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‎seminar03-conv_nets/how_to_shoot_yourself_in_the_foot_with_cnn.ipynb

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"nbformat_minor": 0,
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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"name": "python3",
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"display_name": "Yandex DataSphere Kernel",
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"language": "python"
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},
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"language_info": {
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"file_extension": ".py",
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"version": "3.7.7",
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"mimetype": "text/x-python",
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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"pygments_lexer": "ipython3"
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},
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"colab": {
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"name": "how_to_shoot_yourself_in_the_foot_with_cnn.ipynb",
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"name": "how_to_shoot_yourself_in_the_foot_with_cnn (4).ipynb",
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"provenance": []
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}
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},
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"notebookId": "8f3ba1c3-68b2-462d-8050-ac7d2972fee2"
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},
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"cells": [
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{
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"cell_type": "code",
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"metadata": {
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"id": "A9knWAIelap5",
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"colab_type": "code",
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"colab": {}
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"cellId": "24wzuqlj89wm4ahvxi57j",
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"trusted": true
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},
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"source": [
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"import torch, torch.nn as nn\n",
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"import torch.nn.functional as F\n",
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"from torch.autograd import Variable\n",
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"\n",
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"# a special module that converts [batch, channel, w, h] to [batch, units]\n",
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"class Flatten(nn.Module):\n",
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" def forward(self, input):\n",
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" return input.view(input.size(0), -1)"
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"from torch.autograd import Variable\n"
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],
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"execution_count": 0,
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "XxAOHFvglszx",
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"colab_type": "text"
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"cellId": "2w8bv2xmozhlclkrn7moo"
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},
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"source": [
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_DL/blob/spring20/seminar3/how_to_shoot_yourself_in_the_foot_with_cnn.ipynb)"
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"cell_type": "code",
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"metadata": {
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"id": "4W6k6Bullap_",
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"colab_type": "code",
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"colab": {}
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"cellId": "0zngede38s1g2znau62g8d6"
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},
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"source": [
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"# assuming input shape [batch, 3, 64, 64]\n",
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" nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(20,20)),\n",
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" nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(20,20)),\n",
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" nn.Softmax(),\n",
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" Flatten(),\n",
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" nn.Flatten(),\n",
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" nn.Linear(64, 256),\n",
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" nn.Softmax(),\n",
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" nn.Linear(256, 10),\n",
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" \n",
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")\n"
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],
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"execution_count": 0,
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"execution_count": null,
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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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"cellId": "7y09x5yxognh7glyjh1wk8",
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"trusted": true,
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"id": "c9i_Hj_ZhWIb"
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},
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"source": [
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"import torch, torch.nn as nn\n",
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"import torch.nn.functional as F\n",
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"from torch.autograd import Variable\n",
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"\n",
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"tensor = torch.randn((16, 3, 64, 64), dtype=torch.float)"
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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": "markdown",
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"metadata": {
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"id": "xydlW1NSlaqD",
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"colab_type": "text"
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"cellId": "5yp82sztn9m6jnzw71tvs"
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"source": [
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"cell_type": "code",
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"metadata": {
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"id": "E84fiu7ZlaqE",
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"colab_type": "code",
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"colab": {}
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"cellId": "uwnr6ieobyxcbccqdaz1f"
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},
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"source": [
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""
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],
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"execution_count": 0,
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"execution_count": null,
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"outputs": []
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}
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]

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