Uzupełnienie wykładu 13. CNN
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@ -8,6 +8,7 @@
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"source": [
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"### Uczenie maszynowe\n",
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"# 13. Splotowe sieci neuronowe"
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@ -97,6 +98,17 @@
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"* nie wykrywa własności „lokalnych” wejścia"
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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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"<img style=\"margin: auto\" width=\"80%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv-9-F.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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@ -110,6 +122,28 @@
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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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"<img style=\"margin: auto\" width=\"80%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv-9-Conv2.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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"<img style=\"margin: auto\" width=\"80%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv-9-Conv3.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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@ -123,6 +157,39 @@
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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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"<img style=\"margin: auto\" width=\"60%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv-9-Conv2Conv2.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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"<img style=\"margin: auto\" width=\"50%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv2-9x5-Conv2.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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"<img style=\"margin: auto\" width=\"50%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv2-9x5-Conv2Conv2.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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@ -152,6 +219,19 @@
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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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"Ilustracja działania funkcji splotu:\n",
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"\n",
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"<img style=\"margin: auto\" height=\"80%\" src=\"https://devblogs.nvidia.com/wp-content/uploads/2015/11/Convolution_schematic.gif\"/>"
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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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