wehub-resource-sync
753 lines
26 KiB
Plaintext
753 lines
26 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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"toc": true
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},
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"source": [
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"<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
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"<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Model-Zoo----Convolutional-Neural-Network-(VGG16)\" data-toc-modified-id=\"Model-Zoo----Convolutional-Neural-Network-(VGG16)-1\"><span class=\"toc-item-num\">1 </span>Model Zoo -- Convolutional Neural Network (VGG16)</a></span><ul class=\"toc-item\"><li><span><a href=\"#Imports\" data-toc-modified-id=\"Imports-1.1\"><span class=\"toc-item-num\">1.1 </span>Imports</a></span></li><li><span><a href=\"#Settings-and-Dataset\" data-toc-modified-id=\"Settings-and-Dataset-1.2\"><span class=\"toc-item-num\">1.2 </span>Settings and Dataset</a></span></li><li><span><a href=\"#Model\" data-toc-modified-id=\"Model-1.3\"><span class=\"toc-item-num\">1.3 </span>Model</a></span></li><li><span><a href=\"#Training\" data-toc-modified-id=\"Training-1.4\"><span class=\"toc-item-num\">1.4 </span>Training</a></span></li><li><span><a href=\"#Evaluation\" data-toc-modified-id=\"Evaluation-1.5\"><span class=\"toc-item-num\">1.5 </span>Evaluation</a></span></li></ul></li></ul></div>"
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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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"colab_type": "text",
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"id": "UEBilEjLj5wY"
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},
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"source": [
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"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
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"- Author: Sebastian Raschka\n",
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"- GitHub Repository: https://github.com/rasbt/deeplearning-models"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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},
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"base_uri": "https://localhost:8080/",
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"height": 119
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},
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"colab_type": "code",
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"executionInfo": {
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"elapsed": 536,
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"status": "ok",
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"timestamp": 1524974472601,
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"user": {
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"displayName": "Sebastian Raschka",
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"photoUrl": "//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg",
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"userId": "118404394130788869227"
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},
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"user_tz": 240
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},
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"id": "GOzuY8Yvj5wb",
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"outputId": "c19362ce-f87a-4cc2-84cc-8d7b4b9e6007"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Sebastian Raschka \n",
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"\n",
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"CPython 3.7.3\n",
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"IPython 7.6.1\n",
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"\n",
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"torch 1.3.0\n"
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]
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}
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],
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"source": [
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"%load_ext watermark\n",
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"%watermark -a 'Sebastian Raschka' -v -p torch"
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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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"colab_type": "text",
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"id": "MEu9MiOxj5wk"
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},
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"source": [
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"- Runs on CPU (not recommended here) or GPU (if available)"
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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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"colab_type": "text",
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"id": "rH4XmErYj5wm"
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},
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"source": [
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"# Model Zoo -- Convolutional Neural Network (VGG16)"
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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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"colab_type": "text",
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"id": "MkoGLH_Tj5wn"
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},
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"source": [
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"## Imports"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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}
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},
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"colab_type": "code",
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"id": "ORj09gnrj5wp"
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},
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"outputs": [],
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"source": [
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"import time\n",
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"import numpy as np\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.nn.functional as F\n",
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"from torchvision import datasets\n",
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"from torchvision import transforms\n",
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"from torch.utils.data import DataLoader\n",
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"\n",
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"\n",
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"if torch.cuda.is_available():\n",
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" torch.backends.cudnn.deterministic = True"
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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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"colab_type": "text",
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"id": "PvgJ_0i7j5wt"
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},
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"source": [
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"## Settings and Dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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},
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"base_uri": "https://localhost:8080/",
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"height": 85
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},
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"colab_type": "code",
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"executionInfo": {
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"elapsed": 23936,
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"status": "ok",
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"timestamp": 1524974497505,
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"user": {
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"displayName": "Sebastian Raschka",
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"photoUrl": "//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg",
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"userId": "118404394130788869227"
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},
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"user_tz": 240
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},
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"id": "NnT0sZIwj5wu",
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"outputId": "55aed925-d17e-4c6a-8c71-0d9b3bde5637"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Device: cuda:3\n",
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"Files already downloaded and verified\n",
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"Image batch dimensions: torch.Size([128, 3, 32, 32])\n",
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"Image label dimensions: torch.Size([128])\n"
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]
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}
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],
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"source": [
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"##########################\n",
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"### SETTINGS\n",
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"##########################\n",
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"\n",
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"# Device\n",
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"DEVICE = torch.device(\"cuda:3\" if torch.cuda.is_available() else \"cpu\")\n",
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"print('Device:', DEVICE)\n",
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"\n",
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"# Hyperparameters\n",
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"random_seed = 1\n",
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"learning_rate = 0.001\n",
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"num_epochs = 10\n",
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"batch_size = 128\n",
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"\n",
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"# Architecture\n",
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"num_features = 784\n",
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"num_classes = 10\n",
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"\n",
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"\n",
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"##########################\n",
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"### MNIST DATASET\n",
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"##########################\n",
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"\n",
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"# Note transforms.ToTensor() scales input images\n",
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"# to 0-1 range\n",
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"train_dataset = datasets.CIFAR10(root='data', \n",
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" train=True, \n",
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" transform=transforms.ToTensor(),\n",
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" download=True)\n",
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"\n",
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"test_dataset = datasets.CIFAR10(root='data', \n",
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" train=False, \n",
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" transform=transforms.ToTensor())\n",
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"\n",
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"\n",
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"train_loader = DataLoader(dataset=train_dataset, \n",
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" batch_size=batch_size, \n",
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" shuffle=True)\n",
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"\n",
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"test_loader = DataLoader(dataset=test_dataset, \n",
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" batch_size=batch_size, \n",
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" shuffle=False)\n",
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"\n",
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"# Checking the dataset\n",
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"for images, labels in train_loader: \n",
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" print('Image batch dimensions:', images.shape)\n",
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" print('Image label dimensions:', labels.shape)\n",
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" break"
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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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"colab_type": "text",
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"id": "I6hghKPxj5w0"
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},
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"source": [
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"## Model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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}
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},
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"colab_type": "code",
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"id": "_lza9t_uj5w1"
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},
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"outputs": [],
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"source": [
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"##########################\n",
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"### MODEL\n",
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"##########################\n",
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"\n",
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"\n",
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"class VGG16(torch.nn.Module):\n",
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"\n",
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" def __init__(self, num_features, num_classes):\n",
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" super(VGG16, self).__init__()\n",
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" \n",
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" # calculate same padding:\n",
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" # (w - k + 2*p)/s + 1 = o\n",
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" # => p = (s(o-1) - w + k)/2\n",
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" \n",
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" self.block_1 = nn.Sequential(\n",
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" nn.Conv2d(in_channels=3,\n",
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" out_channels=64,\n",
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" kernel_size=(3, 3),\n",
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" stride=(1, 1),\n",
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" # (1(32-1)- 32 + 3)/2 = 1\n",
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" padding=1), \n",
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" nn.ReLU(),\n",
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" nn.Conv2d(in_channels=64,\n",
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" out_channels=64,\n",
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" kernel_size=(3, 3),\n",
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" stride=(1, 1),\n",
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" padding=1),\n",
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" nn.ReLU(),\n",
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" nn.MaxPool2d(kernel_size=(2, 2),\n",
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" stride=(2, 2))\n",
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" )\n",
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" \n",
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" self.block_2 = nn.Sequential(\n",
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" nn.Conv2d(in_channels=64,\n",
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" out_channels=128,\n",
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" kernel_size=(3, 3),\n",
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" stride=(1, 1),\n",
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" padding=1),\n",
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" nn.ReLU(),\n",
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" nn.Conv2d(in_channels=128,\n",
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" out_channels=128,\n",
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" kernel_size=(3, 3),\n",
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" stride=(1, 1),\n",
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" padding=1),\n",
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" nn.ReLU(),\n",
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" nn.MaxPool2d(kernel_size=(2, 2),\n",
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" stride=(2, 2))\n",
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" )\n",
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" \n",
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" self.block_3 = nn.Sequential( \n",
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" nn.Conv2d(in_channels=128,\n",
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" out_channels=256,\n",
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" kernel_size=(3, 3),\n",
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" stride=(1, 1),\n",
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" padding=1),\n",
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" nn.ReLU(),\n",
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" nn.Conv2d(in_channels=256,\n",
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" out_channels=256,\n",
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" kernel_size=(3, 3),\n",
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" stride=(1, 1),\n",
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" padding=1),\n",
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" nn.ReLU(), \n",
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" nn.Conv2d(in_channels=256,\n",
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" out_channels=256,\n",
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" kernel_size=(3, 3),\n",
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" stride=(1, 1),\n",
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" padding=1),\n",
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" nn.ReLU(),\n",
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" nn.MaxPool2d(kernel_size=(2, 2),\n",
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" stride=(2, 2))\n",
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" )\n",
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" \n",
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" \n",
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" self.block_4 = nn.Sequential( \n",
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" nn.Conv2d(in_channels=256,\n",
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" out_channels=512,\n",
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" kernel_size=(3, 3),\n",
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" stride=(1, 1),\n",
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" padding=1),\n",
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" nn.ReLU(), \n",
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" nn.Conv2d(in_channels=512,\n",
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" out_channels=512,\n",
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" kernel_size=(3, 3),\n",
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" stride=(1, 1),\n",
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" padding=1),\n",
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" nn.ReLU(), \n",
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" nn.Conv2d(in_channels=512,\n",
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" out_channels=512,\n",
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" kernel_size=(3, 3),\n",
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" stride=(1, 1),\n",
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" padding=1),\n",
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" nn.ReLU(), \n",
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" nn.MaxPool2d(kernel_size=(2, 2),\n",
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" stride=(2, 2))\n",
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" )\n",
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" \n",
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" self.block_5 = nn.Sequential(\n",
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" nn.Conv2d(in_channels=512,\n",
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" out_channels=512,\n",
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" kernel_size=(3, 3),\n",
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" stride=(1, 1),\n",
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" padding=1),\n",
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" nn.ReLU(), \n",
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" nn.Conv2d(in_channels=512,\n",
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" out_channels=512,\n",
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" kernel_size=(3, 3),\n",
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" stride=(1, 1),\n",
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" padding=1),\n",
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" nn.ReLU(), \n",
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" nn.Conv2d(in_channels=512,\n",
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" out_channels=512,\n",
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" kernel_size=(3, 3),\n",
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" stride=(1, 1),\n",
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" padding=1),\n",
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" nn.ReLU(), \n",
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" nn.MaxPool2d(kernel_size=(2, 2),\n",
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" stride=(2, 2)) \n",
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" )\n",
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" \n",
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" self.classifier = nn.Sequential(\n",
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" nn.Linear(512, 4096),\n",
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" nn.ReLU(True),\n",
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" #nn.Dropout(p=0.5),\n",
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" nn.Linear(4096, 4096),\n",
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" nn.ReLU(True),\n",
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" #nn.Dropout(p=0.5),\n",
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" nn.Linear(4096, num_classes),\n",
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" )\n",
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" \n",
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" for m in self.modules():\n",
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" if isinstance(m, torch.nn.Conv2d) or isinstance(m, torch.nn.Linear):\n",
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" nn.init.kaiming_uniform_(m.weight, mode='fan_in', nonlinearity='relu')\n",
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" if m.bias is not None:\n",
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" m.bias.detach().zero_()\n",
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" \n",
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" #self.avgpool = nn.AdaptiveAvgPool2d((7, 7))\n",
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" \n",
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" \n",
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" def forward(self, x):\n",
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"\n",
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" x = self.block_1(x)\n",
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" x = self.block_2(x)\n",
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" x = self.block_3(x)\n",
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" x = self.block_4(x)\n",
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" x = self.block_5(x)\n",
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" #x = self.avgpool(x)\n",
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" x = x.view(x.size(0), -1)\n",
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" logits = self.classifier(x)\n",
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" probas = F.softmax(logits, dim=1)\n",
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"\n",
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" return logits, probas\n",
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"\n",
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" \n",
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"torch.manual_seed(random_seed)\n",
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"model = VGG16(num_features=num_features,\n",
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" num_classes=num_classes)\n",
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"\n",
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"model = model.to(DEVICE)\n",
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"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) "
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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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"colab_type": "text",
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"id": "RAodboScj5w6"
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},
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"source": [
|
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"## Training"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"colab": {
|
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"autoexec": {
|
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"startup": false,
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"wait_interval": 0
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},
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"base_uri": "https://localhost:8080/",
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"height": 1547
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},
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"colab_type": "code",
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|
"executionInfo": {
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|
"elapsed": 2384585,
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"status": "ok",
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|
"timestamp": 1524976888520,
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"user": {
|
|
"displayName": "Sebastian Raschka",
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|
"photoUrl": "//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg",
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"userId": "118404394130788869227"
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},
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|
"user_tz": 240
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},
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"id": "Dzh3ROmRj5w7",
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|
"outputId": "5f8fd8c9-b076-403a-b0b7-fd2d498b48d7"
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},
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"outputs": [
|
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch: 001/010 | Batch 0000/0391 | Cost: 2.4443\n",
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"Epoch: 001/010 | Batch 0050/0391 | Cost: 2.2800\n",
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"Epoch: 001/010 | Batch 0100/0391 | Cost: 2.1115\n",
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"Epoch: 001/010 | Batch 0150/0391 | Cost: 1.9596\n",
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"Epoch: 001/010 | Batch 0200/0391 | Cost: 2.0510\n",
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"Epoch: 001/010 | Batch 0250/0391 | Cost: 1.8116\n",
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"Epoch: 001/010 | Batch 0300/0391 | Cost: 1.8209\n",
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"Epoch: 001/010 | Batch 0350/0391 | Cost: 1.7161\n",
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"Epoch: 001/010 | Train: 36.232% | Loss: 1.600\n",
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"Time elapsed: 0.59 min\n",
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"Epoch: 002/010 | Batch 0000/0391 | Cost: 1.6484\n",
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"Epoch: 002/010 | Batch 0050/0391 | Cost: 1.8022\n",
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"Epoch: 002/010 | Batch 0100/0391 | Cost: 1.6488\n",
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"Epoch: 002/010 | Batch 0150/0391 | Cost: 1.5652\n",
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"Epoch: 002/010 | Batch 0200/0391 | Cost: 1.3576\n",
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"Epoch: 002/010 | Batch 0250/0391 | Cost: 1.4434\n",
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"Epoch: 002/010 | Batch 0300/0391 | Cost: 1.3092\n",
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"Epoch: 002/010 | Batch 0350/0391 | Cost: 1.1899\n",
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"Epoch: 002/010 | Train: 55.148% | Loss: 1.229\n",
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"Time elapsed: 1.17 min\n",
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"Epoch: 003/010 | Batch 0000/0391 | Cost: 1.1967\n",
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"Epoch: 003/010 | Batch 0050/0391 | Cost: 1.1714\n",
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"Epoch: 003/010 | Batch 0100/0391 | Cost: 1.3094\n",
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"Epoch: 003/010 | Batch 0150/0391 | Cost: 1.2088\n",
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"Epoch: 003/010 | Batch 0200/0391 | Cost: 1.2108\n",
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"Epoch: 003/010 | Batch 0250/0391 | Cost: 0.9571\n",
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"Epoch: 003/010 | Batch 0300/0391 | Cost: 1.0708\n",
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"Epoch: 003/010 | Batch 0350/0391 | Cost: 0.9150\n",
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"Epoch: 003/010 | Train: 67.218% | Loss: 0.938\n",
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"Time elapsed: 1.77 min\n",
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"Epoch: 004/010 | Batch 0000/0391 | Cost: 0.8263\n",
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"Epoch: 004/010 | Batch 0050/0391 | Cost: 0.9029\n",
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"Epoch: 004/010 | Batch 0100/0391 | Cost: 1.1502\n",
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"Epoch: 004/010 | Batch 0150/0391 | Cost: 0.9787\n",
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"Epoch: 004/010 | Batch 0200/0391 | Cost: 0.8300\n",
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"Epoch: 004/010 | Batch 0250/0391 | Cost: 1.0111\n",
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"Epoch: 004/010 | Batch 0300/0391 | Cost: 0.9322\n",
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"Epoch: 004/010 | Batch 0350/0391 | Cost: 0.9228\n",
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"Epoch: 004/010 | Train: 70.676% | Loss: 0.852\n",
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"Time elapsed: 2.36 min\n",
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"Epoch: 005/010 | Batch 0000/0391 | Cost: 0.8467\n",
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"Epoch: 005/010 | Batch 0050/0391 | Cost: 0.6927\n",
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"Epoch: 005/010 | Batch 0100/0391 | Cost: 0.8755\n",
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"Epoch: 005/010 | Batch 0150/0391 | Cost: 0.8654\n",
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"Epoch: 005/010 | Batch 0200/0391 | Cost: 0.8372\n",
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"Epoch: 005/010 | Batch 0250/0391 | Cost: 0.6731\n",
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"Epoch: 005/010 | Batch 0300/0391 | Cost: 0.6184\n",
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"Epoch: 005/010 | Batch 0350/0391 | Cost: 0.6990\n",
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"Epoch: 005/010 | Train: 73.508% | Loss: 0.764\n",
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"Time elapsed: 2.96 min\n",
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"Epoch: 006/010 | Batch 0000/0391 | Cost: 0.7755\n",
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"Epoch: 006/010 | Batch 0050/0391 | Cost: 0.5677\n",
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"Epoch: 006/010 | Batch 0100/0391 | Cost: 0.8109\n",
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"Epoch: 006/010 | Batch 0150/0391 | Cost: 0.5656\n",
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"Epoch: 006/010 | Batch 0200/0391 | Cost: 0.5641\n",
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"Epoch: 006/010 | Batch 0250/0391 | Cost: 0.8524\n",
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"Epoch: 006/010 | Batch 0300/0391 | Cost: 0.8494\n",
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"Epoch: 006/010 | Batch 0350/0391 | Cost: 0.6887\n",
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"Epoch: 006/010 | Train: 81.112% | Loss: 0.559\n",
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"Time elapsed: 3.56 min\n",
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"Epoch: 007/010 | Batch 0000/0391 | Cost: 0.5640\n",
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"Epoch: 007/010 | Batch 0050/0391 | Cost: 0.5809\n",
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"Epoch: 007/010 | Batch 0100/0391 | Cost: 0.7346\n",
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"Epoch: 007/010 | Batch 0150/0391 | Cost: 0.6881\n",
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"Epoch: 007/010 | Batch 0200/0391 | Cost: 0.4644\n",
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"Epoch: 007/010 | Batch 0250/0391 | Cost: 0.5216\n",
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"Epoch: 007/010 | Batch 0300/0391 | Cost: 0.6380\n",
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"Epoch: 007/010 | Batch 0350/0391 | Cost: 0.5521\n",
|
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"Epoch: 007/010 | Train: 84.438% | Loss: 0.460\n",
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|
"Time elapsed: 4.15 min\n",
|
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"Epoch: 008/010 | Batch 0000/0391 | Cost: 0.4843\n",
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"Epoch: 008/010 | Batch 0050/0391 | Cost: 0.5226\n",
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"Epoch: 008/010 | Batch 0100/0391 | Cost: 0.3682\n",
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"Epoch: 008/010 | Batch 0150/0391 | Cost: 0.4941\n",
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"Epoch: 008/010 | Batch 0200/0391 | Cost: 0.5376\n",
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"Epoch: 008/010 | Batch 0250/0391 | Cost: 0.4707\n",
|
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"Epoch: 008/010 | Batch 0300/0391 | Cost: 0.5882\n",
|
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"Epoch: 008/010 | Batch 0350/0391 | Cost: 0.5355\n",
|
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"Epoch: 008/010 | Train: 85.186% | Loss: 0.442\n",
|
|
"Time elapsed: 4.76 min\n",
|
|
"Epoch: 009/010 | Batch 0000/0391 | Cost: 0.4174\n",
|
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"Epoch: 009/010 | Batch 0050/0391 | Cost: 0.4007\n",
|
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"Epoch: 009/010 | Batch 0100/0391 | Cost: 0.4133\n",
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"Epoch: 009/010 | Batch 0150/0391 | Cost: 0.6204\n",
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"Epoch: 009/010 | Batch 0200/0391 | Cost: 0.3827\n",
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"Epoch: 009/010 | Batch 0250/0391 | Cost: 0.4509\n",
|
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"Epoch: 009/010 | Batch 0300/0391 | Cost: 0.4461\n",
|
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"Epoch: 009/010 | Batch 0350/0391 | Cost: 0.4692\n",
|
|
"Epoch: 009/010 | Train: 89.038% | Loss: 0.328\n",
|
|
"Time elapsed: 5.36 min\n",
|
|
"Epoch: 010/010 | Batch 0000/0391 | Cost: 0.2964\n",
|
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"Epoch: 010/010 | Batch 0050/0391 | Cost: 0.2739\n",
|
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"Epoch: 010/010 | Batch 0100/0391 | Cost: 0.3803\n",
|
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"Epoch: 010/010 | Batch 0150/0391 | Cost: 0.3981\n",
|
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"Epoch: 010/010 | Batch 0200/0391 | Cost: 0.3877\n",
|
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"Epoch: 010/010 | Batch 0250/0391 | Cost: 0.4213\n",
|
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"Epoch: 010/010 | Batch 0300/0391 | Cost: 0.5088\n",
|
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"Epoch: 010/010 | Batch 0350/0391 | Cost: 0.4598\n",
|
|
"Epoch: 010/010 | Train: 89.668% | Loss: 0.321\n",
|
|
"Time elapsed: 5.96 min\n",
|
|
"Total Training Time: 5.96 min\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"def compute_accuracy(model, data_loader):\n",
|
|
" model.eval()\n",
|
|
" correct_pred, num_examples = 0, 0\n",
|
|
" for i, (features, targets) in enumerate(data_loader):\n",
|
|
" \n",
|
|
" features = features.to(DEVICE)\n",
|
|
" targets = targets.to(DEVICE)\n",
|
|
"\n",
|
|
" logits, probas = model(features)\n",
|
|
" _, predicted_labels = torch.max(probas, 1)\n",
|
|
" num_examples += targets.size(0)\n",
|
|
" correct_pred += (predicted_labels == targets).sum()\n",
|
|
" return correct_pred.float()/num_examples * 100\n",
|
|
"\n",
|
|
"\n",
|
|
"def compute_epoch_loss(model, data_loader):\n",
|
|
" model.eval()\n",
|
|
" curr_loss, num_examples = 0., 0\n",
|
|
" with torch.no_grad():\n",
|
|
" for features, targets in data_loader:\n",
|
|
" features = features.to(DEVICE)\n",
|
|
" targets = targets.to(DEVICE)\n",
|
|
" logits, probas = model(features)\n",
|
|
" loss = F.cross_entropy(logits, targets, reduction='sum')\n",
|
|
" num_examples += targets.size(0)\n",
|
|
" curr_loss += loss\n",
|
|
"\n",
|
|
" curr_loss = curr_loss / num_examples\n",
|
|
" return curr_loss\n",
|
|
" \n",
|
|
" \n",
|
|
"\n",
|
|
"start_time = time.time()\n",
|
|
"for epoch in range(num_epochs):\n",
|
|
" \n",
|
|
" model.train()\n",
|
|
" for batch_idx, (features, targets) in enumerate(train_loader):\n",
|
|
" \n",
|
|
" features = features.to(DEVICE)\n",
|
|
" targets = targets.to(DEVICE)\n",
|
|
" \n",
|
|
" ### FORWARD AND BACK PROP\n",
|
|
" logits, probas = model(features)\n",
|
|
" cost = F.cross_entropy(logits, targets)\n",
|
|
" optimizer.zero_grad()\n",
|
|
" \n",
|
|
" cost.backward()\n",
|
|
" \n",
|
|
" ### UPDATE MODEL PARAMETERS\n",
|
|
" optimizer.step()\n",
|
|
" \n",
|
|
" ### LOGGING\n",
|
|
" if not batch_idx % 50:\n",
|
|
" print ('Epoch: %03d/%03d | Batch %04d/%04d | Cost: %.4f' \n",
|
|
" %(epoch+1, num_epochs, batch_idx, \n",
|
|
" len(train_loader), cost))\n",
|
|
"\n",
|
|
" model.eval()\n",
|
|
" with torch.set_grad_enabled(False): # save memory during inference\n",
|
|
" print('Epoch: %03d/%03d | Train: %.3f%% | Loss: %.3f' % (\n",
|
|
" epoch+1, num_epochs, \n",
|
|
" compute_accuracy(model, train_loader),\n",
|
|
" compute_epoch_loss(model, train_loader)))\n",
|
|
"\n",
|
|
"\n",
|
|
" print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\n",
|
|
" \n",
|
|
"print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "paaeEQHQj5xC"
|
|
},
|
|
"source": [
|
|
"## Evaluation"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {
|
|
"colab": {
|
|
"autoexec": {
|
|
"startup": false,
|
|
"wait_interval": 0
|
|
},
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 34
|
|
},
|
|
"colab_type": "code",
|
|
"executionInfo": {
|
|
"elapsed": 6514,
|
|
"status": "ok",
|
|
"timestamp": 1524976895054,
|
|
"user": {
|
|
"displayName": "Sebastian Raschka",
|
|
"photoUrl": "//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg",
|
|
"userId": "118404394130788869227"
|
|
},
|
|
"user_tz": 240
|
|
},
|
|
"id": "gzQMWKq5j5xE",
|
|
"outputId": "de7dc005-5eeb-4177-9f9f-d9b5d1358db9"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Test accuracy: 76.31%\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"with torch.set_grad_enabled(False): # save memory during inference\n",
|
|
" print('Test accuracy: %.2f%%' % (compute_accuracy(model, test_loader)))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"torchvision 0.4.1a0+d94043a\n",
|
|
"numpy 1.16.4\n",
|
|
"torch 1.3.0\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%watermark -iv"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"accelerator": "GPU",
|
|
"colab": {
|
|
"collapsed_sections": [],
|
|
"default_view": {},
|
|
"name": "convnet-vgg16.ipynb",
|
|
"provenance": [],
|
|
"version": "0.3.2",
|
|
"views": {}
|
|
},
|
|
"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": "3.7.3"
|
|
},
|
|
"toc": {
|
|
"nav_menu": {},
|
|
"number_sections": true,
|
|
"sideBar": true,
|
|
"skip_h1_title": false,
|
|
"title_cell": "Table of Contents",
|
|
"title_sidebar": "Contents",
|
|
"toc_cell": true,
|
|
"toc_position": {
|
|
"height": "calc(100% - 180px)",
|
|
"left": "10px",
|
|
"top": "150px",
|
|
"width": "371px"
|
|
},
|
|
"toc_section_display": true,
|
|
"toc_window_display": true
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|