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   "cell_type": "code",
   "source": "import matplotlib.pyplot as plt\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset, DataLoader\nimport pandas as pd",
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   "cell_type": "code",
   "source": "%cd /kaggle/working\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\ntrain_df=pd.read_csv(\"/kaggle/input/competitions/digit-recognizer/train.csv\")\ntest_df=pd.read_csv(\"/kaggle/input/competitions/digit-recognizer/test.csv\")",
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   "execution_count": null
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   "cell_type": "code",
   "source": "fig, ax = plt.subplots(nrows=2, ncols=2, sharex='all', sharey='all')\nax = ax.flatten()\nfor i in range(4):\n    img = train_df.iloc[i][1:].to_numpy().reshape(28,28)\n    # ax[i].imshow(img,cmap='Greys')\n    ax[i].imshow(img)\n    ax[i].set_title(f'{train_df.iloc[i][0]}')",
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  {
   "cell_type": "code",
   "source": "class DatasetMnist(Dataset):\n    def __init__(self,df):\n        self.df=df\n    def __len__(self):\n        return len(self.df)\n    def __getitem__(self, idx):\n        item= {\"Data\": torch.tensor(self.df.iloc[idx][1:].to_numpy().reshape(28, 28),dtype=torch.float), \"label\": torch.tensor(self.df.iloc[idx][0],dtype=torch.long)}\n        return item\nbatch_size =64\ntrain_dataset = DatasetMnist(train_df)\ntrain_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)",
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  {
   "cell_type": "code",
   "source": "class MnistModule(nn.Module):\n    def __init__(self):\n        super(MnistModule, self).__init__()\n        self.fc1 = nn.Linear(28*28, 512)\n        self.relu = nn.ReLU()\n        self.fc2 = nn.Linear(512, 256)\n        self.fc3 = nn.Linear(256, 128)\n        self.fc4 = nn.Linear(128,10)\n    def forward(self,X):\n        return self.fc4(self.relu(self.fc3(self.relu(self.fc2(self.relu(self.fc1(X.view(-1,28*28))))))))",
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   "execution_count": null
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  {
   "cell_type": "code",
   "source": "model = MnistModule()\nmodel=model.to(device)\nloss_func = nn.CrossEntropyLoss()\noptimizer = torch.optim.AdamW(model.parameters(), lr=2e-4)\nscheduler=torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)\n",
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   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": "epochs = 30\nfor epoch in range(epochs):\n    model.train()\n    training_loss=0\n    for batch in train_loader:\n        optimizer.zero_grad()\n        X = batch['Data']\n        labels=batch['label']\n        X=X.to(device)\n        labels=labels.to(device)\n        #print(model(X),'\\n',labels)\n        loss = loss_func(model(X),labels)\n        loss.backward()\n        optimizer.step()\n        training_loss+=loss.item()\n    scheduler.step()\n    print(f\"train_loss: {training_loss/len(train_loader)}\")",
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  {
   "cell_type": "code",
   "source": [
    "class DatasetMnistTest(Dataset):\n",
    "    def __init__(self,df):\n",
    "        self.df=df\n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "    def __getitem__(self, idx):\n",
    "        item= {\"Data\": torch.tensor(self.df.iloc[idx].to_numpy().reshape(28, 28),dtype=torch.float)}\n",
    "        return item\n",
    "test_dataset = DatasetMnistTest(test_df)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
    "all_preds = []\n",
    "with torch.no_grad():\n",
    "    for batch in test_loader:\n",
    "        input_ids = batch['Data'].to(device)\n",
    "        outputs = model(input_ids)\n",
    "        preds = torch.argmax(outputs, dim=1)\n",
    "        all_preds.extend(preds.cpu().numpy())\n",
    "idd = range(1,len(all_preds)+1)\n",
    "submission = pd.DataFrame({\n",
    "    'ImageId':idd,\n",
    "    'Label': all_preds\n",
    "})\n",
    "print(submission)\n",
    "submission.to_csv('submission.csv', index=False)\n",
    "print(\"Submission saved!\")"
   ],
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 ]
}