├── BOSTON_data ├── boston_predict.csv ├── boston_test.csv └── boston_train.csv ├── Figures ├── fig1.png ├── fig2.png ├── fig3.png ├── fig4.png ├── fig5.png └── public-account.jpg ├── IRIS_data ├── iris_test.csv └── iris_training.csv ├── LICENSE ├── MNIST_data ├── t10k-images-idx3-ubyte.gz ├── t10k-labels-idx1-ubyte.gz ├── train-images-idx3-ubyte.gz └── train-labels-idx1-ubyte.gz ├── README.md ├── exp01_basic_usage.py ├── exp02_simple_linear_model.py ├── exp03_SoftmaxRegression_mnist.py ├── exp04_CNN_mnist.py ├── exp05_Iris_data_set.py ├── exp06_Customer_InputFun.py ├── exp07_TensorBoard.py ├── exp08_Embedding_Visualization.py ├── exp09_Graph_Visualization.py ├── exp10_monitor_API.py ├── exp11_user_dataset_low_API_1.py ├── exp12_user_dataset_low_API_2.py ├── exp13_user_dataset_high_API_1.py ├── exp14_user_dataset_high_API_2.py ├── my_data.h5 └── my_data ├── test_001.png ├── test_002.png ├── test_003.png ├── test_004.png ├── test_005.png ├── test_006.png ├── test_007.png ├── 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test_359.png ├── test_360.png ├── test_361.png ├── test_362.png ├── test_363.png ├── test_364.png ├── test_365.png ├── test_366.png ├── test_367.png ├── test_368.png ├── test_369.png ├── test_370.png ├── test_371.png ├── test_372.png ├── test_373.png ├── test_374.png ├── test_375.png ├── test_376.png ├── test_377.png ├── test_378.png ├── test_379.png ├── test_380.png ├── test_381.png ├── test_382.png ├── test_383.png ├── test_384.png ├── test_385.png ├── test_386.png ├── test_387.png ├── test_388.png ├── test_389.png ├── test_390.png ├── test_391.png ├── test_392.png ├── test_393.png ├── test_394.png ├── test_395.png ├── test_396.png ├── test_397.png ├── test_398.png ├── test_399.png └── test_400.png /BOSTON_data/boston_predict.csv: -------------------------------------------------------------------------------- 1 | 6,9,CRIM,ZN,INDUS,NOX,RM,AGE,DIS,TAX,PTRATIO 2 | 0.03359,75.0,2.95,0.428,7.024,15.8,5.4011,252,18.3 3 | 5.09017,0.0,18.1,0.713,6.297,91.8,2.3682,666,20.2 4 | 0.1265,25.0,5.13,0.453,6.762,43.4,7.9809,284,19.7 5 | 0.05515,33.0,2.18,0.472,7.236,41.1,4.022,222,18.4 6 | 8.15174,0.0,18.1,0.7,5.39,98.9,1.7281,666,20.2 7 | 0.24522,0.0,9.9,0.544,5.782,71.7,4.0317,304,18.4 8 | -------------------------------------------------------------------------------- /BOSTON_data/boston_test.csv: -------------------------------------------------------------------------------- 1 | 100,9,CRIM,ZN,INDUS,NOX,RM,AGE,DIS,TAX,PTRATIO,MEDV 2 | 0.13587,0.0,10.59,0.489,6.064,59.1,4.2392,277,18.6,24.4 3 | 0.08664,45.0,3.44,0.437,7.178,26.3,6.4798,398,15.2,36.4 4 | 0.26938,0.0,9.9,0.544,6.266,82.8,3.2628,304,18.4,21.6 5 | 0.05302,0.0,3.41,0.489,7.079,63.1,3.4145,270,17.8,28.7 6 | 0.0686,0.0,2.89,0.445,7.416,62.5,3.4952,276,18.0,33.2 7 | 0.14231,0.0,10.01,0.547,6.254,84.2,2.2565,432,17.8,18.5 8 | 0.1676,0.0,7.38,0.493,6.426,52.3,4.5404,287,19.6,23.8 9 | 0.04301,80.0,1.91,0.413,5.663,21.9,10.5857,334,22.0,18.2 10 | 0.06905,0.0,2.18,0.458,7.147,54.2,6.0622,222,18.7,36.2 11 | 0.21719,0.0,10.59,0.489,5.807,53.8,3.6526,277,18.6,22.4 12 | 0.06076,0.0,11.93,0.573,6.976,91.0,2.1675,273,21.0,23.9 13 | 4.03841,0.0,18.1,0.532,6.229,90.7,3.0993,666,20.2,19.6 14 | 0.16902,0.0,25.65,0.581,5.986,88.4,1.9929,188,19.1,21.4 15 | 0.52693,0.0,6.2,0.504,8.725,83.0,2.8944,307,17.4,50.0 16 | 12.0482,0.0,18.1,0.614,5.648,87.6,1.9512,666,20.2,20.8 17 | 3.1636,0.0,18.1,0.655,5.759,48.2,3.0665,666,20.2,19.9 18 | 0.10008,0.0,2.46,0.488,6.563,95.6,2.847,193,17.8,32.5 19 | 2.15505,0.0,19.58,0.871,5.628,100.0,1.5166,403,14.7,15.6 20 | 0.03584,80.0,3.37,0.398,6.29,17.8,6.6115,337,16.1,23.5 21 | 0.13554,12.5,6.07,0.409,5.594,36.8,6.498,345,18.9,17.4 22 | 17.8667,0.0,18.1,0.671,6.223,100.0,1.3861,666,20.2,10.2 23 | 0.09378,12.5,7.87,0.524,5.889,39.0,5.4509,311,15.2,21.7 24 | 0.05372,0.0,13.92,0.437,6.549,51.0,5.9604,289,16.0,27.1 25 | 9.96654,0.0,18.1,0.74,6.485,100.0,1.9784,666,20.2,15.4 26 | 3.8497,0.0,18.1,0.77,6.395,91.0,2.5052,666,20.2,21.7 27 | 0.47547,0.0,9.9,0.544,6.113,58.8,4.0019,304,18.4,21.0 28 | 0.06642,0.0,4.05,0.51,6.86,74.4,2.9153,296,16.6,29.9 29 | 0.04011,80.0,1.52,0.404,7.287,34.1,7.309,329,12.6,33.3 30 | 0.40202,0.0,9.9,0.544,6.382,67.2,3.5325,304,18.4,23.1 31 | 0.04527,0.0,11.93,0.573,6.12,76.7,2.2875,273,21.0,20.6 32 | 0.06617,0.0,3.24,0.46,5.868,25.8,5.2146,430,16.9,19.3 33 | 0.06417,0.0,5.96,0.499,5.933,68.2,3.3603,279,19.2,18.9 34 | 0.09068,45.0,3.44,0.437,6.951,21.5,6.4798,398,15.2,37.0 35 | 0.10469,40.0,6.41,0.447,7.267,49.0,4.7872,254,17.6,33.2 36 | 0.36894,22.0,5.86,0.431,8.259,8.4,8.9067,330,19.1,42.8 37 | 0.17134,0.0,10.01,0.547,5.928,88.2,2.4631,432,17.8,18.3 38 | 0.77299,0.0,8.14,0.538,6.495,94.4,4.4547,307,21.0,18.4 39 | 0.5405,20.0,3.97,0.575,7.47,52.6,2.872,264,13.0,43.5 40 | 0.1396,0.0,8.56,0.52,6.167,90.0,2.421,384,20.9,20.1 41 | 0.76162,20.0,3.97,0.647,5.56,62.8,1.9865,264,13.0,22.8 42 | 0.09065,20.0,6.96,0.464,5.92,61.5,3.9175,223,18.6,20.7 43 | 0.13117,0.0,8.56,0.52,6.127,85.2,2.1224,384,20.9,20.4 44 | 0.43571,0.0,10.59,0.489,5.344,100.0,3.875,277,18.6,20.0 45 | 1.41385,0.0,19.58,0.871,6.129,96.0,1.7494,403,14.7,17.0 46 | 11.8123,0.0,18.1,0.718,6.824,76.5,1.794,666,20.2,8.4 47 | 7.02259,0.0,18.1,0.718,6.006,95.3,1.8746,666,20.2,14.2 48 | 4.26131,0.0,18.1,0.77,6.112,81.3,2.5091,666,20.2,22.6 49 | 2.3139,0.0,19.58,0.605,5.88,97.3,2.3887,403,14.7,19.1 50 | 0.62739,0.0,8.14,0.538,5.834,56.5,4.4986,307,21.0,19.9 51 | 0.11747,12.5,7.87,0.524,6.009,82.9,6.2267,311,15.2,18.9 52 | 22.5971,0.0,18.1,0.7,5.0,89.5,1.5184,666,20.2,7.4 53 | 4.22239,0.0,18.1,0.77,5.803,89.0,1.9047,666,20.2,16.8 54 | 0.03548,80.0,3.64,0.392,5.876,19.1,9.2203,315,16.4,20.9 55 | 0.08199,0.0,13.92,0.437,6.009,42.3,5.5027,289,16.0,21.7 56 | 0.11432,0.0,8.56,0.52,6.781,71.3,2.8561,384,20.9,26.5 57 | 1.34284,0.0,19.58,0.605,6.066,100.0,1.7573,403,14.7,24.3 58 | 12.2472,0.0,18.1,0.584,5.837,59.7,1.9976,666,20.2,10.2 59 | 0.21977,0.0,6.91,0.448,5.602,62.0,6.0877,233,17.9,19.4 60 | 0.01096,55.0,2.25,0.389,6.453,31.9,7.3073,300,15.3,22.0 61 | 0.12816,12.5,6.07,0.409,5.885,33.0,6.498,345,18.9,20.9 62 | 0.17004,12.5,7.87,0.524,6.004,85.9,6.5921,311,15.2,18.9 63 | 2.63548,0.0,9.9,0.544,4.973,37.8,2.5194,304,18.4,16.1 64 | 0.21409,22.0,5.86,0.431,6.438,8.9,7.3967,330,19.1,24.8 65 | 0.04417,70.0,2.24,0.4,6.871,47.4,7.8278,358,14.8,24.8 66 | 0.75026,0.0,8.14,0.538,5.924,94.1,4.3996,307,21.0,15.6 67 | 13.6781,0.0,18.1,0.74,5.935,87.9,1.8206,666,20.2,8.4 68 | 0.09604,40.0,6.41,0.447,6.854,42.8,4.2673,254,17.6,32.0 69 | 0.1403,22.0,5.86,0.431,6.487,13.0,7.3967,330,19.1,24.4 70 | 0.44178,0.0,6.2,0.504,6.552,21.4,3.3751,307,17.4,31.5 71 | 0.03445,82.5,2.03,0.415,6.162,38.4,6.27,348,14.7,24.1 72 | 0.10084,0.0,10.01,0.547,6.715,81.6,2.6775,432,17.8,22.8 73 | 0.08707,0.0,12.83,0.437,6.14,45.8,4.0905,398,18.7,20.8 74 | 0.35114,0.0,7.38,0.493,6.041,49.9,4.7211,287,19.6,20.4 75 | 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0.02543,55.0,3.78,0.484,6.696,56.4,5.7321,370,17.6,23.9 132 | 0.49298,0.0,9.9,0.544,6.635,82.5,3.3175,304,18.4,22.8 133 | 0.08187,0.0,2.89,0.445,7.82,36.9,3.4952,276,18.0,43.8 134 | 4.83567,0.0,18.1,0.583,5.905,53.2,3.1523,666,20.2,20.6 135 | 25.9406,0.0,18.1,0.679,5.304,89.1,1.6475,666,20.2,10.4 136 | 3.67822,0.0,18.1,0.77,5.362,96.2,2.1036,666,20.2,20.8 137 | 0.22438,0.0,9.69,0.585,6.027,79.7,2.4982,391,19.2,16.8 138 | 15.8744,0.0,18.1,0.671,6.545,99.1,1.5192,666,20.2,10.9 139 | 0.32543,0.0,21.89,0.624,6.431,98.8,1.8125,437,21.2,18.0 140 | 0.03049,55.0,3.78,0.484,6.874,28.1,6.4654,370,17.6,31.2 141 | 0.06588,0.0,2.46,0.488,7.765,83.3,2.741,193,17.8,39.8 142 | 0.03537,34.0,6.09,0.433,6.59,40.4,5.4917,329,16.1,22.0 143 | 0.04544,0.0,3.24,0.46,6.144,32.2,5.8736,430,16.9,19.8 144 | 0.06151,0.0,5.19,0.515,5.968,58.5,4.8122,224,20.2,18.7 145 | 0.05602,0.0,2.46,0.488,7.831,53.6,3.1992,193,17.8,50.0 146 | 73.5341,0.0,18.1,0.679,5.957,100.0,1.8026,666,20.2,8.8 147 | 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1.23247,0.0,8.14,0.538,6.142,91.7,3.9769,307,21.0,15.2 164 | 9.91655,0.0,18.1,0.693,5.852,77.8,1.5004,666,20.2,6.3 165 | 1.61282,0.0,8.14,0.538,6.096,96.9,3.7598,307,21.0,13.5 166 | 0.0351,95.0,2.68,0.4161,7.853,33.2,5.118,224,14.7,48.5 167 | 0.02187,60.0,2.93,0.401,6.8,9.9,6.2196,265,15.6,31.1 168 | 0.06888,0.0,2.46,0.488,6.144,62.2,2.5979,193,17.8,36.2 169 | 0.18337,0.0,27.74,0.609,5.414,98.3,1.7554,711,20.1,7.0 170 | 0.03615,80.0,4.95,0.411,6.63,23.4,5.1167,245,19.2,27.9 171 | 0.84054,0.0,8.14,0.538,5.599,85.7,4.4546,307,21.0,13.9 172 | 0.0578,0.0,2.46,0.488,6.98,58.4,2.829,193,17.8,37.2 173 | 9.92485,0.0,18.1,0.74,6.251,96.6,2.198,666,20.2,12.6 174 | 0.04932,33.0,2.18,0.472,6.849,70.3,3.1827,222,18.4,28.2 175 | 0.25915,0.0,21.89,0.624,5.693,96.0,1.7883,437,21.2,16.2 176 | 6.44405,0.0,18.1,0.584,6.425,74.8,2.2004,666,20.2,16.1 177 | 16.8118,0.0,18.1,0.7,5.277,98.1,1.4261,666,20.2,7.2 178 | 0.54452,0.0,21.89,0.624,6.151,97.9,1.6687,437,21.2,17.8 179 | 0.12204,0.0,2.89,0.445,6.625,57.8,3.4952,276,18.0,28.4 180 | 0.54011,20.0,3.97,0.647,7.203,81.8,2.1121,264,13.0,33.8 181 | 14.2362,0.0,18.1,0.693,6.343,100.0,1.5741,666,20.2,7.2 182 | 0.22927,0.0,6.91,0.448,6.03,85.5,5.6894,233,17.9,16.6 183 | 0.14052,0.0,10.59,0.489,6.375,32.3,3.9454,277,18.6,28.1 184 | 0.26363,0.0,8.56,0.52,6.229,91.2,2.5451,384,20.9,19.4 185 | 0.67191,0.0,8.14,0.538,5.813,90.3,4.682,307,21.0,16.6 186 | 6.65492,0.0,18.1,0.713,6.317,83.0,2.7344,666,20.2,19.5 187 | 14.4383,0.0,18.1,0.597,6.852,100.0,1.4655,666,20.2,27.5 188 | 4.75237,0.0,18.1,0.713,6.525,86.5,2.4358,666,20.2,14.1 189 | 0.0187,85.0,4.15,0.429,6.516,27.7,8.5353,351,17.9,23.1 190 | 7.40389,0.0,18.1,0.597,5.617,97.9,1.4547,666,20.2,17.2 191 | 5.29305,0.0,18.1,0.7,6.051,82.5,2.1678,666,20.2,23.2 192 | 37.6619,0.0,18.1,0.679,6.202,78.7,1.8629,666,20.2,10.9 193 | 0.51183,0.0,6.2,0.507,7.358,71.6,4.148,307,17.4,31.5 194 | 0.10328,25.0,5.13,0.453,5.927,47.2,6.932,284,19.7,19.6 195 | 14.3337,0.0,18.1,0.7,4.88,100.0,1.5895,666,20.2,10.2 196 | 0.06899,0.0,25.65,0.581,5.87,69.7,2.2577,188,19.1,22.0 197 | 0.03659,25.0,4.86,0.426,6.302,32.2,5.4007,281,19.0,24.8 198 | 0.53412,20.0,3.97,0.647,7.52,89.4,2.1398,264,13.0,43.1 199 | 0.02985,0.0,2.18,0.458,6.43,58.7,6.0622,222,18.7,28.7 200 | 0.6147,0.0,6.2,0.507,6.618,80.8,3.2721,307,17.4,30.1 201 | 4.42228,0.0,18.1,0.584,6.003,94.5,2.5403,666,20.2,19.1 202 | 0.02731,0.0,7.07,0.469,6.421,78.9,4.9671,242,17.8,21.6 203 | 0.62976,0.0,8.14,0.538,5.949,61.8,4.7075,307,21.0,20.4 204 | 0.85204,0.0,8.14,0.538,5.965,89.2,4.0123,307,21.0,19.6 205 | 0.08829,12.5,7.87,0.524,6.012,66.6,5.5605,311,15.2,22.9 206 | 0.11069,0.0,13.89,0.55,5.951,93.8,2.8893,276,16.4,21.5 207 | 0.03961,0.0,5.19,0.515,6.037,34.5,5.9853,224,20.2,21.1 208 | 0.27957,0.0,9.69,0.585,5.926,42.6,2.3817,391,19.2,24.5 209 | 0.82526,20.0,3.97,0.647,7.327,94.5,2.0788,264,13.0,31.0 210 | 0.10793,0.0,8.56,0.52,6.195,54.4,2.7778,384,20.9,21.7 211 | 25.0461,0.0,18.1,0.693,5.987,100.0,1.5888,666,20.2,5.6 212 | 0.01709,90.0,2.02,0.41,6.728,36.1,12.1265,187,17.0,30.1 213 | 1.83377,0.0,19.58,0.605,7.802,98.2,2.0407,403,14.7,50.0 214 | 0.06664,0.0,4.05,0.51,6.546,33.1,3.1323,296,16.6,29.4 215 | 0.08244,30.0,4.93,0.428,6.481,18.5,6.1899,300,16.6,23.7 216 | 0.01965,80.0,1.76,0.385,6.23,31.5,9.0892,241,18.2,20.1 217 | 24.8017,0.0,18.1,0.693,5.349,96.0,1.7028,666,20.2,8.3 218 | 3.32105,0.0,19.58,0.871,5.403,100.0,1.3216,403,14.7,13.4 219 | 0.22212,0.0,10.01,0.547,6.092,95.4,2.548,432,17.8,18.7 220 | 14.4208,0.0,18.1,0.74,6.461,93.3,2.0026,666,20.2,9.6 221 | 0.7258,0.0,8.14,0.538,5.727,69.5,3.7965,307,21.0,18.2 222 | 1.42502,0.0,19.58,0.871,6.51,100.0,1.7659,403,14.7,23.3 223 | 14.0507,0.0,18.1,0.597,6.657,100.0,1.5275,666,20.2,17.2 224 | 8.20058,0.0,18.1,0.713,5.936,80.3,2.7792,666,20.2,13.5 225 | 15.0234,0.0,18.1,0.614,5.304,97.3,2.1007,666,20.2,12.0 226 | 1.35472,0.0,8.14,0.538,6.072,100.0,4.175,307,21.0,14.5 227 | 8.05579,0.0,18.1,0.584,5.427,95.4,2.4298,666,20.2,13.8 228 | 0.19657,22.0,5.86,0.431,6.226,79.2,8.0555,330,19.1,20.5 229 | 0.05059,0.0,4.49,0.449,6.389,48.0,4.7794,247,18.5,23.9 230 | 0.09299,0.0,25.65,0.581,5.961,92.9,2.0869,188,19.1,20.5 231 | 1.05393,0.0,8.14,0.538,5.935,29.3,4.4986,307,21.0,23.1 232 | 0.12579,45.0,3.44,0.437,6.556,29.1,4.5667,398,15.2,29.8 233 | 0.17446,0.0,10.59,0.489,5.96,92.1,3.8771,277,18.6,21.7 234 | 0.16211,20.0,6.96,0.464,6.24,16.3,4.429,223,18.6,25.2 235 | 0.22489,12.5,7.87,0.524,6.377,94.3,6.3467,311,15.2,15.0 236 | 45.7461,0.0,18.1,0.693,4.519,100.0,1.6582,666,20.2,7.0 237 | 2.24236,0.0,19.58,0.605,5.854,91.8,2.422,403,14.7,22.7 238 | 0.29916,20.0,6.96,0.464,5.856,42.1,4.429,223,18.6,21.1 239 | 8.71675,0.0,18.1,0.693,6.471,98.8,1.7257,666,20.2,13.1 240 | 0.07022,0.0,4.05,0.51,6.02,47.2,3.5549,296,16.6,23.2 241 | 0.09164,0.0,10.81,0.413,6.065,7.8,5.2873,305,19.2,22.8 242 | 24.3938,0.0,18.1,0.7,4.652,100.0,1.4672,666,20.2,10.5 243 | 0.14932,25.0,5.13,0.453,5.741,66.2,7.2254,284,19.7,18.7 244 | 0.12932,0.0,13.92,0.437,6.678,31.1,5.9604,289,16.0,28.6 245 | 5.20177,0.0,18.1,0.77,6.127,83.4,2.7227,666,20.2,22.7 246 | 0.31827,0.0,9.9,0.544,5.914,83.2,3.9986,304,18.4,17.8 247 | 0.11504,0.0,2.89,0.445,6.163,69.6,3.4952,276,18.0,21.4 248 | 0.10574,0.0,27.74,0.609,5.983,98.8,1.8681,711,20.1,13.6 249 | 0.7842,0.0,8.14,0.538,5.99,81.7,4.2579,307,21.0,17.5 250 | 2.44668,0.0,19.58,0.871,5.272,94.0,1.7364,403,14.7,13.1 251 | 0.06047,0.0,2.46,0.488,6.153,68.8,3.2797,193,17.8,29.6 252 | 0.25387,0.0,6.91,0.448,5.399,95.3,5.87,233,17.9,14.4 253 | 3.77498,0.0,18.1,0.655,5.952,84.7,2.8715,666,20.2,19.0 254 | 0.19539,0.0,10.81,0.413,6.245,6.2,5.2873,305,19.2,23.4 255 | 0.3494,0.0,9.9,0.544,5.972,76.7,3.1025,304,18.4,20.3 256 | 11.0874,0.0,18.1,0.718,6.411,100.0,1.8589,666,20.2,16.7 257 | 5.69175,0.0,18.1,0.583,6.114,79.8,3.5459,666,20.2,19.1 258 | 0.08447,0.0,4.05,0.51,5.859,68.7,2.7019,296,16.6,22.6 259 | 0.44791,0.0,6.2,0.507,6.726,66.5,3.6519,307,17.4,29.0 260 | 28.6558,0.0,18.1,0.597,5.155,100.0,1.5894,666,20.2,16.3 261 | 0.28955,0.0,10.59,0.489,5.412,9.8,3.5875,277,18.6,23.7 262 | 2.01019,0.0,19.58,0.605,7.929,96.2,2.0459,403,14.7,50.0 263 | 4.87141,0.0,18.1,0.614,6.484,93.6,2.3053,666,20.2,16.7 264 | 0.14455,12.5,7.87,0.524,6.172,96.1,5.9505,311,15.2,27.1 265 | 0.05497,0.0,5.19,0.515,5.985,45.4,4.8122,224,20.2,19.0 266 | 4.34879,0.0,18.1,0.58,6.167,84.0,3.0334,666,20.2,19.9 267 | 0.06129,20.0,3.33,0.4429,7.645,49.7,5.2119,216,14.9,46.0 268 | 6.71772,0.0,18.1,0.713,6.749,92.6,2.3236,666,20.2,13.4 269 | 0.07151,0.0,4.49,0.449,6.121,56.8,3.7476,247,18.5,22.2 270 | 0.11329,30.0,4.93,0.428,6.897,54.3,6.3361,300,16.6,22.0 271 | 10.8342,0.0,18.1,0.679,6.782,90.8,1.8195,666,20.2,7.5 272 | 3.83684,0.0,18.1,0.77,6.251,91.1,2.2955,666,20.2,19.9 273 | 0.12757,30.0,4.93,0.428,6.393,7.8,7.0355,300,16.6,23.7 274 | 1.15172,0.0,8.14,0.538,5.701,95.0,3.7872,307,21.0,13.1 275 | 0.01301,35.0,1.52,0.442,7.241,49.3,7.0379,284,15.5,32.7 276 | 0.09178,0.0,4.05,0.51,6.416,84.1,2.6463,296,16.6,23.6 277 | 0.0536,21.0,5.64,0.439,6.511,21.1,6.8147,243,16.8,25.0 278 | 0.12802,0.0,8.56,0.52,6.474,97.1,2.4329,384,20.9,19.8 279 | 0.07978,40.0,6.41,0.447,6.482,32.1,4.1403,254,17.6,29.1 280 | 0.04113,25.0,4.86,0.426,6.727,33.5,5.4007,281,19.0,28.0 281 | 1.13081,0.0,8.14,0.538,5.713,94.1,4.233,307,21.0,12.7 282 | 88.9762,0.0,18.1,0.671,6.968,91.9,1.4165,666,20.2,10.4 283 | 0.26838,0.0,9.69,0.585,5.794,70.6,2.8927,391,19.2,18.3 284 | 11.1081,0.0,18.1,0.668,4.906,100.0,1.1742,666,20.2,13.8 285 | 0.14103,0.0,13.92,0.437,5.79,58.0,6.32,289,16.0,20.3 286 | 8.98296,0.0,18.1,0.77,6.212,97.4,2.1222,666,20.2,17.8 287 | 0.12329,0.0,10.01,0.547,5.913,92.9,2.3534,432,17.8,18.8 288 | 0.25199,0.0,10.59,0.489,5.783,72.7,4.3549,277,18.6,22.5 289 | 0.22188,20.0,6.96,0.464,7.691,51.8,4.3665,223,18.6,35.2 290 | 0.08873,21.0,5.64,0.439,5.963,45.7,6.8147,243,16.8,19.7 291 | 0.02899,40.0,1.25,0.429,6.939,34.5,8.7921,335,19.7,26.6 292 | 0.08308,0.0,2.46,0.488,5.604,89.8,2.9879,193,17.8,26.4 293 | 0.01311,90.0,1.22,0.403,7.249,21.9,8.6966,226,17.9,35.4 294 | 0.13158,0.0,10.01,0.547,6.176,72.5,2.7301,432,17.8,21.2 295 | 0.02009,95.0,2.68,0.4161,8.034,31.9,5.118,224,14.7,50.0 296 | 0.09252,30.0,4.93,0.428,6.606,42.2,6.1899,300,16.6,23.3 297 | 0.15038,0.0,25.65,0.581,5.856,97.0,1.9444,188,19.1,17.3 298 | 0.2498,0.0,21.89,0.624,5.857,98.2,1.6686,437,21.2,13.3 299 | 0.57834,20.0,3.97,0.575,8.297,67.0,2.4216,264,13.0,50.0 300 | 0.05023,35.0,6.06,0.4379,5.706,28.4,6.6407,304,16.9,17.1 301 | 9.72418,0.0,18.1,0.74,6.406,97.2,2.0651,666,20.2,17.1 302 | 0.22969,0.0,10.59,0.489,6.326,52.5,4.3549,277,18.6,24.4 303 | 0.22876,0.0,8.56,0.52,6.405,85.4,2.7147,384,20.9,18.6 304 | 9.59571,0.0,18.1,0.693,6.404,100.0,1.639,666,20.2,12.1 305 | 1.62864,0.0,21.89,0.624,5.019,100.0,1.4394,437,21.2,14.4 306 | 0.05646,0.0,12.83,0.437,6.232,53.7,5.0141,398,18.7,21.2 307 | 0.52058,0.0,6.2,0.507,6.631,76.5,4.148,307,17.4,25.1 308 | 1.20742,0.0,19.58,0.605,5.875,94.6,2.4259,403,14.7,17.4 309 | 0.17171,25.0,5.13,0.453,5.966,93.4,6.8185,284,19.7,16.0 310 | 2.44953,0.0,19.58,0.605,6.402,95.2,2.2625,403,14.7,22.3 311 | 0.04741,0.0,11.93,0.573,6.03,80.8,2.505,273,21.0,11.9 312 | 0.04379,80.0,3.37,0.398,5.787,31.1,6.6115,337,16.1,19.4 313 | 0.01432,100.0,1.32,0.411,6.816,40.5,8.3248,256,15.1,31.6 314 | 0.03427,0.0,5.19,0.515,5.869,46.3,5.2311,224,20.2,19.5 315 | 0.18159,0.0,7.38,0.493,6.376,54.3,4.5404,287,19.6,23.1 316 | 5.58107,0.0,18.1,0.713,6.436,87.9,2.3158,666,20.2,14.3 317 | 23.6482,0.0,18.1,0.671,6.38,96.2,1.3861,666,20.2,13.1 318 | 2.14918,0.0,19.58,0.871,5.709,98.5,1.6232,403,14.7,19.4 319 | 2.33099,0.0,19.58,0.871,5.186,93.8,1.5296,403,14.7,17.8 320 | 0.2896,0.0,9.69,0.585,5.39,72.9,2.7986,391,19.2,19.7 321 | 0.06466,70.0,2.24,0.4,6.345,20.1,7.8278,358,14.8,22.5 322 | 0.01381,80.0,0.46,0.422,7.875,32.0,5.6484,255,14.4,50.0 323 | 0.03306,0.0,5.19,0.515,6.059,37.3,4.8122,224,20.2,20.6 324 | 0.35233,0.0,21.89,0.624,6.454,98.4,1.8498,437,21.2,17.1 325 | 0.06724,0.0,3.24,0.46,6.333,17.2,5.2146,430,16.9,22.6 326 | 0.04294,28.0,15.04,0.464,6.249,77.3,3.615,270,18.2,20.6 327 | 4.55587,0.0,18.1,0.718,3.561,87.9,1.6132,666,20.2,27.5 328 | 0.66351,20.0,3.97,0.647,7.333,100.0,1.8946,264,13.0,36.0 329 | 7.67202,0.0,18.1,0.693,5.747,98.9,1.6334,666,20.2,8.5 330 | 0.0136,75.0,4.0,0.41,5.888,47.6,7.3197,469,21.1,18.9 331 | 0.17142,0.0,6.91,0.448,5.682,33.8,5.1004,233,17.9,19.3 332 | 0.08221,22.0,5.86,0.431,6.957,6.8,8.9067,330,19.1,29.6 333 | 0.31533,0.0,6.2,0.504,8.266,78.3,2.8944,307,17.4,44.8 334 | 6.96215,0.0,18.1,0.7,5.713,97.0,1.9265,666,20.2,15.1 335 | 20.7162,0.0,18.1,0.659,4.138,100.0,1.1781,666,20.2,11.9 336 | 0.12083,0.0,2.89,0.445,8.069,76.0,3.4952,276,18.0,38.7 337 | 0.01951,17.5,1.38,0.4161,7.104,59.5,9.2229,216,18.6,33.0 338 | 0.09512,0.0,12.83,0.437,6.286,45.0,4.5026,398,18.7,21.4 339 | 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6.9,3.1,5.4,2.1,2 4 | 5.1,3.3,1.7,0.5,0 5 | 6.0,3.4,4.5,1.6,1 6 | 5.5,2.5,4.0,1.3,1 7 | 6.2,2.9,4.3,1.3,1 8 | 5.5,4.2,1.4,0.2,0 9 | 6.3,2.8,5.1,1.5,2 10 | 5.6,3.0,4.1,1.3,1 11 | 6.7,2.5,5.8,1.8,2 12 | 7.1,3.0,5.9,2.1,2 13 | 4.3,3.0,1.1,0.1,0 14 | 5.6,2.8,4.9,2.0,2 15 | 5.5,2.3,4.0,1.3,1 16 | 6.0,2.2,4.0,1.0,1 17 | 5.1,3.5,1.4,0.2,0 18 | 5.7,2.6,3.5,1.0,1 19 | 4.8,3.4,1.9,0.2,0 20 | 5.1,3.4,1.5,0.2,0 21 | 5.7,2.5,5.0,2.0,2 22 | 5.4,3.4,1.7,0.2,0 23 | 5.6,3.0,4.5,1.5,1 24 | 6.3,2.9,5.6,1.8,2 25 | 6.3,2.5,4.9,1.5,1 26 | 5.8,2.7,3.9,1.2,1 27 | 6.1,3.0,4.6,1.4,1 28 | 5.2,4.1,1.5,0.1,0 29 | 6.7,3.1,4.7,1.5,1 30 | 6.7,3.3,5.7,2.5,2 31 | 6.4,2.9,4.3,1.3,1 32 | -------------------------------------------------------------------------------- /IRIS_data/iris_training.csv: -------------------------------------------------------------------------------- 1 | 120,4,setosa,versicolor,virginica 2 | 6.4,2.8,5.6,2.2,2 3 | 5.0,2.3,3.3,1.0,1 4 | 4.9,2.5,4.5,1.7,2 5 | 4.9,3.1,1.5,0.1,0 6 | 5.7,3.8,1.7,0.3,0 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6.1,2.8,4.7,1.2,1 94 | 5.1,3.5,1.4,0.3,0 95 | 4.6,3.1,1.5,0.2,0 96 | 6.5,3.0,5.8,2.2,2 97 | 4.6,3.4,1.4,0.3,0 98 | 4.6,3.2,1.4,0.2,0 99 | 7.7,2.8,6.7,2.0,2 100 | 5.9,3.2,4.8,1.8,1 101 | 5.1,3.8,1.6,0.2,0 102 | 4.9,3.0,1.4,0.2,0 103 | 4.9,2.4,3.3,1.0,1 104 | 4.5,2.3,1.3,0.3,0 105 | 5.8,2.7,4.1,1.0,1 106 | 5.0,3.4,1.6,0.4,0 107 | 5.2,3.4,1.4,0.2,0 108 | 5.3,3.7,1.5,0.2,0 109 | 5.0,3.6,1.4,0.2,0 110 | 5.6,2.9,3.6,1.3,1 111 | 4.8,3.1,1.6,0.2,0 112 | 6.3,2.7,4.9,1.8,2 113 | 5.7,2.8,4.1,1.3,1 114 | 5.0,3.0,1.6,0.2,0 115 | 6.3,3.3,6.0,2.5,2 116 | 5.0,3.5,1.6,0.6,0 117 | 5.5,2.6,4.4,1.2,1 118 | 5.7,3.0,4.2,1.2,1 119 | 4.4,2.9,1.4,0.2,0 120 | 4.8,3.0,1.4,0.1,0 121 | 5.5,2.4,3.7,1.0,1 122 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Yiqi Yan 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /MNIST_data/t10k-images-idx3-ubyte.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SaoYan/LearningTensorflow/cc3e5e2583bb9f90f7646486d6c8ed373b5cc844/MNIST_data/t10k-images-idx3-ubyte.gz -------------------------------------------------------------------------------- /MNIST_data/t10k-labels-idx1-ubyte.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SaoYan/LearningTensorflow/cc3e5e2583bb9f90f7646486d6c8ed373b5cc844/MNIST_data/t10k-labels-idx1-ubyte.gz -------------------------------------------------------------------------------- /MNIST_data/train-images-idx3-ubyte.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SaoYan/LearningTensorflow/cc3e5e2583bb9f90f7646486d6c8ed373b5cc844/MNIST_data/train-images-idx3-ubyte.gz -------------------------------------------------------------------------------- /MNIST_data/train-labels-idx1-ubyte.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SaoYan/LearningTensorflow/cc3e5e2583bb9f90f7646486d6c8ed373b5cc844/MNIST_data/train-labels-idx1-ubyte.gz -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # LearningTensorflow 2 | 3 | ## These demos are all based on Tensorflow's low level APIs. 4 | ## Tensorflow has witnessed huge changes. For example, Keras has been merged to Tensorflow as high level APIs. In Tensorflow 2.0, dynamic dynamic computational graph replaces static graph as the default pattern. Therefore, some of the demos in this repo may be out-of-date. Just use them as a reference. 5 | 6 | *** 7 | New Updates: 8 | * March 8, 2018: adding new demo for reading data via queue-based input pipeline; [source code](https://github.com/SaoYan/LearningTensorflow/blob/master/exp11_user_dataset_low_API_1.py) 9 | * March 27, 2018: adding new demo for reading data using high level API tf.data; [source code](https://github.com/SaoYan/LearningTensorflow/blob/master/exp13_user_dataset_high_API_1.py) 10 | 11 | *** 12 | 13 | Tutorials (in Chinese) on my WeChat Public Account: 14 | ![public-account.jpg](https://github.com/SaoYan/LearningTensorflow/blob/master/Figures/public-account.jpg) 15 | 16 | ## exp1_basic_usage.py: 17 | This demo helps to understand basic concepts including graph, Session, placeholder, constant and Variable. 18 | 19 | ## exp2_simple_linear_model.py 20 | This demo shows how to build a linear regression model. 21 | The key is to help understand the framework of a model training pipeline in Tensorflow. 22 | 1. Define tensor objects. 23 | optimizable model parameters: defined as Variable 24 | fixed model parameters: defined as constant 25 | model inputs: defined as placeholder (to be "fed" by training or test data) 26 | 2. Define optimization object 27 | This can be MSE (e.g. linear regression), cross entropy loss (e.g. logistic regression, softmax regression), or other possible ones. 28 | 3. Pick an optimizer 29 | This can be SGD, SGD with momentum, Adam, and so on. This demo is so simple that SGD is enough. 30 | 4. Run optimization 31 | In step (1)~(3), we actually build a Computational Graph, which is no more than a model before we RUN it with Session 32 | In the final step, use Session to run the optimizer (the one defined in step 3). 33 | Remember to FEED each placeholder when running the graph! 34 | 35 | ## exp3_SoftmaxRegression_mnist.py 36 | Let's try something more interesting! 37 | This demo shows how to build a sofmax regression model for the MNIST dataset. 38 | * no hiden layer 39 | * cost function: cross entropy loss 40 | * momentum optimizer, learning rate 0.1, momentum 0.9 41 | * batch size 100 42 | * maximum iteration 1000 43 | * no regularization, no dropout 44 | 45 | ## exp4_CNN_mnist.py 46 | Of course the CNN model cannot be missed! 47 | This demo shows how to build a CNN model for MNIST dataset. 48 | * hidden layer: 2 convolution layers, each of which followed by one max-pooling layer 49 | * output: one fully connected layer (with dropout) 50 | * cost function: cross entropy loss 51 | * Adam optimizer, learning rate 1e-4 52 | * batch size 100 53 | * maximum iteration 20000 54 | * using dropout, dropout probability 0.5 (remember using dropout just in training process, no dropout when testing!) 55 | 56 | ## exp5_Iris_data_set.py 57 | This is a demo for use of Tensorflow high level API tf.contrib. 58 | For simplicity, use the IRIS dataset and a simple 3-layer feed forward network. 59 | 60 | ## exp6_Customer_InputFun.py 61 | It is often the case that we need to pre-processing the dataset. Customizing our own input_function is a good choice. Then you can provide the function handle when running the graph. 62 | For more detail, see [Tensorflow Document](https://www.tensorflow.org/get_started/input_fn). 63 | 64 | ## exp7~exp9 65 | These three demos are all about Tensorboard. All of them are extended based on exp4. (The model is exactly the same, I just add some extra codes for visualizing on Tensorboard.) 66 | 67 | First, you can try Tensorboard and see how amazing it is. 68 | 1. Just run exp9, you will see a folder named 'MNIST_logs' in your current path. 69 | 2. Open command line. Run command 70 | ``` 71 | tensorboard --logdir MNIST_logs 72 | ``` 73 | 3. You will see something similar to the following content: 74 | > TensorBoard 0.4.0 at http://sao:6006 (Press CTRL+C to quit) 75 | 76 | 4. Open your browser, go to the address shown on your command line 77 | 78 | Now you may want to see the details of the demo. Here is the overview: 79 | Generally speaking, I wrap four types of information for visualization on Tensorboard 80 | 1. Training information. For example, record loss value to plot a trainging curve, or record weight values to plot a histogram for visualizing their distribution. 81 | ![fig1.png](https://github.com/SaoYan/LearningTensorflow/blob/master/Figures/fig1.png) 82 | ![fig2.png](https://github.com/SaoYan/LearningTensorflow/blob/master/Figures/fig2.png) 83 | 2. Images. In this demo only original MNIST data is visualized. In practice, it may be more useful for you to visualize features of hidden layers. 84 | ![fig3.png](https://github.com/SaoYan/LearningTensorflow/blob/master/Figures/fig3.png) 85 | 3. Embeddings. 86 | ![fig4.png](https://github.com/SaoYan/LearningTensorflow/blob/master/Figures/fig4.png) 87 | 4. Comnputational Graph. In addition to the architecture of the graph, information on running status is also shown (including memory useage, computing time, etc.) 88 | ![fig5.png](https://github.com/SaoYan/LearningTensorflow/blob/master/Figures/fig5.png) 89 | 90 | **exp7_TensorBoard**: visualizing training information, images, and simplest version of computational graph 91 | **exp8_Embedding_Visualization**: add Embeddings for visualization 92 | **exp9_Graph_Visualization**: add running status information 93 | -------------------------------------------------------------------------------- /exp01_basic_usage.py: -------------------------------------------------------------------------------- 1 | import os 2 | os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" 3 | os.environ["CUDA_VISIBLE_DEVICES"] = "0" 4 | 5 | # This is an example code for getting started with tensorflow 6 | print("getting started with tensorflow\n") 7 | 8 | # import tensorflow 9 | print("import...\n") 10 | import tensorflow as tf 11 | sess = tf.Session() 12 | 13 | # build your first computational graph 14 | # with constant nodes 15 | print("\nbuild computational graph with constant nodes...\n") 16 | node1 = tf.constant(3.0, tf.float32) 17 | node2 = tf.constant(4.0) # also tf.float32 impicitly 18 | node3 = tf.add(node1, node2) 19 | print(node1) 20 | print(node2) 21 | print(node3) 22 | 23 | # this is just BUILDING the graph 24 | # to actually evaluate the graph, you have to run with a SESSION 25 | print("\nrun the graph...\n") 26 | print(sess.run([node1, node2, node3])) 27 | 28 | # you may want a more flexible graph 29 | # with nodes which can FEEDED with exteral inputs 30 | # to do this we use PLACEHODER 31 | print("\nbuild and graph with exteral inputs...\n") 32 | a = tf.placeholder(tf.float32) 33 | b = tf.placeholder(tf.float32) 34 | adder = a + b # opporator + provides a shotcut for tf.add 35 | 36 | # to FEED the PLACEHODERs 37 | # use the feed_dict parameter 38 | print("\nrun the graph...\n") 39 | data1 = {a: 3, b: 2.5} 40 | print(sess.run(adder,feed_dict = data1)) 41 | data2 = {a: [1, 2.5], b: [5, 4]} 42 | print(sess.run(adder, data2)) # "deed_dict" can be omited 43 | 44 | # we can continue to make the graph more complex 45 | print("\na more complex graph...\n") 46 | add_and_triple = adder * 3 # for a shotcut just use "adder*3" 47 | print(sess.run(add_and_triple, {a:1.0, b: 3.5})) 48 | 49 | # in machine learning there are various parameters (weights, bias, etc.) 50 | # use VARIABLE 51 | # having parameters makes a model trainable 52 | print("\nbuild a trainable model...\n") 53 | W = tf.Variable(.3, tf.float32) 54 | # we can also get W by: 55 | # C = tf.constant(.3, tf.float32) 56 | # W = tf.Variable(C) 57 | b = tf.Variable(-.3, tf.float32) 58 | x = tf.placeholder(tf.float32) 59 | linear_model = W * x + b 60 | 61 | # you have to explicitly initialize the Variables 62 | print("\ninitialize model parameters...\n") 63 | init = tf.global_variables_initializer() 64 | sess.run(init) # note that the Variables are uninitialized until we call sess.run 65 | # not that we've initialized the Variables, we can compute 66 | print(sess.run([W, b])) 67 | 68 | # since x is a placeholder, we can feed it and evaluate the model 69 | print("\nrun the model...\n") 70 | print(sess.run(linear_model, {x: 1.0})) 71 | print(sess.run(linear_model, {x: [1,2,3,4]})) # we can evaluate model for several values of x simultaneously 72 | 73 | # you can change the value of variables 74 | print("\nassign new values to variables...\n") 75 | W = tf.assign(W, 1.0) 76 | b = tf.assign(b, -1.0) 77 | sess.run([W, b]) # remember to run ! 78 | print(sess.run(linear_model, {x: [1,2,3,4]})) # the result is computed with new values 79 | -------------------------------------------------------------------------------- /exp02_simple_linear_model.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import numpy as np 3 | import os 4 | 5 | os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" 6 | os.environ["CUDA_VISIBLE_DEVICES"] = "0" 7 | 8 | print("train a simple linear model by yourself !") 9 | 10 | # model parameters 11 | W = tf.Variable(0.3, tf.float32) 12 | b = tf.Variable(-0.3, tf.float32) 13 | 14 | # model inputs & outputs 15 | x = tf.placeholder(tf.float32) 16 | y = tf.placeholder(tf.float32) 17 | 18 | # the model 19 | out = W * x + b 20 | 21 | # loss function 22 | loss = tf.reduce_sum(tf.square(out - y)) 23 | 24 | # optimizer 25 | optimizer = tf.train.GradientDescentOptimizer(0.001) 26 | train = optimizer.minimize(loss) 27 | 28 | # training data 29 | x_train = np.random.random_sample((100,)).astype(np.float32) 30 | y_train = np.random.random_sample((100,)).astype(np.float32) 31 | 32 | # training 33 | init = tf.global_variables_initializer() 34 | with tf.Session() as sess: 35 | sess.run(init) 36 | for i in range(1000): 37 | sess.run(train, {x:x_train, y:y_train}) 38 | current_loss = sess.run(loss, {x:x_train, y:y_train}) 39 | print("iter step %d training loss %f" % (i, current_loss)) 40 | print(sess.run(W)) 41 | print(sess.run(b)) 42 | -------------------------------------------------------------------------------- /exp03_SoftmaxRegression_mnist.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | from tensorflow.examples.tutorials.mnist import input_data 3 | import os 4 | 5 | os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" 6 | os.environ["CUDA_VISIBLE_DEVICES"] = "0" 7 | 8 | print("classify the MNIST dataset using softmax regression") 9 | 10 | # load the dataset 11 | mnist = input_data.read_data_sets("./MNIST_data", one_hot = True) # labels are "one-hot vectors" 12 | 13 | # model parameters 14 | W = tf.Variable(tf.random_uniform([784, 10])) 15 | b = tf.Variable(tf.random_uniform([10])) 16 | learning_rate = 0.1 17 | momentum = 0.9 18 | batch_size = 64 19 | max_iter = 1000 20 | 21 | # model inputs & outputs 22 | x = tf.placeholder(tf.float32, [None, 784]) # "None" means that a dimension can be of any length 23 | y_ = tf.placeholder(tf.float32, [None, 10]) 24 | 25 | # creat model 26 | y = tf.matmul(x, W) + b 27 | 28 | # loss function 29 | cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)) 30 | 31 | # optimizer 32 | #optimizer = tf.train.GradientDescentOptimizer(learning_rate) 33 | optimizer = tf.train.MomentumOptimizer(learning_rate, momentum) 34 | train_step = optimizer.minimize(cross_entropy) 35 | 36 | # accuracy test 37 | correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) # this gives us a list of booleans 38 | accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # booleans->float32 39 | 40 | # training 41 | init = tf.global_variables_initializer() 42 | with tf.Session() as sess: 43 | sess.run(init) 44 | for iter in range(3000): 45 | batch_x, batch_y = mnist.train.next_batch(batch_size) 46 | test_x = mnist.test.images 47 | test_y = mnist.test.labels 48 | sess.run(train_step, {x:batch_x, y_:batch_y}) 49 | if iter%10 == 0: 50 | train_loss = sess.run(cross_entropy,{x:batch_x,y_:batch_y}) 51 | train_accuracy = sess.run(accuracy, {x:batch_x,y_:batch_y}) 52 | test_accuracy = sess.run(accuracy, {x: test_x, y_: test_y}) 53 | print("iter step %d, loss %f, training accuracy %f, test accuracy %f" % 54 | (iter,train_loss,train_accuracy,test_accuracy)) 55 | -------------------------------------------------------------------------------- /exp04_CNN_mnist.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import os 3 | 4 | os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" 5 | os.environ["CUDA_VISIBLE_DEVICES"] = "0" 6 | 7 | print("classify the MNIST dataset using CNN ~~") 8 | 9 | # define some functions 10 | def weight(shape): 11 | initial = tf.truncated_normal(shape, stddev=0.1) 12 | return tf.Variable(initial) 13 | 14 | def bias(shape): 15 | initial = tf.constant(0.1, shape=shape) 16 | return tf.Variable(initial) 17 | 18 | def conv2d(x, W, padding='SAME'): 19 | return tf.nn.conv2d(x, W, strides = [1,1,1,1], padding = padding) 20 | 21 | def max_pool_2x2(x): 22 | return tf.nn.max_pool(x, ksize = [1,2,2,1], strides = [1,2,2,1], padding = 'SAME') 23 | 24 | # load the dataset 25 | from tensorflow.examples.tutorials.mnist import input_data 26 | mnist = input_data.read_data_sets("./MNIST_data", one_hot = True) 27 | 28 | # inputs & outputs 29 | x = tf.placeholder(tf.float32, [None, 28, 28, 1]) 30 | y_ = tf.placeholder(tf.float32, [None, 10]) 31 | 32 | # build CNN model 33 | W1 = 5; H1 = 5; C1 = 32 34 | W2 = 5; H2 = 5; C2 = 64 35 | C3 = 1024 36 | # 1st conv layer 37 | W_conv1 = weight([W1,H1,1,C1]) 38 | b_conv1 = bias([C1]) 39 | h_conv1 = conv2d(x, W_conv1) + b_conv1 40 | h_conv1 = tf.nn.relu(h_conv1) 41 | h_pool1 = max_pool_2x2(h_conv1) 42 | # 2nd conv layer 43 | W_conv2 = weight([W2,H2,C1,C2]) 44 | b_conv2 = bias([C2]) 45 | h_conv2 = conv2d(h_pool1, W_conv2) + b_conv2 46 | h_conv2 = tf.nn.relu(h_conv2) 47 | h_pool2 = max_pool_2x2(h_conv2) # feature size: 7x7xC2 48 | # densely connected layer with 1024 neurons 49 | W_fc = weight([7,7,C2,C3]) 50 | b_fc = bias([C3]) 51 | h_fc = conv2d(h_pool2, W_fc, padding='VALID') + b_fc 52 | h_fc = tf.nn.relu(h_fc) 53 | # dropout 54 | p_keep = tf.placeholder(tf.float32) 55 | h_fc_drop = tf.nn.dropout(h_fc, p_keep) 56 | # output layer 57 | W_output = weight([1,1,C3,10]) 58 | b_output = bias([10]) 59 | y = conv2d(h_fc_drop, W_output, padding='VALID') + b_output 60 | y = tf.squeeze(y, [1,2]) # shape batchx1x1x10---->batchx10 61 | 62 | # cost 63 | cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels = y_, logits = y) 64 | loss = tf.reduce_mean(cross_entropy) # average over the batch 65 | optimizer = tf.train.AdamOptimizer(1e-4) 66 | train_step = optimizer.minimize(loss) 67 | 68 | # accuracy testing 69 | correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) 70 | accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 71 | 72 | # training 73 | max_iter = 20000 74 | batch_size = 64 75 | keep_prob = 0.5 76 | with tf.Session() as sess: 77 | # initialize all parameters 78 | sess.run(tf.global_variables_initializer()) 79 | for iter in range(max_iter): 80 | # extract a batch of training data from MNIST dataset 81 | batch_x, batch_y = mnist.train.next_batch(batch_size) 82 | batch_x = batch_x.reshape((-1,28,28,1)) 83 | # update parameters 84 | sess.run(train_step, {x:batch_x, y_:batch_y, p_keep: keep_prob}) 85 | if iter%100 == 0: 86 | # the accuracy over the training batch 87 | train_accuracy = sess.run(accuracy, {x:batch_x, y_:batch_y, p_keep: 1.0}) # turn off dropout during testing 88 | # the accuracy over the whole test set 89 | # unable to load the whole test set at one time because of memory limitation 90 | test_accuracy = 0.0 91 | for i in range(100): 92 | test_x, test_y = mnist.test.next_batch(100) 93 | test_x = test_x.reshape((-1,28,28,1)) 94 | test_acc = sess.run(accuracy, {x:test_x, y_:test_y, p_keep:1.0}) 95 | test_accuracy = test_accuracy + test_acc 96 | test_accuracy = test_accuracy/100.0 97 | # print the result 98 | print("iter step %d batch accuracy %f test accuracy %f" % 99 | (iter, train_accuracy, test_accuracy)) 100 | -------------------------------------------------------------------------------- /exp05_Iris_data_set.py: -------------------------------------------------------------------------------- 1 | ''' 2 | this code is an example use of tf.contrib.learn to construct a neural network classifier; 3 | train it on the Iris data set to predict flower species based on sepal/petal geometry 4 | ''' 5 | from __future__ import absolute_import 6 | from __future__ import division 7 | from __future__ import print_function 8 | 9 | import tensorflow as tf 10 | import numpy as np 11 | import os 12 | 13 | os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" 14 | os.environ["CUDA_VISIBLE_DEVICES"] = "0" 15 | 16 | # Data sets 17 | IRIS_TRAINING = "./IRIS_data/iris_training.csv" 18 | IRIS_TEST = "./IRIS_data/iris_test.csv" 19 | 20 | # Load datasets 21 | training_set = tf.contrib.learn.datasets.base.load_csv_with_header( 22 | filename=IRIS_TRAINING, 23 | target_dtype=np.int, 24 | features_dtype=np.float32) 25 | test_set = tf.contrib.learn.datasets.base.load_csv_with_header( 26 | filename=IRIS_TEST, 27 | target_dtype=np.int, 28 | features_dtype=np.float32) 29 | 30 | # Specify that all features have real-value data 31 | feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)] 32 | 33 | # Build 3 layer DNN with 10, 20, 10 units respectively 34 | classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns, 35 | hidden_units=[10, 20, 10], 36 | n_classes=3, 37 | model_dir="./IRIS_logs") 38 | 39 | # Fit model 40 | # tf.contrib.learn offers a variety of predefined models, called Estimators 41 | classifier.fit(x=training_set.data, 42 | y=training_set.target, 43 | steps=2000) 44 | 45 | # Evaluate accuracy 46 | accuracy_score = classifier.evaluate(x=test_set.data, 47 | y=test_set.target)["accuracy"] 48 | print('Accuracy: {0:f}'.format(accuracy_score)) 49 | 50 | # Classify two new flower samples 51 | new_samples = np.array( 52 | [[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=float) 53 | y = list(classifier.predict(new_samples, as_iterable=True)) 54 | print('Predictions: {}'.format(str(y))) 55 | -------------------------------------------------------------------------------- /exp06_Customer_InputFun.py: -------------------------------------------------------------------------------- 1 | ''' 2 | in exp5, no manipulation of source data is required; 3 | but in cases where more feature engineering is needed, 4 | tf.contrib.learn supports using a custom input function; 5 | in this function you can do your own preprocessing work 6 | 7 | for more detail, see the following URL 8 | https://www.tensorflow.org/get_started/input_fn 9 | 10 | two important things worth paying attention on: 11 | 1) if our feature/label data is stored in pandas dataframes or numpy arrays, how to convert it to Tensors? 12 | see https://www.tensorflow.org/get_started/input_fn#converting_feature_data_to_tensors 13 | 2) how to pass input_fn Data to Your Model? 14 | see https://www.tensorflow.org/get_started/input_fn#passing_input_fn_data_to_your_model 15 | ''' 16 | 17 | from __future__ import absolute_import 18 | from __future__ import division 19 | from __future__ import print_function 20 | 21 | import itertools 22 | import pandas as pd 23 | import tensorflow as tf 24 | import os 25 | 26 | os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" 27 | os.environ["CUDA_VISIBLE_DEVICES"] = "0" 28 | 29 | # set logging verbosity to INFO for more detailed log output 30 | # see https://www.tensorflow.org/get_started/monitors#enabling_logging_with_tensorflow 31 | tf.logging.set_verbosity(tf.logging.INFO) 32 | 33 | # Importing the Housing Data 34 | COLUMNS = ["crim", "zn", "indus", "nox", "rm", "age", 35 | "dis", "tax", "ptratio", "medv"] 36 | FEATURES = ["crim", "zn", "indus", "nox", "rm", 37 | "age", "dis", "tax", "ptratio"] 38 | LABEL = "medv" 39 | 40 | training_set = pd.read_csv("./BOSTON_data/boston_train.csv", skipinitialspace=True, 41 | skiprows=1, names=COLUMNS) 42 | test_set = pd.read_csv("./BOSTON_data/boston_test.csv", skipinitialspace=True, 43 | skiprows=1, names=COLUMNS) 44 | prediction_set = pd.read_csv("./BOSTON_data/boston_predict.csv", skipinitialspace=True, 45 | skiprows=1, names=COLUMNS) 46 | 47 | # define FeatureColumns 48 | feature_cols = [tf.contrib.layers.real_valued_column(k) 49 | for k in FEATURES] 50 | 51 | # creating the DNNRegressor model 52 | regressor = tf.contrib.learn.DNNRegressor(feature_columns=feature_cols, 53 | hidden_units=[10, 10], 54 | model_dir="./BOSTON_logs") 55 | 56 | # build the input function 57 | def my_input_fun(data_set): 58 | # preprocessing 59 | # for example: feature scaling 60 | # in this exp, none is needed 61 | 62 | # return 63 | # 1) a dict: (key)feature columns->(value)Tensors with the corresponding feature data 64 | # 2) a Tensor containing labels 65 | feature_cols = {k: tf.constant(data_set[k].values) 66 | for k in FEATURES} 67 | labels = tf.constant(data_set[LABEL].values) 68 | return feature_cols, labels 69 | 70 | # training 71 | # see log output for INFO on training loss(every 100 steps) 72 | regressor.fit(input_fn=lambda: my_input_fun(training_set), steps=5000) 73 | 74 | # evaluating 75 | ev = regressor.evaluate(input_fn=lambda: my_input_fun(test_set), steps=1) 76 | loss_score = ev['loss'] 77 | print("Loss: %f"%loss_score) 78 | # or 79 | # print("Loss: {0:f}".format(loss_score)) 80 | 81 | # making prediction 82 | y = regressor.predict(input_fn=lambda: my_input_fun(prediction_set)) 83 | # predict() returns an iterator; convert to a list and print predictions 84 | # itertools.islice(y, 6): get the first 6 items from the iterator 85 | predictions = list(itertools.islice(y, 6)) 86 | print ("Predictions: %s"%(str(predictions))) 87 | -------------------------------------------------------------------------------- /exp07_TensorBoard.py: -------------------------------------------------------------------------------- 1 | ''' 2 | use TensorBoard 3 | based on exp4 4 | ''' 5 | 6 | import tensorflow as tf 7 | import os 8 | 9 | os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" 10 | os.environ["CUDA_VISIBLE_DEVICES"] = "0" 11 | 12 | # reference: 13 | # https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard 14 | 15 | # define some functions 16 | def weight(shape, name): 17 | initial = tf.truncated_normal(shape, stddev=0.1) 18 | return tf.Variable(initial,name=name) 19 | #return tf.Variable(tf.truncated_normal(shape, stddev = .1)) # ValueError: None values not supported 20 | 21 | def bias(shape, name): 22 | initial = tf.constant(0.1, shape=shape) 23 | return tf.Variable(initial,name=name) 24 | 25 | def conv2d(x, W): 26 | return tf.nn.conv2d(x, W, strides = [1,1,1,1], padding = 'SAME') 27 | 28 | def max_pool_2x2(x): 29 | return tf.nn.max_pool(x, ksize = [1,2,2,1], strides = [1,2,2,1], padding = 'SAME') 30 | 31 | # load the dataset 32 | from tensorflow.examples.tutorials.mnist import input_data 33 | mnist = input_data.read_data_sets("MNIST_data", one_hot = True) 34 | 35 | # build CNN model 36 | # initialize weights with small random values for symmetry breaking 37 | # initialize weights with positive values for preventing "dead ReLU neurons" 38 | W1 = 5; H1 = 5; C1 = 32 39 | W2 = 5; H2 = 5; C2 = 64 40 | C3 = 1024 41 | with tf.name_scope('input_data'): 42 | x = tf.placeholder(tf.float32, [None, 784]) 43 | y_ = tf.placeholder(tf.float32, [None, 10]) 44 | x_image = tf.reshape(x, [-1,28,28,1]) 45 | tf.summary.image('input', x_image, max_outputs=10) 46 | with tf.name_scope('hidden_1'): 47 | W_conv1 = weight([W1,H1,1,C1],name='weights') 48 | b_conv1 = bias([C1],name='bias') 49 | h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) 50 | h_pool1 = max_pool_2x2(h_conv1) 51 | # summaries 52 | tf.summary.histogram('histogram of W1', W_conv1) 53 | tf.summary.histogram('histogram of b1', b_conv1) 54 | with tf.name_scope('hidden_2'): 55 | W_conv2 = weight([W2,H2,C1,C2],name='weights') 56 | b_conv2 = bias([C2],name='bias') 57 | h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) 58 | h_pool2 = max_pool_2x2(h_conv2) # now that the image size has been reduced to 7x7 59 | # summaries 60 | tf.summary.histogram('histogram of W2', W_conv2) 61 | tf.summary.histogram('histogram of b2', b_conv2) 62 | with tf.name_scope('fully_connected'): 63 | W_fc = weight([7*7*C2, C3],name='weights') 64 | b_fc = bias([C3],name='bias') 65 | h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*C2]) 66 | h_fc = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc) + b_fc) 67 | # summaries 68 | tf.summary.histogram('histogram of W_fc', W_fc) 69 | tf.summary.histogram('histogram of b_fc', b_fc) 70 | with tf.name_scope('dropout'): 71 | p_keep = tf.placeholder(tf.float32) 72 | h_fc_drop = tf.nn.dropout(h_fc, p_keep) 73 | with tf.name_scope('output_layer'): 74 | W_output = weight([C3, 10],name='weights') 75 | b_output = bias([10],name='bias') 76 | y = tf.matmul(h_fc_drop, W_output) + b_output 77 | # summaries 78 | tf.summary.histogram('histogram of W_out', W_output) 79 | tf.summary.histogram('histogram of b_out', b_output) 80 | 81 | # cost 82 | with tf.name_scope('cost'): 83 | cross_entropy = tf.reduce_mean( 84 | tf.nn.softmax_cross_entropy_with_logits(labels = y_, logits = y) 85 | ) 86 | # summary 87 | tf.summary.scalar('test_loss', cross_entropy) 88 | 89 | # training 90 | with tf.name_scope('train'): 91 | optimizer = tf.train.AdamOptimizer(1e-4) 92 | train_step = optimizer.minimize(cross_entropy) 93 | 94 | # accuracy testing 95 | with tf.name_scope('accuracy'): 96 | correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) 97 | accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 98 | # summary 99 | tf.summary.scalar('accuracy', accuracy) 100 | 101 | # training 102 | max_iter = 20000 103 | batch_size = 64 104 | keep_prob = 0.5 105 | with tf.Session() as sess: 106 | # summary writer 107 | summary = tf.summary.merge_all() 108 | train_writer = tf.summary.FileWriter('MNIST_logs/train', sess.graph) 109 | test_writer = tf.summary.FileWriter('MNIST_logs/test' , sess.graph) 110 | # initialization 111 | sess.run(tf.global_variables_initializer()) 112 | for iter in range(max_iter): 113 | batch_x, batch_y = mnist.train.next_batch(batch_size) 114 | if iter%100 == 0: 115 | # report accuracy 116 | # turn off dropout during testing 117 | train_accuracy = accuracy.eval({x:batch_x, y_:batch_y, p_keep: 1.0}) 118 | test_accuracy = 0.0 119 | for i in range(100): 120 | test_x, test_y = mnist.test.next_batch(100) 121 | test_acc = accuracy.eval({x:test_x, y_:test_y, p_keep:1.0}) 122 | test_accuracy = test_accuracy + test_acc 123 | test_accuracy = test_accuracy/100.0 124 | print("iter step %d batch accuracy %f test accuracy %f" % 125 | (iter, train_accuracy, test_accuracy)) 126 | # write summary file 127 | summary_train = sess.run(summary, {x:batch_x, y_:batch_y, p_keep: 1.0}) 128 | summary_test = sess.run(summary, {x:test_x, y_:test_y, p_keep: 1.0}) 129 | train_writer.add_summary(summary_train, iter) 130 | test_writer.add_summary(summary_test, iter) 131 | train_writer.flush() 132 | test_writer.flush() 133 | # train on step 134 | train_step.run({x:batch_x, y_:batch_y, p_keep: keep_prob}) 135 | train_writer.close() 136 | test_writer.close() 137 | -------------------------------------------------------------------------------- /exp08_Embedding_Visualization.py: -------------------------------------------------------------------------------- 1 | import os.path 2 | import tensorflow as tf 3 | from tensorflow.contrib.tensorboard.plugins import projector 4 | import numpy as np 5 | import os 6 | 7 | os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" 8 | os.environ["CUDA_VISIBLE_DEVICES"] = "0" 9 | 10 | # what is embedding? 11 | # https://www.tensorflow.org/versions/master/programmers_guide/embedding 12 | 13 | # how to visualizing embeddings? 14 | # https://www.tensorflow.org/versions/r1.2/get_started/embedding_viz 15 | 16 | # define some functions 17 | def weight(shape,name): 18 | initial = tf.truncated_normal(shape, stddev=0.1) 19 | return tf.Variable(initial,name=name) 20 | 21 | def bias(shape,name): 22 | initial = tf.constant(0.1, shape=shape) 23 | return tf.Variable(initial,name=name) 24 | 25 | def conv2d(x, W): 26 | return tf.nn.conv2d(x, W, strides = [1,1,1,1], padding = 'SAME') 27 | 28 | def max_pool_2x2(x): 29 | return tf.nn.max_pool(x, ksize = [1,2,2,1], strides = [1,2,2,1], padding = 'SAME') 30 | 31 | # load the dataset 32 | from tensorflow.examples.tutorials.mnist import input_data 33 | mnist = input_data.read_data_sets("MNIST_data", one_hot = True) 34 | 35 | # build CNN model 36 | # initialize weights with small random values for symmetry breaking 37 | # initialize weights with positive values for preventing "dead ReLU neurons" 38 | W1 = 5; H1 = 5; C1 = 32 39 | W2 = 5; H2 = 5; C2 = 64 40 | C3 = 1024 41 | with tf.name_scope('input_data'): 42 | x = tf.placeholder(tf.float32, [None, 784]) 43 | y_ = tf.placeholder(tf.float32, [None, 10]) 44 | x_image = tf.reshape(x, [-1,28,28,1]) 45 | tf.summary.image('input', x_image, max_outputs=10) 46 | # 1st conv layer 47 | with tf.name_scope('hidden_conv_1'): 48 | W_conv1 = weight([W1,H1,1,C1],name='weights') 49 | b_conv1 = bias([C1],name='bias') 50 | h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) 51 | h_pool1 = max_pool_2x2(h_conv1) 52 | # summaries 53 | tf.summary.histogram('histogram of W1', W_conv1) 54 | tf.summary.histogram('histogram of b1', b_conv1) 55 | # 2nd conv layer 56 | with tf.name_scope('hidden_conv_2'): 57 | W_conv2 = weight([W2,H2,C1,C2],name='weights') 58 | b_conv2 = bias([C2],name='bias') 59 | h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) 60 | h_pool2 = max_pool_2x2(h_conv2) # now that the image size has been reduced to 7x7 61 | # summaries 62 | tf.summary.histogram('histogram of W2', W_conv2) 63 | tf.summary.histogram('histogram of b2', b_conv2) 64 | # densely connected layer with 1024 neurons 65 | with tf.name_scope('hidden_fully_connected'): 66 | W_fc = weight([7*7*C2, C3],name='weights') 67 | b_fc = bias([C3],name='bias') 68 | h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*C2]) 69 | h_fc = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc) + b_fc) 70 | # summaries 71 | tf.summary.histogram('histogram of W_fc', W_fc) 72 | tf.summary.histogram('histogram of b_fc', b_fc) 73 | # dropout 74 | with tf.name_scope('dropout'): 75 | p_keep = tf.placeholder(tf.float32) 76 | h_fc_drop = tf.nn.dropout(h_fc, p_keep) 77 | # output layer 78 | with tf.name_scope('output_layer'): 79 | W_output = weight([C3, 10],name='weights') 80 | b_output = bias([10],name='bias') 81 | y = tf.matmul(h_fc_drop, W_output) + b_output 82 | # summaries 83 | tf.summary.histogram('histogram of W_out', W_output) 84 | tf.summary.histogram('histogram of b_out', b_output) 85 | tf.summary.histogram('histogram of prob_dist_out', y_) 86 | 87 | # cost 88 | with tf.name_scope('cost'): 89 | cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y_, logits = y)) 90 | # summary 91 | tf.summary.scalar('loss', cross_entropy) 92 | 93 | # training op 94 | with tf.name_scope('train'): 95 | optimizer = tf.train.AdamOptimizer(1e-4) 96 | train_step = optimizer.minimize(cross_entropy) 97 | 98 | # accuracy testing 99 | with tf.name_scope('accuracy'): 100 | correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) 101 | accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 102 | # summary 103 | tf.summary.scalar('accuracy', accuracy) 104 | 105 | # training 106 | max_iter = 20000 107 | batch_size = 64 108 | keep_prob = 0.5 109 | if not os.path.exists('MNIST_logs/train'): 110 | os.mkdir('MNIST_logs') 111 | os.mkdir('MNIST_logs/train') 112 | if not os.path.exists('MNIST_logs/test'): 113 | os.mkdir('MNIST_logs/test') 114 | with tf.Session() as sess: 115 | ###################################################################################### 116 | # embeddings 117 | test_images = tf.Variable(mnist.test.images, name='test_images') 118 | test_labels = tf.argmax(mnist.test.labels,1).eval(session=sess) 119 | with open('MNIST_logs/test/metadata.tsv', 'w') as metadata_file: 120 | for label in test_labels: 121 | metadata_file.write('%d\n' % label) 122 | # associate metadata with the embedding. 123 | config = projector.ProjectorConfig() 124 | embedding = config.embeddings.add() 125 | embedding.tensor_name = test_images.name 126 | embedding.metadata_path = 'metadata.tsv' # Link this tensor to its metadata file 127 | # writers 128 | summary = tf.summary.merge_all() 129 | train_writer = tf.summary.FileWriter('MNIST_logs/train', sess.graph) 130 | test_writer = tf.summary.FileWriter('MNIST_logs/test' , sess.graph) 131 | saver = tf.train.Saver() # Create a saver for writing training checkpoints. 132 | projector.visualize_embeddings(test_writer, config) 133 | ###################################################################################### 134 | # training 135 | sess.run(tf.global_variables_initializer()) 136 | for iter in range(max_iter): 137 | batch_x, batch_y = mnist.train.next_batch(batch_size) 138 | if iter%100 == 0: 139 | # report accuracy 140 | train_accuracy = accuracy.eval({x:batch_x, y_:batch_y, p_keep: 1.0}) # turn off dropout during testing 141 | test_accuracy = 0.0 142 | for i in range(100): 143 | test_x, test_y = mnist.test.next_batch(100) 144 | test_acc = accuracy.eval({x:test_x, y_:test_y, p_keep:1.0}) 145 | test_accuracy = test_accuracy + test_acc 146 | test_accuracy = test_accuracy/100.0 147 | print("iter step %d batch accuracy %f test accuracy %f"%(iter, train_accuracy, test_accuracy)) 148 | # write summary file 149 | summary_train = sess.run(summary, {x:batch_x, y_:batch_y, p_keep: 1.0}) 150 | summary_test = sess.run(summary, {x:test_x, y_:test_y, p_keep: 1.0}) 151 | train_writer.add_summary(summary_train, iter) 152 | test_writer.add_summary(summary_test, iter) 153 | train_writer.flush() 154 | test_writer.flush() 155 | if iter%1000 == 0: 156 | # save checkpoint file 157 | checkpoint_file = os.path.join('MNIST_logs', 'model.ckpt') 158 | saver.save(sess, checkpoint_file, global_step=iter) 159 | # train on setp 160 | train_step.run({x:batch_x, y_:batch_y, p_keep: keep_prob}) 161 | train_writer.close() 162 | test_writer.close() 163 | embedding_writer.close() 164 | -------------------------------------------------------------------------------- /exp09_Graph_Visualization.py: -------------------------------------------------------------------------------- 1 | import os.path 2 | import tensorflow as tf 3 | from tensorflow.contrib.tensorboard.plugins import projector 4 | import numpy as np 5 | import os 6 | 7 | os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" 8 | os.environ["CUDA_VISIBLE_DEVICES"] = "0" 9 | 10 | # reference: 11 | # https://www.tensorflow.org/programmers_guide/graph_viz 12 | 13 | # define some functions 14 | def weight(shape,name): 15 | initial = tf.truncated_normal(shape, stddev=0.1) 16 | return tf.Variable(initial,name=name) 17 | 18 | def bias(shape,name): 19 | initial = tf.constant(0.1, shape=shape) 20 | return tf.Variable(initial,name=name) 21 | 22 | def conv2d(x, W): 23 | return tf.nn.conv2d(x, W, strides = [1,1,1,1], padding = 'SAME') 24 | 25 | def max_pool_2x2(x): 26 | return tf.nn.max_pool(x, ksize = [1,2,2,1], strides = [1,2,2,1], padding = 'SAME') 27 | 28 | # load the dataset 29 | from tensorflow.examples.tutorials.mnist import input_data 30 | mnist = input_data.read_data_sets("MNIST_data", one_hot = True) 31 | 32 | # build CNN model 33 | # initialize weights with small random values for symmetry breaking 34 | # initialize weights with positive values for preventing "dead ReLU neurons" 35 | W1 = 5; H1 = 5; C1 = 32 36 | W2 = 5; H2 = 5; C2 = 64 37 | C3 = 1024 38 | with tf.name_scope('input_data'): 39 | x = tf.placeholder(tf.float32, [None, 784]) 40 | y_ = tf.placeholder(tf.float32, [None, 10]) 41 | x_image = tf.reshape(x, [-1,28,28,1]) 42 | tf.summary.image('input', x_image, max_outputs=10) 43 | # 1st conv layer 44 | with tf.name_scope('hidden_conv_1'): 45 | W_conv1 = weight([W1,H1,1,C1],name='weights') 46 | b_conv1 = bias([C1],name='bias') 47 | h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) 48 | h_pool1 = max_pool_2x2(h_conv1) 49 | # summaries 50 | tf.summary.histogram('histogram of W1', W_conv1) 51 | tf.summary.histogram('histogram of b1', b_conv1) 52 | # 2nd conv layer 53 | with tf.name_scope('hidden_conv_2'): 54 | W_conv2 = weight([W2,H2,C1,C2],name='weights') 55 | b_conv2 = bias([C2],name='bias') 56 | h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) 57 | h_pool2 = max_pool_2x2(h_conv2) # now that the image size has been reduced to 7x7 58 | # summaries 59 | tf.summary.histogram('histogram of W2', W_conv2) 60 | tf.summary.histogram('histogram of b2', b_conv2) 61 | # densely connected layer with 1024 neurons 62 | with tf.name_scope('hidden_fully_connected'): 63 | W_fc = weight([7*7*C2, C3],name='weights') 64 | b_fc = bias([C3],name='bias') 65 | h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*C2]) 66 | h_fc = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc) + b_fc) 67 | # summaries 68 | tf.summary.histogram('histogram of W_fc', W_fc) 69 | tf.summary.histogram('histogram of b_fc', b_fc) 70 | # dropout 71 | with tf.name_scope('dropout'): 72 | p_keep = tf.placeholder(tf.float32) 73 | h_fc_drop = tf.nn.dropout(h_fc, p_keep) 74 | # output layer 75 | with tf.name_scope('output_layer'): 76 | W_output = weight([C3, 10],name='weights') 77 | b_output = bias([10],name='bias') 78 | y = tf.matmul(h_fc_drop, W_output) + b_output 79 | # summaries 80 | tf.summary.histogram('histogram of W_out', W_output) 81 | tf.summary.histogram('histogram of b_out', b_output) 82 | tf.summary.histogram('histogram of prob_dist_out', y_) 83 | 84 | # cost 85 | with tf.name_scope('cost'): 86 | cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y_, logits = y)) 87 | # summary 88 | tf.summary.scalar('loss', cross_entropy) 89 | 90 | # training op 91 | with tf.name_scope('train'): 92 | optimizer = tf.train.AdamOptimizer(1e-4) 93 | train_step = optimizer.minimize(cross_entropy) 94 | 95 | # accuracy testing 96 | with tf.name_scope('accuracy'): 97 | correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) 98 | accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 99 | # summary 100 | tf.summary.scalar('accuracy', accuracy) 101 | 102 | # training 103 | max_iter = 20000 104 | batch_size = 64 105 | keep_prob = 0.5 106 | if not os.path.exists('MNIST_logs/train'): 107 | os.mkdir('MNIST_logs') 108 | os.mkdir('MNIST_logs/train') 109 | if not os.path.exists('MNIST_logs/test'): 110 | os.mkdir('MNIST_logs/test') 111 | with tf.Session() as sess: 112 | ###################################################################################### 113 | # embeddings 114 | test_images = tf.Variable(mnist.test.images, name='test_images') 115 | test_labels = tf.argmax(mnist.test.labels,1).eval(session=sess) 116 | with open('MNIST_logs/test/metadata.tsv', 'w') as metadata_file: 117 | for label in test_labels: 118 | metadata_file.write('%d\n' % label) 119 | # associate metadata with the embedding. 120 | config = projector.ProjectorConfig() 121 | embedding = config.embeddings.add() 122 | embedding.tensor_name = test_images.name 123 | embedding.metadata_path = 'metadata.tsv' # Link this tensor to its metadata file 124 | # writers 125 | summary = tf.summary.merge_all() 126 | train_writer = tf.summary.FileWriter('MNIST_logs/train', sess.graph) 127 | test_writer = tf.summary.FileWriter('MNIST_logs/test' , sess.graph) 128 | saver = tf.train.Saver() # Create a saver for writing training checkpoints. 129 | projector.visualize_embeddings(test_writer, config) 130 | ###################################################################################### 131 | # training 132 | sess.run(tf.global_variables_initializer()) 133 | for iter in range(max_iter): 134 | batch_x, batch_y = mnist.train.next_batch(batch_size) 135 | # each 100 steps: report accuracy & write summary file 136 | if iter%100 == 0: 137 | # report accuracy 138 | train_accuracy = accuracy.eval({x:batch_x, y_:batch_y, p_keep: 1.0}) # turn off dropout during testing 139 | test_accuracy = 0.0 140 | for i in range(100): 141 | test_x, test_y = mnist.test.next_batch(100) 142 | test_acc = accuracy.eval({x:test_x, y_:test_y, p_keep:1.0}) 143 | test_accuracy = test_accuracy + test_acc 144 | test_accuracy = test_accuracy/100.0 145 | print("iter step %d batch accuracy %f test accuracy %f"%(iter, train_accuracy, test_accuracy)) 146 | # train summary 147 | summary_train = sess.run(summary, {x:batch_x, y_:batch_y, p_keep: 1.0}) 148 | train_writer.add_summary(summary_train, iter) 149 | train_writer.flush() 150 | # test summary 151 | summary_test = sess.run(summary, {x:test_x, y_:test_y, p_keep: 1.0}) 152 | test_writer.add_summary(summary_test, iter) 153 | test_writer.flush() 154 | if iter%1000 == 0: 155 | # save checkpoint file 156 | checkpoint_file = os.path.join('MNIST_logs', 'model.ckpt') 157 | saver.save(sess, checkpoint_file, global_step=iter) 158 | ###################################################################################### 159 | # each 1000 steps: save running status 160 | if iter%1000 == 0: 161 | print('\nsaving running status information ...\n') 162 | run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) 163 | run_metadata = tf.RunMetadata() 164 | sess.run(train_step, {x:batch_x, y_:batch_y, p_keep: keep_prob}, 165 | options=run_options, run_metadata=run_metadata) 166 | train_writer.add_run_metadata(run_metadata, 'step%03d' % iter) 167 | else: 168 | sess.run(train_step, {x:batch_x, y_:batch_y, p_keep: keep_prob}) 169 | ###################################################################################### 170 | train_writer.close() 171 | test_writer.close() 172 | embedding_writer.close() 173 | -------------------------------------------------------------------------------- /exp10_monitor_API.py: -------------------------------------------------------------------------------- 1 | ''' 2 | this code is a modification of Exp5 3 | reference: 4 | https://www.tensorflow.org/get_started/monitors 5 | ''' 6 | from __future__ import absolute_import 7 | from __future__ import division 8 | from __future__ import print_function 9 | 10 | import tensorflow as tf 11 | import numpy as np 12 | import os 13 | 14 | os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" 15 | os.environ["CUDA_VISIBLE_DEVICES"] = "0" 16 | 17 | # change the default logging level 18 | # for details about logging level, see 19 | # https://www.tensorflow.org/get_started/monitors#enabling_logging_with_tensorflow 20 | # tf.logging.set_verbosity(tf.logging.ERROR) # in case you don't want to see warnings(this will disable the display of logs!) 21 | tf.logging.set_verbosity(tf.logging.INFO) # in case you want to see log infor(this will also allow the warings to be shown!) 22 | 23 | # Data sets 24 | IRIS_TRAINING = "./IRIS_data/iris_training.csv" 25 | IRIS_TEST = "./IRIS_data/iris_test.csv" 26 | 27 | # Load datasets 28 | training_set = tf.contrib.learn.datasets.base.load_csv_with_header( 29 | filename=IRIS_TRAINING, 30 | target_dtype=np.int, 31 | features_dtype=np.float32) 32 | test_set = tf.contrib.learn.datasets.base.load_csv_with_header( 33 | filename=IRIS_TEST, 34 | target_dtype=np.int, 35 | features_dtype=np.float32) 36 | 37 | # Specify that all features have real-value data 38 | feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)] 39 | 40 | # set monitor 41 | validation_metrics = { 42 | "accuracy": 43 | tf.contrib.learn.metric_spec.MetricSpec( 44 | metric_fn=tf.contrib.metrics.streaming_accuracy, 45 | prediction_key=tf.contrib.learn.prediction_key.PredictionKey. 46 | CLASSES), 47 | "precision": 48 | tf.contrib.learn.metric_spec.MetricSpec( 49 | metric_fn=tf.contrib.metrics.streaming_precision, 50 | prediction_key=tf.contrib.learn.prediction_key.PredictionKey. 51 | CLASSES), 52 | "recall": 53 | tf.contrib.learn.metric_spec.MetricSpec( 54 | metric_fn=tf.contrib.metrics.streaming_recall, 55 | prediction_key=tf.contrib.learn.prediction_key.PredictionKey. 56 | CLASSES) 57 | } 58 | 59 | validation_monitor = tf.contrib.learn.monitors.ValidationMonitor( 60 | test_set.data, 61 | test_set.target, 62 | every_n_steps=50, 63 | metrics=validation_metrics, 64 | early_stopping_metric="loss", 65 | early_stopping_metric_minimize=True, 66 | early_stopping_rounds=200) 67 | 68 | # Build 3 layer DNN with 10, 20, 10 units respectively 69 | classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns, 70 | hidden_units=[10, 20, 10], 71 | n_classes=3, 72 | model_dir="./IRIS_logs", 73 | config=tf.contrib.learn.RunConfig(save_checkpoints_secs=1)) 74 | 75 | # Fit model 76 | # tf.contrib.learn offers a variety of predefined models, called Estimators 77 | classifier.fit(x=training_set.data, 78 | y=training_set.target, 79 | steps=2000, 80 | monitors=[validation_monitor]) 81 | 82 | # Evaluate accuracy 83 | accuracy_score = classifier.evaluate(x=test_set.data, 84 | y=test_set.target)["accuracy"] 85 | print('Accuracy: {0:f}'.format(accuracy_score)) 86 | 87 | # Classify two new flower samples 88 | new_samples = np.array( 89 | [[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=float) 90 | y = list(classifier.predict(new_samples, as_iterable=True)) 91 | print('Predictions: {}'.format(str(y))) 92 | -------------------------------------------------------------------------------- /exp11_user_dataset_low_API_1.py: -------------------------------------------------------------------------------- 1 | ''' 2 | import user's own dataset using low level API 3 | reference: 4 | https://www.tensorflow.org/api_guides/python/reading_data 5 | ''' 6 | 7 | import tensorflow as tf 8 | import glob 9 | import cv2 10 | import os 11 | 12 | os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" 13 | os.environ["CUDA_VISIBLE_DEVICES"] = "0" 14 | 15 | def _int64_feature(value): 16 | return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) 17 | 18 | def _bytes_feature(value): 19 | return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) 20 | 21 | def read_tfrecord(tf_filename, size): 22 | queue = tf.train.string_input_producer([tf_filename]) 23 | reader = tf.TFRecordReader() 24 | __, serialized_example = reader.read(queue) 25 | feature = { 26 | 'image_raw': tf.FixedLenFeature([], tf.string), 27 | 'height': tf.FixedLenFeature([], tf.int64), 28 | 'width': tf.FixedLenFeature([], tf.int64) 29 | } 30 | features = tf.parse_single_example(serialized_example, features=feature) 31 | image = tf.decode_raw(features['image_raw'], tf.uint8) 32 | image = tf.reshape(image, size) 33 | return image 34 | 35 | if __name__ == "__main__": 36 | data_path = 'my_data' 37 | dataset_name = 'dataset.tfrecords' 38 | ########################################### 39 | # save my own images to dataset (400 images of size 180 x 180) 40 | ########################################### 41 | print("saving data ...\n") 42 | files = glob.glob(os.path.join(data_path, '*.png')) 43 | files.sort() 44 | # build tfrecord file 45 | writer = tf.python_io.TFRecordWriter(dataset_name) 46 | # save my images to dataset 47 | for f in files: 48 | img = cv2.imread(f, cv2.IMREAD_GRAYSCALE) # gray scale image 49 | image_raw = img.tostring() # save as 8bit data 50 | feature = { 51 | 'image_raw': _bytes_feature(image_raw), 52 | 'height': _int64_feature(img.shape[0]), 53 | 'width': _int64_feature(img.shape[1]) 54 | } 55 | example = tf.train.Example(features=tf.train.Features(feature=feature)) 56 | writer.write(example.SerializeToString()) 57 | writer.close() 58 | 59 | ########################################### 60 | # load the dataset and display them with tensorboard 61 | ########################################### 62 | print("loading data ...\n") 63 | # define batch 64 | batch_size = 64 65 | Image = read_tfrecord(dataset_name, size=[180,180,1]) 66 | # min_after_dequeue defines how big a buffer we will randomly sample from 67 | # -- bigger means better shuffling but slower start up and more memory used. 68 | # capacity must be larger than min_after_dequeue 69 | # -- recommendation: 70 | # capacity = min_after_dequeue + (num_threads + a small safety margin) * batch_size 71 | data_batch = tf.train.shuffle_batch([Image], 72 | batch_size = batch_size, 73 | capacity = 1000 + 3 * batch_size, 74 | num_threads = 2, 75 | min_after_dequeue = 1000) 76 | # placeholder 77 | img_batch = tf.placeholder(tf.uint8, [None, 180, 180, 1]) 78 | # summary 79 | # we only display 6 images within each batch 80 | tf.summary.image(name='display', tensor=img_batch, max_outputs=6) 81 | # begin loading 82 | epoch = 0 83 | with tf.Session() as sess: 84 | coord = tf.train.Coordinator() 85 | threads = tf.train.start_queue_runners(sess=sess, coord=coord) 86 | summary = tf.summary.merge_all() 87 | summ_writer = tf.summary.FileWriter('logs', sess.graph) 88 | while epoch < 10: 89 | print("epoch %d" % epoch) 90 | batch = sess.run(data_batch) 91 | summ = sess.run(summary, {img_batch: batch}) 92 | summ_writer.add_summary(summ, epoch) 93 | summ_writer.flush() 94 | epoch += 1 95 | coord.request_stop() 96 | coord.join(threads) 97 | -------------------------------------------------------------------------------- /exp12_user_dataset_low_API_2.py: -------------------------------------------------------------------------------- 1 | ''' 2 | import user's own dataset using low level API when the original data is float 3 | 4 | this demo is modified based on exp11 5 | ''' 6 | 7 | import tensorflow as tf 8 | import numpy as np 9 | import glob 10 | import cv2 11 | import os 12 | import h5py 13 | 14 | os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" 15 | os.environ["CUDA_VISIBLE_DEVICES"] = "0" 16 | 17 | def _int64_feature(value): 18 | return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) 19 | 20 | ############################################################################## 21 | # this part is changed 22 | def _float_feature(value): 23 | return tf.train.Feature(float_list=tf.train.FloatList(value=value)) 24 | # end change 25 | ############################################################################## 26 | 27 | ############################################################################## 28 | # this part is changed 29 | def read_tfrecord(tf_filename, size): 30 | queue = tf.train.string_input_producer([tf_filename]) 31 | reader = tf.TFRecordReader() 32 | __, serialized_example = reader.read(queue) 33 | feature = { 34 | 'image_raw': tf.FixedLenFeature([size[0]*size[1]*size[2]], tf.float32), 35 | 'height': tf.FixedLenFeature([], tf.int64), 36 | 'width': tf.FixedLenFeature([], tf.int64) 37 | } 38 | features = tf.parse_single_example(serialized_example, features=feature) 39 | image = features['image_raw'] 40 | image = tf.reshape(image, size) 41 | return image 42 | # end change 43 | ############################################################################## 44 | 45 | if __name__ == "__main__": 46 | dataset_name = 'dataset.tfrecords' 47 | 48 | ## save my own images to dataset (100 images of size 180 x 180) 49 | print("saving data ...\n") 50 | # build tfrecord file 51 | writer = tf.python_io.TFRecordWriter(dataset_name) 52 | # reader for original data 53 | h5f = h5py.File('my_data.h5', 'r') 54 | keys = list(h5f.keys()) 55 | # save my images to dataset 56 | ############################################################################## 57 | # this part is changed 58 | for key in keys: 59 | img = np.array(h5f[key]).astype(dtype=np.float32) 60 | height = img.shape[0] 61 | width = img.shape[1] 62 | feature = { 63 | 'image_raw': _float_feature(img.reshape( (height*width) )), 64 | 'height': _int64_feature(height), 65 | 'width': _int64_feature(width) 66 | } 67 | example = tf.train.Example(features=tf.train.Features(feature=feature)) 68 | writer.write(example.SerializeToString()) 69 | h5f.close() 70 | writer.close() 71 | # end change 72 | ############################################################################## 73 | 74 | ## load the dataset and display them with tensorboard 75 | print("loading data ...\n") 76 | # define batch 77 | batch_size = 10 78 | Image = read_tfrecord(dataset_name, size=[180,180,1]) 79 | data_batch = tf.train.shuffle_batch([Image], 80 | batch_size = batch_size, 81 | capacity = 1000 + 3 * batch_size, 82 | num_threads = 2, 83 | min_after_dequeue = 1000) 84 | ############################################################################## 85 | # this part is changed 86 | img_batch = tf.placeholder(tf.float32, [None, 180, 180, 1]) 87 | # end change 88 | ############################################################################## 89 | # summary 90 | tf.summary.image(name='display', tensor=img_batch, max_outputs=4) 91 | # begin loading 92 | epoch = 0 93 | with tf.Session() as sess: 94 | coord = tf.train.Coordinator() 95 | threads = tf.train.start_queue_runners(sess=sess, coord=coord) 96 | summary = tf.summary.merge_all() 97 | summ_writer = tf.summary.FileWriter('logs', sess.graph) 98 | while epoch < 10: 99 | print("epoch %d" % epoch) 100 | batch = sess.run(data_batch) 101 | print([np.min(batch), np.max(batch)]) 102 | summ = sess.run(summary, {img_batch: batch}) 103 | summ_writer.add_summary(summ, epoch) 104 | summ_writer.flush() 105 | epoch += 1 106 | coord.request_stop() 107 | coord.join(threads) 108 | -------------------------------------------------------------------------------- /exp13_user_dataset_high_API_1.py: -------------------------------------------------------------------------------- 1 | ''' 2 | import user's own dataset using high level API ---- From Tensor 3 | 4 | reference: 5 | https://www.tensorflow.org/programmers_guide/datasets 6 | ''' 7 | 8 | import tensorflow as tf 9 | import numpy as np 10 | import glob 11 | import cv2 12 | import os 13 | 14 | os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" 15 | os.environ["CUDA_VISIBLE_DEVICES"] = "0" 16 | 17 | def __read__(file, label): 18 | image = cv2.imread(file.decode(), cv2.IMREAD_GRAYSCALE) 19 | image = np.expand_dims(image, 2) 20 | image = np.float32(image) / 255. 21 | return (image, label) 22 | 23 | if __name__ == "__main__": 24 | ## load into memory 25 | print('\nloading data into memory ...\n') 26 | data_path = 'my_data' 27 | files = glob.glob(os.path.join(data_path, '*.png')) 28 | files.sort() 29 | labels = np.random.randint(0,2, size=(len(files),1)) 30 | 31 | ## construct dataset 32 | print('\nconstructing Dataset & Iterator ...\n') 33 | # 1. build Dataset object 34 | dataset = tf.data.Dataset.from_tensor_slices((files, labels)) 35 | # 2. preprocess with Dataset.map() 36 | dataset = dataset.map( 37 | lambda x, y: tf.py_func(__read__, [x, y], [tf.float32, y.dtype]) 38 | ) 39 | # 3. shuffle the dataset 40 | dataset = dataset.shuffle(buffer_size=10000) 41 | # 4. multiple epochs & batching 42 | dataset = dataset.batch(64) # batch size: 64 43 | dataset = dataset.repeat(10) # 10 epoches 44 | 45 | ## construct iterator 46 | iterator = dataset.make_one_shot_iterator() 47 | next_element = iterator.get_next() 48 | 49 | ## read data 50 | print('\nreading dataset ...\n') 51 | img_batch = tf.placeholder(tf.float32, [None, 180, 180, 1]) 52 | tf.summary.image(name='display', tensor=img_batch, max_outputs=6) 53 | step = 0 54 | with tf.Session() as sess: 55 | summary = tf.summary.merge_all() 56 | summ_writer = tf.summary.FileWriter('logs', sess.graph) 57 | while True: 58 | try: 59 | batch_img, batch_label = sess.run(next_element) 60 | # print(batch_img.shape) 61 | # print(batch_label.shape) 62 | # print(type(batch_label[0,0])) 63 | except tf.errors.OutOfRangeError: 64 | print('\nEnd of dataset\n') 65 | break 66 | summ = sess.run(summary, {img_batch: batch_img}) 67 | summ_writer.add_summary(summ, step) 68 | summ_writer.flush() 69 | step += 1 70 | -------------------------------------------------------------------------------- /exp14_user_dataset_high_API_2.py: -------------------------------------------------------------------------------- 1 | ''' 2 | import user's own dataset using high level API ---- From TFrecords 3 | 4 | reference: 5 | https://www.tensorflow.org/programmers_guide/datasets 6 | ''' 7 | 8 | import tensorflow as tf 9 | import numpy as np 10 | import glob 11 | import cv2 12 | import os 13 | import h5py 14 | 15 | os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" 16 | os.environ["CUDA_VISIBLE_DEVICES"] = "0" 17 | 18 | def _int64_feature(value): 19 | return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) 20 | 21 | def _float_feature(value): 22 | return tf.train.Feature(float_list=tf.train.FloatList(value=value)) 23 | 24 | def _parse_function(example_proto): 25 | feature = { 26 | 'image_raw': tf.FixedLenFeature([180*180*1], tf.float32), 27 | 'height': tf.FixedLenFeature([], tf.int64), 28 | 'width': tf.FixedLenFeature([], tf.int64) 29 | } 30 | parsed_features = tf.parse_single_example(example_proto, feature) 31 | image = parsed_features['image_raw'] 32 | image = tf.reshape(image, [180,180,1]) 33 | return image 34 | 35 | if __name__ == "__main__": 36 | dataset_name = 'dataset.tfrecords' 37 | 38 | ## save my own images to dataset (100 images of size 180 x 180) 39 | print("saving data ...\n") 40 | # build tfrecord file 41 | writer = tf.python_io.TFRecordWriter(dataset_name) 42 | # reader for original data 43 | h5f = h5py.File('my_data.h5', 'r') 44 | keys = list(h5f.keys()) 45 | # save my images to dataset 46 | for key in keys: 47 | img = np.array(h5f[key]).astype(dtype=np.float32) 48 | height = img.shape[0] 49 | width = img.shape[1] 50 | feature = { 51 | 'image_raw': _float_feature(img.reshape( (height*width) )), 52 | 'height': _int64_feature(height), 53 | 'width': _int64_feature(width) 54 | } 55 | example = tf.train.Example(features=tf.train.Features(feature=feature)) 56 | writer.write(example.SerializeToString()) 57 | h5f.close() 58 | writer.close() 59 | 60 | ## load the dataset and display them with tensorboard 61 | print("loading data ...\n") 62 | # 1. build Dataset object 63 | dataset = tf.data.TFRecordDataset(dataset_name) 64 | # 2. parsing TFrecords 65 | dataset = dataset.map(_parse_function) 66 | # 3. shuffle the dataset 67 | dataset = dataset.shuffle(buffer_size=10000) 68 | # 4. multiple epochs & batching 69 | dataset = dataset.batch(64) # batch size: 64 70 | dataset = dataset.repeat(10) # 10 epoches 71 | # construct iterator 72 | iterator = dataset.make_one_shot_iterator() 73 | next_element = 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