scratchpad.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install torch\n",
    "!pip install torchvision\n",
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets\n",
    "from torchvision.transforms import ToTensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download training data from open datasets.\n",
    "training_data = datasets.FashionMNIST(\n",
    "    root=\"data\",\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=ToTensor(),\n",
    ")\n",
    "\n",
    "# Download test data from open datasets.\n",
    "test_data = datasets.FashionMNIST(\n",
    "    root=\"data\",\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=ToTensor(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bs = 64\n",
    "train_dl = DataLoader(training_data, batch_size=bs)\n",
    "test_dl = DataLoader(test_data, batch_size=bs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dl.dataset[0][0].shape # 28 x 28 images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(train_dl.dataset[0][0].numpy().squeeze(), cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for X, y in test_dl:\n",
    "  print(f'Shape of X [N, C, H, W]: {X.shape}')\n",
    "  print(f'Shape of y [N]: {y.shape} {y.dtype}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(f\"Using device: {device}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NN(nn.Module):\n",
    "  def __init__(self):\n",
    "    super(NN, self).__init__()\n",
    "    self.flatten = nn.Flatten()\n",
    "    self.linear_relu_stack = nn.Sequential(\n",
    "      nn.Linear(28*28, 512),\n",
    "      nn.ReLU(),\n",
    "      nn.Linear(512, 512),\n",
    "      nn.ReLU(),\n",
    "      nn.Linear(512, 10),\n",
    "    )\n",
    "  \n",
    "  def forward(self, x):\n",
    "    x = self.flatten(x)\n",
    "    logits = self.linear_relu_stack(x)\n",
    "    return logits\n",
    "\n",
    "model = NN().to(device)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=1e-3) # 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(dataloader, model, loss_fn, optimizer):\n",
    "  size = len(dataloader.dataset)\n",
    "  model.train()\n",
    "  for batch, (X, y) in enumerate(dataloader):\n",
    "    X, y = X.to(device), y.to(device) # independent vars (img), dependent vars (actuals)\n",
    "\n",
    "    # Compute prediction error\n",
    "    pred = model(X)\n",
    "    loss = loss_fn(pred, y)\n",
    "\n",
    "    # Bcakpropagate\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    if batch % 100 == 0:\n",
    "      loss, current = loss.item(), batch * len(X)\n",
    "      print(f\"loss: {loss:>7f} [{current:>5d}/{size:>5d}]\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(dataloader, model, loss_fn):\n",
    "    size = len(dataloader.dataset)\n",
    "    num_batches = len(dataloader)\n",
    "    model.eval()\n",
    "    test_loss, correct = 0, 0\n",
    "    # Disabling gradient calculation is useful for inference, when you are sure that you will not call Tensor.backward()\n",
    "    with torch.no_grad():\n",
    "        for X, y in dataloader:\n",
    "            X, y = X.to(device), y.to(device)\n",
    "            pred = model(X)\n",
    "            test_loss += loss_fn(pred, y).item()\n",
    "            correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n",
    "    test_loss /= num_batches\n",
    "    correct /= size\n",
    "    print(f\"Test Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 5\n",
    "for t in range(epochs):\n",
    "  print(f\"Epoch {t+1}/{epochs}\\n\")\n",
    "  train(train_dl, model, loss_fn, optimizer)\n",
    "  test(test_dl, model, loss_fn)\n",
    "\n",
    "print(\"Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), \"model.pth\")\n",
    "print(\"saved model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = NN()\n",
    "model.load_state_dict(torch.load(\"model.pth\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(train_dl.dataset[1][0].numpy().squeeze(), cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = [\n",
    "    \"T-shirt/top\",\n",
    "    \"Trouser\",\n",
    "    \"Pullover\",\n",
    "    \"Dress\",\n",
    "    \"Coat\",\n",
    "    \"Sandal\",\n",
    "    \"Shirt\",\n",
    "    \"Sneaker\",\n",
    "    \"Bag\",\n",
    "    \"Ankle boot\",\n",
    "]\n",
    "\n",
    "model.eval()\n",
    "x, y = test_data[1][0], test_data[1][1]\n",
    "with torch.no_grad():\n",
    "    pred = model(x)\n",
    "    print(pred)\n",
    "    predicted, actual = classes[pred[0].argmax(0)], classes[y]\n",
    "    print(f'Predicted: \"{predicted}\", Actual: \"{actual}\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred[0].argmax(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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