DL之GRU(Tensorflow框架)：基于茅台股票数据集利用GRU算法实现回归预测(保存模型.ckpt.index、.ckpt.data文件)
目录
基于茅台股票数据集利用GRU算法实现回归预测(保存模型.ckpt.index、.ckpt.data文件)
相关文章
DL之GRU(Tensorflow框架)：基于茅台股票数据集利用GRU算法实现回归预测(保存模型.ckpt.index、.ckpt.data文件)
基于茅台股票数据集利用GRU算法实现回归预测(保存模型.ckpt.index、.ckpt.data文件)
#1、定义数据集
数据集下载：http://quotes.money.163.com/trade/lsjysj_600519.html
日期股票代码名称收盘价最高价最低价开盘价前收盘涨跌额涨跌幅换手率成交量成交金额总市值流通市值2022/6/27 '600519 贵州茅台2010.55 2049.94 2000.3 2019.94 2009.01 1.54 0.0767 0.3193 4011517 8124448900 2.53E+12 2.53E+12 2022/6/24 '600519 贵州茅台2009.01 2020 1965 1970 1957.1 51.91 2.6524 0.3155 3963465 7921199792 2.52E+12 2.52E+12 2022/6/23 '600519 贵州茅台1957.1 1965.04 1940 1942.7 1936 21.1 1.0899 0.2137 2684352 5239860443 2.46E+12 2.46E+12 2022/6/22 '600519 贵州茅台1936 1958 1932 1955 1945.74 -9.74 -0.5006 0.1564 1964665 3813775294 2.43E+12 2.43E+12 2022/6/21 '600519 贵州茅台1945.74 1966.99 1928 1949 1942.02 3.72 0.1916 0.1888 2371702 4617805127 2.44E+12 2.44E+12 2022/6/20 '600519 贵州茅台1942.02 1970 1930 1950 1951 -8.98 -0.4603 0.2784 3497478 6802792459 2.44E+12 2.44E+12 2022/6/17 '600519 贵州茅台1951 1952 1878.09 1878.09 1877 74 3.9425 0.4023 5054161 9749530916 2.45E+12 2.45E+12 2022/6/16 '600519 贵州茅台1877 1907.63 1875.33 1894.59 1875.1 1.9 0.1013 0.214 2688670 5087605391 2.36E+12 2.36E+12 2022/6/15 '600519 贵州茅台1875.1 1905 1862.99 1870 1871 4.1 0.2191 0.268 3366362 6354869100 2.36E+12 2.36E+12 2022/6/14 '600519 贵州茅台1871 1875.42 1832 1834 1856 15 0.8082 0.2342 2941623 5467949348 2.35E+12 2.35E+12 2022/6/13 '600519 贵州茅台1856 1892 1848.08 1890 1900.6 -44.6 -2.3466 0.2926 3675518 6847248995 2.33E+12 2.33E+12 2022/6/10 '600519 贵州茅台1900.6 1907 1835 1845.01 1853 47.6 2.5688 0.3769 4734462 8882462598 2.39E+12 2.39E+12 2022/6/9 '600519 贵州茅台1853 1888.35 1849 1872 1865.6 -12.6 -0.6754 0.2096 2632902 4897066622 2.33E+12 2.33E+12 2022/6/8 '600519 贵州茅台1865.6 1882 1825 1825 1817.9 47.7 2.6239 0.3531 4435381 8236953846 2.34E+12 2.34E+12 2022/6/7 '600519 贵州茅台1817.9 1825 1770.31 1784.14 1788 29.9 1.6723 0.279 3504859 6356031009 2.28E+12 2.28E+12 2022/6/6 '600519 贵州茅台1788 1795 1758 1790 1786 2 0.112 0.2925 3674126 6535329352 2.25E+12 2.25E+12 2022/6/2 '600519 贵州茅台1786 1795.8 1780 1787.97 1788.25 -2.25 -0.1258 0.1347 1691473 3019718032 2.24E+12 2.24E+12 2022/6/1 '600519 贵州茅台1788.25 1814.78 1779 1802 1804.03 -15.78 -0.8747 0.1732 2176001 3897858999 2.25E+12 2.25E+12 2022/5/31 '600519 贵州茅台1804.03 1814.9 1766.98 1774.77 1778.41 25.62 1.4406 0.3244 4075082 7329201058 2.27E+12 2.27E+12 2022/5/30 '600519 贵州茅台1778.41 1790.55 1766 1766 1755.16 23.25 1.3247 0.2744 3446569 6135631304 2.23E+12 2.23E+12
# 2、数据集预处理
# 2.1、数据集切分
training_set
[2019.94 1970. 1942.7 ... 26.07 25.92 26.5 ]
test_set
[26.5 0. 25.69 25.6 26.3 25.92 26. 26.24 26.48 26. 25.8 25.8
25.98 25.78 26.05 26.13 27.2 26.75 26.95 26.7 26.22 26.08 26.03 26.25
26.5 26.6 27.11 27.1 27.45 26.97 26.79 27.5 27.91 27.78 27.6 27.9
27.68 27.7 28. 28.15 28.12 28.36 27.98 28.4 28.68 28.97 28.8 28.99
28.75 29.11 29.01 29. 29.46 30. 30.3 30.35 30.52 30.63 30.4 30.45
30.56 30.55 30.89 30.73 31.15 31.15 31. 31. 30.59 30.79 30.5 30.98
30.98 30.7 30.8 31.21 31.42 31.43 31.32 31.44 31.3 31.28 31.52 31.68
32.2 32.5 32.61 36.3 36.45 36.68 36.37 36.05 35.95 35.68 36.01 35.99
35.63 36.12 36.18 36.18 36.06 36.68 36.75 36.8 37.08 36.7 36.9 37.28
39.04 35. 34.98 34.9 34.7 34.55 34.9 35.1 34.8 34.75 35. 34.8
34.38 34.5 34.9 34.9 35. 34.88 35.21 35.2 35. 35.01 35.88 35.1
35.54 34.99 34.89 35.25 35.68 35.4 35.57 36.05 36. 36.31 36.48 36.2
35.5 35.1 35.5 36.19 36. 36.39 37. 38.5 37.88 38.46 37.62 37.49
37.43 37. 37.3 37.78 36.97 37.02 37.61 37.16 38. 38.01 38.15 38.7
38.49 38.92 39.3 38.8 38.1 38.12 38.02 38.11 38.31 39.45 39.69 38.55
38.2 38.8 38.06 37.35 37.95 38. 37.85 37.99 37.6 37.18 37.86 37.93
37.18 37.5 36. 35.6 35.2 37. 37.24 37.36 36.65 35.8 36.3 34.8
36.2 36.48 35.98 35.7 37.01 36.98 36.5 37. 37.15 38.72 37.67 37.3
37.22 36.54 36.45 35.99 34.7 35.9 35.9 35.48 35.11 35.02 35.61 35.6
36. 36. 36.1 35.9 37. 36.25 35.35 34.83 35.01 35.05 34.58 35.
35.01 35.22 35.48 35.2 34.15 36.2 33.65 33.64 33.28 34.4 33.7 33.35
35. 34.8 35. 35.28 35.05 35. 35.25 34.88 34.7 35.7 36.78 36.
33.3 34. 34.2 34.79 35.13 35.9 35.9 36.01 37.3 36.6 37. 36.9
36.08 36.11 36.28 36.06 36.28 36.9 36.3 35.88 36.08 36.01 36.01 35.33
36.8 35.4 36.5 37.35 37.61 37.01 37.2 37.15 36.28 36.98 34.99 34.51]
# 2.2、数据维度转换
进行MinMaxScaler之前，需要将数据从(4754,)→(4754, 1)
before reshape <class 'numpy.ndarray'> (4752,) (300,)
after reshape <class 'numpy.ndarray'> (4752, 1) (300, 1)</class></class>
# 2.3、训练集、测试集进行MinMax归一化
# 2.4、依次构建train、test的时序性数据集矩阵
# (1)、for循环构建train时序性数据集矩阵
提取训练集中连续X_num=60天的开盘价，作为输入特征x_train；以第61天的数据作为label，for循环共构建4752-300-60=4392组数据
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 0 0.78050835 0.761211447 0.750662679 0.755415421 0.75309701 0.753483412 0.725697262 0.732072891 0.722571272 0.708660809 0.730299307 0.712915092 0.723344075 0.705183193 0.689394818 0.691659132 0.690874736 0.696295953 0.685774233 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 1 0.761211447 0.750662679 0.755415421 0.75309701 0.753483412 0.725697262 0.732072891 0.722571272 0.708660809 0.730299307 0.712915092 0.723344075 0.705183193 0.689394818 0.691659132 0.690874736 0.696295953 0.685774233 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 0.661519795 2 0.750662679 0.755415421 0.75309701 0.753483412 0.725697262 0.732072891 0.722571272 0.708660809 0.730299307 0.712915092 0.723344075 0.705183193 0.689394818 0.691659132 0.690874736 0.696295953 0.685774233 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 0.661519795 0.671566241 3 0.755415421 0.75309701 0.753483412 0.725697262 0.732072891 0.722571272 0.708660809 0.730299307 0.712915092 0.723344075 0.705183193 0.689394818 0.691659132 0.690874736 0.696295953 0.685774233 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 0.661519795 0.671566241 0.658814983 4 0.75309701 0.753483412 0.725697262 0.732072891 0.722571272 0.708660809 0.730299307 0.712915092 0.723344075 0.705183193 0.689394818 0.691659132 0.690874736 0.696295953 0.685774233 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 0.661519795 0.671566241 0.658814983 0.666110248 5 0.753483412 0.725697262 0.732072891 0.722571272 0.708660809 0.730299307 0.712915092 0.723344075 0.705183193 0.689394818 0.691659132 0.690874736 0.696295953 0.685774233 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 0.661519795 0.671566241 0.658814983 0.666110248 0.666156616 6 0.725697262 0.732072891 0.722571272 0.708660809 0.730299307 0.712915092 0.723344075 0.705183193 0.689394818 0.691659132 0.690874736 0.696295953 0.685774233 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 0.661519795 0.671566241 0.658814983 0.666110248 0.666156616 0.663838206 7 0.732072891 0.722571272 0.708660809 0.730299307 0.712915092 0.723344075 0.705183193 0.689394818 0.691659132 0.690874736 0.696295953 0.685774233 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 0.661519795 0.671566241 0.658814983 0.666110248 0.666156616 0.663838206 0.664611009 8 0.722571272 0.708660809 0.730299307 0.712915092 0.723344075 0.705183193 0.689394818 0.691659132 0.690874736 0.696295953 0.685774233 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 0.661519795 0.671566241 0.658814983 0.666110248 0.666156616 0.663838206 0.664611009 0.631380459 9 0.708660809 0.730299307 0.712915092 0.723344075 0.705183193 0.689394818 0.691659132 0.690874736 0.696295953 0.685774233 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 0.661519795 0.671566241 0.658814983 0.666110248 0.666156616 0.663838206 0.664611009 0.631380459 0.637562887 10 0.730299307 0.712915092 0.723344075 0.705183193 0.689394818 0.691659132 0.690874736 0.696295953 0.685774233 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 0.661519795 0.671566241 0.658814983 0.666110248 0.666156616 0.663838206 0.664611009 0.631380459 0.637562887 0.668475027 11 0.712915092 0.723344075 0.705183193 0.689394818 0.691659132 0.690874736 0.696295953 0.685774233 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 0.661519795 0.671566241 0.658814983 0.666110248 0.666156616 0.663838206 0.664611009 0.631380459 0.637562887 0.668475027 0.68238549 12 0.723344075 0.705183193 0.689394818 0.691659132 0.690874736 0.696295953 0.685774233 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 0.661519795 0.671566241 0.658814983 0.666110248 0.666156616 0.663838206 0.664611009 0.631380459 0.637562887 0.668475027 0.68238549 0.698614363 13 0.705183193 0.689394818 0.691659132 0.690874736 0.696295953 0.685774233 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 0.661519795 0.671566241 0.658814983 0.666110248 0.666156616 0.663838206 0.664611009 0.631380459 0.637562887 0.668475027 0.68238549 0.698614363 0.681651327 14 0.689394818 0.691659132 0.690874736 0.696295953 0.685774233 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 0.661519795 0.671566241 0.658814983 0.666110248 0.666156616 0.663838206 0.664611009 0.631380459 0.637562887 0.668475027 0.68238549 0.698614363 0.681651327 0.680059351 15 0.691659132 0.690874736 0.696295953 0.685774233 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 0.661519795 0.671566241 0.658814983 0.666110248 0.666156616 0.663838206 0.664611009 0.631380459 0.637562887 0.668475027 0.68238549 0.698614363 0.681651327 0.680059351 0.68014436 16 0.690874736 0.696295953 0.685774233 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 0.661519795 0.671566241 0.658814983 0.666110248 0.666156616 0.663838206 0.664611009 0.631380459 0.637562887 0.668475027 0.68238549 0.698614363 0.681651327 0.680059351 0.68014436 0.6847039 17 0.696295953 0.685774233 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 0.661519795 0.671566241 0.658814983 0.666110248 0.666156616 0.663838206 0.664611009 0.631380459 0.637562887 0.668475027 0.68238549 0.698614363 0.681651327 0.680059351 0.68014436 0.6847039 0.713220349 18 0.685774233 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 0.661519795 0.671566241 0.658814983 0.666110248 0.666156616 0.663838206 0.664611009 0.631380459 0.637562887 0.668475027 0.68238549 0.698614363 0.681651327 0.680059351 0.68014436 0.6847039 0.713220349 0.710979219 19 0.68238549 0.680063215 0.680453481 0.682103417 0.690113525 0.695523149 0.680063215 0.674271053 0.685345327 0.683884729 0.694363944 0.687795114 0.677362267 0.678135071 0.664611009 0.687795114 0.701315312 0.707115202 0.709433612 0.692818337 0.68281826 0.658169692 0.676203062 0.681226285 0.691736412 0.696176168 0.695523149 0.688231748 0.696373233 0.690886328 0.682771892 0.668088625 0.683931097 0.683158293 0.679294276 0.677362267 0.668451843 0.664611009 0.656110171 0.642006507 0.627902843 0.661519795 0.671566241 0.658814983 0.666110248 0.666156616 0.663838206 0.664611009 0.631380459 0.637562887 0.668475027 0.68238549 0.698614363 0.681651327 0.680059351 0.68014436 0.6847039 0.713220349 0.710979219 0.696295953
依次对x_train、y_train打乱数据并转为array格式
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 0 0.076739387 0.078284994 0.077284214 0.07651141 0.074355289 0.074142768 0.073563165 0.073470429 0.072821274 0.074420977 0.073458837 0.074119584 0.073161307 0.075348341 0.073416332 0.068300373 0.064706837 0.069938717 0.074003663 0.077601063 0.077183749 0.079977434 0.080379292 0.081337568 0.08172397 0.081140503 0.082033091 0.079989026 0.080174499 0.079687633 0.082276525 0.08085843 0.079212359 0.079869242 0.080692277 0.080286556 0.077226254 0.08384918 0.085201586 0.084231717 0.085008385 0.085684588 0.087771157 0.088486001 0.097087304 0.095634433 0.098532446 0.099691651 0.09930525 0.091523118 0.088486001 0.096600437 0.103536349 0.098532446 0.098648367 0.098532446 0.095302128 0.095970603 0.096608165 0.101044058 1 0.449385235 0.449771637 0.453998872 0.455954065 0.446294021 0.447066824 0.444748414 0.443589209 0.428326339 0.422723514 0.418473095 0.411904265 0.432468566 0.438295505 0.442430003 0.442236802 0.436247575 0.440807116 0.440497995 0.440494131 0.434701968 0.426973933 0.42851954 0.431610754 0.430065147 0.426973933 0.414605213 0.413488512 0.407653846 0.409972256 0.405660013 0.397220999 0.39800153 0.392646002 0.390385552 0.378094112 0.368434068 0.366888461 0.359740029 0.365149653 0.364763252 0.377325173 0.376741706 0.377321309 0.371730075 0.371706891 0.365524463 0.370292661 0.371834404 0.37094568 0.369013671 0.371525282 0.374036894 0.376915587 0.373959613 0.379176037 0.382522276 0.379033068 0.378403233 0.384489061 2 0.019961514 0.020201083 0.019629209 0.019895826 0.018933686 0.018740485 0.018431363 0.018203386 0.018160882 0.018218842 0.018593652 0.019049606 0.017310798 0.017368759 0.016163185 0.015649271 0.017194878 0.017233518 0.017252838 0.017001677 0.01758128 0.018508644 0.018570468 0.018697981 0.01893755 0.01893755 0.018547284 0.019277583 0.018933686 0.018732757 0.018744349 0.018740485 0.019590569 0.019706489 0.020286092 0.020208812 0.02040974 0.020046523 0.020089027 0.018972326 0.018674797 0.018006322 0.017959953 0.018354083 0.018810037 0.019126887 0.018895046 0.018663205 0.018632292 0.019242807 0.019351 0.01978377 0.019385776 0.019397368 0.018922094 0.018578196 0.01816861 0.018547284 0.018968462 0.018276803 3 0 0.034389756 0.034582957 0.032368875 0.034768429 0.032028841 0.029115372 0.02646852 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.022685647 0.022546542 0.021978532 0.022326293 0.022666327 0.022276061 0.022990904 0 0.022326293 0.018701845 0.019223487 0.019192575 0.018315443 0.018373403 0.017627648 0.017391943 4 0.101044058 0.096600437 0.09265914 0.095004598 0.097767371 0.098130588 0.100078053 0.103161539 0.101283627 0.100464455 0.098779743 0.102396464 0.097183904 0.099734156 0.101615932 0.102466016 0.104521673 0.101986878 0.100468319 0.106357082 0.110325428 0.110742741 0.104328472 0.100340806 0.098918848 0.097373241 0.093142142 0.094668429 0.097145264 0.099015448 0.098068764 0.09938253 0.09718004 0.095101199 0.094668429 0.094274299 0.097373241 0.097628266 0.097044799 0.095093471 0.098146044 0.099529363 0.097326873 0.102010062 0.10386479 0.09273642 0.088872402 0.092311378 0.080584085 0.080796606 0.078825957 0.077879273 0.077276486 0.07901143 0.078246354 0.076063184 0.074961939 0.075719287 0.075746335 0.075997496 5 0.073690678 0.073744774 0.074988988 0.076256385 0.079591032 0.080371564 0.081913307 0.076507546 0.076240929 0.076314346 0.074710778 0.075730879 0.076314346 0.074146632 0.07722239 0.076507546 0.078903237 0.078439555 0.083605747 0.079308959 0.078427963 0.077485143 0.074189136 0.068926344 0.067573938 0.066113339 0.064355211 0.067968068 0.071866861 0.070321254 0.067832827 0.070321254 0.064374531 0.063215326 0.063366023 0.063872209 0.063350567 0.062983485 0.063570816 0.063060766 0.063655824 0.064876854 0.065205295 0.066770222 0.065301896 0.064714565 0.064760933 0.06186292 0.06237297 0.06182428 0.061252405 0.06221841 0.06414269 0.064842078 0.067233904 0.065301896 0.064359075 0.065842858 0.065143471 0.064181331 6 0.019049606 0.017310798 0.017368759 0.016163185 0.015649271 0.017194878 0.017233518 0.017252838 0.017001677 0.01758128 0.018508644 0.018570468 0.018697981 0.01893755 0.01893755 0.018547284 0.019277583 0.018933686 0.018732757 0.018744349 0.018740485 0.019590569 0.019706489 0.020286092 0.020208812 0.02040974 0.020046523 0.020089027 0.018972326 0.018674797 0.018006322 0.017959953 0.018354083 0.018810037 0.019126887 0.018895046 0.018663205 0.018632292 0.019242807 0.019351 0.01978377 0.019385776 0.019397368 0.018922094 0.018578196 0.01816861 0.018547284 0.018968462 0.018276803 0.018160882 0.018933686 0.018895046 0.018160882 0.018160882 0.018350219 0.018044962 0.017967681 0.018276803 0.0176972 0.01761992 7 0.087535452 0.088482137 0.087713197 0.090147528 0.092006121 0.087666829 0.086940394 0.084621983 0.083447322 0.084618119 0.082342213 0.084544703 0.081994451 0.080282692 0.07959876 0.077906321 0.078516836 0.078725492 0.077496735 0.07728035 0.079208495 0.079135078 0.078277266 0.078053153 0.078091794 0.076306618 0.077183749 0.077272622 0.077662888 0.078439555 0.078482059 0.077767216 0.079985162 0.077666752 0.076507546 0.079115758 0.076163649 0.076932588 0.077705392 0.078501379 0.078891645 0.080754102 0.082218564 0.081144367 0.084235581 0.08432059 0.085077937 0.084475151 0.087125867 0.086940394 0.086546264 0.087114274 0.087635917 0.084235581 0.083269577 0.082840671 0.082848399 0.082612694 0.081217784 0.082110372 8 0.078825957 0.07952148 0.078748677 0.080023802 0.083115016 0.076893948 0.07728035 0.077821312 0.076893948 0 0.071441819 0.071472732 0.068238549 0.067427105 0.066665894 0.068702231 0.067628034 0.069629595 0.069127273 0.068586311 0.068779511 0.070325118 0.071870725 0.070518319 0.069552315 0.070904721 0.072284175 0.068694503 0.066461101 0.066310404 0.066468829 0.069057721 0.070518319 0.069583227 0.071097922 0.07071152 0.074382337 0.072570113 0.074196864 0.074579402 0.07370227 0.073029931 0.072662849 0.070904721 0.068779511 0.068199909 0.069042265 0.071097922 0.073026067 0.068045348 0.067658946 0.063694464 0.062098625 0.056607856 0.058551457 0.056406927 0.056287143 0.053713707 0.054660392 0.055641852 9 0.096310636 0.096527021 0.094710933 0.094579556 0.093748792 0.093644464 0.093895625 0.093115094 0.094784349 0.092357746 0.090804411 0.091963616 0.089826815 0.088486001 0.088219383 0.089220164 0.092558675 0.093041677 0.094668429 0.093122822 0.092910301 0.093316023 0.09285234 0.093397167 0.090108888 0.09051461 0.089838407 0.091577215 0 0.084235581 0.083845316 0.086940394 0.088486001 0.089065603 0.087794342 0.089606566 0.091546303 0.089904095 0.088721706 0.092125905 0.094382491 0.095433504 0.095827634 0.096013107 0.096986839 0.097295961 0.097550986 0.096144483 0.095302128 0.093702424 0.093938129 0.095124383 0.096407237 0.096310636 0.096940471 0.098493806 0.096600437 0.092972125 0.092690052 0.094857766 10 0.772370729 0.772842139 0.78632756 0.78632756 0.772803499 0.763116407 0.792123587 0.799851622 0.763143456 0.763916259 0.755415421 0.801768174 0.772803499 0.809511665 0.788259569 0.842355814 0.841969412 0.811443674 0.853561465 0.891811374 0.875254059 0.948616295 0.947132513 1 0.960208348 0.915308465 0.903020889 0.898384068 0.846606233 0.830763762 0.816165504 0.823035727 0.811903492 0.803715639 0.827630044 0.844287823 0.804874844 0.79946522 0.791350783 0.775894713 0.801053331 0.79639719 0.8172397 0.833082172 0.836173386 0.806806853 0.807579657 0.827672548 0.810284469 0.797842333 0.768939482 0.772795771 0.750005796 0.722571272 0.723730477 0.705801436 0.696678491 0.702478381 0.733386657 0.714831645 11 0.3091214 0.300620561 0.299867078 0.301393365 0.305249654 0.304484579 0.303904976 0.290709356 0.290237946 0.282169878 0.28099908 0.285473613 0.277436456 0.275118046 0.27820926 0.285937295 0.284090294 0.287482902 0.279751003 0.284051654 0.285937295 0.288255705 0.279754867 0.277703073 0.271833631 0.273989753 0.269720013 0.255005835 0.259267846 0.257343565 0.255025155 0.264781799 0.267390011 0.266230805 0.261207583 0.257347429 0.263139591 0.254967194 0.259275574 0.264685198 0.262977303 0.269901622 0.272026832 0.274345242 0.272335953 0.26936066 0.263525993 0.261980386 0.26275319 0.2650716 0.263603274 0.270469633 0.279368465 0.273572439 0.270867626 0.287482902 0.289843816 0.289801312 0.28747131 0.288873948 12 0.061051476 0.061360598 0.061256269 0.058609417 0.057960262 0.057921622 0.058153463 0.058083911 0.057284059 0.058420081 0.059073099 0.059884543 0.059042187 0.058118687 0.057527492 0.057110179 0.056414655 0.058582369 0.057284059 0.057110179 0.055672764 0.054869048 0.05525545 0.053566875 0.053818036 0.055228402 0.055100889 0.054134885 0.05437059 0.053392994 0.053632563 0.052747703 0.05254291 0.051410753 0.050579989 0.050425428 0.050000386 0.050518165 0.050541349 0.049683537 0.049115526 0.049304863 0.04864798 0.048879821 0.048899141 0.049401464 0.04868662 0.049656489 0.050309508 0.050154947 0.050970255 0.051005031 0.051491897 0.051159592 0.052554502 0.053787124 0.054702896 0.054637207 0.054181253 0.054505831 13 0.118857178 0.120905107 0.119842503 0.119251308 0.121909752 0.122682556 0.122535723 0.122137729 0.124819357 0.122010216 0.119409733 0.121148541 0.122025673 0.115843245 0.114000108 0.110897302 0.110974582 0.112064235 0.111109823 0.11090503 0.106260481 0.106368674 0.108180898 0.105785207 0.107048741 0.108103617 0.105719519 0.106206385 0.104602818 0.103443612 0.105622918 0.106801444 0.106840084 0.108965293 0.103169267 0.101627524 0.102551024 0.098918848 0.09718004 0.09765145 0.097369377 0.098331517 0.098304469 0.096492245 0.095379408 0.094730253 0.096244948 0.096990703 0.096932743 0.09891112 0.098153772 0.097508482 0.096407237 0.09665067 0.099425034 0.099112049 0.100464455 0.096812958 0.095591929 0.094989142 14 0.088099599 0.089258804 0.089305172 0.09059189 0.090727131 0.087519996 0.087319067 0.087326795 0.083729395 0.082786575 0.082091052 0.081990587 0.081808978 0.080487484 0.082604966 0.081302792 0.082241748 0.082110372 0.082110372 0.080700005 0.079212359 0.080866158 0.080197683 0.079451928 0.075773383 0.077728576 0.077589471 0.076893948 0.081229376 0.081577137 0.080178363 0.078949605 0.080294284 0.080197683 0.080294284 0.077585607 0.076909404 0.077894729 0.07731899 0.077326718 0.075228557 0.074656682 0.075240149 0.076893948 0.077608791 0.078296587 0.078640484 0.077666752 0.077060101 0.07680894 0.075031492 0.074390065 0.074482801 0.074107992 0.074560082 0.074915571 0.074776467 0.074471209 0.074455753 0.073223132 15 0.009273642 0.00937797 0.009273642 0.009111353 0.009115217 0.009088169 0.009092033 0.009177042 0.009188634 0.009149993 0.009235002 0.009041801 0.009103625 0.008983841 0.008960657 0.009003161 0.008922016 0.008922016 0.00888724 0.009003161 0.008918152 0.009034073 0.00882928 0.00879064 0.00888724 0.008918152 0.00880996 0.008883376 0.00888724 0.00890656 0.00888724 0.009003161 0.009146129 0.008979977 0.008794504 0.008763592 0.008694039 0.008539479 0.008241949 0.008191717 0.00829991 0.008346278 0.008427422 0.008288318 0.008265133 0.008261269 0.008292182 0.008369462 0.008404238 0.00833855 0.008346278 0.008164669 0.008191717 0.008160805 0.008160805 0.008153077 0.008180125 0.008191717 0.008176261 0.008075797 16 0.875254059 0.948616295 0.947132513 1 0.960208348 0.915308465 0.903020889 0.898384068 0.846606233 0.830763762 0.816165504 0.823035727 0.811903492 0.803715639 0.827630044 0.844287823 0.804874844 0.79946522 0.791350783 0.775894713 0.801053331 0.79639719 0.8172397 0.833082172 0.836173386 0.806806853 0.807579657 0.827672548 0.810284469 0.797842333 0.768939482 0.772795771 0.750005796 0.722571272 0.723730477 0.705801436 0.696678491 0.702478381 0.733386657 0.714831645 0.710979219 0.715542624 0.713104429 0.707308403 0.708467608 0.706253526 0.708962202 0.710979219 0.721006345 0.701319176 0.696566434 0.676975865 0.671570105 0.674657455 0.66692942 0.670407036 0.669502856 0.66692942 0.680646682 0.683927233 17 0.173540754 0.171937187 0.17312344 0.173880787 0.170982774 0.173880787 0.174197637 0.173382329 0.175001352 0.170886174 0.169645824 0.1659866 0.164240064 0.165766351 0.162134174 0.159630291 0.1598119 0.157806475 0.157806475 0.156276324 0.16081268 0.160820408 0.160213757 0.158811119 0.15904296 0.163057674 0.160561519 0.161844373 0.156415428 0.154800269 0.160356726 0.156913887 0.155719905 0.15247413 0.151083084 0.154340451 0.150998076 0.14887673 0.149943199 0.152242289 0.151852024 0.150314145 0.149019699 0.149228356 0.14799187 0.147222931 0.146813345 0.149162667 0.151593134 0.151481078 0.150901475 0.149363596 0.147025866 0.144166493 0.145503443 0.142775446 0.14356757 0.142987967 0.142937735 0.141809442 18 0.288622787 0.283108834 0.284391688 0.286516897 0.285550893 0.286876251 0.292506124 0.290690036 0.286826019 0.277436456 0.274345242 0.281686875 0.278220852 0.273186037 0.270481225 0.267390011 0.274252506 0.283819813 0.275233966 0.275890849 0.290574116 0.295717123 0.29598374 0.291346919 0.295682347 0.286323696 0.297142945 0.298765833 0.305257382 0.3091214 0.300620561 0.299867078 0.301393365 0.305249654 0.304484579 0.303904976 0.290709356 0.290237946 0.282169878 0.28099908 0.285473613 0.277436456 0.275118046 0.27820926 0.285937295 0.284090294 0.287482902 0.279751003 0.284051654 0.285937295 0.288255705 0.279754867 0.277703073 0.271833631 0.273989753 0.269720013 0.255005835 0.259267846 0.257343565 0.255025155 19 0.715731961 0.720264453 0.726435289 0.716775246 0.711404261 0.708274407 0.695523149 0.729526503 0.730337947 0.745341927 0.722571272 0.721025665 0.710789882 0.704186277 0.702547933 0.699000765 0.714070433 0.676203062 0.629062048 0.632926066 0.640974814 0.625970834 0.619402005 0.637562887 0.641040503 0.643358913 0.631411371 0.628289245 0.641426904 0.641824898 0.637176485 0.622114545 0.630990193 0.602400328 0.614301502 0.620174808 0.61399238 0.643355049 0.636650979 0.608582756 0.594239523 0.600920409 0.626828646 0.627771467 0.656882974 0.655337367 0.653018957 0.66924783 0.687798978 0.656882974 0.637949289 0.652632555 0.654131794 0.670043818 0.668475027 0.642972511 0.666543018 0.699391031 0.65804218 0.696682355
# (2)、for循环构建test时序性数据集矩阵
测试集：csv表格中后300天数据，for循环共构建300-60=240组数据。
将df格式数据转为array格式
# 3、模构建GRU模型
# 3.1、模型构建
# 3.2、模型编译并定义优化器、损失函数
# 3.3、模型训练并保存checkpoint文件
# 使入模数据维度标准化
x_train要reshape成符合RNN输入要求：[样本数， 循环核时间展开步数， 每个时间步输入特征个数]
before x_train.shape[0]： 4692
after x_train.shape： (4692, 60, 1)
# 创建并保存weights.tx权重文件
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
gru (GRU) (None, 60, 80) 19680
_________________________________________________________________
dropout (Dropout) (None, 60, 80) 0
_________________________________________________________________
gru_1 (GRU) (None, 100) 54300
_________________________________________________________________
dropout_1 (Dropout) (None, 100) 0
_________________________________________________________________
dense (Dense) (None, 1) 101
=================================================================
Total params: 74,081
Trainable params: 74,081
Non-trainable params: 0
_________________________________________________________________
# 模型训练过程可视化：绘制loss
epoch=5
# 3.4、模型评估
# 对真实、预测数据进行MinMax反归一化还原
# 画出真实数据和预测数据的对比曲线
# 输出模型评估指标
R2: 0.5177
MSE: 1.8693
RMSE: 1.3672
MAE: 1.2081
None
R2: 0.8342
MSE: 0.6269
RMSE: 0.7918
MAE: 0.5756
# 保存预测结果
Original: https://blog.csdn.net/qq_41185868/article/details/125510867
Author: 一个处女座的程序猿
Title: DL之GRU(Tensorflow框架)：基于茅台股票数据集利用GRU算法实现回归预测(保存模型.ckpt.index、.ckpt.data文件)
相关阅读
Title: 机器学习TensorFlow安装之CPU版本
TensorFlow1.5发布的时候，出于好奇下载安装跑了一下演示的几个代码。最近，重新拿起书本学习深度学习，发现TensorFlow已经更新发布到了2.6版本了。由于这期间电换了电脑，为了学TensorFlow只有再次安装了！
两台电脑，一台笔记本电脑，没有独立显卡；另一台台式机，安装了NVIDIV的T1000入门显卡。所以我安装了两个版本的TensorFlow，cup版本的还算顺利，GPU版本的折腾了一个下午。
一、无显卡笔记本安装TensorFlow的cpu版本
笔记本配置：win10-64位专业版，I7,16G。cup版本的TensorFlow安装比较简单，目前最新版本的TensorFlow2.6.0 支持Python3.9.7，经过测试完全可以跑官网的示例。当然如果安装GPU版本的建议还是下载Python3.6版本的，可能会比较稳定，因为我在台式机上用Python3.9版本安装TensorFlow2.6没有成功，当然也可能会成功，（后面详述）反正我是后来用3.6版本装好的GPU版本。
1、安装Anaconda3
下载地址：Anaconda https://www.anaconda.com/products/individual-d
安装方法网上有很多，介绍的已经很全面了： https://blog.csdn.net/weixin_39618121/article/details/112610492
2、新建虚拟环境Tensor
conda create -n Tensor python=3
运行自动创建一个名称位Tensor的虚拟环境，python版本位最新的3.9.7。
python安装后，运行一下看看，一切正常，提示Python3.9.7已经安装。
接下来我们根据自己喜好下载一个代码编辑器，当然也可以用Python自带的，不过我还是喜欢用sublimetext，开源软件、插件很多基本都能满足需求，当然最主要原因的是免费^_^，下载地址： http://www.sublimetext.com/3
配置编辑器方法网上很多，这里给个链接：https://blog.csdn.net/samenmoer/article/details/89740271?utm_medium=distribute.pc_relevant_t0.none-task-blog-
3、安装TensorFlow
首先激活上一步创建的Tensor虚拟环境
activate Tensor
这里如果直接安装TensorFlow因为网络的问题可能会失败，所以我们先配置pip使用清华源：
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
或者我们可以直接将对应版本的TensorFlow下载到本地安装，下载地址在官网
Python 3.9（仅支持 CPU）https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.6.0-cp39-cp39-win_amd64.whl
然后使用安装命令在本地安装，（安装时将文件放在CMD窗口命令的当前目录下）
pip install tensorflow_cpu-2.6.0-cp39-cp39-win_amd64.whl
安装tensorflow
pip install tensorflow
安装后运行一段Tensor代码测试
python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
如果你是按照我上面的步骤进行的，那么大概率这里会报错！并报出下面的提示：Cannot register 2 metrics with the same name: /tensorflow/api/keras/optimize
不过大家完全不用担心，这个是因为Keras版本问题造成的，使用conda list,命令查看一下当前Tensor虚拟环境下的包，找到其中的Keras看看版本号，我的解决办法是重新安装Keras的2.6版本版本
pip install keras==2.6.0
安装后重新运行TensorFlow测试代码，如果出现下面的输出那么恭喜你安装成功，可以愉快的学习了！
总体来说安装CPU版本的还是比较顺利的，但是安装GPU版本就比较折腾人了，我是花了一个下午的时间不断踩坑才安装成功。
Original: https://blog.csdn.net/tangqxj/article/details/122932752
Author: tangqxj
Title: 机器学习TensorFlow安装之CPU版本
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