y_weights = np.argmax(train_y, axis=2)
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train_y_weights = np.reshape(class_weight.compute_sample_weight('balanced', train_y_weights.flatten()),
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np.shape(train_y_weights))
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logging.info("数据预处理完成")
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logging.info("可能的最大召回率: %s",
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metrics.recall(test_answer,
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postprocessing.get_words(test_doc, postprocessing.undo_sequential(test_y)),
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STEM_MODE))
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if not SAVE_MODEL or not os.path.isfile(MODEL_PATH):
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logging.debug("建立网络...")
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model = Sequential()
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print("-------",np.shape(embedding_matrix)[0])
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embedding_layer = Embedding(np.shape(embedding_matrix)[0],
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EMBEDDINGS_SIZE,
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weights=[embedding_matrix],
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input_length=MAX_DOCUMENT_LENGTH,
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trainable=False)
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model.add(embedding_layer)
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model.add(LSTM(300, activation='tanh', recurrent_activation='hard_sigmoid', return_sequences=True))
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model.add(Dropout(0.25))
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model.add(TimeDistributed(Dense(150, activation='relu', kernel_regularizer=regularizers.l2(0.01))))
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model.add(Dropout(0.25))
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model.add(TimeDistributed(Dense(2, activation='softmax')))
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logging.info("编译网络...")
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model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'],
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sample_weight_mode="temporal")
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print(model.summary())
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metrics_callback = keras_metrics.MetricsCallback(val_x, val_y)
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logging.info("拟合网络...")
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history = model.fit(train_x, train_y,
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validation_data=(val_x, val_y),
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epochs=epochs,
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batch_size=batch_size,
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sample_weight=train_y_weights,
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callbacks=[metrics_callback])
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if SHOW_PLOTS:
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plots.plot_accuracy(history)
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plots.plot_loss(history)
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plots.plot_prf(metrics_callback)
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if SAVE_MODEL:
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model.save(MODEL_PATH)
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logging.info("模型保存路径 in %s", MODEL_PATH)
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else:
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logging.info("加载模型 %s...", MODEL_PATH)
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model = load_model(MODEL_PATH)
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logging.info("加载模型完成")
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logging.info("在测试集上预测...")
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output = model.predict(x=test_x, verbose=1)
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logging.debug("输出格式: %s", np.shape(output))
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obtained_tokens = postprocessing.undo_sequential(output)
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obtained_words = postprocessing.get_words(test_doc, obtained_tokens)
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precision = metrics.precision(test_answer, obtained_words,STEM_MODE)
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recall = metrics.recall(test_answer, obtained_words,STEM_MODE)
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f1 = metrics.f1(precision, recall)
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print("### 获得的分数 ###")
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print("###")
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print("### Precision : %.4f" % precision)
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print("### Recall : %.4f" % recall)
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print("### F1 : %.4f" % f1)
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print("### ###")
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keras_precision = keras_metrics.keras_precision(test_y, output)
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keras_recall = keras_metrics.keras_recall(test_y, output)
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keras_f1 = keras_metrics.keras_f1(test_y, output)
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print("### 获得的分数 ###")
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print("###")
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print("### Precision : %.4f" % keras_precision)
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print("### Recall : %.4f" % keras_recall)
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print("### F1 : %.4f" % keras_f1)
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print("### ###")
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