门户网站系统开发,交通局网站建设方案策划书,百度ai智能搜索引擎,网站 服务器 虚拟主机文章目录1. 谷歌Colab设置2. 编写代码3. flask 微服务4. 打包到容器5. 容器托管参考
基于深度学习的自然语言处理使用这篇文章的数据(情感分类)进行学习。
1. 谷歌Colab设置
Colab 地址 新建笔记本 设置 选择 GPU/TPU 加速计算 测试 GPU 是否分配
import tensorflow…
文章目录1. 谷歌Colab设置2. 编写代码3. flask 微服务4. 打包到容器5. 容器托管参考
基于深度学习的自然语言处理使用这篇文章的数据(情感分类)进行学习。
1. 谷歌Colab设置
Colab 地址 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 tf
tf.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 np
import 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 y
sentence data[sentence].values
label data[label].values# 训练集 测试集拆分
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test train_test_split(sentence, label, test_size0.2, random_state1)#%%max_features 2000# 文本向量化
from keras.preprocessing.text import Tokenizer
tokenizer Tokenizer(num_wordsmax_features)
tokenizer.fit_on_texts(X_train) # 训练tokenizer
X_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_sequences
X_train pad_sequences(X_train, maxlenmaxlen, paddingpost)
# post 尾部补0pre 前部补0
X_test pad_sequences(X_test, maxlenmaxlen, paddingpost)#%%embed_dim 256
hidden_units 64from keras.models import Model, Sequential
from keras.layers import Dense, LSTM, Embedding, Bidirectional, Dropout
model Sequential()
model.add(Embedding(input_dimmax_features,output_dimembed_dim,input_lengthmaxlen))
model.add(Bidirectional(LSTM(hidden_units)))
model.add(Dropout(0.3))
model.add(Dense(1, activationsigmoid)) # 二分类sigmoid, 多分类 softmaxmodel.compile(optimizeradam, lossbinary_crossentropy,metrics[accuracy])
model.summary()
from keras.utils import plot_model
plot_model(model, show_shapesTrue, to_filemodel.jpg) # 绘制模型结构到文件#%%history model.fit(X_train,y_train,batch_size64,epochs100,verbose2,validation_split0.1)
# 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, verbose1)
print(训练集loss {0:.3f}, 准确率{1:.3f}.format(loss, accuracy))
loss, accuracy model.evaluate(X_test, y_test, verbose1)
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 plt
import pandas as pd
his 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, labeltrain Loss)
plt.plot(val_loss, labelvalid Loss)
plt.title(Training and Validation Loss)
plt.legend()
plt.grid()
plt.show()plt.plot(acc, labeltrain Acc)
plt.plot(val_acc, labelvalid Acc)
plt.title(Training and Validation Acc)
plt.legend()
plt.grid()
plt.show()#%%model.save(trained_model.h5)import pickle
with open(trained_tokenizer.pkl,wb) as f:pickle.dump(tokenizer, f)# 下载到本地
from google.colab import files
files.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
# Flask
import pickle
import numpy as np
from keras.preprocessing.sequence import pad_sequences
from keras.models import load_model
def load_var():global model, tokenizermodel load_model(trained_model.h5)model.make_predict_function()with open(trained_tokenizer.pkl,rb) as f:tokenizer pickle.load(f)maxlen 50
def process_txt(text):x tokenizer.texts_to_sequences(text)x pad_sequences(x, maxlenmaxlen, paddingpost)return x#%%from flask import Flask, request, jsonify
app 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(debugTrue)# 上线阶段应该为 app.run(host0.0.0.0, port80)运行 python app.pywindows 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
