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使用这篇文章的数据()进行学习。

1. 谷歌Colab设置

• 新建笔记本

  

• 设置

  

• 选择 GPU/TPU 加速计算

  

• 测试 GPU 是否分配

import tensorflow as tftf.test.gpu_device_name()

输出：

/device:GPU:0

• 上传数据至谷歌云硬盘，并在Colab中加载
  
  
  
• 解压数据
  

2. 编写代码

import numpy as npimport pandas as pddata = pd.read_csv("yelp_labelled.txt", sep='\t', names=['sentence', 'label'])data.head() # 1000条数据# 数据 X 和 标签 ysentence = data['sentence'].valueslabel = data['label'].values# 训练集 测试集拆分from sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(sentence, label, test_size=0.2, random_state=1)#%%max_features = 2000# 文本向量化from keras.preprocessing.text import Tokenizertokenizer = Tokenizer(num_words=max_features)tokenizer.fit_on_texts(X_train) # 训练tokenizerX_train = tokenizer.texts_to_sequences(X_train) # 转成 [[ids...],[ids...],...]X_test = tokenizer.texts_to_sequences(X_test)vocab_size = len(tokenizer.word_index)+1 # +1 是因为index 0, 0 不对应任何词，用来padmaxlen = 50# pad 保证每个句子的长度相等from keras.preprocessing.sequence import pad_sequencesX_train = pad_sequences(X_train, maxlen=maxlen, padding='post')# post 尾部补0，pre 前部补0X_test = pad_sequences(X_test, maxlen=maxlen, padding='post')#%%embed_dim = 256hidden_units = 64from keras.models import Model, Sequentialfrom keras.layers import Dense, LSTM, Embedding, Bidirectional, Dropoutmodel = Sequential()model.add(Embedding(input_dim=max_features,output_dim=embed_dim,                    input_length=maxlen))model.add(Bidirectional(LSTM(hidden_units)))model.add(Dropout(0.3))model.add(Dense(1, activation='sigmoid')) # 二分类sigmoid, 多分类 softmaxmodel.compile(optimizer='adam', loss='binary_crossentropy',metrics=['accuracy'])model.summary()from keras.utils import plot_modelplot_model(model, show_shapes=True, to_file='model.jpg') # 绘制模型结构到文件#%%history = model.fit(X_train,y_train,batch_size=64,             epochs=100,verbose=2,validation_split=0.1)# verbose 是否显示日志信息，0不显示，1显示进度条，2不显示进度条loss, accuracy = model.evaluate(X_train, y_train, verbose=1)print("训练集：loss {0:.3f}, 准确率：{1:.3f}".format(loss, accuracy))loss, accuracy = model.evaluate(X_test, y_test, verbose=1)print("测试集：loss {0:.3f}, 准确率：{1:.3f}".format(loss, accuracy))# 绘制训练曲线from matplotlib import pyplot as pltimport pandas as pdhis = pd.DataFrame(history.history)loss = history.history['loss']val_loss = history.history['val_loss']acc = history.history['accuracy']val_acc = history.history['val_accuracy']plt.plot(loss, label='train Loss')plt.plot(val_loss, label='valid Loss')plt.title('Training and Validation Loss')plt.legend()plt.grid()plt.show()plt.plot(acc, label='train Acc')plt.plot(val_acc, label='valid Acc')plt.title('Training and Validation Acc')plt.legend()plt.grid()plt.show()#%%model.save('trained_model.h5')import picklewith open('trained_tokenizer.pkl','wb') as f:    pickle.dump(tokenizer, f)# 下载到本地from google.colab import filesfiles.download('trained_model.h5')files.download('trained_tokenizer.pkl')

3. flask 微服务

• 以下内容不懂，抄一遍

编写 app.py

# Flaskimport pickleimport numpy as npfrom keras.preprocessing.sequence import pad_sequencesfrom keras.models import load_modeldef load_var():    global model, tokenizer    model = load_model('trained_model.h5')    model.make_predict_function()    with open('trained_tokenizer.pkl','rb') as f:        tokenizer = pickle.load(f)maxlen = 50def process_txt(text):    x = tokenizer.texts_to_sequences(text)    x = pad_sequences(x, maxlen=maxlen, padding='post')    return x#%%from flask import Flask, request, jsonifyapp = Flask(__name__)@app.route('/')def home_routine():    return "hello NLP!"#%%@app.route("/prediction",methods=['POST'])def get_prediction():    if request.method == 'POST':        data = request.get_json()    x = process_txt(data)    prob = model.predict(x)    pred = np.argmax(prob, axis=-1)    return str(pred)#%%if __name__ == "__main__":    load_var()    app.run(debug=True)    # 上线阶段应该为 app.run(host=0.0.0.0, port=80)

• 运行 python app.py
• windows cmd 输入：
  Invoke-WebRequest -Uri 127.0.0.1:5000/prediction -ContentType 'application/json' -Body '["The book was very poor", "Very nice", "bad, oh no", "i love you"]' -Method 'POST'

返回预测结果：

4. 打包到容器

• 后序需要用 Docker 将 应用程序包装到容器中

5. 容器托管

• 容器托管到网络服务，如 AWS EC2 实例

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