unit04-multilayer-nets/exercises/2_fashion-mnist/exercise_2_fashion-mnist.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d71bce70-9dc3-448b-9f9a-8896e83b6d09",
   "metadata": {},
   "source": [
    "# Implementing a Multilayer Perceptron (MNIST)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5b48fc7-4f46-4d5a-8558-cd06892aaa27",
   "metadata": {},
   "source": [
    "## 1) Installing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "be1f5a9a-b3ee-424b-ab02-4371f49bd786",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !conda install numpy pandas matplotlib --yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ea7b3b8-9092-4b37-8b7f-57362be611ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install torch torchvision torchaudio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "79dd2077-ba5c-4ab5-95fc-6ee4d8a9f811",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !conda install watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bc4fa295-5c62-4888-bcf8-d07d6a7afc47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.10.9\n",
      "IPython version      : 8.7.0\n",
      "\n",
      "numpy     : 1.23.5\n",
      "pandas    : not installed\n",
      "matplotlib: 3.6.3\n",
      "torch     : 1.13.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -p numpy,pandas,matplotlib,torch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9549676-2fa5-41a7-bbb9-ce03f5797c34",
   "metadata": {},
   "source": [
    "## 2) Loading the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e002ad95-a1f7-4c33-826a-4a45944f2687",
   "metadata": {},
   "source": [
    "- MNIST website: http://yann.lecun.com/exdb/mnist/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f609024c-3eae-4ad5-8cb8-b95b403b7606",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "\n",
    "def load_mnist(path, kind='train'):\n",
    "    import os\n",
    "    import gzip\n",
    "    import numpy as np\n",
    "\n",
    "    \"\"\"Load MNIST data from `path`\"\"\"\n",
    "    labels_path = os.path.join(path,\n",
    "                               '%s-labels-idx1-ubyte.gz'\n",
    "                               % kind)\n",
    "    images_path = os.path.join(path,\n",
    "                               '%s-images-idx3-ubyte.gz'\n",
    "                               % kind)\n",
    "\n",
    "    with gzip.open(labels_path, 'rb') as lbpath:\n",
    "        labels = np.frombuffer(lbpath.read(), dtype=np.uint8,\n",
    "                               offset=8)\n",
    "\n",
    "    with gzip.open(images_path, 'rb') as imgpath:\n",
    "        images = np.frombuffer(imgpath.read(), dtype=np.uint8,\n",
    "                               offset=16).reshape(len(labels), 784)\n",
    "\n",
    "    return images, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "53652f7f-1327-4992-a78a-345921eeb854",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader, Dataset\n",
    "\n",
    "\n",
    "class MyDataset(Dataset):\n",
    "    def __init__(self, ..., transform=None):\n",
    "\n",
    "        self.transform = transform\n",
    "        # ...\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        # ...\n",
    "        img = torch.tensor(img).to(torch.float32)\n",
    "        img = img/255.\n",
    "        # ...\n",
    "\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "\n",
    "        # ...\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.labels.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6661307a-6220-48d5-b965-4cd36e29e54c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "78adc94e-5418-4aac-9a82-9a9cbf8fc688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "765adcf0-9147-434b-917a-f6d736a7117e",
   "metadata": {},
   "source": [
    "### Create a validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b39a42a2-cd46-46cf-ba93-d3f2f232f29c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data.dataset import random_split\n",
    "\n",
    "torch.manual_seed(1)\n",
    "train_dataset, val_dataset = random_split(train_dataset, lengths=[55000, 5000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b30e4a70-55b3-4fb0-b28d-b0fddd193fae",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = DataLoader(\n",
    "    dataset=train_dataset,\n",
    "    batch_size=64,\n",
    "    shuffle=True,\n",
    ")\n",
    "\n",
    "val_loader = DataLoader(\n",
    "    dataset=val_dataset,\n",
    "    batch_size=64,\n",
    "    shuffle=False,\n",
    ")\n",
    "\n",
    "test_loader = DataLoader(\n",
    "    dataset=test_dataset,\n",
    "    batch_size=64,\n",
    "    shuffle=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db50db02-3696-4f86-b149-74baabeec6c4",
   "metadata": {},
   "source": [
    "## 3) Implementing the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "971389a7-5424-4141-a3ee-9399eebbbb6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "class PyTorchMLP(torch.nn.Module):\n",
    "    def __init__(self, num_features, num_classes):\n",
    "        super().__init__()\n",
    "\n",
    "        self.all_layers = torch.nn.Sequential(\n",
    "            # 1st hidden layer\n",
    "            torch.nn.Linear(num_features, 50),\n",
    "            torch.nn.ReLU(),\n",
    "            # 2nd hidden layer\n",
    "            torch.nn.Linear(50, 25),\n",
    "            torch.nn.ReLU(),\n",
    "            # output layer\n",
    "            torch.nn.Linear(25, num_classes),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = torch.flatten(x, start_dim=1)\n",
    "        logits = self.all_layers(x)\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46bc16a0-ec59-4c54-a209-0a5e22406287",
   "metadata": {},
   "source": [
    "## 4) The training loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8de213fc-48b0-4f7c-af9e-8e2da068e351",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_accuracy(model, dataloader):\n",
    "\n",
    "    model = model.eval()\n",
    "\n",
    "    correct = 0.0\n",
    "    total_examples = 0\n",
    "\n",
    "    for idx, (features, labels) in enumerate(dataloader):\n",
    "\n",
    "        with torch.no_grad():\n",
    "            logits = model(features)\n",
    "\n",
    "        predictions = torch.argmax(logits, dim=1)\n",
    "\n",
    "        compare = labels == predictions\n",
    "        correct += torch.sum(compare)\n",
    "        total_examples += len(compare)\n",
    "\n",
    "    return correct / total_examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3dcaa2b1-4019-4128-9ff5-6a966c3abdf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 001/030 | Batch 000/860 | Train Loss: 2.29\n",
      "Epoch: 001/030 | Batch 250/860 | Train Loss: 1.90\n",
      "Epoch: 001/030 | Batch 500/860 | Train Loss: 1.26\n",
      "Epoch: 001/030 | Batch 750/860 | Train Loss: 0.83\n",
      "Train Acc 68.13% | Val Acc 68.92%\n",
      "Epoch: 002/030 | Batch 000/860 | Train Loss: 0.90\n",
      "Epoch: 002/030 | Batch 250/860 | Train Loss: 0.93\n",
      "Epoch: 002/030 | Batch 500/860 | Train Loss: 0.77\n",
      "Epoch: 002/030 | Batch 750/860 | Train Loss: 0.75\n",
      "Train Acc 76.77% | Val Acc 76.88%\n",
      "Epoch: 003/030 | Batch 000/860 | Train Loss: 0.91\n",
      "Epoch: 003/030 | Batch 250/860 | Train Loss: 0.62\n",
      "Epoch: 003/030 | Batch 500/860 | Train Loss: 0.58\n",
      "Epoch: 003/030 | Batch 750/860 | Train Loss: 0.43\n",
      "Train Acc 79.60% | Val Acc 80.12%\n",
      "Epoch: 004/030 | Batch 000/860 | Train Loss: 0.68\n",
      "Epoch: 004/030 | Batch 250/860 | Train Loss: 0.61\n",
      "Epoch: 004/030 | Batch 500/860 | Train Loss: 0.39\n",
      "Epoch: 004/030 | Batch 750/860 | Train Loss: 0.69\n",
      "Train Acc 80.65% | Val Acc 80.94%\n",
      "Epoch: 005/030 | Batch 000/860 | Train Loss: 0.68\n",
      "Epoch: 005/030 | Batch 250/860 | Train Loss: 0.47\n",
      "Epoch: 005/030 | Batch 500/860 | Train Loss: 0.40\n",
      "Epoch: 005/030 | Batch 750/860 | Train Loss: 0.51\n",
      "Train Acc 81.45% | Val Acc 81.66%\n",
      "Epoch: 006/030 | Batch 000/860 | Train Loss: 0.61\n",
      "Epoch: 006/030 | Batch 250/860 | Train Loss: 0.50\n",
      "Epoch: 006/030 | Batch 500/860 | Train Loss: 0.47\n",
      "Epoch: 006/030 | Batch 750/860 | Train Loss: 0.56\n",
      "Train Acc 83.29% | Val Acc 83.02%\n",
      "Epoch: 007/030 | Batch 000/860 | Train Loss: 0.42\n",
      "Epoch: 007/030 | Batch 250/860 | Train Loss: 0.39\n",
      "Epoch: 007/030 | Batch 500/860 | Train Loss: 0.48\n",
      "Epoch: 007/030 | Batch 750/860 | Train Loss: 0.64\n",
      "Train Acc 83.49% | Val Acc 83.20%\n",
      "Epoch: 008/030 | Batch 000/860 | Train Loss: 0.52\n",
      "Epoch: 008/030 | Batch 250/860 | Train Loss: 0.65\n",
      "Epoch: 008/030 | Batch 500/860 | Train Loss: 0.48\n",
      "Epoch: 008/030 | Batch 750/860 | Train Loss: 0.73\n",
      "Train Acc 83.99% | Val Acc 83.82%\n",
      "Epoch: 009/030 | Batch 000/860 | Train Loss: 0.41\n",
      "Epoch: 009/030 | Batch 250/860 | Train Loss: 0.37\n",
      "Epoch: 009/030 | Batch 500/860 | Train Loss: 0.29\n",
      "Epoch: 009/030 | Batch 750/860 | Train Loss: 0.56\n",
      "Train Acc 83.81% | Val Acc 84.08%\n",
      "Epoch: 010/030 | Batch 000/860 | Train Loss: 0.59\n",
      "Epoch: 010/030 | Batch 250/860 | Train Loss: 0.53\n",
      "Epoch: 010/030 | Batch 500/860 | Train Loss: 0.58\n",
      "Epoch: 010/030 | Batch 750/860 | Train Loss: 0.42\n",
      "Train Acc 84.65% | Val Acc 83.98%\n",
      "Epoch: 011/030 | Batch 000/860 | Train Loss: 0.35\n",
      "Epoch: 011/030 | Batch 250/860 | Train Loss: 0.32\n",
      "Epoch: 011/030 | Batch 500/860 | Train Loss: 0.52\n",
      "Epoch: 011/030 | Batch 750/860 | Train Loss: 0.43\n",
      "Train Acc 84.35% | Val Acc 84.08%\n",
      "Epoch: 012/030 | Batch 000/860 | Train Loss: 0.30\n",
      "Epoch: 012/030 | Batch 250/860 | Train Loss: 0.45\n",
      "Epoch: 012/030 | Batch 500/860 | Train Loss: 0.48\n",
      "Epoch: 012/030 | Batch 750/860 | Train Loss: 0.49\n",
      "Train Acc 84.23% | Val Acc 83.96%\n",
      "Epoch: 013/030 | Batch 000/860 | Train Loss: 0.48\n",
      "Epoch: 013/030 | Batch 250/860 | Train Loss: 0.49\n",
      "Epoch: 013/030 | Batch 500/860 | Train Loss: 0.41\n",
      "Epoch: 013/030 | Batch 750/860 | Train Loss: 0.42\n",
      "Train Acc 84.20% | Val Acc 83.46%\n",
      "Epoch: 014/030 | Batch 000/860 | Train Loss: 0.58\n",
      "Epoch: 014/030 | Batch 250/860 | Train Loss: 0.48\n",
      "Epoch: 014/030 | Batch 500/860 | Train Loss: 0.37\n",
      "Epoch: 014/030 | Batch 750/860 | Train Loss: 0.52\n",
      "Train Acc 85.13% | Val Acc 84.40%\n",
      "Epoch: 015/030 | Batch 000/860 | Train Loss: 0.31\n",
      "Epoch: 015/030 | Batch 250/860 | Train Loss: 0.51\n",
      "Epoch: 015/030 | Batch 500/860 | Train Loss: 0.31\n",
      "Epoch: 015/030 | Batch 750/860 | Train Loss: 0.36\n",
      "Train Acc 85.69% | Val Acc 85.02%\n",
      "Epoch: 016/030 | Batch 000/860 | Train Loss: 0.30\n",
      "Epoch: 016/030 | Batch 250/860 | Train Loss: 0.46\n",
      "Epoch: 016/030 | Batch 500/860 | Train Loss: 0.43\n",
      "Epoch: 016/030 | Batch 750/860 | Train Loss: 0.30\n",
      "Train Acc 84.77% | Val Acc 84.28%\n",
      "Epoch: 017/030 | Batch 000/860 | Train Loss: 0.40\n",
      "Epoch: 017/030 | Batch 250/860 | Train Loss: 0.41\n",
      "Epoch: 017/030 | Batch 500/860 | Train Loss: 0.32\n",
      "Epoch: 017/030 | Batch 750/860 | Train Loss: 0.39\n",
      "Train Acc 85.69% | Val Acc 85.04%\n",
      "Epoch: 018/030 | Batch 000/860 | Train Loss: 0.36\n",
      "Epoch: 018/030 | Batch 250/860 | Train Loss: 0.43\n",
      "Epoch: 018/030 | Batch 500/860 | Train Loss: 0.41\n",
      "Epoch: 018/030 | Batch 750/860 | Train Loss: 0.49\n",
      "Train Acc 85.44% | Val Acc 84.82%\n",
      "Epoch: 019/030 | Batch 000/860 | Train Loss: 0.54\n",
      "Epoch: 019/030 | Batch 250/860 | Train Loss: 0.38\n",
      "Epoch: 019/030 | Batch 500/860 | Train Loss: 0.53\n",
      "Epoch: 019/030 | Batch 750/860 | Train Loss: 0.35\n",
      "Train Acc 86.14% | Val Acc 85.44%\n",
      "Epoch: 020/030 | Batch 000/860 | Train Loss: 0.67\n",
      "Epoch: 020/030 | Batch 250/860 | Train Loss: 0.43\n",
      "Epoch: 020/030 | Batch 500/860 | Train Loss: 0.30\n",
      "Epoch: 020/030 | Batch 750/860 | Train Loss: 0.50\n",
      "Train Acc 86.65% | Val Acc 85.80%\n",
      "Epoch: 021/030 | Batch 000/860 | Train Loss: 0.38\n",
      "Epoch: 021/030 | Batch 250/860 | Train Loss: 0.32\n",
      "Epoch: 021/030 | Batch 500/860 | Train Loss: 0.65\n",
      "Epoch: 021/030 | Batch 750/860 | Train Loss: 0.32\n",
      "Train Acc 86.43% | Val Acc 85.56%\n",
      "Epoch: 022/030 | Batch 000/860 | Train Loss: 0.48\n",
      "Epoch: 022/030 | Batch 250/860 | Train Loss: 0.40\n",
      "Epoch: 022/030 | Batch 500/860 | Train Loss: 0.33\n",
      "Epoch: 022/030 | Batch 750/860 | Train Loss: 0.41\n",
      "Train Acc 87.04% | Val Acc 86.12%\n",
      "Epoch: 023/030 | Batch 000/860 | Train Loss: 0.26\n",
      "Epoch: 023/030 | Batch 250/860 | Train Loss: 0.43\n",
      "Epoch: 023/030 | Batch 500/860 | Train Loss: 0.34\n",
      "Epoch: 023/030 | Batch 750/860 | Train Loss: 0.34\n",
      "Train Acc 87.01% | Val Acc 85.98%\n",
      "Epoch: 024/030 | Batch 000/860 | Train Loss: 0.40\n",
      "Epoch: 024/030 | Batch 250/860 | Train Loss: 0.32\n",
      "Epoch: 024/030 | Batch 500/860 | Train Loss: 0.35\n",
      "Epoch: 024/030 | Batch 750/860 | Train Loss: 0.37\n",
      "Train Acc 87.26% | Val Acc 86.04%\n",
      "Epoch: 025/030 | Batch 000/860 | Train Loss: 0.38\n",
      "Epoch: 025/030 | Batch 250/860 | Train Loss: 0.49\n",
      "Epoch: 025/030 | Batch 500/860 | Train Loss: 0.37\n",
      "Epoch: 025/030 | Batch 750/860 | Train Loss: 0.54\n",
      "Train Acc 87.10% | Val Acc 86.30%\n",
      "Epoch: 026/030 | Batch 000/860 | Train Loss: 0.32\n",
      "Epoch: 026/030 | Batch 250/860 | Train Loss: 0.17\n",
      "Epoch: 026/030 | Batch 500/860 | Train Loss: 0.47\n",
      "Epoch: 026/030 | Batch 750/860 | Train Loss: 0.21\n",
      "Train Acc 85.55% | Val Acc 84.84%\n",
      "Epoch: 027/030 | Batch 000/860 | Train Loss: 0.33\n",
      "Epoch: 027/030 | Batch 250/860 | Train Loss: 0.26\n",
      "Epoch: 027/030 | Batch 500/860 | Train Loss: 0.40\n",
      "Epoch: 027/030 | Batch 750/860 | Train Loss: 0.26\n",
      "Train Acc 87.84% | Val Acc 86.70%\n",
      "Epoch: 028/030 | Batch 000/860 | Train Loss: 0.38\n",
      "Epoch: 028/030 | Batch 250/860 | Train Loss: 0.82\n",
      "Epoch: 028/030 | Batch 500/860 | Train Loss: 0.28\n",
      "Epoch: 028/030 | Batch 750/860 | Train Loss: 0.20\n",
      "Train Acc 87.57% | Val Acc 86.36%\n",
      "Epoch: 029/030 | Batch 000/860 | Train Loss: 0.53\n",
      "Epoch: 029/030 | Batch 250/860 | Train Loss: 0.27\n",
      "Epoch: 029/030 | Batch 500/860 | Train Loss: 0.39\n",
      "Epoch: 029/030 | Batch 750/860 | Train Loss: 0.44\n",
      "Train Acc 86.38% | Val Acc 85.20%\n",
      "Epoch: 030/030 | Batch 000/860 | Train Loss: 0.80\n",
      "Epoch: 030/030 | Batch 250/860 | Train Loss: 0.26\n",
      "Epoch: 030/030 | Batch 500/860 | Train Loss: 0.39\n",
      "Epoch: 030/030 | Batch 750/860 | Train Loss: 0.49\n",
      "Train Acc 87.67% | Val Acc 86.46%\n"
     ]
    }
   ],
   "source": [
    "import torch.nn.functional as F\n",
    "\n",
    "torch.manual_seed(1)\n",
    "model = PyTorchMLP(num_features=784, num_classes=10)\n",
    "\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n",
    "\n",
    "num_epochs = 30\n",
    "\n",
    "loss_list = []\n",
    "train_acc_list, val_acc_list = [], []\n",
    "for epoch in range(num_epochs):\n",
    "\n",
    "    model = model.train()\n",
    "    for batch_idx, (features, labels) in enumerate(train_loader):\n",
    "\n",
    "        logits = model(features)\n",
    "\n",
    "        loss = F.cross_entropy(logits, labels)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        if not batch_idx % 250:\n",
    "            ### LOGGING\n",
    "            print(\n",
    "                f\"Epoch: {epoch+1:03d}/{num_epochs:03d}\"\n",
    "                f\" | Batch {batch_idx:03d}/{len(train_loader):03d}\"\n",
    "                f\" | Train Loss: {loss:.2f}\"\n",
    "            )\n",
    "        loss_list.append(loss.item())\n",
    "\n",
    "    train_acc = compute_accuracy(model, train_loader)\n",
    "    val_acc = compute_accuracy(model, val_loader)\n",
    "    print(f\"Train Acc {train_acc*100:.2f}% | Val Acc {val_acc*100:.2f}%\")\n",
    "    train_acc_list.append(train_acc)\n",
    "    val_acc_list.append(val_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb0d5821-7c8d-46b5-9e7d-02e72cac2acc",
   "metadata": {},
   "source": [
    "## 5) Evaluating the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "27538c8d-61bc-47b0-8289-b6aab4aa16ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Acc 87.67%\n",
      "Val Acc 86.46%\n",
      "Test Acc 85.68%\n"
     ]
    }
   ],
   "source": [
    "train_acc = compute_accuracy(model, train_loader)\n",
    "val_acc = compute_accuracy(model, val_loader)\n",
    "test_acc = compute_accuracy(model, test_loader)\n",
    "\n",
    "print(f\"Train Acc {train_acc*100:.2f}%\")\n",
    "print(f\"Val Acc {val_acc*100:.2f}%\")\n",
    "print(f\"Test Acc {test_acc*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0f1f8499-3191-4f78-b2d4-82daba9b9bc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from helper_plotting import plot_training_loss\n",
    "\n",
    "plot_training_loss(minibatch_loss_list=loss_list,\n",
    "                   num_epochs=num_epochs,\n",
    "                   iter_per_epoch=len(loss_list)//num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bf9d4519-b7a2-4382-90d0-a0dce2ee608e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from helper_plotting import plot_accuracy\n",
    "\n",
    "plot_accuracy(train_acc_list=train_acc_list, valid_acc_list=val_acc_list)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}