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CM20315_2023/CM20315_Coursework_II.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyM+iKos5DEoHUxL8+9oxA2A",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/CM20315_2023/CM20315_Coursework_II.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Coursework II -- Training hyperparameters\n",
+        "\n",
+        "The goal of the coursework is to modify a simple bit of numpy code that trains a network and measures the performance on a validation set for the MNist 1D dataset.\n",
+        "\n",
+        "In this coursework, you need to modify the **training hyperparameters** (only) to improve the performance over the current attempt. This could mean the training algorithm, learning rate, learning rate schedule, momentum term, initialization etc.  \n",
+        "\n",
+        "You must improve the performance by at least 2% to get full marks.\n",
+        "\n",
+        "You will need to upload three things to Moodle:\n",
+        "1.   The image that this notebook saves (click the folder icon on the left on colab to download it)\n",
+        "2.   The lines of code you changed\n",
+        "3.   The whole notebook as a .ipynb file.  You can do this on the File menu"
+      ],
+      "metadata": {
+        "id": "t9vk9Elugvmi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import os\n",
+        "import torch, torch.nn as nn\n",
+        "from torch.utils.data import TensorDataset, DataLoader\n",
+        "from torch.optim.lr_scheduler import StepLR\n",
+        "import matplotlib.pyplot as plt\n",
+        "import random\n",
+        "import gdown"
+      ],
+      "metadata": {
+        "id": "YrXWAH7sUWvU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Run this once to copy the train and validation data to your CoLab environment\n",
+        "if not os.path.exists('./Data.zip'):\n",
+        "  !gdown 1HtnCrncY6dFCYqzgPf1HtPVAerTpwFRm\n",
+        "  !unzip Data.zip"
+      ],
+      "metadata": {
+        "id": "wScBGXXFVadm"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Load in the data\n",
+        "train_data_x = np.load('train_data_x.npy',allow_pickle=True)\n",
+        "train_data_y = np.load('train_data_y.npy',allow_pickle=True)\n",
+        "val_data_x = np.load('val_data_x.npy',allow_pickle=True)\n",
+        "val_data_y = np.load('val_data_y.npy',allow_pickle=True)\n",
+        "# Print out sizes\n",
+        "print(\"Train data: %d examples (columns), each of which has %d dimensions (rows)\"%((train_data_x.shape[1],train_data_x.shape[0])))\n",
+        "print(\"Validation data: %d examples (columns), each of which has %d dimensions (rows)\"%((val_data_x.shape[1],val_data_x.shape[0])))"
+      ],
+      "metadata": {
+        "id": "8bKADvLHbiV5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Define the network"
+      ],
+      "metadata": {
+        "id": "_sFvRDGrl4qe"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# YOU SHOULD NOT CHANGE THIS CELL!\n",
+        "\n",
+        "# There are 40 input dimensions and 10 output dimensions for this data\n",
+        "# The inputs correspond to the 40 offsets in the MNIST1D template.\n",
+        "D_i = 40\n",
+        "# The outputs correspond to the 10 digits\n",
+        "D_o = 10\n",
+        "\n",
+        "# Number of hidden units in layers 1 and 2\n",
+        "D_1 = 100\n",
+        "D_2 = 100\n",
+        "\n",
+        "# create model with two hidden layers\n",
+        "model = nn.Sequential(\n",
+        "nn.Linear(D_i, D_1),\n",
+        "nn.ReLU(),\n",
+        "nn.Linear(D_1, D_2),\n",
+        "nn.ReLU(),\n",
+        "nn.Linear(D_2, D_o))"
+      ],
+      "metadata": {
+        "id": "FslroPJJffrh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# He initialization of weights\n",
+        "def weights_init(layer_in):\n",
+        "  if isinstance(layer_in, nn.Linear):\n",
+        "    nn.init.kaiming_uniform_(layer_in.weight)\n",
+        "    layer_in.bias.data.fill_(0.0)"
+      ],
+      "metadata": {
+        "id": "YgLaex1pfhqz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# You need all this stuff to ensure that PyTorch is deterministic\n",
+        "def set_seed(seed):\n",
+        "    torch.manual_seed(seed)\n",
+        "    torch.cuda.manual_seed_all(seed)\n",
+        "    torch.backends.cudnn.deterministic = True\n",
+        "    torch.backends.cudnn.benchmark = False\n",
+        "    np.random.seed(seed)\n",
+        "    random.seed(seed)\n",
+        "    os.environ['PYTHONHASHSEED'] = str(seed)"
+      ],
+      "metadata": {
+        "id": "zXRmxCQNnL_M"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set seed so always get same result (do not change)\n",
+        "set_seed(1)\n",
+        "\n",
+        "# choose cross entropy loss function (equation 5.24 in the loss notes)\n",
+        "loss_function = nn.CrossEntropyLoss()\n",
+        "# construct SGD optimizer and initialize learning rate and momentum\n",
+        "optimizer = torch.optim.SGD(model.parameters(), lr = 0.05, momentum=0.9)\n",
+        "# object that decreases learning rate by half every 10 epochs\n",
+        "scheduler = StepLR(optimizer, step_size=10, gamma=0.5)\n",
+        "# create 100 dummy data points and store in data loader class\n",
+        "x_train = torch.tensor(train_data_x.transpose().astype('float32'))\n",
+        "print(x_train.shape)\n",
+        "y_train = torch.tensor(train_data_y.astype('long'))\n",
+        "print(y_train.shape)\n",
+        "x_val= torch.tensor(val_data_x.transpose().astype('float32'))\n",
+        "y_val = torch.tensor(val_data_y.astype('long'))\n",
+        "\n",
+        "# load the data into a class that creates the batches\n",
+        "data_loader = DataLoader(TensorDataset(x_train,y_train), batch_size=100, shuffle=True, worker_init_fn=np.random.seed(1))\n",
+        "\n",
+        "# Initialize model weights\n",
+        "model.apply(weights_init)\n",
+        "\n",
+        "# loop over the dataset n_epoch times\n",
+        "n_epoch = 50\n",
+        "# store the loss and the % correct at each epoch\n",
+        "losses_train = np.zeros((n_epoch))\n",
+        "errors_train = np.zeros((n_epoch))\n",
+        "losses_val = np.zeros((n_epoch))\n",
+        "errors_val = np.zeros((n_epoch))\n",
+        "\n",
+        "for epoch in range(n_epoch):\n",
+        "  # loop over batches\n",
+        "  for i, data in enumerate(data_loader):\n",
+        "    # retrieve inputs and labels for this batch\n",
+        "    x_batch, y_batch = data\n",
+        "    # zero the parameter gradients\n",
+        "    optimizer.zero_grad()\n",
+        "    # forward pass -- calculate model output\n",
+        "    pred = model(x_batch)\n",
+        "    # compute the lss\n",
+        "    loss = loss_function(pred, y_batch)\n",
+        "    # backward pass\n",
+        "    loss.backward()\n",
+        "    # SGD update\n",
+        "    optimizer.step()\n",
+        "\n",
+        "  # Run whole dataset to get statistics -- normally wouldn't do this\n",
+        "  pred_train = model(x_train)\n",
+        "  pred_val = model(x_val)\n",
+        "  _, predicted_train_class = torch.max(pred_train.data, 1)\n",
+        "  _, predicted_val_class = torch.max(pred_val.data, 1)\n",
+        "  errors_train[epoch] = 100 - 100 * (predicted_train_class == y_train).float().sum() / len(y_train)\n",
+        "  errors_val[epoch]= 100 - 100 * (predicted_val_class == y_val).float().sum() / len(y_val)\n",
+        "  losses_train[epoch] = loss_function(pred_train, y_train).item()\n",
+        "  losses_val[epoch]= loss_function(pred_val, y_val).item()\n",
+        "  print(f'Epoch {epoch:5d}, train loss {losses_train[epoch]:.6f}, train error {errors_train[epoch]:3.2f},  val loss {losses_val[epoch]:.6f}, percent error {errors_val[epoch]:3.2f}')\n",
+        "\n",
+        "  # tell scheduler to consider updating learning rate\n",
+        "  scheduler.step()\n",
+        "\n",
+        "# Plot the results\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.plot(errors_train,'r-',label='train')\n",
+        "ax.plot(errors_val,'b-',label='validation')\n",
+        "ax.set_ylim(0,100); ax.set_xlim(0,n_epoch)\n",
+        "ax.set_xlabel('Epoch'); ax.set_ylabel('Error')\n",
+        "ax.set_title('Part II: Validation Result %3.2f'%(errors_val[-1]))\n",
+        "ax.legend()\n",
+        "ax.plot([0,n_epoch],[37.45, 37.45],'k:') # Original results. You should be better than this!\n",
+        "plt.savefig('Coursework_II_Results.png',format='png')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "NYw8I_3mmX5c"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Leave this all commented for now\n",
+        "# We'll see how well you did on the test data after the coursework is submitted\n",
+        "\n",
+        "# # I haven't given you this yet, leave commented\n",
+        "# test_data_x = np.load('test_data_x.npy')\n",
+        "# test_data_y = np.load('test_data_y.npy')\n",
+        "# x_test = torch.tensor(test_data_x.transpose().astype('float32'))\n",
+        "# y_test = torch.tensor(test_data_y.astype('long'))\n",
+        "# pred_test = model(x_test)\n",
+        "# _, predicted_test_class = torch.max(pred_test.data, 1)\n",
+        "# errors_test = 100 - 100 * (predicted_test_class == y_test).float().sum() / len(y_test)\n",
+        "# print(\"Test error = %3.3f\"%(errors_test))"
+      ],
+      "metadata": {
+        "id": "O7nBz-R84QdJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}