Add image classification notebook
This commit is contained in:
commit
6e54cbf5b9
7 changed files with 4286 additions and 0 deletions
1
.gitattributes
vendored
Normal file
1
.gitattributes
vendored
Normal file
|
|
@ -0,0 +1 @@
|
|||
*.jpg filter=lfs diff=lfs merge=lfs -text
|
||||
1
.gitignore
vendored
Normal file
1
.gitignore
vendored
Normal file
|
|
@ -0,0 +1 @@
|
|||
cifar-10-batches-py
|
||||
359
1_image_classification.ipynb
Normal file
359
1_image_classification.ipynb
Normal file
|
|
@ -0,0 +1,359 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Image Classification\n",
|
||||
"\n",
|
||||
"Simple image classification using the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html).\n",
|
||||
"\n",
|
||||
"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",
|
||||
"\n",
|
||||
"Here are the classes:\n",
|
||||
"1. Airplane\n",
|
||||
"2. Car\n",
|
||||
"3. Bird\n",
|
||||
"4. Cat\n",
|
||||
"5. Deer\n",
|
||||
"6. Dog\n",
|
||||
"7. Frog\n",
|
||||
"8. Horse\n",
|
||||
"9. Ship\n",
|
||||
"10. Truck"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import keras\n",
|
||||
"import numpy as np\n",
|
||||
"import os\n",
|
||||
"from keras.src.datasets.cifar import load_batch\n",
|
||||
"from keras import backend\n",
|
||||
"from skimage.transform import resize\n",
|
||||
"\n",
|
||||
"classes = [\n",
|
||||
" \"airplane\",\n",
|
||||
" \"car\",\n",
|
||||
" \"bird\",\n",
|
||||
" \"cat\",\n",
|
||||
" \"deer\",\n",
|
||||
" \"dog\",\n",
|
||||
" \"frog\",\n",
|
||||
" \"horse\",\n",
|
||||
" \"ship\",\n",
|
||||
" \"truck\",\n",
|
||||
"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load the dataset"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"num_train_samples = 50000\n",
|
||||
"\n",
|
||||
"x_train = np.empty((num_train_samples, 3, 32, 32), dtype=\"uint8\")\n",
|
||||
"y_train = np.empty((num_train_samples,), dtype=\"uint8\")\n",
|
||||
"\n",
|
||||
"for i in range(1, 6):\n",
|
||||
" file_path = os.path.join(\"cifar-10-batches-py\", f\"data_batch_{i}\")\n",
|
||||
" (\n",
|
||||
" x_train[(i - 1) * 10000 : i * 10000, :, :, :],\n",
|
||||
" y_train[(i - 1) * 10000 : i * 10000],\n",
|
||||
" ) = load_batch(file_path)\n",
|
||||
"\n",
|
||||
"file_path = os.path.join(\"cifar-10-batches-py\", \"test_batch\")\n",
|
||||
"x_test, y_test = load_batch(file_path)\n",
|
||||
"\n",
|
||||
"y_train = np.reshape(y_train, (len(y_train), 1))\n",
|
||||
"y_test = np.reshape(y_test, (len(y_test), 1))\n",
|
||||
"\n",
|
||||
"if backend.image_data_format() == \"channels_last\":\n",
|
||||
" x_train = x_train.transpose(0, 2, 3, 1)\n",
|
||||
" x_test = x_test.transpose(0, 2, 3, 1)\n",
|
||||
"\n",
|
||||
"x_test = x_test.astype(x_train.dtype)\n",
|
||||
"y_test = y_test.astype(y_train.dtype)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Exploring"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(x_train.shape)\n",
|
||||
"print(y_train.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"`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",
|
||||
"`y_train` is the category for each image, this is just a single number between 0 and 9."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"x_train[1]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.imshow(x_train[1])\n",
|
||||
"print(y_train[1])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Processing\n",
|
||||
"\n",
|
||||
"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",
|
||||
"\n",
|
||||
"For example, the number 2 would get encoded to `[0, 0, 1, ...]`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_train_one_hot = keras.src.utils.numerical_utils.to_categorical(y_train, 10)\n",
|
||||
"y_test_one_hot = keras.src.utils.numerical_utils.to_categorical(y_test, 10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# task: can you print out the one hot encoded label for the truck above?\n",
|
||||
"print()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"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."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"x_train = x_train.astype(\"float32\")\n",
|
||||
"x_test = x_test.astype(\"float32\")\n",
|
||||
"x_train = x_train / 255\n",
|
||||
"x_test = x_test / 255"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"x_train[0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Build and Train CNN"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from keras.models import Sequential\n",
|
||||
"from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D\n",
|
||||
"\n",
|
||||
"model = Sequential()\n",
|
||||
"model.add(\n",
|
||||
" Conv2D(32, (3, 3), activation=\"relu\", padding=\"same\", input_shape=(32, 32, 3))\n",
|
||||
")\n",
|
||||
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
|
||||
"model.add(Dropout(0.25))\n",
|
||||
"model.add(Conv2D(64, (3, 3), activation=\"relu\", padding=\"same\"))\n",
|
||||
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
|
||||
"model.add(Dropout(0.25))\n",
|
||||
"model.add(Flatten())\n",
|
||||
"model.add(Dense(512, activation=\"relu\"))\n",
|
||||
"model.add(Dropout(0.5))\n",
|
||||
"model.add(Dense(10, activation=\"softmax\"))\n",
|
||||
"\n",
|
||||
"model.summary()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"hist = model.fit(\n",
|
||||
" x_train, y_train_one_hot, batch_size=32, epochs=1, validation_split=0.2\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Evaluate"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.evaluate(x_test, y_test_one_hot)[1]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"~50% accuracy... not great"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### What about for something it's not been trained on?\n",
|
||||
"\n",
|
||||
"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."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cat = plt.imread(\"cat.jpg\")\n",
|
||||
"cat_resized = resize(cat, (32, 32, 3))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.imshow(cat_resized)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"probabilities = model.predict(\n",
|
||||
" np.array(\n",
|
||||
" [\n",
|
||||
" cat_resized,\n",
|
||||
" ]\n",
|
||||
" )\n",
|
||||
")\n",
|
||||
"probabilities"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"index = np.argsort(probabilities[0, :])\n",
|
||||
"print(f\"Most likely: {classes[index[9]]}, probability={probabilities[0,index[9]]}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"More tasks\n",
|
||||
"\n",
|
||||
"- Try adding in some more layers to the neural network, adding a second `Conv2D` layer under both of the existing ones.\n",
|
||||
"- Try increasing the number of `epochs` when training.\n",
|
||||
"- Save/load your model with `model.save('mymodel.h5')` and `keras.models.load_model('mymodel.h5')`."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "cads-ai-NGxhrgr5-py3.12",
|
||||
"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": "3.9.19"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
BIN
cat.jpg
(Stored with Git LFS)
Normal file
BIN
cat.jpg
(Stored with Git LFS)
Normal file
Binary file not shown.
4
download-data.sh
Executable file
4
download-data.sh
Executable file
|
|
@ -0,0 +1,4 @@
|
|||
#!/bin/bash
|
||||
wget -O cifar-10-python.tar.gz https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
|
||||
tar -xvf cifar-10-python.tar.gz
|
||||
rm cifar-10-python.tar.gz
|
||||
3895
poetry.lock
generated
Normal file
3895
poetry.lock
generated
Normal file
File diff suppressed because it is too large
Load diff
23
pyproject.toml
Normal file
23
pyproject.toml
Normal file
|
|
@ -0,0 +1,23 @@
|
|||
[tool.poetry]
|
||||
name = "cads-ai"
|
||||
version = "0.1.0"
|
||||
description = "Practial resources for CADS session on AI"
|
||||
authors = ["Jake Walker <hi@jakew.me>"]
|
||||
readme = "README.md"
|
||||
package-mode = false
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.9,<3.12"
|
||||
jupyterlab = "^4.2.0"
|
||||
keras = "^3.3.3"
|
||||
matplotlib = "^3.9.0"
|
||||
numpy = "^1.26.4"
|
||||
stable-baselines3 = "^2.3.2"
|
||||
gymnasium = "^0.29.1"
|
||||
tensorflow = "^2.16.1"
|
||||
scikit-image = "0.22.0"
|
||||
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
Loading…
Reference in a new issue