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log2.txt
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adyotagupta@hurleyserv /e/a/s/S/hw8SL> python hw8_2.py (SL)
2020-11-23 20:05:39.559645: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-11-23 20:05:39.593802: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
pciBusID: 0000:d8:00.0 name: Quadro RTX 8000 computeCapability: 7.5
coreClock: 1.77GHz coreCount: 72 deviceMemorySize: 47.46GiB deviceMemoryBandwidth: 625.94GiB/s
2020-11-23 20:05:39.594146: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-11-23 20:05:39.596640: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-11-23 20:05:39.599204: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-11-23 20:05:39.599585: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-11-23 20:05:39.602378: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-11-23 20:05:39.603716: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-11-23 20:05:39.609230: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-11-23 20:05:39.612139: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2020-11-23 20:05:39.612890: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA
2020-11-23 20:05:39.629277: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2200000000 Hz
2020-11-23 20:05:39.632159: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55eb41556be0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-11-23 20:05:39.632235: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2020-11-23 20:05:39.633912: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
pciBusID: 0000:d8:00.0 name: Quadro RTX 8000 computeCapability: 7.5
coreClock: 1.77GHz coreCount: 72 deviceMemorySize: 47.46GiB deviceMemoryBandwidth: 625.94GiB/s
2020-11-23 20:05:39.633978: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-11-23 20:05:39.634031: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-11-23 20:05:39.634050: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-11-23 20:05:39.634071: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-11-23 20:05:39.634088: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-11-23 20:05:39.634105: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-11-23 20:05:39.634122: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-11-23 20:05:39.636976: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2020-11-23 20:05:39.637023: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-11-23 20:05:39.781878: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-11-23 20:05:39.781937: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0
2020-11-23 20:05:39.781956: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N
2020-11-23 20:05:39.786323: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 45281 MB memory) -> physical GPU (device: 0, name: Quadro RTX 8000, pci bus id: 0000:d8:00.0, compute capability: 7.5)
2020-11-23 20:05:39.789670: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55eb41db58f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-11-23 20:05:39.789704: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Quadro RTX 8000, Compute Capability 7.5
Epoch 1/100
2020-11-23 20:05:41.946897: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4908 - accuracy: 0.8129 - val_loss: 0.4808 - val_accuracy: 0.8174
Epoch 2/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4821 - accuracy: 0.8157 - val_loss: 0.4768 - val_accuracy: 0.8186
Epoch 3/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4790 - accuracy: 0.8169 - val_loss: 0.4744 - val_accuracy: 0.8186
Epoch 4/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4770 - accuracy: 0.8169 - val_loss: 0.4731 - val_accuracy: 0.8186
Epoch 5/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4758 - accuracy: 0.8169 - val_loss: 0.4719 - val_accuracy: 0.8186
Epoch 6/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4748 - accuracy: 0.8169 - val_loss: 0.4710 - val_accuracy: 0.8186
Epoch 7/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4741 - accuracy: 0.8169 - val_loss: 0.4704 - val_accuracy: 0.8186
Epoch 8/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4735 - accuracy: 0.8169 - val_loss: 0.4698 - val_accuracy: 0.8186
Epoch 9/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4730 - accuracy: 0.8169 - val_loss: 0.4693 - val_accuracy: 0.8186
Epoch 10/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4725 - accuracy: 0.8169 - val_loss: 0.4689 - val_accuracy: 0.8186
Epoch 11/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4721 - accuracy: 0.8169 - val_loss: 0.4685 - val_accuracy: 0.8186
Epoch 12/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4718 - accuracy: 0.8169 - val_loss: 0.4682 - val_accuracy: 0.8186
Epoch 13/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4715 - accuracy: 0.8169 - val_loss: 0.4679 - val_accuracy: 0.8186
Epoch 14/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4712 - accuracy: 0.8169 - val_loss: 0.4676 - val_accuracy: 0.8186
Epoch 15/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4709 - accuracy: 0.8169 - val_loss: 0.4673 - val_accuracy: 0.8186
Epoch 16/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4707 - accuracy: 0.8169 - val_loss: 0.4671 - val_accuracy: 0.8186
Epoch 17/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4705 - accuracy: 0.8169 - val_loss: 0.4670 - val_accuracy: 0.8186
Epoch 18/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4703 - accuracy: 0.8169 - val_loss: 0.4667 - val_accuracy: 0.8186
Epoch 19/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4701 - accuracy: 0.8169 - val_loss: 0.4665 - val_accuracy: 0.8186
Epoch 20/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4698 - accuracy: 0.8169 - val_loss: 0.4667 - val_accuracy: 0.8186
Epoch 21/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4697 - accuracy: 0.8169 - val_loss: 0.4663 - val_accuracy: 0.8186
Epoch 22/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4695 - accuracy: 0.8169 - val_loss: 0.4660 - val_accuracy: 0.8186
Epoch 23/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4694 - accuracy: 0.8169 - val_loss: 0.4659 - val_accuracy: 0.8186
Epoch 24/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4693 - accuracy: 0.8169 - val_loss: 0.4659 - val_accuracy: 0.8186
Epoch 25/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4691 - accuracy: 0.8169 - val_loss: 0.4657 - val_accuracy: 0.8186
Epoch 26/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4690 - accuracy: 0.8169 - val_loss: 0.4655 - val_accuracy: 0.8186
Epoch 27/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4689 - accuracy: 0.8169 - val_loss: 0.4654 - val_accuracy: 0.8186
Epoch 28/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4688 - accuracy: 0.8169 - val_loss: 0.4653 - val_accuracy: 0.8186
Epoch 29/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4687 - accuracy: 0.8169 - val_loss: 0.4652 - val_accuracy: 0.8186
Epoch 30/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4686 - accuracy: 0.8169 - val_loss: 0.4651 - val_accuracy: 0.8186
Epoch 31/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4685 - accuracy: 0.8169 - val_loss: 0.4651 - val_accuracy: 0.8186
Epoch 32/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4684 - accuracy: 0.8169 - val_loss: 0.4649 - val_accuracy: 0.8186
Epoch 33/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4683 - accuracy: 0.8169 - val_loss: 0.4649 - val_accuracy: 0.8186
Epoch 34/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4683 - accuracy: 0.8169 - val_loss: 0.4650 - val_accuracy: 0.8186
Epoch 35/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4682 - accuracy: 0.8169 - val_loss: 0.4648 - val_accuracy: 0.8186
Epoch 36/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4681 - accuracy: 0.8169 - val_loss: 0.4647 - val_accuracy: 0.8186
Epoch 37/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4680 - accuracy: 0.8169 - val_loss: 0.4646 - val_accuracy: 0.8186
Epoch 38/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4680 - accuracy: 0.8169 - val_loss: 0.4645 - val_accuracy: 0.8186
Epoch 39/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4679 - accuracy: 0.8169 - val_loss: 0.4645 - val_accuracy: 0.8186
Epoch 40/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4679 - accuracy: 0.8169 - val_loss: 0.4645 - val_accuracy: 0.8186
Epoch 41/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4678 - accuracy: 0.8169 - val_loss: 0.4643 - val_accuracy: 0.8186
Epoch 42/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4677 - accuracy: 0.8169 - val_loss: 0.4643 - val_accuracy: 0.8186
Epoch 43/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4677 - accuracy: 0.8169 - val_loss: 0.4643 - val_accuracy: 0.8186
Epoch 44/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4677 - accuracy: 0.8169 - val_loss: 0.4642 - val_accuracy: 0.8186
Epoch 45/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4676 - accuracy: 0.8169 - val_loss: 0.4642 - val_accuracy: 0.8186
Epoch 46/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4675 - accuracy: 0.8169 - val_loss: 0.4643 - val_accuracy: 0.8186
Epoch 47/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4675 - accuracy: 0.8169 - val_loss: 0.4641 - val_accuracy: 0.8186
Epoch 48/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4675 - accuracy: 0.8169 - val_loss: 0.4640 - val_accuracy: 0.8186
Epoch 49/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4674 - accuracy: 0.8169 - val_loss: 0.4640 - val_accuracy: 0.8186
Epoch 50/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4674 - accuracy: 0.8169 - val_loss: 0.4639 - val_accuracy: 0.8186
Epoch 51/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4673 - accuracy: 0.8169 - val_loss: 0.4639 - val_accuracy: 0.8186
Epoch 52/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4673 - accuracy: 0.8169 - val_loss: 0.4639 - val_accuracy: 0.8186
Epoch 53/100
1213/1213 [==============================] - 2s 2ms/step - loss: 0.4673 - accuracy: 0.8169 - val_loss: 0.4638 - val_accuracy: 0.8186
Epoch 54/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4672 - accuracy: 0.8169 - val_loss: 0.4638 - val_accuracy: 0.8186
Epoch 55/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4672 - accuracy: 0.8169 - val_loss: 0.4638 - val_accuracy: 0.8186
Epoch 56/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4671 - accuracy: 0.8169 - val_loss: 0.4638 - val_accuracy: 0.8186
Epoch 57/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4671 - accuracy: 0.8169 - val_loss: 0.4637 - val_accuracy: 0.8186
Epoch 58/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4671 - accuracy: 0.8169 - val_loss: 0.4637 - val_accuracy: 0.8186
Epoch 59/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4670 - accuracy: 0.8169 - val_loss: 0.4637 - val_accuracy: 0.8186
Epoch 60/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4670 - accuracy: 0.8169 - val_loss: 0.4636 - val_accuracy: 0.8186
Epoch 61/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4670 - accuracy: 0.8169 - val_loss: 0.4636 - val_accuracy: 0.8186
Epoch 62/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4670 - accuracy: 0.8169 - val_loss: 0.4636 - val_accuracy: 0.8186
Epoch 63/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4669 - accuracy: 0.8169 - val_loss: 0.4635 - val_accuracy: 0.8186
Epoch 64/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4669 - accuracy: 0.8169 - val_loss: 0.4635 - val_accuracy: 0.8186
Epoch 65/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4668 - accuracy: 0.8169 - val_loss: 0.4636 - val_accuracy: 0.8186
Epoch 66/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4669 - accuracy: 0.8169 - val_loss: 0.4635 - val_accuracy: 0.8186
Epoch 67/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4668 - accuracy: 0.8169 - val_loss: 0.4638 - val_accuracy: 0.8186
Epoch 68/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4668 - accuracy: 0.8169 - val_loss: 0.4635 - val_accuracy: 0.8186
Epoch 69/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4668 - accuracy: 0.8169 - val_loss: 0.4636 - val_accuracy: 0.8186
Epoch 70/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4667 - accuracy: 0.8169 - val_loss: 0.4634 - val_accuracy: 0.8186
Epoch 71/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4667 - accuracy: 0.8169 - val_loss: 0.4634 - val_accuracy: 0.8186
Epoch 72/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4667 - accuracy: 0.8169 - val_loss: 0.4634 - val_accuracy: 0.8186
Epoch 73/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4667 - accuracy: 0.8169 - val_loss: 0.4633 - val_accuracy: 0.8186
Epoch 74/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4666 - accuracy: 0.8169 - val_loss: 0.4633 - val_accuracy: 0.8186
Epoch 75/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4666 - accuracy: 0.8169 - val_loss: 0.4633 - val_accuracy: 0.8186
Epoch 76/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4666 - accuracy: 0.8169 - val_loss: 0.4633 - val_accuracy: 0.8186
Epoch 77/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4666 - accuracy: 0.8169 - val_loss: 0.4633 - val_accuracy: 0.8186
Epoch 78/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4666 - accuracy: 0.8169 - val_loss: 0.4632 - val_accuracy: 0.8186
Epoch 79/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4665 - accuracy: 0.8169 - val_loss: 0.4632 - val_accuracy: 0.8186
Epoch 80/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4665 - accuracy: 0.8169 - val_loss: 0.4633 - val_accuracy: 0.8186
Epoch 81/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4665 - accuracy: 0.8169 - val_loss: 0.4632 - val_accuracy: 0.8186
Epoch 82/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4665 - accuracy: 0.8169 - val_loss: 0.4632 - val_accuracy: 0.8186
Epoch 83/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4665 - accuracy: 0.8169 - val_loss: 0.4632 - val_accuracy: 0.8186
Epoch 84/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4665 - accuracy: 0.8169 - val_loss: 0.4631 - val_accuracy: 0.8186
Epoch 85/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4664 - accuracy: 0.8169 - val_loss: 0.4631 - val_accuracy: 0.8186
Epoch 86/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4664 - accuracy: 0.8169 - val_loss: 0.4631 - val_accuracy: 0.8186
Epoch 87/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4664 - accuracy: 0.8169 - val_loss: 0.4631 - val_accuracy: 0.8186
Epoch 88/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4664 - accuracy: 0.8169 - val_loss: 0.4631 - val_accuracy: 0.8186
Epoch 89/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4664 - accuracy: 0.8169 - val_loss: 0.4631 - val_accuracy: 0.8186
Epoch 90/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4663 - accuracy: 0.8169 - val_loss: 0.4632 - val_accuracy: 0.8186
Epoch 91/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4663 - accuracy: 0.8169 - val_loss: 0.4630 - val_accuracy: 0.8186
Epoch 92/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4663 - accuracy: 0.8169 - val_loss: 0.4631 - val_accuracy: 0.8186
Epoch 93/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4663 - accuracy: 0.8169 - val_loss: 0.4630 - val_accuracy: 0.8186
Epoch 94/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4663 - accuracy: 0.8169 - val_loss: 0.4630 - val_accuracy: 0.8186
Epoch 95/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4663 - accuracy: 0.8169 - val_loss: 0.4630 - val_accuracy: 0.8186
Epoch 96/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4663 - accuracy: 0.8169 - val_loss: 0.4630 - val_accuracy: 0.8186
Epoch 97/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4663 - accuracy: 0.8169 - val_loss: 0.4630 - val_accuracy: 0.8186
Epoch 98/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4663 - accuracy: 0.8169 - val_loss: 0.4629 - val_accuracy: 0.8186
Epoch 99/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4662 - accuracy: 0.8169 - val_loss: 0.4630 - val_accuracy: 0.8186
Epoch 100/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4662 - accuracy: 0.8169 - val_loss: 0.4630 - val_accuracy: 0.8186
193/193 [==============================] - 0s 2ms/step - loss: 0.4765 - accuracy: 0.8097
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 1) 22
=================================================================
Total params: 22
Trainable params: 22
Non-trainable params: 0
_________________________________________________________________
None
Accuracy: 80.97
Epoch 1/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.6943 - accuracy: 0.7741 - val_loss: 0.4694 - val_accuracy: 0.8186
Epoch 2/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4726 - accuracy: 0.8169 - val_loss: 0.4649 - val_accuracy: 0.8186
Epoch 3/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4728 - accuracy: 0.8168 - val_loss: 0.4660 - val_accuracy: 0.8186
Epoch 4/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4846 - accuracy: 0.8095 - val_loss: 0.4672 - val_accuracy: 0.8186
Epoch 5/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4718 - accuracy: 0.8169 - val_loss: 0.4621 - val_accuracy: 0.8186
Epoch 6/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4742 - accuracy: 0.8151 - val_loss: 0.4656 - val_accuracy: 0.8186
Epoch 7/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4713 - accuracy: 0.8169 - val_loss: 0.4640 - val_accuracy: 0.8186
Epoch 8/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4864 - accuracy: 0.8057 - val_loss: 0.5133 - val_accuracy: 0.7727
Epoch 9/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4773 - accuracy: 0.8146 - val_loss: 0.4965 - val_accuracy: 0.8186
Epoch 10/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4695 - accuracy: 0.8169 - val_loss: 0.5067 - val_accuracy: 0.8186
Epoch 11/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4962 - accuracy: 0.8045 - val_loss: 0.4713 - val_accuracy: 0.8186
Epoch 12/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.5268 - accuracy: 0.7941 - val_loss: 0.4713 - val_accuracy: 0.8186
Epoch 13/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4738 - accuracy: 0.8169 - val_loss: 0.4659 - val_accuracy: 0.8186
Epoch 14/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4715 - accuracy: 0.8169 - val_loss: 0.4694 - val_accuracy: 0.8186
Epoch 15/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4703 - accuracy: 0.8169 - val_loss: 0.4725 - val_accuracy: 0.8186
Epoch 16/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4689 - accuracy: 0.8169 - val_loss: 0.5052 - val_accuracy: 0.8186
Epoch 17/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4695 - accuracy: 0.8169 - val_loss: 0.4737 - val_accuracy: 0.8186
Epoch 18/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4707 - accuracy: 0.8169 - val_loss: 0.4637 - val_accuracy: 0.8186
Epoch 19/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4748 - accuracy: 0.8129 - val_loss: 0.4629 - val_accuracy: 0.8186
Epoch 20/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4694 - accuracy: 0.8169 - val_loss: 0.4679 - val_accuracy: 0.8186
Epoch 21/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4722 - accuracy: 0.8126 - val_loss: 0.4636 - val_accuracy: 0.8186
Epoch 22/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4867 - accuracy: 0.8057 - val_loss: 0.4681 - val_accuracy: 0.8186
Epoch 23/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4705 - accuracy: 0.8169 - val_loss: 0.4640 - val_accuracy: 0.8186
Epoch 24/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4690 - accuracy: 0.8169 - val_loss: 0.4635 - val_accuracy: 0.8186
Epoch 25/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4685 - accuracy: 0.8169 - val_loss: 0.4623 - val_accuracy: 0.8186
Epoch 26/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4679 - accuracy: 0.8169 - val_loss: 0.4623 - val_accuracy: 0.8186
Epoch 27/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.5143 - accuracy: 0.8018 - val_loss: 0.4795 - val_accuracy: 0.8132
Epoch 28/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.5213 - accuracy: 0.7930 - val_loss: 0.4860 - val_accuracy: 0.8151
Epoch 29/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4907 - accuracy: 0.8080 - val_loss: 0.4806 - val_accuracy: 0.8150
Epoch 30/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.5163 - accuracy: 0.7960 - val_loss: 0.5140 - val_accuracy: 0.7653
Epoch 31/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4774 - accuracy: 0.8149 - val_loss: 0.4729 - val_accuracy: 0.8181
Epoch 32/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4742 - accuracy: 0.8168 - val_loss: 0.4658 - val_accuracy: 0.8186
Epoch 33/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4797 - accuracy: 0.8119 - val_loss: 0.4812 - val_accuracy: 0.8188
Epoch 34/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4736 - accuracy: 0.8158 - val_loss: 0.4691 - val_accuracy: 0.8186
Epoch 35/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4738 - accuracy: 0.8168 - val_loss: 0.5344 - val_accuracy: 0.8186
Epoch 36/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4762 - accuracy: 0.8131 - val_loss: 0.4625 - val_accuracy: 0.8179
Epoch 37/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4746 - accuracy: 0.8167 - val_loss: 0.4646 - val_accuracy: 0.8186
Epoch 38/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4724 - accuracy: 0.8168 - val_loss: 0.4969 - val_accuracy: 0.8188
Epoch 39/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.6404 - accuracy: 0.7777 - val_loss: 0.4837 - val_accuracy: 0.8015
Epoch 40/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.5726 - accuracy: 0.7905 - val_loss: 0.4783 - val_accuracy: 0.8148
Epoch 41/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4844 - accuracy: 0.8153 - val_loss: 0.4821 - val_accuracy: 0.8186
Epoch 42/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4866 - accuracy: 0.8095 - val_loss: 0.4697 - val_accuracy: 0.8186
Epoch 43/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4900 - accuracy: 0.8020 - val_loss: 0.4646 - val_accuracy: 0.8186
Epoch 44/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.6698 - accuracy: 0.7691 - val_loss: 0.5350 - val_accuracy: 0.7566
Epoch 45/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.5097 - accuracy: 0.7915 - val_loss: 0.4857 - val_accuracy: 0.8078
Epoch 46/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4946 - accuracy: 0.8053 - val_loss: 0.4692 - val_accuracy: 0.8171
Epoch 47/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4829 - accuracy: 0.8133 - val_loss: 0.4771 - val_accuracy: 0.8186
Epoch 48/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4814 - accuracy: 0.8144 - val_loss: 0.5126 - val_accuracy: 0.8186
Epoch 49/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.6781 - accuracy: 0.7635 - val_loss: 0.4696 - val_accuracy: 0.8186
Epoch 50/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4778 - accuracy: 0.8152 - val_loss: 0.4856 - val_accuracy: 0.8186
Epoch 51/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4753 - accuracy: 0.8169 - val_loss: 0.4787 - val_accuracy: 0.8183
Epoch 52/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4761 - accuracy: 0.8151 - val_loss: 0.4655 - val_accuracy: 0.8186
Epoch 53/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4837 - accuracy: 0.8090 - val_loss: 0.4652 - val_accuracy: 0.8186
Epoch 54/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.5086 - accuracy: 0.7965 - val_loss: 0.4715 - val_accuracy: 0.8186
Epoch 55/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4760 - accuracy: 0.8145 - val_loss: 0.4635 - val_accuracy: 0.8186
Epoch 56/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4774 - accuracy: 0.8129 - val_loss: 0.4712 - val_accuracy: 0.8186
Epoch 57/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4813 - accuracy: 0.8116 - val_loss: 0.4690 - val_accuracy: 0.8186
Epoch 58/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4990 - accuracy: 0.8041 - val_loss: 0.4719 - val_accuracy: 0.8186
Epoch 59/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.5432 - accuracy: 0.7943 - val_loss: 0.4664 - val_accuracy: 0.8148
Epoch 60/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4777 - accuracy: 0.8109 - val_loss: 0.4853 - val_accuracy: 0.8186
Epoch 61/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.5418 - accuracy: 0.7857 - val_loss: 0.4657 - val_accuracy: 0.8186
Epoch 62/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4776 - accuracy: 0.8113 - val_loss: 0.4655 - val_accuracy: 0.8186
Epoch 63/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4726 - accuracy: 0.8163 - val_loss: 0.4721 - val_accuracy: 0.8183
Epoch 64/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4723 - accuracy: 0.8148 - val_loss: 0.4625 - val_accuracy: 0.8186
Epoch 65/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4818 - accuracy: 0.8089 - val_loss: 0.4706 - val_accuracy: 0.8167
Epoch 66/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4726 - accuracy: 0.8166 - val_loss: 0.5870 - val_accuracy: 0.8186
Epoch 67/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4710 - accuracy: 0.8168 - val_loss: 0.4625 - val_accuracy: 0.8186
Epoch 68/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4827 - accuracy: 0.8081 - val_loss: 0.4687 - val_accuracy: 0.8186
Epoch 69/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4730 - accuracy: 0.8154 - val_loss: 0.4678 - val_accuracy: 0.8186
Epoch 70/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4691 - accuracy: 0.8169 - val_loss: 0.4719 - val_accuracy: 0.8186
Epoch 71/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4723 - accuracy: 0.8156 - val_loss: 0.4646 - val_accuracy: 0.8186
Epoch 72/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4742 - accuracy: 0.8122 - val_loss: 0.4702 - val_accuracy: 0.8186
Epoch 73/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4726 - accuracy: 0.8150 - val_loss: 0.4703 - val_accuracy: 0.8184
Epoch 74/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4693 - accuracy: 0.8168 - val_loss: 0.4735 - val_accuracy: 0.8188
Epoch 75/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4698 - accuracy: 0.8168 - val_loss: 0.4716 - val_accuracy: 0.8188
Epoch 76/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.8640 - accuracy: 0.7902 - val_loss: 2.8196 - val_accuracy: 0.8132
Epoch 77/100
1213/1213 [==============================] - 4s 3ms/step - loss: 2.8271 - accuracy: 0.8159 - val_loss: 2.7983 - val_accuracy: 0.8183
Epoch 78/100
1213/1213 [==============================] - 4s 3ms/step - loss: 2.8246 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 79/100
1213/1213 [==============================] - 4s 3ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 80/100
1213/1213 [==============================] - 3s 3ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 81/100
1213/1213 [==============================] - 3s 3ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 82/100
1213/1213 [==============================] - 3s 2ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 83/100
1213/1213 [==============================] - 3s 3ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 84/100
1213/1213 [==============================] - 3s 3ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 85/100
1213/1213 [==============================] - 3s 3ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 86/100
1213/1213 [==============================] - 3s 3ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 87/100
1213/1213 [==============================] - 3s 3ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 88/100
1213/1213 [==============================] - 3s 2ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 89/100
1213/1213 [==============================] - 3s 2ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 90/100
1213/1213 [==============================] - 3s 2ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 91/100
1213/1213 [==============================] - 3s 2ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 92/100
1213/1213 [==============================] - 3s 3ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 93/100
1213/1213 [==============================] - 3s 3ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 94/100
1213/1213 [==============================] - 4s 3ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 95/100
1213/1213 [==============================] - 3s 3ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 96/100
1213/1213 [==============================] - 3s 2ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 97/100
1213/1213 [==============================] - 3s 3ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 98/100
1213/1213 [==============================] - 3s 3ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 99/100
1213/1213 [==============================] - 3s 3ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
Epoch 100/100
1213/1213 [==============================] - 3s 3ms/step - loss: 2.8245 - accuracy: 0.8169 - val_loss: 2.7976 - val_accuracy: 0.8186
193/193 [==============================] - 0s 2ms/step - loss: 2.9352 - accuracy: 0.8097
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 15) 330
_________________________________________________________________
dense_2 (Dense) (None, 1) 16
=================================================================
Total params: 346
Trainable params: 346
Non-trainable params: 0
_________________________________________________________________
None
Accuracy: 80.97
Epoch 1/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4765 - accuracy: 0.8146 - val_loss: 0.4688 - val_accuracy: 0.8186
Epoch 2/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4716 - accuracy: 0.8169 - val_loss: 0.4674 - val_accuracy: 0.8186
Epoch 3/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4706 - accuracy: 0.8169 - val_loss: 0.4667 - val_accuracy: 0.8186
Epoch 4/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4699 - accuracy: 0.8169 - val_loss: 0.4659 - val_accuracy: 0.8186
Epoch 5/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4693 - accuracy: 0.8169 - val_loss: 0.4655 - val_accuracy: 0.8186
Epoch 6/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4689 - accuracy: 0.8169 - val_loss: 0.4650 - val_accuracy: 0.8186
Epoch 7/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4685 - accuracy: 0.8169 - val_loss: 0.4647 - val_accuracy: 0.8186
Epoch 8/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4681 - accuracy: 0.8169 - val_loss: 0.4643 - val_accuracy: 0.8186
Epoch 9/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4677 - accuracy: 0.8169 - val_loss: 0.4640 - val_accuracy: 0.8186
Epoch 10/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4674 - accuracy: 0.8169 - val_loss: 0.4641 - val_accuracy: 0.8186
Epoch 11/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4672 - accuracy: 0.8169 - val_loss: 0.4637 - val_accuracy: 0.8186
Epoch 12/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4670 - accuracy: 0.8169 - val_loss: 0.4633 - val_accuracy: 0.8186
Epoch 13/100
1213/1213 [==============================] - 5s 4ms/step - loss: 0.4668 - accuracy: 0.8169 - val_loss: 0.4631 - val_accuracy: 0.8186
Epoch 14/100
1213/1213 [==============================] - 5s 4ms/step - loss: 0.4665 - accuracy: 0.8169 - val_loss: 0.4628 - val_accuracy: 0.8186
Epoch 15/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4664 - accuracy: 0.8169 - val_loss: 0.4627 - val_accuracy: 0.8186
Epoch 16/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4661 - accuracy: 0.8169 - val_loss: 0.4626 - val_accuracy: 0.8186
Epoch 17/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4659 - accuracy: 0.8169 - val_loss: 0.4624 - val_accuracy: 0.8186
Epoch 18/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4659 - accuracy: 0.8169 - val_loss: 0.4627 - val_accuracy: 0.8186
Epoch 19/100
1213/1213 [==============================] - 5s 4ms/step - loss: 0.4658 - accuracy: 0.8169 - val_loss: 0.4623 - val_accuracy: 0.8186
Epoch 20/100
1213/1213 [==============================] - 5s 4ms/step - loss: 0.4656 - accuracy: 0.8169 - val_loss: 0.4625 - val_accuracy: 0.8186
Epoch 21/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4654 - accuracy: 0.8169 - val_loss: 0.4620 - val_accuracy: 0.8186
Epoch 22/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4653 - accuracy: 0.8169 - val_loss: 0.4619 - val_accuracy: 0.8186
Epoch 23/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4653 - accuracy: 0.8169 - val_loss: 0.4622 - val_accuracy: 0.8186
Epoch 24/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4651 - accuracy: 0.8169 - val_loss: 0.4627 - val_accuracy: 0.8186
Epoch 25/100
1213/1213 [==============================] - 5s 4ms/step - loss: 0.4651 - accuracy: 0.8169 - val_loss: 0.4619 - val_accuracy: 0.8186
Epoch 26/100
1213/1213 [==============================] - 5s 4ms/step - loss: 0.4651 - accuracy: 0.8169 - val_loss: 0.4616 - val_accuracy: 0.8186
Epoch 27/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4648 - accuracy: 0.8169 - val_loss: 0.4619 - val_accuracy: 0.8186
Epoch 28/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4649 - accuracy: 0.8169 - val_loss: 0.4622 - val_accuracy: 0.8186
Epoch 29/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4648 - accuracy: 0.8169 - val_loss: 0.4616 - val_accuracy: 0.8186
Epoch 30/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4646 - accuracy: 0.8169 - val_loss: 0.4615 - val_accuracy: 0.8186
Epoch 31/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4646 - accuracy: 0.8168 - val_loss: 0.4640 - val_accuracy: 0.8187
Epoch 32/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4645 - accuracy: 0.8170 - val_loss: 0.4618 - val_accuracy: 0.8186
Epoch 33/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4645 - accuracy: 0.8169 - val_loss: 0.4617 - val_accuracy: 0.8186
Epoch 34/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4646 - accuracy: 0.8169 - val_loss: 0.4623 - val_accuracy: 0.8187
Epoch 35/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4647 - accuracy: 0.8170 - val_loss: 0.4615 - val_accuracy: 0.8186
Epoch 36/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4643 - accuracy: 0.8170 - val_loss: 0.4628 - val_accuracy: 0.8186
Epoch 37/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4645 - accuracy: 0.8170 - val_loss: 0.4613 - val_accuracy: 0.8187
Epoch 38/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4642 - accuracy: 0.8171 - val_loss: 0.4622 - val_accuracy: 0.8186
Epoch 39/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4642 - accuracy: 0.8170 - val_loss: 0.4611 - val_accuracy: 0.8187
Epoch 40/100
1213/1213 [==============================] - 5s 4ms/step - loss: 0.4642 - accuracy: 0.8170 - val_loss: 0.4614 - val_accuracy: 0.8187
Epoch 41/100
1213/1213 [==============================] - 5s 4ms/step - loss: 0.4641 - accuracy: 0.8170 - val_loss: 0.4618 - val_accuracy: 0.8187
Epoch 42/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4641 - accuracy: 0.8171 - val_loss: 0.4622 - val_accuracy: 0.8187
Epoch 43/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4641 - accuracy: 0.8171 - val_loss: 0.4610 - val_accuracy: 0.8187
Epoch 44/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4639 - accuracy: 0.8171 - val_loss: 0.4612 - val_accuracy: 0.8187
Epoch 45/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4641 - accuracy: 0.8171 - val_loss: 0.4618 - val_accuracy: 0.8187
Epoch 46/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4640 - accuracy: 0.8171 - val_loss: 0.4612 - val_accuracy: 0.8188
Epoch 47/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4641 - accuracy: 0.8170 - val_loss: 0.4610 - val_accuracy: 0.8187
Epoch 48/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4638 - accuracy: 0.8171 - val_loss: 0.4608 - val_accuracy: 0.8187
Epoch 49/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4639 - accuracy: 0.8170 - val_loss: 0.4612 - val_accuracy: 0.8187
Epoch 50/100
1213/1213 [==============================] - 5s 4ms/step - loss: 0.4638 - accuracy: 0.8171 - val_loss: 0.4610 - val_accuracy: 0.8187
Epoch 51/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4638 - accuracy: 0.8171 - val_loss: 0.4610 - val_accuracy: 0.8187
Epoch 52/100
1213/1213 [==============================] - 5s 4ms/step - loss: 0.4638 - accuracy: 0.8171 - val_loss: 0.4614 - val_accuracy: 0.8187
Epoch 53/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4636 - accuracy: 0.8171 - val_loss: 0.4608 - val_accuracy: 0.8187
Epoch 54/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4638 - accuracy: 0.8170 - val_loss: 0.4620 - val_accuracy: 0.8188
Epoch 55/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4637 - accuracy: 0.8170 - val_loss: 0.4610 - val_accuracy: 0.8191
Epoch 56/100
1213/1213 [==============================] - 5s 4ms/step - loss: 0.4638 - accuracy: 0.8170 - val_loss: 0.4610 - val_accuracy: 0.8187
Epoch 57/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4635 - accuracy: 0.8171 - val_loss: 0.4607 - val_accuracy: 0.8187
Epoch 58/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4638 - accuracy: 0.8171 - val_loss: 0.4607 - val_accuracy: 0.8187
Epoch 59/100
1213/1213 [==============================] - 5s 4ms/step - loss: 0.4635 - accuracy: 0.8172 - val_loss: 0.4616 - val_accuracy: 0.8188
Epoch 60/100
1213/1213 [==============================] - 5s 4ms/step - loss: 0.4637 - accuracy: 0.8170 - val_loss: 0.4608 - val_accuracy: 0.8187
Epoch 61/100
1213/1213 [==============================] - 5s 4ms/step - loss: 0.4636 - accuracy: 0.8171 - val_loss: 0.4616 - val_accuracy: 0.8188
Epoch 62/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4635 - accuracy: 0.8171 - val_loss: 0.4609 - val_accuracy: 0.8190
Epoch 63/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4634 - accuracy: 0.8171 - val_loss: 0.4607 - val_accuracy: 0.8187
Epoch 64/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4635 - accuracy: 0.8171 - val_loss: 0.4605 - val_accuracy: 0.8187
Epoch 65/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4635 - accuracy: 0.8172 - val_loss: 0.4606 - val_accuracy: 0.8187
Epoch 66/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4635 - accuracy: 0.8170 - val_loss: 0.4608 - val_accuracy: 0.8187
Epoch 67/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4634 - accuracy: 0.8171 - val_loss: 0.4610 - val_accuracy: 0.8188
Epoch 68/100
1213/1213 [==============================] - 5s 4ms/step - loss: 0.4633 - accuracy: 0.8170 - val_loss: 0.4610 - val_accuracy: 0.8187
Epoch 69/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4634 - accuracy: 0.8171 - val_loss: 0.4607 - val_accuracy: 0.8188
Epoch 70/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4633 - accuracy: 0.8172 - val_loss: 0.4605 - val_accuracy: 0.8191
Epoch 71/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4632 - accuracy: 0.8169 - val_loss: 0.4605 - val_accuracy: 0.8189
Epoch 72/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4634 - accuracy: 0.8170 - val_loss: 0.4604 - val_accuracy: 0.8191
Epoch 73/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4633 - accuracy: 0.8170 - val_loss: 0.4610 - val_accuracy: 0.8189
Epoch 74/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4632 - accuracy: 0.8171 - val_loss: 0.4607 - val_accuracy: 0.8188
Epoch 75/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4633 - accuracy: 0.8170 - val_loss: 0.4604 - val_accuracy: 0.8190
Epoch 76/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4632 - accuracy: 0.8170 - val_loss: 0.4605 - val_accuracy: 0.8190
Epoch 77/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4632 - accuracy: 0.8170 - val_loss: 0.4607 - val_accuracy: 0.8190
Epoch 78/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4631 - accuracy: 0.8170 - val_loss: 0.4608 - val_accuracy: 0.8188
Epoch 79/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4631 - accuracy: 0.8171 - val_loss: 0.4605 - val_accuracy: 0.8191
Epoch 80/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4632 - accuracy: 0.8171 - val_loss: 0.4606 - val_accuracy: 0.8191
Epoch 81/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4631 - accuracy: 0.8171 - val_loss: 0.4615 - val_accuracy: 0.8188
Epoch 82/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4633 - accuracy: 0.8170 - val_loss: 0.4619 - val_accuracy: 0.8192
Epoch 83/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4632 - accuracy: 0.8171 - val_loss: 0.4605 - val_accuracy: 0.8190
Epoch 84/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4631 - accuracy: 0.8170 - val_loss: 0.4603 - val_accuracy: 0.8190
Epoch 85/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4631 - accuracy: 0.8171 - val_loss: 0.4604 - val_accuracy: 0.8191
Epoch 86/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4630 - accuracy: 0.8169 - val_loss: 0.4603 - val_accuracy: 0.8190
Epoch 87/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4631 - accuracy: 0.8169 - val_loss: 0.4602 - val_accuracy: 0.8191
Epoch 88/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4632 - accuracy: 0.8170 - val_loss: 0.4620 - val_accuracy: 0.8189
Epoch 89/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4630 - accuracy: 0.8169 - val_loss: 0.4602 - val_accuracy: 0.8191
Epoch 90/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4630 - accuracy: 0.8171 - val_loss: 0.4611 - val_accuracy: 0.8190
Epoch 91/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4631 - accuracy: 0.8170 - val_loss: 0.4603 - val_accuracy: 0.8190
Epoch 92/100
1213/1213 [==============================] - 3s 2ms/step - loss: 0.4631 - accuracy: 0.8170 - val_loss: 0.4620 - val_accuracy: 0.8189
Epoch 93/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4629 - accuracy: 0.8169 - val_loss: 0.4604 - val_accuracy: 0.8192
Epoch 94/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4628 - accuracy: 0.8170 - val_loss: 0.4613 - val_accuracy: 0.8189
Epoch 95/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4630 - accuracy: 0.8170 - val_loss: 0.4610 - val_accuracy: 0.8190
Epoch 96/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4628 - accuracy: 0.8171 - val_loss: 0.4603 - val_accuracy: 0.8192
Epoch 97/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4628 - accuracy: 0.8171 - val_loss: 0.4601 - val_accuracy: 0.8191
Epoch 98/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4629 - accuracy: 0.8171 - val_loss: 0.4601 - val_accuracy: 0.8191
Epoch 99/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4630 - accuracy: 0.8169 - val_loss: 0.4604 - val_accuracy: 0.8192
Epoch 100/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4629 - accuracy: 0.8170 - val_loss: 0.4606 - val_accuracy: 0.8191
193/193 [==============================] - 0s 2ms/step - loss: 0.4731 - accuracy: 0.8104
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_3 (Dense) (None, 25) 550
_________________________________________________________________
dense_4 (Dense) (None, 15) 390
_________________________________________________________________
dense_5 (Dense) (None, 1) 16
=================================================================
Total params: 956
Trainable params: 956
Non-trainable params: 0
_________________________________________________________________
None
Accuracy: 81.04
Epoch 1/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.8019 - accuracy: 0.8120 - val_loss: 0.7215 - val_accuracy: 0.8186
Epoch 2/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.6722 - accuracy: 0.8169 - val_loss: 0.6263 - val_accuracy: 0.8186
Epoch 3/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.5974 - accuracy: 0.8169 - val_loss: 0.5680 - val_accuracy: 0.8186
Epoch 4/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.5516 - accuracy: 0.8169 - val_loss: 0.5325 - val_accuracy: 0.8186
Epoch 5/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.5235 - accuracy: 0.8169 - val_loss: 0.5106 - val_accuracy: 0.8186
Epoch 6/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.5061 - accuracy: 0.8169 - val_loss: 0.4970 - val_accuracy: 0.8186
Epoch 7/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4953 - accuracy: 0.8169 - val_loss: 0.4885 - val_accuracy: 0.8186
Epoch 8/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4886 - accuracy: 0.8169 - val_loss: 0.4833 - val_accuracy: 0.8186
Epoch 9/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4844 - accuracy: 0.8169 - val_loss: 0.4801 - val_accuracy: 0.8186
Epoch 10/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4817 - accuracy: 0.8169 - val_loss: 0.4779 - val_accuracy: 0.8186
Epoch 11/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4801 - accuracy: 0.8169 - val_loss: 0.4765 - val_accuracy: 0.8186
Epoch 12/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4789 - accuracy: 0.8169 - val_loss: 0.4756 - val_accuracy: 0.8186
Epoch 13/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4782 - accuracy: 0.8169 - val_loss: 0.4751 - val_accuracy: 0.8186
Epoch 14/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4777 - accuracy: 0.8169 - val_loss: 0.4747 - val_accuracy: 0.8186
Epoch 15/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4774 - accuracy: 0.8169 - val_loss: 0.4744 - val_accuracy: 0.8186
Epoch 16/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4771 - accuracy: 0.8169 - val_loss: 0.4742 - val_accuracy: 0.8186
Epoch 17/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4769 - accuracy: 0.8169 - val_loss: 0.4740 - val_accuracy: 0.8186
Epoch 18/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4768 - accuracy: 0.8169 - val_loss: 0.4739 - val_accuracy: 0.8186
Epoch 19/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4767 - accuracy: 0.8169 - val_loss: 0.4740 - val_accuracy: 0.8186
Epoch 20/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4766 - accuracy: 0.8169 - val_loss: 0.4736 - val_accuracy: 0.8186
Epoch 21/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4765 - accuracy: 0.8169 - val_loss: 0.4737 - val_accuracy: 0.8186
Epoch 22/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4765 - accuracy: 0.8169 - val_loss: 0.4735 - val_accuracy: 0.8186
Epoch 23/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4763 - accuracy: 0.8169 - val_loss: 0.4735 - val_accuracy: 0.8186
Epoch 24/100
1213/1213 [==============================] - 4s 4ms/step - loss: 0.4764 - accuracy: 0.8169 - val_loss: 0.4734 - val_accuracy: 0.8186
Epoch 25/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4763 - accuracy: 0.8169 - val_loss: 0.4733 - val_accuracy: 0.8186
Epoch 26/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4762 - accuracy: 0.8169 - val_loss: 0.4733 - val_accuracy: 0.8186
Epoch 27/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4761 - accuracy: 0.8169 - val_loss: 0.4733 - val_accuracy: 0.8186
Epoch 28/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4762 - accuracy: 0.8169 - val_loss: 0.4732 - val_accuracy: 0.8186
Epoch 29/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4761 - accuracy: 0.8169 - val_loss: 0.4732 - val_accuracy: 0.8186
Epoch 30/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4760 - accuracy: 0.8169 - val_loss: 0.4732 - val_accuracy: 0.8186
Epoch 31/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4760 - accuracy: 0.8169 - val_loss: 0.4731 - val_accuracy: 0.8186
Epoch 32/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4760 - accuracy: 0.8169 - val_loss: 0.4730 - val_accuracy: 0.8186
Epoch 33/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4760 - accuracy: 0.8169 - val_loss: 0.4733 - val_accuracy: 0.8186
Epoch 34/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4760 - accuracy: 0.8169 - val_loss: 0.4730 - val_accuracy: 0.8186
Epoch 35/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4759 - accuracy: 0.8169 - val_loss: 0.4730 - val_accuracy: 0.8186
Epoch 36/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4759 - accuracy: 0.8169 - val_loss: 0.4730 - val_accuracy: 0.8186
Epoch 37/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4759 - accuracy: 0.8169 - val_loss: 0.4730 - val_accuracy: 0.8186
Epoch 38/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4759 - accuracy: 0.8169 - val_loss: 0.4730 - val_accuracy: 0.8186
Epoch 39/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4758 - accuracy: 0.8169 - val_loss: 0.4730 - val_accuracy: 0.8186
Epoch 40/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4759 - accuracy: 0.8169 - val_loss: 0.4728 - val_accuracy: 0.8186
Epoch 41/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4758 - accuracy: 0.8169 - val_loss: 0.4731 - val_accuracy: 0.8186
Epoch 42/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4758 - accuracy: 0.8169 - val_loss: 0.4733 - val_accuracy: 0.8186
Epoch 43/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4758 - accuracy: 0.8169 - val_loss: 0.4728 - val_accuracy: 0.8186
Epoch 44/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4757 - accuracy: 0.8169 - val_loss: 0.4728 - val_accuracy: 0.8186
Epoch 45/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4757 - accuracy: 0.8169 - val_loss: 0.4737 - val_accuracy: 0.8186
Epoch 46/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4758 - accuracy: 0.8169 - val_loss: 0.4728 - val_accuracy: 0.8186
Epoch 47/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4757 - accuracy: 0.8169 - val_loss: 0.4727 - val_accuracy: 0.8186
Epoch 48/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4757 - accuracy: 0.8169 - val_loss: 0.4727 - val_accuracy: 0.8186
Epoch 49/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4757 - accuracy: 0.8169 - val_loss: 0.4726 - val_accuracy: 0.8186
Epoch 50/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4757 - accuracy: 0.8169 - val_loss: 0.4730 - val_accuracy: 0.8186
Epoch 51/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4756 - accuracy: 0.8169 - val_loss: 0.4728 - val_accuracy: 0.8186
Epoch 52/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4756 - accuracy: 0.8169 - val_loss: 0.4726 - val_accuracy: 0.8186
Epoch 53/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4756 - accuracy: 0.8169 - val_loss: 0.4726 - val_accuracy: 0.8186
Epoch 54/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4756 - accuracy: 0.8169 - val_loss: 0.4725 - val_accuracy: 0.8186
Epoch 55/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4756 - accuracy: 0.8169 - val_loss: 0.4725 - val_accuracy: 0.8186
Epoch 56/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4755 - accuracy: 0.8169 - val_loss: 0.4725 - val_accuracy: 0.8186
Epoch 57/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4755 - accuracy: 0.8169 - val_loss: 0.4725 - val_accuracy: 0.8186
Epoch 58/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4756 - accuracy: 0.8169 - val_loss: 0.4725 - val_accuracy: 0.8186
Epoch 59/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4755 - accuracy: 0.8169 - val_loss: 0.4725 - val_accuracy: 0.8186
Epoch 60/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4756 - accuracy: 0.8169 - val_loss: 0.4725 - val_accuracy: 0.8186
Epoch 61/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4755 - accuracy: 0.8169 - val_loss: 0.4726 - val_accuracy: 0.8186
Epoch 62/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4754 - accuracy: 0.8169 - val_loss: 0.4725 - val_accuracy: 0.8186
Epoch 63/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4755 - accuracy: 0.8169 - val_loss: 0.4724 - val_accuracy: 0.8186
Epoch 64/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4755 - accuracy: 0.8169 - val_loss: 0.4725 - val_accuracy: 0.8186
Epoch 65/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4755 - accuracy: 0.8169 - val_loss: 0.4725 - val_accuracy: 0.8186
Epoch 66/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4754 - accuracy: 0.8169 - val_loss: 0.4726 - val_accuracy: 0.8186
Epoch 67/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4754 - accuracy: 0.8169 - val_loss: 0.4724 - val_accuracy: 0.8186
Epoch 68/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4755 - accuracy: 0.8169 - val_loss: 0.4723 - val_accuracy: 0.8186
Epoch 69/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4755 - accuracy: 0.8169 - val_loss: 0.4723 - val_accuracy: 0.8186
Epoch 70/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4754 - accuracy: 0.8169 - val_loss: 0.4723 - val_accuracy: 0.8186
Epoch 71/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4755 - accuracy: 0.8169 - val_loss: 0.4725 - val_accuracy: 0.8186
Epoch 72/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4754 - accuracy: 0.8169 - val_loss: 0.4725 - val_accuracy: 0.8186
Epoch 73/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4754 - accuracy: 0.8169 - val_loss: 0.4726 - val_accuracy: 0.8186
Epoch 74/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4754 - accuracy: 0.8169 - val_loss: 0.4723 - val_accuracy: 0.8186
Epoch 75/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4754 - accuracy: 0.8169 - val_loss: 0.4723 - val_accuracy: 0.8186
Epoch 76/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4753 - accuracy: 0.8169 - val_loss: 0.4722 - val_accuracy: 0.8186
Epoch 77/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4754 - accuracy: 0.8169 - val_loss: 0.4722 - val_accuracy: 0.8186
Epoch 78/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4753 - accuracy: 0.8169 - val_loss: 0.4723 - val_accuracy: 0.8186
Epoch 79/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4753 - accuracy: 0.8169 - val_loss: 0.4725 - val_accuracy: 0.8186
Epoch 80/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4754 - accuracy: 0.8169 - val_loss: 0.4722 - val_accuracy: 0.8186
Epoch 81/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4754 - accuracy: 0.8169 - val_loss: 0.4723 - val_accuracy: 0.8186
Epoch 82/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4753 - accuracy: 0.8169 - val_loss: 0.4722 - val_accuracy: 0.8186
Epoch 83/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4753 - accuracy: 0.8169 - val_loss: 0.4722 - val_accuracy: 0.8186
Epoch 84/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4752 - accuracy: 0.8169 - val_loss: 0.4732 - val_accuracy: 0.8186
Epoch 85/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4753 - accuracy: 0.8169 - val_loss: 0.4721 - val_accuracy: 0.8186
Epoch 86/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4753 - accuracy: 0.8169 - val_loss: 0.4721 - val_accuracy: 0.8186
Epoch 87/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4752 - accuracy: 0.8169 - val_loss: 0.4721 - val_accuracy: 0.8186
Epoch 88/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4752 - accuracy: 0.8169 - val_loss: 0.4723 - val_accuracy: 0.8186
Epoch 89/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4753 - accuracy: 0.8169 - val_loss: 0.4725 - val_accuracy: 0.8186
Epoch 90/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4753 - accuracy: 0.8169 - val_loss: 0.4720 - val_accuracy: 0.8186
Epoch 91/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4753 - accuracy: 0.8169 - val_loss: 0.4721 - val_accuracy: 0.8186
Epoch 92/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4753 - accuracy: 0.8169 - val_loss: 0.4720 - val_accuracy: 0.8186
Epoch 93/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4753 - accuracy: 0.8169 - val_loss: 0.4721 - val_accuracy: 0.8186
Epoch 94/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4752 - accuracy: 0.8169 - val_loss: 0.4725 - val_accuracy: 0.8186
Epoch 95/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4752 - accuracy: 0.8169 - val_loss: 0.4723 - val_accuracy: 0.8186
Epoch 96/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4752 - accuracy: 0.8169 - val_loss: 0.4721 - val_accuracy: 0.8186
Epoch 97/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4752 - accuracy: 0.8169 - val_loss: 0.4719 - val_accuracy: 0.8186
Epoch 98/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4752 - accuracy: 0.8169 - val_loss: 0.4720 - val_accuracy: 0.8186
Epoch 99/100
1213/1213 [==============================] - 4s 3ms/step - loss: 0.4752 - accuracy: 0.8169 - val_loss: 0.4719 - val_accuracy: 0.8186
Epoch 100/100
1213/1213 [==============================] - 3s 3ms/step - loss: 0.4752 - accuracy: 0.8169 - val_loss: 0.4719 - val_accuracy: 0.8186
193/193 [==============================] - 0s 2ms/step - loss: 0.4855 - accuracy: 0.8097
Model: "sequential_3"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_6 (Dense) (None, 25) 550
_________________________________________________________________
dense_7 (Dense) (None, 15) 390
_________________________________________________________________
dense_8 (Dense) (None, 1) 16
=================================================================
Total params: 956
Trainable params: 956
Non-trainable params: 0
_________________________________________________________________
None
Accuracy: 80.97