demo.ipynb

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   "source": [
    "import pandas as pd\n",
    "import torch \n",
    "\n",
    "df=pd.read_csv(\"train_clean_kalman.csv\")\n",
    "df['signal']=df['signal']/df['signal'].max()"
   ]
  },
  {
   "cell_type": "code",
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    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['open_channels'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create time series dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
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   "source": [
    "class TimesereisDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, df:pd.Series(), seqLen=10):\n",
    "        super().__init__()\n",
    "        self.df=df\n",
    "        self.seqLen=seqLen\n",
    "    \n",
    "    def __len__(self):\n",
    "        return self.df.shape[0]-self.seqLen-1\n",
    "    \n",
    "    def __getitem__(self,index):\n",
    "        x=self.df.iloc[index:index+self.seqLen,1].values\n",
    "        y=self.df.iloc[index+self.seqLen-1,2] \n",
    "        return x,y   "
   ]
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   "execution_count": 4,
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    "execution": {
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   "outputs": [],
   "source": [
    "seqLen=50\n",
    "timeSeriesDataset = TimesereisDataset(df,seqLen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
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   "source": [
    "trainNumbers=int(len(timeSeriesDataset)*0.9)\n",
    "trainDataset,testDataset=torch.utils.data.random_split(timeSeriesDataset,[trainNumbers,len(timeSeriesDataset)-trainNumbers])"
   ]
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   "execution_count": 6,
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   "source": [
    "trainDataLoader=torch.utils.data.DataLoader(trainDataset,batch_size=8,shuffle=True)\n",
    "testDataLoader=torch.utils.data.DataLoader(testDataset,batch_size=8,shuffle=True)"
   ]
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
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    },
    "tags": []
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   "outputs": [],
   "source": [
    "from Wavenet import WaveNet,WaveNetClassifier\n",
    "from tqdm import tqdm \n",
    "\n",
    "# device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "device=\"cpu\"\n",
    "wavenetClassifierModel=WaveNetClassifier(seqLen,df['open_channels'].max()+1)\n",
    "wavenetClassifierModel.to(device)\n",
    "\n",
    "wavenetClassifierModel.train()\n",
    "\n",
    "optimizer=torch.optim.AdamW(wavenetClassifierModel.parameters(), lr=0.01)\n",
    "scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)\n",
    "lossFunction = torch.nn.CrossEntropyLoss()"
   ]
  },
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   "execution_count": 8,
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   "source": [
    "def calc_accuracy(Out,Y):\n",
    "    max_vals, max_indices = torch.max(Out,1)\n",
    "    train_acc = (max_indices == Y).sum().item()/max_indices.size()[0]\n",
    "    return train_acc"
   ]
  },
  {
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   "execution_count": 9,
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     "text": [
      "Training: 0it [00:00, ?it/s]/home/adhiraj/miniconda3/envs/wavenet/lib/python3.10/site-packages/torch/nn/modules/conv.py:309: UserWarning: Using padding='same' with even kernel lengths and odd dilation may require a zero-padded copy of the input be created (Triggered internally at /opt/conda/conda-bld/pytorch_1682343967769/work/aten/src/ATen/native/Convolution.cpp:1003.)\n",
      "  return F.conv1d(input, weight, bias, self.stride,\n",
      "\n",
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      "Training: 6it [00:02,  2.89it/s]"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss for step 0 : 2.4244583080064004  :  Accuracy: 3.3582089552238807 %\n"
     ]
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    {
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     ]
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      "loss for step 500 : 2.305035905458441  :  Accuracy: 23.942786069651742 %\n"
     ]
    },
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     ]
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     ]
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     ]
    },
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     ]
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     ]
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      "loss for step 59500 : 2.295165765937881  :  Accuracy: 24.62686567164179 %\n"
     ]
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     ]
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      "loss for step 60000 : 2.286797534173994  :  Accuracy: 25.497512437810947 %\n"
     ]
    },
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     ]
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      "loss for step 60500 : 2.2960530703340596  :  Accuracy: 24.129353233830845 %\n"
     ]
    },
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     ]
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      "loss for step 61000 : 2.29391723841577  :  Accuracy: 23.880597014925375 %\n"
     ]
    },
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     ]
    },
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      "loss for step 61500 : 2.297572692235311  :  Accuracy: 24.564676616915424 %\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training: 61674it [29:59, 34.28it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[9], line 18\u001b[0m\n\u001b[1;32m     15\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m     17\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[0;32m---> 18\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m step \u001b[38;5;241m%\u001b[39m globalStep \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     21\u001b[0m    \n\u001b[1;32m     22\u001b[0m    \u001b[38;5;66;03m# scheduler.step()\u001b[39;00m\n\u001b[1;32m     23\u001b[0m    \u001b[38;5;66;03m# print(output.detach().numpy())\u001b[39;00m\n\u001b[1;32m     24\u001b[0m    \u001b[38;5;66;03m# print(y_train.numpy())\u001b[39;00m\n\u001b[1;32m     26\u001b[0m    \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n",
      "File \u001b[0;32m~/miniconda3/envs/wavenet/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:69\u001b[0m, in \u001b[0;36mLRScheduler.__init__.<locals>.with_counter.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     67\u001b[0m instance\u001b[38;5;241m.\u001b[39m_step_count \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m     68\u001b[0m wrapped \u001b[38;5;241m=\u001b[39m func\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__get__\u001b[39m(instance, \u001b[38;5;28mcls\u001b[39m)\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mwrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/wavenet/lib/python3.10/site-packages/torch/optim/optimizer.py:280\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    276\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    277\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs),\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    278\u001b[0m                                \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 280\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    281\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[1;32m    283\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/wavenet/lib/python3.10/site-packages/torch/optim/optimizer.py:33\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.<locals>._use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     32\u001b[0m     torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m---> 33\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     34\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     35\u001b[0m     torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(prev_grad)\n",
      "File \u001b[0;32m~/miniconda3/envs/wavenet/lib/python3.10/site-packages/torch/optim/adamw.py:171\u001b[0m, in \u001b[0;36mAdamW.step\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m    158\u001b[0m     beta1, beta2 \u001b[38;5;241m=\u001b[39m group[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbetas\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    160\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_group(\n\u001b[1;32m    161\u001b[0m         group,\n\u001b[1;32m    162\u001b[0m         params_with_grad,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    168\u001b[0m         state_steps,\n\u001b[1;32m    169\u001b[0m     )\n\u001b[0;32m--> 171\u001b[0m     \u001b[43madamw\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    172\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams_with_grad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    173\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    174\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    175\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    176\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    177\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    178\u001b[0m \u001b[43m        \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamsgrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    179\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    180\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    181\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    182\u001b[0m \u001b[43m        \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mweight_decay\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    183\u001b[0m \u001b[43m        \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meps\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    184\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmaximize\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    185\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforeach\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mforeach\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    186\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcapturable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    187\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdifferentiable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    188\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfused\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfused\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    189\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgrad_scale\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    190\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfound_inf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    191\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    193\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
      "File \u001b[0;32m~/miniconda3/envs/wavenet/lib/python3.10/site-packages/torch/optim/adamw.py:321\u001b[0m, in \u001b[0;36madamw\u001b[0;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[1;32m    318\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    319\u001b[0m     func \u001b[38;5;241m=\u001b[39m _single_tensor_adamw\n\u001b[0;32m--> 321\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    322\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    323\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    324\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    325\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    326\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    327\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    328\u001b[0m \u001b[43m    \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamsgrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    329\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    330\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    331\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    332\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    333\u001b[0m \u001b[43m    \u001b[49m\u001b[43meps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    334\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaximize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    335\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcapturable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    336\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdifferentiable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    337\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrad_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    338\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfound_inf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    339\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/wavenet/lib/python3.10/site-packages/torch/optim/adamw.py:390\u001b[0m, in \u001b[0;36m_single_tensor_adamw\u001b[0;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable)\u001b[0m\n\u001b[1;32m    388\u001b[0m \u001b[38;5;66;03m# Decay the first and second moment running average coefficient\u001b[39;00m\n\u001b[1;32m    389\u001b[0m exp_avg\u001b[38;5;241m.\u001b[39mmul_(beta1)\u001b[38;5;241m.\u001b[39madd_(grad, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m beta1)\n\u001b[0;32m--> 390\u001b[0m \u001b[43mexp_avg_sq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmul_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbeta2\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39maddcmul_(grad, grad, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m beta2)\n\u001b[1;32m    392\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m capturable \u001b[38;5;129;01mor\u001b[39;00m differentiable:\n\u001b[1;32m    393\u001b[0m     step \u001b[38;5;241m=\u001b[39m step_t\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "epochs=1\n",
    "globalStep=500\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    for step, (x_train,y_train) in tqdm(enumerate(trainDataLoader),desc=\"Training\"):\n",
    "         x_train = torch.unsqueeze(x_train,dim=1).float()\n",
    "            \n",
    "         x_train.to(device)\n",
    "         y_train.to(device)\n",
    "            \n",
    "         output = wavenetClassifierModel(x_train)\n",
    "         output = torch.squeeze(output,dim=1)\n",
    "\n",
    "         loss= lossFunction(output,y_train)\n",
    "         optimizer.zero_grad()\n",
    "            \n",
    "         loss.backward()\n",
    "         optimizer.step()\n",
    "            \n",
    "         if step % globalStep == 0:\n",
    "            \n",
    "            # scheduler.step()\n",
    "            # print(output.detach().numpy())\n",
    "            # print(y_train.numpy())\n",
    "            \n",
    "            with torch.no_grad():\n",
    "                accuracy=0\n",
    "                loss=0\n",
    "                \n",
    "                for stepTest, (x_test,y_test) in tqdm(enumerate(testDataLoader),desc=\"Validation\"):\n",
    "                    x_test.to(device)\n",
    "                    y_test.to(device)\n",
    "                    \n",
    "                    x_test = torch.unsqueeze(x_test,dim=1).float()\n",
    "                    \n",
    "                    output=wavenetClassifierModel(x_test)\n",
    "                    output = torch.squeeze(output,dim=1)\n",
    "                    \n",
    "                    accuracy+=calc_accuracy(output,y_test)*100\n",
    "                    loss+= lossFunction(output,y_test).item()\n",
    "                    \n",
    "                    if stepTest>200:\n",
    "                        break\n",
    "                        \n",
    "            print(f\"loss for step {step} : {loss/stepTest}  :  Accuracy: {accuracy/stepTest} %\")\n",
    "         \n",
    "    print(f\"epoch {epoch}\")"
   ]
  }
 ],
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