217 lines
8.4 KiB
Plaintext
217 lines
8.4 KiB
Plaintext
{
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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"language_info": {
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"name": "python",
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"version": "3.12.13",
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"nbformat": 4,
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"cells": [
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{
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"cell_type": "code",
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"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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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-07-03T10:17:12.326817130Z",
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"start_time": "2026-07-03T10:17:12.293250353Z"
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"execution": {
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"iopub.status.busy": "2026-07-04T16:16:09.940312Z",
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"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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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-07-03T10:17:15.014258006Z",
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"start_time": "2026-07-03T10:17:12.346500913Z"
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"execution": {
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"iopub.status.busy": "2026-07-04T16:16:12.857730Z",
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"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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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-07-03T10:17:15.565961732Z",
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"start_time": "2026-07-03T10:17:15.073738808Z"
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},
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"trusted": true,
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"execution": {
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"iopub.status.busy": "2026-07-04T16:16:16.049873Z",
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"iopub.execute_input": "2026-07-04T16:16:16.050089Z",
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}
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},
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"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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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-07-03T10:17:15.669173315Z",
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"start_time": "2026-07-03T10:17:15.616933550Z"
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},
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"trusted": true,
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"execution": {
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"iopub.status.busy": "2026-07-04T16:16:16.370253Z",
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"iopub.execute_input": "2026-07-04T16:16:16.370426Z",
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"iopub.status.idle": "2026-07-04T16:16:16.375630Z",
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"shell.execute_reply.started": "2026-07-04T16:16:16.370409Z",
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"shell.execute_reply": "2026-07-04T16:16:16.375171Z"
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}
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},
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"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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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-07-03T10:17:15.729366448Z",
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"start_time": "2026-07-03T10:17:15.670668930Z"
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},
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"trusted": true,
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"execution": {
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"iopub.status.busy": "2026-07-04T16:16:16.376258Z",
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"iopub.execute_input": "2026-07-04T16:16:16.376401Z",
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"iopub.status.idle": "2026-07-04T16:16:16.389616Z",
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"shell.execute_reply.started": "2026-07-04T16:16:16.376386Z",
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"shell.execute_reply": "2026-07-04T16:16:16.388703Z"
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}
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},
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"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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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-07-03T10:17:15.792463697Z",
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"start_time": "2026-07-03T10:17:15.730536380Z"
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},
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"trusted": true,
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"execution": {
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"iopub.status.busy": "2026-07-04T16:16:16.390304Z",
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"iopub.execute_input": "2026-07-04T16:16:16.390504Z",
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"iopub.status.idle": "2026-07-04T16:16:18.580638Z",
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"shell.execute_reply.started": "2026-07-04T16:16:16.390482Z",
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"shell.execute_reply": "2026-07-04T16:16:18.580038Z"
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}
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},
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"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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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-07-03T10:18:55.758148762Z",
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"start_time": "2026-07-03T10:17:31.736342843Z"
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},
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"trusted": true,
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"execution": {
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"iopub.status.busy": "2026-07-04T16:16:18.582512Z",
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"iopub.execute_input": "2026-07-04T16:16:18.582874Z",
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"iopub.status.idle": "2026-07-04T16:18:53.082304Z",
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"shell.execute_reply.started": "2026-07-04T16:16:18.582856Z",
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"shell.execute_reply": "2026-07-04T16:18:53.081057Z"
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}
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},
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"source": [
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"class DatasetMnistTest(Dataset):\n",
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" def __init__(self,df):\n",
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" self.df=df\n",
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" def __len__(self):\n",
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" return len(self.df)\n",
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" def __getitem__(self, idx):\n",
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" item= {\"Data\": torch.tensor(self.df.iloc[idx].to_numpy().reshape(28, 28),dtype=torch.float)}\n",
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" return item\n",
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"test_dataset = DatasetMnistTest(test_df)\n",
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"test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
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"all_preds = []\n",
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"with torch.no_grad():\n",
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" for batch in test_loader:\n",
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" input_ids = batch['Data'].to(device)\n",
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" outputs = model(input_ids)\n",
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" preds = torch.argmax(outputs, dim=1)\n",
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" all_preds.extend(preds.cpu().numpy())\n",
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"idd = range(1,len(all_preds)+1)\n",
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"submission = pd.DataFrame({\n",
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" 'ImageId':idd,\n",
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" 'Label': all_preds\n",
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"})\n",
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"print(submission)\n",
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"submission.to_csv('submission.csv', index=False)\n",
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"print(\"Submission saved!\")"
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],
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"metadata": {
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"trusted": true,
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"execution": {
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"iopub.status.busy": "2026-07-04T16:19:40.600448Z",
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"iopub.execute_input": "2026-07-04T16:19:40.601006Z",
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"iopub.status.idle": "2026-07-04T16:19:41.910494Z",
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"shell.execute_reply.started": "2026-07-04T16:19:40.600982Z",
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"shell.execute_reply": "2026-07-04T16:19:41.909862Z"
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}
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},
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"source": "",
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"metadata": {
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"trusted": true
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},
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"outputs": [],
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"execution_count": null
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}
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]
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}
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