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- # -*- encoding:utf-8 -*-
- import numpy as np
- from keras.models import load_model
- def read_data(path):
- lines = []
- with open(path) as f:
- for line in f.readlines()[:]:
- 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),lines
- def predict(file_path='', model_path='15min_dnn_seq.h5'):
- test_x,test_y,lines=read_data(file_path)
- model=load_model(model_path)
- score = model.evaluate(test_x, test_y)
- print('DNN', score)
- up_num = 0
- up_right = 0
- i = 0
- result=model.predict(test_x)
- win_dnn = []
- with open('dnn_predict_5d.txt', 'a') as f:
- for r in result:
- fact = test_y[i]
- if r[0] > 0.5:
- f.write(str([lines[i][-2], lines[i][-1]]) + "\n")
- win_dnn.append([lines[i][-2], lines[i][-1]])
- if fact[0] == 1:
- up_right = up_right + 1.15
- elif fact[1] == 1:
- up_right = up_right + 1.05
- elif fact[2] == 1:
- up_right = up_right + 1
- else:
- up_right = up_right - 0.15
- up_num = up_num + 1
- elif r[1] > 0.5:
- f.write(str([lines[i][-2], lines[i][-1]]) + "\n")
- win_dnn.append([lines[i][-2], lines[i][-1]])
- if fact[0] == 1:
- up_right = up_right + 1.15
- elif fact[1] == 1:
- up_right = up_right + 1.05
- elif fact[2] == 1:
- up_right = up_right + 1
- else:
- up_right = up_right - 0.15
- up_num = up_num + 1
- i = i + 1
- print('DNN', up_right, up_num, up_right/up_num)
- return win_dnn
- if __name__ == '__main__':
- predict(file_path='D:\\data\\quantization\\stock6_5_test.log', model_path='5d_dnn_seq.h5')
- # predict(file_path='D:\\data\\quantization\\stock6_test.log', model_path='15m_dnn_seq.h5')
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