PyTorchMNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Setup of all libraries (follow straight order to not get errors!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision.datasets import MNIST\n",
    "from torchvision import transforms as tfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data    import DataLoader\n",
    "from torch.nn.functional import cross_entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "from torchsummary import summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Device Config "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda:0') if torch.cuda.is_available else torch.device('cpu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Get and transform data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_tfs = tfs.Compose([\n",
    "    tfs.ToTensor(),\n",
    "    tfs.Normalize((0.5), (0.5))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "root = './'\n",
    "train = MNIST(root, train = True, transform = data_tfs, download = True)\n",
    "test = MNIST(root, train = False, transform = data_tfs, download = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data size:\n",
      "\t train 60000,\n",
      "\t test 60000\n",
      "Data shape:\n",
      "\t train torch.Size([1, 28, 28]),\n",
      "\t test torch.Size([1, 28, 28])\n"
     ]
    }
   ],
   "source": [
    "print(f'Data size:\\n\\t train {len(train)},\\n\\t test {len(train)}')\n",
    "print(f'Data shape:\\n\\t train {train[0][0].shape},\\n\\t test {test[0][0].shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Create Batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "\n",
    "train_loader = DataLoader(train, batch_size = batch_size, drop_last = True)\n",
    "test_loader  = DataLoader(test,  batch_size = batch_size, drop_last = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([128, 1, 28, 28]), torch.Size([128]))"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_batch, y_batch = next(iter(train_loader))\n",
    "x_batch.shape, y_batch.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Set up the first model\n",
    "\n",
    "Fitting the linear model:\n",
    "1. For every class computate his own weights `logits` \n",
    "2. Use `softmax` on every `logits` to get probability\n",
    "3. Learning with `sgd` based on `cross-entropy` function of losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = 28 * 28\n",
    "classes  = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 5\n",
    "lr      = 1e-2\n",
    "history = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.0145,  0.0217, -0.0019,  ...,  0.0110, -0.0316,  0.0147],\n",
       "        [-0.0099,  0.0233,  0.0149,  ...,  0.0016, -0.0317, -0.0268],\n",
       "        [-0.0123,  0.0235, -0.0010,  ..., -0.0226,  0.0094, -0.0327],\n",
       "        ...,\n",
       "        [-0.0312,  0.0289,  0.0217,  ..., -0.0011, -0.0321, -0.0062],\n",
       "        [ 0.0224,  0.0009,  0.0134,  ..., -0.0127, -0.0332,  0.0350],\n",
       "        [ 0.0116, -0.0002,  0.0203,  ..., -0.0336,  0.0350, -0.0335]],\n",
       "       requires_grad=True)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Initial weights \n",
    "w = torch.FloatTensor(features, classes).uniform_(-1, 1) / features**0.5\n",
    "w.requires_grad_()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1, \t loss: 0.1966225802898407\n",
      "2, \t loss: 0.14063414931297302\n",
      "3, \t loss: 0.12039943039417267\n",
      "4, \t loss: 0.10950277745723724\n",
      "5, \t loss: 0.1024676188826561\n"
     ]
    }
   ],
   "source": [
    "for i in range(epochs):\n",
    "    for x_batch, y_batch in train_loader:\n",
    "\n",
    "        x_batch = x_batch.reshape(x_batch.shape[0], -1)\n",
    "\n",
    "        logits         = x_batch @ w\n",
    "        probabilities  = torch.exp(logits) / torch.exp(logits).sum(dim = 1, keepdim = True)\n",
    "\n",
    "        loss = -torch.log(probabilities[range(batch_size), y_batch]).mean()\n",
    "        history.append(loss.item())\n",
    "        loss.backward() #computates gradients \n",
    "\n",
    "        grad = w.grad\n",
    "        with torch.no_grad():\n",
    "            w -= lr * grad\n",
    "        w.grad.zero_()\n",
    "    print(f'{i+1}, \\t loss: {history[-1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize  = (10, 7))\n",
    "plt.plot(history)\n",
    "plt.title('Loss by batch iteration')\n",
    "plt.ylabel('Entropy')\n",
    "plt.xlabel('Iteration')\n",
    "plt.grid(alpha = 0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy 0.907\n"
     ]
    }
   ],
   "source": [
    "acc = 0\n",
    "batches = 0\n",
    "\n",
    "for x_batch, y_batch in test_loader:\n",
    "    batches += 1\n",
    "\n",
    "    x_batch = x_batch.view(x_batch.shape[0], -1)\n",
    "    y_batch = y_batch\n",
    "\n",
    "    preds = torch.argmax(x_batch @ w, dim = 1)\n",
    "    acc += (preds == y_batch).cpu().numpy().mean()\n",
    "\n",
    "print(f'Test accuracy {acc / batches:.3}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Set up of second, more complicated model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chosing the model\n",
    "\n",
    "`nn.Linear`\n",
    "\n",
    "`nn.Softmax`, `nn.LogSoftmax`\n",
    "\n",
    "`nn.ReLU`, `nn.ELU`, `nn.LeakyReLU`\n",
    "\n",
    "`nn.Tanh`, `nn.Sigmoid`\n",
    "\n",
    "`nn.LSTM`, `nn.GRU`\n",
    "\n",
    "`nn.Conv1d`, `nn.Conv2d`\n",
    "\n",
    "`nn.MaxPool1d`, `nn.AdaptiveMaxPool1d` and others pooling\n",
    "\n",
    "`nn.BatchNorm1d`, `nn.BatchNorm2d`\n",
    "\n",
    "`nn.Dropout`\n",
    "\n",
    "losses: `nn.CrossEntropyLoss`, `nn.NLLLoss`, `nn.MSELoss`\n",
    "etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Linear(in_features=784, out_features=64, bias=True)\n",
       "  (1): ReLU()\n",
       "  (2): Linear(in_features=64, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = nn.Sequential(\n",
    "    nn.Linear(features, 64),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(64, classes)\n",
    ")\n",
    "\n",
    "# It is the same as:\n",
    "# model = nn.Sequential()\n",
    "# model.add_module('0', nn.Linear(features, 64))\n",
    "# model.add_module('1', nn.Linear(64, classes))\n",
    "\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Linear-1                  [228, 64]          50,240\n",
      "              ReLU-2                  [228, 64]               0\n",
      "            Linear-3                  [228, 10]             650\n",
      "================================================================\n",
      "Total params: 50,890\n",
      "Trainable params: 50,890\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.68\n",
      "Forward/backward pass size (MB): 0.24\n",
      "Params size (MB): 0.19\n",
      "Estimated Total Size (MB): 1.12\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "summary(model, (features,), batch_size = 228) #to check if everything is ok"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ideas for optimizer\n",
    "\n",
    "`torch.optim.Adam` - [paper] (https://arxiv.org/abs/1412.6980)\n",
    "\n",
    "`torch.optim.SGD` - casual SGD\n",
    "\n",
    "`torch.optim.Adagrad` - [article] (https://jmlr.org/papers/v12/duchi11a.html)\n",
    "\n",
    "`torch.optim.RMSprop` - [slides] (https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "criteria  = nn.CrossEntropyLoss() #(logsoftmax + negative likelihood)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3, betas = (0.9, 0.99))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 3\n",
    "history = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1, \t loss: 0.03192576393485069\n",
      "2, \t loss: 0.025674520060420036\n",
      "3, \t loss: 0.02014310471713543\n"
     ]
    }
   ],
   "source": [
    "for i in range(epochs):\n",
    "    for x_batch, y_batch in train_loader:\n",
    "\n",
    "        x_batch = x_batch.view(x_batch.shape[0], -1).to(device)\n",
    "        y_batch = y_batch.to(device)\n",
    "\n",
    "        logits = model(x_batch)\n",
    "\n",
    "        loss = criteria(logits, y_batch)\n",
    "        history.append(loss.item())\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "\n",
    "        optimizer.step()\n",
    "    print(f'{i+1}, \\t loss: {history[-1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 7))\n",
    "plt.plot(history)\n",
    "plt.title('Loss during the iterations')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Entropy')\n",
    "plt.grid(alpha = 0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score is 0.959\n"
     ]
    }
   ],
   "source": [
    "acc     = 0\n",
    "batches = 0\n",
    "\n",
    "for x_batch, y_batch in test_loader:\n",
    "    batches += 1\n",
    "\n",
    "    x_batch = x_batch.view(x_batch.shape[0], -1).to(device)\n",
    "    y_batch = y_batch.to(device)\n",
    "\n",
    "    preds = torch.argmax(model(x_batch), dim = 1)\n",
    "    acc   += (preds == y_batch).cpu().numpy().mean()\n",
    "\n",
    "print(f'Accuracy score is {acc / batches:.3}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our two-layers Neural Network (without Softmax) gave us worse results than usual SGD Linear Regression model"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.4 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}