hw4_A6_final.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UNmkdWoXvkBE"
      },
      "source": [
        "##Image Classification on CIFAR-10\n",
        "In this problem we will explore different deep learning architectures for image classification on the CIFAR-10 dataset. Make sure that you are familiar with torch `Tensor`s, two-dimensional convolutions (`nn.Conv2d`) and fully-connected layers (`nn.Linear`), ReLU non-linearities (`F.relu`), pooling (`nn.MaxPool2d`), and tensor reshaping (`view`).\n",
        "\n",
        "We will use Colab because it has free GPU runtimes available; GPUs can accelerate training times for this problem by 10-100x. **You will need to enable the GPU runtime to use it**. To do so, click \"Runtime\" above and then \"Change runtime type\". There under hardware accelerator choose \"GPU\".\n",
        "\n",
        "This notebook provides some starter code for the CIFAR-10 problem on HW4, including a completed training loop to assist with some of the Pytorch setup. You'll need to modify this code to implement the layers required for the assignment, but this provides a working training loop to start from.\n",
        "\n",
        "*Note: GPU runtimes are limited on Colab. Limit your training to short-running jobs (around 20mins or less) and spread training out over time, if possible. Colab WILL limit your usage of GPU time, so plan ahead and be prepared to take breaks during training.* We also suggest performing your early coding/sweeps on a small fraction of the dataset (~10%) to minimize training time and GPU usage."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bb7WymOmv_cx"
      },
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "from typing import Tuple, Union, List, Callable\n",
        "from torch.optim import SGD\n",
        "import torchvision\n",
        "from torch.utils.data import DataLoader, TensorDataset, random_split\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm, trange"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-SusLoH91CEz"
      },
      "source": [
        "Let's verify that we are using a gpu:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vmxNdxwvxNs1",
        "outputId": "8d0c7146-b0f5-48bd-c5d1-98a35516d67f"
      },
      "source": [
        "assert torch.cuda.is_available(), \"GPU is not available, check the directions above (or disable this assertion to use CPU)\"\n",
        "\n",
        "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "print(DEVICE)  # this should print out CUDA"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LbFk2t2RxYcn"
      },
      "source": [
        "To use the GPU you will need to send both the model and data to a device; this transfers the model from its default location on CPU to the GPU.\n",
        "\n",
        "Note that torch operations on Tensors will fail if they are not located on the same device.\n",
        "\n",
        "```python\n",
        "model = model.to(DEVICE)  # Sending a model to GPU\n",
        "\n",
        "for x, y in tqdm(data_loader):\n",
        "  x, y = x.to(DEVICE), y.to(DEVICE)\n",
        "```\n",
        "When reading tensors you may need to send them back to cpu, you can do so with `x = x.cpu()`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xODE5P5D1Wwy"
      },
      "source": [
        "Let's load CIFAR-10 data. This is how we load datasets using PyTorch in the real world!"
      ]
    },
    {
      "cell_type": "code",
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        "id": "wOvLuZry1cKc",
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      "source": [
        "train_dataset = torchvision.datasets.CIFAR10(\"./data\", train=True, download=True, transform=torchvision.transforms.ToTensor())\n",
        "test_dataset = torchvision.datasets.CIFAR10(\"./data\", train=False, download=True, transform=torchvision.transforms.ToTensor())"
      ],
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        {
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          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ]
        },
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          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, we'll use the torch `DataLoader` to wrap our datasets. `DataLoader`s handle batching, shuffling, and iterating over data; they can also be useful for building more complex input pipelines that perform transfoermations such as data augmentation."
      ],
      "metadata": {
        "id": "oG78KMOj61HJ"
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    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 128\n",
        "\n",
        "train_dataset, val_dataset = random_split(train_dataset, [int(0.9 * len(train_dataset)), int( 0.1 * len(train_dataset))])\n",
        "\n",
        "# Create separate dataloaders for the train, test, and validation set\n",
        "train_loader = DataLoader(\n",
        "    train_dataset,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=True\n",
        ")\n",
        "\n",
        "val_loader = DataLoader(\n",
        "    val_dataset,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=True\n",
        ")\n",
        "\n",
        "test_loader = DataLoader(\n",
        "    test_dataset,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=True\n",
        ")"
      ],
      "metadata": {
        "id": "mHIZY4BoRxvC"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##For Reference: Logistic Regression\n",
        "\n",
        "This problem is about deep learning architectures, not pytorch. We are providing an implementation of logistic regression using SGD in torch, which can serve as a template for the rest of your implementation in this problem."
      ],
      "metadata": {
        "id": "ju8QRAyv2u7F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before we get started, let's take a look at our data to get an understanding of what we are doing. CIFAR-10 is a dataset containing images split into 10 classes."
      ],
      "metadata": {
        "id": "ELjydYRt5Frf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "imgs, labels = next(iter(train_loader))\n",
        "print(f\"A single batch of images has shape: {imgs.size()}\")\n",
        "example_image, example_label = imgs[0], labels[0]\n",
        "c, w, h = example_image.size()\n",
        "print(f\"A single RGB image has {c} channels, width {w}, and height {h}.\")\n",
        "\n",
        "# This is one way to flatten our images\n",
        "batch_flat_view = imgs.view(-1, c * w * h)\n",
        "print(f\"Size of a batch of images flattened with view: {batch_flat_view.size()}\")\n",
        "\n",
        "# This is another equivalent way\n",
        "batch_flat_flatten = imgs.flatten(1)\n",
        "print(f\"Size of a batch of images flattened with flatten: {batch_flat_flatten.size()}\")\n",
        "\n",
        "# The new dimension is just the product of the ones we flattened\n",
        "d = example_image.flatten().size()[0]\n",
        "print(c * w * h == d)\n",
        "\n",
        "# View the image\n",
        "t =  torchvision.transforms.ToPILImage()\n",
        "plt.imshow(t(example_image))\n",
        "\n",
        "# These are what the class labels in CIFAR-10 represent. For more information,\n",
        "# visit https://www.cs.toronto.edu/~kriz/cifar.html\n",
        "classes = [\"airplane\", \"automobile\", \"bird\", \"cat\", \"deer\", \"dog\", \"frog\",\n",
        "           \"horse\", \"ship\", \"truck\"]\n",
        "print(f\"This image is labeled as class {classes[example_label]}\")\n"
      ],
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      },
      "execution_count": 5,
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          "text": [
            "A single batch of images has shape: torch.Size([128, 3, 32, 32])\n",
            "A single RGB image has 3 channels, width 32, and height 32.\n",
            "Size of a batch of images flattened with view: torch.Size([128, 3072])\n",
            "Size of a batch of images flattened with flatten: torch.Size([128, 3072])\n",
            "True\n",
            "This image is labeled as class automobile\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this problem, we will attempt to predict what class an image is labeled as."
      ],
      "metadata": {
        "id": "JdBbLQdC_mA_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, let's create our model. For a linear model we could flatten the data before passing it into the model, but that is not be the case for the convolutional neural network."
      ],
      "metadata": {
        "id": "J3HQyGMn_42D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def linear_model() -> nn.Module:\n",
        "  \"\"\"Instantiate a linear model and send it to device.\"\"\"\n",
        "  model =  nn.Sequential(\n",
        "            nn.Flatten(),\n",
        "            nn.Linear(d, 10)\n",
        "         )\n",
        "  return model.to(DEVICE)"
      ],
      "metadata": {
        "id": "5qLVC9PbACDt"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def neural_network_A(M: int) -> nn.Module:\n",
        "  \"\"\"\n",
        "  Instantiate the model with 1 fully-connected hidden layer for part a and send\n",
        "  it to device.\n",
        "  \"\"\"\n",
        "  model =  nn.Sequential(\n",
        "            nn.Flatten(),\n",
        "            nn.Linear(3072, M),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(M, 10)\n",
        "         )\n",
        "  return model.to(DEVICE)"
      ],
      "metadata": {
        "id": "18pDAClBzWU9"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def neural_network_B(M: int, k: int, N: int) -> nn.Module:\n",
        "  \"\"\"\n",
        "  Instantiate the model with a convolutional layer with max-pool and fully\n",
        "  connnected output for part b and send it to device.\n",
        "\n",
        "  Inputs: \n",
        "    M: number of filters (100)\n",
        "    k: size of filters (5)\n",
        "    N: size of max-pool (14)\n",
        "\n",
        "  Returns:\n",
        "    nn.Module\n",
        "  \"\"\"\n",
        "  n_conv = int(M * ((33 - k) / N)**2)\n",
        "  model =  nn.Sequential(\n",
        "            nn.Conv2d(in_channels=3, out_channels=M, kernel_size=k),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(N),\n",
        "            nn.Flatten(),\n",
        "            nn.Linear(n_conv, 10))\n",
        "  return model.to(DEVICE)"
      ],
      "metadata": {
        "id": "FqlWAoP20QV6"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's define a method to train this model using SGD as our optimizer."
      ],
      "metadata": {
        "id": "kd49udL8AZ_E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train(\n",
        "    model: nn.Module, optimizer: SGD,\n",
        "    train_loader: DataLoader, val_loader: DataLoader,\n",
        "    epochs: int = 20\n",
        "    )-> Tuple[List[float], List[float], List[float], List[float]]:\n",
        "  \"\"\"\n",
        "  Trains a model for the specified number of epochs using the loaders.\n",
        "\n",
        "  Returns: \n",
        "    Lists of training loss, training accuracy, validation loss, validation accuracy for each epoch.\n",
        "  \"\"\"\n",
        "\n",
        "  loss = nn.CrossEntropyLoss()\n",
        "  train_losses = []\n",
        "  train_accuracies = []\n",
        "  val_losses = []\n",
        "  val_accuracies = []\n",
        "  for e in range(epochs):\n",
        "    model.train()\n",
        "    train_loss = 0.0\n",
        "    train_acc = 0.0\n",
        "\n",
        "    # Main training loop; iterate over train_loader. The loop\n",
        "    # terminates when the train loader finishes iterating, which is one epoch.\n",
        "    for (x_batch, labels) in train_loader:\n",
        "      x_batch, labels = x_batch.to(DEVICE), labels.to(DEVICE)\n",
        "      optimizer.zero_grad()\n",
        "      labels_pred = model(x_batch)\n",
        "      batch_loss = loss(labels_pred, labels)\n",
        "      train_loss = train_loss + batch_loss.item()\n",
        "\n",
        "      labels_pred_max = torch.argmax(labels_pred, 1)\n",
        "      batch_acc = torch.sum(labels_pred_max == labels)\n",
        "      train_acc = train_acc + batch_acc.item()\n",
        "\n",
        "      batch_loss.backward()\n",
        "      optimizer.step()\n",
        "    train_losses.append(train_loss / len(train_loader))\n",
        "    train_accuracies.append(train_acc / (batch_size * len(train_loader)))\n",
        "\n",
        "    # Validation loop; use .no_grad() context manager to save memory.\n",
        "    model.eval()\n",
        "    val_loss = 0.0\n",
        "    val_acc = 0.0\n",
        "\n",
        "    with torch.no_grad():\n",
        "      for (v_batch, labels) in val_loader:\n",
        "        v_batch, labels = v_batch.to(DEVICE), labels.to(DEVICE)\n",
        "        labels_pred = model(v_batch)\n",
        "        v_batch_loss = loss(labels_pred, labels)\n",
        "        val_loss = val_loss + v_batch_loss.item()\n",
        "\n",
        "        v_pred_max = torch.argmax(labels_pred, 1)\n",
        "        batch_acc = torch.sum(v_pred_max == labels)\n",
        "        val_acc = val_acc + batch_acc.item()\n",
        "      val_losses.append(val_loss / len(val_loader))\n",
        "      val_accuracies.append(val_acc / (batch_size * len(val_loader)))\n",
        "\n",
        "  return train_losses, train_accuracies, val_losses, val_accuracies\n"
      ],
      "metadata": {
        "id": "YgcFP1-UBj1Z"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For this problem, we will be using SGD. The two hyperparameters for our linear model trained with SGD are the learning rate and momentum. Only learning rate will be searched for in this example.\n",
        "\n",
        "Note: We ask you to plot the accuracies for the best 5 models for each structure, so you will need to return multiple sets of hyperparameters for the homework, or, if you do random search, run your hyperparameter search multiple times."
      ],
      "metadata": {
        "id": "2ZUqV2ZrEf-u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(torch.linspace(d/2, 972, 3, dtype=int))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a11vNeDKOCZN",
        "outputId": "c0865775-e5d1-4fa3-ca20-b2fc72526b07"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([1536, 1254,  972])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def parameter_search(train_loader: DataLoader, \n",
        "                     val_loader: DataLoader, \n",
        "                     model_fn:Callable[[], nn.Module]) -> float:\n",
        "  \"\"\"\n",
        "  Parameter search for our linear model using SGD.\n",
        "\n",
        "  Args:\n",
        "    train_loader: the train dataloader.\n",
        "    val_loader: the validation dataloader.\n",
        "    model_fn: a function that, when called, returns a torch.nn.Module.\n",
        "\n",
        "  Returns:\n",
        "    The learning rate with the least validation loss.\n",
        "    NOTE: you may need to modify this function to search over and return\n",
        "     other parameters beyond learning rate.\n",
        "  \"\"\"\n",
        "  num_iter = 4\n",
        "  losses = {}\n",
        "\n",
        "  lrs = torch.linspace(10 ** (-3), 10 ** (-1), num_iter)\n",
        "  momentums = [0.5, 0.9, 0.99]\n",
        "  Ms = torch.linspace(d/2, 972, 3, dtype=int)\n",
        "\n",
        "  for lr in lrs:\n",
        "    for mom in momentums:\n",
        "      for M in Ms:\n",
        "        print(f\"trying learning rate {lr:0.3f}, momentum {mom}, M {M}\")\n",
        "        model = model_fn(M)\n",
        "        optim = SGD(model.parameters(), lr, momentum=mom)\n",
        "\n",
        "        train_loss, train_acc, val_loss, val_acc = train(\n",
        "            model,\n",
        "            optim,\n",
        "            train_loader,\n",
        "            val_loader,\n",
        "            epochs=3\n",
        "            )\n",
        "        print(f\"loss: {min(val_loss)}\")\n",
        "        \n",
        "        losses[min(val_loss)] = (lr, mom, M)\n",
        "\n",
        "  return losses"
      ],
      "metadata": {
        "id": "3vszLpnyEOEF"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def parameter_search_B(train_loader: DataLoader, \n",
        "                     val_loader: DataLoader, \n",
        "                     model_fn:Callable[[], nn.Module]) -> float:\n",
        "  \"\"\"\n",
        "  Parameter search for our linear model using SGD.\n",
        "\n",
        "  Args:\n",
        "    train_loader: the train dataloader.\n",
        "    val_loader: the validation dataloader.\n",
        "    model_fn: a function that, when called, returns a torch.nn.Module.\n",
        "\n",
        "  Returns:\n",
        "    The learning rate with the least validation loss.\n",
        "    NOTE: you may need to modify this function to search over and return\n",
        "     other parameters beyond learning rate.\n",
        "  \"\"\"\n",
        "  losses = {}\n",
        "\n",
        "  lrs = torch.linspace(10 ** (-3), 10 ** (-1), 4)\n",
        "  momentums = [0.9, 0.99]\n",
        "  Ms = torch.linspace(2000, 400, 4, dtype=int)\n",
        "  k = 5\n",
        "  N = 14\n",
        "\n",
        "  for lr in lrs:\n",
        "    for mom in momentums:\n",
        "      for M in Ms:\n",
        "        print(f\"trying learning rate {lr:0.3f}, momentum {mom}, M {M}\")\n",
        "        model = model_fn(M=M, k=k, N=N)\n",
        "        optim = SGD(model.parameters(), lr, momentum=mom)\n",
        "\n",
        "        train_loss, train_acc, val_loss, val_acc = train(\n",
        "            model,\n",
        "            optim,\n",
        "            train_loader,\n",
        "            val_loader,\n",
        "            epochs=3\n",
        "            )\n",
        "        print(f\"loss: {min(val_loss)}\")\n",
        "        \n",
        "        losses[min(val_loss)] = (lr, mom, M)\n",
        "\n",
        "  return losses"
      ],
      "metadata": {
        "id": "hJiRVPrZKfO_"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we have everything, we can train and evaluate our model."
      ],
      "metadata": {
        "id": "ZULxD9sGHm1D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "losses_A = parameter_search(train_loader, val_loader, neural_network_A)\n"
      ],
      "metadata": {
        "id": "azwXX-AEIGKx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dae2fc85-4e1f-49ed-baa0-7c81221677cc"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trying learning rate 0.001, momentum 0.5, M 1536\n",
            "loss: 2.033295002579689\n",
            "trying learning rate 0.001, momentum 0.5, M 1254\n",
            "loss: 2.0398558616638183\n",
            "trying learning rate 0.001, momentum 0.5, M 972\n",
            "loss: 2.0403724044561384\n",
            "trying learning rate 0.001, momentum 0.9, M 1536\n",
            "loss: 1.8431220084428788\n",
            "trying learning rate 0.001, momentum 0.9, M 1254\n",
            "loss: 1.8469954937696458\n",
            "trying learning rate 0.001, momentum 0.9, M 972\n",
            "loss: 1.8355518877506256\n",
            "trying learning rate 0.001, momentum 0.99, M 1536\n",
            "loss: 1.6031416177749633\n",
            "trying learning rate 0.001, momentum 0.99, M 1254\n",
            "loss: 1.6141704738140106\n",
            "trying learning rate 0.001, momentum 0.99, M 972\n",
            "loss: 1.6337729126214982\n",
            "trying learning rate 0.034, momentum 0.5, M 1536\n",
            "loss: 1.6523005783557891\n",
            "trying learning rate 0.034, momentum 0.5, M 1254\n",
            "loss: 1.6756217032670975\n",
            "trying learning rate 0.034, momentum 0.5, M 972\n",
            "loss: 1.6910900503396988\n",
            "trying learning rate 0.034, momentum 0.9, M 1536\n",
            "loss: 1.7742464423179627\n",
            "trying learning rate 0.034, momentum 0.9, M 1254\n",
            "loss: 1.7633924037218094\n",
            "trying learning rate 0.034, momentum 0.9, M 972\n",
            "loss: 1.735081323981285\n",
            "trying learning rate 0.034, momentum 0.99, M 1536\n",
            "loss: 2.200031480193138\n",
            "trying learning rate 0.034, momentum 0.99, M 1254\n",
            "loss: 2.194085031747818\n",
            "trying learning rate 0.034, momentum 0.99, M 972\n",
            "loss: 2.2245341897010804\n",
            "trying learning rate 0.067, momentum 0.5, M 1536\n",
            "loss: 1.672406941652298\n",
            "trying learning rate 0.067, momentum 0.5, M 1254\n",
            "loss: 1.6682529002428055\n",
            "trying learning rate 0.067, momentum 0.5, M 972\n",
            "loss: 1.7177605390548707\n",
            "trying learning rate 0.067, momentum 0.9, M 1536\n",
            "loss: 2.0082029700279236\n",
            "trying learning rate 0.067, momentum 0.9, M 1254\n",
            "loss: 1.9224770218133926\n",
            "trying learning rate 0.067, momentum 0.9, M 972\n",
            "loss: 1.9628413408994674\n",
            "trying learning rate 0.067, momentum 0.99, M 1536\n",
            "loss: 2.2239093720912932\n",
            "trying learning rate 0.067, momentum 0.99, M 1254\n",
            "loss: 2.2972966134548187\n",
            "trying learning rate 0.067, momentum 0.99, M 972\n",
            "loss: 2.317035287618637\n",
            "trying learning rate 0.100, momentum 0.5, M 1536\n",
            "loss: 1.7875254333019257\n",
            "trying learning rate 0.100, momentum 0.5, M 1254\n",
            "loss: 1.7085938692092895\n",
            "trying learning rate 0.100, momentum 0.5, M 972\n",
            "loss: 1.7090945273637772\n",
            "trying learning rate 0.100, momentum 0.9, M 1536\n",
            "loss: 2.027724716067314\n",
            "trying learning rate 0.100, momentum 0.9, M 1254\n",
            "loss: 2.1336381793022157\n",
            "trying learning rate 0.100, momentum 0.9, M 972\n",
            "loss: 2.169624590873718\n",
            "trying learning rate 0.100, momentum 0.99, M 1536\n",
            "loss: 2.260811525583267\n",
            "trying learning rate 0.100, momentum 0.99, M 1254\n",
            "loss: 2.310474157333374\n",
            "trying learning rate 0.100, momentum 0.99, M 972\n",
            "loss: 2.315341019630432\n",
            "CPU times: user 11min 28s, sys: 7.35 s, total: 11min 35s\n",
            "Wall time: 11min 42s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "losses_B = parameter_search_B(train_loader, val_loader, neural_network_B)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_h542ZHKN5hQ",
        "outputId": "220a9af1-7f5b-4aa3-aad8-63185d15ba81"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trying learning rate 0.001, momentum 0.9, M 2000\n",
            "loss: 1.7315086454153061\n",
            "trying learning rate 0.001, momentum 0.9, M 1466\n",
            "loss: 1.7533951938152312\n",
            "trying learning rate 0.001, momentum 0.9, M 933\n",
            "loss: 1.8038274198770523\n",
            "trying learning rate 0.001, momentum 0.9, M 400\n",
            "loss: 1.9110151946544647\n",
            "trying learning rate 0.001, momentum 0.99, M 2000\n",
            "loss: 1.341507402062416\n",
            "trying learning rate 0.001, momentum 0.99, M 1466\n",
            "loss: 1.3733620256185533\n",
            "trying learning rate 0.001, momentum 0.99, M 933\n",
            "loss: 1.422749677300453\n",
            "trying learning rate 0.001, momentum 0.99, M 400\n",
            "loss: 1.4636527955532075\n",
            "trying learning rate 0.034, momentum 0.9, M 2000\n",
            "loss: 1.2154807671904564\n",
            "trying learning rate 0.034, momentum 0.9, M 1466\n",
            "loss: 1.2457669109106064\n",
            "trying learning rate 0.034, momentum 0.9, M 933\n",
            "loss: 1.2106488212943076\n",
            "trying learning rate 0.034, momentum 0.9, M 400\n",
            "loss: 1.2367557018995285\n",
            "trying learning rate 0.034, momentum 0.99, M 2000\n",
            "loss: 1.7377695560455322\n",
            "trying learning rate 0.034, momentum 0.99, M 1466\n",
            "loss: 1.7717148035764694\n",
            "trying learning rate 0.034, momentum 0.99, M 933\n",
            "loss: 1.7491302520036698\n",
            "trying learning rate 0.034, momentum 0.99, M 400\n",
            "loss: 1.8452925086021423\n",
            "trying learning rate 0.067, momentum 0.9, M 2000\n",
            "loss: 1.312419566512108\n",
            "trying learning rate 0.067, momentum 0.9, M 1466\n",
            "loss: 1.2549321860074998\n",
            "trying learning rate 0.067, momentum 0.9, M 933\n",
            "loss: 1.359355866909027\n",
            "trying learning rate 0.067, momentum 0.9, M 400\n",
            "loss: 1.304487195611\n",
            "trying learning rate 0.067, momentum 0.99, M 2000\n",
            "loss: 2.0537298053503035\n",
            "trying learning rate 0.067, momentum 0.99, M 1466\n",
            "loss: 1.9526885151863098\n",
            "trying learning rate 0.067, momentum 0.99, M 933\n",
            "loss: 2.0925994902849196\n",
            "trying learning rate 0.067, momentum 0.99, M 400\n",
            "loss: 2.100120317935944\n",
            "trying learning rate 0.100, momentum 0.9, M 2000\n",
            "loss: 1.4384219393134117\n",
            "trying learning rate 0.100, momentum 0.9, M 1466\n",
            "loss: 1.344945427775383\n",
            "trying learning rate 0.100, momentum 0.9, M 933\n",
            "loss: 1.3387846052646637\n",
            "trying learning rate 0.100, momentum 0.9, M 400\n",
            "loss: 1.3489358395338058\n",
            "trying learning rate 0.100, momentum 0.99, M 2000\n",
            "loss: 2.1165257722139357\n",
            "trying learning rate 0.100, momentum 0.99, M 1466\n",
            "loss: 2.232659214735031\n",
            "trying learning rate 0.100, momentum 0.99, M 933\n",
            "loss: 2.220948851108551\n",
            "trying learning rate 0.100, momentum 0.99, M 400\n",
            "loss: 2.133948928117752\n",
            "CPU times: user 30min 36s, sys: 1min 1s, total: 31min 37s\n",
            "Wall time: 31min 38s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"A\")\n",
        "for loss in sorted(losses_A)[0:5]:\n",
        "  lr, mom, M = losses_A[loss]\n",
        "  print(f\"Loss: {loss}, lr {lr}, momentum {mom}, M {M}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NOxxba89g5LP",
        "outputId": "264ad972-27f1-4bf5-8d75-204ac0344dbe"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A\n",
            "Loss: 1.6031416177749633, lr 0.0010000000474974513, momentum 0.99, M 1536\n",
            "Loss: 1.6141704738140106, lr 0.0010000000474974513, momentum 0.99, M 1254\n",
            "Loss: 1.6337729126214982, lr 0.0010000000474974513, momentum 0.99, M 972\n",
            "Loss: 1.6523005783557891, lr 0.03399999812245369, momentum 0.5, M 1536\n",
            "Loss: 1.6682529002428055, lr 0.06700000166893005, momentum 0.5, M 1254\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"B\")\n",
        "for loss in sorted(losses_B)[0:5]:\n",
        "  lr, mom, M = losses_B[loss]\n",
        "  print(f\"Loss: {loss}, lr {lr}, momentum {mom}, M {M}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GnqDq26OPtfM",
        "outputId": "9463fbb4-501e-459b-a9f3-0c366543ede2"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "B\n",
            "Loss: 1.2106488212943076, lr 0.03399999812245369, momentum 0.9, M 933\n",
            "Loss: 1.2154807671904564, lr 0.03399999812245369, momentum 0.9, M 2000\n",
            "Loss: 1.2367557018995285, lr 0.03399999812245369, momentum 0.9, M 400\n",
            "Loss: 1.2457669109106064, lr 0.03399999812245369, momentum 0.9, M 1466\n",
            "Loss: 1.2549321860074998, lr 0.06700000166893005, momentum 0.9, M 1466\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr, mom, M = losses_B[sorted(losses_B)[0]]\n",
        "model = neural_network_B(M, k=5, N=14)\n",
        "optimizer = SGD(model.parameters(), lr, momentum=mom)\n",
        "train_loss, train_accuracy, val_loss, val_accuracy = train(\n",
        "  model, optimizer, train_loader, val_loader, 30)\n",
        "print(f\"Validation accuracy for best model: {max(val_accuracy)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ebRnhgXAP6a_",
        "outputId": "3447fb46-584a-4317-86d1-177cc94b77ca"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation accuracy for best model: 0.6685546875\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plot the training and validation accuracy for each epoch, and evaluate our first model on the testing data."
      ],
      "metadata": {
        "id": "zu8AaNRl4FDi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate(\n",
        "    model: nn.Module, loader: DataLoader\n",
        ") -> Tuple[float, float]:\n",
        "  \"\"\"Computes test loss and accuracy of model on loader.\"\"\"\n",
        "  loss = nn.CrossEntropyLoss()\n",
        "  model.eval()\n",
        "  test_loss = 0.0\n",
        "  test_acc = 0.0\n",
        "  with torch.no_grad():\n",
        "    for (batch, labels) in loader:\n",
        "      batch, labels = batch.to(DEVICE), labels.to(DEVICE)\n",
        "      y_batch_pred = model(batch)\n",
        "      batch_loss = loss(y_batch_pred, labels)\n",
        "      test_loss = test_loss + batch_loss.item()\n",
        "\n",
        "      pred_max = torch.argmax(y_batch_pred, 1)\n",
        "      batch_acc = torch.sum(pred_max == labels)\n",
        "      test_acc = test_acc + batch_acc.item()\n",
        "    test_loss = test_loss / len(loader)\n",
        "    test_acc = test_acc / (batch_size * len(loader))\n",
        "    return test_loss, test_acc"
      ],
      "metadata": {
        "id": "KzxYGlYH4MwC"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 10))\n",
        "for i, loss in enumerate(sorted(losses_A)[0:5]):\n",
        "  lr, mom, M = losses_A[loss]\n",
        "  model = neural_network_A(M)\n",
        "  optimizer = SGD(model.parameters(), lr, momentum=mom)\n",
        "  train_loss, train_accuracy, val_loss, val_accuracy = train(\n",
        "    model, optimizer, train_loader, val_loader, 25)\n",
        "  if i == 0:\n",
        "    print(f\"Validation accuracy for best model: {max(val_accuracy)}\")\n",
        "    test_loss, test_acc = evaluate(model, test_loader)\n",
        "    print(f\"Test Accuracy for best model: {test_acc}\")\n",
        "\n",
        "  epochs = range(1, 26)\n",
        "  plt.plot(epochs, train_accuracy, label=f\"Train Accuracy, configuration {i+1}\")\n",
        "  plt.plot(epochs, val_accuracy, label=f\"Validation Accuracy, configuration {i+1}\")\n",
        "  plt.xlabel(\"Epoch\")\n",
        "  plt.ylabel(\"Accuracy\")\n",
        "  plt.legend()\n",
        "  #plt.title(\"Logistic Regression Accuracy for CIFAR-10 vs Epoch\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "-i_KVlY3WthL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "e29a1912-2e2f-401b-efb0-bd5261b797a8"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation accuracy for best model: 0.5037109375\n",
            "Test Accuracy for best model: 0.4994066455696203\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr_best, mom_best, M_best = losses_A[sorted(losses_A)[4]]\n",
        "model = neural_network_B(M, k=5, N=14)\n",
        "optimizer = SGD(model.parameters(), lr, momentum=mom)\n",
        "train_loss, train_accuracy, val_loss, val_accuracy = train(\n",
        "  model, optimizer, train_loader, val_loader, 25)\n",
        "print(f\"Validation accuracy for best model: {max(val_accuracy)}\")\n",
        "test_loss, test_acc = evaluate(model, test_loader)\n",
        "print(f\"Test Accuracy for best model: {test_acc}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X8KwUB7Fm_Bm",
        "outputId": "78784134-c6c8-4e8e-9db9-e2a95c32b319"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation accuracy for best model: 0.61640625\n",
            "Test Accuracy for best model: 0.5850474683544303\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 10))\n",
        "for i, loss in enumerate(sorted(losses_B)[0:5]):\n",
        "  lr, mom, M = losses_B[loss]\n",
        "  model = neural_network_B(M, k=5, N=14)\n",
        "  optimizer = SGD(model.parameters(), lr, momentum=mom)\n",
        "  train_loss, train_accuracy, val_loss, val_accuracy = train(\n",
        "    model, optimizer, train_loader, val_loader, 30)\n",
        "  if i == 0:\n",
        "    print(f\"Validation accuracy for best model: {max(val_accuracy)}\")\n",
        "    test_loss, test_acc = evaluate(model, test_loader)\n",
        "    print(f\"Test Accuracy for best model: {test_acc}\")\n",
        "\n",
        "  epochs = range(1, 31)\n",
        "  plt.plot(epochs, train_accuracy, label=f\"Train Accuracy, configuration {i+1}\")\n",
        "  plt.plot(epochs, val_accuracy, label=f\"Validation Accuracy, configuration {i+1}\")\n",
        "  plt.xlabel(\"Epoch\")\n",
        "  plt.ylabel(\"Accuracy\")\n",
        "  plt.legend()\n",
        "  #plt.title(\"Logistic Regression Accuracy for CIFAR-10 vs Epoch\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "5EPClxw7Qzzp",
        "outputId": "543da160-86ef-41eb-ea6e-9bba8ee8fd39"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation accuracy for best model: 0.66171875\n",
            "Test Accuracy for best model: 0.6600079113924051\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
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      "metadata": {
        "id": "lnlRsk76tuGU"
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}