Add image classification solutions
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1a_image_classification_solutions.ipynb
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359
1a_image_classification_solutions.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Image Classification\n",
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"\n",
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"Simple image classification using the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html).\n",
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"\n",
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"The CIFAR-10 dataset has 60,000 32x32 colour images in 10 classes (6,000 per class). These are split into 50,000 training images and 10,000 testing images.\n",
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"\n",
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"Here are the classes:\n",
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"1. Airplane\n",
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"2. Car\n",
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"3. Bird\n",
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"4. Cat\n",
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"5. Deer\n",
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"6. Dog\n",
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"7. Frog\n",
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"8. Horse\n",
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"9. Ship\n",
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"10. Truck"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import keras\n",
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"import numpy as np\n",
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"import os\n",
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"from keras.src.datasets.cifar import load_batch\n",
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"from keras import backend\n",
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"from skimage.transform import resize\n",
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"\n",
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"classes = [\n",
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" \"airplane\",\n",
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" \"car\",\n",
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" \"bird\",\n",
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" \"cat\",\n",
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" \"deer\",\n",
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" \"dog\",\n",
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" \"frog\",\n",
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" \"horse\",\n",
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" \"ship\",\n",
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" \"truck\",\n",
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"]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load the dataset 💿"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"num_train_samples = 50000\n",
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"\n",
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"x_train = np.empty((num_train_samples, 3, 32, 32), dtype=\"uint8\")\n",
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"y_train = np.empty((num_train_samples,), dtype=\"uint8\")\n",
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"\n",
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"for i in range(1, 6):\n",
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" file_path = os.path.join(\"cifar-10-batches-py\", f\"data_batch_{i}\")\n",
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" (\n",
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" x_train[(i - 1) * 10000 : i * 10000, :, :, :],\n",
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" y_train[(i - 1) * 10000 : i * 10000],\n",
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" ) = load_batch(file_path)\n",
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"\n",
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"file_path = os.path.join(\"cifar-10-batches-py\", \"test_batch\")\n",
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"x_test, y_test = load_batch(file_path)\n",
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"\n",
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"y_train = np.reshape(y_train, (len(y_train), 1))\n",
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"y_test = np.reshape(y_test, (len(y_test), 1))\n",
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"\n",
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"if backend.image_data_format() == \"channels_last\":\n",
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" x_train = x_train.transpose(0, 2, 3, 1)\n",
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" x_test = x_test.transpose(0, 2, 3, 1)\n",
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"\n",
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"x_test = x_test.astype(x_train.dtype)\n",
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"y_test = y_test.astype(y_train.dtype)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Exploring 🔎"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(x_train.shape)\n",
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"print(y_train.shape)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"`x_train` is the actual images in the dataset. You can see they are 32x32 and the 3 is for red, green and blue values.\n",
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"`y_train` is the category for each image, this is just a single number between 0 and 9."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train[1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.imshow(x_train[1])\n",
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"print(y_train[1])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Processing 🫧\n",
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"\n",
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"Our neural network works with decimal numbers between 0 and 1, so we need to convert the categories into 0s and 1s. We take an array of 0s and set a 1 for the category.\n",
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"\n",
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"For example, the number 2 would get encoded to `[0, 0, 1, ...]`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_train_one_hot = keras.src.utils.numerical_utils.to_categorical(y_train, 10)\n",
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"y_test_one_hot = keras.src.utils.numerical_utils.to_categorical(y_test, 10)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# task: can you print out the one hot encoded label for the truck above?\n",
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"print(y_train_one_hot[1])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"At the moment each pixel is represented by a number from 0 to 255. We also need to convert these to be between 0 and 1."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train = x_train.astype(\"float32\")\n",
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"x_test = x_test.astype(\"float32\")\n",
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"x_train = x_train / 255\n",
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"x_test = x_test / 255"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train[0]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Build and Train CNN 🔨"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from keras.models import Sequential\n",
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"from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D\n",
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"\n",
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"model = Sequential()\n",
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"model.add(\n",
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" Conv2D(32, (3, 3), activation=\"relu\", padding=\"same\", input_shape=(32, 32, 3))\n",
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")\n",
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"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
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"model.add(Dropout(0.25))\n",
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"model.add(Conv2D(64, (3, 3), activation=\"relu\", padding=\"same\"))\n",
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"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
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"model.add(Dropout(0.25))\n",
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"model.add(Flatten())\n",
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"model.add(Dense(512, activation=\"relu\"))\n",
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"model.add(Dropout(0.5))\n",
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"model.add(Dense(10, activation=\"softmax\"))\n",
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"\n",
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"model.summary()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"hist = model.fit(\n",
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" x_train, y_train_one_hot, batch_size=32, epochs=1, validation_split=0.2\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Evaluate 🧪"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model.evaluate(x_test, y_test_one_hot)[1]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"~50% accuracy... not great"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### What about for something it's not been trained on?\n",
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"\n",
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"Let's try and feed a picture of a cat to the model, and see what it thinks... As a reminder, the model hasn't been trained on pictures of cats."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"cat = plt.imread(\"cat.jpg\")\n",
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"cat_resized = resize(cat, (32, 32, 3))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.imshow(cat_resized)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"probabilities = model.predict(\n",
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" np.array(\n",
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" [\n",
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" cat_resized,\n",
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" ]\n",
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" )\n",
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")\n",
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"probabilities"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"index = np.argsort(probabilities[0, :])\n",
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"print(f\"Most likely: {classes[index[9]]}, probability={probabilities[0,index[9]]}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Additional Challenges 🏆\n",
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"\n",
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"- Try adding in some more layers to the neural network, adding a second `Conv2D` layer under both of the existing ones.\n",
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"- Try increasing the number of `epochs` when training.\n",
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"- Save/load your model with `model.save('mymodel.h5')` and `keras.models.load_model('mymodel.h5')`."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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