615 lines
25 KiB
Plaintext
615 lines
25 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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"source": [
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"# 13. Splotowe sieci neuronowe"
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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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"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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"Konwolucyjne sieci neuronowe wykorzystuje się do:\n",
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"\n",
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"* rozpoznawania obrazu\n",
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"* analizy wideo\n",
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"* innych zagadnień o podobnej strukturze"
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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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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"source": [
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"Innymi słowy, CNN przydają się, gdy mamy bardzo dużo danych wejściowych, w których istotne jest ich sąsiedztwo."
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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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"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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"### Warstwy konwolucyjne"
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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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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"source": [
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"Dla uproszczenia przyjmijmy, że mamy dane w postaci jendowymiarowej – np. chcemy stwierdzić, czy na danym nagraniu obecny jest głos człowieka."
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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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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"Nasze nagranie możemy reprezentować jako ciąg $n$ próbek dźwiękowych:\n",
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"$$(x_0, x_1, \\ldots, x_n)$$\n",
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"(możemy traktować je jak jednowymiarowe „piksele”)."
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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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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"Najprostsza metoda – „zwykła” jednowarstwowa sieć neuronowa (każdy z każdym):"
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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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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"Najprostsza metoda – „zwykła” jednowarstwowa sieć neuronowa (każdy z każdym) nie poradzi sobie zbyt dobrze w tym przypadku:\n",
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"\n",
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"* dużo danych wejściowych\n",
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"* nie wykrywa własności „lokalnych” wejścia"
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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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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"source": [
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"Chcielibyśmy wykrywać pewne lokalne „wzory” w danych wejściowych.\n",
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"\n",
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"W tym celu tworzymy mniejszą sieć neuronową (mniej neuronów wejściowych) i _kopiujemy_ ją tak, żeby każda jej kopia działała na pewnym fragmencie wejścia (fragmenty mogą nachodzić na siebie)."
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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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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"Warstwę sieci A nazywamy **warstwą konwolucyjną** (konwolucja = splot).\n",
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"\n",
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"Warstw konwolucyjnych może być więcej niż jedna."
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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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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"Tak definiujemy formalnie funckję splotu dla 2 wymiarów:\n",
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"\n",
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"$$\n",
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"\\left[\\begin{array}{ccc}\n",
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"a & b & c\\\\\n",
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"d & e & f\\\\\n",
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"g & h & i\\\\\n",
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"\\end{array}\\right]\n",
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"*\n",
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"\\left[\\begin{array}{ccc}\n",
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"1 & 2 & 3\\\\\n",
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"4 & 5 & 6\\\\\n",
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"7 & 8 & 9\\\\\n",
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"\\end{array}\\right] \n",
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"=\\\\\n",
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"(1 \\cdot a)+(2 \\cdot b)+(3 \\cdot c)+(4 \\cdot d)+(5 \\cdot e)\\\\+(6 \\cdot f)+(7 \\cdot g)+(8 \\cdot h)+(9 \\cdot i)\n",
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"$$\n",
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"\n",
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"Więcej: https://en.wikipedia.org/wiki/Kernel_(image_processing)"
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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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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"Jednostka warstwy konwolucyjnej może się składać z jednej lub kilku warstw neuronów."
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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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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"source": [
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"Jeden neuron może odpowiadać np. za wykrywanie pionowych krawędzi, drugi poziomych, a jeszcze inny np. krzyżujących się linii."
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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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"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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"### _Pooling_"
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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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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"source": [
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"Obrazy składają się na ogół z milionów pikseli. Oznacza to, że nawet po zastosowaniu kilku warstw konwolucyjnych mielibyśmy sporo parametrów do wytrenowania.\n",
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"\n",
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"Żeby zredukować liczbę parametrów, a dzięki temu uprościć obliczenia, stosuje się warstwy ***pooling***.\n",
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"\n",
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"*Pooling* to rodzaj próbkowania. Najpopularniejszą jego odmianą jest *max-pooling*, czyli wybieranie najwyższej wartości spośród kilku sąsiadujących pikseli (rys. 13.1)."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"![Rys. 13.1. Pooling](Max_pooling.png \"Rys. 13.1. Pooling\")\n",
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"\n",
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"Rys. 13.1. - źródło: [Aphex34](https://commons.wikimedia.org/wiki/File:Max_pooling.png), [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0), Wikimedia Commons"
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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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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"Warstwy _pooling_ i konwolucyjne można przeplatać ze sobą (rys. 13.2)."
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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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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"source": [
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"![Rys. 13.2. CNN](Typical_cnn.png \"Rys. 13.2. CNN\")\n",
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"\n",
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"Rys. 13.2. - źródło: [Aphex34](https://commons.wikimedia.org/wiki/File:Typical_cnn.png), [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0), Wikimedia Commons"
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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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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"_Pooling_ – idea: nie jest istotne, w którym *dokładnie* miejscu na obrazku dana cecha (krawędź, oko, itp.) się znajduje, wystarczy przybliżona lokalizacja."
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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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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"Do sieci konwolucujnych możemy dokładać też warstwy ReLU."
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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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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"https://www.youtube.com/watch?v=FmpDIaiMIeA"
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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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"slideshow": {
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"slide_type": "notes"
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}
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},
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"source": [
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"Zobacz też: https://colah.github.io/posts/2014-07-Conv-Nets-Modular/"
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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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"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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"### Przykład: MNIST"
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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": 23,
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"metadata": {
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"slideshow": {
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"slide_type": "notes"
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}
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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"\n",
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"import math\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import random\n",
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"\n",
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"from IPython.display import YouTubeVideo"
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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": 24,
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"metadata": {
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"slideshow": {
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"slide_type": "notes"
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}
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},
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"outputs": [],
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"source": [
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"import keras\n",
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"from keras.datasets import mnist\n",
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"\n",
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"from keras.models import Sequential\n",
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"from keras.layers import Dense, Dropout, Flatten\n",
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"from keras.layers import Conv2D, MaxPooling2D\n",
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"\n",
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"# załaduj dane i podziel je na zbiory uczący i testowy\n",
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"(x_train, y_train), (x_test, y_test) = mnist.load_data()"
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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": 25,
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"metadata": {
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"slideshow": {
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"slide_type": "notes"
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}
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},
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"outputs": [],
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"source": [
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"def draw_examples(examples, captions=None):\n",
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" plt.figure(figsize=(16, 4))\n",
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" m = len(examples)\n",
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" for i, example in enumerate(examples):\n",
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" plt.subplot(100 + m * 10 + i + 1)\n",
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" plt.imshow(example, cmap=plt.get_cmap('gray'))\n",
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" plt.show()\n",
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" if captions is not None:\n",
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" print(6 * ' ' + (10 * ' ').join(str(captions[i]) for i in range(m)))"
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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": 26,
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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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"image/png": 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",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f70ba2e9090>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" 5 0 4 1 9 2 1\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"draw_examples(x_train[:7], captions=y_train)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"batch_size = 128\n",
|
||
"num_classes = 10\n",
|
||
"epochs = 12\n",
|
||
"\n",
|
||
"# input image dimensions\n",
|
||
"img_rows, img_cols = 28, 28"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "notes"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"if keras.backend.image_data_format() == 'channels_first':\n",
|
||
" x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
|
||
" x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
|
||
" input_shape = (1, img_rows, img_cols)\n",
|
||
"else:\n",
|
||
" x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
|
||
" x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
|
||
" input_shape = (img_rows, img_cols, 1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"x_train shape: (60000, 28, 28, 1)\n",
|
||
"60000 train samples\n",
|
||
"10000 test samples\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"x_train = x_train.astype('float32')\n",
|
||
"x_test = x_test.astype('float32')\n",
|
||
"x_train /= 255\n",
|
||
"x_test /= 255\n",
|
||
"print('x_train shape: {}'.format(x_train.shape))\n",
|
||
"print('{} train samples'.format(x_train.shape[0]))\n",
|
||
"print('{} test samples'.format(x_test.shape[0]))\n",
|
||
"\n",
|
||
"# convert class vectors to binary class matrices\n",
|
||
"y_train = keras.utils.to_categorical(y_train, num_classes)\n",
|
||
"y_test = keras.utils.to_categorical(y_test, num_classes)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"model = Sequential()\n",
|
||
"model.add(Conv2D(32, kernel_size=(3, 3),\n",
|
||
" activation='relu',\n",
|
||
" input_shape=input_shape))\n",
|
||
"model.add(Conv2D(64, (3, 3), activation='relu'))\n",
|
||
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
|
||
"model.add(Dropout(0.25))\n",
|
||
"model.add(Flatten())\n",
|
||
"model.add(Dense(128, activation='relu'))\n",
|
||
"model.add(Dropout(0.5))\n",
|
||
"model.add(Dense(num_classes, activation='softmax'))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"model.compile(loss=keras.losses.categorical_crossentropy,\n",
|
||
" optimizer=keras.optimizers.Adadelta(),\n",
|
||
" metrics=['accuracy'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Train on 60000 samples, validate on 10000 samples\n",
|
||
"Epoch 1/12\n",
|
||
"60000/60000 [==============================] - 333s - loss: 0.3256 - acc: 0.9037 - val_loss: 0.0721 - val_acc: 0.9780\n",
|
||
"Epoch 2/12\n",
|
||
"60000/60000 [==============================] - 342s - loss: 0.1088 - acc: 0.9683 - val_loss: 0.0501 - val_acc: 0.9835\n",
|
||
"Epoch 3/12\n",
|
||
"60000/60000 [==============================] - 366s - loss: 0.0837 - acc: 0.9748 - val_loss: 0.0429 - val_acc: 0.9860\n",
|
||
"Epoch 4/12\n",
|
||
"60000/60000 [==============================] - 311s - loss: 0.0694 - acc: 0.9788 - val_loss: 0.0380 - val_acc: 0.9878\n",
|
||
"Epoch 5/12\n",
|
||
"60000/60000 [==============================] - 325s - loss: 0.0626 - acc: 0.9815 - val_loss: 0.0334 - val_acc: 0.9886\n",
|
||
"Epoch 6/12\n",
|
||
"60000/60000 [==============================] - 262s - loss: 0.0552 - acc: 0.9835 - val_loss: 0.0331 - val_acc: 0.9890\n",
|
||
"Epoch 7/12\n",
|
||
"60000/60000 [==============================] - 218s - loss: 0.0494 - acc: 0.9852 - val_loss: 0.0291 - val_acc: 0.9903\n",
|
||
"Epoch 8/12\n",
|
||
"60000/60000 [==============================] - 218s - loss: 0.0461 - acc: 0.9859 - val_loss: 0.0294 - val_acc: 0.9902\n",
|
||
"Epoch 9/12\n",
|
||
"60000/60000 [==============================] - 219s - loss: 0.0423 - acc: 0.9869 - val_loss: 0.0287 - val_acc: 0.9907\n",
|
||
"Epoch 10/12\n",
|
||
"60000/60000 [==============================] - 218s - loss: 0.0418 - acc: 0.9875 - val_loss: 0.0299 - val_acc: 0.9906\n",
|
||
"Epoch 11/12\n",
|
||
"60000/60000 [==============================] - 218s - loss: 0.0388 - acc: 0.9879 - val_loss: 0.0304 - val_acc: 0.9905\n",
|
||
"Epoch 12/12\n",
|
||
"60000/60000 [==============================] - 218s - loss: 0.0366 - acc: 0.9889 - val_loss: 0.0275 - val_acc: 0.9910\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<keras.callbacks.History at 0x7f70b80b1a10>"
|
||
]
|
||
},
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"model.fit(x_train, y_train,\n",
|
||
" batch_size=batch_size,\n",
|
||
" epochs=epochs,\n",
|
||
" verbose=1,\n",
|
||
" validation_data=(x_test, y_test))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"('Test loss:', 0.027530849870144449)\n",
|
||
"('Test accuracy:', 0.99099999999999999)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"score = model.evaluate(x_test, y_test, verbose=0)\n",
|
||
"print('Test loss:', score[0])\n",
|
||
"print('Test accuracy:', score[1])"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"author": "Paweł Skórzewski",
|
||
"celltoolbar": "Slideshow",
|
||
"email": "pawel.skorzewski@amu.edu.pl",
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"lang": "pl",
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.10.6"
|
||
},
|
||
"livereveal": {
|
||
"start_slideshow_at": "selected",
|
||
"theme": "white"
|
||
},
|
||
"subtitle": "12.Splotowe sieci neuronowe[wykład]",
|
||
"title": "Uczenie maszynowe",
|
||
"vscode": {
|
||
"interpreter": {
|
||
"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
|
||
}
|
||
},
|
||
"year": "2021"
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|