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- import keras
- # -*- encoding:utf-8 -*-
- import numpy as np
- from keras.models import Sequential
- from keras.layers import Dense,Dropout
- import random
- from keras.models import load_model
- def read_data(path):
- lines = []
- with open(path) as f:
- for line in f.readlines()[:1000]:
- lines.append(eval(line.strip()))
- size = len(lines[0])
- train_x=[s[:size - 2] for s in lines]
- train_y=[s[size-1] for s in lines]
- return np.array(train_x),np.array(train_y)
- test_x,test_y=read_data("D:\\data\\quantization\\stock_test.log")
- path="model_seq.h5"
- model=load_model(path)
- score = model.evaluate(test_x, test_y)
- print(score)
- result=model.predict(test_x)
- # print(result)
- i = 0
- for x in test_y:
- # print(str(i) + ":" + str(x))
- i = i + 1
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