s5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/azureml_py38_PT_TF/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from model import Net\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA Available? False\n"
     ]
    }
   ],
   "source": [
    "# CUDA?\n",
    "cuda = torch.cuda.is_available()\n",
    "print(\"CUDA Available?\", cuda)\n",
    "device = torch.device(\"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train data transformations\n",
    "train_transforms = transforms.Compose([\n",
    "    transforms.RandomApply([transforms.CenterCrop(22), ], p=0.1),\n",
    "    transforms.Resize((28, 28)),\n",
    "    transforms.RandomRotation((-15., 15.), fill=0),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.1307,), (0.3081,)),\n",
    "    ])\n",
    "\n",
    "# Test data transformations\n",
    "test_transforms = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.1307,), (0.3081,))\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = datasets.MNIST('../data', train=True, download=True, transform=train_transforms)\n",
    "test_data = datasets.MNIST('../data', train=False, download=True, transform=test_transforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 512\n",
    "\n",
    "kwargs = {'batch_size': batch_size, 'shuffle': False, 'num_workers': 2, 'pin_memory': True}\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(test_data, **kwargs)\n",
    "train_loader = torch.utils.data.DataLoader(train_data, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_data, batch_label = next(iter(train_loader)) \n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "for i in range(12):\n",
    "  plt.subplot(3,4,i+1)\n",
    "  plt.tight_layout()\n",
    "  plt.imshow(batch_data[i].squeeze(0), cmap='gray')\n",
    "  plt.title(batch_label[i].item())\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data to plot accuracy and loss graphs\n",
    "train_losses = []\n",
    "test_losses = []\n",
    "train_acc = []\n",
    "test_acc = []\n",
    "\n",
    "test_incorrect_pred = {'images': [], 'ground_truths': [], 'predicted_vals': []}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusting learning rate of group 0 to 1.0000e-02.\n",
      "Epoch 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.5931 Batch_id=117 Accuracy=40.28: 100%|██████████| 118/118 [01:00<00:00,  1.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.3689, Accuracy: 8909/10000 (89.09%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-02.\n",
      "Epoch 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.3315 Batch_id=117 Accuracy=92.25: 100%|██████████| 118/118 [00:58<00:00,  2.02it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0993, Accuracy: 9692/10000 (96.92%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-02.\n",
      "Epoch 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.2811 Batch_id=117 Accuracy=95.84: 100%|██████████| 118/118 [00:58<00:00,  2.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0665, Accuracy: 9798/10000 (97.98%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-02.\n",
      "Epoch 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.2817 Batch_id=117 Accuracy=96.91: 100%|██████████| 118/118 [00:58<00:00,  2.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0605, Accuracy: 9800/10000 (98.00%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-02.\n",
      "Epoch 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.2142 Batch_id=117 Accuracy=97.38: 100%|██████████| 118/118 [00:57<00:00,  2.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0468, Accuracy: 9842/10000 (98.42%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-02.\n",
      "Epoch 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.1860 Batch_id=117 Accuracy=97.71: 100%|██████████| 118/118 [00:59<00:00,  1.97it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0405, Accuracy: 9872/10000 (98.72%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-02.\n",
      "Epoch 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.1594 Batch_id=117 Accuracy=97.92: 100%|██████████| 118/118 [00:58<00:00,  2.02it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0395, Accuracy: 9878/10000 (98.78%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-02.\n",
      "Epoch 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.1736 Batch_id=117 Accuracy=98.14: 100%|██████████| 118/118 [00:57<00:00,  2.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0338, Accuracy: 9892/10000 (98.92%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-02.\n",
      "Epoch 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.1601 Batch_id=117 Accuracy=98.31: 100%|██████████| 118/118 [00:57<00:00,  2.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0307, Accuracy: 9910/10000 (99.10%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-02.\n",
      "Epoch 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.1427 Batch_id=117 Accuracy=98.41: 100%|██████████| 118/118 [00:57<00:00,  2.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0291, Accuracy: 9906/10000 (99.06%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-02.\n",
      "Epoch 11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.1507 Batch_id=117 Accuracy=98.51: 100%|██████████| 118/118 [00:57<00:00,  2.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0269, Accuracy: 9917/10000 (99.17%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-02.\n",
      "Epoch 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.1056 Batch_id=117 Accuracy=98.60: 100%|██████████| 118/118 [01:00<00:00,  1.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0302, Accuracy: 9910/10000 (99.10%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-02.\n",
      "Epoch 13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.1232 Batch_id=117 Accuracy=98.57: 100%|██████████| 118/118 [00:57<00:00,  2.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0300, Accuracy: 9897/10000 (98.97%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-02.\n",
      "Epoch 14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.1037 Batch_id=117 Accuracy=98.71: 100%|██████████| 118/118 [00:58<00:00,  2.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0273, Accuracy: 9906/10000 (99.06%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-02.\n",
      "Epoch 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.0868 Batch_id=117 Accuracy=98.84: 100%|██████████| 118/118 [00:57<00:00,  2.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0298, Accuracy: 9903/10000 (99.03%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-03.\n",
      "Epoch 16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.0746 Batch_id=117 Accuracy=99.04: 100%|██████████| 118/118 [00:57<00:00,  2.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0202, Accuracy: 9929/10000 (99.29%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-03.\n",
      "Epoch 17\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.0662 Batch_id=117 Accuracy=99.09: 100%|██████████| 118/118 [00:58<00:00,  2.01it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0197, Accuracy: 9933/10000 (99.33%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-03.\n",
      "Epoch 18\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.0367 Batch_id=117 Accuracy=99.09: 100%|██████████| 118/118 [00:58<00:00,  2.02it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0197, Accuracy: 9937/10000 (99.37%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-03.\n",
      "Epoch 19\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.0583 Batch_id=117 Accuracy=99.09: 100%|██████████| 118/118 [00:57<00:00,  2.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0198, Accuracy: 9930/10000 (99.30%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-03.\n",
      "Epoch 20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train: Loss=0.0573 Batch_id=117 Accuracy=99.12: 100%|██████████| 118/118 [00:58<00:00,  2.01it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0194, Accuracy: 9936/10000 (99.36%)\n",
      "\n",
      "Adjusting learning rate of group 0 to 1.0000e-03.\n"
     ]
    }
   ],
   "source": [
    "model = Net().to(device)\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.1, verbose=True)\n",
    "# New Line\n",
    "criterion =  F.nll_loss\n",
    "num_epochs = 20\n",
    "\n",
    "for epoch in range(1, num_epochs+1):\n",
    "  print(f'Epoch {epoch}')\n",
    "  train_acc,train_losses  =  train(model, device, train_loader, optimizer, criterion)\n",
    "  test_acc,test_losses =  test(model, device, test_loader, criterion)\n",
    "  scheduler.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Test Accuracy')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,2,figsize=(15,10))\n",
    "axs[0, 0].plot(train_losses)\n",
    "axs[0, 0].set_title(\"Training Loss\")\n",
    "axs[1, 0].plot(train_acc)\n",
    "axs[1, 0].set_title(\"Training Accuracy\")\n",
    "axs[0, 1].plot(test_losses)\n",
    "axs[0, 1].set_title(\"Test Loss\")\n",
    "axs[1, 1].plot(test_acc)\n",
    "axs[1, 1].set_title(\"Test Accuracy\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8 - Pytorch and Tensorflow",
   "language": "python",
   "name": "python38-azureml-pt-tf"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}