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265 lines
31 KiB
Plaintext
265 lines
31 KiB
Plaintext
{
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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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"collapsed": true,
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"# Multi-Class Classification with Perceptron\n",
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"\n",
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"Lab Assignment from [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners)."
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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": 1,
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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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 numpy as np\n",
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"import pickle\n",
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"import os"
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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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"You can use the following perceptron training code from the lecture:"
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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": 6,
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"outputs": [],
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"source": [
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"def train(positive_examples, negative_examples, num_iterations = 100):\n",
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" num_dims = positive_examples.shape[1]\n",
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" weights = np.zeros((num_dims,1)) # initialize weights\n",
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" \n",
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" pos_count = positive_examples.shape[0]\n",
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" neg_count = negative_examples.shape[0]\n",
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" \n",
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" report_frequency = 10\n",
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" \n",
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" for i in range(num_iterations):\n",
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" pos = random.choice(positive_examples)\n",
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" neg = random.choice(negative_examples)\n",
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"\n",
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" z = np.dot(pos, weights) \n",
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" if z < 0:\n",
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" weights = weights + pos.reshape(weights.shape)\n",
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"\n",
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" z = np.dot(neg, weights)\n",
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" if z >= 0:\n",
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" weights = weights - neg.reshape(weights.shape)\n",
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" \n",
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" if i % report_frequency == 0: \n",
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" pos_out = np.dot(positive_examples, weights)\n",
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" neg_out = np.dot(negative_examples, weights) \n",
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" pos_correct = (pos_out >= 0).sum() / float(pos_count)\n",
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" neg_correct = (neg_out < 0).sum() / float(neg_count)\n",
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" print(\"Iteration={}, pos correct={}, neg correct={}\".format(i,pos_correct,neg_correct))\n",
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"\n",
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" return weights"
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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": 13,
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1.0"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"def accuracy(weights, test_x, test_labels):\n",
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" res = np.dot(np.c_[test_x,np.ones(len(test_x))],weights)\n",
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" return (res.reshape(test_labels.shape)*test_labels>=0).sum()/float(len(test_labels))\n",
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"\n",
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"accuracy(wts, test_x, test_labels)"
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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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"collapsed": true,
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"### Reading the Dataset\n",
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"\n",
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"This code download the dataset from the repository on the internet. You can also manually copy the dataset from `/data` directory of AI Curriculum repo."
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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": 8,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [],
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"source": [
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"!rm *.pkl\n",
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"https://github.com/mnielsen/neural-networks-and-deep-learning/blob/master/data/mnist.pkl.gz",
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"!gzip -d mnist.pkl.gz"
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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": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('mnist.pkl', 'rb') as mnist_pickle:\n",
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" MNIST = pickle.load(mnist_pickle)"
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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": 20,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" 0 0 0 0 0 0 0 0 0 0 0 191 250 253 93 0 0 0\n",
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" 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
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"1\n"
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]
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},
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{
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"data": {
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",
|
|
"text/plain": [
|
|
"<Figure size 720x360 with 10 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"print(MNIST['Train']['Features'][0][130:180])\n",
|
|
"print(MNIST['Train']['Labels'][0])\n",
|
|
"features = MNIST['Train']['Features'].astype(np.float32) / 256.0\n",
|
|
"labels = MNIST['Train']['Labels']\n",
|
|
"fig = plt.figure(figsize=(10,5))\n",
|
|
"for i in range(10):\n",
|
|
" ax = fig.add_subplot(1,10,i+1)\n",
|
|
" plt.imshow(features[i].reshape(28,28))\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Code to create *one-vs-other* dataset for two-digit classification. You need to modify this code to create *one-vs-all* dateset."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "slide"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def set_mnist_pos_neg(positive_label, negative_label):\n",
|
|
" positive_indices = [i for i, j in enumerate(MNIST['Train']['Labels']) \n",
|
|
" if j == positive_label]\n",
|
|
" negative_indices = [i for i, j in enumerate(MNIST['Train']['Labels']) \n",
|
|
" if j == negative_label]\n",
|
|
"\n",
|
|
" positive_images = MNIST['Train']['Features'][positive_indices]\n",
|
|
" negative_images = MNIST['Train']['Features'][negative_indices]\n",
|
|
"\n",
|
|
" return positive_images, negative_images"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now you need to:\n",
|
|
"1. Create 10 *one-vs-all* datasets for all digits\n",
|
|
"1. Train 10 perceptrons\n",
|
|
"1. Define `classify` function to perform digit classification\n",
|
|
"1. Measure the accuracy of classification and print *confusion matrix*\n",
|
|
"1. [Optional] Create improved `classify` function that performs the classification using one matrix multiplication."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"celltoolbar": "Slideshow",
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"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": "3.9.5"
|
|
},
|
|
"livereveal": {
|
|
"start_slideshow_at": "selected"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|