2605 lines
301 KiB
Plaintext
2605 lines
301 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ab021475",
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"metadata": {},
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"source": [
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"## Jupyter notebook Lars Rook & Sem van der Hoeven"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ffa039c8",
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"metadata": {},
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"source": [
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"# Week 4"
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]
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},
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{
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"cell_type": "markdown",
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"id": "239c1156",
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"metadata": {},
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"source": [
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"## 4.1: ZTDL 1 - First Deep Learning Model\n",
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"We hebben bij elk code blok comments gezet die uitleggen wat de code doet."
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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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"id": "155b55bd",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np # import the numpy library and assign the name np to it\n",
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"\n",
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"# magic function that sets the backend of matplotlib to the inline backend\n",
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"# source: https://stackoverflow.com/questions/43027980/purpose-of-matplotlib-inline\n",
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"%matplotlib inline \n",
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"import matplotlib.pyplot as plt # import the matplotlib.pyplot and assign the name plt to it"
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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": 2,
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"id": "feeaffc8",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.datasets import make_circles # import the make_circles module from the sklearn.datasets module"
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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": 3,
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"id": "c1db0180",
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"metadata": {},
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"outputs": [],
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"source": [
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"\"\"\"\n",
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"roept de make_circles functie aan van de sklearn.datasets module\n",
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"de make_circles functie maakt een cirkel met een kleinere cirkel hier binnen in.\n",
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"- de n_samples staat voor hoeveel points gegenereerd moeten worden.\n",
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"- de noise variabele staat voor hoeveel noise eraan toegeoegd moet worden\n",
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"- de factor staat voor de schaling tussen de binnenste en buitenste cirkel\n",
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"- de random_state variabele wordt gebruikt voor het genereren van een random nummer die gebruikt wordt voor het shufflen van de dataset en de noise.\n",
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"\"\"\"\n",
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"\n",
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"X, y = make_circles(n_samples=1000,\n",
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" noise=0.1,\n",
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" factor=0.2,\n",
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" random_state=0)"
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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": 4,
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"id": "46267e93",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[ 0.24265541, 0.0383196 ],\n",
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" [ 0.04433036, -0.05667334],\n",
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" [-0.78677748, -0.75718576],\n",
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" ...,\n",
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" [ 0.0161236 , -0.00548034],\n",
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" [ 0.20624715, 0.09769677],\n",
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" [-0.19186631, 0.08916672]])"
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]
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},
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"execution_count": 4,
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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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"# X bevat de gegenereerde samples van de make_circles methode.\n",
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"\n",
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"X"
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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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"id": "34c5864c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(1000, 2)"
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]
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},
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"execution_count": 6,
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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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"# aangezien x de array of shape [n_samples, 2] bevat, geeft dit de gegeven aantal samples en 2.\n",
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"X.shape"
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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": 5,
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"id": "fa762402",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Text(0.5, 1.0, 'Blue circles and Red crosses')"
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]
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},
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"execution_count": 5,
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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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"data": {
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"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 360x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\"\"\"\n",
|
||
"Hier wordt met matplotlib een grafiek van de samples getekend.\n",
|
||
"allereerst wordt er een figuur aangemaakt, daarna worden de waarden die zijn gegenereerd getekend op deze figuur.\n",
|
||
"Hierna worden de limits van de x en y assen gezet.\n",
|
||
"Daarna wordt de legenda aangemaakt, hierbij zijn de bolletjes een 0 en de kruisjes een 1.\n",
|
||
"Als laatst wordt de titel van de figuur aangemaakt.\n",
|
||
"\"\"\"\n",
|
||
"\n",
|
||
"plt.figure(figsize=(5, 5))\n",
|
||
"plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
|
||
"plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
|
||
"plt.xlim(-1.5, 1.5)\n",
|
||
"plt.ylim(-1.5, 1.5)\n",
|
||
"plt.legend(['0', '1'])\n",
|
||
"plt.title(\"Blue circles and Red crosses\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "9efb32c3",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# importeer modules van tensorflow\n",
|
||
"from tensorflow.keras.models import Sequential\n",
|
||
"from tensorflow.keras.layers import Dense\n",
|
||
"from tensorflow.keras.optimizers import SGD"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "43c0a1d3",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Maak een sequentieel model aan\n",
|
||
"model = Sequential()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "585aa0e2",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# voeg een laag aan het model toe met 4 output neurons en 2 input neurons. Deze laag gebruikt de hyperbolic tangent activation functie.\n",
|
||
"model.add(Dense(4, input_shape=(2,), activation='tanh'))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "84e68325",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# voeg nog een laag toe met 1 output neuron. Deze laag gebruikt de Sigmoid activation function, sigmoid(x) = 1 / (1 + exp(-x))\n",
|
||
"model.add(Dense(1, activation='sigmoid'))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "fef8e12a",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"\"\"\"\n",
|
||
"compile configureert het model voor het trainen.\n",
|
||
"- als optimizer wordt de gradient descent optimizer gebruikt met een learning rate van 0.5\n",
|
||
"- als loss wordt binary cross entropy gebruikt. \n",
|
||
"- bij de metrics parameter wordt een lijst van attributen gezet waarop het model wordt geëvalueerd. In dit geval is het alleen de nauwkeurigheid\n",
|
||
"\"\"\" \n",
|
||
"model.compile(SGD(learning_rate=0.5), 'binary_crossentropy', metrics=['accuracy'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "0e1bcf7b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch 1/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.6605 - accuracy: 0.6200\n",
|
||
"Epoch 2/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.5513 - accuracy: 0.8240\n",
|
||
"Epoch 3/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.4052 - accuracy: 0.9510\n",
|
||
"Epoch 4/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.2894 - accuracy: 0.9940\n",
|
||
"Epoch 5/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.2142 - accuracy: 1.0000\n",
|
||
"Epoch 6/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.1680 - accuracy: 1.0000\n",
|
||
"Epoch 7/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.1372 - accuracy: 1.0000\n",
|
||
"Epoch 8/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.1153 - accuracy: 1.0000\n",
|
||
"Epoch 9/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.0988 - accuracy: 1.0000\n",
|
||
"Epoch 10/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.0865 - accuracy: 1.0000\n",
|
||
"Epoch 11/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.0765 - accuracy: 1.0000\n",
|
||
"Epoch 12/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.0686 - accuracy: 1.0000\n",
|
||
"Epoch 13/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.0622 - accuracy: 1.0000\n",
|
||
"Epoch 14/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.0567 - accuracy: 1.0000\n",
|
||
"Epoch 15/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.0519 - accuracy: 1.0000\n",
|
||
"Epoch 16/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.0482 - accuracy: 1.0000\n",
|
||
"Epoch 17/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.0447 - accuracy: 1.0000\n",
|
||
"Epoch 18/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.0418 - accuracy: 0.9990\n",
|
||
"Epoch 19/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.0392 - accuracy: 0.9990\n",
|
||
"Epoch 20/20\n",
|
||
"32/32 [==============================] - 0s 1ms/step - loss: 0.0369 - accuracy: 0.9990\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<tensorflow.python.keras.callbacks.History at 0x26d8d1e0588>"
|
||
]
|
||
},
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"\"\"\"\n",
|
||
"fit traint het model voor het aantal gegeven epochs.\n",
|
||
"- X staat voor de input samples.\n",
|
||
"- y staat voor de target data (tensors)\n",
|
||
"- epochs staat voor hoe vaak het model getrained wordt.\n",
|
||
"\"\"\"\n",
|
||
"model.fit(X, y, epochs=20)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "20f9bb50",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"\"\"\"\n",
|
||
"met de linspace functies worden nummers gegenereerd over een gelijk interval.\n",
|
||
"met de meshgrid functies worden coordinate matrices gemaakt van coordinate vectors.\n",
|
||
"met de c_ functie wordt een matrix gemaakt van de genenereerde arrays.\n",
|
||
"met de predict functie worden output predictions gegenereerd voor de input samples.\n",
|
||
"met de reshape functie wordt de shape aangepast naar die van de meshgrid.\n",
|
||
"\"\"\"\n",
|
||
"hticks = np.linspace(-1.5, 1.5, 101)\n",
|
||
"vticks = np.linspace(-1.5, 1.5, 101)\n",
|
||
"aa, bb = np.meshgrid(hticks, vticks)\n",
|
||
"ab = np.c_[aa.ravel(), bb.ravel()]\n",
|
||
"c = model.predict(ab)\n",
|
||
"cc = c.reshape(aa.shape)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "a2e4f082",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[-1.5 , -1.5 ],\n",
|
||
" [-1.47, -1.5 ],\n",
|
||
" [-1.44, -1.5 ],\n",
|
||
" ...,\n",
|
||
" [ 1.44, 1.5 ],\n",
|
||
" [ 1.47, 1.5 ],\n",
|
||
" [ 1.5 , 1.5 ]])"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"ab"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "1e5b54db",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0.5, 1.0, 'Blue circles and Red crosses')"
|
||
]
|
||
},
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 360x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\"\"\"\n",
|
||
"Hier wordt met matplotlib een grafiek van de samples getekend.\n",
|
||
"allereerst wordt er een figuur aangemaakt\n",
|
||
"Daarna wordt de contour getekend aan de hand van de output van het model.\n",
|
||
"Daarna worden de waarden die zijn gegenereerd getekend op deze figuur.\n",
|
||
"Hierna worden de limits van de x en y assen gezet.\n",
|
||
"Daarna wordt de legenda aangemaakt, hierbij zijn de bolletjes een 0 en de kruisjes een 1.\n",
|
||
"Als laatst wordt de titel van de figuur aangemaakt.\n",
|
||
"\"\"\"\n",
|
||
"plt.figure(figsize=(5, 5))\n",
|
||
"plt.contourf(aa, bb, cc, cmap='bwr', alpha=0.2)\n",
|
||
"plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
|
||
"plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
|
||
"plt.xlim(-1.5, 1.5)\n",
|
||
"plt.ylim(-1.5, 1.5)\n",
|
||
"plt.legend(['0', '1'])\n",
|
||
"plt.title(\"Blue circles and Red crosses\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9267f151",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 4.2 : ZTDL 2 - Data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "20cea62e",
|
||
"metadata": {},
|
||
"source": [
|
||
"a. we hebben allebei het notebook bestudeerd.\n",
|
||
"b. we hebben een spreadsheet gevonden over heart attack analysis and predictions.\n",
|
||
"c. Hieronder zijn een aantal technieken en plots te zien die wij op de data hebben uitgevoerd. Om het beter te begrijpen hebben wij allebei samen de technieken en plots uitgevoerd."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "eaab705e",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Standaard info\n",
|
||
"We hebben eerst de standaard gegevens van de dataset verkend."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "4deee696",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"%matplotlib inline\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import pandas as pd\n",
|
||
"import numpy as np"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "6b792675",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df = pd.read_csv('../data/heart.csv')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "81faf37f",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"pandas.core.frame.DataFrame"
|
||
]
|
||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"type(df)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "6417b5c6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
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|
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|
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|
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>age</th>\n",
|
||
" <th>sex</th>\n",
|
||
" <th>cp</th>\n",
|
||
" <th>trtbps</th>\n",
|
||
" <th>chol</th>\n",
|
||
" <th>fbs</th>\n",
|
||
" <th>restecg</th>\n",
|
||
" <th>thalachh</th>\n",
|
||
" <th>exng</th>\n",
|
||
" <th>oldpeak</th>\n",
|
||
" <th>slp</th>\n",
|
||
" <th>caa</th>\n",
|
||
" <th>thall</th>\n",
|
||
" <th>output</th>\n",
|
||
" </tr>\n",
|
||
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|
||
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|
||
" <tr>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
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|
||
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|
||
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|
||
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|
||
" <td>37</td>\n",
|
||
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|
||
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|
||
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|
||
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|
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||
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|
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" <tr>\n",
|
||
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|
||
" <td>41</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>130</td>\n",
|
||
" <td>204</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>172</td>\n",
|
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" <td>0</td>\n",
|
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|
||
" <td>2</td>\n",
|
||
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|
||
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|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>56</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>120</td>\n",
|
||
" <td>236</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>178</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.8</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>57</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>120</td>\n",
|
||
" <td>354</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>163</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.6</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" age sex cp trtbps chol fbs restecg thalachh exng oldpeak slp \\\n",
|
||
"0 63 1 3 145 233 1 0 150 0 2.3 0 \n",
|
||
"1 37 1 2 130 250 0 1 187 0 3.5 0 \n",
|
||
"2 41 0 1 130 204 0 0 172 0 1.4 2 \n",
|
||
"3 56 1 1 120 236 0 1 178 0 0.8 2 \n",
|
||
"4 57 0 0 120 354 0 1 163 1 0.6 2 \n",
|
||
"\n",
|
||
" caa thall output \n",
|
||
"0 0 1 1 \n",
|
||
"1 0 2 1 \n",
|
||
"2 0 2 1 \n",
|
||
"3 0 2 1 \n",
|
||
"4 0 2 1 "
|
||
]
|
||
},
|
||
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|
||
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|
||
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 303 entries, 0 to 302\n",
|
||
"Data columns (total 14 columns):\n",
|
||
" # Column Non-Null Count Dtype \n",
|
||
"--- ------ -------------- ----- \n",
|
||
" 0 age 303 non-null int64 \n",
|
||
" 1 sex 303 non-null int64 \n",
|
||
" 2 cp 303 non-null int64 \n",
|
||
" 3 trtbps 303 non-null int64 \n",
|
||
" 4 chol 303 non-null int64 \n",
|
||
" 5 fbs 303 non-null int64 \n",
|
||
" 6 restecg 303 non-null int64 \n",
|
||
" 7 thalachh 303 non-null int64 \n",
|
||
" 8 exng 303 non-null int64 \n",
|
||
" 9 oldpeak 303 non-null float64\n",
|
||
" 10 slp 303 non-null int64 \n",
|
||
" 11 caa 303 non-null int64 \n",
|
||
" 12 thall 303 non-null int64 \n",
|
||
" 13 output 303 non-null int64 \n",
|
||
"dtypes: float64(1), int64(13)\n",
|
||
"memory usage: 33.3 KB\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"df.info()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "9a94370a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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"<div>\n",
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"<style scoped>\n",
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||
" }\n",
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||
"\n",
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||
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|
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|
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||
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||
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|
||
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|
||
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|
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>age</th>\n",
|
||
" <th>sex</th>\n",
|
||
" <th>cp</th>\n",
|
||
" <th>trtbps</th>\n",
|
||
" <th>chol</th>\n",
|
||
" <th>fbs</th>\n",
|
||
" <th>restecg</th>\n",
|
||
" <th>thalachh</th>\n",
|
||
" <th>exng</th>\n",
|
||
" <th>oldpeak</th>\n",
|
||
" <th>slp</th>\n",
|
||
" <th>caa</th>\n",
|
||
" <th>thall</th>\n",
|
||
" <th>output</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>count</th>\n",
|
||
" <td>303.000000</td>\n",
|
||
" <td>303.000000</td>\n",
|
||
" <td>303.000000</td>\n",
|
||
" <td>303.000000</td>\n",
|
||
" <td>303.000000</td>\n",
|
||
" <td>303.000000</td>\n",
|
||
" <td>303.000000</td>\n",
|
||
" <td>303.000000</td>\n",
|
||
" <td>303.000000</td>\n",
|
||
" <td>303.000000</td>\n",
|
||
" <td>303.000000</td>\n",
|
||
" <td>303.000000</td>\n",
|
||
" <td>303.000000</td>\n",
|
||
" <td>303.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>54.366337</td>\n",
|
||
" <td>0.683168</td>\n",
|
||
" <td>0.966997</td>\n",
|
||
" <td>131.623762</td>\n",
|
||
" <td>246.264026</td>\n",
|
||
" <td>0.148515</td>\n",
|
||
" <td>0.528053</td>\n",
|
||
" <td>149.646865</td>\n",
|
||
" <td>0.326733</td>\n",
|
||
" <td>1.039604</td>\n",
|
||
" <td>1.399340</td>\n",
|
||
" <td>0.729373</td>\n",
|
||
" <td>2.313531</td>\n",
|
||
" <td>0.544554</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>9.082101</td>\n",
|
||
" <td>0.466011</td>\n",
|
||
" <td>1.032052</td>\n",
|
||
" <td>17.538143</td>\n",
|
||
" <td>51.830751</td>\n",
|
||
" <td>0.356198</td>\n",
|
||
" <td>0.525860</td>\n",
|
||
" <td>22.905161</td>\n",
|
||
" <td>0.469794</td>\n",
|
||
" <td>1.161075</td>\n",
|
||
" <td>0.616226</td>\n",
|
||
" <td>1.022606</td>\n",
|
||
" <td>0.612277</td>\n",
|
||
" <td>0.498835</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>29.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>94.000000</td>\n",
|
||
" <td>126.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>71.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25%</th>\n",
|
||
" <td>47.500000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>120.000000</td>\n",
|
||
" <td>211.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>133.500000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>55.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>130.000000</td>\n",
|
||
" <td>240.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>153.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.800000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>61.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>140.000000</td>\n",
|
||
" <td>274.500000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>166.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.600000</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>3.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>77.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>3.000000</td>\n",
|
||
" <td>200.000000</td>\n",
|
||
" <td>564.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>202.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>6.200000</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>4.000000</td>\n",
|
||
" <td>3.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" age sex cp trtbps chol fbs \\\n",
|
||
"count 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \n",
|
||
"mean 54.366337 0.683168 0.966997 131.623762 246.264026 0.148515 \n",
|
||
"std 9.082101 0.466011 1.032052 17.538143 51.830751 0.356198 \n",
|
||
"min 29.000000 0.000000 0.000000 94.000000 126.000000 0.000000 \n",
|
||
"25% 47.500000 0.000000 0.000000 120.000000 211.000000 0.000000 \n",
|
||
"50% 55.000000 1.000000 1.000000 130.000000 240.000000 0.000000 \n",
|
||
"75% 61.000000 1.000000 2.000000 140.000000 274.500000 0.000000 \n",
|
||
"max 77.000000 1.000000 3.000000 200.000000 564.000000 1.000000 \n",
|
||
"\n",
|
||
" restecg thalachh exng oldpeak slp caa \\\n",
|
||
"count 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \n",
|
||
"mean 0.528053 149.646865 0.326733 1.039604 1.399340 0.729373 \n",
|
||
"std 0.525860 22.905161 0.469794 1.161075 0.616226 1.022606 \n",
|
||
"min 0.000000 71.000000 0.000000 0.000000 0.000000 0.000000 \n",
|
||
"25% 0.000000 133.500000 0.000000 0.000000 1.000000 0.000000 \n",
|
||
"50% 1.000000 153.000000 0.000000 0.800000 1.000000 0.000000 \n",
|
||
"75% 1.000000 166.000000 1.000000 1.600000 2.000000 1.000000 \n",
|
||
"max 2.000000 202.000000 1.000000 6.200000 2.000000 4.000000 \n",
|
||
"\n",
|
||
" thall output \n",
|
||
"count 303.000000 303.000000 \n",
|
||
"mean 2.313531 0.544554 \n",
|
||
"std 0.612277 0.498835 \n",
|
||
"min 0.000000 0.000000 \n",
|
||
"25% 2.000000 0.000000 \n",
|
||
"50% 2.000000 1.000000 \n",
|
||
"75% 3.000000 1.000000 \n",
|
||
"max 3.000000 1.000000 "
|
||
]
|
||
},
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.describe()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "345b68bd",
|
||
"metadata": {},
|
||
"source": [
|
||
"### indexing\n",
|
||
"We zullen nu verschillende items in de dataset indexeren"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "ce906842",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"age 54.0\n",
|
||
"sex 1.0\n",
|
||
"cp 0.0\n",
|
||
"trtbps 140.0\n",
|
||
"chol 239.0\n",
|
||
"fbs 0.0\n",
|
||
"restecg 1.0\n",
|
||
"thalachh 160.0\n",
|
||
"exng 0.0\n",
|
||
"oldpeak 1.2\n",
|
||
"slp 2.0\n",
|
||
"caa 0.0\n",
|
||
"thall 2.0\n",
|
||
"output 1.0\n",
|
||
"Name: 10, dtype: float64"
|
||
]
|
||
},
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# het 10de element ophalen uit de dataset\n",
|
||
"df.iloc[10]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "5f8d9afe",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>age</th>\n",
|
||
" <th>sex</th>\n",
|
||
" <th>oldpeak</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>63</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2.3</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>37</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>3.5</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>41</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1.4</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>56</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.8</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>57</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.6</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>57</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.4</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>56</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1.3</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>44</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>52</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.5</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" age sex oldpeak\n",
|
||
"0 63 1 2.3\n",
|
||
"1 37 1 3.5\n",
|
||
"2 41 0 1.4\n",
|
||
"3 56 1 0.8\n",
|
||
"4 57 0 0.6\n",
|
||
"5 57 1 0.4\n",
|
||
"6 56 0 1.3\n",
|
||
"7 44 1 0.0\n",
|
||
"8 52 1 0.5"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# de age, sex en oldpeak van de eerste 9 elementen ophalen\n",
|
||
"df.loc[0:8,['age','sex','oldpeak']]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "ce4a5f85",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 1\n",
|
||
"1 1\n",
|
||
"2 0\n",
|
||
"3 1\n",
|
||
"4 0\n",
|
||
"Name: sex, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# de head opvragen van de elementen met het sex attribuut\n",
|
||
"df['sex'].head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "d129b322",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([1, 0], dtype=int64)"
|
||
]
|
||
},
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# unieke element van sex ophalen\n",
|
||
"df['sex'].unique()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "37d36915",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>age</th>\n",
|
||
" <th>sex</th>\n",
|
||
" <th>cp</th>\n",
|
||
" <th>trtbps</th>\n",
|
||
" <th>chol</th>\n",
|
||
" <th>fbs</th>\n",
|
||
" <th>restecg</th>\n",
|
||
" <th>thalachh</th>\n",
|
||
" <th>exng</th>\n",
|
||
" <th>oldpeak</th>\n",
|
||
" <th>slp</th>\n",
|
||
" <th>caa</th>\n",
|
||
" <th>thall</th>\n",
|
||
" <th>output</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>72</th>\n",
|
||
" <td>29</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>130</td>\n",
|
||
" <td>204</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>202</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>58</th>\n",
|
||
" <td>34</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>118</td>\n",
|
||
" <td>182</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>174</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>125</th>\n",
|
||
" <td>34</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>118</td>\n",
|
||
" <td>210</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>192</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.7</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>239</th>\n",
|
||
" <td>35</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>126</td>\n",
|
||
" <td>282</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>156</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>65</th>\n",
|
||
" <td>35</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>138</td>\n",
|
||
" <td>183</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>182</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1.4</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>60</th>\n",
|
||
" <td>71</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>110</td>\n",
|
||
" <td>265</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>130</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>151</th>\n",
|
||
" <td>71</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>112</td>\n",
|
||
" <td>149</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>125</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1.6</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>129</th>\n",
|
||
" <td>74</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>120</td>\n",
|
||
" <td>269</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>121</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.2</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>144</th>\n",
|
||
" <td>76</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>140</td>\n",
|
||
" <td>197</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>116</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1.1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>238</th>\n",
|
||
" <td>77</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>125</td>\n",
|
||
" <td>304</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>162</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>303 rows × 14 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" age sex cp trtbps chol fbs restecg thalachh exng oldpeak slp \\\n",
|
||
"72 29 1 1 130 204 0 0 202 0 0.0 2 \n",
|
||
"58 34 1 3 118 182 0 0 174 0 0.0 2 \n",
|
||
"125 34 0 1 118 210 0 1 192 0 0.7 2 \n",
|
||
"239 35 1 0 126 282 0 0 156 1 0.0 2 \n",
|
||
"65 35 0 0 138 183 0 1 182 0 1.4 2 \n",
|
||
".. ... ... .. ... ... ... ... ... ... ... ... \n",
|
||
"60 71 0 2 110 265 1 0 130 0 0.0 2 \n",
|
||
"151 71 0 0 112 149 0 1 125 0 1.6 1 \n",
|
||
"129 74 0 1 120 269 0 0 121 1 0.2 2 \n",
|
||
"144 76 0 2 140 197 0 2 116 0 1.1 1 \n",
|
||
"238 77 1 0 125 304 0 0 162 1 0.0 2 \n",
|
||
"\n",
|
||
" caa thall output \n",
|
||
"72 0 2 1 \n",
|
||
"58 0 2 1 \n",
|
||
"125 0 2 1 \n",
|
||
"239 0 3 0 \n",
|
||
"65 0 2 1 \n",
|
||
".. ... ... ... \n",
|
||
"60 1 2 1 \n",
|
||
"151 0 2 1 \n",
|
||
"129 1 2 1 \n",
|
||
"144 0 2 1 \n",
|
||
"238 3 2 0 \n",
|
||
"\n",
|
||
"[303 rows x 14 columns]"
|
||
]
|
||
},
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# sorteer de values van attribuut age op optellende manier\n",
|
||
"df.sort_values('age', ascending = True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"id": "c379d785",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<AxesSubplot:title={'center':'age chol relation'}>"
|
||
]
|
||
},
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"dfplot = df[['age','chol']]\n",
|
||
"dfplot.plot(title='age chol relation')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"id": "601da936",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"True 207\n",
|
||
"False 96\n",
|
||
"Name: sex, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"amountMale = df['sex'] > 0 # assuming male = 1\n",
|
||
"piecounts = amountMale.value_counts()\n",
|
||
"piecounts"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"id": "a128aabd",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 360x360 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"piecounts.plot(kind='pie',\n",
|
||
" figsize=(5, 5),\n",
|
||
" explode=[0, 0.12],\n",
|
||
" labels=['male', 'female'],\n",
|
||
" autopct='%1.1f%%',\n",
|
||
" shadow=True,\n",
|
||
" startangle=60,\n",
|
||
" fontsize=16);"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "95e144df",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 4.3 : ZTDL 3 – Machine Learning"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "79972227",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Linear regression\n",
|
||
"Deze code blokken zijn nodig als setup voor de linear regression met keras\n",
|
||
"We hebben gekozen voor een dataset die gebruikt kan worden voor linear regression. De dataset heeft geen bepaalde betekenis."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "09ee5be2",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"%matplotlib inline\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import pandas as pd\n",
|
||
"import numpy as np"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "37389405",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# read the csv file into the df variable\n",
|
||
"df = pd.read_csv('../data/test.csv')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "a7ea6eb2",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>x</th>\n",
|
||
" <th>y</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>77</td>\n",
|
||
" <td>79.775152</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>21</td>\n",
|
||
" <td>23.177279</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>22</td>\n",
|
||
" <td>25.609262</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>20</td>\n",
|
||
" <td>17.857388</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>36</td>\n",
|
||
" <td>41.849864</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" x y\n",
|
||
"0 77 79.775152\n",
|
||
"1 21 23.177279\n",
|
||
"2 22 25.609262\n",
|
||
"3 20 17.857388\n",
|
||
"4 36 41.849864"
|
||
]
|
||
},
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# check out what is in the first few lines\n",
|
||
"df.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "952802ba",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<AxesSubplot:title={'center':'x and y in the dataset'}, xlabel='x', ylabel='y'>"
|
||
]
|
||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# plot the points in the dataset\n",
|
||
"df.plot(kind='scatter',\n",
|
||
" x='x',\n",
|
||
" y='y',\n",
|
||
" title='x and y in the dataset')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "8e9d0f89",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[<matplotlib.lines.Line2D at 0x251e44ef948>]"
|
||
]
|
||
},
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# plot the points again\n",
|
||
"df.plot(kind='scatter',\n",
|
||
" x='x',\n",
|
||
" y='y',\n",
|
||
" title='y and x in the data set')\n",
|
||
"\n",
|
||
"# Here we're plotting the red line 'by hand' with fixed values\n",
|
||
"# We'll try to learn this line with an algorithm below\n",
|
||
"plt.plot([55, 78], [75, 250], color='red', linewidth=3)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "bf4cd5b6",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# define a method to calculate a point for a line given the input values\n",
|
||
"def line(x, w=0, b=0):\n",
|
||
" return x * w + b"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "b4842e99",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# generate evenly spaced numbers \n",
|
||
"x = np.linspace(55, 80, 100)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "479278e7",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([55. , 55.25252525, 55.50505051, 55.75757576, 56.01010101,\n",
|
||
" 56.26262626, 56.51515152, 56.76767677, 57.02020202, 57.27272727,\n",
|
||
" 57.52525253, 57.77777778, 58.03030303, 58.28282828, 58.53535354,\n",
|
||
" 58.78787879, 59.04040404, 59.29292929, 59.54545455, 59.7979798 ,\n",
|
||
" 60.05050505, 60.3030303 , 60.55555556, 60.80808081, 61.06060606,\n",
|
||
" 61.31313131, 61.56565657, 61.81818182, 62.07070707, 62.32323232,\n",
|
||
" 62.57575758, 62.82828283, 63.08080808, 63.33333333, 63.58585859,\n",
|
||
" 63.83838384, 64.09090909, 64.34343434, 64.5959596 , 64.84848485,\n",
|
||
" 65.1010101 , 65.35353535, 65.60606061, 65.85858586, 66.11111111,\n",
|
||
" 66.36363636, 66.61616162, 66.86868687, 67.12121212, 67.37373737,\n",
|
||
" 67.62626263, 67.87878788, 68.13131313, 68.38383838, 68.63636364,\n",
|
||
" 68.88888889, 69.14141414, 69.39393939, 69.64646465, 69.8989899 ,\n",
|
||
" 70.15151515, 70.4040404 , 70.65656566, 70.90909091, 71.16161616,\n",
|
||
" 71.41414141, 71.66666667, 71.91919192, 72.17171717, 72.42424242,\n",
|
||
" 72.67676768, 72.92929293, 73.18181818, 73.43434343, 73.68686869,\n",
|
||
" 73.93939394, 74.19191919, 74.44444444, 74.6969697 , 74.94949495,\n",
|
||
" 75.2020202 , 75.45454545, 75.70707071, 75.95959596, 76.21212121,\n",
|
||
" 76.46464646, 76.71717172, 76.96969697, 77.22222222, 77.47474747,\n",
|
||
" 77.72727273, 77.97979798, 78.23232323, 78.48484848, 78.73737374,\n",
|
||
" 78.98989899, 79.24242424, 79.49494949, 79.74747475, 80. ])"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"x"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "56cd60cd",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# generate line points\n",
|
||
"yhat = line(x, w=0, b=0)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "bd73d23f",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
|
||
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
|
||
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
|
||
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
|
||
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
|
||
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
|
||
]
|
||
},
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"yhat"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "7af98bd1",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[<matplotlib.lines.Line2D at 0x251e4561388>]"
|
||
]
|
||
},
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# draw the points again, now with the line\n",
|
||
"df.plot(kind='scatter',\n",
|
||
" x='x',\n",
|
||
" y='y',\n",
|
||
" title='y en x in the data set')\n",
|
||
"plt.plot(x, yhat, color='red', linewidth=3)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "077d7978",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Cost function"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "62838ac9",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# calculate the mean squared error given the input parameters.\n",
|
||
"def mean_squared_error(y_true, y_pred):\n",
|
||
" s = (y_true - y_pred)**2\n",
|
||
" return s.mean()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "4bea9b4a",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# get the x and y values from the dataset\n",
|
||
"X = df[['x']].values\n",
|
||
"y_true = df['y'].values"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "392f50b2",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([ 79.77515201, 23.17727887, 25.60926156, 17.85738813,\n",
|
||
" 41.84986439, 9.80523488, 58.87465933, 97.61793701,\n",
|
||
" 18.39512747, 8.74674765, 2.81141583, 17.09537241,\n",
|
||
" 95.14907176, 61.38800663, 40.24701716, 14.82248589,\n",
|
||
" 66.95806869, 16.63507984, 90.65513736, 77.22982636,\n",
|
||
" 92.11906278, 46.91387709, 89.82634442, 21.71380347,\n",
|
||
" 97.41206981, 57.01631363, 78.31056542, 19.1315097 ,\n",
|
||
" 93.03483388, 26.59112396, 97.55155344, 31.43524822,\n",
|
||
" 35.12724777, 78.61042432, 33.07112825, 51.69967172,\n",
|
||
" 53.62235225, 69.46306072, 27.42497237, 36.34644189,\n",
|
||
" 95.06140858, 68.16724757, 50.96155532, 78.04237454,\n",
|
||
" 5.60766487, 36.11334779, 67.2352155 , 65.01324035,\n",
|
||
" 38.14753871, 34.31141446, 95.28503937, 87.84749912,\n",
|
||
" 54.08170635, 31.93063515, 59.61247085, -1.04011421,\n",
|
||
" 47.49374765, 62.60089773, 70.9146434 , 56.14834113,\n",
|
||
" 14.05572877, 68.11367147, 75.59701346, 59.225745 ,\n",
|
||
" 85.45504157, 17.76197116, 38.68888682, 50.96343637,\n",
|
||
" 51.83503872, 17.0761107 , 46.56141773, 10.34754461,\n",
|
||
" 77.91032969, 50.17008622, 13.25690647, 31.32274932,\n",
|
||
" 73.9308764 , 74.45114379, 52.01932286, 83.68820499,\n",
|
||
" 70.3698748 , 23.44479161, 49.83051801, 49.88226593,\n",
|
||
" 41.04525583, 33.37834391, 81.29750133, 105.5918375 ,\n",
|
||
" 56.82457013, 48.67252645, 67.02150613, 38.43076389,\n",
|
||
" 58.61466887, 89.12377509, 60.9105427 , 13.83959878,\n",
|
||
" 16.89085185, 84.06676818, 70.34969772, 33.38474138,\n",
|
||
" -1.63296825, 88.54475895, 17.44047622, 75.69298554,\n",
|
||
" 41.97607107, 12.59244741, 0.27530726, 98.13258005,\n",
|
||
" 87.45721555, -2.34473854, 39.3294153 , 16.68715211,\n",
|
||
" 96.58888601, 97.70342201, 67.01715955, 25.63476257,\n",
|
||
" 13.41310757, 95.15647284, 9.74416426, -3.46788379,\n",
|
||
" 62.82816355, 97.27405461, 95.58017185, 7.46850184,\n",
|
||
" 45.44599591, 46.69013968, 74.4993599 , 21.63500655,\n",
|
||
" 91.59548851, 26.49487961, 67.38654703, 74.25362837,\n",
|
||
" 12.07991648, 21.32273728, 29.31770045, 26.48713683,\n",
|
||
" 68.94699774, 59.10598995, 64.37521087, 60.20758349,\n",
|
||
" 70.34329706, 97.1082562 , 75.7584178 , 10.80462727,\n",
|
||
" 12.11219941, 63.28312382, 98.03017721, 63.19354354,\n",
|
||
" 34.8534823 , -2.81991397, 59.8313966 , 29.38505024,\n",
|
||
" 97.00148372, 85.18657275, 61.74063192, 18.84798163,\n",
|
||
" 78.79008525, 95.12400481, 30.48881287, 10.41468095,\n",
|
||
" 38.98317436, 46.11021062, 52.45103628, 21.16523945,\n",
|
||
" 52.28620611, 44.18863945, 97.13832018, 67.22008001,\n",
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||
" 18.98322306, 24.3884599 , 79.44769523, 40.03504862,\n",
|
||
" 53.32005764, 54.55446979, -2.7611826 , 37.80182795,\n",
|
||
" 57.48741435, 36.06292994, 49.83538167, 74.68953276,\n",
|
||
" 14.86159401, 101.0697879 , 99.43577876, 91.69240746,\n",
|
||
" 34.12473248, 6.07939007, 59.07247174, 56.43046022,\n",
|
||
" 30.49412933, 48.35172635, 89.73153611, 72.86282528,\n",
|
||
" 80.97144285, 91.36566374, 60.07137496, 99.87382707,\n",
|
||
" 8.65571417, 69.39858505, 19.38780134, 53.11628433,\n",
|
||
" 78.39683006, 25.75612514, 75.07484683, 92.88772282,\n",
|
||
" 69.45498498, 13.12109842, 48.09843134, 79.3142548 ,\n",
|
||
" 68.48820749, 73.2300846 , 24.68362712, 41.90368917,\n",
|
||
" 62.22635684, 45.96396877, 23.52647153, 51.80035866,\n",
|
||
" 51.10774273, 95.79747345, 9.24113898, 7.64652976,\n",
|
||
" 9.28169975, 103.5266162 , 47.41006725, 42.03835773,\n",
|
||
" 96.11982476, 38.05766408, 105.4503788 , 88.80306911,\n",
|
||
" 15.49301141, 12.42624606, 40.00709598, 5.6340309 ,\n",
|
||
" 87.36938931, 89.73951993, 66.61499643, 72.9138853 ,\n",
|
||
" 57.19103506, 11.21710477, 0.67607675, 28.15668543,\n",
|
||
" 95.3958003 , 52.05490703, 59.70864577, 36.79224762,\n",
|
||
" 37.08457698, 24.18437976, 67.28725332, 82.870594 ,\n",
|
||
" 89.899991 , 36.94173178, 19.87562242, 90.71481654,\n",
|
||
" 61.09367762, 60.11134958, 64.83296316, 81.40381769,\n",
|
||
" 92.40217686, 2.57662538, 63.80768172, 38.67780759,\n",
|
||
" 16.82839701, 99.78687252, 44.68913433, 71.00377824,\n",
|
||
" 51.57326718, 19.87846479, 79.50341495, 34.58876491,\n",
|
||
" 55.7383467 , 68.19721905, 55.81628509, 9.3914168 ,\n",
|
||
" 56.01448111, 77.9969477 , 55.37049953, 11.89457829,\n",
|
||
" 94.79081712, 25.69041546, 53.52042319, 18.31396758,\n",
|
||
" 21.42637785, 30.41303282, 67.68142149, 17.0854783 ,\n",
|
||
" 60.91792707, 14.99514319, 16.74923937, 41.46923883,\n",
|
||
" 42.84526108, 59.12912974, 91.30863673, 8.67333636,\n",
|
||
" 39.31485292, 5.3136862 , 5.40522052, 68.5458879 ,\n",
|
||
" 47.33487629, 54.09063686, 63.29717058, 52.45946688])"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"y_true"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "0806d238",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# convert the points of the y values to line points\n",
|
||
"y_pred = line(X)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "aa0ef0f8",
|
||
"metadata": {},
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"outputs": [
|
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{
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"data": {
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" [0]], dtype=int64)"
|
||
]
|
||
},
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"y_pred"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "55ff0a77",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"3464.291087139079"
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# calculate the mean square error for the points\n",
|
||
"mean_squared_error(y_true, y_pred.ravel())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a60e5104",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Linear regression with keras"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "ccde02ca",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from tensorflow.keras.models import Sequential\n",
|
||
"from tensorflow.keras.layers import Dense\n",
|
||
"from tensorflow.keras.optimizers import Adam, SGD"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "3c2b554a",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"model = Sequential()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "4da8d3b5",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# make a model with 1 layer, 1 output node and 1 input node\n",
|
||
"model.add(Dense(1, input_shape=(1,)))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"id": "afbf17ff",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Model: \"sequential\"\n",
|
||
"_________________________________________________________________\n",
|
||
"Layer (type) Output Shape Param # \n",
|
||
"=================================================================\n",
|
||
"dense (Dense) (None, 1) 2 \n",
|
||
"=================================================================\n",
|
||
"Total params: 2\n",
|
||
"Trainable params: 2\n",
|
||
"Non-trainable params: 0\n",
|
||
"_________________________________________________________________\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"model.summary()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"id": "35dda627",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# get the model ready for training\n",
|
||
"model.compile(Adam(learning_rate=0.8), 'mean_squared_error')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"id": "fe11b9d0",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch 1/40\n",
|
||
"10/10 [==============================] - 0s 889us/step - loss: 276.5771\n",
|
||
"Epoch 2/40\n",
|
||
"10/10 [==============================] - 0s 889us/step - loss: 70.1189\n",
|
||
"Epoch 3/40\n",
|
||
"10/10 [==============================] - 0s 893us/step - loss: 38.0515\n",
|
||
"Epoch 4/40\n",
|
||
"10/10 [==============================] - 0s 1ms/step - loss: 27.3967\n",
|
||
"Epoch 5/40\n",
|
||
"10/10 [==============================] - 0s 1ms/step - loss: 25.7747\n",
|
||
"Epoch 6/40\n",
|
||
"10/10 [==============================] - 0s 777us/step - loss: 20.8342\n",
|
||
"Epoch 7/40\n",
|
||
"10/10 [==============================] - 0s 1ms/step - loss: 15.4722\n",
|
||
"Epoch 8/40\n",
|
||
"10/10 [==============================] - 0s 997us/step - loss: 9.9356\n",
|
||
"Epoch 9/40\n",
|
||
"10/10 [==============================] - 0s 778us/step - loss: 9.9688\n",
|
||
"Epoch 10/40\n",
|
||
"10/10 [==============================] - 0s 1ms/step - loss: 10.1344\n",
|
||
"Epoch 11/40\n",
|
||
"10/10 [==============================] - 0s 1ms/step - loss: 10.7054\n",
|
||
"Epoch 12/40\n",
|
||
"10/10 [==============================] - 0s 889us/step - loss: 9.7593\n",
|
||
"Epoch 13/40\n",
|
||
"10/10 [==============================] - 0s 1ms/step - loss: 9.4868\n",
|
||
"Epoch 14/40\n",
|
||
"10/10 [==============================] - 0s 1ms/step - loss: 9.8315\n",
|
||
"Epoch 15/40\n",
|
||
"10/10 [==============================] - 0s 889us/step - loss: 10.3213\n",
|
||
"Epoch 16/40\n",
|
||
"10/10 [==============================] - 0s 999us/step - loss: 17.9203\n",
|
||
"Epoch 17/40\n",
|
||
"10/10 [==============================] - 0s 889us/step - loss: 14.1653\n",
|
||
"Epoch 18/40\n",
|
||
"10/10 [==============================] - 0s 1ms/step - loss: 18.5441\n",
|
||
"Epoch 19/40\n",
|
||
"10/10 [==============================] - 0s 1ms/step - loss: 15.2991\n",
|
||
"Epoch 20/40\n",
|
||
"10/10 [==============================] - 0s 778us/step - loss: 10.4892\n",
|
||
"Epoch 21/40\n",
|
||
"10/10 [==============================] - 0s 1ms/step - loss: 9.3215\n",
|
||
"Epoch 22/40\n",
|
||
"10/10 [==============================] - ETA: 0s - loss: 9.491 - 0s 1ms/step - loss: 10.7091\n",
|
||
"Epoch 23/40\n",
|
||
"10/10 [==============================] - 0s 885us/step - loss: 10.4797\n",
|
||
"Epoch 24/40\n",
|
||
"10/10 [==============================] - 0s 1ms/step - loss: 11.5123\n",
|
||
"Epoch 25/40\n",
|
||
"10/10 [==============================] - 0s 1ms/step - loss: 13.3325\n",
|
||
"Epoch 26/40\n",
|
||
"10/10 [==============================] - 0s 775us/step - loss: 13.3366\n",
|
||
"Epoch 27/40\n",
|
||
"10/10 [==============================] - 0s 1ms/step - loss: 16.0355\n",
|
||
"Epoch 28/40\n",
|
||
"10/10 [==============================] - ETA: 0s - loss: 24.20 - 0s 1ms/step - loss: 14.7609\n",
|
||
"Epoch 29/40\n",
|
||
"10/10 [==============================] - 0s 889us/step - loss: 10.3856\n",
|
||
"Epoch 30/40\n",
|
||
"10/10 [==============================] - 0s 1ms/step - loss: 10.1707\n",
|
||
"Epoch 31/40\n",
|
||
"10/10 [==============================] - 0s 778us/step - loss: 9.4444\n",
|
||
"Epoch 32/40\n",
|
||
"10/10 [==============================] - 0s 1ms/step - loss: 10.1405\n",
|
||
"Epoch 33/40\n",
|
||
"10/10 [==============================] - 0s 890us/step - loss: 13.1112\n",
|
||
"Epoch 34/40\n",
|
||
"10/10 [==============================] - 0s 778us/step - loss: 10.2187\n",
|
||
"Epoch 35/40\n",
|
||
"10/10 [==============================] - 0s 1ms/step - loss: 10.7566\n",
|
||
"Epoch 36/40\n",
|
||
"10/10 [==============================] - 0s 1ms/step - loss: 11.3941\n",
|
||
"Epoch 37/40\n",
|
||
"10/10 [==============================] - 0s 889us/step - loss: 15.5596\n",
|
||
"Epoch 38/40\n",
|
||
"10/10 [==============================] - 0s 1ms/step - loss: 15.3198\n",
|
||
"Epoch 39/40\n",
|
||
"10/10 [==============================] - 0s 1ms/step - loss: 13.1469\n",
|
||
"Epoch 40/40\n",
|
||
"10/10 [==============================] - 0s 888us/step - loss: 9.4861\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<tensorflow.python.keras.callbacks.History at 0x251ea00efc8>"
|
||
]
|
||
},
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# start training the model for 40 epochs\n",
|
||
"model.fit(X, y_true, epochs=40)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"id": "a22c9fb1",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"y_pred = model.predict(X)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"id": "9787ea0d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[<matplotlib.lines.Line2D at 0x251f2e13148>]"
|
||
]
|
||
},
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.plot(kind='scatter',\n",
|
||
" x='x',\n",
|
||
" y='y',\n",
|
||
" title='x and y in the dataset, with prediction')\n",
|
||
"plt.plot(X, y_pred, color='red')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"id": "ccaf0068",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"W, B = model.get_weights()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"id": "5e966641",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[1.030289]], dtype=float32)"
|
||
]
|
||
},
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"W"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"id": "d7d8eb87",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([-0.44838893], dtype=float32)"
|
||
]
|
||
},
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"B"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "18ef5e31",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"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": "3.7.10"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|