64 lines
1.7 KiB
Plaintext
64 lines
1.7 KiB
Plaintext
{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "Resnet_block_architecture.ipynb",
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"provenance": [],
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"authorship_tag": "ABX9TyOTLHszFiFWrGkTp/f0kcnn",
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"accelerator": "GPU"
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},
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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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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/PacktPublishing/Modern-Computer-Vision-with-PyTorch/blob/master/Chapter05/Resnet_block_architecture.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "cuS9OIFRPtEW"
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},
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"source": [
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"import torch\n",
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"from torch import nn"
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],
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"execution_count": 1,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "S339gpWBP06u"
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},
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"source": [
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"class ResLayer(nn.Module):\n",
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" def __init__(self,ni,no,kernel_size,stride=1):\n",
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" super(ResLayer, self).__init__()\n",
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" padding = kernel_size - 2\n",
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" self.conv = nn.Sequential(\n",
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" nn.Conv2d(ni, no, kernel_size, stride, \n",
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" padding=padding),\n",
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" nn.ReLU()\n",
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" )\n",
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"\n",
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" def forward(self, x):\n",
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" return self.conv(x) + x\n",
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"\n"
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],
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"execution_count": null,
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"outputs": []
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}
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]
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} |