機器學習實驗（十二）：深度學習之圖像分類模型AlexNet結構分析和tensorflow實現

雪柳花明 2017-03-14
展開全文
Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
Iter 1280, Minibatch Loss= 105135.500000, Training Accuracy= 0.20312
Iter 2560, Minibatch Loss= 53449.058594, Training Accuracy= 0.26562
Iter 3840, Minibatch Loss= 46481.890625, Training Accuracy= 0.45312
Iter 5120, Minibatch Loss= 27159.894531, Training Accuracy= 0.46875
Iter 6400, Minibatch Loss= 24144.722656, Training Accuracy= 0.59375
Iter 7680, Minibatch Loss= 34462.066406, Training Accuracy= 0.51562
Iter 8960, Minibatch Loss= 29766.037109, Training Accuracy= 0.54688
Iter 10240, Minibatch Loss= 18833.246094, Training Accuracy= 0.62500
Iter 11520, Minibatch Loss= 14885.839844, Training Accuracy= 0.67188
Iter 12800, Minibatch Loss= 29879.960938, Training Accuracy= 0.56250
Iter 14080, Minibatch Loss= 24039.386719, Training Accuracy= 0.50000
Iter 15360, Minibatch Loss= 6645.172852, Training Accuracy= 0.76562
Iter 16640, Minibatch Loss= 18668.121094, Training Accuracy= 0.64062
Iter 17920, Minibatch Loss= 12958.103516, Training Accuracy= 0.78125
Iter 19200, Minibatch Loss= 11467.254883, Training Accuracy= 0.75000
Iter 20480, Minibatch Loss= 11780.928711, Training Accuracy= 0.78125
Iter 21760, Minibatch Loss= 17288.359375, Training Accuracy= 0.68750
Iter 23040, Minibatch Loss= 10288.304688, Training Accuracy= 0.79688
Iter 24320, Minibatch Loss= 10797.081055, Training Accuracy= 0.73438
Iter 25600, Minibatch Loss= 15526.394531, Training Accuracy= 0.68750
Iter 26880, Minibatch Loss= 5896.966309, Training Accuracy= 0.78125
Iter 28160, Minibatch Loss= 5986.314941, Training Accuracy= 0.78125
Iter 29440, Minibatch Loss= 14352.033203, Training Accuracy= 0.70312
Iter 30720, Minibatch Loss= 9299.230469, Training Accuracy= 0.79688
Iter 32000, Minibatch Loss= 12997.154297, Training Accuracy= 0.73438
Iter 33280, Minibatch Loss= 8444.686523, Training Accuracy= 0.78125
Iter 34560, Minibatch Loss= 8713.619141, Training Accuracy= 0.79688
Iter 35840, Minibatch Loss= 11031.250000, Training Accuracy= 0.76562
Iter 37120, Minibatch Loss= 14087.941406, Training Accuracy= 0.78125
Iter 38400, Minibatch Loss= 6499.219727, Training Accuracy= 0.78125
Iter 39680, Minibatch Loss= 7266.004883, Training Accuracy= 0.79688
Iter 40960, Minibatch Loss= 13199.544922, Training Accuracy= 0.65625
Iter 42240, Minibatch Loss= 7316.147949, Training Accuracy= 0.82812
Iter 43520, Minibatch Loss= 5919.269531, Training Accuracy= 0.84375
Iter 44800, Minibatch Loss= 4456.823242, Training Accuracy= 0.81250
Iter 46080, Minibatch Loss= 2113.104492, Training Accuracy= 0.93750
Iter 47360, Minibatch Loss= 4923.742188, Training Accuracy= 0.85938
Iter 48640, Minibatch Loss= 7970.073730, Training Accuracy= 0.75000
Iter 49920, Minibatch Loss= 5625.089844, Training Accuracy= 0.79688
Iter 51200, Minibatch Loss= 8557.619141, Training Accuracy= 0.82812
Iter 52480, Minibatch Loss= 4743.790039, Training Accuracy= 0.78125
Iter 53760, Minibatch Loss= 972.031982, Training Accuracy= 0.90625
Iter 55040, Minibatch Loss= 6338.499023, Training Accuracy= 0.81250
Iter 56320, Minibatch Loss= 9248.547852, Training Accuracy= 0.76562
Iter 57600, Minibatch Loss= 5703.125000, Training Accuracy= 0.85938
Iter 58880, Minibatch Loss= 10426.742188, Training Accuracy= 0.78125
Iter 60160, Minibatch Loss= 4342.888672, Training Accuracy= 0.84375
Iter 61440, Minibatch Loss= 7261.451660, Training Accuracy= 0.84375
Iter 62720, Minibatch Loss= 3899.342285, Training Accuracy= 0.89062
Iter 64000, Minibatch Loss= 3670.834229, Training Accuracy= 0.85938
Iter 65280, Minibatch Loss= 5787.583008, Training Accuracy= 0.82812
Iter 66560, Minibatch Loss= 6649.801758, Training Accuracy= 0.78125
Iter 67840, Minibatch Loss= 2080.366455, Training Accuracy= 0.92188
Iter 69120, Minibatch Loss= 7153.665039, Training Accuracy= 0.84375
Iter 70400, Minibatch Loss= 5228.269043, Training Accuracy= 0.82812
Iter 71680, Minibatch Loss= 9432.683594, Training Accuracy= 0.78125
Iter 72960, Minibatch Loss= 1656.271851, Training Accuracy= 0.92188
Iter 74240, Minibatch Loss= 2362.667236, Training Accuracy= 0.89062
Iter 75520, Minibatch Loss= 4994.963867, Training Accuracy= 0.85938
Iter 76800, Minibatch Loss= 691.407837, Training Accuracy= 0.92188
Iter 78080, Minibatch Loss= 3261.704102, Training Accuracy= 0.89062
Iter 79360, Minibatch Loss= 4440.437500, Training Accuracy= 0.84375
Iter 80640, Minibatch Loss= 5165.506348, Training Accuracy= 0.85938
Iter 81920, Minibatch Loss= 2232.877441, Training Accuracy= 0.92188
Iter 83200, Minibatch Loss= 2232.522949, Training Accuracy= 0.85938
Iter 84480, Minibatch Loss= 1921.781006, Training Accuracy= 0.93750
Iter 85760, Minibatch Loss= 5770.326172, Training Accuracy= 0.79688
Iter 87040, Minibatch Loss= 2022.952881, Training Accuracy= 0.92188
Iter 88320, Minibatch Loss= 1921.537109, Training Accuracy= 0.90625
Iter 89600, Minibatch Loss= 8427.068359, Training Accuracy= 0.75000
Iter 90880, Minibatch Loss= 3777.687012, Training Accuracy= 0.85938
Iter 92160, Minibatch Loss= 1407.593140, Training Accuracy= 0.90625
Iter 93440, Minibatch Loss= 3493.047363, Training Accuracy= 0.82812
Iter 94720, Minibatch Loss= 3549.630371, Training Accuracy= 0.90625
Iter 96000, Minibatch Loss= 4050.996338, Training Accuracy= 0.89062
Iter 97280, Minibatch Loss= 2689.620605, Training Accuracy= 0.87500
Iter 98560, Minibatch Loss= 775.571289, Training Accuracy= 0.92188
Iter 99840, Minibatch Loss= 4966.501465, Training Accuracy= 0.81250
Iter 101120, Minibatch Loss= 468.303955, Training Accuracy= 0.96875
Iter 102400, Minibatch Loss= 4239.565430, Training Accuracy= 0.87500
Iter 103680, Minibatch Loss= 2601.228760, Training Accuracy= 0.90625
Iter 104960, Minibatch Loss= 2539.465820, Training Accuracy= 0.84375
Iter 106240, Minibatch Loss= 3445.990234, Training Accuracy= 0.92188
Iter 107520, Minibatch Loss= 2020.942261, Training Accuracy= 0.89062
Iter 108800, Minibatch Loss= 2290.115479, Training Accuracy= 0.84375
Iter 110080, Minibatch Loss= 5105.082520, Training Accuracy= 0.84375
Iter 111360, Minibatch Loss= 2792.384521, Training Accuracy= 0.90625
Iter 112640, Minibatch Loss= 3714.771973, Training Accuracy= 0.84375
Iter 113920, Minibatch Loss= 2331.506348, Training Accuracy= 0.82812
Iter 115200, Minibatch Loss= 5542.223633, Training Accuracy= 0.87500
Iter 116480, Minibatch Loss= 2068.789795, Training Accuracy= 0.87500
Iter 117760, Minibatch Loss= 3032.279541, Training Accuracy= 0.85938
Iter 119040, Minibatch Loss= 2303.545166, Training Accuracy= 0.89062
Iter 120320, Minibatch Loss= 1151.952393, Training Accuracy= 0.93750
Iter 121600, Minibatch Loss= 2172.850342, Training Accuracy= 0.92188
Iter 122880, Minibatch Loss= 1365.023438, Training Accuracy= 0.96875
Iter 124160, Minibatch Loss= 2074.203613, Training Accuracy= 0.90625
Iter 125440, Minibatch Loss= 3134.413330, Training Accuracy= 0.90625
Iter 126720, Minibatch Loss= 1457.720703, Training Accuracy= 0.92188
Iter 128000, Minibatch Loss= 7626.540039, Training Accuracy= 0.87500
Iter 129280, Minibatch Loss= 332.535400, Training Accuracy= 0.95312
Iter 130560, Minibatch Loss= 3173.010010, Training Accuracy= 0.81250
Iter 131840, Minibatch Loss= 2052.775879, Training Accuracy= 0.89062
Iter 133120, Minibatch Loss= 915.511108, Training Accuracy= 0.93750
Iter 134400, Minibatch Loss= 2391.861572, Training Accuracy= 0.89062
Iter 135680, Minibatch Loss= 1909.811890, Training Accuracy= 0.87500
Iter 136960, Minibatch Loss= 1870.513428, Training Accuracy= 0.87500
Iter 138240, Minibatch Loss= 3669.521973, Training Accuracy= 0.87500
Iter 139520, Minibatch Loss= 3938.454834, Training Accuracy= 0.87500
Iter 140800, Minibatch Loss= 3682.827393, Training Accuracy= 0.85938
Iter 142080, Minibatch Loss= 773.881226, Training Accuracy= 0.89062
Iter 143360, Minibatch Loss= 2542.113770, Training Accuracy= 0.82812
Iter 144640, Minibatch Loss= 1390.634399, Training Accuracy= 0.95312
Iter 145920, Minibatch Loss= 1816.718994, Training Accuracy= 0.92188
Iter 147200, Minibatch Loss= 2383.392822, Training Accuracy= 0.84375
Iter 148480, Minibatch Loss= 2805.485352, Training Accuracy= 0.90625
Iter 149760, Minibatch Loss= 4273.811035, Training Accuracy= 0.85938
Iter 151040, Minibatch Loss= 2829.233154, Training Accuracy= 0.90625
Iter 152320, Minibatch Loss= 2350.943848, Training Accuracy= 0.87500
Iter 153600, Minibatch Loss= 1390.092407, Training Accuracy= 0.92188
Iter 154880, Minibatch Loss= 1447.536255, Training Accuracy= 0.85938
Iter 156160, Minibatch Loss= 2814.929688, Training Accuracy= 0.92188
Iter 157440, Minibatch Loss= 1454.184570, Training Accuracy= 0.92188
Iter 158720, Minibatch Loss= 1053.621826, Training Accuracy= 0.90625
Iter 160000, Minibatch Loss= 268.273071, Training Accuracy= 0.96875
Iter 161280, Minibatch Loss= 421.640625, Training Accuracy= 0.93750
Iter 162560, Minibatch Loss= 554.997803, Training Accuracy= 0.95312
Iter 163840, Minibatch Loss= 756.904907, Training Accuracy= 0.92188
Iter 165120, Minibatch Loss= 2533.083496, Training Accuracy= 0.90625
Iter 166400, Minibatch Loss= 1262.960327, Training Accuracy= 0.90625
Iter 167680, Minibatch Loss= 987.682190, Training Accuracy= 0.93750
Iter 168960, Minibatch Loss= 2693.651367, Training Accuracy= 0.87500
Iter 170240, Minibatch Loss= 2195.717285, Training Accuracy= 0.92188
Iter 171520, Minibatch Loss= 2571.887451, Training Accuracy= 0.87500
Iter 172800, Minibatch Loss= 632.802551, Training Accuracy= 0.96875
Iter 174080, Minibatch Loss= 1144.768799, Training Accuracy= 0.90625
Iter 175360, Minibatch Loss= 1107.609863, Training Accuracy= 0.89062
Iter 176640, Minibatch Loss= 962.998108, Training Accuracy= 0.93750
Iter 177920, Minibatch Loss= 475.736450, Training Accuracy= 0.95312
Iter 179200, Minibatch Loss= 454.031738, Training Accuracy= 0.96875
Iter 180480, Minibatch Loss= 1643.504272, Training Accuracy= 0.93750
Iter 181760, Minibatch Loss= 520.336853, Training Accuracy= 0.96875
Iter 183040, Minibatch Loss= 5099.615723, Training Accuracy= 0.82812
Iter 184320, Minibatch Loss= 1832.333374, Training Accuracy= 0.92188
Iter 185600, Minibatch Loss= 5085.391602, Training Accuracy= 0.87500
Iter 186880, Minibatch Loss= 1165.275635, Training Accuracy= 0.90625
Iter 188160, Minibatch Loss= 436.611694, Training Accuracy= 0.95312
Iter 189440, Minibatch Loss= 729.550781, Training Accuracy= 0.92188
Iter 190720, Minibatch Loss= 631.992798, Training Accuracy= 0.92188
Iter 192000, Minibatch Loss= 254.609497, Training Accuracy= 0.95312
Iter 193280, Minibatch Loss= 1927.740479, Training Accuracy= 0.90625
Iter 194560, Minibatch Loss= 0.000000, Training Accuracy= 1.00000
Iter 195840, Minibatch Loss= 735.920166, Training Accuracy= 0.95312
Iter 197120, Minibatch Loss= 93.257446, Training Accuracy= 0.98438
Iter 198400, Minibatch Loss= 328.502441, Training Accuracy= 0.93750
Iter 199680, Minibatch Loss= 1295.930298, Training Accuracy= 0.92188
Optimization Finished!
('Testing Accuracy:', 0.96875)