def Finalsummary(Num, JueLv, Keyword):
# characters = input("Please input characters：")
characters = Keyword
# # 根据关键词返回类别标签
label = word2vec_demo.Word2vec_similar.class_tags(characters)
print(label)
Imbalance_words = word2vec_demo.Word2vec_similar.similar_6words(
characters, label)
if '边塞征战' == label:
class_tag = 'biansai'
elif '写景咏物' == label:
class_tag = 'jingwu'
elif '山水田园' == label:
class_tag = 'shanshui'
elif '思乡羁旅' == label:
class_tag = 'sixiang'
else:
class_tag = 'poetrySong'
checkpointsPath = "E:\Desk\MyProjects\Python/NLP_Demo1\File_jar\generate_poem/" + class_tag # checkpoints location
trainPoems = "E:\Desk\MyProjects\Python/NLP_Demo1\File_jar\generate_poem\Poetry_class/" + class_tag + ".txt" # training file location
# 训练数据时用，依次更改诗的种类，路径
# trainPoems = "E:\Desk\MyProjects\Python/NLP_Demo1\File_jar\generate_poem\Poetry_class/yongshi.txt"
# checkpointsPath = "E:\Desk\MyProjects\Python/NLP_Demo1\File_jar\generate_poem/yongshi"
trainData = data.POEMS(trainPoems)
MCPangHu = model.MODEL(trainData) # 带参初始化
#***** 分别训练5类模型
# MCPangHu.train(checkpointsPath)
poems = MCPangHu.testHead(characters, Imbalance_words, checkpointsPath,
Num, JueLv)
return poems
def main(trainPoems, checkpointsPath):
global Str
args = defineArgs()
trainData = data.POEMS(trainPoems)
MCPangHu = model.MODEL(trainData)
if args.mode == "train":
MCPangHu.train()
else:
if args.mode == "test":
poems = MCPangHu.test(checkpointsPath)
Str = MCPangHu.Get_Str()
else:
characters = input("please input chinese character:")
poems = MCPangHu.testHead(characters)
print("no checkpoint found!")
for epoch in range(epochNum):
X, Y = self.trainData.generateBatch()
epochSteps = len(X) # equal to batch
for step, (x, y) in enumerate(zip(X, Y)):
a, loss, gStep = sess.run([trainOP, cost, addGlobalStep], feed_dict={gtX: x, gtY: y})
print("epoch: %d, steps: %d/%d, loss: %3f" % (epoch + 1, step + 1, epochSteps, loss))
if gStep % saveStep == saveStep - 1: # prevent save at the beginning
print("save model")
saver.save(sess, os.path.join(evaluateCheckpointsPath, type), global_step=gStep)
X, Y = self.trainData.generateBatch(isTrain=False)
print("evaluating testing error...")
wrongNum = 0
totalNum = 0
testBatchNum = len(X)
for step, (x, y) in enumerate(zip(X, Y)):
print("test batch %d/%d" % (step + 1, testBatchNum))
testProbs, testTargets = sess.run([probs, targets], feed_dict={gtX: x, gtY: y})
wrongNum += len(np.nonzero(np.argmax(testProbs, axis=1) - testTargets)[0])
totalNum += len(testTargets)
print("accuracy: %.2f" % ((totalNum - wrongNum) / totalNum))
if __name__ == "__main__":
trainData = data.POEMS(trainPoems, isEvaluate=True)
MCPangHu = EVALUATE_MODEL(trainData)
MCPangHu.evaluate()
# coding: UTF-8
from config import *
import data
import model
def defineArgs():
"""define args"""
parser = argparse.ArgumentParser(description="Chinese_poem_generator.")
parser.add_argument("-m",
"--mode",
help="select mode by 'train' or test or head",
choices=["train", "test", "head"],
default="test")
return parser.parse_args()
if __name__ == "__main__":
args = defineArgs()
trainData = data.POEMS(trainPoems)
MCPangHu = model.MODEL(trainData)
if args.mode == "train":
MCPangHu.train()
else:
if args.mode == "test":
poems = MCPangHu.test()
else:
characters = input("please input chinese character:")
poems = MCPangHu.testHead(characters)
github: https://github.com/hjptriplebee
''' '''''' '''''' '''''' '''''' '''''' '''''' '''''' '''''' ''
from config import *
import data
import model
def defineArgs():
"""define args"""
parser = argparse.ArgumentParser(description="Chinese_poem_generator.")
parser.add_argument("-m",
"--mode",
help="select mode by 'train' or test or head",
choices=["train", "test", "head", "header"],
default="test")
return parser.parse_args()
if __name__ == "__main__":
args = defineArgs()
trainData = data.POEMS(trainPoems, path_word2vec)
MCPangHu = model.MODEL(trainData)
if args.mode == "train":
MCPangHu.train()
else:
if args.mode == "test":
poems = MCPangHu.test()
elif args.mode == 'head':
poems = MCPangHu.testHead(characters)
elif args.mode == 'header':
poems = MCPangHu.testHeader(header)
