{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "authorship_tag": "ABX9TyMcvUAtcMw0yuDfUkbsEDLD", "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": [ "\"Open" ] }, { "cell_type": "markdown", "source": [ "# Coursework III -- Regularization\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 add **regularization** of some kind to improve the performance. Anything from chapter 8 of the book or anything else you can find is fine *except* early stopping. You must not change the model hyperparameters or the training algorithm.\n", "\n", "You don't have to improve the performance much. A few tenths of a percent is fine. It just has to be better 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\n", "\n", "\n" ], "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" ], "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", "# or download from my github to your local machine if you are doing this locally\n", "if not os.path.exists('./train_data_x.npy'):\n", " !wget https://github.com/udlbook/udlbook/raw/main/practicals/train_data_x.npy\n", " !wget https://github.com/udlbook/udlbook/raw/main/practicals/train_data_y.npy\n", " !wget https://github.com/udlbook/udlbook/raw/main/practicals/val_data_x.npy\n", " !wget https://github.com/udlbook/udlbook/raw/main/practicals/val_data_y.npy " ], "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')\n", "train_data_y = np.load('train_data_y.npy')\n", "val_data_x = np.load('val_data_x.npy')\n", "val_data_y = np.load('val_data_y.npy')\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": [ "# 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", "y_train = torch.tensor(train_data_y.astype('long'))\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 III: 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_III_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", "# if not os.path.exists('./test_data_x.npy'):\n", "# !wget https://github.com/udlbook/udlbook/raw/main/practicals/test_data_x.npy\n", "# !wget https://github.com/udlbook/udlbook/raw/main/practicals/test_data_y.npy\n", "\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": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "zXccksoD1Eww" }, "execution_count": null, "outputs": [] } ] }