340 lines
9.7 KiB
Plaintext
340 lines
9.7 KiB
Plaintext
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{
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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": "Sequential_method_to_build_a_neural_network.ipynb",
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"provenance": [],
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"include_colab_link": true
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},
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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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},
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"language_info": {
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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": "3.7.6"
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}
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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/Hands-On-Computer-Vision-with-PyTorch/blob/master/Chapter02/Sequential_method_to_build_a_neural_network.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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"ExecuteTime": {
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"end_time": "2020-09-25T19:49:17.305235Z",
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"start_time": "2020-09-25T19:49:17.302498Z"
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},
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"id": "D5_lUQ_JzxNQ"
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},
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"source": [
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"x = [[1,2],[3,4],[5,6],[7,8]]\n",
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"y = [[3],[7],[11],[15]]"
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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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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-09-25T19:49:17.611616Z",
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"start_time": "2020-09-25T19:49:17.306365Z"
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},
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"id": "TG0fNwONz6yn"
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},
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"source": [
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"import torch\n",
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"import torch.nn as nn\n",
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"import numpy as np\n",
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"from torch.utils.data import Dataset, DataLoader\n",
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"device = 'cuda' if torch.cuda.is_available() else 'cpu'"
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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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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-09-25T19:49:17.616192Z",
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"start_time": "2020-09-25T19:49:17.613054Z"
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},
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"id": "f4-xTYoCz8U9"
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},
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"source": [
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"class MyDataset(Dataset):\n",
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" def __init__(self, x, y):\n",
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" self.x = torch.tensor(x).float().to(device)\n",
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" self.y = torch.tensor(y).float().to(device)\n",
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" def __getitem__(self, ix):\n",
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" return self.x[ix], self.y[ix]\n",
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" def __len__(self): \n",
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" return len(self.x)"
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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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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-09-25T19:49:19.209881Z",
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"start_time": "2020-09-25T19:49:17.617151Z"
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},
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"id": "WeBe83XQz9we"
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},
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"source": [
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"ds = MyDataset(x, y)\n",
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"dl = DataLoader(ds, batch_size=2, shuffle=True)"
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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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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-09-25T19:49:19.213866Z",
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"start_time": "2020-09-25T19:49:19.210841Z"
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},
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"id": "Vcg57P86z_oF"
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},
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"source": [
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"model = nn.Sequential(\n",
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" nn.Linear(2, 8),\n",
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" nn.ReLU(),\n",
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" nn.Linear(8, 1)\n",
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").to(device)"
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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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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-09-25T19:49:21.005946Z",
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"start_time": "2020-09-25T19:49:19.215403Z"
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},
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"id": "7FGa-UWK0BIX",
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"outputId": "570c4f77-ef48-46c7-85b9-49b41eec4088",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 85
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}
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},
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"source": [
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"!pip install torch_summary\n",
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"from torchsummary import summary"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: torch_summary in /home/yyr/anaconda3/lib/python3.7/site-packages (1.4.1)\n",
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"\u001b[33mWARNING: You are using pip version 20.2.2; however, version 20.2.3 is available.\n",
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"You should consider upgrading via the '/home/yyr/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
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],
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"name": "stdout"
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}
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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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"ExecuteTime": {
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"end_time": "2020-09-25T19:49:21.040105Z",
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"start_time": "2020-09-25T19:49:21.011241Z"
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},
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"id": "UVZlHyXh0Fyd",
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"outputId": "1b7c50ea-f954-4a56-8eb0-8095891c943c",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 595
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}
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},
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"source": [
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"summary(model, torch.zeros(1,2));"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"==========================================================================================\n",
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"Layer (type:depth-idx) Output Shape Param #\n",
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"==========================================================================================\n",
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"├─Linear: 1-1 [-1, 8] 24\n",
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"├─ReLU: 1-2 [-1, 8] --\n",
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"├─Linear: 1-3 [-1, 1] 9\n",
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"==========================================================================================\n",
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"Total params: 33\n",
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"Trainable params: 33\n",
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"Non-trainable params: 0\n",
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"Total mult-adds (M): 0.00\n",
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"==========================================================================================\n",
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"Input size (MB): 0.00\n",
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"Forward/backward pass size (MB): 0.00\n",
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"Params size (MB): 0.00\n",
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"Estimated Total Size (MB): 0.00\n",
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"==========================================================================================\n"
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],
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"name": "stdout"
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}
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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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"ExecuteTime": {
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"end_time": "2020-09-25T19:49:21.127594Z",
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"start_time": "2020-09-25T19:49:21.044743Z"
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},
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"id": "NDHfUDbW0Lh_",
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||
|
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"outputId": "c0f4620b-4479-4ecc-d3e2-77e7d067b5d8",
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"colab": {
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||
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"base_uri": "https://localhost:8080/",
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"height": 34
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}
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},
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"source": [
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"loss_func = nn.MSELoss()\n",
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"from torch.optim import SGD\n",
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"opt = SGD(model.parameters(), lr = 0.001)\n",
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"import time\n",
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"loss_history = []\n",
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"start = time.time()\n",
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"for _ in range(50):\n",
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" for ix, iy in dl:\n",
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" opt.zero_grad()\n",
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" loss_value = loss_func(model(ix),iy)\n",
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" loss_value.backward()\n",
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" opt.step()\n",
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" loss_history.append(loss_value)\n",
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"end = time.time()\n",
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"print(end - start)"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"0.0754392147064209\n"
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],
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"name": "stdout"
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}
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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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"ExecuteTime": {
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"end_time": "2020-09-25T19:49:21.130860Z",
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"start_time": "2020-09-25T19:49:21.128656Z"
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},
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"id": "-Y-j0JeW0WKz"
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},
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"source": [
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"val = [[8,9],[10,11],[1.5,2.5]]\n",
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"val = torch.tensor(val).float()"
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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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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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|
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"end_time": "2020-09-25T19:49:21.141201Z",
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"start_time": "2020-09-25T19:49:21.132039Z"
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},
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"id": "KdNMIy4u0Xkt",
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||
|
|
"outputId": "1fc3883d-0692-409d-ecb8-d5dd98583285",
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"colab": {
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||
|
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"base_uri": "https://localhost:8080/",
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"height": 68
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}
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},
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"source": [
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"model(val.to(device))"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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||
|
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"tensor([[16.7953],\n",
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|
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" [20.6512],\n",
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|
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" [ 4.2647]], device='cuda:0', grad_fn=<AddmmBackward>)"
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]
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||
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},
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||
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"metadata": {
|
||
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"tags": []
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},
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||
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"execution_count": 10
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}
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]
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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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||
|
|
"ExecuteTime": {
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||
|
|
"end_time": "2020-09-25T19:49:21.145210Z",
|
||
|
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"start_time": "2020-09-25T19:49:21.142226Z"
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|
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},
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||
|
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"id": "0fgQGXEX0YK_",
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||
|
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"outputId": "a8c760d3-bae4-4954-89cf-8e87d222fc5b"
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||
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},
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||
|
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"source": [
|
||
|
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"val.sum(-1)"
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],
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"execution_count": null,
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||
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"outputs": [
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||
|
|
{
|
||
|
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"output_type": "execute_result",
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||
|
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"data": {
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||
|
|
"text/plain": [
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||
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"tensor([17., 21., 4.])"
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||
|
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]
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||
|
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},
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||
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"metadata": {
|
||
|
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"tags": []
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||
|
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},
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||
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"execution_count": 11
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||
|
|
}
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||
|
|
]
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||
|
|
},
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||
|
|
{
|
||
|
|
"cell_type": "code",
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||
|
|
"metadata": {
|
||
|
|
"id": "5HF-DDp50YLB"
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||
|
|
},
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||
|
|
"source": [
|
||
|
|
""
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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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||
|
|
]
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||
|
|
}
|