forked from pms/uczenie-maszynowe
616 lines
25 KiB
Plaintext
616 lines
25 KiB
Plaintext
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"cells": [
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"attachments": {},
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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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"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. 12.1)."
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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. 12.1. Pooling](Max_pooling.png \"Rys. 12.1. Pooling\")\n",
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"\n",
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"Rys. 12.1. - 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. 12.2)."
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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. 12.2. CNN](Typical_cnn.png \"Rys. 12.2. CNN\")\n",
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"\n",
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"Rys. 12.2. - 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,
|
|||
|
|
"metadata": {
|
|||
|
|
"slideshow": {
|
|||
|
|
"slide_type": "fragment"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"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",
|
|||
|
|
"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 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]"
|
|||
|
|
},
|
|||
|
|
"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
|
|||
|
|
}
|