rasbt--deeplearning-models
rasbt
767 行
23 KiB
Plaintext
767 行
23 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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"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 3.6.8\n",
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"IPython 7.2.0\n",
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"\n",
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"torch 1.0.0\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": "rH4XmErYj5wm"
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},
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"source": [
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"# Network in Network CIFAR-10 Classifier"
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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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"source": [
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"based on \n",
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"\n",
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"- Lin, Min, Qiang Chen, and Shuicheng Yan. \"Network in network.\" arXiv preprint arXiv:1312.4400 (2013)."
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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 os\n",
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"import time\n",
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"\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"\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 torch.utils.data import DataLoader\n",
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"\n",
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"from torchvision import datasets\n",
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"from torchvision import transforms\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"from PIL import Image\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": "I6hghKPxj5w0"
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},
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"source": [
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"## Model Settings"
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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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"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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"# Hyperparameters\n",
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"RANDOM_SEED = 1\n",
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"LEARNING_RATE = 0.001\n",
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"BATCH_SIZE = 256\n",
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"NUM_EPOCHS = 10\n",
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"\n",
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"# Architecture\n",
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"NUM_CLASSES = 10\n",
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"\n",
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"# Other\n",
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"DEVICE = \"cuda:0\"\n",
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"GRAYSCALE = False"
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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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"source": [
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"The following code cell that implements the ResNet-34 architecture is a derivative of the code provided at https://pytorch.org/docs/0.4.0/_modules/torchvision/models/resnet.html."
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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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"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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"Files already downloaded and verified\n",
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"Image batch dimensions: torch.Size([256, 3, 32, 32])\n",
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"Image label dimensions: torch.Size([256])\n",
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"Image batch dimensions: torch.Size([256, 3, 32, 32])\n",
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"Image label dimensions: torch.Size([256])\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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"### CIFAR-10 Dataset\n",
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"##########################\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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" num_workers=8,\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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" num_workers=8,\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\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": "code",
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"execution_count": 4,
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"metadata": {},
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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 NiN(nn.Module):\n",
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" def __init__(self, num_classes):\n",
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" super(NiN, self).__init__()\n",
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" self.num_classes = num_classes\n",
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" self.classifier = nn.Sequential(\n",
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" nn.Conv2d(3, 192, kernel_size=5, stride=1, padding=2),\n",
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" nn.ReLU(inplace=True),\n",
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" nn.Conv2d(192, 160, kernel_size=1, stride=1, padding=0),\n",
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" nn.ReLU(inplace=True),\n",
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" nn.Conv2d(160, 96, kernel_size=1, stride=1, padding=0),\n",
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" nn.ReLU(inplace=True),\n",
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" nn.MaxPool2d(kernel_size=3, stride=2, padding=1),\n",
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" nn.Dropout(0.5),\n",
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"\n",
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" nn.Conv2d(96, 192, kernel_size=5, stride=1, padding=2),\n",
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" nn.ReLU(inplace=True),\n",
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" nn.Conv2d(192, 192, kernel_size=1, stride=1, padding=0),\n",
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" nn.ReLU(inplace=True),\n",
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" nn.Conv2d(192, 192, kernel_size=1, stride=1, padding=0),\n",
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" nn.ReLU(inplace=True),\n",
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" nn.AvgPool2d(kernel_size=3, stride=2, padding=1),\n",
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" nn.Dropout(0.5),\n",
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"\n",
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" nn.Conv2d(192, 192, kernel_size=3, stride=1, padding=1),\n",
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" nn.ReLU(inplace=True),\n",
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" nn.Conv2d(192, 192, kernel_size=1, stride=1, padding=0),\n",
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" nn.ReLU(inplace=True),\n",
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" nn.Conv2d(192, 10, kernel_size=1, stride=1, padding=0),\n",
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" nn.ReLU(inplace=True),\n",
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" nn.AvgPool2d(kernel_size=8, stride=1, padding=0),\n",
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"\n",
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" )\n",
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"\n",
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" def forward(self, x):\n",
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" x = self.classifier(x)\n",
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" logits = x.view(x.size(0), self.num_classes)\n",
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" probas = torch.softmax(x, dim=1)\n",
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" return logits, probas"
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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 without Pinned Memory"
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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": 6,
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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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"torch.manual_seed(RANDOM_SEED)\n",
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"\n",
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"model = NiN(NUM_CLASSES)\n",
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"model.to(DEVICE)\n",
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"\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": "code",
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"execution_count": 7,
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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": {
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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": "Dzh3ROmRj5w7",
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"outputId": "5f8fd8c9-b076-403a-b0b7-fd2d498b48d7",
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"scrolled": false
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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 0000/0196 | Cost: 2.6021\n",
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"Epoch: 001/010 | Batch 0150/0196 | Cost: 1.3961\n",
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"Epoch: 001/010 | Train: 45.084%\n",
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"Time elapsed: 0.26 min\n",
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"Epoch: 002/010 | Batch 0000/0196 | Cost: 1.1228\n",
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"Epoch: 002/010 | Batch 0150/0196 | Cost: 1.0426\n",
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"Epoch: 002/010 | Train: 56.166%\n",
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"Time elapsed: 0.52 min\n",
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"Epoch: 003/010 | Batch 0000/0196 | Cost: 0.9980\n",
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"Epoch: 003/010 | Batch 0150/0196 | Cost: 0.8279\n",
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"Epoch: 003/010 | Train: 66.702%\n",
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"Time elapsed: 0.80 min\n",
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"Epoch: 004/010 | Batch 0000/0196 | Cost: 0.6384\n",
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"Epoch: 004/010 | Batch 0150/0196 | Cost: 0.7103\n",
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"Epoch: 004/010 | Train: 65.330%\n",
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"Time elapsed: 1.08 min\n",
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"Epoch: 005/010 | Batch 0000/0196 | Cost: 0.6308\n",
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"Epoch: 005/010 | Batch 0150/0196 | Cost: 0.5913\n",
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"Epoch: 005/010 | Train: 79.636%\n",
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"Time elapsed: 1.36 min\n",
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"Epoch: 006/010 | Batch 0000/0196 | Cost: 0.4409\n",
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"Epoch: 006/010 | Batch 0150/0196 | Cost: 0.5557\n",
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"Epoch: 006/010 | Train: 76.456%\n",
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"Time elapsed: 1.62 min\n",
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"Epoch: 007/010 | Batch 0000/0196 | Cost: 0.4778\n",
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"Epoch: 007/010 | Batch 0150/0196 | Cost: 0.4815\n",
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"Epoch: 007/010 | Train: 65.890%\n",
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"Time elapsed: 1.89 min\n",
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"Epoch: 008/010 | Batch 0000/0196 | Cost: 0.3782\n",
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"Epoch: 008/010 | Batch 0150/0196 | Cost: 0.4339\n",
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"Epoch: 008/010 | Train: 85.200%\n",
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"Time elapsed: 2.16 min\n",
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"Epoch: 009/010 | Batch 0000/0196 | Cost: 0.3083\n",
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"Epoch: 009/010 | Batch 0150/0196 | Cost: 0.3290\n",
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"Epoch: 009/010 | Train: 78.108%\n",
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"Time elapsed: 2.42 min\n",
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"Epoch: 010/010 | Batch 0000/0196 | Cost: 0.2229\n",
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"Epoch: 010/010 | Batch 0150/0196 | Cost: 0.1945\n",
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"Epoch: 010/010 | Train: 87.384%\n",
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"Time elapsed: 2.70 min\n",
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"Total Training Time: 2.70 min\n",
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"Test accuracy: 70.67%\n",
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"Total Time: 2.71 min\n"
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]
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}
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],
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"source": [
|
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"def compute_accuracy(model, data_loader, device):\n",
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" correct_pred, num_examples = 0, 0\n",
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" for i, (features, targets) in enumerate(data_loader):\n",
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" \n",
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" features = features.to(device)\n",
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" targets = targets.to(device)\n",
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"\n",
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" logits, probas = model(features)\n",
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" _, predicted_labels = torch.max(probas, 1)\n",
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" num_examples += targets.size(0)\n",
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" correct_pred += (predicted_labels == targets).sum()\n",
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" return correct_pred.float()/num_examples * 100\n",
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" \n",
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"\n",
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"start_time = time.time()\n",
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"for epoch in range(NUM_EPOCHS):\n",
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" \n",
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" model.train()\n",
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" for batch_idx, (features, targets) in enumerate(train_loader):\n",
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" \n",
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" features = features.to(DEVICE)\n",
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" targets = targets.to(DEVICE)\n",
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" \n",
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" ### FORWARD AND BACK PROP\n",
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" logits, probas = model(features)\n",
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" cost = F.cross_entropy(logits, targets)\n",
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" optimizer.zero_grad()\n",
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" \n",
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" cost.backward()\n",
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" \n",
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" ### UPDATE MODEL PARAMETERS\n",
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" optimizer.step()\n",
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" \n",
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" ### LOGGING\n",
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" if not batch_idx % 150:\n",
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" print ('Epoch: %03d/%03d | Batch %04d/%04d | Cost: %.4f' \n",
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" %(epoch+1, NUM_EPOCHS, batch_idx, \n",
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" len(train_loader), cost))\n",
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"\n",
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" \n",
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"\n",
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" model.eval()\n",
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" with torch.set_grad_enabled(False): # save memory during inference\n",
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" print('Epoch: %03d/%03d | Train: %.3f%%' % (\n",
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" epoch+1, NUM_EPOCHS, \n",
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" compute_accuracy(model, train_loader, device=DEVICE)))\n",
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" \n",
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" print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\n",
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" \n",
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"print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))\n",
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"\n",
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"\n",
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"with torch.set_grad_enabled(False): # save memory during inference\n",
|
|
" print('Test accuracy: %.2f%%' % (compute_accuracy(model, test_loader, device=DEVICE)))\n",
|
|
" \n",
|
|
"print('Total Time: %.2f min' % ((time.time() - start_time)/60))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Training with Pinned Memory"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Files already downloaded and verified\n",
|
|
"Image batch dimensions: torch.Size([256, 3, 32, 32])\n",
|
|
"Image label dimensions: torch.Size([256])\n",
|
|
"Image batch dimensions: torch.Size([256, 3, 32, 32])\n",
|
|
"Image label dimensions: torch.Size([256])\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"##########################\n",
|
|
"### CIFAR-10 Dataset\n",
|
|
"##########################\n",
|
|
"\n",
|
|
"\n",
|
|
"# Note transforms.ToTensor() scales input images\n",
|
|
"# to 0-1 range\n",
|
|
"train_dataset = datasets.CIFAR10(root='data', \n",
|
|
" train=True, \n",
|
|
" transform=transforms.ToTensor(),\n",
|
|
" download=True)\n",
|
|
"\n",
|
|
"test_dataset = datasets.CIFAR10(root='data', \n",
|
|
" train=False, \n",
|
|
" transform=transforms.ToTensor())\n",
|
|
"\n",
|
|
"\n",
|
|
"train_loader = DataLoader(dataset=train_dataset, \n",
|
|
" batch_size=BATCH_SIZE, \n",
|
|
" pin_memory=True,\n",
|
|
" shuffle=True)\n",
|
|
"\n",
|
|
"test_loader = DataLoader(dataset=test_dataset, \n",
|
|
" batch_size=BATCH_SIZE,\n",
|
|
" pin_memory=True,\n",
|
|
" shuffle=False)\n",
|
|
"\n",
|
|
"# Checking the dataset\n",
|
|
"for images, labels in train_loader: \n",
|
|
" print('Image batch dimensions:', images.shape)\n",
|
|
" print('Image label dimensions:', labels.shape)\n",
|
|
" break\n",
|
|
"\n",
|
|
"# Checking the dataset\n",
|
|
"for images, labels in train_loader: \n",
|
|
" print('Image batch dimensions:', images.shape)\n",
|
|
" print('Image label dimensions:', labels.shape)\n",
|
|
" break"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {
|
|
"colab": {
|
|
"autoexec": {
|
|
"startup": false,
|
|
"wait_interval": 0
|
|
}
|
|
},
|
|
"colab_type": "code",
|
|
"id": "_lza9t_uj5w1"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"torch.manual_seed(RANDOM_SEED)\n",
|
|
"\n",
|
|
"model = resnet34(NUM_CLASSES)\n",
|
|
"model.to(DEVICE)\n",
|
|
"\n",
|
|
"optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE) "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {
|
|
"colab": {
|
|
"autoexec": {
|
|
"startup": false,
|
|
"wait_interval": 0
|
|
},
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 1547
|
|
},
|
|
"colab_type": "code",
|
|
"executionInfo": {
|
|
"elapsed": 2384585,
|
|
"status": "ok",
|
|
"timestamp": 1524976888520,
|
|
"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": "Dzh3ROmRj5w7",
|
|
"outputId": "5f8fd8c9-b076-403a-b0b7-fd2d498b48d7",
|
|
"scrolled": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch: 001/010 | Batch 0000/0196 | Cost: 2.6021\n",
|
|
"Epoch: 001/010 | Batch 0150/0196 | Cost: 1.3961\n",
|
|
"Epoch: 001/010 | Train: 45.084%\n",
|
|
"Time elapsed: 0.39 min\n",
|
|
"Epoch: 002/010 | Batch 0000/0196 | Cost: 1.1228\n",
|
|
"Epoch: 002/010 | Batch 0150/0196 | Cost: 1.0426\n",
|
|
"Epoch: 002/010 | Train: 56.166%\n",
|
|
"Time elapsed: 0.77 min\n",
|
|
"Epoch: 003/010 | Batch 0000/0196 | Cost: 0.9980\n",
|
|
"Epoch: 003/010 | Batch 0150/0196 | Cost: 0.8279\n",
|
|
"Epoch: 003/010 | Train: 66.702%\n",
|
|
"Time elapsed: 1.16 min\n",
|
|
"Epoch: 004/010 | Batch 0000/0196 | Cost: 0.6384\n",
|
|
"Epoch: 004/010 | Batch 0150/0196 | Cost: 0.7103\n",
|
|
"Epoch: 004/010 | Train: 65.330%\n",
|
|
"Time elapsed: 1.55 min\n",
|
|
"Epoch: 005/010 | Batch 0000/0196 | Cost: 0.6308\n",
|
|
"Epoch: 005/010 | Batch 0150/0196 | Cost: 0.5913\n",
|
|
"Epoch: 005/010 | Train: 79.636%\n",
|
|
"Time elapsed: 1.94 min\n",
|
|
"Epoch: 006/010 | Batch 0000/0196 | Cost: 0.4409\n",
|
|
"Epoch: 006/010 | Batch 0150/0196 | Cost: 0.5557\n",
|
|
"Epoch: 006/010 | Train: 76.456%\n",
|
|
"Time elapsed: 2.33 min\n",
|
|
"Epoch: 007/010 | Batch 0000/0196 | Cost: 0.4778\n",
|
|
"Epoch: 007/010 | Batch 0150/0196 | Cost: 0.4815\n",
|
|
"Epoch: 007/010 | Train: 65.890%\n",
|
|
"Time elapsed: 2.71 min\n",
|
|
"Epoch: 008/010 | Batch 0000/0196 | Cost: 0.3782\n",
|
|
"Epoch: 008/010 | Batch 0150/0196 | Cost: 0.4339\n",
|
|
"Epoch: 008/010 | Train: 85.200%\n",
|
|
"Time elapsed: 3.10 min\n",
|
|
"Epoch: 009/010 | Batch 0000/0196 | Cost: 0.3083\n",
|
|
"Epoch: 009/010 | Batch 0150/0196 | Cost: 0.3290\n",
|
|
"Epoch: 009/010 | Train: 78.108%\n",
|
|
"Time elapsed: 3.49 min\n",
|
|
"Epoch: 010/010 | Batch 0000/0196 | Cost: 0.2229\n",
|
|
"Epoch: 010/010 | Batch 0150/0196 | Cost: 0.1945\n",
|
|
"Epoch: 010/010 | Train: 87.384%\n",
|
|
"Time elapsed: 3.88 min\n",
|
|
"Total Training Time: 3.88 min\n",
|
|
"Test accuracy: 70.67%\n",
|
|
"Total Time: 3.91 min\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"def compute_accuracy(model, data_loader, device):\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",
|
|
"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 % 150:\n",
|
|
" print ('Epoch: %03d/%03d | Batch %04d/%04d | Cost: %.4f' \n",
|
|
" %(epoch+1, NUM_EPOCHS, batch_idx, \n",
|
|
" len(train_loader), cost))\n",
|
|
"\n",
|
|
" \n",
|
|
"\n",
|
|
" model.eval()\n",
|
|
" with torch.set_grad_enabled(False): # save memory during inference\n",
|
|
" print('Epoch: %03d/%03d | Train: %.3f%%' % (\n",
|
|
" epoch+1, NUM_EPOCHS, \n",
|
|
" compute_accuracy(model, train_loader, device=DEVICE)))\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))\n",
|
|
"\n",
|
|
"\n",
|
|
"with torch.set_grad_enabled(False): # save memory during inference\n",
|
|
" print('Test accuracy: %.2f%%' % (compute_accuracy(model, test_loader, device=DEVICE)))\n",
|
|
" \n",
|
|
"print('Total Time: %.2f min' % ((time.time() - start_time)/60))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"numpy 1.15.4\n",
|
|
"pandas 0.23.4\n",
|
|
"torch 1.0.0\n",
|
|
"PIL.Image 5.3.0\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": "3.7.1"
|
|
},
|
|
"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": 2
|
|
}
|