From 2c2b053d54c1f79d14e9f05f419e1207bf82ddaa Mon Sep 17 00:00:00 2001 From: Tobias Arndt Date: Wed, 22 Jul 2020 17:11:09 +0200 Subject: [PATCH] mnsit udn adadelta alg --- Cluster/gd_10min.out | 1831 ++++++++++++++++++++++++++++ TeX/Plots/SGD_vs_GD.tex | 1 + TeX/Plots/pfg_test.tex | 49 +- TeX/bibliograpy.bib | 17 + TeX/further_applications_of_nn.tex | 105 +- 5 files changed, 1980 insertions(+), 23 deletions(-) create mode 100644 Cluster/gd_10min.out diff --git a/Cluster/gd_10min.out b/Cluster/gd_10min.out new file mode 100644 index 0000000..e75009e --- /dev/null +++ b/Cluster/gd_10min.out @@ -0,0 +1,1831 @@ +2020-07-22 15:10:00.479052: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.2 +2020-07-22 15:10:01.694057: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1 +2020-07-22 15:10:01.741512: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1683] Found device 0 with properties: +pciBusID: 0000:5e:00.0 name: Tesla V100-PCIE-32GB computeCapability: 7.0 +coreClock: 1.38GHz coreCount: 80 deviceMemorySize: 31.75GiB deviceMemoryBandwidth: 836.37GiB/s +2020-07-22 15:10:01.741540: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.2 +2020-07-22 15:10:01.742978: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10 +2020-07-22 15:10:01.744513: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10 +2020-07-22 15:10:01.744777: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10 +2020-07-22 15:10:01.746346: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10 +2020-07-22 15:10:01.747269: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10 +2020-07-22 15:10:01.750841: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7 +2020-07-22 15:10:01.754864: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1825] Adding visible gpu devices: 0 +2020-07-22 15:10:01.779370: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2600000000 Hz +2020-07-22 15:10:01.779525: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x54e6f60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: +2020-07-22 15:10:01.779533: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version +2020-07-22 15:10:01.904227: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5e892c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: +2020-07-22 15:10:01.904249: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla V100-PCIE-32GB, Compute Capability 7.0 +2020-07-22 15:10:01.905154: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1683] Found device 0 with properties: +pciBusID: 0000:5e:00.0 name: Tesla V100-PCIE-32GB computeCapability: 7.0 +coreClock: 1.38GHz coreCount: 80 deviceMemorySize: 31.75GiB deviceMemoryBandwidth: 836.37GiB/s +2020-07-22 15:10:01.905178: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.2 +2020-07-22 15:10:01.905195: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10 +2020-07-22 15:10:01.905201: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10 +2020-07-22 15:10:01.905208: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10 +2020-07-22 15:10:01.905214: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10 +2020-07-22 15:10:01.905220: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10 +2020-07-22 15:10:01.905226: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7 +2020-07-22 15:10:01.906694: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1825] Adding visible gpu devices: 0 +2020-07-22 15:10:01.906715: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.2 +2020-07-22 15:10:02.404971: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1224] Device interconnect StreamExecutor with strength 1 edge matrix: +2020-07-22 15:10:02.405010: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1230] 0 +2020-07-22 15:10:02.405016: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1243] 0: N +2020-07-22 15:10:02.406779: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1369] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 30073 MB memory) -> physical GPU (device: 0, name: Tesla V100-PCIE-32GB, pci bus id: 0000:5e:00.0, compute capability: 7.0) +2020-07-22 15:10:03.401057: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10 +2020-07-22 15:10:03.893028: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7 +2020-07-22 15:10:04.956173: W tensorflow/core/kernels/gpu_utils.cc:49] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. +Epoch 1/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.3014 - accuracy: 0.0971 1/1 [==============================] - 0s 394ms/step - loss: 2.3014 - accuracy: 0.0971 - val_loss: 2.3006 - val_accuracy: 0.0972 +Epoch 2/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.3002 - accuracy: 0.1017 1/1 [==============================] - 0s 102ms/step - loss: 2.3002 - accuracy: 0.1017 - val_loss: 2.2994 - val_accuracy: 0.1017 +Epoch 3/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2991 - accuracy: 0.1062 1/1 [==============================] - 0s 94ms/step - loss: 2.2991 - accuracy: 0.1062 - val_loss: 2.2983 - val_accuracy: 0.1053 +Epoch 4/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2979 - accuracy: 0.1102 1/1 [==============================] - 0s 93ms/step - loss: 2.2979 - accuracy: 0.1102 - val_loss: 2.2971 - val_accuracy: 0.1091 +Epoch 5/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2968 - accuracy: 0.1153 1/1 [==============================] - 0s 98ms/step - loss: 2.2968 - accuracy: 0.1153 - val_loss: 2.2959 - val_accuracy: 0.1139 +Epoch 6/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2956 - accuracy: 0.1205 1/1 [==============================] - 0s 93ms/step - loss: 2.2956 - accuracy: 0.1205 - val_loss: 2.2947 - val_accuracy: 0.1178 +Epoch 7/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2945 - accuracy: 0.1257 1/1 [==============================] - 0s 99ms/step - loss: 2.2945 - accuracy: 0.1257 - val_loss: 2.2936 - val_accuracy: 0.1226 +Epoch 8/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2933 - accuracy: 0.1310 1/1 [==============================] - 0s 93ms/step - loss: 2.2933 - accuracy: 0.1310 - val_loss: 2.2924 - val_accuracy: 0.1276 +Epoch 9/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2922 - accuracy: 0.1361 1/1 [==============================] - 0s 93ms/step - loss: 2.2922 - accuracy: 0.1361 - val_loss: 2.2912 - val_accuracy: 0.1331 +Epoch 10/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2910 - accuracy: 0.1413 1/1 [==============================] - 0s 95ms/step - loss: 2.2910 - accuracy: 0.1413 - val_loss: 2.2900 - val_accuracy: 0.1381 +Epoch 11/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2899 - accuracy: 0.1461 1/1 [==============================] - 0s 98ms/step - loss: 2.2899 - accuracy: 0.1461 - val_loss: 2.2889 - val_accuracy: 0.1446 +Epoch 12/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2887 - accuracy: 0.1511 1/1 [==============================] - 0s 88ms/step - loss: 2.2887 - accuracy: 0.1511 - val_loss: 2.2877 - val_accuracy: 0.1487 +Epoch 13/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2875 - accuracy: 0.1554 1/1 [==============================] - 0s 89ms/step - loss: 2.2875 - accuracy: 0.1554 - val_loss: 2.2865 - val_accuracy: 0.1544 +Epoch 14/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2864 - accuracy: 0.1604 1/1 [==============================] - 0s 84ms/step - loss: 2.2864 - accuracy: 0.1604 - val_loss: 2.2853 - val_accuracy: 0.1593 +Epoch 15/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2852 - accuracy: 0.1651 1/1 [==============================] - 0s 80ms/step - loss: 2.2852 - accuracy: 0.1651 - val_loss: 2.2841 - val_accuracy: 0.1636 +Epoch 16/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2841 - accuracy: 0.1692 1/1 [==============================] - 0s 81ms/step - loss: 2.2841 - accuracy: 0.1692 - val_loss: 2.2830 - val_accuracy: 0.1686 +Epoch 17/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2829 - accuracy: 0.1735 1/1 [==============================] - 0s 80ms/step - loss: 2.2829 - accuracy: 0.1735 - val_loss: 2.2818 - val_accuracy: 0.1728 +Epoch 18/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2817 - accuracy: 0.1777 1/1 [==============================] - 0s 81ms/step - loss: 2.2817 - accuracy: 0.1777 - val_loss: 2.2806 - val_accuracy: 0.1762 +Epoch 19/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2806 - accuracy: 0.1819 1/1 [==============================] - 0s 104ms/step - loss: 2.2806 - accuracy: 0.1819 - val_loss: 2.2794 - val_accuracy: 0.1799 +Epoch 20/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2794 - accuracy: 0.1860 1/1 [==============================] - 0s 157ms/step - loss: 2.2794 - accuracy: 0.1860 - val_loss: 2.2782 - val_accuracy: 0.1842 +Epoch 21/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2782 - accuracy: 0.1900 1/1 [==============================] - 0s 87ms/step - loss: 2.2782 - accuracy: 0.1900 - val_loss: 2.2770 - val_accuracy: 0.1880 +Epoch 22/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2770 - accuracy: 0.1940 1/1 [==============================] - 0s 87ms/step - loss: 2.2770 - accuracy: 0.1940 - val_loss: 2.2757 - val_accuracy: 0.1909 +Epoch 23/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2758 - accuracy: 0.1982 1/1 [==============================] - 0s 86ms/step - loss: 2.2758 - accuracy: 0.1982 - val_loss: 2.2745 - val_accuracy: 0.1946 +Epoch 24/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2746 - accuracy: 0.2025 1/1 [==============================] - 0s 86ms/step - loss: 2.2746 - accuracy: 0.2025 - val_loss: 2.2733 - val_accuracy: 0.1974 +Epoch 25/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2734 - accuracy: 0.2059 1/1 [==============================] - 0s 87ms/step - loss: 2.2734 - accuracy: 0.2059 - val_loss: 2.2720 - val_accuracy: 0.2014 +Epoch 26/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2722 - accuracy: 0.2091 1/1 [==============================] - 0s 88ms/step - loss: 2.2722 - accuracy: 0.2091 - val_loss: 2.2708 - val_accuracy: 0.2047 +Epoch 27/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2709 - accuracy: 0.2124 1/1 [==============================] - 0s 86ms/step - loss: 2.2709 - accuracy: 0.2124 - val_loss: 2.2695 - val_accuracy: 0.2081 +Epoch 28/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2697 - accuracy: 0.2154 1/1 [==============================] - 0s 99ms/step - loss: 2.2697 - accuracy: 0.2154 - val_loss: 2.2682 - val_accuracy: 0.2110 +Epoch 29/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2684 - accuracy: 0.2187 1/1 [==============================] - 0s 78ms/step - loss: 2.2684 - accuracy: 0.2187 - val_loss: 2.2669 - val_accuracy: 0.2140 +Epoch 30/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2671 - accuracy: 0.2212 1/1 [==============================] - 0s 81ms/step - loss: 2.2671 - accuracy: 0.2212 - val_loss: 2.2656 - val_accuracy: 0.2165 +Epoch 31/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2658 - accuracy: 0.2246 1/1 [==============================] - 0s 82ms/step - loss: 2.2658 - accuracy: 0.2246 - val_loss: 2.2643 - val_accuracy: 0.2199 +Epoch 32/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2645 - accuracy: 0.2276 1/1 [==============================] - 0s 80ms/step - loss: 2.2645 - accuracy: 0.2276 - val_loss: 2.2629 - val_accuracy: 0.2240 +Epoch 33/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2632 - accuracy: 0.2303 1/1 [==============================] - 0s 98ms/step - loss: 2.2632 - accuracy: 0.2303 - val_loss: 2.2616 - val_accuracy: 0.2274 +Epoch 34/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2619 - accuracy: 0.2330 1/1 [==============================] - 0s 81ms/step - loss: 2.2619 - accuracy: 0.2330 - val_loss: 2.2602 - val_accuracy: 0.2311 +Epoch 35/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2605 - accuracy: 0.2360 1/1 [==============================] - 0s 99ms/step - loss: 2.2605 - accuracy: 0.2360 - val_loss: 2.2588 - val_accuracy: 0.2339 +Epoch 36/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2592 - accuracy: 0.2390 1/1 [==============================] - 0s 93ms/step - loss: 2.2592 - accuracy: 0.2390 - val_loss: 2.2574 - val_accuracy: 0.2372 +Epoch 37/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2578 - accuracy: 0.2416 1/1 [==============================] - 0s 99ms/step - loss: 2.2578 - accuracy: 0.2416 - val_loss: 2.2560 - val_accuracy: 0.2396 +Epoch 38/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2564 - accuracy: 0.2444 1/1 [==============================] - 0s 81ms/step - loss: 2.2564 - accuracy: 0.2444 - val_loss: 2.2545 - val_accuracy: 0.2427 +Epoch 39/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2549 - accuracy: 0.2475 1/1 [==============================] - 0s 80ms/step - loss: 2.2549 - accuracy: 0.2475 - val_loss: 2.2531 - val_accuracy: 0.2444 +Epoch 40/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2535 - accuracy: 0.2502 1/1 [==============================] - 0s 80ms/step - loss: 2.2535 - accuracy: 0.2502 - val_loss: 2.2516 - val_accuracy: 0.2472 +Epoch 41/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2520 - accuracy: 0.2533 1/1 [==============================] - 0s 87ms/step - loss: 2.2520 - accuracy: 0.2533 - val_loss: 2.2501 - val_accuracy: 0.2502 +Epoch 42/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2505 - accuracy: 0.2562 1/1 [==============================] - 0s 87ms/step - loss: 2.2505 - accuracy: 0.2562 - val_loss: 2.2485 - val_accuracy: 0.2539 +Epoch 43/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2490 - accuracy: 0.2594 1/1 [==============================] - 0s 87ms/step - loss: 2.2490 - accuracy: 0.2594 - val_loss: 2.2470 - val_accuracy: 0.2584 +Epoch 44/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2475 - accuracy: 0.2624 1/1 [==============================] - 0s 81ms/step - loss: 2.2475 - accuracy: 0.2624 - val_loss: 2.2454 - val_accuracy: 0.2620 +Epoch 45/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2459 - accuracy: 0.2655 1/1 [==============================] - 0s 92ms/step - loss: 2.2459 - accuracy: 0.2655 - val_loss: 2.2438 - val_accuracy: 0.2661 +Epoch 46/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2444 - accuracy: 0.2688 1/1 [==============================] - 0s 78ms/step - loss: 2.2444 - accuracy: 0.2688 - val_loss: 2.2422 - val_accuracy: 0.2704 +Epoch 47/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2428 - accuracy: 0.2725 1/1 [==============================] - 0s 80ms/step - loss: 2.2428 - accuracy: 0.2725 - val_loss: 2.2405 - val_accuracy: 0.2735 +Epoch 48/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2411 - accuracy: 0.2757 1/1 [==============================] - 0s 81ms/step - loss: 2.2411 - accuracy: 0.2757 - val_loss: 2.2389 - val_accuracy: 0.2765 +Epoch 49/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2395 - accuracy: 0.2788 1/1 [==============================] - 0s 80ms/step - loss: 2.2395 - accuracy: 0.2788 - val_loss: 2.2372 - val_accuracy: 0.2804 +Epoch 50/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2378 - accuracy: 0.2825 1/1 [==============================] - 0s 88ms/step - loss: 2.2378 - accuracy: 0.2825 - val_loss: 2.2354 - val_accuracy: 0.2826 +Epoch 51/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2361 - accuracy: 0.2858 1/1 [==============================] - 0s 87ms/step - loss: 2.2361 - accuracy: 0.2858 - val_loss: 2.2337 - val_accuracy: 0.2866 +Epoch 52/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2344 - accuracy: 0.2891 1/1 [==============================] - 0s 87ms/step - loss: 2.2344 - accuracy: 0.2891 - val_loss: 2.2319 - val_accuracy: 0.2916 +Epoch 53/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2326 - accuracy: 0.2930 1/1 [==============================] - 0s 99ms/step - loss: 2.2326 - accuracy: 0.2930 - val_loss: 2.2301 - val_accuracy: 0.2952 +Epoch 54/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2308 - accuracy: 0.2967 1/1 [==============================] - 0s 99ms/step - loss: 2.2308 - accuracy: 0.2967 - val_loss: 2.2283 - val_accuracy: 0.2994 +Epoch 55/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2290 - accuracy: 0.3011 1/1 [==============================] - 0s 92ms/step - loss: 2.2290 - accuracy: 0.3011 - val_loss: 2.2264 - val_accuracy: 0.3039 +Epoch 56/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2271 - accuracy: 0.3052 1/1 [==============================] - 0s 80ms/step - loss: 2.2271 - accuracy: 0.3052 - val_loss: 2.2245 - val_accuracy: 0.3078 +Epoch 57/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2253 - accuracy: 0.3092 1/1 [==============================] - 0s 81ms/step - loss: 2.2253 - accuracy: 0.3092 - val_loss: 2.2225 - val_accuracy: 0.3128 +Epoch 58/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2233 - accuracy: 0.3136 1/1 [==============================] - 0s 87ms/step - loss: 2.2233 - accuracy: 0.3136 - val_loss: 2.2206 - val_accuracy: 0.3171 +Epoch 59/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2214 - accuracy: 0.3181 1/1 [==============================] - 0s 80ms/step - loss: 2.2214 - accuracy: 0.3181 - val_loss: 2.2185 - val_accuracy: 0.3220 +Epoch 60/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2194 - accuracy: 0.3221 1/1 [==============================] - 0s 80ms/step - loss: 2.2194 - accuracy: 0.3221 - val_loss: 2.2165 - val_accuracy: 0.3260 +Epoch 61/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2174 - accuracy: 0.3260 1/1 [==============================] - 0s 81ms/step - loss: 2.2174 - accuracy: 0.3260 - val_loss: 2.2144 - val_accuracy: 0.3310 +Epoch 62/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2153 - accuracy: 0.3304 1/1 [==============================] - 0s 93ms/step - loss: 2.2153 - accuracy: 0.3304 - val_loss: 2.2123 - val_accuracy: 0.3361 +Epoch 63/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2132 - accuracy: 0.3344 1/1 [==============================] - 0s 100ms/step - loss: 2.2132 - accuracy: 0.3344 - val_loss: 2.2101 - val_accuracy: 0.3404 +Epoch 64/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2111 - accuracy: 0.3386 1/1 [==============================] - 0s 74ms/step - loss: 2.2111 - accuracy: 0.3386 - val_loss: 2.2079 - val_accuracy: 0.3451 +Epoch 65/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2089 - accuracy: 0.3427 1/1 [==============================] - 0s 80ms/step - loss: 2.2089 - accuracy: 0.3427 - val_loss: 2.2056 - val_accuracy: 0.3491 +Epoch 66/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2066 - accuracy: 0.3473 1/1 [==============================] - 0s 86ms/step - loss: 2.2066 - accuracy: 0.3473 - val_loss: 2.2033 - val_accuracy: 0.3529 +Epoch 67/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2044 - accuracy: 0.3511 1/1 [==============================] - 0s 80ms/step - loss: 2.2044 - accuracy: 0.3511 - val_loss: 2.2010 - val_accuracy: 0.3569 +Epoch 68/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.2020 - accuracy: 0.3553 1/1 [==============================] - 0s 83ms/step - loss: 2.2020 - accuracy: 0.3553 - val_loss: 2.1986 - val_accuracy: 0.3615 +Epoch 69/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.1997 - accuracy: 0.3594 1/1 [==============================] - 0s 81ms/step - loss: 2.1997 - accuracy: 0.3594 - val_loss: 2.1961 - val_accuracy: 0.3655 +Epoch 70/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.1972 - accuracy: 0.3637 1/1 [==============================] - 0s 83ms/step - loss: 2.1972 - accuracy: 0.3637 - val_loss: 2.1936 - val_accuracy: 0.3714 +Epoch 71/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.1947 - accuracy: 0.3680 1/1 [==============================] - 0s 86ms/step - loss: 2.1947 - accuracy: 0.3680 - val_loss: 2.1910 - val_accuracy: 0.3757 +Epoch 72/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.1922 - accuracy: 0.3733 1/1 [==============================] - 0s 74ms/step - loss: 2.1922 - accuracy: 0.3733 - val_loss: 2.1884 - val_accuracy: 0.3798 +Epoch 73/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.1896 - accuracy: 0.3778 1/1 [==============================] - 0s 81ms/step - loss: 2.1896 - accuracy: 0.3778 - val_loss: 2.1857 - val_accuracy: 0.3844 +Epoch 74/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.1869 - accuracy: 0.3823 1/1 [==============================] - 0s 80ms/step - loss: 2.1869 - accuracy: 0.3823 - val_loss: 2.1829 - val_accuracy: 0.3885 +Epoch 75/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.1842 - accuracy: 0.3871 1/1 [==============================] - 0s 80ms/step - loss: 2.1842 - accuracy: 0.3871 - val_loss: 2.1801 - val_accuracy: 0.3927 +Epoch 76/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.1814 - accuracy: 0.3927 1/1 [==============================] - 0s 88ms/step - loss: 2.1814 - accuracy: 0.3927 - val_loss: 2.1772 - val_accuracy: 0.3982 +Epoch 77/1875 + 1/1 [==============================] - 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loss: 2.1665 - accuracy: 0.4188 - val_loss: 2.1616 - val_accuracy: 0.4284 +Epoch 82/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.1633 - accuracy: 0.4243 1/1 [==============================] - 0s 89ms/step - loss: 2.1633 - accuracy: 0.4243 - val_loss: 2.1583 - val_accuracy: 0.4348 +Epoch 83/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.1600 - accuracy: 0.4302 1/1 [==============================] - 0s 87ms/step - loss: 2.1600 - accuracy: 0.4302 - val_loss: 2.1549 - val_accuracy: 0.4411 +Epoch 84/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.1566 - accuracy: 0.4358 1/1 [==============================] - 0s 81ms/step - loss: 2.1566 - accuracy: 0.4358 - val_loss: 2.1513 - val_accuracy: 0.4474 +Epoch 85/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.1531 - accuracy: 0.4416 1/1 [==============================] - 0s 81ms/step - loss: 2.1531 - accuracy: 0.4416 - val_loss: 2.1477 - val_accuracy: 0.4520 +Epoch 86/1875 + 1/1 [==============================] - 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loss: 2.1345 - accuracy: 0.4721 - val_loss: 2.1282 - val_accuracy: 0.4865 +Epoch 91/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.1304 - accuracy: 0.4783 1/1 [==============================] - 0s 80ms/step - loss: 2.1304 - accuracy: 0.4783 - val_loss: 2.1240 - val_accuracy: 0.4932 +Epoch 92/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.1263 - accuracy: 0.4856 1/1 [==============================] - 0s 81ms/step - loss: 2.1263 - accuracy: 0.4856 - val_loss: 2.1196 - val_accuracy: 0.5002 +Epoch 93/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.1220 - accuracy: 0.4922 1/1 [==============================] - 0s 80ms/step - loss: 2.1220 - accuracy: 0.4922 - val_loss: 2.1152 - val_accuracy: 0.5075 +Epoch 94/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.1176 - accuracy: 0.4992 1/1 [==============================] - 0s 80ms/step - loss: 2.1176 - accuracy: 0.4992 - val_loss: 2.1106 - val_accuracy: 0.5135 +Epoch 95/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.1131 - accuracy: 0.5060 1/1 [==============================] - 0s 80ms/step - loss: 2.1131 - accuracy: 0.5060 - val_loss: 2.1059 - val_accuracy: 0.5192 +Epoch 96/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.1085 - accuracy: 0.5130 1/1 [==============================] - 0s 84ms/step - loss: 2.1085 - accuracy: 0.5130 - val_loss: 2.1010 - val_accuracy: 0.5258 +Epoch 97/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.1037 - accuracy: 0.5198 1/1 [==============================] - 0s 86ms/step - loss: 2.1037 - accuracy: 0.5198 - val_loss: 2.0960 - val_accuracy: 0.5335 +Epoch 98/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.0988 - accuracy: 0.5269 1/1 [==============================] - 0s 74ms/step - loss: 2.0988 - accuracy: 0.5269 - val_loss: 2.0909 - val_accuracy: 0.5407 +Epoch 99/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.0937 - accuracy: 0.5336 1/1 [==============================] - 0s 83ms/step - loss: 2.0937 - accuracy: 0.5336 - val_loss: 2.0856 - val_accuracy: 0.5479 +Epoch 100/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.0886 - accuracy: 0.5400 1/1 [==============================] - 0s 80ms/step - loss: 2.0886 - accuracy: 0.5400 - val_loss: 2.0802 - val_accuracy: 0.5547 +Epoch 101/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.0832 - accuracy: 0.5468 1/1 [==============================] - 0s 80ms/step - loss: 2.0832 - accuracy: 0.5468 - val_loss: 2.0747 - val_accuracy: 0.5623 +Epoch 102/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.0778 - accuracy: 0.5535 1/1 [==============================] - 0s 82ms/step - loss: 2.0778 - accuracy: 0.5535 - val_loss: 2.0690 - val_accuracy: 0.5695 +Epoch 103/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.0722 - accuracy: 0.5594 1/1 [==============================] - 0s 80ms/step - loss: 2.0722 - accuracy: 0.5594 - val_loss: 2.0631 - val_accuracy: 0.5752 +Epoch 104/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.0664 - accuracy: 0.5657 1/1 [==============================] - 0s 80ms/step - loss: 2.0664 - accuracy: 0.5657 - val_loss: 2.0571 - val_accuracy: 0.5819 +Epoch 105/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.0605 - accuracy: 0.5724 1/1 [==============================] - 0s 94ms/step - loss: 2.0605 - accuracy: 0.5724 - val_loss: 2.0509 - val_accuracy: 0.5890 +Epoch 106/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.0544 - accuracy: 0.5789 1/1 [==============================] - 0s 98ms/step - loss: 2.0544 - accuracy: 0.5789 - val_loss: 2.0446 - val_accuracy: 0.5954 +Epoch 107/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.0482 - accuracy: 0.5854 1/1 [==============================] - 0s 74ms/step - loss: 2.0482 - accuracy: 0.5854 - val_loss: 2.0380 - val_accuracy: 0.6003 +Epoch 108/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.0418 - accuracy: 0.5919 1/1 [==============================] - 0s 81ms/step - loss: 2.0418 - accuracy: 0.5919 - val_loss: 2.0313 - val_accuracy: 0.6055 +Epoch 109/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.0352 - accuracy: 0.5976 1/1 [==============================] - 0s 81ms/step - loss: 2.0352 - accuracy: 0.5976 - val_loss: 2.0244 - val_accuracy: 0.6121 +Epoch 110/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.0284 - accuracy: 0.6037 1/1 [==============================] - 0s 81ms/step - loss: 2.0284 - accuracy: 0.6037 - val_loss: 2.0173 - val_accuracy: 0.6189 +Epoch 111/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.0215 - accuracy: 0.6094 1/1 [==============================] - 0s 80ms/step - loss: 2.0215 - accuracy: 0.6094 - val_loss: 2.0101 - val_accuracy: 0.6257 +Epoch 112/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.0143 - accuracy: 0.6152 1/1 [==============================] - 0s 81ms/step - loss: 2.0143 - accuracy: 0.6152 - val_loss: 2.0026 - val_accuracy: 0.6319 +Epoch 113/1875 + 1/1 [==============================] - ETA: 0s - loss: 2.0069 - accuracy: 0.6203 1/1 [==============================] - 0s 87ms/step - loss: 2.0069 - accuracy: 0.6203 - val_loss: 1.9948 - val_accuracy: 0.6373 +Epoch 114/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.9994 - accuracy: 0.6261 1/1 [==============================] - 0s 93ms/step - loss: 1.9994 - accuracy: 0.6261 - val_loss: 1.9869 - val_accuracy: 0.6419 +Epoch 115/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.9916 - accuracy: 0.6312 1/1 [==============================] - 0s 105ms/step - loss: 1.9916 - accuracy: 0.6312 - val_loss: 1.9787 - val_accuracy: 0.6471 +Epoch 116/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.9835 - accuracy: 0.6368 1/1 [==============================] - 0s 77ms/step - loss: 1.9835 - accuracy: 0.6368 - val_loss: 1.9702 - val_accuracy: 0.6530 +Epoch 117/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.9752 - accuracy: 0.6423 1/1 [==============================] - 0s 87ms/step - loss: 1.9752 - accuracy: 0.6423 - val_loss: 1.9616 - val_accuracy: 0.6583 +Epoch 118/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.9667 - accuracy: 0.6476 1/1 [==============================] - 0s 81ms/step - loss: 1.9667 - accuracy: 0.6476 - val_loss: 1.9527 - val_accuracy: 0.6636 +Epoch 119/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.9580 - accuracy: 0.6520 1/1 [==============================] - 0s 81ms/step - loss: 1.9580 - accuracy: 0.6520 - val_loss: 1.9435 - val_accuracy: 0.6690 +Epoch 120/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.9490 - accuracy: 0.6573 1/1 [==============================] - 0s 87ms/step - loss: 1.9490 - accuracy: 0.6573 - val_loss: 1.9342 - val_accuracy: 0.6727 +Epoch 121/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.9398 - accuracy: 0.6610 1/1 [==============================] - 0s 81ms/step - loss: 1.9398 - accuracy: 0.6610 - val_loss: 1.9246 - val_accuracy: 0.6766 +Epoch 122/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.9304 - accuracy: 0.6654 1/1 [==============================] - 0s 87ms/step - loss: 1.9304 - accuracy: 0.6654 - val_loss: 1.9148 - val_accuracy: 0.6805 +Epoch 123/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.9208 - accuracy: 0.6693 1/1 [==============================] - 0s 85ms/step - loss: 1.9208 - accuracy: 0.6693 - val_loss: 1.9047 - val_accuracy: 0.6841 +Epoch 124/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.9109 - accuracy: 0.6721 1/1 [==============================] - 0s 74ms/step - loss: 1.9109 - accuracy: 0.6721 - val_loss: 1.8944 - val_accuracy: 0.6875 +Epoch 125/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.9008 - accuracy: 0.6752 1/1 [==============================] - 0s 80ms/step - loss: 1.9008 - accuracy: 0.6752 - val_loss: 1.8838 - val_accuracy: 0.6896 +Epoch 126/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.8904 - accuracy: 0.6777 1/1 [==============================] - 0s 80ms/step - loss: 1.8904 - accuracy: 0.6777 - val_loss: 1.8729 - val_accuracy: 0.6916 +Epoch 127/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.8798 - accuracy: 0.6803 1/1 [==============================] - 0s 82ms/step - loss: 1.8798 - accuracy: 0.6803 - val_loss: 1.8618 - val_accuracy: 0.6936 +Epoch 128/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.8689 - accuracy: 0.6829 1/1 [==============================] - 0s 80ms/step - loss: 1.8689 - accuracy: 0.6829 - val_loss: 1.8504 - val_accuracy: 0.6959 +Epoch 129/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.8577 - accuracy: 0.6856 1/1 [==============================] - 0s 80ms/step - loss: 1.8577 - accuracy: 0.6856 - val_loss: 1.8387 - val_accuracy: 0.6993 +Epoch 130/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.8462 - accuracy: 0.6879 1/1 [==============================] - 0s 93ms/step - loss: 1.8462 - accuracy: 0.6879 - val_loss: 1.8268 - val_accuracy: 0.7018 +Epoch 131/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.8345 - accuracy: 0.6899 1/1 [==============================] - 0s 93ms/step - loss: 1.8345 - accuracy: 0.6899 - val_loss: 1.8145 - val_accuracy: 0.7046 +Epoch 132/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.8225 - accuracy: 0.6926 1/1 [==============================] - 0s 98ms/step - loss: 1.8225 - accuracy: 0.6926 - val_loss: 1.8020 - val_accuracy: 0.7074 +Epoch 133/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.8102 - accuracy: 0.6954 1/1 [==============================] - 0s 75ms/step - loss: 1.8102 - accuracy: 0.6954 - val_loss: 1.7891 - val_accuracy: 0.7090 +Epoch 134/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.7976 - accuracy: 0.6975 1/1 [==============================] - 0s 80ms/step - loss: 1.7976 - accuracy: 0.6975 - val_loss: 1.7760 - val_accuracy: 0.7115 +Epoch 135/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.7846 - accuracy: 0.7002 1/1 [==============================] - 0s 80ms/step - loss: 1.7846 - accuracy: 0.7002 - val_loss: 1.7625 - val_accuracy: 0.7141 +Epoch 136/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.7715 - accuracy: 0.7035 1/1 [==============================] - 0s 83ms/step - loss: 1.7715 - accuracy: 0.7035 - val_loss: 1.7488 - val_accuracy: 0.7170 +Epoch 137/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.7580 - accuracy: 0.7056 1/1 [==============================] - 0s 80ms/step - loss: 1.7580 - accuracy: 0.7056 - val_loss: 1.7347 - val_accuracy: 0.7191 +Epoch 138/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.7442 - accuracy: 0.7082 1/1 [==============================] - 0s 80ms/step - loss: 1.7442 - accuracy: 0.7082 - val_loss: 1.7203 - val_accuracy: 0.7214 +Epoch 139/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.7301 - accuracy: 0.7098 1/1 [==============================] - 0s 80ms/step - loss: 1.7301 - accuracy: 0.7098 - val_loss: 1.7057 - val_accuracy: 0.7226 +Epoch 140/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.7157 - accuracy: 0.7121 1/1 [==============================] - 0s 82ms/step - loss: 1.7157 - accuracy: 0.7121 - val_loss: 1.6907 - val_accuracy: 0.7251 +Epoch 141/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.7010 - accuracy: 0.7143 1/1 [==============================] - 0s 99ms/step - loss: 1.7010 - accuracy: 0.7143 - val_loss: 1.6755 - val_accuracy: 0.7264 +Epoch 142/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.6861 - accuracy: 0.7167 1/1 [==============================] - 0s 74ms/step - loss: 1.6861 - accuracy: 0.7167 - val_loss: 1.6600 - val_accuracy: 0.7293 +Epoch 143/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.6709 - accuracy: 0.7191 1/1 [==============================] - 0s 80ms/step - loss: 1.6709 - accuracy: 0.7191 - val_loss: 1.6442 - val_accuracy: 0.7311 +Epoch 144/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.6554 - accuracy: 0.7210 1/1 [==============================] - 0s 81ms/step - loss: 1.6554 - accuracy: 0.7210 - val_loss: 1.6282 - val_accuracy: 0.7327 +Epoch 145/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.6397 - accuracy: 0.7228 1/1 [==============================] - 0s 80ms/step - loss: 1.6397 - accuracy: 0.7228 - val_loss: 1.6119 - val_accuracy: 0.7355 +Epoch 146/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.6237 - accuracy: 0.7251 1/1 [==============================] - 0s 80ms/step - loss: 1.6237 - accuracy: 0.7251 - val_loss: 1.5954 - val_accuracy: 0.7373 +Epoch 147/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.6075 - accuracy: 0.7275 1/1 [==============================] - 0s 88ms/step - loss: 1.6075 - accuracy: 0.7275 - val_loss: 1.5787 - val_accuracy: 0.7398 +Epoch 148/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.5911 - accuracy: 0.7295 1/1 [==============================] - 0s 87ms/step - loss: 1.5911 - accuracy: 0.7295 - val_loss: 1.5617 - val_accuracy: 0.7424 +Epoch 149/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.5745 - accuracy: 0.7321 1/1 [==============================] - 0s 99ms/step - loss: 1.5745 - accuracy: 0.7321 - val_loss: 1.5446 - val_accuracy: 0.7448 +Epoch 150/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.5577 - accuracy: 0.7343 1/1 [==============================] - 0s 75ms/step - loss: 1.5577 - accuracy: 0.7343 - val_loss: 1.5273 - val_accuracy: 0.7483 +Epoch 151/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.5408 - accuracy: 0.7368 1/1 [==============================] - 0s 82ms/step - loss: 1.5408 - accuracy: 0.7368 - val_loss: 1.5098 - val_accuracy: 0.7506 +Epoch 152/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.5236 - accuracy: 0.7389 1/1 [==============================] - 0s 81ms/step - loss: 1.5236 - accuracy: 0.7389 - val_loss: 1.4922 - val_accuracy: 0.7522 +Epoch 153/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.5063 - accuracy: 0.7407 1/1 [==============================] - 0s 81ms/step - loss: 1.5063 - accuracy: 0.7407 - val_loss: 1.4744 - val_accuracy: 0.7542 +Epoch 154/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.4889 - accuracy: 0.7429 1/1 [==============================] - 0s 92ms/step - loss: 1.4889 - accuracy: 0.7429 - val_loss: 1.4565 - val_accuracy: 0.7561 +Epoch 155/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.4713 - accuracy: 0.7451 1/1 [==============================] - 0s 87ms/step - loss: 1.4713 - accuracy: 0.7451 - val_loss: 1.4385 - val_accuracy: 0.7588 +Epoch 156/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.4537 - accuracy: 0.7473 1/1 [==============================] - 0s 81ms/step - loss: 1.4537 - accuracy: 0.7473 - val_loss: 1.4204 - val_accuracy: 0.7602 +Epoch 157/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.4360 - accuracy: 0.7494 1/1 [==============================] - 0s 93ms/step - loss: 1.4360 - accuracy: 0.7494 - val_loss: 1.4023 - val_accuracy: 0.7629 +Epoch 158/1875 + 1/1 [==============================] - 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loss: 1.3468 - accuracy: 0.7593 - val_loss: 1.3114 - val_accuracy: 0.7712 +Epoch 163/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.3290 - accuracy: 0.7613 1/1 [==============================] - 0s 80ms/step - loss: 1.3290 - accuracy: 0.7613 - val_loss: 1.2934 - val_accuracy: 0.7728 +Epoch 164/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.3112 - accuracy: 0.7632 1/1 [==============================] - 0s 81ms/step - loss: 1.3112 - accuracy: 0.7632 - val_loss: 1.2754 - val_accuracy: 0.7747 +Epoch 165/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.2936 - accuracy: 0.7647 1/1 [==============================] - 0s 80ms/step - loss: 1.2936 - accuracy: 0.7647 - val_loss: 1.2575 - val_accuracy: 0.7769 +Epoch 166/1875 + 1/1 [==============================] - ETA: 0s - loss: 1.2760 - accuracy: 0.7666 1/1 [==============================] - 0s 80ms/step - loss: 1.2760 - accuracy: 0.7666 - val_loss: 1.2398 - val_accuracy: 0.7794 +Epoch 167/1875 + 1/1 [==============================] - 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ETA: 0s - loss: 0.8740 - accuracy: 0.8077 1/1 [==============================] - 0s 75ms/step - loss: 0.8740 - accuracy: 0.8077 - val_loss: 0.8406 - val_accuracy: 0.8166 +Epoch 195/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.8636 - accuracy: 0.8089 1/1 [==============================] - 0s 74ms/step - loss: 0.8636 - accuracy: 0.8089 - val_loss: 0.8305 - val_accuracy: 0.8174 +Epoch 196/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.8535 - accuracy: 0.8098 1/1 [==============================] - 0s 81ms/step - loss: 0.8535 - accuracy: 0.8098 - val_loss: 0.8206 - val_accuracy: 0.8187 +Epoch 197/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.8436 - accuracy: 0.8110 1/1 [==============================] - 0s 81ms/step - loss: 0.8436 - accuracy: 0.8110 - val_loss: 0.8109 - val_accuracy: 0.8205 +Epoch 198/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.8339 - accuracy: 0.8123 1/1 [==============================] - 0s 80ms/step - 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ETA: 0s - loss: 0.7892 - accuracy: 0.8181 1/1 [==============================] - 0s 76ms/step - loss: 0.7892 - accuracy: 0.8181 - val_loss: 0.7578 - val_accuracy: 0.8282 +Epoch 204/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.7809 - accuracy: 0.8191 1/1 [==============================] - 0s 87ms/step - loss: 0.7809 - accuracy: 0.8191 - val_loss: 0.7497 - val_accuracy: 0.8292 +Epoch 205/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.7728 - accuracy: 0.8200 1/1 [==============================] - 0s 81ms/step - loss: 0.7728 - accuracy: 0.8200 - val_loss: 0.7418 - val_accuracy: 0.8304 +Epoch 206/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.7649 - accuracy: 0.8211 1/1 [==============================] - 0s 81ms/step - loss: 0.7649 - accuracy: 0.8211 - val_loss: 0.7341 - val_accuracy: 0.8313 +Epoch 207/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.7571 - accuracy: 0.8221 1/1 [==============================] - 0s 80ms/step - loss: 0.7571 - accuracy: 0.8221 - val_loss: 0.7266 - val_accuracy: 0.8322 +Epoch 208/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.7496 - accuracy: 0.8233 1/1 [==============================] - 0s 80ms/step - loss: 0.7496 - accuracy: 0.8233 - val_loss: 0.7193 - val_accuracy: 0.8337 +Epoch 209/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.7423 - accuracy: 0.8243 1/1 [==============================] - 0s 99ms/step - loss: 0.7423 - accuracy: 0.8243 - val_loss: 0.7122 - val_accuracy: 0.8346 +Epoch 210/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.7351 - accuracy: 0.8255 1/1 [==============================] - 0s 99ms/step - loss: 0.7351 - accuracy: 0.8255 - val_loss: 0.7052 - val_accuracy: 0.8358 +Epoch 211/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.7281 - accuracy: 0.8267 1/1 [==============================] - 0s 75ms/step - loss: 0.7281 - accuracy: 0.8267 - val_loss: 0.6984 - val_accuracy: 0.8373 +Epoch 212/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.7213 - accuracy: 0.8278 1/1 [==============================] - 0s 74ms/step - loss: 0.7213 - accuracy: 0.8278 - val_loss: 0.6917 - val_accuracy: 0.8377 +Epoch 213/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.7146 - accuracy: 0.8287 1/1 [==============================] - 0s 80ms/step - loss: 0.7146 - accuracy: 0.8287 - val_loss: 0.6852 - val_accuracy: 0.8391 +Epoch 214/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.7081 - accuracy: 0.8296 1/1 [==============================] - 0s 81ms/step - loss: 0.7081 - accuracy: 0.8296 - val_loss: 0.6789 - val_accuracy: 0.8395 +Epoch 215/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.7017 - accuracy: 0.8306 1/1 [==============================] - 0s 80ms/step - loss: 0.7017 - accuracy: 0.8306 - val_loss: 0.6727 - val_accuracy: 0.8412 +Epoch 216/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6955 - accuracy: 0.8318 1/1 [==============================] - 0s 80ms/step - loss: 0.6955 - accuracy: 0.8318 - val_loss: 0.6667 - val_accuracy: 0.8420 +Epoch 217/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6894 - accuracy: 0.8331 1/1 [==============================] - 0s 81ms/step - loss: 0.6894 - accuracy: 0.8331 - val_loss: 0.6608 - val_accuracy: 0.8429 +Epoch 218/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6834 - accuracy: 0.8340 1/1 [==============================] - 0s 80ms/step - loss: 0.6834 - accuracy: 0.8340 - val_loss: 0.6550 - val_accuracy: 0.8437 +Epoch 219/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6776 - accuracy: 0.8348 1/1 [==============================] - 0s 99ms/step - loss: 0.6776 - accuracy: 0.8348 - val_loss: 0.6494 - val_accuracy: 0.8449 +Epoch 220/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6719 - accuracy: 0.8355 1/1 [==============================] - 0s 74ms/step - loss: 0.6719 - accuracy: 0.8355 - val_loss: 0.6438 - val_accuracy: 0.8465 +Epoch 221/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6664 - accuracy: 0.8364 1/1 [==============================] - 0s 74ms/step - loss: 0.6664 - accuracy: 0.8364 - val_loss: 0.6385 - val_accuracy: 0.8478 +Epoch 222/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6609 - accuracy: 0.8372 1/1 [==============================] - 0s 74ms/step - loss: 0.6609 - accuracy: 0.8372 - val_loss: 0.6332 - val_accuracy: 0.8490 +Epoch 223/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6556 - accuracy: 0.8382 1/1 [==============================] - 0s 80ms/step - loss: 0.6556 - accuracy: 0.8382 - val_loss: 0.6280 - val_accuracy: 0.8502 +Epoch 224/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6504 - accuracy: 0.8390 1/1 [==============================] - 0s 91ms/step - loss: 0.6504 - accuracy: 0.8390 - val_loss: 0.6230 - val_accuracy: 0.8513 +Epoch 225/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6453 - accuracy: 0.8398 1/1 [==============================] - 0s 80ms/step - loss: 0.6453 - accuracy: 0.8398 - val_loss: 0.6180 - val_accuracy: 0.8525 +Epoch 226/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6403 - accuracy: 0.8405 1/1 [==============================] - 0s 80ms/step - loss: 0.6403 - accuracy: 0.8405 - val_loss: 0.6132 - val_accuracy: 0.8534 +Epoch 227/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6354 - accuracy: 0.8414 1/1 [==============================] - 0s 86ms/step - loss: 0.6354 - accuracy: 0.8414 - val_loss: 0.6085 - val_accuracy: 0.8538 +Epoch 228/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6306 - accuracy: 0.8424 1/1 [==============================] - 0s 74ms/step - loss: 0.6306 - accuracy: 0.8424 - val_loss: 0.6039 - val_accuracy: 0.8548 +Epoch 229/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6259 - accuracy: 0.8433 1/1 [==============================] - 0s 74ms/step - loss: 0.6259 - accuracy: 0.8433 - val_loss: 0.5993 - val_accuracy: 0.8557 +Epoch 230/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6214 - accuracy: 0.8442 1/1 [==============================] - 0s 74ms/step - loss: 0.6214 - accuracy: 0.8442 - val_loss: 0.5949 - val_accuracy: 0.8569 +Epoch 231/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6169 - accuracy: 0.8448 1/1 [==============================] - 0s 74ms/step - loss: 0.6169 - accuracy: 0.8448 - val_loss: 0.5906 - val_accuracy: 0.8578 +Epoch 232/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6125 - accuracy: 0.8459 1/1 [==============================] - 0s 80ms/step - loss: 0.6125 - accuracy: 0.8459 - val_loss: 0.5863 - val_accuracy: 0.8585 +Epoch 233/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6082 - accuracy: 0.8465 1/1 [==============================] - 0s 80ms/step - loss: 0.6082 - accuracy: 0.8465 - val_loss: 0.5822 - val_accuracy: 0.8593 +Epoch 234/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6039 - accuracy: 0.8476 1/1 [==============================] - 0s 80ms/step - loss: 0.6039 - accuracy: 0.8476 - val_loss: 0.5781 - val_accuracy: 0.8601 +Epoch 235/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5998 - accuracy: 0.8486 1/1 [==============================] - 0s 93ms/step - loss: 0.5998 - accuracy: 0.8486 - val_loss: 0.5741 - val_accuracy: 0.8607 +Epoch 236/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5957 - accuracy: 0.8492 1/1 [==============================] - 0s 100ms/step - loss: 0.5957 - accuracy: 0.8492 - val_loss: 0.5702 - val_accuracy: 0.8613 +Epoch 237/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5917 - accuracy: 0.8500 1/1 [==============================] - 0s 74ms/step - loss: 0.5917 - accuracy: 0.8500 - val_loss: 0.5663 - val_accuracy: 0.8617 +Epoch 238/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5878 - accuracy: 0.8508 1/1 [==============================] - 0s 85ms/step - loss: 0.5878 - accuracy: 0.8508 - val_loss: 0.5626 - val_accuracy: 0.8623 +Epoch 239/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5840 - accuracy: 0.8514 1/1 [==============================] - 0s 74ms/step - loss: 0.5840 - accuracy: 0.8514 - val_loss: 0.5589 - val_accuracy: 0.8630 +Epoch 240/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5802 - accuracy: 0.8522 1/1 [==============================] - 0s 77ms/step - loss: 0.5802 - accuracy: 0.8522 - val_loss: 0.5552 - val_accuracy: 0.8638 +Epoch 241/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5766 - accuracy: 0.8529 1/1 [==============================] - 0s 81ms/step - loss: 0.5766 - accuracy: 0.8529 - val_loss: 0.5517 - val_accuracy: 0.8644 +Epoch 242/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5729 - accuracy: 0.8538 1/1 [==============================] - 0s 80ms/step - loss: 0.5729 - accuracy: 0.8538 - val_loss: 0.5482 - val_accuracy: 0.8650 +Epoch 243/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5694 - accuracy: 0.8543 1/1 [==============================] - 0s 80ms/step - loss: 0.5694 - accuracy: 0.8543 - val_loss: 0.5448 - val_accuracy: 0.8651 +Epoch 244/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5659 - accuracy: 0.8550 1/1 [==============================] - 0s 80ms/step - loss: 0.5659 - accuracy: 0.8550 - val_loss: 0.5414 - val_accuracy: 0.8656 +Epoch 245/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5625 - accuracy: 0.8555 1/1 [==============================] - 0s 102ms/step - loss: 0.5625 - accuracy: 0.8555 - val_loss: 0.5381 - val_accuracy: 0.8662 +Epoch 246/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5591 - accuracy: 0.8559 1/1 [==============================] - 0s 74ms/step - loss: 0.5591 - accuracy: 0.8559 - val_loss: 0.5349 - val_accuracy: 0.8670 +Epoch 247/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5558 - accuracy: 0.8566 1/1 [==============================] - 0s 74ms/step - loss: 0.5558 - accuracy: 0.8566 - val_loss: 0.5317 - val_accuracy: 0.8679 +Epoch 248/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5526 - accuracy: 0.8573 1/1 [==============================] - 0s 75ms/step - loss: 0.5526 - accuracy: 0.8573 - val_loss: 0.5286 - val_accuracy: 0.8681 +Epoch 249/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5494 - accuracy: 0.8581 1/1 [==============================] - 0s 75ms/step - loss: 0.5494 - accuracy: 0.8581 - val_loss: 0.5255 - val_accuracy: 0.8682 +Epoch 250/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5463 - accuracy: 0.8587 1/1 [==============================] - 0s 81ms/step - loss: 0.5463 - accuracy: 0.8587 - val_loss: 0.5225 - val_accuracy: 0.8690 +Epoch 251/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5432 - accuracy: 0.8594 1/1 [==============================] - 0s 81ms/step - loss: 0.5432 - accuracy: 0.8594 - val_loss: 0.5196 - val_accuracy: 0.8697 +Epoch 252/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5402 - accuracy: 0.8600 1/1 [==============================] - 0s 80ms/step - loss: 0.5402 - accuracy: 0.8600 - val_loss: 0.5167 - val_accuracy: 0.8699 +Epoch 253/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5372 - accuracy: 0.8607 1/1 [==============================] - 0s 99ms/step - loss: 0.5372 - accuracy: 0.8607 - val_loss: 0.5138 - val_accuracy: 0.8703 +Epoch 254/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5343 - accuracy: 0.8613 1/1 [==============================] - 0s 74ms/step - loss: 0.5343 - accuracy: 0.8613 - val_loss: 0.5110 - val_accuracy: 0.8705 +Epoch 255/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5315 - accuracy: 0.8619 1/1 [==============================] - 0s 74ms/step - loss: 0.5315 - accuracy: 0.8619 - val_loss: 0.5083 - val_accuracy: 0.8711 +Epoch 256/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5287 - accuracy: 0.8626 1/1 [==============================] - 0s 76ms/step - loss: 0.5287 - accuracy: 0.8626 - val_loss: 0.5056 - val_accuracy: 0.8718 +Epoch 257/1875 + 1/1 [==============================] - 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loss: 0.5153 - accuracy: 0.8654 - val_loss: 0.4927 - val_accuracy: 0.8740 +Epoch 262/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5127 - accuracy: 0.8659 1/1 [==============================] - 0s 99ms/step - loss: 0.5127 - accuracy: 0.8659 - val_loss: 0.4902 - val_accuracy: 0.8750 +Epoch 263/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5102 - accuracy: 0.8663 1/1 [==============================] - 0s 74ms/step - loss: 0.5102 - accuracy: 0.8663 - val_loss: 0.4878 - val_accuracy: 0.8749 +Epoch 264/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5078 - accuracy: 0.8669 1/1 [==============================] - 0s 74ms/step - loss: 0.5078 - accuracy: 0.8669 - val_loss: 0.4854 - val_accuracy: 0.8761 +Epoch 265/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5054 - accuracy: 0.8673 1/1 [==============================] - 0s 74ms/step - loss: 0.5054 - accuracy: 0.8673 - val_loss: 0.4832 - val_accuracy: 0.8759 +Epoch 266/1875 + 1/1 [==============================] - 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loss: 0.4941 - accuracy: 0.8694 - val_loss: 0.4723 - val_accuracy: 0.8788 +Epoch 271/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4921 - accuracy: 0.8697 1/1 [==============================] - 0s 99ms/step - loss: 0.4921 - accuracy: 0.8697 - val_loss: 0.4711 - val_accuracy: 0.8766 +Epoch 272/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4904 - accuracy: 0.8698 1/1 [==============================] - 0s 82ms/step - loss: 0.4904 - accuracy: 0.8698 - val_loss: 0.4693 - val_accuracy: 0.8793 +Epoch 273/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4890 - accuracy: 0.8698 1/1 [==============================] - 0s 74ms/step - loss: 0.4890 - accuracy: 0.8698 - val_loss: 0.4696 - val_accuracy: 0.8763 +Epoch 274/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4885 - accuracy: 0.8694 1/1 [==============================] - 0s 80ms/step - loss: 0.4885 - accuracy: 0.8694 - val_loss: 0.4694 - val_accuracy: 0.8760 +Epoch 275/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4893 - accuracy: 0.8667 1/1 [==============================] - 0s 75ms/step - loss: 0.4893 - accuracy: 0.8667 - val_loss: 0.4744 - val_accuracy: 0.8710 +Epoch 276/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4927 - accuracy: 0.8651 1/1 [==============================] - 0s 75ms/step - loss: 0.4927 - accuracy: 0.8651 - val_loss: 0.4798 - val_accuracy: 0.8684 +Epoch 277/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5001 - accuracy: 0.8576 1/1 [==============================] - 0s 86ms/step - loss: 0.5001 - accuracy: 0.8576 - val_loss: 0.4984 - val_accuracy: 0.8535 +Epoch 278/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5154 - accuracy: 0.8480 1/1 [==============================] - 0s 80ms/step - loss: 0.5154 - accuracy: 0.8480 - val_loss: 0.5158 - val_accuracy: 0.8458 +Epoch 279/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5365 - accuracy: 0.8341 1/1 [==============================] - 0s 93ms/step - loss: 0.5365 - accuracy: 0.8341 - val_loss: 0.5571 - val_accuracy: 0.8192 +Epoch 280/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5720 - accuracy: 0.8165 1/1 [==============================] - 0s 74ms/step - loss: 0.5720 - accuracy: 0.8165 - val_loss: 0.5650 - val_accuracy: 0.8183 +Epoch 281/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5857 - accuracy: 0.8082 1/1 [==============================] - 0s 74ms/step - loss: 0.5857 - accuracy: 0.8082 - val_loss: 0.5940 - val_accuracy: 0.8030 +Epoch 282/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.6075 - accuracy: 0.8003 1/1 [==============================] - 0s 74ms/step - loss: 0.6075 - accuracy: 0.8003 - val_loss: 0.5686 - val_accuracy: 0.8161 +Epoch 283/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5890 - accuracy: 0.8055 1/1 [==============================] - 0s 74ms/step - loss: 0.5890 - accuracy: 0.8055 - val_loss: 0.5705 - val_accuracy: 0.8058 +Epoch 284/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5844 - accuracy: 0.8025 1/1 [==============================] - 0s 85ms/step - loss: 0.5844 - accuracy: 0.8025 - val_loss: 0.5593 - val_accuracy: 0.8183 +Epoch 285/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5796 - accuracy: 0.8080 1/1 [==============================] - 0s 74ms/step - loss: 0.5796 - accuracy: 0.8080 - val_loss: 0.5604 - val_accuracy: 0.8104 +Epoch 286/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5744 - accuracy: 0.8053 1/1 [==============================] - 0s 74ms/step - loss: 0.5744 - accuracy: 0.8053 - val_loss: 0.5496 - val_accuracy: 0.8233 +Epoch 287/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5698 - accuracy: 0.8120 1/1 [==============================] - 0s 93ms/step - loss: 0.5698 - accuracy: 0.8120 - val_loss: 0.5452 - val_accuracy: 0.8195 +Epoch 288/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5595 - accuracy: 0.8137 1/1 [==============================] - 0s 99ms/step - loss: 0.5595 - accuracy: 0.8137 - val_loss: 0.5321 - val_accuracy: 0.8319 +Epoch 289/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5524 - accuracy: 0.8206 1/1 [==============================] - 0s 74ms/step - loss: 0.5524 - accuracy: 0.8206 - val_loss: 0.5272 - val_accuracy: 0.8293 +Epoch 290/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5419 - accuracy: 0.8244 1/1 [==============================] - 0s 75ms/step - loss: 0.5419 - accuracy: 0.8244 - val_loss: 0.5148 - val_accuracy: 0.8402 +Epoch 291/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5350 - accuracy: 0.8287 1/1 [==============================] - 0s 74ms/step - loss: 0.5350 - accuracy: 0.8287 - val_loss: 0.5113 - val_accuracy: 0.8389 +Epoch 292/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5264 - accuracy: 0.8348 1/1 [==============================] - 0s 74ms/step - loss: 0.5264 - accuracy: 0.8348 - val_loss: 0.4997 - val_accuracy: 0.8483 +Epoch 293/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5199 - accuracy: 0.8362 1/1 [==============================] - 0s 74ms/step - loss: 0.5199 - accuracy: 0.8362 - val_loss: 0.4978 - val_accuracy: 0.8456 +Epoch 294/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5132 - accuracy: 0.8424 1/1 [==============================] - 0s 74ms/step - loss: 0.5132 - accuracy: 0.8424 - val_loss: 0.4870 - val_accuracy: 0.8555 +Epoch 295/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5071 - accuracy: 0.8425 1/1 [==============================] - 0s 74ms/step - loss: 0.5071 - accuracy: 0.8425 - val_loss: 0.4865 - val_accuracy: 0.8531 +Epoch 296/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.5021 - accuracy: 0.8482 1/1 [==============================] - 0s 80ms/step - loss: 0.5021 - accuracy: 0.8482 - val_loss: 0.4764 - val_accuracy: 0.8614 +Epoch 297/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4964 - accuracy: 0.8485 1/1 [==============================] - 0s 99ms/step - loss: 0.4964 - accuracy: 0.8485 - val_loss: 0.4770 - val_accuracy: 0.8587 +Epoch 298/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4928 - accuracy: 0.8530 1/1 [==============================] - 0s 75ms/step - loss: 0.4928 - accuracy: 0.8530 - val_loss: 0.4676 - val_accuracy: 0.8657 +Epoch 299/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4874 - accuracy: 0.8528 1/1 [==============================] - 0s 74ms/step - loss: 0.4874 - accuracy: 0.8528 - val_loss: 0.4688 - val_accuracy: 0.8625 +Epoch 300/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4848 - accuracy: 0.8569 1/1 [==============================] - 0s 74ms/step - loss: 0.4848 - accuracy: 0.8569 - val_loss: 0.4600 - val_accuracy: 0.8686 +Epoch 301/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4798 - accuracy: 0.8564 1/1 [==============================] - 0s 74ms/step - loss: 0.4798 - accuracy: 0.8564 - val_loss: 0.4618 - val_accuracy: 0.8655 +Epoch 302/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4778 - accuracy: 0.8599 1/1 [==============================] - 0s 74ms/step - loss: 0.4778 - accuracy: 0.8599 - val_loss: 0.4536 - val_accuracy: 0.8714 +Epoch 303/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4732 - accuracy: 0.8593 1/1 [==============================] - 0s 74ms/step - loss: 0.4732 - accuracy: 0.8593 - val_loss: 0.4556 - val_accuracy: 0.8689 +Epoch 304/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4717 - accuracy: 0.8624 1/1 [==============================] - 0s 74ms/step - loss: 0.4717 - accuracy: 0.8624 - val_loss: 0.4479 - val_accuracy: 0.8736 +Epoch 305/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4674 - accuracy: 0.8618 1/1 [==============================] - 0s 79ms/step - loss: 0.4674 - accuracy: 0.8618 - val_loss: 0.4500 - val_accuracy: 0.8715 +Epoch 306/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4662 - accuracy: 0.8647 1/1 [==============================] - 0s 74ms/step - loss: 0.4662 - accuracy: 0.8647 - val_loss: 0.4428 - val_accuracy: 0.8754 +Epoch 307/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4622 - accuracy: 0.8643 1/1 [==============================] - 0s 75ms/step - loss: 0.4622 - accuracy: 0.8643 - val_loss: 0.4451 - val_accuracy: 0.8739 +Epoch 308/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4612 - accuracy: 0.8666 1/1 [==============================] - 0s 74ms/step - loss: 0.4612 - accuracy: 0.8666 - val_loss: 0.4382 - val_accuracy: 0.8771 +Epoch 309/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4575 - accuracy: 0.8660 1/1 [==============================] - 0s 74ms/step - loss: 0.4575 - accuracy: 0.8660 - val_loss: 0.4405 - val_accuracy: 0.8749 +Epoch 310/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4566 - accuracy: 0.8686 1/1 [==============================] - 0s 77ms/step - loss: 0.4566 - accuracy: 0.8686 - val_loss: 0.4339 - val_accuracy: 0.8785 +Epoch 311/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4531 - accuracy: 0.8677 1/1 [==============================] - 0s 75ms/step - loss: 0.4531 - accuracy: 0.8677 - val_loss: 0.4362 - val_accuracy: 0.8762 +Epoch 312/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4523 - accuracy: 0.8702 1/1 [==============================] - 0s 74ms/step - loss: 0.4523 - accuracy: 0.8702 - val_loss: 0.4300 - val_accuracy: 0.8807 +Epoch 313/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4491 - accuracy: 0.8690 1/1 [==============================] - 0s 93ms/step - loss: 0.4491 - accuracy: 0.8690 - val_loss: 0.4322 - val_accuracy: 0.8775 +Epoch 314/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4484 - accuracy: 0.8717 1/1 [==============================] - 0s 99ms/step - loss: 0.4484 - accuracy: 0.8717 - val_loss: 0.4263 - val_accuracy: 0.8816 +Epoch 315/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4452 - accuracy: 0.8704 1/1 [==============================] - 0s 174ms/step - loss: 0.4452 - accuracy: 0.8704 - val_loss: 0.4284 - val_accuracy: 0.8788 +Epoch 316/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4446 - accuracy: 0.8728 1/1 [==============================] - 0s 90ms/step - loss: 0.4446 - accuracy: 0.8728 - val_loss: 0.4228 - val_accuracy: 0.8826 +Epoch 317/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4416 - accuracy: 0.8717 1/1 [==============================] - 0s 87ms/step - loss: 0.4416 - accuracy: 0.8717 - val_loss: 0.4248 - val_accuracy: 0.8798 +Epoch 318/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4410 - accuracy: 0.8739 1/1 [==============================] - 0s 86ms/step - loss: 0.4410 - accuracy: 0.8739 - val_loss: 0.4194 - val_accuracy: 0.8844 +Epoch 319/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4382 - accuracy: 0.8728 1/1 [==============================] - 0s 86ms/step - loss: 0.4382 - accuracy: 0.8728 - val_loss: 0.4214 - val_accuracy: 0.8806 +Epoch 320/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4376 - accuracy: 0.8753 1/1 [==============================] - 0s 86ms/step - loss: 0.4376 - accuracy: 0.8753 - val_loss: 0.4162 - val_accuracy: 0.8855 +Epoch 321/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4349 - accuracy: 0.8739 1/1 [==============================] - 0s 86ms/step - loss: 0.4349 - accuracy: 0.8739 - val_loss: 0.4182 - val_accuracy: 0.8813 +Epoch 322/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4343 - accuracy: 0.8765 1/1 [==============================] - 0s 81ms/step - loss: 0.4343 - accuracy: 0.8765 - val_loss: 0.4131 - val_accuracy: 0.8859 +Epoch 323/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4318 - accuracy: 0.8749 1/1 [==============================] - 0s 85ms/step - loss: 0.4318 - accuracy: 0.8749 - val_loss: 0.4150 - val_accuracy: 0.8827 +Epoch 324/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4311 - accuracy: 0.8774 1/1 [==============================] - 0s 74ms/step - loss: 0.4311 - accuracy: 0.8774 - val_loss: 0.4101 - val_accuracy: 0.8872 +Epoch 325/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4287 - accuracy: 0.8760 1/1 [==============================] - 0s 80ms/step - loss: 0.4287 - accuracy: 0.8760 - val_loss: 0.4119 - val_accuracy: 0.8836 +Epoch 326/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4280 - accuracy: 0.8783 1/1 [==============================] - 0s 80ms/step - loss: 0.4280 - accuracy: 0.8783 - val_loss: 0.4072 - val_accuracy: 0.8880 +Epoch 327/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4257 - accuracy: 0.8770 1/1 [==============================] - 0s 80ms/step - loss: 0.4257 - accuracy: 0.8770 - val_loss: 0.4090 - val_accuracy: 0.8846 +Epoch 328/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4250 - accuracy: 0.8792 1/1 [==============================] - 0s 81ms/step - loss: 0.4250 - accuracy: 0.8792 - val_loss: 0.4044 - val_accuracy: 0.8882 +Epoch 329/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4228 - accuracy: 0.8779 1/1 [==============================] - 0s 82ms/step - loss: 0.4228 - accuracy: 0.8779 - val_loss: 0.4061 - val_accuracy: 0.8865 +Epoch 330/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4221 - accuracy: 0.8800 1/1 [==============================] - 0s 80ms/step - loss: 0.4221 - accuracy: 0.8800 - val_loss: 0.4017 - val_accuracy: 0.8889 +Epoch 331/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4200 - accuracy: 0.8788 1/1 [==============================] - 0s 92ms/step - loss: 0.4200 - accuracy: 0.8788 - val_loss: 0.4033 - val_accuracy: 0.8866 +Epoch 332/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4193 - accuracy: 0.8809 1/1 [==============================] - 0s 74ms/step - loss: 0.4193 - accuracy: 0.8809 - val_loss: 0.3990 - val_accuracy: 0.8896 +Epoch 333/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4172 - accuracy: 0.8795 1/1 [==============================] - 0s 87ms/step - loss: 0.4172 - accuracy: 0.8795 - val_loss: 0.4005 - val_accuracy: 0.8873 +Epoch 334/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4165 - accuracy: 0.8816 1/1 [==============================] - 0s 87ms/step - loss: 0.4165 - accuracy: 0.8816 - val_loss: 0.3964 - val_accuracy: 0.8900 +Epoch 335/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4145 - accuracy: 0.8804 1/1 [==============================] - 0s 86ms/step - loss: 0.4145 - accuracy: 0.8804 - val_loss: 0.3978 - val_accuracy: 0.8880 +Epoch 336/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4138 - accuracy: 0.8825 1/1 [==============================] - 0s 87ms/step - loss: 0.4138 - accuracy: 0.8825 - val_loss: 0.3938 - val_accuracy: 0.8905 +Epoch 337/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.4119 - accuracy: 0.8813 1/1 [==============================] - 0s 86ms/step - loss: 0.4119 - accuracy: 0.8813 - val_loss: 0.3952 - val_accuracy: 0.8890 +Epoch 338/1875 + 1/1 [==============================] - 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loss: 0.3472 - accuracy: 0.9017 - val_loss: 0.3314 - val_accuracy: 0.9082 +Epoch 406/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.3465 - accuracy: 0.9023 1/1 [==============================] - 0s 80ms/step - loss: 0.3465 - accuracy: 0.9023 - val_loss: 0.3298 - val_accuracy: 0.9083 +Epoch 407/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.3458 - accuracy: 0.9020 1/1 [==============================] - 0s 80ms/step - loss: 0.3458 - accuracy: 0.9020 - val_loss: 0.3301 - val_accuracy: 0.9088 +Epoch 408/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.3451 - accuracy: 0.9027 1/1 [==============================] - 0s 80ms/step - loss: 0.3451 - accuracy: 0.9027 - val_loss: 0.3284 - val_accuracy: 0.9085 +Epoch 409/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.3444 - accuracy: 0.9024 1/1 [==============================] - 0s 85ms/step - loss: 0.3444 - accuracy: 0.9024 - val_loss: 0.3287 - val_accuracy: 0.9088 +Epoch 410/1875 + 1/1 [==============================] - 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loss: 0.3353 - accuracy: 0.9052 - val_loss: 0.3198 - val_accuracy: 0.9110 +Epoch 424/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.3347 - accuracy: 0.9048 1/1 [==============================] - 0s 80ms/step - loss: 0.3347 - accuracy: 0.9048 - val_loss: 0.3184 - val_accuracy: 0.9112 +Epoch 425/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.3341 - accuracy: 0.9056 1/1 [==============================] - 0s 81ms/step - loss: 0.3341 - accuracy: 0.9056 - val_loss: 0.3186 - val_accuracy: 0.9111 +Epoch 426/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.3335 - accuracy: 0.9053 1/1 [==============================] - 0s 87ms/step - loss: 0.3335 - accuracy: 0.9053 - val_loss: 0.3173 - val_accuracy: 0.9115 +Epoch 427/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.3329 - accuracy: 0.9059 1/1 [==============================] - 0s 92ms/step - loss: 0.3329 - accuracy: 0.9059 - val_loss: 0.3174 - val_accuracy: 0.9113 +Epoch 428/1875 + 1/1 [==============================] - 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loss: 0.3037 - accuracy: 0.9130 - val_loss: 0.2888 - val_accuracy: 0.9195 +Epoch 487/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.3033 - accuracy: 0.9137 1/1 [==============================] - 0s 86ms/step - loss: 0.3033 - accuracy: 0.9137 - val_loss: 0.2886 - val_accuracy: 0.9193 +Epoch 488/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.3029 - accuracy: 0.9131 1/1 [==============================] - 0s 74ms/step - loss: 0.3029 - accuracy: 0.9131 - val_loss: 0.2880 - val_accuracy: 0.9192 +Epoch 489/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.3025 - accuracy: 0.9139 1/1 [==============================] - 0s 74ms/step - loss: 0.3025 - accuracy: 0.9139 - val_loss: 0.2878 - val_accuracy: 0.9194 +Epoch 490/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.3021 - accuracy: 0.9134 1/1 [==============================] - 0s 75ms/step - loss: 0.3021 - accuracy: 0.9134 - val_loss: 0.2872 - val_accuracy: 0.9194 +Epoch 491/1875 + 1/1 [==============================] - 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loss: 0.3001 - accuracy: 0.9143 - val_loss: 0.2854 - val_accuracy: 0.9196 +Epoch 496/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2997 - accuracy: 0.9141 1/1 [==============================] - 0s 92ms/step - loss: 0.2997 - accuracy: 0.9141 - val_loss: 0.2848 - val_accuracy: 0.9196 +Epoch 497/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2993 - accuracy: 0.9146 1/1 [==============================] - 0s 75ms/step - loss: 0.2993 - accuracy: 0.9146 - val_loss: 0.2847 - val_accuracy: 0.9198 +Epoch 498/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2989 - accuracy: 0.9144 1/1 [==============================] - 0s 74ms/step - loss: 0.2989 - accuracy: 0.9144 - val_loss: 0.2841 - val_accuracy: 0.9197 +Epoch 499/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2985 - accuracy: 0.9148 1/1 [==============================] - 0s 74ms/step - loss: 0.2985 - accuracy: 0.9148 - val_loss: 0.2839 - val_accuracy: 0.9199 +Epoch 500/1875 + 1/1 [==============================] - 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loss: 0.2965 - accuracy: 0.9151 - val_loss: 0.2818 - val_accuracy: 0.9201 +Epoch 505/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2961 - accuracy: 0.9154 1/1 [==============================] - 0s 92ms/step - loss: 0.2961 - accuracy: 0.9154 - val_loss: 0.2816 - val_accuracy: 0.9202 +Epoch 506/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2957 - accuracy: 0.9153 1/1 [==============================] - 0s 75ms/step - loss: 0.2957 - accuracy: 0.9153 - val_loss: 0.2811 - val_accuracy: 0.9202 +Epoch 507/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2954 - accuracy: 0.9157 1/1 [==============================] - 0s 74ms/step - loss: 0.2954 - accuracy: 0.9157 - val_loss: 0.2809 - val_accuracy: 0.9203 +Epoch 508/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2950 - accuracy: 0.9155 1/1 [==============================] - 0s 74ms/step - loss: 0.2950 - accuracy: 0.9155 - val_loss: 0.2804 - val_accuracy: 0.9201 +Epoch 509/1875 + 1/1 [==============================] - 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loss: 0.2931 - accuracy: 0.9164 - val_loss: 0.2787 - val_accuracy: 0.9212 +Epoch 514/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2927 - accuracy: 0.9162 1/1 [==============================] - 0s 75ms/step - loss: 0.2927 - accuracy: 0.9162 - val_loss: 0.2782 - val_accuracy: 0.9204 +Epoch 515/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2924 - accuracy: 0.9165 1/1 [==============================] - 0s 75ms/step - loss: 0.2924 - accuracy: 0.9165 - val_loss: 0.2780 - val_accuracy: 0.9215 +Epoch 516/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2920 - accuracy: 0.9163 1/1 [==============================] - 0s 74ms/step - loss: 0.2920 - accuracy: 0.9163 - val_loss: 0.2775 - val_accuracy: 0.9208 +Epoch 517/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2916 - accuracy: 0.9167 1/1 [==============================] - 0s 80ms/step - loss: 0.2916 - accuracy: 0.9167 - val_loss: 0.2773 - val_accuracy: 0.9217 +Epoch 518/1875 + 1/1 [==============================] - 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loss: 0.2751 - accuracy: 0.9205 - val_loss: 0.2613 - val_accuracy: 0.9243 +Epoch 568/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2748 - accuracy: 0.9205 1/1 [==============================] - 0s 74ms/step - loss: 0.2748 - accuracy: 0.9205 - val_loss: 0.2610 - val_accuracy: 0.9240 +Epoch 569/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2745 - accuracy: 0.9207 1/1 [==============================] - 0s 77ms/step - loss: 0.2745 - accuracy: 0.9207 - val_loss: 0.2607 - val_accuracy: 0.9244 +Epoch 570/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2742 - accuracy: 0.9207 1/1 [==============================] - 0s 74ms/step - loss: 0.2742 - accuracy: 0.9207 - val_loss: 0.2604 - val_accuracy: 0.9241 +Epoch 571/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2739 - accuracy: 0.9208 1/1 [==============================] - 0s 74ms/step - loss: 0.2739 - accuracy: 0.9208 - val_loss: 0.2601 - val_accuracy: 0.9245 +Epoch 572/1875 + 1/1 [==============================] - 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loss: 0.2724 - accuracy: 0.9212 - val_loss: 0.2587 - val_accuracy: 0.9248 +Epoch 577/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2721 - accuracy: 0.9213 1/1 [==============================] - 0s 74ms/step - loss: 0.2721 - accuracy: 0.9213 - val_loss: 0.2584 - val_accuracy: 0.9252 +Epoch 578/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2718 - accuracy: 0.9213 1/1 [==============================] - 0s 74ms/step - loss: 0.2718 - accuracy: 0.9213 - val_loss: 0.2581 - val_accuracy: 0.9252 +Epoch 579/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2715 - accuracy: 0.9215 1/1 [==============================] - 0s 75ms/step - loss: 0.2715 - accuracy: 0.9215 - val_loss: 0.2578 - val_accuracy: 0.9254 +Epoch 580/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2712 - accuracy: 0.9215 1/1 [==============================] - 0s 74ms/step - loss: 0.2712 - accuracy: 0.9215 - val_loss: 0.2575 - val_accuracy: 0.9255 +Epoch 581/1875 + 1/1 [==============================] - 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loss: 0.2698 - accuracy: 0.9219 - val_loss: 0.2562 - val_accuracy: 0.9261 +Epoch 586/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2695 - accuracy: 0.9219 1/1 [==============================] - 0s 75ms/step - loss: 0.2695 - accuracy: 0.9219 - val_loss: 0.2559 - val_accuracy: 0.9261 +Epoch 587/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2692 - accuracy: 0.9220 1/1 [==============================] - 0s 74ms/step - loss: 0.2692 - accuracy: 0.9220 - val_loss: 0.2556 - val_accuracy: 0.9263 +Epoch 588/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2690 - accuracy: 0.9220 1/1 [==============================] - 0s 74ms/step - loss: 0.2690 - accuracy: 0.9220 - val_loss: 0.2553 - val_accuracy: 0.9261 +Epoch 589/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2687 - accuracy: 0.9220 1/1 [==============================] - 0s 74ms/step - loss: 0.2687 - accuracy: 0.9220 - val_loss: 0.2551 - val_accuracy: 0.9263 +Epoch 590/1875 + 1/1 [==============================] - 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loss: 0.2673 - accuracy: 0.9225 - val_loss: 0.2537 - val_accuracy: 0.9270 +Epoch 595/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2670 - accuracy: 0.9225 1/1 [==============================] - 0s 80ms/step - loss: 0.2670 - accuracy: 0.9225 - val_loss: 0.2535 - val_accuracy: 0.9270 +Epoch 596/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2667 - accuracy: 0.9226 1/1 [==============================] - 0s 80ms/step - loss: 0.2667 - accuracy: 0.9226 - val_loss: 0.2532 - val_accuracy: 0.9272 +Epoch 597/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2664 - accuracy: 0.9227 1/1 [==============================] - 0s 74ms/step - loss: 0.2664 - accuracy: 0.9227 - val_loss: 0.2529 - val_accuracy: 0.9272 +Epoch 598/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2662 - accuracy: 0.9228 1/1 [==============================] - 0s 74ms/step - loss: 0.2662 - accuracy: 0.9228 - val_loss: 0.2526 - val_accuracy: 0.9273 +Epoch 599/1875 + 1/1 [==============================] - 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loss: 0.2648 - accuracy: 0.9231 - val_loss: 0.2513 - val_accuracy: 0.9277 +Epoch 604/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2645 - accuracy: 0.9232 1/1 [==============================] - 0s 80ms/step - loss: 0.2645 - accuracy: 0.9232 - val_loss: 0.2511 - val_accuracy: 0.9278 +Epoch 605/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2643 - accuracy: 0.9233 1/1 [==============================] - 0s 74ms/step - loss: 0.2643 - accuracy: 0.9233 - val_loss: 0.2508 - val_accuracy: 0.9277 +Epoch 606/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2640 - accuracy: 0.9234 1/1 [==============================] - 0s 79ms/step - loss: 0.2640 - accuracy: 0.9234 - val_loss: 0.2506 - val_accuracy: 0.9278 +Epoch 607/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2637 - accuracy: 0.9235 1/1 [==============================] - 0s 80ms/step - loss: 0.2637 - accuracy: 0.9235 - val_loss: 0.2503 - val_accuracy: 0.9278 +Epoch 608/1875 + 1/1 [==============================] - 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loss: 0.2511 - accuracy: 0.9268 - val_loss: 0.2380 - val_accuracy: 0.9312 +Epoch 658/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2509 - accuracy: 0.9269 1/1 [==============================] - 0s 80ms/step - loss: 0.2509 - accuracy: 0.9269 - val_loss: 0.2378 - val_accuracy: 0.9314 +Epoch 659/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2506 - accuracy: 0.9269 1/1 [==============================] - 0s 81ms/step - loss: 0.2506 - accuracy: 0.9269 - val_loss: 0.2375 - val_accuracy: 0.9317 +Epoch 660/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2504 - accuracy: 0.9270 1/1 [==============================] - 0s 80ms/step - loss: 0.2504 - accuracy: 0.9270 - val_loss: 0.2373 - val_accuracy: 0.9316 +Epoch 661/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2502 - accuracy: 0.9270 1/1 [==============================] - 0s 86ms/step - loss: 0.2502 - accuracy: 0.9270 - val_loss: 0.2371 - val_accuracy: 0.9318 +Epoch 662/1875 + 1/1 [==============================] - 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loss: 0.2490 - accuracy: 0.9274 - val_loss: 0.2359 - val_accuracy: 0.9324 +Epoch 667/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2488 - accuracy: 0.9275 1/1 [==============================] - 0s 87ms/step - loss: 0.2488 - accuracy: 0.9275 - val_loss: 0.2357 - val_accuracy: 0.9325 +Epoch 668/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2485 - accuracy: 0.9276 1/1 [==============================] - 0s 81ms/step - loss: 0.2485 - accuracy: 0.9276 - val_loss: 0.2355 - val_accuracy: 0.9326 +Epoch 669/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2483 - accuracy: 0.9277 1/1 [==============================] - 0s 91ms/step - loss: 0.2483 - accuracy: 0.9277 - val_loss: 0.2352 - val_accuracy: 0.9327 +Epoch 670/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2481 - accuracy: 0.9278 1/1 [==============================] - 0s 80ms/step - loss: 0.2481 - accuracy: 0.9278 - val_loss: 0.2350 - val_accuracy: 0.9330 +Epoch 671/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2479 - accuracy: 0.9278 1/1 [==============================] - 0s 81ms/step - loss: 0.2479 - accuracy: 0.9278 - val_loss: 0.2348 - val_accuracy: 0.9330 +Epoch 672/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2476 - accuracy: 0.9279 1/1 [==============================] - 0s 81ms/step - loss: 0.2476 - accuracy: 0.9279 - val_loss: 0.2346 - val_accuracy: 0.9330 +Epoch 673/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2474 - accuracy: 0.9280 1/1 [==============================] - 0s 87ms/step - loss: 0.2474 - accuracy: 0.9280 - val_loss: 0.2344 - val_accuracy: 0.9331 +Epoch 674/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2472 - accuracy: 0.9281 1/1 [==============================] - 0s 80ms/step - loss: 0.2472 - accuracy: 0.9281 - val_loss: 0.2341 - val_accuracy: 0.9331 +Epoch 675/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2469 - accuracy: 0.9282 1/1 [==============================] - 0s 80ms/step - loss: 0.2469 - accuracy: 0.9282 - val_loss: 0.2339 - val_accuracy: 0.9331 +Epoch 676/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2467 - accuracy: 0.9283 1/1 [==============================] - 0s 80ms/step - loss: 0.2467 - accuracy: 0.9283 - val_loss: 0.2337 - val_accuracy: 0.9334 +Epoch 677/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2465 - accuracy: 0.9284 1/1 [==============================] - 0s 87ms/step - loss: 0.2465 - accuracy: 0.9284 - val_loss: 0.2335 - val_accuracy: 0.9334 +Epoch 678/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2463 - accuracy: 0.9284 1/1 [==============================] - 0s 86ms/step - loss: 0.2463 - accuracy: 0.9284 - val_loss: 0.2333 - val_accuracy: 0.9334 +Epoch 679/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2460 - accuracy: 0.9284 1/1 [==============================] - 0s 74ms/step - loss: 0.2460 - accuracy: 0.9284 - val_loss: 0.2330 - val_accuracy: 0.9334 +Epoch 680/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2458 - accuracy: 0.9285 1/1 [==============================] - 0s 80ms/step - loss: 0.2458 - accuracy: 0.9285 - val_loss: 0.2328 - val_accuracy: 0.9335 +Epoch 681/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2456 - accuracy: 0.9285 1/1 [==============================] - 0s 80ms/step - loss: 0.2456 - accuracy: 0.9285 - val_loss: 0.2326 - val_accuracy: 0.9334 +Epoch 682/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2454 - accuracy: 0.9287 1/1 [==============================] - 0s 80ms/step - loss: 0.2454 - accuracy: 0.9287 - val_loss: 0.2324 - val_accuracy: 0.9334 +Epoch 683/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2452 - accuracy: 0.9287 1/1 [==============================] - 0s 80ms/step - loss: 0.2452 - accuracy: 0.9287 - val_loss: 0.2322 - val_accuracy: 0.9334 +Epoch 684/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2449 - accuracy: 0.9288 1/1 [==============================] - 0s 91ms/step - loss: 0.2449 - accuracy: 0.9288 - val_loss: 0.2320 - val_accuracy: 0.9334 +Epoch 685/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2447 - accuracy: 0.9289 1/1 [==============================] - 0s 85ms/step - loss: 0.2447 - accuracy: 0.9289 - val_loss: 0.2317 - val_accuracy: 0.9335 +Epoch 686/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2445 - accuracy: 0.9289 1/1 [==============================] - 0s 80ms/step - loss: 0.2445 - accuracy: 0.9289 - val_loss: 0.2315 - val_accuracy: 0.9335 +Epoch 687/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2443 - accuracy: 0.9290 1/1 [==============================] - 0s 92ms/step - loss: 0.2443 - accuracy: 0.9290 - val_loss: 0.2313 - val_accuracy: 0.9335 +Epoch 688/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2441 - accuracy: 0.9290 1/1 [==============================] - 0s 74ms/step - loss: 0.2441 - accuracy: 0.9290 - val_loss: 0.2311 - val_accuracy: 0.9335 +Epoch 689/1875 + 1/1 [==============================] - 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loss: 0.2430 - accuracy: 0.9293 - val_loss: 0.2300 - val_accuracy: 0.9337 +Epoch 694/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2428 - accuracy: 0.9294 1/1 [==============================] - 0s 80ms/step - loss: 0.2428 - accuracy: 0.9294 - val_loss: 0.2298 - val_accuracy: 0.9338 +Epoch 695/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2425 - accuracy: 0.9295 1/1 [==============================] - 0s 85ms/step - loss: 0.2425 - accuracy: 0.9295 - val_loss: 0.2296 - val_accuracy: 0.9337 +Epoch 696/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2423 - accuracy: 0.9296 1/1 [==============================] - 0s 74ms/step - loss: 0.2423 - accuracy: 0.9296 - val_loss: 0.2294 - val_accuracy: 0.9338 +Epoch 697/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2421 - accuracy: 0.9297 1/1 [==============================] - 0s 80ms/step - loss: 0.2421 - accuracy: 0.9297 - val_loss: 0.2292 - val_accuracy: 0.9340 +Epoch 698/1875 + 1/1 [==============================] - 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ETA: 0s - loss: 0.2080 - accuracy: 0.9393 1/1 [==============================] - 0s 75ms/step - loss: 0.2080 - accuracy: 0.9393 - val_loss: 0.1962 - val_accuracy: 0.9422 +Epoch 888/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2078 - accuracy: 0.9393 1/1 [==============================] - 0s 74ms/step - loss: 0.2078 - accuracy: 0.9393 - val_loss: 0.1961 - val_accuracy: 0.9422 +Epoch 889/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2077 - accuracy: 0.9394 1/1 [==============================] - 0s 75ms/step - loss: 0.2077 - accuracy: 0.9394 - val_loss: 0.1959 - val_accuracy: 0.9422 +Epoch 890/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2075 - accuracy: 0.9394 1/1 [==============================] - 0s 75ms/step - loss: 0.2075 - accuracy: 0.9394 - val_loss: 0.1958 - val_accuracy: 0.9422 +Epoch 891/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2074 - accuracy: 0.9395 1/1 [==============================] - 0s 74ms/step - loss: 0.2074 - accuracy: 0.9395 - val_loss: 0.1956 - val_accuracy: 0.9422 +Epoch 892/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2072 - accuracy: 0.9395 1/1 [==============================] - 0s 80ms/step - loss: 0.2072 - accuracy: 0.9395 - val_loss: 0.1955 - val_accuracy: 0.9422 +Epoch 893/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2071 - accuracy: 0.9396 1/1 [==============================] - 0s 81ms/step - loss: 0.2071 - accuracy: 0.9396 - val_loss: 0.1954 - val_accuracy: 0.9423 +Epoch 894/1875 + 1/1 [==============================] - ETA: 0s - loss: 0.2069 - accuracy: 0.9397 1/1 [==============================] - 0s 80ms/step - loss: 0.2069 - accuracy: 0.9397 - val_loss: 0.1952 - val_accuracy: 0.9424 +Epoch 895/1875 +srun: Job step aborted: Waiting up to 32 seconds for job step to finish. +slurmstepd-node18: error: *** JOB 18932 ON node18 CANCELLED AT 2020-07-22T15:20:07 DUE TO TIME LIMIT *** +slurmstepd-node18: error: *** STEP 18932.0 ON node18 CANCELLED AT 2020-07-22T15:20:07 DUE TO TIME LIMIT *** +srun: got SIGCONT +srun: forcing job termination diff --git a/TeX/Plots/SGD_vs_GD.tex b/TeX/Plots/SGD_vs_GD.tex index 0bb9bbe..0f0eff1 100644 --- a/TeX/Plots/SGD_vs_GD.tex +++ b/TeX/Plots/SGD_vs_GD.tex @@ -84,6 +84,7 @@ plot coordinates { \end{tabu} \caption{Performace metrics of the networks trained in Figure~\ref{ref:sdg_vs_gd} after 20 training epochs.} + \label{sgd_vs_gd} \end{table} %%% Local Variables: %%% mode: latex diff --git a/TeX/Plots/pfg_test.tex b/TeX/Plots/pfg_test.tex index 391dbf4..66d52b7 100644 --- a/TeX/Plots/pfg_test.tex +++ b/TeX/Plots/pfg_test.tex @@ -30,7 +30,7 @@ plot coordinates { $10$,$12$,$14$,$16$,$18$,$20$}, xlabel = {epoch}, ylabel = {Classification Accuracy}] \addplot table - [x=epoch, y=val_accuracy, col sep=comma] {Data/GD_01.log}; + [x=epoch, y=val_accuracy, col sep=comma, mark = none] {Data/gd_10min.log}; \addplot table [x=epoch, y=val_accuracy, col sep=comma] {Data/GD_05.log}; \addplot table @@ -89,29 +89,48 @@ plot coordinates { \end{subfigure} \caption{The neural network given in ?? trained with different algorithms on the MNIST handwritten digits data set. For gradient - descent the learning rated 0.01, 0.05 and 0.1 are (GD$_{\text{rate}}$). For + descent the learning rated 0.01, 0.05 and 0.1 are (GD$_{ + rate}$). For stochastic gradient descend a batch size of 32 and learning rate of 0.01 is used (SDG$_{0.01}$)} \end{figure} \begin{center} -\begin{figure}[h] - \begin{subfigure}{0.49\textwidth} - \includegraphics[width=\textwidth]{Data/klammern.jpg} - \caption{Original Picure} + \begin{figure}[h] + \centering + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Data/mnist0.pdf} \end{subfigure} - \begin{subfigure}{0.49\textwidth} - \includegraphics[width=\textwidth]{Data/image_conv4.png} - \caption{test} + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Data/mnist1.pdf} \end{subfigure} - \begin{subfigure}{0.49\textwidth} - \includegraphics[width=\textwidth]{Data/image_conv5.png} - \caption{test} + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Data/mnist2.pdf} \end{subfigure} - \begin{subfigure}{0.49\textwidth} - \includegraphics[width=\textwidth]{Data/image_conv6.png} - \caption{test} + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Data/mnist3.pdf} \end{subfigure} + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Data/mnist4.pdf} + \end{subfigure}\\ + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Data/mnist5.pdf} + \end{subfigure} + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Data/mnist6.pdf} + \end{subfigure} + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Data/mnist7.pdf} + \end{subfigure} + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Data/mnist8.pdf} + \end{subfigure} + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Data/mnist9.pdf} + \end{subfigure} + \caption{The MNIST data set contains 70.000 images of preprocessed handwritten + digits. Of these images 60.000 are used as training images, while + the rest are used to validate the models trained.} \end{figure} \end{center} diff --git a/TeX/bibliograpy.bib b/TeX/bibliograpy.bib index ef748cb..588b172 100644 --- a/TeX/bibliograpy.bib +++ b/TeX/bibliograpy.bib @@ -56,3 +56,20 @@ issn={1476-4687}, doi={10.1038/323533a0}, url={https://doi.org/10.1038/323533a0} } + +@article{MNIST, + added-at = {2010-06-28T21:16:30.000+0200}, + author = {LeCun, Yann and Cortes, Corinna}, + biburl = {https://www.bibsonomy.org/bibtex/2935bad99fa1f65e03c25b315aa3c1032/mhwombat}, + groups = {public}, + howpublished = {http://yann.lecun.com/exdb/mnist/}, + interhash = {21b9d0558bd66279df9452562df6e6f3}, + intrahash = {935bad99fa1f65e03c25b315aa3c1032}, + keywords = {MSc _checked character_recognition mnist network neural}, + lastchecked = {2016-01-14 14:24:11}, + timestamp = {2016-07-12T19:25:30.000+0200}, + title = {{MNIST} handwritten digit database}, + url = {http://yann.lecun.com/exdb/mnist/}, + username = {mhwombat}, + year = 2010 +} diff --git a/TeX/further_applications_of_nn.tex b/TeX/further_applications_of_nn.tex index 242a456..3d8f999 100644 --- a/TeX/further_applications_of_nn.tex +++ b/TeX/further_applications_of_nn.tex @@ -263,20 +263,91 @@ dataset a (different) subset of data is chosen to compute the gradient in each iteration. The amount of iterations until each data point has been considered in updating the parameters is commonly called a ``epoch''. -This reduces the amount of memory and computing power required for -each iteration. This allows for use of very large training -sets. Additionally the noise introduced on the gradient can improve +Using subsets reduces the amount of memory and computing power required for +each iteration. This makes it possible to use very large training +sets to fit the model. +Additionally the noise introduced on the gradient can improve the accuracy of the fit as stochastic gradient descent algorithms are less likely to get stuck on local extrema. \input{Plots/SGD_vs_GD.tex} -Another benefit of using subsets even if enough memory is available to -use the whole dataset is that depending on the size of the subsets the +Another important benefit in using subsets is that depending on their size the gradient can be calculated far quicker which allows to make more steps in the same time. If the approximated gradient is close enough to the ``real'' one this can drastically cut down the time required for -training the model. +training the model. And improve the accuracy achievable in a given +mount of training time. +In order to illustrate this behavior we modeled a convolutional neural +network to ... handwritten digits. The data set used for this is the +MNIST database of handwritten digits (\textcite{MNIST}, +Figure~\ref{fig:MNIST}). +The network used consists of two convolution and max pooling layers +followed by one fully connected hidden layer and the output layer. +Both covolutional layers utilize square filters of size five which are +applied with a stride of one. +The first layer consists of 32 filters and the second of 64. Both +pooling layers pool a $2\times 2$ area. The fully connected layer +consists of 256 nodes and the output layer of 10, one for each digit. +All layers except the output layer use RELU as activation function +with the output layer using softmax (\ref{def:softmax}). +As loss function categorical crossentropy is used (\ref{def:...}). +In Figure~\ref{fig:mnist_architecture} the architecture of the network +is summarized. +Here it can be seen that the network trained with stochstic gradient +descent is more accurate after the first epoch than the ones trained +with gradient descent after 20 epochs. +This is due to the former using a batch size of 32 and thus having +made 1.875 updates to the weights +after the first epoch. While each of these updates uses a approximate +gradient calculated on the subset it performs far better than the +network using true gradients when training for the same mount of time. +\todo{vergleich training time} +The difficulty of choosing the learning rate ALSO ILLUSTRATED IN +FUGURE... + + +The results of the network being trained with gradient descent and +stochastic gradient descent are given in Figure~\ref{fig:sgd_vs_gd} +and Table~\ref{table:sgd_vs_dg} + + \begin{figure}[h] + \centering + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Plots/Data/mnist0.pdf} + \end{subfigure} + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Plots/Data/mnist1.pdf} + \end{subfigure} + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Plots/Data/mnist2.pdf} + \end{subfigure} + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Plots/Data/mnist3.pdf} + \end{subfigure} + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Plots/Data/mnist4.pdf} + \end{subfigure}\\ + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Plots/Data/mnist5.pdf} + \end{subfigure} + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Plots/Data/mnist6.pdf} + \end{subfigure} + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Plots/Data/mnist7.pdf} + \end{subfigure} + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Plots/Data/mnist8.pdf} + \end{subfigure} + \begin{subfigure}{0.19\textwidth} + \includegraphics[width=\textwidth]{Plots/Data/mnist9.pdf} + \end{subfigure} + \caption{The MNIST data set contains 70.000 images of preprocessed handwritten + digits. Of these images 60.000 are used as training images, while + the rest are used to validate the models trained.} + \label{fig:MNIST} +\end{figure} \begin{itemize} \item ADAM @@ -285,7 +356,24 @@ training the model. \end{itemize} - +\begin{algorithm}[H] + \SetAlgoLined + \KwInput{Decay Rate $\rho$, Constant $\varepsilon$} + \KwInput{Initial parameter $x_1$} + Initialize accumulation variables $E[g^2]_0 = 0, E[\Delta x^2]_0 =0$\; + \For{$t \in \left\{1,\dots,T\right\};\, t+1$}{ + Compute Gradient: $g_t$\; + Accumulate Gradient: $[E[g^2]_t \leftarrow \roh D[g^2]_{t-1} + + (1-\roh)g_t^2$\; + Compute Update: $\Delta x_t \leftarrow -\frac{\sqrt{E[\Delta + x^2]_{t-1} + \varepsilon}}{\sqrt{E[g^2]_t + \varepsilon}} g_t$\; + Accumulate Updates: $E[\Delta x^2]_t \leftarrow \rho E[\Delta + x^2]_{t-1} + (1+p)\Delta x_t^2$\; + Apply Update: $x_{t+1} \leftarrow x_t + \Delta x_t$\; + } + \caption{ADADELTA, \textcite{ADADELTA}} + \label{alg:gd} +\end{algorithm} % \subsubsubsection{Stochastic Gradient Descent} @@ -319,7 +407,8 @@ networks. The nodes are chosen at random and change in every iteration, this practice is called Dropout and was introduced by \textcite{Dropout}. -\todo{Vergleich verschiedene dropout größen auf MNSIT o.ä.} +\todo{Vergleich verschiedene dropout größen auf MNSIT o.ä., subset als +training set?}