diff options
Diffstat (limited to 'transformer_shortest_paths.ipynb')
| -rw-r--r-- | transformer_shortest_paths.ipynb | 1445 |
1 files changed, 836 insertions, 609 deletions
diff --git a/transformer_shortest_paths.ipynb b/transformer_shortest_paths.ipynb index 3949fd5..c9ff777 100644 --- a/transformer_shortest_paths.ipynb +++ b/transformer_shortest_paths.ipynb @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 3, "execution_state": "idle", "metadata": { "colab": { @@ -391,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "execution_state": "idle", "metadata": { "id": "tLOWhg_CeWzH" @@ -432,7 +432,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "execution_state": "idle", "metadata": { "colab": { @@ -446,8 +446,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Training data: 1048M\n", - "Trainable parameters in the model: 200K\n" + "Training data: 104M\n", + "Trainable parameters in the model: 200545\n" ] } ], @@ -455,7 +455,7 @@ "# PARAMS\n", "VOCAB_SIZE = 1 + MAX_VTXS + 1 # pad plus max number of vertices plus target token\n", "MODEL_DIM = 64 # Dimension of model (embedding and transformer)\n", - "NEPOCHS = 1000\n", + "NEPOCHS = 100\n", "BSZ = 2**17 # Batch size\n", "BPE = 8 # Batches per epoch\n", "NHEADS = 2\n", @@ -469,9 +469,13 @@ "\n", "trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", "print(f\"Training data: {NEPOCHS*BPE*BSZ//10**6}M\")\n", - "print(f\"Trainable parameters in the model: {trainable_params//1000}K\")\n", + "print(f\"Trainable parameters in the model: {trainable_params}\")\n", "\n", "train_err = []\n", + "len1 = []\n", + "len2 = []\n", + "len3 = []\n", + "len15 = []\n", "epoch = 0\n", "\n", "# clear loss file\n", @@ -495,17 +499,17 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "execution_state": "idle", "metadata": {}, "outputs": [], "source": [ - "model = TransformerModel(input_dim=VOCAB_SIZE, model_dim=MODEL_DIM,\n", - " output_dim=1, num_heads=NHEADS,\n", - " num_layers=NLAYERS, seq_len=SEQ_LEN,\n", - " dropout=DROPOUT).to(device)\n", - "model = torch.compile(model)\n", - "model.load_state_dict(torch.load('model.pth', weights_only=True))\n", + "# model = TransformerModel(input_dim=VOCAB_SIZE, model_dim=MODEL_DIM,\n", + "# output_dim=1, num_heads=NHEADS,\n", + "# num_layers=NLAYERS, seq_len=SEQ_LEN,\n", + "# dropout=DROPOUT).to(device)\n", + "# model = torch.compile(model)\n", + "# model.load_state_dict(torch.load('model.pth', weights_only=True))\n", "\n", "LR = 8e-4\n", "WD = 0 # 1e-5\n", @@ -526,7 +530,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "execution_state": "idle", "metadata": {}, "outputs": [], @@ -553,8 +557,8 @@ }, { "cell_type": "code", - "execution_count": null, - "execution_state": "running", + "execution_count": 12, + "execution_state": "idle", "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -570,298 +574,768 @@ "text": [ "/home/sipb/.venv/lib64/python3.12/site-packages/torch/nn/functional.py:6278: UserWarning: Memory Efficient attention on Navi31 GPU is still experimental. Enable it with TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1. (Triggered internally at ../aten/src/ATen/native/transformers/hip/sdp_utils.cpp:269.)\n", " attn_output = scaled_dot_product_attention(\n", - "/home/sipb/.venv/lib64/python3.12/site-packages/torch/_inductor/compile_fx.py:167: UserWarning: TensorFloat32 tensor cores for float32 matrix multiplication available but not enabled. Consider setting `torch.set_float32_matmul_precision('high')` for better performance.\n", - " warnings.warn(\n", - "/tmp/torchinductor_sipb/nj/cnjfg6sudczhbwjig6u6ixumyik7x7ugjn4x43lbushjy4vv4pwz.py:883: UserWarning: Attempting to use hipBLASLt on an unsupported architecture! Overriding blas backend to hipblas (Triggered internally at ../aten/src/ATen/Context.cpp:296.)\n", - " extern_kernels.mm(reinterpret_tensor(buf1, (1048576, 64), (64, 1), 0), reinterpret_tensor(primals_5, (64, 192), (1, 64), 0), out=buf2)\n" + "/tmp/torchinductor_sipb/bn/cbngaobakjqlwlijvkqph5lgddb2z2kzjaln3b2g2j75b6snskdn.py:859: UserWarning: Attempting to use hipBLASLt on an unsupported architecture! Overriding blas backend to hipblas (Triggered internally at ../aten/src/ATen/Context.cpp:296.)\n", + " extern_kernels.mm(reinterpret_tensor(buf1, (2097152, 64), (64, 1), 0), reinterpret_tensor(primals_5, (64, 192), (1, 64), 0), out=buf2)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 0/1000 \t Train Err: 85.0000\n", - "Epoch 0/1000 \t Train Err: 72.0000\n", - "Epoch 0/1000 \t Train Err: 63.5000\n", - "Epoch 0/1000 \t Train Err: 58.0000\n", - "Epoch 0/1000 \t Train Err: 53.7500\n", - "Epoch 0/1000 \t Train Err: 51.0000\n", - "Epoch 0/1000 \t Train Err: 49.2500\n", - "Epoch 0/1000 \t Train Err: 48.0000\n", - "Epoch 0/1000 \t Train Err: 47.2500\n", - "Epoch 0/1000 \t Train Err: 46.2500\n", - "Epoch 0/1000 \t Train Err: 45.5000\n", - "Epoch 0/1000 \t Train Err: 45.2500\n", - "Epoch 0/1000 \t Train Err: 44.5000\n", - "Epoch 0/1000 \t Train Err: 44.2500\n", - "Epoch 0/1000 \t Train Err: 44.2500\n", - "Epoch 0/1000 \t Train Err: 44.2500\n", - "Epoch 1/1000 \t Train 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Train Err: 24.7500\n", - "Epoch 17/1000 \t Train Err: 24.1250\n", - "Epoch 17/1000 \t Train Err: 23.8750\n", - "Epoch 17/1000 \t Train Err: 23.7500\n", - "Epoch 17/1000 \t Train Err: 23.5000\n", - "Epoch 17/1000 \t Train Err: 23.1250\n", - "Epoch 17/1000 \t Train Err: 22.8750\n" + "Epoch 0/100 \t Train Err: 87.5000 0.62109375 3.28125 8.125 222.0\n", + "Epoch 0/100 \t Train Err: 70.5000 0.5078125 0.173828125 1.953125 182.0\n", + "Epoch 0/100 \t Train Err: 59.7500 2.828125 0.4140625 0.134765625 154.0\n", + "Epoch 0/100 \t Train Err: 54.0000 5.5 1.734375 0.1279296875 137.0\n", + "Epoch 0/100 \t Train Err: 50.7500 7.9375 3.21875 0.6953125 126.0\n", + "Epoch 0/100 \t Train Err: 48.5000 10.0 4.625 1.40625 118.0\n", + "Epoch 0/100 \t Train Err: 46.7500 11.75 5.84375 2.109375 111.5\n", + "Epoch 0/100 \t Train Err: 45.7500 13.125 6.90625 2.75 107.5\n", + "Epoch 1/100 \t Train Err: 44.7500 14.25 7.75 3.28125 104.0\n", + "Epoch 1/100 \t Train Err: 44.5000 15.1875 8.4375 3.71875 102.0\n", + "Epoch 1/100 \t Train Err: 44.2500 15.875 9.0 4.09375 100.0\n", + "Epoch 1/100 \t Train Err: 43.7500 16.5 9.4375 4.34375 98.5\n", + "Epoch 1/100 \t Train Err: 43.7500 16.875 9.8125 4.59375 97.5\n", + "Epoch 1/100 \t Train Err: 43.5000 17.25 10.1875 4.8125 96.5\n", + "Epoch 1/100 \t Train Err: 43.2500 17.625 10.4375 5.0 95.0\n", + "Epoch 1/100 \t Train Err: 43.2500 18.0 10.6875 5.1875 95.0\n", + "Epoch 2/100 \t Train Err: 43.0000 18.5 11.0 5.34375 94.0\n", + "Epoch 2/100 \t Train Err: 42.5000 18.75 11.25 5.5625 92.5\n", + "Epoch 2/100 \t Train Err: 42.7500 19.125 11.5625 5.75 92.5\n", + "Epoch 2/100 \t Train Err: 42.5000 19.5 11.8125 5.9375 91.5\n", + "Epoch 2/100 \t Train Err: 42.0000 19.875 12.1875 6.1875 90.0\n", + "Epoch 2/100 \t Train Err: 42.2500 20.25 12.5 6.40625 90.0\n", + "Epoch 2/100 \t Train Err: 42.0000 20.625 12.6875 6.59375 89.0\n", + "Epoch 2/100 \t Train Err: 41.7500 21.0 13.0625 6.84375 88.0\n", + "Epoch 3/100 \t Train Err: 42.2500 21.375 13.375 7.0625 88.0\n", + "Epoch 3/100 \t Train Err: 41.7500 21.75 13.6875 7.28125 86.0\n", + "Epoch 3/100 \t Train Err: 41.5000 22.125 14.0 7.5625 85.5\n", + "Epoch 3/100 \t Train Err: 41.7500 22.5 14.3125 7.75 85.5\n", + "Epoch 3/100 \t Train Err: 41.2500 22.875 14.5625 7.9375 84.5\n", + "Epoch 3/100 \t Train Err: 41.2500 23.25 14.875 8.1875 83.5\n", + "Epoch 3/100 \t Train Err: 41.5000 23.5 15.1875 8.4375 83.5\n", + "Epoch 3/100 \t Train Err: 41.2500 23.75 15.4375 8.625 82.0\n", + "Epoch 4/100 \t Train Err: 41.0000 24.125 15.75 8.8125 81.0\n", + "Epoch 4/100 \t Train Err: 40.7500 24.375 16.0 9.0625 81.0\n", + "Epoch 4/100 \t Train Err: 40.7500 24.5 16.25 9.25 80.5\n", + "Epoch 4/100 \t Train Err: 40.7500 24.625 16.5 9.4375 79.5\n", + "Epoch 4/100 \t Train Err: 40.5000 24.75 16.75 9.625 79.0\n", + "Epoch 4/100 \t Train Err: 40.5000 24.625 16.875 9.75 79.0\n", + "Epoch 4/100 \t Train Err: 40.2500 24.375 17.125 9.875 78.5\n", + "Epoch 4/100 \t Train Err: 40.0000 23.75 17.125 10.0 78.0\n", + "Epoch 5/100 \t Train Err: 39.7500 23.0 17.125 10.0625 77.5\n", + "Epoch 5/100 \t Train Err: 39.5000 21.5 17.0 10.0 78.0\n", + "Epoch 5/100 \t Train Err: 38.7500 19.375 16.75 9.875 78.0\n", + "Epoch 5/100 \t Train Err: 38.5000 16.5 16.25 9.6875 78.5\n", + "Epoch 5/100 \t Train Err: 37.5000 12.9375 15.625 9.375 79.0\n", + "Epoch 5/100 \t Train Err: 36.5000 8.875 14.9375 9.125 80.0\n", + "Epoch 5/100 \t Train Err: 35.5000 5.09375 14.6875 9.25 79.5\n", + "Epoch 5/100 \t Train Err: 34.5000 2.390625 15.5 10.0 78.0\n", + "Epoch 6/100 \t Train Err: 33.5000 0.9140625 17.5 11.3125 75.0\n", + "Epoch 6/100 \t Train Err: 33.0000 0.38671875 19.875 12.4375 72.5\n", + "Epoch 6/100 \t Train Err: 32.7500 0.4921875 21.0 12.9375 71.5\n", + "Epoch 6/100 \t Train Err: 33.0000 0.85546875 21.375 13.0 71.0\n", + "Epoch 6/100 \t Train Err: 33.0000 1.1328125 21.5 13.125 70.5\n", + "Epoch 6/100 \t Train Err: 32.7500 1.1875 21.875 13.4375 69.5\n", + "Epoch 6/100 \t Train Err: 32.5000 1.0234375 22.5 13.9375 68.5\n", + "Epoch 6/100 \t Train Err: 32.2500 0.73828125 23.125 14.5 67.5\n", + "Epoch 7/100 \t Train Err: 31.8750 0.451171875 23.875 15.0625 66.0\n", + "Epoch 7/100 \t Train Err: 31.6250 0.251953125 24.625 15.625 64.5\n", + "Epoch 7/100 \t Train Err: 31.5000 0.2060546875 25.25 16.125 63.75\n", + "Epoch 7/100 \t Train Err: 31.2500 0.2734375 25.625 16.5 63.0\n", + "Epoch 7/100 \t Train Err: 31.1250 0.37109375 26.125 17.0 62.25\n", + "Epoch 7/100 \t Train Err: 30.8750 0.400390625 26.625 17.25 61.5\n", + "Epoch 7/100 \t Train Err: 30.8750 0.353515625 26.875 17.5 61.0\n", + "Epoch 7/100 \t Train Err: 30.7500 0.275390625 27.25 17.75 60.5\n", + "Epoch 8/100 \t Train Err: 30.6250 0.18359375 27.625 18.125 59.75\n", + "Epoch 8/100 \t Train Err: 30.5000 0.10986328125 28.125 18.625 59.0\n", + "Epoch 8/100 \t Train Err: 30.3750 0.06640625 28.625 19.0 58.5\n", + "Epoch 8/100 \t Train Err: 30.3750 0.04931640625 29.125 19.375 57.75\n", + "Epoch 8/100 \t Train Err: 30.1250 0.048583984375 29.75 19.875 57.0\n", + "Epoch 8/100 \t Train Err: 30.0000 0.054443359375 30.25 20.25 56.0\n", + "Epoch 8/100 \t Train Err: 29.8750 0.0576171875 30.875 20.875 55.25\n", + "Epoch 8/100 \t Train Err: 29.8750 0.056884765625 31.5 21.25 54.5\n", + "Epoch 9/100 \t Train Err: 29.7500 0.051025390625 32.0 21.75 53.75\n", + "Epoch 9/100 \t Train Err: 29.5000 0.04296875 32.75 22.25 53.0\n", + "Epoch 9/100 \t Train Err: 29.5000 0.03369140625 33.0 22.625 52.25\n", + "Epoch 9/100 \t Train Err: 29.5000 0.0260009765625 33.75 23.125 51.75\n", + "Epoch 9/100 \t Train Err: 29.3750 0.02197265625 34.25 23.5 51.25\n", + "Epoch 9/100 \t Train Err: 29.3750 0.0216064453125 35.0 24.125 50.25\n", + "Epoch 9/100 \t Train Err: 29.2500 0.0238037109375 35.25 24.375 50.0\n", + "Epoch 9/100 \t Train Err: 29.1250 0.02734375 35.75 24.75 49.5\n", + "Epoch 10/100 \t Train Err: 29.1250 0.0301513671875 36.0 25.0 49.0\n", + "Epoch 10/100 \t Train Err: 29.1250 0.032470703125 36.75 25.625 48.25\n", + "Epoch 10/100 \t Train Err: 29.0000 0.03271484375 37.25 26.125 47.5\n", + "Epoch 10/100 \t Train Err: 28.8750 0.03125 37.5 26.25 47.25\n", + "Epoch 10/100 \t Train Err: 29.0000 0.027587890625 38.0 26.75 46.5\n", + "Epoch 10/100 \t Train Err: 28.8750 0.023193359375 38.25 26.875 46.5\n", + "Epoch 10/100 \t Train Err: 28.8750 0.0196533203125 38.25 26.875 46.5\n", + "Epoch 10/100 \t Train Err: 28.7500 0.0172119140625 38.75 27.375 45.75\n", + "Epoch 11/100 \t Train Err: 28.7500 0.0166015625 39.0 27.5 45.5\n", + "Epoch 11/100 \t Train Err: 28.8750 0.0169677734375 39.0 27.5 45.5\n", + "Epoch 11/100 \t Train Err: 28.7500 0.0172119140625 39.0 27.5 45.5\n", + "Epoch 11/100 \t Train Err: 28.7500 0.017578125 39.75 28.25 44.75\n", + "Epoch 11/100 \t Train Err: 28.7500 0.017578125 39.75 28.25 44.75\n", + "Epoch 11/100 \t Train Err: 28.7500 0.017333984375 39.75 28.25 44.75\n", + "Epoch 11/100 \t Train Err: 28.7500 0.016845703125 39.75 28.25 44.75\n", + "Epoch 11/100 \t Train Err: 28.7500 0.016357421875 39.75 28.25 44.75\n", + "Epoch 12/100 \t Train Err: 28.7500 0.015869140625 40.0 28.5 44.25\n", + "Epoch 12/100 \t Train Err: 28.7500 0.01513671875 40.75 28.875 44.0\n", + "Epoch 12/100 \t Train Err: 28.7500 0.01483154296875 40.75 28.875 44.0\n", + "Epoch 12/100 \t Train Err: 28.8750 0.01416015625 40.75 28.875 44.0\n", + "Epoch 12/100 \t Train Err: 28.7500 0.0140380859375 40.75 28.875 44.0\n", + "Epoch 12/100 \t Train Err: 28.7500 0.01397705078125 40.75 28.875 44.0\n", + "Epoch 12/100 \t Train Err: 28.7500 0.0140380859375 40.75 28.875 44.0\n", + "Epoch 12/100 \t Train Err: 28.6250 0.01422119140625 40.75 29.0 43.75\n", + "Epoch 13/100 \t Train Err: 28.6250 0.01422119140625 41.0 29.375 43.25\n", + "Epoch 13/100 \t Train Err: 28.7500 0.01416015625 41.5 29.5 43.0\n", + "Epoch 13/100 \t Train Err: 28.7500 0.0142822265625 41.5 29.625 43.0\n", + "Epoch 13/100 \t Train Err: 28.6250 0.01446533203125 41.5 29.625 43.0\n", + "Epoch 13/100 \t Train Err: 28.6250 0.01422119140625 41.5 29.625 43.0\n", + "Epoch 13/100 \t Train Err: 28.6250 0.013916015625 41.5 29.625 43.0\n", + "Epoch 13/100 \t Train Err: 28.6250 0.01373291015625 41.5 29.625 43.0\n", + "Epoch 13/100 \t Train Err: 28.6250 0.0135498046875 41.5 29.625 43.0\n", + "Epoch 14/100 \t Train Err: 28.6250 0.01318359375 41.5 29.625 43.0\n", + "Epoch 14/100 \t Train Err: 28.5000 0.012939453125 41.5 29.625 42.75\n", + "Epoch 14/100 \t Train Err: 28.6250 0.01275634765625 41.75 29.875 42.5\n", + "Epoch 14/100 \t Train Err: 28.6250 0.012451171875 42.0 30.125 42.5\n", + "Epoch 14/100 \t Train Err: 28.6250 0.01220703125 42.25 30.25 42.25\n", + "Epoch 14/100 \t Train Err: 28.6250 0.01226806640625 42.25 30.25 42.25\n", + "Epoch 14/100 \t Train Err: 28.6250 0.01190185546875 42.25 30.25 42.25\n", + "Epoch 14/100 \t Train Err: 28.6250 0.01190185546875 42.25 30.25 42.25\n", + "Epoch 15/100 \t Train Err: 28.7500 0.0118408203125 42.25 30.25 42.25\n", + "Epoch 15/100 \t Train Err: 28.6250 0.0115966796875 42.25 30.25 42.25\n", + "Epoch 15/100 \t Train Err: 28.6250 0.0115966796875 42.25 30.25 42.25\n", + "Epoch 15/100 \t Train Err: 28.6250 0.01141357421875 42.25 30.25 42.25\n", + "Epoch 15/100 \t Train Err: 28.6250 0.011474609375 42.25 30.25 42.0\n", + "Epoch 15/100 \t Train Err: 28.6250 0.01123046875 42.25 30.375 42.0\n", + "Epoch 15/100 \t Train Err: 28.5000 0.0111083984375 42.5 30.625 41.75\n", + "Epoch 15/100 \t Train Err: 28.6250 0.010986328125 42.5 30.75 41.75\n", + "Epoch 16/100 \t Train Err: 28.6250 0.01104736328125 42.75 30.875 41.5\n", + "Epoch 16/100 \t Train Err: 28.6250 0.01092529296875 42.75 30.875 41.5\n", + "Epoch 16/100 \t Train Err: 28.6250 0.0107421875 43.0 31.0 41.5\n", + "Epoch 16/100 \t Train Err: 28.6250 0.0107421875 43.0 31.0 41.5\n", + "Epoch 16/100 \t Train Err: 28.6250 0.01068115234375 43.0 31.0 41.5\n", + "Epoch 16/100 \t Train Err: 28.6250 0.01043701171875 43.0 31.0 41.5\n", + "Epoch 16/100 \t Train Err: 28.6250 0.0103759765625 43.0 31.0 41.5\n", + "Epoch 16/100 \t Train Err: 28.6250 0.01025390625 43.0 31.0 41.5\n", + "Epoch 17/100 \t Train Err: 28.6250 0.0101318359375 43.0 31.0 41.5\n", + "Epoch 17/100 \t Train Err: 28.6250 0.0098876953125 43.0 31.0 41.5\n", + "Epoch 17/100 \t Train Err: 28.6250 0.00982666015625 43.0 31.0 41.5\n", + "Epoch 17/100 \t Train Err: 28.6250 0.009765625 43.0 31.0 41.5\n", + "Epoch 17/100 \t Train Err: 28.6250 0.00958251953125 43.0 31.0 41.5\n", + "Epoch 17/100 \t Train Err: 28.6250 0.00946044921875 43.0 31.0 41.5\n", + "Epoch 17/100 \t Train Err: 28.5000 0.0093994140625 43.0 31.0 41.5\n", + "Epoch 17/100 \t Train Err: 28.6250 0.0091552734375 43.0 31.0 41.5\n", + "Epoch 18/100 \t Train Err: 28.6250 0.00897216796875 43.0 31.0 41.25\n", + "Epoch 18/100 \t Train Err: 28.5000 0.0089111328125 43.0 31.0 41.25\n", + "Epoch 18/100 \t Train Err: 28.3750 0.00885009765625 43.0 31.0 41.25\n", + "Epoch 18/100 \t Train Err: 28.3750 0.0087890625 43.0 31.125 41.25\n", + "Epoch 18/100 \t Train Err: 28.6250 0.0086669921875 43.0 31.125 41.25\n", + "Epoch 18/100 \t Train Err: 28.5000 0.008544921875 43.0 31.125 41.25\n", + "Epoch 18/100 \t Train Err: 28.5000 0.00836181640625 43.0 31.125 41.25\n", + "Epoch 18/100 \t Train Err: 28.5000 0.0081787109375 43.0 31.125 41.25\n", + "Epoch 19/100 \t Train Err: 28.3750 0.0079345703125 43.0 31.125 41.25\n", + "Epoch 19/100 \t Train Err: 28.5000 0.0078125 43.0 31.125 41.25\n", + "Epoch 19/100 \t Train Err: 28.5000 0.007781982421875 43.0 31.0 41.25\n", + "Epoch 19/100 \t Train Err: 28.5000 0.00750732421875 43.0 31.0 41.25\n", + "Epoch 19/100 \t Train Err: 28.5000 0.00738525390625 42.75 30.875 41.25\n", + "Epoch 19/100 \t Train Err: 28.5000 0.00714111328125 42.5 30.75 41.5\n", + "Epoch 19/100 \t Train Err: 28.3750 0.006866455078125 42.25 30.5 41.5\n", + "Epoch 19/100 \t Train Err: 28.3750 0.0067138671875 41.75 30.125 42.0\n", + "Epoch 20/100 \t Train Err: 28.2500 0.006591796875 40.5 29.25 42.5\n", + "Epoch 20/100 \t Train Err: 28.1250 0.00634765625 37.5 27.125 44.5\n", + "Epoch 20/100 \t Train Err: 27.8750 0.0067138671875 27.75 19.875 52.0\n", + "Epoch 20/100 \t Train Err: 27.8750 0.0040283203125 25.875 18.5 53.5\n", + "Epoch 20/100 \t Train Err: 27.7500 0.011962890625 34.0 24.5 46.5\n", + "Epoch 20/100 \t Train Err: 27.8750 0.0240478515625 36.5 26.125 44.75\n", + "Epoch 20/100 \t Train Err: 27.6250 0.0267333984375 35.5 25.5 45.0\n", + "Epoch 20/100 \t Train Err: 27.2500 0.016357421875 30.125 21.5 48.5\n", + "Epoch 21/100 \t Train Err: 27.5000 0.005279541015625 19.5 13.5 57.5\n", + "Epoch 21/100 \t Train Err: 26.8750 0.00982666015625 28.875 20.875 48.25\n", + "Epoch 21/100 \t Train Err: 26.7500 0.01019287109375 32.5 23.875 45.0\n", + "Epoch 21/100 \t Train Err: 26.5000 0.0057373046875 27.75 20.625 47.5\n", + "Epoch 21/100 \t Train Err: 26.5000 0.0111083984375 14.0 10.375 58.5\n", + "Epoch 21/100 \t Train Err: 25.7500 0.007110595703125 27.625 21.875 45.0\n", + "Epoch 21/100 \t Train Err: 25.3750 0.0081787109375 27.625 22.25 44.25\n", + "Epoch 21/100 \t Train Err: 24.7500 0.0101318359375 11.4375 9.5 55.5\n", + "Epoch 22/100 \t Train Err: 23.7500 0.0091552734375 14.8125 12.625 50.0\n", + "Epoch 22/100 \t Train Err: 23.3750 0.0196533203125 18.5 16.5 45.5\n", + "Epoch 22/100 \t Train Err: 22.8750 0.0205078125 9.5625 8.25 52.0\n", + "Epoch 22/100 \t Train Err: 22.3750 0.045654296875 9.1875 7.90625 50.75\n", + "Epoch 22/100 \t Train Err: 22.2500 0.1318359375 15.375 13.8125 45.0\n", + "Epoch 22/100 \t Train Err: 21.6250 0.150390625 11.4375 9.5625 47.25\n", + "Epoch 22/100 \t Train Err: 21.3750 0.126953125 8.4375 6.34375 49.75\n", + "Epoch 22/100 \t Train Err: 20.8750 0.1455078125 11.0625 8.75 46.0\n", + "Epoch 23/100 \t Train Err: 20.6250 0.125 13.6875 11.4375 43.0\n", + "Epoch 23/100 \t Train Err: 20.3750 0.04931640625 11.625 9.625 44.0\n", + "Epoch 23/100 \t Train Err: 20.0000 0.033935546875 9.3125 7.6875 45.25\n", + "Epoch 23/100 \t Train Err: 19.6250 0.07275390625 10.0625 8.875 43.25\n", + "Epoch 23/100 \t Train Err: 19.5000 0.1181640625 11.5625 10.9375 41.0\n", + "Epoch 23/100 \t Train Err: 19.0000 0.1787109375 11.0 11.1875 40.25\n", + "Epoch 23/100 \t Train Err: 18.7500 0.25 8.1875 8.9375 41.75\n", + "Epoch 23/100 \t Train Err: 18.5000 0.2216796875 8.1875 9.9375 40.25\n", + "Epoch 24/100 \t Train Err: 18.1250 0.1513671875 10.0625 13.4375 37.0\n", + "Epoch 24/100 \t Train Err: 17.6250 0.12890625 7.6875 11.6875 37.75\n", + "Epoch 24/100 \t Train Err: 17.3750 0.1201171875 6.28125 10.875 38.0\n", + "Epoch 24/100 \t Train Err: 17.0000 0.126953125 7.53125 14.5625 35.0\n", + "Epoch 24/100 \t Train Err: 16.7500 0.11181640625 7.3125 15.6875 33.75\n", + "Epoch 24/100 \t Train Err: 16.5000 0.08203125 4.75 13.3125 35.5\n", + "Epoch 24/100 \t Train Err: 16.3750 0.068359375 5.75 17.125 32.75\n", + "Epoch 24/100 \t Train Err: 15.9375 0.057861328125 6.34375 19.25 30.75\n", + "Epoch 25/100 \t Train Err: 15.7500 0.051025390625 3.578125 14.5 33.5\n", + "Epoch 25/100 \t Train Err: 15.2500 0.04248046875 5.0 18.625 29.625\n", + "Epoch 25/100 \t Train Err: 15.0000 0.040771484375 5.53125 21.125 27.875\n", + "Epoch 25/100 \t Train Err: 14.8125 0.033935546875 3.171875 16.0 30.375\n", + "Epoch 25/100 \t Train Err: 14.6250 0.0322265625 3.734375 18.875 28.5\n", + "Epoch 25/100 \t Train Err: 14.3750 0.03369140625 5.09375 23.0 25.5\n", + "Epoch 25/100 \t Train Err: 14.1250 0.028076171875 2.046875 14.3125 30.125\n", + "Epoch 25/100 \t Train Err: 13.8125 0.023681640625 3.234375 19.375 26.625\n", + "Epoch 26/100 \t Train Err: 13.6875 0.023681640625 4.75 24.875 23.125\n", + "Epoch 26/100 \t Train Err: 13.5625 0.0245361328125 1.515625 13.6875 29.0\n", + "Epoch 26/100 \t Train Err: 13.0625 0.0179443359375 2.875 20.5 24.25\n", + "Epoch 26/100 \t Train Err: 13.0000 0.016845703125 3.5 24.0 22.25\n", + "Epoch 26/100 \t Train Err: 12.8750 0.02197265625 1.46875 15.625 26.5\n", + "Epoch 26/100 \t Train Err: 12.5000 0.0174560546875 2.03125 19.5 23.5\n", + "Epoch 26/100 \t Train Err: 12.4375 0.014404296875 3.0 24.75 20.625\n", + "Epoch 26/100 \t Train Err: 12.1250 0.0230712890625 1.46875 17.625 23.875\n", + "Epoch 27/100 \t Train Err: 11.9375 0.022705078125 1.421875 17.75 23.125\n", + "Epoch 27/100 \t Train Err: 11.7500 0.0150146484375 2.09375 22.625 20.0\n", + "Epoch 27/100 \t Train Err: 11.6250 0.01531982421875 1.6796875 20.875 20.75\n", + "Epoch 27/100 \t Train Err: 11.3750 0.0177001953125 1.0546875 17.25 22.0\n", + "Epoch 27/100 \t Train Err: 11.0625 0.0128173828125 1.359375 20.375 19.875\n", + "Epoch 27/100 \t Train Err: 11.0000 0.0128173828125 1.5078125 22.0 18.875\n", + "Epoch 27/100 \t Train Err: 10.8125 0.01190185546875 1.03125 18.125 20.125\n", + "Epoch 27/100 \t Train Err: 10.7500 0.01165771484375 0.99609375 18.125 20.25\n", + "Epoch 28/100 \t Train Err: 10.5625 0.012451171875 1.328125 21.125 18.125\n", + "Epoch 28/100 \t Train Err: 10.3750 0.01104736328125 1.15625 19.375 18.625\n", + "Epoch 28/100 \t Train Err: 10.3125 0.01025390625 0.953125 17.25 19.5\n", + "Epoch 28/100 \t Train Err: 10.1250 0.010498046875 1.171875 19.875 17.75\n", + "Epoch 28/100 \t Train Err: 10.0625 0.0101318359375 1.109375 20.0 17.5\n", + "Epoch 28/100 \t Train Err: 10.0000 0.0111083984375 0.7578125 16.75 18.875\n", + "Epoch 28/100 \t Train Err: 9.8125 0.0093994140625 0.87109375 18.375 17.5\n", + "Epoch 28/100 \t Train Err: 9.7500 0.01043701171875 1.0390625 20.625 16.375\n", + "Epoch 29/100 \t Train Err: 9.4375 0.00921630859375 0.828125 18.5 16.75\n", + "Epoch 29/100 \t Train Err: 9.5000 0.00836181640625 0.59375 16.5 17.75\n", + "Epoch 29/100 \t Train Err: 9.4375 0.0115966796875 0.796875 20.375 15.8125\n", + "Epoch 29/100 \t Train Err: 9.3125 0.010986328125 0.72265625 20.25 15.625\n", + "Epoch 29/100 \t Train Err: 9.1875 0.00762939453125 0.51953125 16.75 16.875\n", + "Epoch 29/100 \t Train Err: 9.0625 0.00799560546875 0.56640625 18.375 15.8125\n", + "Epoch 29/100 \t Train Err: 9.0625 0.00946044921875 0.66796875 20.625 14.625\n", + "Epoch 29/100 \t Train Err: 9.0000 0.00665283203125 0.46484375 16.125 16.5\n", + "Epoch 30/100 \t Train Err: 8.8750 0.008056640625 0.5234375 18.25 15.3125\n", + "Epoch 30/100 \t Train Err: 8.7500 0.0111083984375 0.59375 20.0 14.1875\n", + "Epoch 36/100 \t Train Err: 7.3750 0.00799560546875 0.302734375 13.5 12.9375\n", + "Epoch 36/100 \t Train Err: 7.2188 0.00799560546875 0.369140625 15.5625 11.375\n", + "Epoch 36/100 \t Train Err: 7.2188 0.00823974609375 0.4296875 17.375 10.375\n", + "Epoch 36/100 \t Train Err: 7.2500 0.00860595703125 0.412109375 18.0 10.125\n", + "Epoch 36/100 \t Train Err: 7.1875 0.01171875 0.33984375 15.625 11.1875\n", + "Epoch 36/100 \t Train Err: 7.0625 0.0177001953125 0.2890625 12.875 12.1875\n", + "Epoch 36/100 \t Train Err: 7.1562 0.01806640625 0.271484375 11.8125 13.0\n", + "Epoch 36/100 \t Train Err: 7.1875 0.0120849609375 0.24609375 11.5625 13.0625\n", + "Epoch 37/100 \t Train Err: 7.0625 0.007171630859375 0.2431640625 12.375 12.3125\n", + "Epoch 37/100 \t Train Err: 7.0625 0.0101318359375 0.2490234375 14.0 11.5\n", + "Epoch 37/100 \t Train Err: 7.0625 0.0181884765625 0.28125 15.4375 10.875\n", + "Epoch 37/100 \t Train Err: 7.0938 0.0244140625 0.287109375 15.8125 10.6875\n", + "Epoch 37/100 \t Train Err: 6.9375 0.0230712890625 0.27734375 15.1875 10.625\n", + "Epoch 37/100 \t Train Err: 6.8750 0.01556396484375 0.255859375 13.5625 11.4375\n", + "Epoch 37/100 \t Train Err: 6.9375 0.0091552734375 0.220703125 12.4375 12.0625\n", + "Epoch 37/100 \t Train Err: 6.9375 0.006011962890625 0.2158203125 12.4375 12.0625\n", + "Epoch 38/100 \t Train Err: 6.8438 0.004791259765625 0.232421875 12.9375 11.625\n", + "Epoch 38/100 \t Train Err: 6.8750 0.004486083984375 0.2421875 14.3125 10.875\n", + "Epoch 38/100 \t Train Err: 6.8438 0.00433349609375 0.28125 15.0625 10.1875\n", + "Epoch 38/100 \t Train Err: 6.9375 0.004241943359375 0.25 14.9375 10.4375\n", + "Epoch 38/100 \t Train Err: 6.7500 0.004241943359375 0.23828125 13.75 10.625\n", + "Epoch 38/100 \t Train Err: 6.7812 0.0042724609375 0.2109375 12.25 11.5625\n", + "Epoch 38/100 \t Train Err: 6.8438 0.003692626953125 0.1943359375 11.9375 11.875\n", + "Epoch 38/100 \t Train Err: 6.6875 0.004119873046875 0.197265625 11.5 11.5\n", + "Epoch 39/100 \t Train Err: 6.6562 0.007720947265625 0.193359375 12.1875 11.0625\n", + "Epoch 39/100 \t Train Err: 6.6250 0.01318359375 0.2080078125 13.25 10.4375\n", + "Epoch 39/100 \t Train Err: 6.6562 0.016357421875 0.224609375 13.9375 10.3125\n", + "Epoch 39/100 \t Train Err: 6.6562 0.0159912109375 0.2021484375 13.75 10.375\n", + "Epoch 39/100 \t Train Err: 6.5312 0.0126953125 0.19140625 12.9375 10.5\n", + "Epoch 39/100 \t Train Err: 6.5938 0.0081787109375 0.1796875 11.9375 11.0625\n", + "Epoch 39/100 \t Train Err: 6.6250 0.005401611328125 0.1796875 11.875 11.375\n", + "Epoch 39/100 \t Train Err: 6.5000 0.0040283203125 0.1787109375 12.125 10.9375\n", + "Epoch 40/100 \t Train Err: 6.5312 0.0031890869140625 0.1962890625 12.8125 10.5625\n", + "Epoch 40/100 \t Train Err: 6.5625 0.0029296875 0.2080078125 13.25 10.3125\n", + "Epoch 40/100 \t Train Err: 6.5625 0.0026702880859375 0.189453125 13.5 10.25\n", + "Epoch 40/100 \t Train Err: 6.5312 0.002685546875 0.177734375 12.5625 10.4375\n", + "Epoch 40/100 \t Train Err: 6.4375 0.0027008056640625 0.169921875 11.625 10.8125\n", + "Epoch 40/100 \t Train Err: 6.5000 0.0026092529296875 0.1630859375 11.6875 11.0625\n", + "Epoch 40/100 \t Train Err: 6.5000 0.0030670166015625 0.162109375 11.9375 10.875\n", + "Epoch 40/100 \t Train Err: 6.5000 0.004486083984375 0.1630859375 12.4375 10.5625\n", + "Epoch 41/100 \t Train Err: 6.4375 0.006011962890625 0.1875 13.3125 9.9375\n", + "Epoch 41/100 \t Train Err: 6.4688 0.005706787109375 0.1708984375 12.75 10.25\n", + "Epoch 41/100 \t Train Err: 6.4688 0.00445556640625 0.15234375 12.25 10.625\n", + "Epoch 41/100 \t Train Err: 6.4688 0.0032501220703125 0.166015625 11.8125 10.875\n", + "Epoch 41/100 \t Train Err: 6.3750 0.0027008056640625 0.166015625 12.0625 10.5\n", + "Epoch 41/100 \t Train Err: 6.3125 0.0023040771484375 0.158203125 12.0 10.25\n", + "Epoch 41/100 \t Train Err: 6.4062 0.002227783203125 0.1640625 12.4375 10.125\n", + "Epoch 41/100 \t Train Err: 6.3438 0.002227783203125 0.171875 12.6875 9.9375\n", + "Epoch 42/100 \t Train Err: 6.3125 0.002197265625 0.1591796875 12.0625 10.1875\n", + "Epoch 42/100 \t Train Err: 6.2500 0.0021209716796875 0.1513671875 11.4375 10.3125\n", + "Epoch 42/100 \t Train Err: 6.2812 0.0022430419921875 0.1396484375 11.5 10.5\n", + "Epoch 42/100 \t Train Err: 6.1875 0.002838134765625 0.146484375 11.8125 9.9375\n", + "Epoch 42/100 \t Train Err: 6.3125 0.0037078857421875 0.150390625 12.125 10.0625\n", + "Epoch 42/100 \t Train Err: 6.2812 0.004425048828125 0.1591796875 12.375 9.875\n", + "Epoch 42/100 \t Train Err: 6.2188 0.004150390625 0.1357421875 11.625 10.0625\n", + "Epoch 42/100 \t Train Err: 6.2188 0.0035858154296875 0.1416015625 11.4375 10.25\n", + "Epoch 43/100 \t Train Err: 6.2500 0.0028839111328125 0.1328125 11.1875 10.4375\n", + "Epoch 43/100 \t Train Err: 6.1562 0.0025482177734375 0.13671875 11.1875 10.125\n", + "Epoch 43/100 \t Train Err: 6.0938 0.002227783203125 0.142578125 11.625 9.75\n", + "Epoch 43/100 \t Train Err: 6.1875 0.002105712890625 0.1435546875 12.0625 9.8125\n", + "Epoch 43/100 \t Train Err: 6.2812 0.001983642578125 0.150390625 12.125 9.9375\n", + "Epoch 43/100 \t Train Err: 6.0938 0.0019683837890625 0.1396484375 11.5 9.8125\n", + "Epoch 43/100 \t Train Err: 6.2188 0.00191497802734375 0.1337890625 11.5 10.125\n", + "Epoch 43/100 \t Train Err: 6.0938 0.00201416015625 0.1337890625 11.4375 9.875\n", + "Epoch 44/100 \t Train Err: 6.0938 0.0023040771484375 0.140625 11.375 9.9375\n", + "Epoch 44/100 \t Train Err: 6.0938 0.002960205078125 0.1298828125 11.125 10.0\n", + "Epoch 44/100 \t Train Err: 6.1562 0.003662109375 0.1357421875 11.375 10.0\n", + "Epoch 44/100 \t Train Err: 6.0625 0.003997802734375 0.130859375 11.4375 9.75\n", + "Epoch 44/100 \t Train Err: 6.0312 0.003997802734375 0.134765625 11.4375 9.5625\n", + "Epoch 44/100 \t Train Err: 6.0000 0.003265380859375 0.1337890625 11.4375 9.625\n", + "Epoch 44/100 \t Train Err: 6.0000 0.0024871826171875 0.1337890625 11.5 9.625\n", + "Epoch 44/100 \t Train Err: 6.0312 0.0020904541015625 0.1376953125 11.0625 9.8125\n", + "Epoch 45/100 \t Train Err: 5.9688 0.0020294189453125 0.125 10.8125 9.6875\n", + "Epoch 45/100 \t Train Err: 6.0000 0.0019683837890625 0.1318359375 10.75 9.75\n", + "Epoch 45/100 \t Train Err: 6.0312 0.002044677734375 0.12890625 11.0625 9.6875\n", + "Epoch 45/100 \t Train Err: 5.9688 0.002197265625 0.12353515625 11.0625 9.6875\n", + "Epoch 45/100 \t Train Err: 5.8750 0.0026397705078125 0.1318359375 11.3125 9.375\n", + "Epoch 45/100 \t Train Err: 5.9375 0.003204345703125 0.1201171875 11.25 9.4375\n", + "Epoch 45/100 \t Train Err: 5.8125 0.003326416015625 0.115234375 11.0 9.375\n", + "Epoch 45/100 \t Train Err: 5.9062 0.0030975341796875 0.111328125 11.0 9.5625\n", + "Epoch 46/100 \t Train Err: 5.9062 0.0026702880859375 0.10498046875 10.5 9.75\n", + "Epoch 46/100 \t Train Err: 5.8125 0.0024566650390625 0.1044921875 10.375 9.625\n", + "Epoch 46/100 \t Train Err: 5.8438 0.0024566650390625 0.11474609375 10.875 9.5\n", + "Epoch 46/100 \t Train Err: 5.8438 0.0023956298828125 0.11962890625 11.375 9.1875\n", + "Epoch 46/100 \t Train Err: 5.7812 0.0023651123046875 0.12060546875 11.125 9.125\n", + "Epoch 46/100 \t Train Err: 5.9062 0.0023193359375 0.11767578125 10.875 9.5625\n", + "Epoch 46/100 \t Train Err: 5.7500 0.002349853515625 0.09912109375 10.25 9.5625\n", + "Epoch 46/100 \t Train Err: 5.7812 0.0024871826171875 0.10986328125 9.9375 9.8125\n", + "Epoch 47/100 \t Train Err: 5.7812 0.002960205078125 0.107421875 10.5 9.375\n", + "Epoch 47/100 \t Train Err: 5.7812 0.0032501220703125 0.1123046875 10.875 9.1875\n", + "Epoch 47/100 \t Train Err: 5.7188 0.0033111572265625 0.11767578125 10.5625 9.125\n", + "Epoch 47/100 \t Train Err: 5.7812 0.0030517578125 0.10986328125 10.4375 9.5\n", + "Epoch 47/100 \t Train Err: 5.6562 0.002899169921875 0.1181640625 10.625 9.0\n", + "Epoch 47/100 \t Train Err: 5.6562 0.0026702880859375 0.12109375 11.0 8.875\n", + "Epoch 47/100 \t Train Err: 5.7812 0.00262451171875 0.10302734375 10.4375 9.375\n", + "Epoch 47/100 \t Train Err: 5.7812 0.0026702880859375 0.1015625 9.8125 9.75\n", + "Epoch 48/100 \t Train Err: 5.7188 0.002655029296875 0.09814453125 9.9375 9.4375\n", + "Epoch 48/100 \t Train Err: 5.6562 0.003143310546875 0.111328125 10.75 8.875\n", + "Epoch 48/100 \t Train Err: 5.5625 0.00335693359375 0.111328125 10.6875 8.75\n", + "Epoch 48/100 \t Train Err: 5.5625 0.003326416015625 0.1044921875 9.8125 9.1875\n", + "Epoch 48/100 \t Train Err: 5.6562 0.003265380859375 0.099609375 10.125 9.25\n", + "Epoch 48/100 \t Train Err: 5.6875 0.0030670166015625 0.10888671875 10.875 8.875\n", + "Epoch 48/100 \t Train Err: 5.5938 0.0027923583984375 0.09619140625 10.25 8.9375\n", + "Epoch 48/100 \t Train Err: 5.5938 0.0027313232421875 0.10400390625 9.4375 9.4375\n", + "Epoch 49/100 \t Train Err: 5.5312 0.002777099609375 0.09326171875 10.0 8.9375\n", + "Epoch 49/100 \t Train Err: 5.5938 0.0031890869140625 0.1015625 10.9375 8.5\n", + "Epoch 49/100 \t Train Err: 5.5000 0.00341796875 0.08984375 10.25 8.8125\n", + "Epoch 49/100 \t Train Err: 5.5938 0.003662109375 0.07666015625 8.8125 9.75\n", + "Epoch 49/100 \t Train Err: 5.5312 0.004547119140625 0.09375 9.6875 9.0625\n", + "Epoch 49/100 \t Train Err: 5.5000 0.004638671875 0.1103515625 11.125 8.125\n", + "Epoch 49/100 \t Train Err: 5.4375 0.0031890869140625 0.08447265625 8.875 9.3125\n", + "Epoch 49/100 \t Train Err: 5.5312 0.0031890869140625 0.0908203125 9.3125 9.375\n", + "Epoch 50/100 \t Train Err: 5.5625 0.003265380859375 0.10693359375 11.75 7.625\n", + "Epoch 50/100 \t Train Err: 5.5000 0.00341796875 0.07763671875 7.5625 10.1875\n", + "Epoch 50/100 \t Train Err: 5.3750 0.00323486328125 0.08056640625 8.8125 9.125\n", + "Epoch 50/100 \t Train Err: 5.4688 0.00433349609375 0.11767578125 11.875 7.46875\n", + "Epoch 50/100 \t Train Err: 5.5312 0.00433349609375 0.09130859375 8.625 9.75\n", + "Epoch 50/100 \t Train Err: 5.4062 0.0047607421875 0.087890625 9.3125 9.0\n", + "Epoch 50/100 \t Train Err: 5.4375 0.00457763671875 0.11767578125 11.875 7.4375\n", + "Epoch 50/100 \t Train Err: 5.5312 0.003387451171875 0.06640625 6.75 10.8125\n", + "Epoch 51/100 \t Train Err: 5.4062 0.003448486328125 0.08984375 9.5 8.75\n", + "Epoch 51/100 \t Train Err: 5.4062 0.003662109375 0.11279296875 12.0 7.40625\n", + "Epoch 51/100 \t Train Err: 5.3125 0.003692626953125 0.087890625 8.4375 9.1875\n", + "Epoch 51/100 \t Train Err: 5.3750 0.003692626953125 0.08349609375 8.5625 9.3125\n", + "Epoch 51/100 \t Train Err: 5.3125 0.0037994384765625 0.09765625 10.8125 7.9375\n", + "Epoch 51/100 \t Train Err: 5.3750 0.0036773681640625 0.0791015625 9.6875 8.4375\n", + "Epoch 51/100 \t Train Err: 5.3125 0.0037078857421875 0.06787109375 8.375 9.25\n", + "Epoch 51/100 \t Train Err: 5.2500 0.003936767578125 0.076171875 9.25 8.625\n", + "Epoch 52/100 \t Train Err: 5.3125 0.00433349609375 0.08349609375 10.375 8.0\n", + "Epoch 52/100 \t Train Err: 5.1875 0.00457763671875 0.0732421875 8.8125 8.6875\n", + "Epoch 52/100 \t Train Err: 5.2188 0.0047607421875 0.06787109375 8.25 8.9375\n", + "Epoch 52/100 \t Train Err: 5.2812 0.00518798828125 0.08056640625 9.5 8.3125\n", + "Epoch 52/100 \t Train Err: 5.0938 0.0047607421875 0.08154296875 9.5 7.9375\n", + "Epoch 52/100 \t Train Err: 5.2188 0.00396728515625 0.06591796875 8.0625 9.1875\n", + "Epoch 52/100 \t Train Err: 5.1250 0.004180908203125 0.07421875 9.5 8.0\n", + "Epoch 52/100 \t Train Err: 5.1250 0.004150390625 0.078125 9.5 8.0625\n", + "Epoch 53/100 \t Train Err: 5.1562 0.004180908203125 0.064453125 8.0 8.875\n", + "Epoch 53/100 \t Train Err: 5.0312 0.004608154296875 0.0703125 8.3125 8.4375\n", + "Epoch 53/100 \t Train Err: 5.0625 0.00531005859375 0.07861328125 8.625 8.1875\n", + "Epoch 53/100 \t Train Err: 5.0312 0.005340576171875 0.07763671875 9.0 8.0625\n", + "Epoch 53/100 \t Train Err: 5.0312 0.004791259765625 0.07421875 8.4375 8.25\n", + "Epoch 53/100 \t Train Err: 5.0312 0.00445556640625 0.0673828125 8.3125 8.375\n", + "Epoch 53/100 \t Train Err: 4.9688 0.004486083984375 0.06591796875 8.625 8.0\n", + "Epoch 53/100 \t Train Err: 5.0312 0.004486083984375 0.06396484375 8.25 8.4375\n", + "Epoch 54/100 \t Train Err: 4.9688 0.004425048828125 0.06689453125 8.3125 8.3125\n", + "Epoch 54/100 \t Train Err: 5.0000 0.00457763671875 0.07470703125 8.75 7.96875\n", + "Epoch 54/100 \t Train Err: 4.9375 0.004669189453125 0.07080078125 7.96875 8.25\n", + "Epoch 54/100 \t Train Err: 4.8750 0.004852294921875 0.07275390625 7.90625 8.1875\n", + "Epoch 54/100 \t Train Err: 4.9062 0.00494384765625 0.0791015625 8.5625 7.8125\n", + "Epoch 54/100 \t Train Err: 4.9688 0.0045166015625 0.0732421875 7.90625 8.3125\n", + "Epoch 54/100 \t Train Err: 4.9375 0.0045166015625 0.06689453125 7.625 8.4375\n", + "Epoch 54/100 \t Train Err: 4.8438 0.004608154296875 0.0791015625 8.625 7.5\n", + "Epoch 55/100 \t Train Err: 4.9062 0.0045166015625 0.0693359375 7.3125 8.5625\n", + "Epoch 55/100 \t Train Err: 4.9062 0.00494384765625 0.07958984375 8.3125 7.875\n", + "Epoch 55/100 \t Train Err: 4.8750 0.00531005859375 0.0849609375 8.6875 7.6875\n", + "Epoch 55/100 \t Train Err: 4.7812 0.004638671875 0.06494140625 6.8125 8.5625\n", + "Epoch 55/100 \t Train Err: 4.8438 0.004791259765625 0.08203125 9.0 7.1875\n", + "Epoch 55/100 \t Train Err: 4.9688 0.004241943359375 0.055419921875 5.40625 10.0\n", + "Epoch 55/100 \t Train Err: 6.1562 0.005401611328125 0.19140625 20.125 4.1875\n", + "Epoch 55/100 \t Train Err: 14.0000 0.005706787109375 0.107421875 0.53515625 36.0\n", + "Epoch 56/100 \t Train Err: 11.1875 0.314453125 0.1689453125 0.56640625 29.0\n", + "Epoch 56/100 \t Train Err: 7.6250 1.75 3.984375 17.0 9.1875\n", + "Epoch 56/100 \t Train Err: 12.3125 2.03125 11.1875 47.25 3.21875\n", + "Epoch 56/100 \t Train Err: 7.0625 0.88671875 1.78125 10.1875 12.5625\n", + "Epoch 56/100 \t Train Err: 8.7500 0.05078125 0.1953125 2.484375 21.875\n", + "Epoch 56/100 \t Train Err: 8.8750 0.1328125 0.0419921875 2.5625 22.0\n", + "Epoch 56/100 \t Train Err: 6.6562 0.32421875 0.0888671875 9.6875 12.8125\n", + "Epoch 56/100 \t Train Err: 7.5000 0.349609375 0.51171875 28.375 5.90625\n", + "Epoch 57/100 \t Train Err: 8.4375 0.337890625 0.66796875 35.75 4.53125\n", + "Epoch 57/100 \t Train Err: 7.1562 0.3125 0.265625 25.375 6.375\n", + "Epoch 57/100 \t Train Err: 6.3750 0.259765625 0.095703125 10.75 11.5\n", + "Epoch 57/100 \t Train Err: 7.0625 0.125 0.08837890625 5.75 15.75\n", + "Epoch 57/100 \t Train Err: 7.1562 0.022705078125 0.06005859375 5.75 16.0\n", + "Epoch 57/100 \t Train Err: 6.5625 0.1787109375 0.2412109375 8.625 13.0625\n", + "Epoch 57/100 \t Train Err: 6.4375 0.443359375 0.59765625 14.5 9.375\n", + "Epoch 57/100 \t Train Err: 6.5625 0.408203125 0.6484375 18.875 7.34375\n", + "Epoch 58/100 \t Train Err: 6.4062 0.150390625 0.337890625 19.25 6.9375\n", + "Epoch 58/100 \t Train Err: 6.1875 0.0218505859375 0.11865234375 16.5 7.65625\n", + "Epoch 58/100 \t Train Err: 5.9688 0.1796875 0.30078125 11.4375 9.4375\n", + "Epoch 58/100 \t Train Err: 6.1562 0.35546875 0.5703125 8.625 11.0625\n", + "Epoch 58/100 \t Train Err: 6.0312 0.31640625 0.55859375 8.125 11.375\n", + "Epoch 58/100 \t Train Err: 5.8125 0.130859375 0.333984375 9.375 10.125\n", + "Epoch 58/100 \t Train Err: 5.5938 0.01422119140625 0.138671875 12.125 8.4375\n", + "Epoch 58/100 \t Train Err: 5.6562 0.06884765625 0.1396484375 14.4375 7.28125\n", + "Epoch 59/100 \t Train Err: 5.6875 0.1884765625 0.20703125 14.5625 7.28125\n", + "Epoch 59/100 \t Train Err: 5.6562 0.24609375 0.216796875 12.9375 7.96875\n", + "Epoch 59/100 \t Train Err: 5.4688 0.2109375 0.162109375 10.3125 9.0\n", + "Epoch 59/100 \t Train Err: 5.4688 0.134765625 0.10888671875 8.6875 9.9375\n", + "Epoch 59/100 \t Train Err: 5.4375 0.06689453125 0.0966796875 8.125 10.25\n", + "Epoch 59/100 \t Train Err: 5.2812 0.0262451171875 0.11279296875 8.3125 9.6875\n", + "Epoch 59/100 \t Train Err: 5.2812 0.0106201171875 0.1416015625 10.375 8.5\n", + "Epoch 59/100 \t Train Err: 5.3125 0.0084228515625 0.177734375 12.5 7.46875\n", + "Epoch 60/100 \t Train Err: 5.3125 0.0126953125 0.1875 12.1875 7.28125\n", + "Epoch 60/100 \t Train Err: 5.2188 0.019775390625 0.1982421875 11.1875 7.5625\n", + "Epoch 60/100 \t Train Err: 5.1250 0.0240478515625 0.203125 9.3125 8.375\n", + "Epoch 60/100 \t Train Err: 5.1250 0.019775390625 0.1875 8.25 9.1875\n", + "Epoch 60/100 \t Train Err: 5.1250 0.010986328125 0.1572265625 7.71875 9.1875\n", + "Epoch 60/100 \t Train Err: 5.0312 0.007171630859375 0.1259765625 8.375 8.625\n", + "Epoch 60/100 \t Train Err: 4.9688 0.0150146484375 0.10400390625 9.125 8.0\n", + "Epoch 60/100 \t Train Err: 4.9375 0.0308837890625 0.09033203125 9.625 7.625\n", + "Epoch 61/100 \t Train Err: 4.9688 0.046630859375 0.08447265625 9.5625 7.65625\n", + "Epoch 61/100 \t Train Err: 4.9688 0.0546875 0.08154296875 9.125 7.875\n", + "Epoch 61/100 \t Train Err: 4.9688 0.054443359375 0.0712890625 8.1875 8.5\n", + "Epoch 61/100 \t Train Err: 4.9062 0.049072265625 0.07275390625 7.875 8.4375\n", + "Epoch 61/100 \t Train Err: 4.8125 0.040771484375 0.0693359375 7.71875 8.25\n", + "Epoch 61/100 \t Train Err: 4.8750 0.031494140625 0.06884765625 7.9375 8.4375\n", + "Epoch 61/100 \t Train Err: 4.8125 0.0228271484375 0.072265625 8.5625 7.8125\n", + "Epoch 61/100 \t Train Err: 4.8125 0.01611328125 0.07568359375 9.0 7.59375\n", + "Epoch 62/100 \t Train Err: 4.7500 0.010986328125 0.07861328125 8.5625 7.625\n", + "Epoch 62/100 \t Train Err: 4.6875 0.0079345703125 0.08203125 7.9375 7.625\n", + "Epoch 62/100 \t Train Err: 4.7500 0.00665283203125 0.0810546875 7.5 8.3125\n", + "Epoch 62/100 \t Train Err: 4.7188 0.00634765625 0.07861328125 7.09375 8.3125\n", + "Epoch 62/100 \t Train Err: 4.6562 0.006317138671875 0.08349609375 7.40625 8.0\n", + "Epoch 62/100 \t Train Err: 4.6562 0.006500244140625 0.0791015625 7.9375 7.625\n", + "Epoch 62/100 \t Train Err: 4.7188 0.006500244140625 0.0732421875 8.1875 7.6875\n", + "Epoch 62/100 \t Train Err: 4.6875 0.00677490234375 0.0771484375 8.5 7.53125\n", + "Epoch 63/100 \t Train Err: 4.6875 0.00732421875 0.07421875 7.9375 7.78125\n", + "Epoch 63/100 \t Train Err: 4.6562 0.00836181640625 0.0703125 7.84375 7.8125\n", + "Epoch 63/100 \t Train Err: 4.6875 0.0093994140625 0.0654296875 7.65625 7.90625\n", + "Epoch 63/100 \t Train Err: 4.6875 0.010498046875 0.0654296875 7.75 7.96875\n", + "Epoch 63/100 \t Train Err: 4.6250 0.01116943359375 0.0634765625 7.6875 7.71875\n", + "Epoch 63/100 \t Train Err: 4.6562 0.01153564453125 0.064453125 7.75 7.78125\n", + "Epoch 63/100 \t Train Err: 4.6562 0.0118408203125 0.060302734375 7.875 7.78125\n", + "Epoch 63/100 \t Train Err: 4.5938 0.01171875 0.0634765625 7.625 7.71875\n", + "Epoch 64/100 \t Train Err: 4.6562 0.010986328125 0.060791015625 7.53125 7.90625\n", + "Epoch 64/100 \t Train Err: 4.5312 0.01019287109375 0.05908203125 7.15625 7.6875\n", + "Epoch 64/100 \t Train Err: 4.5312 0.009033203125 0.064453125 7.28125 7.75\n", + "Epoch 64/100 \t Train Err: 4.5625 0.00836181640625 0.06298828125 7.5 7.75\n", + "Epoch 64/100 \t Train Err: 4.5625 0.007598876953125 0.059326171875 7.5625 7.6875\n", + "Epoch 64/100 \t Train Err: 4.5938 0.007110595703125 0.0654296875 7.5625 7.75\n", + "Epoch 64/100 \t Train Err: 4.5312 0.00689697265625 0.06396484375 7.53125 7.625\n", + "Epoch 64/100 \t Train Err: 4.5625 0.006591796875 0.06494140625 7.59375 7.59375\n", + "Epoch 65/100 \t Train Err: 4.5000 0.0064697265625 0.061767578125 7.40625 7.625\n", + "Epoch 65/100 \t Train Err: 4.5938 0.006439208984375 0.061767578125 7.625 7.71875\n", + "Epoch 65/100 \t Train Err: 4.5625 0.006500244140625 0.0615234375 7.5 7.6875\n", + "Epoch 65/100 \t Train Err: 4.5625 0.0064697265625 0.055908203125 7.25 7.75\n", + "Epoch 65/100 \t Train Err: 4.5625 0.00640869140625 0.056640625 7.34375 7.75\n", + "Epoch 65/100 \t Train Err: 4.4688 0.0064697265625 0.0625 7.375 7.5625\n", + "Epoch 65/100 \t Train Err: 4.4688 0.00640869140625 0.060302734375 7.34375 7.53125\n", + "Epoch 65/100 \t Train Err: 4.4375 0.00640869140625 0.059814453125 7.03125 7.6875\n", + "Epoch 66/100 \t Train Err: 4.5000 0.00628662109375 0.06005859375 6.96875 7.71875\n", + "Epoch 66/100 \t Train Err: 4.5312 0.006622314453125 0.058349609375 7.125 7.71875\n", + "Epoch 66/100 \t Train Err: 4.4688 0.006500244140625 0.05908203125 7.34375 7.53125\n", + "Epoch 66/100 \t Train Err: 4.5000 0.006683349609375 0.06298828125 7.46875 7.4375\n", + "Epoch 66/100 \t Train Err: 4.3750 0.006744384765625 0.057861328125 7.0625 7.3125\n", + "Epoch 66/100 \t Train Err: 4.4688 0.006683349609375 0.060791015625 7.125 7.5625\n", + "Epoch 66/100 \t Train Err: 4.5000 0.006744384765625 0.060302734375 7.25 7.65625\n", + "Epoch 66/100 \t Train Err: 4.5000 0.006744384765625 0.056884765625 7.125 7.625\n", + "Epoch 67/100 \t Train Err: 4.4688 0.006683349609375 0.05712890625 6.96875 7.65625\n", + "Epoch 67/100 \t Train Err: 4.3750 0.006683349609375 0.060546875 6.875 7.4375\n", + "Epoch 67/100 \t Train Err: 4.3438 0.0067138671875 0.06298828125 7.21875 7.25\n", + "Epoch 67/100 \t Train Err: 4.4688 0.00665283203125 0.06201171875 7.25 7.5625\n", + "Epoch 67/100 \t Train Err: 4.4375 0.00628662109375 0.057861328125 6.96875 7.5625\n", + "Epoch 67/100 \t Train Err: 4.2500 0.00640869140625 0.055419921875 6.53125 7.4375\n", + "Epoch 67/100 \t Train Err: 4.3750 0.0062255859375 0.051513671875 6.96875 7.46875\n", + "Epoch 67/100 \t Train Err: 4.4062 0.006195068359375 0.055419921875 6.96875 7.46875\n", + "Epoch 68/100 \t Train Err: 4.3438 0.00604248046875 0.052734375 6.71875 7.34375\n", + "Epoch 68/100 \t Train Err: 4.4062 0.005950927734375 0.055419921875 6.65625 7.59375\n", + "Epoch 68/100 \t Train Err: 4.3750 0.005859375 0.0546875 6.90625 7.34375\n", + "Epoch 68/100 \t Train Err: 4.3125 0.005950927734375 0.057373046875 6.6875 7.375\n", + "Epoch 68/100 \t Train Err: 4.3750 0.005889892578125 0.05517578125 6.53125 7.625\n", + "Epoch 68/100 \t Train Err: 4.3438 0.00592041015625 0.053955078125 6.53125 7.65625\n", + "Epoch 68/100 \t Train Err: 4.3438 0.005889892578125 0.056640625 6.65625 7.34375\n", + "Epoch 68/100 \t Train Err: 4.3125 0.005950927734375 0.05419921875 7.21875 7.25\n", + "Epoch 69/100 \t Train Err: 4.3438 0.005859375 0.057373046875 6.9375 7.25\n", + "Epoch 69/100 \t Train Err: 4.4062 0.00592041015625 0.0576171875 6.875 7.4375\n", + "Epoch 69/100 \t Train Err: 4.3125 0.00592041015625 0.0556640625 6.65625 7.375\n", + "Epoch 69/100 \t Train Err: 4.3438 0.00592041015625 0.0556640625 7.03125 7.40625\n", + "Epoch 69/100 \t Train Err: 4.2812 0.005889892578125 0.058837890625 6.53125 7.34375\n", + "Epoch 69/100 \t Train Err: 4.2500 0.00579833984375 0.05419921875 6.46875 7.25\n", + "Epoch 69/100 \t Train Err: 4.3125 0.005828857421875 0.051513671875 6.625 7.34375\n", + "Epoch 69/100 \t Train Err: 4.3125 0.005859375 0.052978515625 6.78125 7.21875\n", + "Epoch 70/100 \t Train Err: 4.2812 0.005859375 0.04931640625 6.625 7.25\n", + "Epoch 70/100 \t Train Err: 4.1875 0.005767822265625 0.051025390625 6.21875 7.1875\n", + "Epoch 70/100 \t Train Err: 4.2500 0.005828857421875 0.05126953125 6.34375 7.4375\n", + "Epoch 70/100 \t Train Err: 4.2812 0.005706787109375 0.05126953125 6.28125 7.5\n", + "Epoch 70/100 \t Train Err: 4.3438 0.0057373046875 0.05078125 6.40625 7.5\n", + "Epoch 70/100 \t Train Err: 4.3125 0.0057373046875 0.05517578125 6.875 7.1875\n", + "Epoch 70/100 \t Train Err: 4.1562 0.005828857421875 0.052734375 6.78125 6.875\n", + "Epoch 70/100 \t Train Err: 4.2188 0.00579833984375 0.05419921875 6.59375 7.15625\n", + "Epoch 71/100 \t Train Err: 4.3125 0.00567626953125 0.052490234375 6.5 7.46875\n", + "Epoch 71/100 \t Train Err: 4.2500 0.00567626953125 0.05126953125 6.125 7.5\n", + "Epoch 71/100 \t Train Err: 4.2500 0.005706787109375 0.05224609375 6.5 7.4375\n", + "Epoch 71/100 \t Train Err: 4.1875 0.0057373046875 0.053466796875 6.65625 6.96875\n", + "Epoch 71/100 \t Train Err: 4.2812 0.005767822265625 0.0546875 6.875 7.09375\n", + "Epoch 71/100 \t Train Err: 4.1562 0.005645751953125 0.054443359375 6.40625 6.96875\n", + "Epoch 71/100 \t Train Err: 4.2188 0.005615234375 0.0498046875 6.3125 7.4375\n", + "Epoch 71/100 \t Train Err: 4.2500 0.00555419921875 0.05224609375 6.40625 7.40625\n", + "Epoch 72/100 \t Train Err: 4.2500 0.005615234375 0.052978515625 6.15625 7.375\n", + "Epoch 72/100 \t Train Err: 4.2500 0.00537109375 0.0517578125 6.5 7.25\n", + "Epoch 72/100 \t Train Err: 4.1562 0.00543212890625 0.05029296875 6.4375 7.0\n", + "Epoch 72/100 \t Train Err: 4.1875 0.005401611328125 0.04638671875 6.3125 7.09375\n", + "Epoch 72/100 \t Train Err: 4.1562 0.00537109375 0.048828125 6.34375 7.09375\n", + "Epoch 72/100 \t Train Err: 4.2500 0.00537109375 0.0498046875 6.21875 7.3125\n", + "Epoch 72/100 \t Train Err: 4.2188 0.00531005859375 0.05078125 6.46875 7.21875\n", + "Epoch 72/100 \t Train Err: 4.1250 0.00537109375 0.05078125 6.46875 6.90625\n", + "Epoch 73/100 \t Train Err: 4.2500 0.005340576171875 0.053955078125 6.40625 7.1875\n", + "Epoch 73/100 \t Train Err: 4.1875 0.005279541015625 0.0517578125 6.59375 6.96875\n", + "Epoch 73/100 \t Train Err: 4.2500 0.00537109375 0.0556640625 6.40625 7.21875\n", + "Epoch 73/100 \t Train Err: 4.1250 0.00531005859375 0.051513671875 6.34375 6.96875\n", + "Epoch 73/100 \t Train Err: 4.2188 0.005279541015625 0.04833984375 6.0 7.46875\n", + "Epoch 73/100 \t Train Err: 4.1250 0.00537109375 0.050537109375 6.0625 7.1875\n", + "Epoch 73/100 \t Train Err: 4.1250 0.00531005859375 0.048095703125 5.90625 7.09375\n", + "Epoch 73/100 \t Train Err: 4.1562 0.0052490234375 0.05029296875 6.375 6.90625\n", + "Epoch 74/100 \t Train Err: 4.0625 0.005523681640625 0.049560546875 6.4375 6.8125\n", + "Epoch 74/100 \t Train Err: 4.1875 0.005279541015625 0.049072265625 5.90625 7.34375\n", + "Epoch 74/100 \t Train Err: 4.2188 0.00531005859375 0.04931640625 6.09375 7.375\n", + "Epoch 74/100 \t Train Err: 4.0938 0.00531005859375 0.05029296875 6.21875 6.96875\n", + "Epoch 74/100 \t Train Err: 4.1562 0.0052490234375 0.053955078125 6.3125 7.09375\n", + "Epoch 74/100 \t Train Err: 4.1250 0.00518798828125 0.05126953125 6.3125 7.0625\n", + "Epoch 74/100 \t Train Err: 4.1562 0.0052490234375 0.051513671875 6.21875 7.0625\n", + "Epoch 74/100 \t Train Err: 4.0938 0.005218505859375 0.049560546875 6.1875 7.0\n", + "Epoch 75/100 \t Train Err: 4.1250 0.005218505859375 0.0517578125 6.3125 7.0625\n", + "Epoch 75/100 \t Train Err: 4.0312 0.005218505859375 0.050048828125 6.03125 6.875\n", + "Epoch 75/100 \t Train Err: 4.0938 0.005157470703125 0.0498046875 5.84375 7.15625\n", + "Epoch 75/100 \t Train Err: 4.1250 0.005126953125 0.04541015625 5.875 7.21875\n", + "Epoch 75/100 \t Train Err: 4.0625 0.005126953125 0.05126953125 6.09375 6.9375\n", + "Epoch 75/100 \t Train Err: 4.0000 0.005218505859375 0.050537109375 6.40625 6.53125\n", + "Epoch 75/100 \t Train Err: 4.1250 0.00518798828125 0.049560546875 6.09375 6.9375\n", + "Epoch 75/100 \t Train Err: 4.0625 0.005126953125 0.046875 5.59375 7.1875\n", + "Epoch 76/100 \t Train Err: 4.0000 0.00506591796875 0.045654296875 5.5 7.15625\n", + "Epoch 76/100 \t Train Err: 4.1562 0.005157470703125 0.0458984375 6.0 7.25\n", + "Epoch 76/100 \t Train Err: 4.0625 0.00518798828125 0.048095703125 6.5 6.59375\n", + "Epoch 76/100 \t Train Err: 4.0625 0.005096435546875 0.0458984375 6.0 6.875\n", + "Epoch 76/100 \t Train Err: 4.0312 0.0052490234375 0.044921875 5.75 7.03125\n", + "Epoch 76/100 \t Train Err: 4.1562 0.00518798828125 0.043701171875 5.75 7.28125\n", + "Epoch 76/100 \t Train Err: 4.0625 0.005096435546875 0.0498046875 5.84375 7.09375\n", + "Epoch 76/100 \t Train Err: 4.0625 0.005126953125 0.044189453125 6.25 6.8125\n", + "Epoch 77/100 \t Train Err: 3.9688 0.005126953125 0.0478515625 6.15625 6.59375\n", + "Epoch 77/100 \t Train Err: 4.0312 0.005126953125 0.046142578125 5.84375 6.90625\n", + "Epoch 77/100 \t Train Err: 4.0000 0.00506591796875 0.04541015625 5.71875 6.96875\n", + "Epoch 77/100 \t Train Err: 3.9531 0.004974365234375 0.046142578125 5.46875 7.0\n", + "Epoch 77/100 \t Train Err: 4.0312 0.004974365234375 0.044677734375 6.0625 6.8125\n", + "Epoch 77/100 \t Train Err: 4.0312 0.005035400390625 0.047607421875 6.125 6.6875\n", + "Epoch 77/100 \t Train Err: 4.0312 0.004852294921875 0.047607421875 6.09375 6.84375\n", + "Epoch 77/100 \t Train Err: 3.9844 0.0048828125 0.043701171875 5.53125 6.96875\n", + "Epoch 78/100 \t Train Err: 3.9844 0.004852294921875 0.045654296875 5.875 6.875\n", + "Epoch 78/100 \t Train Err: 3.9844 0.004913330078125 0.0458984375 5.8125 6.78125\n", + "Epoch 78/100 \t Train Err: 3.9375 0.0048828125 0.046142578125 5.6875 6.65625\n", + "Epoch 78/100 \t Train Err: 3.9531 0.004974365234375 0.04541015625 5.71875 6.78125\n", + "Epoch 78/100 \t Train Err: 4.0000 0.004913330078125 0.0458984375 5.5625 7.0\n", + "Epoch 78/100 \t Train Err: 3.9375 0.0048828125 0.041748046875 5.46875 6.90625\n", + "Epoch 78/100 \t Train Err: 3.9688 0.00494384765625 0.043701171875 5.71875 6.875\n", + "Epoch 78/100 \t Train Err: 3.9375 0.005035400390625 0.048095703125 5.96875 6.46875\n", + "Epoch 79/100 \t Train Err: 3.9844 0.0050048828125 0.049072265625 6.03125 6.625\n", + "Epoch 79/100 \t Train Err: 3.9688 0.0050048828125 0.040283203125 4.90625 7.25\n", + "Epoch 79/100 \t Train Err: 4.0312 0.004974365234375 0.04345703125 5.4375 7.1875\n", + "Epoch 79/100 \t Train Err: 3.9688 0.005035400390625 0.045166015625 6.03125 6.5625\n", + "Epoch 79/100 \t Train Err: 4.0000 0.005035400390625 0.0478515625 6.46875 6.28125\n", + "Epoch 79/100 \t Train Err: 3.9219 0.005035400390625 0.043701171875 5.03125 7.03125\n", + "Epoch 79/100 \t Train Err: 4.0312 0.004974365234375 0.044921875 4.90625 7.375\n", + "Epoch 79/100 \t Train Err: 3.9062 0.004974365234375 0.04296875 5.8125 6.625\n", + "Epoch 80/100 \t Train Err: 3.9219 0.0050048828125 0.04638671875 6.40625 6.25\n", + "Epoch 80/100 \t Train Err: 3.8750 0.004974365234375 0.045654296875 5.65625 6.5\n", + "Epoch 80/100 \t Train Err: 3.9531 0.0048828125 0.04345703125 5.15625 7.15625\n", + "Epoch 80/100 \t Train Err: 3.9688 0.004913330078125 0.043701171875 5.25 7.0\n", + "Epoch 80/100 \t Train Err: 3.8906 0.004974365234375 0.04345703125 5.875 6.53125\n", + "Epoch 80/100 \t Train Err: 3.8281 0.00494384765625 0.044677734375 5.65625 6.4375\n", + "Epoch 80/100 \t Train Err: 3.9531 0.004852294921875 0.041259765625 5.4375 6.96875\n", + "Epoch 80/100 \t Train Err: 3.9531 0.00494384765625 0.04345703125 5.15625 7.0\n", + "Epoch 81/100 \t Train Err: 3.9844 0.0048828125 0.0419921875 5.5625 6.8125\n", + "Epoch 81/100 \t Train Err: 3.9219 0.0048828125 0.04443359375 5.71875 6.46875\n", + "Epoch 81/100 \t Train Err: 3.9375 0.00482177734375 0.046630859375 5.71875 6.625\n", + "Epoch 81/100 \t Train Err: 3.9219 0.00469970703125 0.042236328125 5.03125 6.90625\n", + "Epoch 81/100 \t Train Err: 3.9219 0.0047607421875 0.042236328125 5.0625 7.03125\n", + "Epoch 81/100 \t Train Err: 3.9844 0.004791259765625 0.044189453125 5.5625 6.90625\n", + "Epoch 81/100 \t Train Err: 3.8750 0.0048828125 0.044677734375 5.875 6.375\n", + "Epoch 81/100 \t Train Err: 3.8438 0.00482177734375 0.045166015625 5.40625 6.5625\n", + "Epoch 82/100 \t Train Err: 3.8281 0.0048828125 0.044189453125 5.0625 6.8125\n", + "Epoch 82/100 \t Train Err: 3.8906 0.004913330078125 0.042724609375 5.15625 7.03125\n", + "Epoch 82/100 \t Train Err: 3.8750 0.00482177734375 0.048583984375 5.90625 6.34375\n", + "Epoch 82/100 \t Train Err: 3.8594 0.004913330078125 0.045166015625 5.6875 6.28125\n", + "Epoch 82/100 \t Train Err: 3.8594 0.004852294921875 0.043701171875 5.28125 6.78125\n", + "Epoch 82/100 \t Train Err: 3.8594 0.004852294921875 0.042236328125 4.84375 6.9375\n", + "Epoch 82/100 \t Train Err: 3.7969 0.0048828125 0.0400390625 5.15625 6.625\n", + "Epoch 82/100 \t Train Err: 3.8281 0.004730224609375 0.04736328125 5.53125 6.28125\n", + "Epoch 83/100 \t Train Err: 3.8438 0.0047607421875 0.044921875 5.3125 6.53125\n", + "Epoch 83/100 \t Train Err: 3.7969 0.00469970703125 0.043212890625 5.09375 6.625\n", + "Epoch 83/100 \t Train Err: 3.8125 0.004669189453125 0.043701171875 5.1875 6.71875\n", + "Epoch 83/100 \t Train Err: 3.8281 0.004669189453125 0.042236328125 5.09375 6.6875\n", + "Epoch 83/100 \t Train Err: 3.8125 0.0047607421875 0.042236328125 5.40625 6.46875\n", + "Epoch 83/100 \t Train Err: 3.8594 0.00482177734375 0.04150390625 5.34375 6.59375\n", + "Epoch 83/100 \t Train Err: 3.7500 0.00482177734375 0.041748046875 5.3125 6.375\n", + "Epoch 83/100 \t Train Err: 3.7812 0.004791259765625 0.040771484375 4.75 6.75\n", + "Epoch 84/100 \t Train Err: 3.7188 0.0047607421875 0.0390625 5.21875 6.34375\n", + "Epoch 84/100 \t Train Err: 3.7656 0.0047607421875 0.040771484375 5.09375 6.40625\n", + "Epoch 84/100 \t Train Err: 3.7969 0.004852294921875 0.04248046875 5.25 6.34375\n", + "Epoch 84/100 \t Train Err: 3.7344 0.0047607421875 0.03955078125 5.125 6.3125\n", + "Epoch 84/100 \t Train Err: 3.7500 0.004791259765625 0.03857421875 4.5 6.90625\n", + "Epoch 84/100 \t Train Err: 3.7656 0.00482177734375 0.04248046875 5.1875 6.40625\n", + "Epoch 84/100 \t Train Err: 3.7656 0.00482177734375 0.042724609375 5.3125 6.21875\n", + "Epoch 84/100 \t Train Err: 3.7188 0.004730224609375 0.04345703125 5.21875 6.28125\n", + "Epoch 85/100 \t Train Err: 3.7500 0.004608154296875 0.039794921875 4.65625 6.625\n", + "Epoch 85/100 \t Train Err: 3.7344 0.004608154296875 0.04150390625 4.71875 6.53125\n", + "Epoch 85/100 \t Train Err: 3.7344 0.004730224609375 0.042236328125 5.375 6.3125\n", + "Epoch 85/100 \t Train Err: 3.7656 0.004730224609375 0.040283203125 5.4375 6.28125\n", + "Epoch 85/100 \t Train Err: 3.6875 0.00469970703125 0.0400390625 5.0 6.28125\n", + "Epoch 85/100 \t Train Err: 3.7031 0.00469970703125 0.037109375 4.8125 6.53125\n", + "Epoch 85/100 \t Train Err: 3.7500 0.00469970703125 0.038330078125 4.78125 6.625\n", + "Epoch 85/100 \t Train Err: 3.6875 0.004669189453125 0.0400390625 5.34375 6.0625\n", + "Epoch 86/100 \t Train Err: 3.7344 0.004638671875 0.037841796875 5.40625 6.25\n", + "Epoch 86/100 \t Train Err: 3.7031 0.004638671875 0.0380859375 4.875 6.46875\n", + "Epoch 86/100 \t Train Err: 3.7344 0.00457763671875 0.0390625 4.59375 6.6875\n", + "Epoch 86/100 \t Train Err: 3.7031 0.004638671875 0.036865234375 5.09375 6.21875\n", + "Epoch 86/100 \t Train Err: 3.7031 0.004638671875 0.0390625 5.28125 6.15625\n", + "Epoch 86/100 \t Train Err: 3.7500 0.004669189453125 0.037841796875 4.875 6.59375\n", + "Epoch 86/100 \t Train Err: 3.7188 0.004638671875 0.0390625 4.625 6.5625\n", + "Epoch 86/100 \t Train Err: 3.6406 0.004730224609375 0.039306640625 5.125 5.9375\n", + "Epoch 87/100 \t Train Err: 3.6875 0.00469970703125 0.03759765625 4.9375 6.28125\n", + "Epoch 87/100 \t Train Err: 3.6875 0.004730224609375 0.039794921875 4.78125 6.25\n", + "Epoch 87/100 \t Train Err: 3.7031 0.004791259765625 0.0361328125 4.65625 6.53125\n", + "Epoch 87/100 \t Train Err: 3.6719 0.0047607421875 0.03662109375 4.625 6.5\n", + "Epoch 87/100 \t Train Err: 3.6094 0.004791259765625 0.037109375 5.125 6.0\n", + "Epoch 87/100 \t Train Err: 3.7344 0.00482177734375 0.036865234375 5.375 6.15625\n", + "Epoch 87/100 \t Train Err: 3.6719 0.0047607421875 0.037353515625 4.65625 6.53125\n", + "Epoch 87/100 \t Train Err: 3.6562 0.004791259765625 0.03662109375 4.59375 6.4375\n", + "Epoch 88/100 \t Train Err: 3.6562 0.004638671875 0.03857421875 5.28125 6.0\n", + "Epoch 88/100 \t Train Err: 3.5938 0.004730224609375 0.040283203125 4.875 6.03125\n", + "Epoch 88/100 \t Train Err: 3.6875 0.004608154296875 0.03955078125 4.5625 6.5\n", + "Epoch 88/100 \t Train Err: 3.6875 0.004730224609375 0.0380859375 4.71875 6.46875\n", + "Epoch 88/100 \t Train Err: 3.5469 0.004608154296875 0.037353515625 4.53125 6.21875\n", + "Epoch 88/100 \t Train Err: 3.6250 0.004608154296875 0.040771484375 4.9375 6.125\n", + "Epoch 88/100 \t Train Err: 3.6094 0.004486083984375 0.038330078125 4.8125 6.21875\n", + "Epoch 88/100 \t Train Err: 3.5938 0.0045166015625 0.0400390625 4.75 6.21875\n", + "Epoch 89/100 \t Train Err: 3.6406 0.0045166015625 0.038330078125 4.875 6.21875\n", + "Epoch 89/100 \t Train Err: 3.5469 0.00457763671875 0.041748046875 5.21875 5.6875\n", + "Epoch 89/100 \t Train Err: 3.6250 0.004608154296875 0.03759765625 4.46875 6.40625\n", + "Epoch 89/100 \t Train Err: 3.5469 0.004638671875 0.035400390625 4.625 6.15625\n", + "Epoch 89/100 \t Train Err: 3.6094 0.00469970703125 0.0341796875 4.40625 6.375\n", + "Epoch 89/100 \t Train Err: 3.5781 0.00469970703125 0.0361328125 4.75 6.0625\n", + "Epoch 89/100 \t Train Err: 3.4688 0.0047607421875 0.0341796875 4.53125 5.96875\n", + "Epoch 89/100 \t Train Err: 3.5781 0.00469970703125 0.033935546875 4.40625 6.34375\n", + "Epoch 90/100 \t Train Err: 3.5625 0.0047607421875 0.03369140625 4.53125 6.25\n", + "Epoch 90/100 \t Train Err: 3.6094 0.004791259765625 0.033447265625 4.875 6.0625\n", + "Epoch 90/100 \t Train Err: 3.6250 0.0048828125 0.034423828125 4.40625 6.34375\n", + "Epoch 90/100 \t Train Err: 3.5469 0.004913330078125 0.03662109375 4.71875 6.0\n", + "Epoch 90/100 \t Train Err: 3.5000 0.004913330078125 0.035400390625 4.40625 6.125\n", + "Epoch 90/100 \t Train Err: 3.5469 0.00494384765625 0.03369140625 4.125 6.375\n", + "Epoch 90/100 \t Train Err: 3.5156 0.004852294921875 0.035400390625 4.53125 5.96875\n", + "Epoch 90/100 \t Train Err: 3.5156 0.00469970703125 0.036376953125 4.71875 5.90625\n", + "Epoch 91/100 \t Train Err: 3.5312 0.0047607421875 0.033447265625 4.375 6.28125\n", + "Epoch 91/100 \t Train Err: 3.5469 0.004730224609375 0.032958984375 4.34375 6.3125\n", + "Epoch 91/100 \t Train Err: 3.6094 0.004669189453125 0.0390625 5.34375 5.65625\n", + "Epoch 91/100 \t Train Err: 3.5156 0.004608154296875 0.032958984375 4.28125 6.0625\n", + "Epoch 91/100 \t Train Err: 3.5469 0.00457763671875 0.03125 4.03125 6.4375\n", + "Epoch 91/100 \t Train Err: 3.5312 0.004608154296875 0.03564453125 4.84375 5.71875\n", + "Epoch 91/100 \t Train Err: 3.4844 0.004547119140625 0.0322265625 4.5 5.90625\n", + "Epoch 91/100 \t Train Err: 3.5312 0.004608154296875 0.0299072265625 3.953125 6.625\n", + "Epoch 92/100 \t Train Err: 3.5000 0.004547119140625 0.03515625 5.21875 5.46875\n", + "Epoch 92/100 \t Train Err: 3.4688 0.004486083984375 0.034423828125 4.53125 5.9375\n", + "Epoch 92/100 \t Train Err: 3.4531 0.00457763671875 0.032470703125 3.875 6.375\n", + "Epoch 92/100 \t Train Err: 3.5938 0.004669189453125 0.038330078125 4.9375 5.84375\n", + "Epoch 92/100 \t Train Err: 3.5156 0.004730224609375 0.03369140625 5.03125 5.6875\n", + "Epoch 92/100 \t Train Err: 3.5625 0.004791259765625 0.029052734375 3.890625 6.65625\n", + "Epoch 92/100 \t Train Err: 3.4531 0.004730224609375 0.032470703125 4.5 5.90625\n", + "Epoch 92/100 \t Train Err: 3.4531 0.0047607421875 0.030517578125 4.71875 5.6875\n", + "Epoch 93/100 \t Train Err: 3.5156 0.00482177734375 0.0281982421875 3.78125 6.53125\n", + "Epoch 93/100 \t Train Err: 3.4531 0.00482177734375 0.03173828125 4.6875 5.5625\n", + "Epoch 93/100 \t Train Err: 3.4531 0.004791259765625 0.03271484375 4.6875 5.6875\n", + "Epoch 93/100 \t Train Err: 3.4375 0.00469970703125 0.0279541015625 3.96875 6.25\n", + "Epoch 93/100 \t Train Err: 3.3594 0.004730224609375 0.03076171875 4.125 5.9375\n", + "Epoch 93/100 \t Train Err: 3.4688 0.004730224609375 0.0301513671875 4.96875 5.625\n", + "Epoch 93/100 \t Train Err: 3.3906 0.004730224609375 0.0296630859375 4.03125 6.0625\n", + "Epoch 93/100 \t Train Err: 3.4688 0.0047607421875 0.0294189453125 4.25 6.0625\n", + "Epoch 94/100 \t Train Err: 3.3906 0.0047607421875 0.031005859375 4.375 5.6875\n", + "Epoch 94/100 \t Train Err: 3.4219 0.004791259765625 0.031494140625 4.53125 5.8125\n", + "Epoch 94/100 \t Train Err: 3.4375 0.004791259765625 0.0281982421875 4.25 6.03125\n", + "Epoch 94/100 \t Train Err: 3.4219 0.004791259765625 0.02978515625 4.34375 5.8125\n", + "Epoch 94/100 \t Train Err: 3.4219 0.00469970703125 0.0308837890625 4.375 5.78125\n", + "Epoch 94/100 \t Train Err: 3.4375 0.004669189453125 0.028564453125 4.25 6.0\n", + "Epoch 94/100 \t Train Err: 3.3594 0.00469970703125 0.0283203125 4.25 5.875\n", + "Epoch 94/100 \t Train Err: 3.3594 0.004852294921875 0.03125 4.28125 5.6875\n", + "Epoch 95/100 \t Train Err: 3.3750 0.004791259765625 0.0284423828125 4.25 5.6875\n", + "Epoch 95/100 \t Train Err: 3.3750 0.0047607421875 0.0279541015625 3.90625 5.96875\n", + "Epoch 95/100 \t Train Err: 3.3594 0.004730224609375 0.029052734375 4.28125 5.71875\n", + "Epoch 95/100 \t Train Err: 3.3906 0.004791259765625 0.0296630859375 4.3125 5.78125\n", + "Epoch 95/100 \t Train Err: 3.3750 0.004791259765625 0.03076171875 4.125 5.78125\n", + "Epoch 95/100 \t Train Err: 3.3594 0.0047607421875 0.0291748046875 4.25 5.8125\n", + "Epoch 95/100 \t Train Err: 3.3438 0.004669189453125 0.02783203125 4.15625 5.875\n", + "Epoch 95/100 \t Train Err: 3.3281 0.0047607421875 0.0299072265625 4.375 5.5\n", + "Epoch 96/100 \t Train Err: 3.3438 0.0048828125 0.02880859375 3.84375 5.90625\n", + "Epoch 96/100 \t Train Err: 3.3750 0.0047607421875 0.030029296875 4.0625 5.8125\n", + "Epoch 96/100 \t Train Err: 3.3438 0.00482177734375 0.0308837890625 3.9375 5.71875\n", + "Epoch 96/100 \t Train Err: 3.3125 0.004730224609375 0.028564453125 4.21875 5.59375\n", + "Epoch 96/100 \t Train Err: 3.2969 0.004638671875 0.0291748046875 3.765625 5.90625\n", + "Epoch 96/100 \t Train Err: 3.3750 0.004638671875 0.034912109375 4.375 5.75\n", + "Epoch 96/100 \t Train Err: 3.2656 0.004638671875 0.029296875 4.125 5.53125\n", + "Epoch 96/100 \t Train Err: 3.2500 0.004638671875 0.0286865234375 3.984375 5.625\n", + "Epoch 97/100 \t Train Err: 3.2656 0.00457763671875 0.028564453125 4.125 5.59375\n", + "Epoch 97/100 \t Train Err: 3.3438 0.004547119140625 0.02587890625 3.859375 5.9375\n", + "Epoch 97/100 \t Train Err: 3.3125 0.00457763671875 0.02783203125 4.03125 5.6875\n", + "Epoch 97/100 \t Train Err: 3.3438 0.004608154296875 0.02783203125 4.125 5.6875\n", + "Epoch 97/100 \t Train Err: 3.3125 0.004730224609375 0.0279541015625 3.875 5.75\n", + "Epoch 97/100 \t Train Err: 3.2500 0.004730224609375 0.0289306640625 3.78125 5.6875\n", + "Epoch 97/100 \t Train Err: 3.2812 0.004669189453125 0.030517578125 4.03125 5.5625\n", + "Epoch 97/100 \t Train Err: 3.3281 0.004638671875 0.029296875 4.1875 5.625\n", + "Epoch 98/100 \t Train Err: 3.2969 0.004669189453125 0.0264892578125 3.734375 6.0\n", + "Epoch 98/100 \t Train Err: 3.2031 0.0047607421875 0.0262451171875 3.921875 5.5\n", + "Epoch 98/100 \t Train Err: 3.2969 0.004791259765625 0.02685546875 4.59375 5.1875\n", + "Epoch 98/100 \t Train Err: 3.3750 0.0047607421875 0.02392578125 3.34375 6.5\n", + "Epoch 98/100 \t Train Err: 3.4688 0.004791259765625 0.033447265625 5.625 4.625\n", + "Epoch 98/100 \t Train Err: 4.5625 0.00482177734375 0.01953125 1.4296875 11.0625\n", + "Epoch 98/100 \t Train Err: 10.6875 0.00567626953125 0.44140625 46.5 1.109375\n", + "Epoch 98/100 \t Train Err: 11.5625 0.0096435546875 0.0252685546875 0.322265625 29.875\n", + "Epoch 99/100 \t Train Err: 12.5000 0.1318359375 0.0341796875 0.1640625 32.25\n", + "Epoch 99/100 \t Train Err: 7.3125 0.71484375 0.66796875 2.953125 17.125\n", + "Epoch 99/100 \t Train Err: 9.1250 1.265625 3.046875 38.0 3.265625\n", + "Epoch 99/100 \t Train Err: 9.4375 1.078125 3.578125 40.5 2.859375\n", + "Epoch 99/100 \t Train Err: 5.9688 0.419921875 1.484375 14.3125 8.125\n", + "Epoch 99/100 \t Train Err: 6.6250 0.01513671875 0.3125 4.5 15.25\n", + "Epoch 99/100 \t Train Err: 7.5000 0.326171875 0.042236328125 2.78125 18.0\n", + "Epoch 99/100 \t Train Err: 6.6250 0.765625 0.087890625 5.0 14.375\n" ] } ], @@ -889,9 +1363,14 @@ " \n", " # test_err.append(test_loss)\n", " train_err.append(train_loss)\n", + " len1.append(criterion(output[batch_labels == 1].squeeze(1), batch_labels[batch_labels==1]))\n", + " len2.append(criterion(output[batch_labels == 2].squeeze(1), batch_labels[batch_labels==2]))\n", + " len3.append(criterion(output[batch_labels == 3].squeeze(1), batch_labels[batch_labels==3]))\n", + " len15.append(criterion(output[batch_labels == 15].squeeze(1), batch_labels[batch_labels==15]))\n", + " \n", " with open('loss', 'a') as f:\n", " f.write(f\"{train_loss}\\n\")\n", - " print(f\"Epoch {epoch}/{NEPOCHS} \\t Train Err: {train_loss:.4f}\")\n", + " print(f\"Epoch {epoch}/{NEPOCHS} \\t Train Err: {train_loss:.4f} {len1[-1]} {len2[-1]} {len3[-1]} {len15[-1]}\")\n", "\n", " epoch += 1\n", " if epoch % 100 == 0:\n", @@ -900,7 +1379,8 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 16, + "execution_state": "idle", "metadata": {}, "outputs": [], "source": [ @@ -929,21 +1409,10 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "execution_state": "idle", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 640x480 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.suptitle('MSE vs Epochs')\n", "plt.plot(train_err, label='Train', color='blue')\n", @@ -954,7 +1423,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 41, "execution_state": "idle", "metadata": { "id": "LoGEmM5lH7_A" @@ -962,46 +1431,9 @@ "outputs": [ { "data": { + "image/png": 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G/B/zVxqJ03uOkTCSJOscM3PFW1rPNRLn/e8tMxJHkm6rKzUWC/g8f1Lkm/L9LnPdBU6NkYi3dSQG3CJRU1OjuXPnKjs7Wy6XS1u3bu33dcuytGrVKo0dO1bDhw9XcXGx9u/f72TOAAAgRgy4kOjs7FR+fv5pm2QeeeQRPf7441q/fr3efPNNpaSkaM6cOerq6nIiXwAAosKpro14M+CujZKSEpWUlAT9mmVZqqys1D333KPvfOc7kqRf/vKXyszM1NatW3XjjTeGnzEAAFHA9M/gHB1s2dTUpJaWFhUXFwfOpaena8aMGdq1a5eToQAAMMoKszUiXgsJRwdbtrS0SJIyMzP7nc/MzAx87WTd3d3q7u4OvPZ6vU6mBAAAIijq0z8rKiqUnp4eOHJycqKdEgAAp7AkWVYYR7QfIEIcbZHIysqSJLW2tmrs2LGB862trSooKAh6z4oVK1ReXh547fV6KSbC1JlnZlrRsA4jYSRJvZ+amamcNaXVSByT9ntHRzsFDFGJxwxM2za4jbhfLrlY2fIUjrZI5OXlKSsrS9XV1YFzXq9Xb775pi677LKg93g8HqWlpfU7AADA4DDgj3kdHR06cOBA4HVTU5MaGxuVkZGh3NxcLV26VA8++KDOO+885eXlaeXKlcrOzta8efOczh0AAGOYtRHcgAuJuro6FRUVBV73dUuUlpZq48aN+pd/+Rd1dnbqtttu05EjR3TFFVdo+/btSk5OdjZzAAAM8lsuucIoBj6/RLbb7VZZWZnKysoczDA6BlxIzJo1S5Z1+iEjLpdLDzzwgB544IFwcwMAIO7E2xLZ7LUBAIANfbMvwrk/HlFIAABgA2Mkgov6OhIAAGDwokXCkJKsHxqLlZI5ykictkszjMQxqeu4mV+J2TvLbVzljJ1XrjUWC/g8V68/8jGORz5GH1okgqOQAADABqdmbcQbCgkAAGxgsGVwjJEAAAAho0UCAAAbPmuRCGeMhKPpxAwKCQAAbGCwZXB0bQAAgJDRIhGHekanRDsFx/lGHjcSJyvlqJE4LZ2pRuIA0dQ9KiniMY4bmGLaxzpxhHN/PKKQAADABro2gqNrAwAAgwoLCzVx4kRVVVVFOxVH0CIBAIAdDvVtsPsnAABDUZhdG4rTrg0KCQAAbGBly+AYIwEAAEJGiwQAADYwayM4CglDrDHmttzuGj3MSBx3j5EwkqTk9C4jcf7yYZaROO/ddLeROJJU9MqdRuLsvPIxI3EweKR80BnxGMd9Zv42SCfGODBG4hR0bQAAgJDRIgEAgA0MtgyOQgIAADtYIzsoujYAAEDIaJEAAMAGZm0ERyEBAIBdcdo9EQ4KCUO2//eDxmJdOfthI3G6Lov8FsF9zExolSaN+8hInKv/UGYkjiTtvDI+NgbC4NN7rifiMY4f53/2aKOQAADABro2gqOQAADADmZtBEUhAQCALa4TRzj3xx+mfwIAgJDRIgEAgB10bQRFIQEAgB0UEkFRSBjyjxffYyxW75fSjcQZ5jXX32dqtPP7n5rZpbWzI/LT4gJmmgsFfJ772PGIx7CORz5GPGtubtaCBQvU1tamxMRErVy5Utddd92A3oNCAgAAO+JwG/HExERVVlaqoKBALS0tmjp1qr75zW8qJSXF/ntENEMAAOJEPO7+OXbsWI0dO1aSlJWVpdGjR+uTTz4ZUCHBrA0AAGJUTU2N5s6dq+zsbLlcLm3duvWUa6qqqjRhwgQlJydrxowZeuutt0KKVV9fL5/Pp5ycnAHdRyEBAIAdlgPHAHV2dio/P19VVcGXut+8ebPKy8t17733qqGhQfn5+ZozZ47a2toC1xQUFGjy5MmnHIcOHQpc88knn+h73/ueNmzYMOAc6doAAMAOh8ZIeL3efqc9Ho88nuADsEtKSlRSUnLat1y7dq1uvfVWLVy4UJK0fv16bdu2TU8//bSWL18uSWpsbDxjWt3d3Zo3b56WL1+uyy+/fMCPRYsEAAAG5eTkKD09PXBUVFSE9D49PT2qr69XcXFx4FxCQoKKi4u1a9cuW+9hWZZuvvlmXXnllVqwYEFIedAiAQCADS7rsyOc+3ViymVaWlrg/OlaI87m8OHD8vl8yszM7Hc+MzNTe/futfUef/jDH7R582ZdfPHFgfEX//Zv/6YpU6bYzoNCwhCT24hf+oOfGYtlSu/fzfyojkjuMRLnvX+620gcIJr8HnfkY7gjHyPAoQWp0tLS+hUS0XTFFVfI7/eH9R4UEgAA2BFj60iMHj1abrdbra2t/c63trYqKyvL0VhnwhgJAAAMKiws1MSJE087E8OupKQkTZ06VdXV1YFzfr9f1dXVuuyyyxzI1B7HWyR8Pp/uu+8+/fu//7taWlqUnZ2tm2++Wffcc49crthb1QsAAFsc6tqora213bXR0dGhAwcOBF43NTWpsbFRGRkZys3NVXl5uUpLSzVt2jRNnz5dlZWV6uzsDMziMMHxQmLNmjVat26dnn32WU2aNEl1dXVauHCh0tPTtXjxYqfDAQBgRhQ27aqrq1NRUVHgdXl5uSSptLRUGzdu1A033KCPP/5Yq1atUktLiwoKCrR9+/ZTBmBGkuOFxB//+Ed95zvf0VVXXSVJmjBhgn71q1+FvNIWAABD1axZs2SdZW3tRYsWadGiRcZyOpnjYyQuv/xyVVdX691335Ukvf3223rjjTdOu6BGd3e3vF5vvwMAgJjj0MqWTo2RiBWOt0gsX75cXq9XF154odxut3w+n1avXq358+cHvb6iokL333+/02kMacmfhDeVx64jFxkJI0nyd5mZYOROMPNv96VNDxmJI0nzp9QaifOTKVuMxMHg4UuK/Hh+X4LBOQMOzdoYyBiJwcDx78Cvf/1rPffcc9q0aZMaGhr07LPP6qc//ameffbZoNevWLFC7e3tgaO5udnplAAAQIQ4/jHvrrvu0vLly3XjjTdKkqZMmaKDBw+qoqJCpaWlp1x/pjXGAQCIFU6tbBlvHC8kjh07poSTmprcbnfYK2cBABBVUZi1MRg43rUxd+5crV69Wtu2bdP777+vLVu2aO3atbr66qudDgUAwKDDYMuzeOKJJ7Ry5Ur98Ic/VFtbm7Kzs/XP//zPWrVqldOhAAAYdOJtsKXjhURqaqoqKytVWVnp9FsDABA1rjDHOcTr2s5s2hWH3N1mxqNYLnPTrjwju4zESfOYiTPxy602rnLGGx9/yVgsoB8T2yKY3HohxjbtihVs2gUAAEJGIQEAgB2sbBkUXRsAANgRhd0/BwNaJAAAQMhokQAAwAZWtgyOQgIAADtY2TIoujYAAEDIaJEwpCTrh8ZidX3zK4YimSuvz035u5E4PT4zvxKHjpkbaLXzyseMxQI+L+F45Ne0MREjgBaJoGiRAADAhr4xEuEcYvonAAAIR7xN/6SQAADADpbIDopCAgAAOxgjERSFBAAANrCORHAMtgQAACGjRSIO+RPN9MP5Rxw3EkeSenxuI3GGuX1G4qQldRuJI0lFr9xpJA7TTHEyly/yH8FNxAigayMoCgkAAOwIs2sjXgsJujYAADCIdSQAABiK2EY8KAoJAADsYIxEUHRtAACAkNEiAQCADawjERwtEgAAIGS0SMSh7nQzcVx+c+vGJ8RZKb9lZnyM1gbOJPFoT+SD+AzEwBlRSAAAYAeDLYOikAAAwAbGSARHIQEAgF1xWgyEg8GWAAAgZBQSAADYYTlwsEQ2AABDk1NjJFgiGzHP7zETJ+GcXjOBJH0hpcNInI4eM/94s3eWG4kjSdVFa43FAj7veGpS5GMc90c8Bs6MQgIAADuY/hkUhQQAADYw/TM4BlsCAICQ0SIBAIAddG0ERSEBAIAdFBJB0bUBAABCRotEHPIb+q4mJvnMBJI0ItHMVFO/ZWZH0x2zfmYkDhBN7r8fj3gM63jkY/RhsGVwFBIAANhB10ZQFBIAANhBIREUYyQAAEDIaJEAAMAGxkgER4sEAAB2sPtnUBEpJD788EN997vf1ahRozR8+HBNmTJFdXV1kQgFAMCgUltbq3feeUdlZWXRTsURjndtfPrpp5o5c6aKior08ssv6wtf+IL279+vkSNHOh0KAABj6NoIzvFCYs2aNcrJydEzzzwTOJeXl+d0mEGne1KusVguQ8s7DPf0mAkkKXfEJ0bivNOeZSTO1X8w90nkSHeykTg7r3zMSBwMHq6uyK/x4PKZW8+GWRvBOd618eKLL2ratGm67rrrNGbMGF1yySV66qmnTnt9d3e3vF5vvwMAAAwOjhcS7733ntatW6fzzjtPv//97/WDH/xAixcv1rPPPhv0+oqKCqWnpweOnJwcp1MCACB8Dg22jDeOFxJ+v1+XXnqpHnroIV1yySW67bbbdOutt2r9+vVBr1+xYoXa29sDR3Nzs9MpAQAQNpcDRzxyvJAYO3asJk6c2O/cRRddpA8++CDo9R6PR2lpaf0OAAAwODg+2HLmzJnat29fv3Pvvvuuxo8f73QoAADMYbBlUI63SNxxxx3avXu3HnroIR04cECbNm3Shg0b4ma+LABgaOqb/hnOEY8cb5EoLCzUli1btGLFCj3wwAPKy8tTZWWl5s+f73SoQSXp4w5jsXrP8RiJMyHN3AybbkN7ox81NFXyfw59wUgcSXrvn+42Fgv4PCs58r+3JrcRp0UiuIh8l7/1rW/pW9/6ViTeGgAAxBA27QIAwK44bVUIB4UEAAA2sER2cOz+CQAAQkaLBAAAdjDYMigKCQAAbKBrIzi6NgAAQMhokTCk5wvnGIt1PN3Mtrp55/zNSBxJSkvsMhJn+DAzW6OPHNlpJA4QTa7j/sjH8EU+RgBdG0FRSAAAYANdG8HRtQEAwBB15MgRTZs2TQUFBZo8ebKeeuqpAb8HLRIAANgRh10bqampqqmp0YgRI9TZ2anJkyfrmmuu0ahRo2y/B4UEAAB2xGEh4Xa7NWLECElSd3e3LMuSZQ0sUbo2AACwIRq7f9bU1Gju3LnKzs6Wy+XS1q1bT7mmqqpKEyZMUHJysmbMmKG33nprQDGOHDmi/Px8jRs3TnfddZdGjx49oPspJAAAiFGdnZ3Kz89XVVVV0K9v3rxZ5eXluvfee9XQ0KD8/HzNmTNHbW1tgWv6xj+cfBw6dEiSdO655+rtt99WU1OTNm3apNbW1gHlSNeGIe6/G9zq1mXm2zoh2dz0z4960o3EOdhqv18wHO/dxNbeiH+urshPp3b5eiMeI8Chrg2v19vvtMfjkcfjCXpLSUmJSkpKTvuWa9eu1a233qqFCxdKktavX69t27bp6aef1vLlyyVJjY2NttLLzMxUfn6+Xn/9dV177bV2n4oWCQAA7HBZVtiHJOXk5Cg9PT1wVFRUhJRPT0+P6uvrVVxcHDiXkJCg4uJi7dq1y9Z7tLa26ujRo5Kk9vZ21dTU6IILLhhQHrRIAABgUHNzs9LS0gKvT9cacTaHDx+Wz+dTZmZmv/OZmZnau3evrfc4ePCgbrvttsAgy9tvv11TpkwZUB4UEgAA2OFQ10ZaWlq/QiKapk+fbrvr43QoJAAAsCHWVrYcPXq03G73KYMjW1tblZWV5WywM2CMBAAABhUWFmrixImnnYlhV1JSkqZOnarq6urAOb/fr+rqal122WUOZGoPLRIAANjhUNdGbW2t7a6Njo4OHThwIPC6qalJjY2NysjIUG5ursrLy1VaWqpp06Zp+vTpqqysVGdnZ2AWhwkUEob8fWyysVgZOUeMxBmfdNhIHEn6fx3ZxmIBcEa87f4Zja6Nuro6FRUVBV6Xl5dLkkpLS7Vx40bdcMMN+vjjj7Vq1Sq1tLSooKBA27dvP2UAZiRRSAAAEKNmzZp11iWrFy1apEWLFhnL6WSMkQAAwA7LgcPBMRKxghYJAABscKprYyBjJAYDCgkAAOyIw90/nUDXBgAACBktEgAA2OT0olLxgBYJAADssKzwDwZbIlQJx82VsZNGtRiJ8zffOUbiSFLNu18xEqfpuyuMxAGGguP7Dti4KswYlsFtxB3CYEsAAIagWNtrI1ZQSAAAYAezNoJijAQAAAgZhQQAADa4/OEfYrAlAABDVBR2/xwMaJEAAAAho0XCkKNfNPdPPS39fSNxftd6sZE4kuT+0Nw27ACcscP/m4jH8Hq9Sk9Pj3gcMWvjtCgkAACw43OLSoV8fxyikAAAwAZaJIJjjAQAAAgZhQQAAHZYDhxM/wQAYGhyqmuD6Z8AAAAnRLxF4uGHH9aKFSu0ZMkSVVZWRjrcgP2fhOuMxOn68eVG4khSzrC/GYnzlz05RuJIUsqnxkIBQHDM2ggqooVEbW2tfvGLX+jii82tNwAAQCQwayO4iHVtdHR0aP78+Xrqqac0cuTISIUBAABRFLFCoqysTFdddZWKi4vPeF13d7e8Xm+/AwCAmOPQrI14E5Gujeeff14NDQ2qra0967UVFRW6//77I5EGAACOoWsjOMdbJJqbm7VkyRI999xzSk4++/4IK1asUHt7e+Bobm52OiUAAGIG60icRX19vdra2nTppZcGzvl8PtXU1OjJJ59Ud3e33G534Gsej0cej8fpNAAAcJbf+uwI5/44XEfC8UJi9uzZ+vOf/9zv3MKFC3XhhRdq2bJl/YoIAAAGjXDHOcRp14bjhURqaqomT57c71xKSopGjRp1yvlYYGKbW0masP6nRuJIUpeVZCROxn9TFAIYOlxhjnNwOZlMDGFlSwAAEDIje228+uqrJsIAABA5rGwZFJt2AQBgA9M/g6NrAwAAhIwWCQAA7GDWRlAUEgAA2OCyLLnCGOcQzr2xjELCkEumNBmLtcv7FSNxGtbfYSQOACB2MUYCAAA7/A4cLJENAMDQ5FTXRrwtkU2LBAAACBktEgAA2MGsjaAoJAAAsIOVLYOikAAAwAZWtgyOMRIAACBktEgYUv7F3xuLVbrrFiNxnrjUSBgAiA10bQRFIQEAgA0u/2dHOPfHI7o2AABAyGiRAADADro2gqKQAADADtaRCIquDQAAEDJaJAAAsIFtxIMb8oVEyRdvNxJn9a4eI3EkKXX3cDOBbjITBgBigkNjJAoLC+V2u1VWVqaysjLn8ouSIV9IAABgUrzt/kkhAQCAHZakcNaCiM+eDQoJAADsYIxEcBQSAADYYYW5FkR81hFM/wQAAKGjRQIAADtY2TIoCgkAAOzwS3KFeX8cGvKFxKHrvmQkzqOH/tFIHEl6++d3GIsFABjahnwhAQCAHczaCI5CAgAAOxgjERSzNgAAQMhokQAAwA5aJIKikAAAwA4KiaDo2gAAACEb8i0S3mldRuL8aftFRuJIkr5qLhQADBmsIxHUkC8kAACwg+mfwVFIAABgB2MkgmKMBAAACBktEgAA2OG3JFcYrQp+WiQAABi6+ro2wjli1LFjxzR+/Hj96Ec/GvC9FBIAAAxxq1ev1le/GtqUvyHftbF39lNG4sz7kbndP3WfuVAAMHSE26oQmy0S+/fv1969ezV37lzt2bNnwPfTIgEAgB1R6NqoqanR3LlzlZ2dLZfLpa1bt55yTVVVlSZMmKDk5GTNmDFDb7311oBi/OhHP1JFRcWAc+vjeCFRUVGhwsJCpaamasyYMZo3b5727dvndBgAAOJeZ2en8vPzVVVVFfTrmzdvVnl5ue699141NDQoPz9fc+bMUVtbW+CagoICTZ48+ZTj0KFDeuGFF3T++efr/PPPDzlHx7s2XnvtNZWVlamwsFDHjx/X3XffrW984xt65513lJKS4nQ4AADM8FvhdU+cmLXh9Xr7nfZ4PPJ4PEFvKSkpUUlJyWnfcu3atbr11lu1cOFCSdL69eu1bds2Pf3001q+fLkkqbGx8bT37969W88//7x+85vfqKOjQ729vUpLS9OqVatsP5bjhcT27dv7vd64caPGjBmj+vp6fe1rX3M6HAAAZlj+z45w7peUk5PT7/S9996r++4b+OC2np4e1dfXa8WKFYFzCQkJKi4u1q5du2y9R0VFRaBbY+PGjdqzZ8+AigiZGGzZ3t4uScrIyAj69e7ubnV3dwden1ypAQAQT5qbm5WWlhZ4fbrWiLM5fPiwfD6fMjMz+53PzMzU3r17w87TrogWEn6/X0uXLtXMmTM1efLkoNdUVFTo/vvvj2QaAACEz6ElstPS0voVErHi5ptvDum+iM7aKCsr0549e/T888+f9poVK1aovb09cDQ3N0cyJQAAQuO3wj8cNHr0aLndbrW2tvY739raqqysLEdjnUnEWiQWLVqkl156STU1NRo3btxprzvTIBMTbvlgtpE4L7f8q5E4AIAIcahForCwUG63W2VlZSorKwv57ZKSkjR16lRVV1dr3rx50omegOrqai1atCj0PAfI8ULCsizdfvvt2rJli1599VXl5eU5HQIAgEGrtrbWdtdGR0eHDhw4EHjd1NSkxsZGZWRkKDc3V+Xl5SotLdW0adM0ffp0VVZWqrOzMzCLwwTHC4mysjJt2rRJL7zwglJTU9XS0iJJSk9P1/Dhw50OBwCAGVaYW4GHcGtdXZ2KiooCr8vLyyVJpaWl2rhxo2644QZ9/PHHWrVqlVpaWlRQUKDt27efMgAzkhwvJNatWydJmjVrVr/zzzzzTMgDOQAAiDqHujYGYtasWbLOct+iRYuMdmWcLCJdGwAAIDinxkjEiiG/aRcAALb4/ZLCWJDK/9m9AxkjMRhQSAAAYEcUujYGgyFfSDw3w8w24gAAxKMhX0gAAGALLRJBRXRlSwAA4oZDK1sWFhZq4sSJp90afLChRQIAAIMYbAkAwBBkWX5ZYWwjHs69sYxCAgAAO6wwN96K0zESFBIAANhhWaGtc93v/vgTs4XE1xZXyZ2UHPE49RvuiHgMAAD6sLIlAABDkd8vucIY52CxsiUAAEMXXRtBsY4EAAAIGS0SAADYYPn9ssLo2mD6JwAAQxldG0HRtQEAAEJGIQEAgB3stRFUzHZtvPrj/6u0VLeBSKwjAQCwwbIkhTP987NCIt6mf9IiAQAAQhazLRIAAMQSy2/JcoU+YNKK08GWFBIAANhh+cPs2mD6JwAAQxYtEsExRgIAAIQs5lok+io2b4eZJqCEEV4jcQAAzvN6P/sbbuLT/nGrO6zuiePqdTSfWOGyYqyt5a9//atycnKinQYAYBBpbm7WuHHjIvLeXV1dysvLU0tLS9jvlZaWprFjxyohISFuthGPuULC7/fr0KFDSk1NlcvlMhLT6/UqJydHzc3NcTW3t0+8P594xrgQ788nnjEiLMvS0aNHlZ2drYSEyPXWd3V1qaenJ+z3SUpKUnJysiM5xYqY69pISEiIWFV5NmlpaXH7y60h8HziGeNCvD+feEbHpaenRzxGcnJy3BUATmGwJQAACBmFBAAACBmFhCSPx6N7771XHo8n2qlERLw/n3jGuBDvzyeeEXEq5gZbAgCAwYMWCQAAEDIKCQAAEDIKCQAAEDIKCQAAELIhW0hUVFSosLBQqampGjNmjObNm6d9+/ZFO62Ievjhh+VyubR06dJop+KoDz/8UN/97nc1atQoDR8+XFOmTFFdXV2003KEz+fTypUrlZeXp+HDh+vLX/6yfvKTnwzqXQRramo0d+5cZWdny+VyaevWrf2+blmWVq1apbFjx2r48OEqLi7W/v37o5ZvKM70jL29vVq2bJmmTJmilJQUZWdn63vf+54OHToU1ZwH6mzfx8/7/ve/L5fLpcrKSqM5wowhW0i89tprKisr0+7du7Vjxw719vbqG9/4hjo7O6OdWkTU1tbqF7/4hS6++OJop+KoTz/9VDNnztSwYcP08ssv65133tFjjz2mkSNHRjs1R6xZs0br1q3Tk08+qb/85S9as2aNHnnkET3xxBPRTi1knZ2dys/PV1VVVdCvP/LII3r88ce1fv16vfnmm0pJSdGcOXPU1dVlPNdQnekZjx07poaGBq1cuVINDQ367W9/q3379unb3/52VHIN1dm+j322bNmi3bt3Kzs721huMMyCZVmW1dbWZkmyXnvttWin4rijR49a5513nrVjxw7r61//urVkyZJop+SYZcuWWVdccUW004iYq666yrrlllv6nbvmmmus+fPnRy0nJ0mytmzZEnjt9/utrKws69FHHw2cO3LkiOXxeKxf/epXUcoyPCc/YzBvvfWWJck6ePCgsbycdLpn/Otf/2p98YtftPbs2WONHz/e+tnPfhaV/BBZQ7ZF4mTt7e2SpIyMjGin4riysjJdddVVKi4ujnYqjnvxxRc1bdo0XXfddRozZowuueQSPfXUU9FOyzGXX365qqur9e6770qS3n77bb3xxhsqKSmJdmoR0dTUpJaWln4/q+np6ZoxY4Z27doV1dwiqb29XS6XS+eee260U3GM3+/XggULdNddd2nSpEnRTgcRFHObdkWD3+/X0qVLNXPmTE2ePDna6Tjq+eefV0NDg2pra6OdSkS89957WrduncrLy3X33XertrZWixcvVlJSkkpLS6OdXtiWL18ur9erCy+8UG63Wz6fT6tXr9b8+fOjnVpE9G3TnJmZ2e98ZmamI1s4x6Kuri4tW7ZMN910U1xt5LVmzRolJiZq8eLF0U4FEUYhceIT+549e/TGG29EOxVHNTc3a8mSJdqxY0fc7lrn9/s1bdo0PfTQQ5KkSy65RHv27NH69evjopD49a9/reeee06bNm3SpEmT1NjYqKVLlyo7Ozsunm+o6+3t1fXXXy/LsrRu3bpop+OY+vp6/fznP1dDQ4NcLle000GEDfmujUWLFumll17Szp07o7Z9eaTU19erra1Nl156qRITE5WYmKjXXntNjz/+uBITE+Xz+aKdYtjGjh2riRMn9jt30UUX6YMPPohaTk666667tHz5ct14442aMmWKFixYoDvuuEMVFRXRTi0isrKyJEmtra39zre2tga+Fi/6ioiDBw9qx44dcdUa8frrr6utrU25ubmBvz0HDx7UnXfeqQkTJkQ7PThsyLZIWJal22+/XVu2bNGrr76qvLy8aKfkuNmzZ+vPf/5zv3MLFy7UhRdeqGXLlsntdkctN6fMnDnzlGm77777rsaPHx+1nJx07NgxJST0r/fdbrf8fn/UcoqkvLw8ZWVlqbq6WgUFBZIkr9erN998Uz/4wQ+inZ5j+oqI/fv3a+fOnRo1alS0U3LUggULThmTNWfOHC1YsEALFy6MWl6IjCFbSJSVlWnTpk164YUXlJqaGuh/TU9P1/Dhw6OdniNSU1NPGfORkpKiUaNGxc1YkDvuuEOXX365HnroIV1//fV66623tGHDBm3YsCHaqTli7ty5Wr16tXJzczVp0iT96U9/0tq1a3XLLbdEO7WQdXR06MCBA4HXTU1NamxsVEZGhnJzc7V06VI9+OCDOu+885SXl6eVK1cqOztb8+bNi2reA3GmZxw7dqyuvfZaNTQ06KWXXpLP5wv8/cnIyFBSUlIUM7fvbN/Hk4ujYcOGKSsrSxdccEEUskVERXvaSLRICno888wz0U4touJt+qdlWdbvfvc7a/LkyZbH47EuvPBCa8OGDdFOyTFer9dasmSJlZubayUnJ1tf+tKXrB//+MdWd3d3tFML2c6dO4P+7pWWllrWiSmgK1eutDIzMy2Px2PNnj3b2rdvX7TTHpAzPWNTU9Np//7s3Lkz2qnbdrbv48mY/hm/2EYcAACEbMgPtgQAAKGjkAAAACGjkAAAACGjkAAAACGjkAAAACGjkAAAACGjkAAAACGjkAAAACGjkAAAACGjkAAAACGjkAAAACGjkAAAACH7/1iL6hoUmWwOAAAAAElFTkSuQmCC", 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14.164,\n", - " 14.44 , 14.72 , 15. ], dtype=float16),\n", - " array([ 0.824, 1.1 , 1.376, 1.652, 1.928, 2.203, 2.48 , 2.756,\n", - " 3.031, 3.307, 3.582, 3.86 , 4.133, 4.41 , 4.688, 4.96 ,\n", - " 5.24 , 5.516, 5.79 , 6.066, 6.34 , 6.617, 6.895, 7.168,\n", - " 7.445, 7.723, 7.996, 8.27 , 8.55 , 8.83 , 9.09 , 9.375,\n", - " 9.66 , 9.92 , 10.2 , 10.484, 10.75 , 11.03 , 11.31 , 11.58 ,\n", - " 11.86 , 12.14 , 12.41 , 12.69 , 12.97 , 13.234, 13.516, 13.8 ,\n", - " 14.06 , 14.34 , 14.625], dtype=float16),\n", - " <matplotlib.collections.QuadMesh at 0x7fe60c0a49b0>)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 640x480 with 1 Axes>" + "<Figure size 640x480 with 2 Axes>" ] }, "metadata": {}, @@ -1013,29 +1445,22 @@ "model.eval()\n", "with torch.no_grad():\n", " output = model(batch_src, batch_padding_mask)\n", - "batch_src[0], batch_labels[0], output[0]\n", - "x = batch_labels.detach().to(torch.float16).cpu().numpy().flatten()\n", - "y = output.detach().to(torch.float16).cpu().numpy().flatten()\n", - "plt.hist2d(x, y, bins=50, norm=mpl.colors.LogNorm())" + "x = batch_labels.detach().to(torch.uint8)\n", + "y = output.detach()\n", + "cnts = torch.bincount(x)\n", + "weights = [1/cnts[i.item()].item() for i in x] # normalize by label count\n", + "fig, ax = plt.subplots()\n", + "h = ax.hist2d(x.cpu().numpy().flatten(), y.to(torch.float16).cpu().numpy().flatten(), weights=weights, bins=[15,50], norm=mpl.colors.LogNorm())\n", + "fig.colorbar(h[3], ax=ax)\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "execution_state": "idle", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.353515625" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "evaluate()" ] @@ -1051,7 +1476,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "execution_state": "idle", "metadata": {}, "outputs": [], @@ -1071,7 +1496,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "execution_state": "idle", "metadata": {}, "outputs": [], @@ -1088,117 +1513,10 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "execution_state": "idle", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/100 \t Train Err: 2.8906\n", - "Epoch 2/100 \t Train Err: 0.3340\n", - "Epoch 3/100 \t Train Err: 0.1709\n", - "Epoch 4/100 \t Train Err: 0.2373\n", - "Epoch 5/100 \t Train Err: 0.2520\n", - "Epoch 6/100 \t Train Err: 0.1953\n", - "Epoch 7/100 \t Train Err: 0.1963\n", - "Epoch 8/100 \t Train Err: 0.2236\n", - "Epoch 9/100 \t Train Err: 0.2119\n", - "Epoch 10/100 \t Train Err: 0.1777\n", - "Epoch 11/100 \t Train Err: 0.1660\n", - "Epoch 12/100 \t Train Err: 0.1787\n", - "Epoch 13/100 \t Train Err: 0.1816\n", - "Epoch 14/100 \t Train Err: 0.1562\n", - "Epoch 15/100 \t Train Err: 0.1377\n", - "Epoch 16/100 \t Train Err: 0.1377\n", - "Epoch 17/100 \t Train Err: 0.1387\n", - "Epoch 18/100 \t Train Err: 0.1289\n", - "Epoch 19/100 \t Train Err: 0.1162\n", - "Epoch 20/100 \t Train Err: 0.1079\n", - "Epoch 21/100 \t Train Err: 0.1108\n", - "Epoch 22/100 \t Train Err: 0.1099\n", - "Epoch 23/100 \t Train Err: 0.1021\n", - "Epoch 24/100 \t Train Err: 0.0918\n", - "Epoch 25/100 \t Train Err: 0.0913\n", - "Epoch 26/100 \t Train Err: 0.0913\n", - "Epoch 27/100 \t Train Err: 0.0859\n", - "Epoch 28/100 \t Train Err: 0.0820\n", - "Epoch 29/100 \t Train Err: 0.0767\n", - "Epoch 30/100 \t Train Err: 0.0776\n", - "Epoch 31/100 \t Train Err: 0.0747\n", - "Epoch 32/100 \t Train Err: 0.0713\n", - "Epoch 33/100 \t Train Err: 0.0698\n", - "Epoch 34/100 \t Train Err: 0.0679\n", - "Epoch 35/100 \t Train Err: 0.0664\n", - "Epoch 36/100 \t Train Err: 0.0669\n", - "Epoch 37/100 \t Train Err: 0.0645\n", - "Epoch 38/100 \t Train Err: 0.0601\n", - "Epoch 39/100 \t Train Err: 0.0583\n", - "Epoch 40/100 \t Train Err: 0.0569\n", - "Epoch 41/100 \t Train Err: 0.0564\n", - "Epoch 42/100 \t Train Err: 0.0554\n", - "Epoch 43/100 \t Train Err: 0.0532\n", - "Epoch 44/100 \t Train Err: 0.0520\n", - "Epoch 45/100 \t Train Err: 0.0500\n", - "Epoch 46/100 \t Train Err: 0.0483\n", - "Epoch 47/100 \t Train Err: 0.0457\n", - "Epoch 48/100 \t Train Err: 0.0452\n", - "Epoch 49/100 \t Train Err: 0.0444\n", - "Epoch 50/100 \t Train Err: 0.0430\n", - "Epoch 51/100 \t Train Err: 0.0422\n", - "Epoch 52/100 \t Train Err: 0.0405\n", - "Epoch 53/100 \t Train Err: 0.0408\n", - "Epoch 54/100 \t Train Err: 0.0378\n", - "Epoch 55/100 \t Train Err: 0.0378\n", - "Epoch 56/100 \t Train Err: 0.0369\n", - "Epoch 57/100 \t Train Err: 0.0354\n", - "Epoch 58/100 \t Train Err: 0.0344\n", - "Epoch 59/100 \t Train Err: 0.0337\n", - "Epoch 60/100 \t Train Err: 0.0334\n", - "Epoch 61/100 \t Train Err: 0.0322\n", - "Epoch 62/100 \t Train Err: 0.0312\n", - "Epoch 63/100 \t Train Err: 0.0304\n", - "Epoch 64/100 \t Train Err: 0.0310\n", - "Epoch 65/100 \t Train Err: 0.0304\n", - "Epoch 66/100 \t Train Err: 0.0297\n", - "Epoch 67/100 \t Train Err: 0.0283\n", - "Epoch 68/100 \t Train Err: 0.0281\n", - "Epoch 69/100 \t Train Err: 0.0280\n", - "Epoch 70/100 \t Train Err: 0.0273\n", - "Epoch 71/100 \t Train Err: 0.0267\n", - "Epoch 72/100 \t Train Err: 0.0277\n", - "Epoch 73/100 \t Train Err: 0.0269\n", - "Epoch 74/100 \t Train Err: 0.0258\n", - "Epoch 75/100 \t Train Err: 0.0249\n", - "Epoch 76/100 \t Train Err: 0.0254\n", - "Epoch 77/100 \t Train Err: 0.0245\n", - "Epoch 78/100 \t Train Err: 0.0244\n", - "Epoch 79/100 \t Train Err: 0.0242\n", - "Epoch 80/100 \t Train Err: 0.0237\n", - "Epoch 81/100 \t Train Err: 0.0243\n", - "Epoch 82/100 \t Train Err: 0.0225\n", - "Epoch 83/100 \t Train Err: 0.0225\n", - "Epoch 84/100 \t Train Err: 0.0221\n", - "Epoch 85/100 \t Train Err: 0.0227\n", - "Epoch 86/100 \t Train Err: 0.0222\n", - "Epoch 87/100 \t Train Err: 0.0219\n", - "Epoch 88/100 \t Train Err: 0.0220\n", - "Epoch 89/100 \t Train Err: 0.0210\n", - "Epoch 90/100 \t Train Err: 0.0210\n", - "Epoch 91/100 \t Train Err: 0.0211\n", - "Epoch 92/100 \t Train Err: 0.0208\n", - "Epoch 93/100 \t Train Err: 0.0205\n", - "Epoch 94/100 \t Train Err: 0.0200\n", - "Epoch 95/100 \t Train Err: 0.0208\n", - "Epoch 96/100 \t Train Err: 0.0198\n", - "Epoch 97/100 \t Train Err: 0.0195\n", - "Epoch 98/100 \t Train Err: 0.0197\n", - "Epoch 99/100 \t Train Err: 0.0190\n", - "Epoch 100/100 \t Train Err: 0.0192\n" - ] - } - ], + "outputs": [], "source": [ "for epoch in range(N_TUNE_EPOCHS):\n", " model.train()\n", @@ -1222,21 +1540,10 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "execution_state": "idle", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 640x480 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.suptitle('MSE vs Epochs')\n", "plt.plot(tune_train_err, label='Train', color='blue')\n", @@ -1247,78 +1554,20 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "execution_state": "idle", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0189208984375" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "tune_evaluate()" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "execution_state": "idle", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([[2.6100e+02, 8.9530e+03, 8.2329e+04, ..., 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00],\n", - " [0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00],\n", - " [0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00],\n", - " ...,\n", - " [0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00],\n", - " [0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00],\n", - " [0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 1.0000e+00,\n", - " 0.0000e+00]]),\n", - " array([1. , 1.1 , 1.2 , 1.3 , 1.4 , 1.5 , 1.6 , 1.699, 1.8 ,\n", - " 1.9 , 2. , 2.1 , 2.2 , 2.3 , 2.398, 2.5 , 2.6 , 2.7 ,\n", - " 2.8 , 2.898, 3. , 3.1 , 3.2 , 3.299, 3.398, 3.5 , 3.6 ,\n", - " 3.7 , 3.799, 3.898, 4. , 4.1 , 4.2 , 4.297, 4.4 , 4.5 ,\n", - " 4.6 , 4.7 , 4.797, 4.9 , 5. , 5.098, 5.2 , 5.3 , 5.4 ,\n", - " 5.5 , 5.598, 5.7 , 5.797, 5.9 , 6. ], dtype=float16),\n", - " array([0.8477, 0.913 , 0.9785, 1.044 , 1.109 , 1.176 , 1.241 , 1.307 ,\n", - " 1.372 , 1.4375, 1.503 , 1.568 , 1.635 , 1.699 , 1.766 , 1.831 ,\n", - " 1.896 , 1.962 , 2.027 , 2.094 , 2.158 , 2.225 , 2.29 , 2.355 ,\n", - " 2.422 , 2.486 , 2.55 , 2.617 , 2.684 , 2.75 , 2.814 , 2.879 ,\n", - " 2.945 , 3.012 , 3.076 , 3.143 , 3.207 , 3.273 , 3.338 , 3.404 ,\n", - " 3.469 , 3.535 , 3.602 , 3.666 , 3.732 , 3.797 , 3.863 , 3.928 ,\n", - " 3.994 , 4.062 , 4.125 ], dtype=float16),\n", - " <matplotlib.collections.QuadMesh at 0x7fe6040e22a0>)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 640x480 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "batch_src, batch_labels, batch_padding_mask = mktunebatch(BSZ)\n", "model.eval()\n", @@ -1340,51 +1589,30 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, + "execution_state": "idle", + "metadata": {}, + "outputs": [], + "source": [ + "batch_src, batch_labels, batch_padding_mask = mktunebatch(BSZ, test=True)\n", + "model.eval()\n", + "with torch.no_grad():\n", + " output = model(batch_src, batch_padding_mask)\n", + "print(criterion(output.squeeze(1), batch_labels).item())\n", + "x = batch_labels.detach().to(torch.float16).cpu().numpy().flatten()\n", + "y = output.detach().to(torch.float16).cpu().numpy().flatten()\n", + "plt.hist2d(x, y, bins=50, norm=mpl.colors.LogNorm())" + ] + }, + { + "cell_type": "code", + "execution_count": 14, "execution_state": "idle", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.1767578125\n" - ] - }, - { "data": { - "text/plain": [ - "(array([[ 241., 824., 9690., ..., 0., 0., 0.],\n", - " [ 0., 0., 0., ..., 0., 0., 0.],\n", - " [ 0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [ 0., 0., 0., ..., 0., 0., 0.],\n", - " [ 0., 0., 0., ..., 0., 0., 0.],\n", - " [ 0., 0., 0., ..., 0., 0., 0.]]),\n", - " array([ 1. , 1.18 , 1.36 , 1.54 , 1.721, 1.9 , 2.08 , 2.262,\n", - " 2.441, 2.621, 2.8 , 2.98 , 3.16 , 3.34 , 3.521, 3.701,\n", - " 3.88 , 4.062, 4.242, 4.42 , 4.6 , 4.78 , 4.96 , 5.14 ,\n", - " 5.32 , 5.5 , 5.68 , 5.863, 6.043, 6.223, 6.402, 6.582,\n", - " 6.76 , 6.94 , 7.12 , 7.3 , 7.48 , 7.66 , 7.844, 8.02 ,\n", - " 8.2 , 8.38 , 8.56 , 8.74 , 8.92 , 9.1 , 9.28 , 9.46 ,\n", - " 9.64 , 9.82 , 10. ], dtype=float16),\n", - " array([0.7344, 0.818 , 0.9014, 0.9844, 1.068 , 1.151 , 1.234 , 1.318 ,\n", - " 1.402 , 1.485 , 1.568 , 1.652 , 1.735 , 1.819 , 1.902 , 1.986 ,\n", - " 2.07 , 2.152 , 2.236 , 2.32 , 2.402 , 2.486 , 2.57 , 2.652 ,\n", - " 2.736 , 2.82 , 2.904 , 2.986 , 3.07 , 3.154 , 3.238 , 3.32 ,\n", - " 3.404 , 3.488 , 3.57 , 3.654 , 3.738 , 3.822 , 3.904 , 3.988 ,\n", - " 4.07 , 4.156 , 4.24 , 4.32 , 4.406 , 4.49 , 4.57 , 4.656 ,\n", - " 4.74 , 4.824 , 4.906 ], dtype=float16),\n", - " <matplotlib.collections.QuadMesh at 0x7fe607ee0110>)" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1394,14 +1622,13 @@ } ], "source": [ - "batch_src, batch_labels, batch_padding_mask = mktunebatch(BSZ, test=True)\n", - "model.eval()\n", - "with torch.no_grad():\n", - " output = model(batch_src, batch_padding_mask)\n", - "print(criterion(output.squeeze(1), batch_labels).item())\n", - "x = batch_labels.detach().to(torch.float16).cpu().numpy().flatten()\n", - "y = output.detach().to(torch.float16).cpu().numpy().flatten()\n", - "plt.hist2d(x, y, bins=50, norm=mpl.colors.LogNorm())" + "with open('training-loss') as f:\n", + " train_err = list(map(float, f.read().split()))\n", + " plt.suptitle('Log MSE vs Epochs')\n", + " plt.plot(torch.log(torch.tensor(train_err)[:500]), label='Train', color='blue')\n", + " plt.xlabel('Epochs')\n", + " plt.ylabel('Log MSE')\n", + " plt.show()" ] } ], |