# -*- coding: utf-8 -*- """ Created on Sat Mar 28 22:10:20 2020 @author: Eastmount CSDN """ from jieba import lcut from numpy import zeros import matplotlib.pyplot as plt from gensim.models import Word2Vec from sklearn.model_selection import train_test_split from tensorflow.python.keras.preprocessing.sequence import pad_sequences from tensorflow.python.keras.models import Sequential from tensorflow.python.keras.layers import Dense, GRU, Bidirectional from tensorflow.python.keras.callbacks import EarlyStopping#-----------------------------------定义参数---------------------------------- max_features = 20#词向量维度 units = 30#RNN神经元数量 maxlen = 40#序列最大长度 epochs = 9#训练最大轮数 batch_size = 12#每批数据量大小 verbose = 1#训练过程展示 patience = 1#没有进步的训练轮数callbacks = [EarlyStopping(val_acc, patience=patience)]#--------------------------------载入数据及预处理------------------------------- data = https://www.songbingjia.com/android/[[0, 小米粥是以小米作为主要食材熬制而成的粥，口味清淡，清香味，具有简单易制，健胃消食的特点],[0, 煮粥时一定要先烧开水然后放入洗净后的小米],[0, 蛋白质及氨基酸、脂肪、维生素、矿物质],[0, 小米是传统健康食品，可单独焖饭和熬粥],[0, 苹果，是水果中的一种],[0, 粥的营养价值很高，富含矿物质和维生素，含钙量丰富，有助于代谢掉体内多余盐分],[0, 鸡蛋有很高的营养价值，是优质蛋白质、B族维生素的良好来源，还能提供脂肪、维生素和矿物质],[0, 这家超市的苹果都非常新鲜],[0, 在北方小米是主要食物之一，很多地区有晚餐吃小米粥的习俗],[0, 小米营养价值高，营养全面均衡 ，主要含有碳水化合物],[0, 蛋白质及氨基酸、脂肪、维生素、盐分],[1, 小米、三星、华为，作为安卓三大手机旗舰],[1, 别再管小米华为了！魅族手机再曝光：这次真的完美了],[1, 苹果手机或将重陷2016年困境，但这次它无法再大幅提价了],[1, 三星想要继续压制华为，仅凭A70还不够],[1, 三星手机屏占比将再创新高，超华为及苹果旗舰],[1, 华为P30、三星A70爆卖，斩获苏宁最佳手机营销奖],[1, 雷军，用一张图告诉你：小米和三星的差距在哪里],[1, 小米米聊APP官方Linux版上线，适配深度系统],[1, 三星刚刚更新了自家的可穿戴设备APP],[1, 华为、小米跨界并不可怕，可怕的打不破内心的“天花板”], ]#中文分析 X, Y = [lcut(i[1]) for i in data], [i[0] for i in data]#划分训练集和预测集 X_train, X_test, y_train, y_test = train_test_split(X, Y) #print(X_train) print(len(X_train), len(X_test)) print(len(y_train), len(y_test))"""[三星, 刚刚, 更新, 了, 自家, 的, 可, 穿戴, 设备, APP]"""#--------------------------------Word2Vec词向量------------------------------- word2vec = Word2Vec(X_train, size=max_features, min_count=1) #最大特征 最低过滤频次1 print(word2vec)#映射特征词 w2i = {w:i for i, w in enumerate(word2vec.wv.index2word)} print("【显示词语】") print(word2vec.wv.index2word) print(w2i) """[小米, 三星, 是, 维生素, 蛋白质, 及, APP, 氨基酸,...""" """{，: 0, 的: 1, 小米: 2, 、: 3, 华为: 4, ....}"""#词向量计算 vectors = word2vec.wv.vectors print("【词向量矩阵】") print(vectors.shape) print(vectors)#自定义函数-获取词向量 def w2v(w):i = w2i.get(w)return vectors[i] if i else zeros(max_features)#自定义函数-序列预处理 def pad(ls_of_words):a = [[w2v(i) for i in x] for x in ls_of_words]a = pad_sequences(a, maxlen, dtype=float)return a#序列化处理 转换为词向量 X_train, X_test = pad(X_train), pad(X_test)#--------------------------------建模与训练------------------------------- model = Sequential()#双向RNN model.add(Bidirectional(GRU(units), input_shape=(maxlen, max_features)))#输出层 2分类 model.add(Dense(units=1, activation=sigmoid))#模型可视化 model.summary()#激活神经网络model.compile(optimizer = rmsprop,#RMSprop优化器loss = binary_crossentropy,#二元交叉熵损失metrics = [acc]#计算误差或准确率)#训练 history = model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs,verbose=verbose, validation_data=https://www.songbingjia.com/android/(X_test, y_test))#----------------------------------预测与可视化------------------------------ #预测 score = model.evaluate(X_test, y_test, batch_size=batch_size) print(test loss:, score[0]) print(test accuracy:, score[1])#可视化 acc = history.history[acc] val_acc = history.history[val_acc]# 设置类标 plt.xlabel("Iterations") plt.ylabel("Accuracy")#绘图 plt.plot(range(epochs), acc, "bo-", linewidth=2, markersize=12, label="accuracy") plt.plot(range(epochs), val_acc, "gs-", linewidth=2, markersize=12, label="val_accuracy") plt.legend(loc="upper left") plt.title("RNN-Word2vec") plt.show()

• X_train = X_train.reshape(len(y_train), maxlen*max_features)
• X_test = X_test.reshape(len(y_test), maxlen*max_features)

# -*- coding: utf-8 -*- """ Created on Sat Mar 28 22:10:20 2020 @author: Eastmount CSDN """ from jieba import lcut from numpy import zeros import matplotlib.pyplot as plt from gensim.models import Word2Vec from sklearn.model_selection import train_test_split from tensorflow.python.keras.preprocessing.sequence import pad_sequences from tensorflow.python.keras.models import Sequential from tensorflow.python.keras.layers import Dense, LSTM, GRU, Embedding from tensorflow.python.keras.callbacks import EarlyStopping#-----------------------------------定义参数---------------------------------- max_features = 20#词向量维度 units = 30#RNN神经元数量 maxlen = 40#序列最大长度 epochs = 9#训练最大轮数 batch_size = 12#每批数据量大小 verbose = 1#训练过程展示 patience = 1#没有进步的训练轮数callbacks = [EarlyStopping(val_acc, patience=patience)]#--------------------------------载入数据及预处理------------------------------- data = https://www.songbingjia.com/android/[[0, 小米粥是以小米作为主要食材熬制而成的粥，口味清淡，清香味，具有简单易制，健胃消食的特点],[0, 煮粥时一定要先烧开水然后放入洗净后的小米],[0, 蛋白质及氨基酸、脂肪、维生素、矿物质],[0, 小米是传统健康食品，可单独焖饭和熬粥],[0, 苹果，是水果中的一种],[0, 粥的营养价值很高，富含矿物质和维生素，含钙量丰富，有助于代谢掉体内多余盐分],[0, 鸡蛋有很高的营养价值，是优质蛋白质、B族维生素的良好来源，还能提供脂肪、维生素和矿物质],[0, 这家超市的苹果都非常新鲜],[0, 在北方小米是主要食物之一，很多地区有晚餐吃小米粥的习俗],[0, 小米营养价值高，营养全面均衡 ，主要含有碳水化合物],[0, 蛋白质及氨基酸、脂肪、维生素、盐分],[1, 小米、三星、华为，作为安卓三大手机旗舰],[1, 别再管小米华为了！魅族手机再曝光：这次真的完美了],[1, 苹果手机或将重陷2016年困境，但这次它无法再大幅提价了],[1, 三星想要继续压制华为，仅凭A70还不够],[1, 三星手机屏占比将再创新高，超华为及苹果旗舰],[1, 华为P30、三星A70爆卖，斩获苏宁最佳手机营销奖],[1, 雷军，用一张图告诉你：小米和三星的差距在哪里],[1, 小米米聊APP官方Linux版上线，适配深度系统],[1, 三星刚刚更新了自家的可穿戴设备APP],[1, 华为、小米跨界并不可怕，可怕的打不破内心的“天花板”], ]#中文分析 X, Y = [lcut(i[1]) for i in data], [i[0] for i in data]#划分训练集和预测集 X_train, X_test, y_train, y_test = train_test_split(X, Y) #print(X_train) print(len(X_train), len(X_test)) print(len(y_train), len(y_test))"""[三星, 刚刚, 更新, 了, 自家, 的, 可, 穿戴, 设备, APP]"""#--------------------------------Word2Vec词向量------------------------------- word2vec = Word2Vec(X_train, size=max_features, min_count=1) #最大特征 最低过滤频次1 print(word2vec)#映射特征词 w2i = {w:i for i, w in enumerate(word2vec.wv.index2word)} print("【显示词语】") print(word2vec.wv.index2word) print(w2i) """[小米, 三星, 是, 维生素, 蛋白质, 及, APP, 氨基酸,...""" """{，: 0, 的: 1, 小米: 2, 、: 3, 华为: 4, ....}"""#词向量计算 vectors = word2vec.wv.vectors print("【词向量矩阵】") print(vectors.shape) print(vectors)#自定义函数-获取词向量 def w2v(w):i = w2i.get(w)return vectors[i] if i else zeros(max_features)#自定义函数-序列预处理 def pad(ls_of_words):a = [[w2v(i) for i in x] for x in ls_of_words]a = pad_sequences(a, maxlen, dtype=float)return a#序列化处理 转换为词向量 X_train, X_test = pad(X_train), pad(X_test) print(X_train.shape) print(X_test.shape) """(15, 40, 20) 15个样本 40个特征 每个特征用20词向量表示"""#拉直形状 (15, 40, 20)=> (15, 40*20) (6, 40, 20)=> (6, 40*20) X_train = X_train.reshape(len(y_train), maxlen*max_features) X_test = X_test.reshape(len(y_test), maxlen*max_features)#--------------------------------建模与训练------------------------------- model = Sequential()#构建Embedding层 128代表Embedding层的向量维度 model.add(Embedding(max_features, 128))#构建LSTM层 model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))#构建全连接层 #注意上面构建LSTM层时只会得到最后一个节点的输出,如果需要输出每个时间点的结果需将return_sequences=True model.add(Dense(units=1, activation=sigmoid))#模型可视化 model.summary()#激活神经网络model.compile(optimizer = rmsprop,#RMSprop优化器loss = binary_crossentropy,#二元交叉熵损失metrics = [acc]#计算误差或准确率)#训练 history = model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs,verbose=verbose, validation_data=https://www.songbingjia.com/android/(X_test, y_test))#----------------------------------预测与可视化------------------------------ #预测 score = model.evaluate(X_test, y_test, batch_size=batch_size) print(test loss:, score[0]) print(test accuracy:, score[1])#可视化 acc = history.history[acc] val_acc = history.history[val_acc]# 设置类标 plt.xlabel("Iterations") plt.ylabel("Accuracy")#绘图 plt.plot(range(epochs), acc, "bo-", linewidth=2, markersize=12, label="accuracy") plt.plot(range(epochs), val_acc, "gs-", linewidth=2, markersize=12, label="val_accuracy") plt.legend(loc="upper left") plt.title("LSTM-Word2vec") plt.show()

• test loss: 0.712007462978363
• test accuracy: 0.33333334

Model: "sequential_22" _________________________________________________________________ Layer (type)Output ShapeParam #================================================================= embedding_8 (Embedding)(None, None, 128)2560_________________________________________________________________ lstm_8 (LSTM)(None, 128)131584_________________________________________________________________ dense_21 (Dense)(None, 1)129================================================================= Total params: 134,273 Trainable params: 134,273 Non-trainable params: 0 _________________________________________________________________ Train on 15 samples, validate on 6 samples Epoch 1/9 15/15 [==============================] - 8s 552ms/sample - loss: 0.6971 - acc: 0.5333 - val_loss: 0.6911 - val_acc: 0.6667 Epoch 2/9 15/15 [==============================] - 5s 304ms/sample - loss: 0.6910 - acc: 0.7333 - val_loss: 0.7111 - val_acc: 0.3333 Epoch 3/9 15/15 [==============================] - 3s 208ms/sample - loss: 0.7014 - acc: 0.4667 - val_loss: 0.7392 - val_acc: 0.3333 Epoch 4/9 15/15 [==============================] - 4s 261ms/sample - loss: 0.6890 - acc: 0.5333 - val_loss: 0.7471 - val_acc: 0.3333 Epoch 5/9 15/15 [==============================] - 4s 248ms/sample - loss: 0.6912 - acc: 0.5333 - val_loss: 0.7221 - val_acc: 0.3333 Epoch 6/9 15/15 [==============================] - 3s 210ms/sample - loss: 0.6857 - acc: 0.5333 - val_loss: 0.7143 - val_acc: 0.3333 Epoch 7/9 15/15 [==============================] - 3s 187ms/sample - loss: 0.6906 - acc: 0.5333 - val_loss: 0.7346 - val_acc: 0.3333 Epoch 8/9 15/15 [==============================] - 3s 185ms/sample - loss: 0.7066 - acc: 0.5333 - val_loss: 0.7578 - val_acc: 0.3333 Epoch 9/9 15/15 [==============================] - 4s 235ms/sample - loss: 0.7197 - acc: 0.5333 - val_loss: 0.7120 - val_acc: 0.3333 6/6 [==============================] - 0s 43ms/sample - loss: 0.7120 - acc: 0.3333 test loss: 0.712007462978363 test accuracy: 0.33333334