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trainning
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trainning
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2020-01-10 20:02:45.297670: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
WARNING:tensorflow:From run_cnn.py:87: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.
Training and evaluating...
Epoch: 1
Iter: 0, Train Loss: 2.6, Train Acc: 7.81%, Val Loss: 2.6, Val Acc: 7.60%, Time: 0:00:03 *
Iter: 100, Train Loss: 1.6, Train Acc: 64.06%, Val Loss: 1.6, Val Acc: 61.45%, Time: 0:00:17 *
Iter: 200, Train Loss: 0.51, Train Acc: 87.50%, Val Loss: 0.6, Val Acc: 84.54%, Time: 0:00:32 *
Iter: 300, Train Loss: 0.32, Train Acc: 90.62%, Val Loss: 0.41, Val Acc: 88.42%, Time: 0:00:46 *
Iter: 400, Train Loss: 0.46, Train Acc: 89.06%, Val Loss: 0.35, Val Acc: 90.32%, Time: 0:01:01 *
Iter: 500, Train Loss: 0.28, Train Acc: 89.06%, Val Loss: 0.31, Val Acc: 90.91%, Time: 0:01:16 *
Iter: 600, Train Loss: 0.3, Train Acc: 92.19%, Val Loss: 0.28, Val Acc: 91.77%, Time: 0:01:31 *
Iter: 700, Train Loss: 0.25, Train Acc: 92.19%, Val Loss: 0.26, Val Acc: 92.37%, Time: 0:01:45 *
Iter: 800, Train Loss: 0.11, Train Acc: 96.88%, Val Loss: 0.24, Val Acc: 92.97%, Time: 0:02:01 *
Iter: 900, Train Loss: 0.17, Train Acc: 95.31%, Val Loss: 0.25, Val Acc: 92.83%, Time: 0:02:17
Iter: 1000, Train Loss: 0.25, Train Acc: 95.31%, Val Loss: 0.24, Val Acc: 93.31%, Time: 0:02:32 *
Epoch: 2
Iter: 1100, Train Loss: 0.13, Train Acc: 98.44%, Val Loss: 0.23, Val Acc: 93.32%, Time: 0:02:47 *
Iter: 1200, Train Loss: 0.28, Train Acc: 93.75%, Val Loss: 0.22, Val Acc: 93.71%, Time: 0:03:02 *
Iter: 1300, Train Loss: 0.24, Train Acc: 93.75%, Val Loss: 0.21, Val Acc: 93.95%, Time: 0:03:17 *
Iter: 1400, Train Loss: 0.3, Train Acc: 93.75%, Val Loss: 0.21, Val Acc: 94.08%, Time: 0:03:32 *
Iter: 1500, Train Loss: 0.17, Train Acc: 92.19%, Val Loss: 0.2, Val Acc: 94.23%, Time: 0:03:47 *
Iter: 1600, Train Loss: 0.32, Train Acc: 92.19%, Val Loss: 0.21, Val Acc: 93.77%, Time: 0:04:02
Iter: 1700, Train Loss: 0.036, Train Acc: 98.44%, Val Loss: 0.21, Val Acc: 94.14%, Time: 0:04:17
Iter: 1800, Train Loss: 0.059, Train Acc: 98.44%, Val Loss: 0.2, Val Acc: 94.11%, Time: 0:04:33
Iter: 1900, Train Loss: 0.2, Train Acc: 92.19%, Val Loss: 0.2, Val Acc: 94.31%, Time: 0:04:48 *
Iter: 2000, Train Loss: 0.12, Train Acc: 95.31%, Val Loss: 0.21, Val Acc: 94.09%, Time: 0:05:04
Iter: 2100, Train Loss: 0.1, Train Acc: 95.31%, Val Loss: 0.19, Val Acc: 94.48%, Time: 0:05:19 *
Epoch: 3
Iter: 2200, Train Loss: 0.049, Train Acc: 100.00%, Val Loss: 0.19, Val Acc: 94.48%, Time: 0:05:35
Iter: 2300, Train Loss: 0.064, Train Acc: 96.88%, Val Loss: 0.19, Val Acc: 94.66%, Time: 0:05:51 *
Iter: 2400, Train Loss: 0.073, Train Acc: 98.44%, Val Loss: 0.19, Val Acc: 94.52%, Time: 0:06:06
Iter: 2500, Train Loss: 0.21, Train Acc: 93.75%, Val Loss: 0.2, Val Acc: 94.00%, Time: 0:06:21
Iter: 2600, Train Loss: 0.096, Train Acc: 96.88%, Val Loss: 0.19, Val Acc: 94.52%, Time: 0:06:35
Iter: 2700, Train Loss: 0.23, Train Acc: 92.19%, Val Loss: 0.19, Val Acc: 94.48%, Time: 0:06:50
Iter: 2800, Train Loss: 0.12, Train Acc: 93.75%, Val Loss: 0.2, Val Acc: 94.45%, Time: 0:07:05
Iter: 2900, Train Loss: 0.16, Train Acc: 98.44%, Val Loss: 0.19, Val Acc: 94.60%, Time: 0:07:20
Iter: 3000, Train Loss: 0.019, Train Acc: 100.00%, Val Loss: 0.18, Val Acc: 94.91%, Time: 0:07:34 *
Iter: 3100, Train Loss: 0.098, Train Acc: 96.88%, Val Loss: 0.19, Val Acc: 94.42%, Time: 0:07:49
Iter: 3200, Train Loss: 0.13, Train Acc: 92.19%, Val Loss: 0.18, Val Acc: 94.88%, Time: 0:08:04
Epoch: 4
Iter: 3300, Train Loss: 0.061, Train Acc: 96.88%, Val Loss: 0.18, Val Acc: 94.95%, Time: 0:08:19 *
Iter: 3400, Train Loss: 0.13, Train Acc: 96.88%, Val Loss: 0.19, Val Acc: 94.77%, Time: 0:08:34
Iter: 3500, Train Loss: 0.18, Train Acc: 95.31%, Val Loss: 0.18, Val Acc: 94.92%, Time: 0:08:48
Iter: 3600, Train Loss: 0.051, Train Acc: 98.44%, Val Loss: 0.19, Val Acc: 95.08%, Time: 0:09:04 *
Iter: 3700, Train Loss: 0.024, Train Acc: 100.00%, Val Loss: 0.19, Val Acc: 94.80%, Time: 0:09:20
Iter: 3800, Train Loss: 0.06, Train Acc: 98.44%, Val Loss: 0.19, Val Acc: 94.85%, Time: 0:09:35
Iter: 3900, Train Loss: 0.098, Train Acc: 96.88%, Val Loss: 0.18, Val Acc: 94.80%, Time: 0:09:51
Iter: 4000, Train Loss: 0.094, Train Acc: 96.88%, Val Loss: 0.19, Val Acc: 94.97%, Time: 0:10:06
Iter: 4100, Train Loss: 0.022, Train Acc: 98.44%, Val Loss: 0.18, Val Acc: 95.00%, Time: 0:10:22
Iter: 4200, Train Loss: 0.075, Train Acc: 96.88%, Val Loss: 0.18, Val Acc: 95.06%, Time: 0:10:37
Epoch: 5
Iter: 4300, Train Loss: 0.1, Train Acc: 93.75%, Val Loss: 0.19, Val Acc: 95.06%, Time: 0:10:52
Iter: 4400, Train Loss: 0.0076, Train Acc: 100.00%, Val Loss: 0.19, Val Acc: 95.11%, Time: 0:11:08 *
Iter: 4500, Train Loss: 0.039, Train Acc: 98.44%, Val Loss: 0.2, Val Acc: 94.69%, Time: 0:11:23
Iter: 4600, Train Loss: 0.023, Train Acc: 98.44%, Val Loss: 0.21, Val Acc: 94.72%, Time: 0:11:38
Iter: 4700, Train Loss: 0.0085, Train Acc: 100.00%, Val Loss: 0.19, Val Acc: 95.02%, Time: 0:11:53
Iter: 4800, Train Loss: 0.096, Train Acc: 96.88%, Val Loss: 0.19, Val Acc: 95.12%, Time: 0:12:08 *
Iter: 4900, Train Loss: 0.046, Train Acc: 100.00%, Val Loss: 0.19, Val Acc: 95.00%, Time: 0:12:23
Iter: 5000, Train Loss: 0.03, Train Acc: 98.44%, Val Loss: 0.18, Val Acc: 95.28%, Time: 0:12:39 *
Iter: 5100, Train Loss: 0.025, Train Acc: 98.44%, Val Loss: 0.18, Val Acc: 94.97%, Time: 0:12:53
Iter: 5200, Train Loss: 0.023, Train Acc: 100.00%, Val Loss: 0.18, Val Acc: 95.17%, Time: 0:13:11
Iter: 5300, Train Loss: 0.11, Train Acc: 96.88%, Val Loss: 0.19, Val Acc: 95.23%, Time: 0:13:26
Epoch: 6
Iter: 5400, Train Loss: 0.0041, Train Acc: 100.00%, Val Loss: 0.2, Val Acc: 95.14%, Time: 0:13:41
Iter: 5500, Train Loss: 0.1, Train Acc: 98.44%, Val Loss: 0.2, Val Acc: 94.89%, Time: 0:13:56
Iter: 5600, Train Loss: 0.047, Train Acc: 98.44%, Val Loss: 0.2, Val Acc: 95.18%, Time: 0:14:11
Iter: 5700, Train Loss: 0.0097, Train Acc: 100.00%, Val Loss: 0.2, Val Acc: 95.26%, Time: 0:14:26
Iter: 5800, Train Loss: 0.014, Train Acc: 100.00%, Val Loss: 0.2, Val Acc: 95.20%, Time: 0:14:41
Iter: 5900, Train Loss: 0.0065, Train Acc: 100.00%, Val Loss: 0.2, Val Acc: 95.00%, Time: 0:14:56
Iter: 6000, Train Loss: 0.011, Train Acc: 100.00%, Val Loss: 0.19, Val Acc: 95.18%, Time: 0:15:10
No optimization for a long time, auto-stopping...