1417 lines
44 KiB
Plaintext
1417 lines
44 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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"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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"### Uczenie maszynowe\n",
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"# 5. Rekurencyjne sieci neuronowe (RNN), splotowe sieci neuronowe (CNN)"
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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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"## 5.1. Rekurencyjne sieci neuronowe (*Recurrent Neural Networks* – RNN)"
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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=WCUNPb-5EYI"
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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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"### Rekurencyjna sieć neuronowa – schemat\n",
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"\n",
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"<img style=\"margin: auto\" width=\"20%\" src=\"http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/RNN-rolled.png\"/>"
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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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"### Rekurencyjna sieć neuronowa – schemat\n",
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"\n",
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"<img style=\"margin: auto\" width=\"80%\" src=\"http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/RNN-unrolled.png\"/>"
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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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"### Zależności długodystansowe (*long-distance dependencies*) w sieciach rekurencyjnych\n",
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"\n",
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"<img style=\"margin: auto\" width=\"60%\" src=\"http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/RNN-longtermdependencies.png\"/>"
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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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"### RNN – typy sekwencji\n",
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"\n",
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"<img style=\"margin: auto\" width=\"80%\" src=\"http://karpathy.github.io/assets/rnn/diags.jpeg\"/>"
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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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"### Prosta sieć RNN – schemat\n",
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"\n",
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"<img src=\"rnn.png\" style=\"margin: auto;\" width=\"80%\" />"
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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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"### LSTM – *Long Short Term Memory*\n",
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"\n",
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"<img src=\"lstm.jpg\" style=\"margin: auto;\" width=\"80%\" />"
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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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"* Rekurencyjne sieci neuronowe znajduja zastosowanie w przetwarzaniu sekwencji, np. szeregów czasowych i tekstów.\n",
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"* LSTM są rozwinięciem RNN, umożliwiają „zapamiętywanie” i „zapominanie”."
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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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"### Co potrafią generować rekurencyjne sieci neuronowe?\n",
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"\n",
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"http://karpathy.github.io/2015/05/21/rnn-effectiveness/"
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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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"### Przewidywanie ciągów czasowych za pomocą LSTM – przykład\n",
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"\n",
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"https://machinelearningmastery.com/time-series-forecasting-long-short-term-memory-network-python/"
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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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"### GRU – *Gated Recurrent Unit*\n",
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"\n",
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"* Rodzaj rekurencyjnej sieci neuronowej wprwadzony w 2014 roku\n",
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"* Ma prostszą budowę niż LSTM (2 bramki zamiast 3).\n"
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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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"### GRU – schemat\n",
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"\n",
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"<img src=\"gru.png\" style=\"margin: auto;\" width=\"50%\" />\n"
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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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"### GRU vs LSTM\n",
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"LSTM – 3 bramki: wejścia (*input*), wyjścia (*output*) i zapomnienia (*forget*); GRU – 2 bramki: resetu (*reset*) i aktualizacji (*update*). Bramka resetu pełni podwójną funkcję: zastępuje bramki wyjścia i zapomnienia."
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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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"### GRU vs LSTM\n",
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"* GRU i LSTM mają podobną skuteczność, ale GRU dzięki prostszej budowie bywa bardziej wydajna.\n",
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"* LSTM sprawdza się lepiej w przetwarzaniu tekstu, ponieważ lepiej zapamiętuje zależności długosystansowe."
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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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||
"## 5.2. Splotowe sieci neuronowe (*Convolutional Neural Networks* – CNN)"
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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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||
"Splotowe (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": {
|
||
"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": "subslide"
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||
}
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||
},
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||
"source": [
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||
"### Przykład\n",
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||
"\n",
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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": {
|
||
"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 wygląda nasze nagranie:"
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||
]
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||
},
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||
{
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||
"cell_type": "markdown",
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||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "fragment"
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||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" width=\"80%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv-9-xs.png\"/>"
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||
]
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||
},
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||
{
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||
"cell_type": "markdown",
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||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "fragment"
|
||
}
|
||
},
|
||
"source": [
|
||
"(ciąg próbek dźwiękowych – 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",
|
||
"metadata": {
|
||
"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",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "fragment"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" width=\"80%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv-9-F.png\"/>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
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||
}
|
||
},
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||
"source": [
|
||
"Wady:\n",
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||
"\n",
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||
"* dużo danych wejściowych\n",
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||
"* nie wykrywa własności „lokalnych” wejścia"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "fragment"
|
||
}
|
||
},
|
||
"source": [
|
||
"Chcielibyśmy wykrywać pewne lokalne „wzory” w danych wejściowych."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
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||
}
|
||
},
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||
"source": [
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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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||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "fragment"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" width=\"60%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv-9-Conv2.png\"/>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "fragment"
|
||
}
|
||
},
|
||
"source": [
|
||
"Każda z sieci A ma 2 neurony wejściowe (mało realistycznie). "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" width=\"80%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv-9-Conv3.png\"/>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "fragment"
|
||
}
|
||
},
|
||
"source": [
|
||
"Każda z sieci A ma 3 neurony wejściowe (wciąż mało realistycznie, ale już trochę bardziej). "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"Warstwę sieci A nazywamy **warstwą splotową (konwolucyjną)**.\n",
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||
"\n",
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||
"Warstw splotowych może być więcej niż jedna:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "fragment"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" width=\"50%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv-9-Conv2Conv2.png\"/>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"W dwóch wymiarach wygląda to tak:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "fragment"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" width=\"50%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv2-9x5-Conv2.png\"/>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" width=\"50%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv2-9x5-Conv2Conv2.png\"/>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"Zblizenie na pojedynczą jednostkę A:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "fragment"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" height=\"80%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv2-unit.png\"/>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"Tak definiujemy formalnie funckję splotu dla 2 wymiarów:\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\left[\\begin{array}{ccc}\n",
|
||
"a & b & c\\\\\n",
|
||
"d & e & f\\\\\n",
|
||
"g & h & i\\\\\n",
|
||
"\\end{array}\\right]\n",
|
||
"*\n",
|
||
"\\left[\\begin{array}{ccc}\n",
|
||
"1 & 2 & 3\\\\\n",
|
||
"4 & 5 & 6\\\\\n",
|
||
"7 & 8 & 9\\\\\n",
|
||
"\\end{array}\\right] \n",
|
||
"=\\\\\n",
|
||
"(1 \\cdot a)+(2 \\cdot b)+(3 \\cdot c)+(4 \\cdot d)+(5 \\cdot e)\\\\+(6 \\cdot f)+(7 \\cdot g)+(8 \\cdot h)+(9 \\cdot i)\n",
|
||
"$$\n",
|
||
"\n",
|
||
"Więcej: https://en.wikipedia.org/wiki/Kernel_(image_processing)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"A tak to mniej więcej działa:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "fragment"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" height=\"70%\" src=\"https://devblogs.nvidia.com/wp-content/uploads/2015/11/Convolution_schematic.gif\"/>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"Jednostka warstwy konwolucyjnej może się składać z jednej lub kilku warstw neuronów:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "fragment"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" width=\"40%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv-A.png\"/>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" width=\"60%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv-A-NIN.png\"/>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"Jeden neuron może odpowiadać np. za wykrywanie pionowych krawędzi, drugi poziomych, a jeszcze inny np. krzyżujących się linii."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"Przykładowe filtry, których może nauczyć się pierwsza warstwa konwolucyjna:\n",
|
||
"\n",
|
||
"<img style=\"margin: auto\" width=\"80%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/KSH-filters.png\"/>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" width=\"70%\" src=\"cnn-features.png\"/>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"### _Pooling_"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "fragment"
|
||
}
|
||
},
|
||
"source": [
|
||
"Obrazy składają się na ogół z milionów pikseli. Oznacza to, że nawet po zastosowaniu kilku warstw konwolucyjnych mielibyśmy sporo parametrów do wytrenowania.\n",
|
||
"\n",
|
||
"Żeby zredukować liczbę parametrów, a dzięki temu uprościć obliczenia, stosuje się warstwy ***pooling***."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"*Pooling* to rodzaj próbkowania. Najpopularniejszą jego odmianą jest *max-pooling*, czyli wybieranie najwyższej wartości spośród kilku sąsiadujących pikseli."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" width=\"60%\" src=\"https://upload.wikimedia.org/wikipedia/commons/e/e9/Max_pooling.png\"/>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"Warstwy _pooling_ i konwolucyjne można przeplatać ze sobą:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "fragment"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" width=\"40%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv2-9x5-Conv2Max2Conv2.png\"/>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"_Pooling_ – idea: nie jest istotne, w którym *dokładnie* miejscu na obrazku dana cecha (krawędź, oko, itp.) się znajduje, wystarczy przybliżona lokalizacja."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"Do sieci konwolucujnych możemy dokładać też warstwy ReLU."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"https://www.youtube.com/watch?v=FmpDIaiMIeA"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Możliwości konwolucyjnych sieci neuronowych"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "fragment"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" width=\"40%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/KSH-results.png\"/>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Przykład: MNIST"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "notes"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"%matplotlib inline\n",
|
||
"\n",
|
||
"import math\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"import random\n",
|
||
"\n",
|
||
"from IPython.display import YouTubeVideo"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "notes"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# źródło: https://github.com/keras-team/keras/examples/minst_mlp.py\n",
|
||
"\n",
|
||
"import keras\n",
|
||
"from keras.datasets import mnist\n",
|
||
"\n",
|
||
"from keras.models import Sequential\n",
|
||
"from keras.layers import Dense, Dropout, Flatten\n",
|
||
"from keras.layers import Conv2D, MaxPooling2D\n",
|
||
"\n",
|
||
"# załaduj dane i podziel je na zbiory uczący i testowy\n",
|
||
"(x_train, y_train), (x_test, y_test) = mnist.load_data()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "notes"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def draw_examples(examples, captions=None):\n",
|
||
" plt.figure(figsize=(16, 4))\n",
|
||
" m = len(examples)\n",
|
||
" for i, example in enumerate(examples):\n",
|
||
" plt.subplot(100 + m * 10 + i + 1)\n",
|
||
" plt.imshow(example, cmap=plt.get_cmap('gray'))\n",
|
||
" plt.show()\n",
|
||
" if captions is not None:\n",
|
||
" print(6 * ' ' + (10 * ' ').join(str(captions[i]) for i in range(m)))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "fragment"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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SO3TooPJTTz2l8mWXXabyuHHjVN6/f3+px1nWdO7cWeXmzZurbPfhLF26NOtjyhS7b8S+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\n",
|
||
"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])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 5.3. Autoencoder"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"* Uczenie nienadzorowane\n",
|
||
"* Dane: zbiór nieanotowanych przykładów uczących $\\{ x^{(1)}, x^{(2)}, x^{(3)}, \\ldots \\}$, $x^{(i)} \\in \\mathbb{R}^{n}$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Autoencoder (encoder-decoder)\n",
|
||
"\n",
|
||
"Sieć neuronowa taka, że:\n",
|
||
"* warstwa wejściowa ma $n$ neuronów\n",
|
||
"* warstwa wyjściowa ma $n$ neuronów\n",
|
||
"* warstwa środkowa ma $k < n$ neuronów\n",
|
||
"* $y^{(i)} = x^{(i)}$ dla każdego $i$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" width=\"40%\" src=\"http://ufldl.stanford.edu/tutorial/images/Autoencoder636.png\" />"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"Co otrzymujemy dzięki takiej sieci?\n",
|
||
"\n",
|
||
"* $y^{(i)} = x^{(i)} \\; \\Longrightarrow \\;$ Autoencoder próbuje nauczyć się funkcji $h(x) \\approx x$, czyli funkcji identycznościowej.\n",
|
||
"* Warstwy środkowe mają mniej neuronów niż warstwy zewnętrzne, więc żeby to osiągnąć, sieć musi znaleźć bardziej kompaktową (tu: $k$-wymiarową) reprezentację informacji zawartej w wektorach $x_{(i)}$.\n",
|
||
"* Otrzymujemy metodę kompresji danych."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"Innymi słowy:\n",
|
||
"* Ograniczenia nałożone na reprezentację danych w warstwie ukrytej pozwala na „odkrycie” pewnej **struktury** w danych.\n",
|
||
"* _Decoder_ musi odtworzyć do pierwotnej postaci reprezentację danych skompresowaną przez _encoder_."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" width=\"60%\" src=\"https://upload.wikimedia.org/wikipedia/commons/2/28/Autoencoder_structure.png\" />"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" width=\"70%\" src=\"autoencoder_schema.jpg\" />"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"* Całkowita liczba warstw w sieci autoencodera może być większa niż 3.\n",
|
||
"* Jako funkcji kosztu na ogół używa się błędu średniokwadratowego (*mean squared error*, MSE) lub entropii krzyżowej (*binary crossentropy*).\n",
|
||
"* Autoencoder może wykryć ciekawe struktury w danych nawet jeżeli $k \\geq n$, jeżeli na sieć nałoży się inne ograniczenia.\n",
|
||
"* W wyniku działania autoencodera uzyskujemy na ogół kompresję **stratną**."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Autoencoder a PCA\n",
|
||
"\n",
|
||
"Widzimy, że autoencoder można wykorzystać do redukcji liczby wymiarów. Podobną rolę pełni poznany na jednym z poprzednich wykładów algorytm PCA (analiza głównych składowych, *principal component analysis*).\n",
|
||
"\n",
|
||
"Faktycznie, jeżeli zastosujemy autoencoder z liniowymi funkcjami aktywacji i pojedynczą sigmoidalną warstwą ukrytą, to na podstawie uzyskanych wag można odtworzyć główne składowe używając rozkładu według wartości osobliwych (*singular value decomposition*, SVD)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Autoencoder odszumiający\n",
|
||
"\n",
|
||
"Jeżeli na wejściu zamiast „czystych” danych użyjemy danych zaszumionych, to otrzymamy sieć, która może usuwać szum z danych:\n",
|
||
"\n",
|
||
"<img style=\"margin: auto\" width=\"70%\" src=\"denoising_autoencoder.png\" />"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" width=\"60%\" src=\"denoising.png\" />"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Autoencoder – zastosowania\n",
|
||
"\n",
|
||
"Autoencoder sprawdza się gorzej niż inne algorytmy kompresji, więc nie stosuje się go raczej jako metody kompresji danych, ale ma inne zastosowania:\n",
|
||
"* odszumianie danych\n",
|
||
"* redukcja wymiarowości\n",
|
||
"* VAE (*variational autoencoders*) – http://kvfrans.com/variational-autoencoders-explained/"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 5.4. Word embeddings"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"_Word embeddings_ – sposoby reprezentacji słów jako wektorów liczbowych"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"Znaczenie wyrazu jest reprezentowane przez sąsiednie wyrazy:\n",
|
||
"\n",
|
||
"“A word is characterized by the company it keeps.” (John R. Firth, 1957)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"* Pomysł pojawił sie jeszcze w latach 60. XX w.\n",
|
||
"* _Word embeddings_ można uzyskiwać na różne sposoby, ale dopiero w ostatnim dziesięcioleciu stało się opłacalne użycie w tym celu sieci neuronowych."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"Przykład – 2 zdania: \n",
|
||
"* \"have a good day\"\n",
|
||
"* \"have a great day\"\n",
|
||
"\n",
|
||
"Słownik:\n",
|
||
"* {\"a\", \"day\", \"good\", \"great\", \"have\"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"* Aby wykorzystać metody uczenia maszynowego do analizy danych tekstowych, musimy je jakoś reprezentować jako liczby."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"* Najprostsza metoda to wektory jednostkowe:\n",
|
||
" * \"a\" = $(1, 0, 0, 0, 0)$\n",
|
||
" * \"day\" = $(0, 1, 0, 0, 0)$\n",
|
||
" * \"good\" = $(0, 0, 1, 0, 0)$\n",
|
||
" * \"great\" = $(0, 0, 0, 1, 0)$\n",
|
||
" * \"have\" = $(0, 0, 0, 0, 1)$\n",
|
||
"* Taka metoda nie uwzględnia jednak podobieństw i różnic między znaczeniami wyrazów."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"Metody uzyskiwania *word embeddings*:\n",
|
||
"* Common Bag of Words (CBOW)\n",
|
||
"* Skip Gram\n",
|
||
"\n",
|
||
"Obie opierają się na odpowiednim użyciu autoencodera."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" width=\"80%\" src=\"we_autoencoder.png\" />"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"*Common Bag of Words*\n",
|
||
"\n",
|
||
"<img style=\"margin: auto\" width=\"40%\" src=\"cbow.png\" />"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"*Skip Gram*\n",
|
||
"\n",
|
||
"<img style=\"margin: auto\" width=\"30%\" src=\"skipgram.png\" />"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Skip Gram a CBOW\n",
|
||
"\n",
|
||
"* Skip Gram lepiej reprezentuje rzadkie wyrazy i lepiej działa, jeżeli mamy mało danych.\n",
|
||
"* CBOW jest szybszy i lepiej reprezentuje częste wyrazy."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Popularne modele _word embeddings_\n",
|
||
"* Word2Vec (Google)\n",
|
||
"* GloVe (Stanford)\n",
|
||
"* FastText (Facebook)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 5.5. Tłumaczenie neuronowe\n",
|
||
"\n",
|
||
"*Neural Machine Translation* (NMT)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"Neuronowe tłumaczenie maszynowe również opiera się na modelu *encoder-decoder*:\n",
|
||
"* *Encoder* koduje z języka źródłowego na abstrakcyjną reprezentację.\n",
|
||
"* *Decoder* odkodowuje z abstrakcyjnej reprezentacji na język docelowy."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"<img style=\"margin: auto\" width=\"40%\" src=\"http://devblogs.nvidia.com/parallelforall/wp-content/uploads/2015/06/Figure2_NMT_system.png\"/>"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"celltoolbar": "Slideshow",
|
||
"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": "3.8.3"
|
||
},
|
||
"livereveal": {
|
||
"start_slideshow_at": "selected",
|
||
"theme": "white"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|