wehub-resource-sync
788 lines
26 KiB
Plaintext
788 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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"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": "markdown",
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"metadata": {},
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"source": [
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"# Custom DataLoader Example for PNG files"
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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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"Illustration of how we can efficiently iterate through custom (image) datasets. For this, suppose \n",
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"- mnist_train, mnist_valid, and mnist_test are image folders you created with your own custom images\n",
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"- mnist_train.csv, mnist_valid.csv, and mnist_test.csv are tables that store the image names with their associated class labels"
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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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"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 3.7.1\n",
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"IPython 7.2.0\n",
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"\n",
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"torch 1.0.1\n",
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"pandas 0.24.0\n",
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"numpy 1.15.4\n",
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"matplotlib 3.0.2\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,pandas,numpy,matplotlib"
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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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"# 1) Inspecting the 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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import matplotlib.pyplot as plt\n",
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"from PIL import Image"
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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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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.image.AxesImage at 0x11ea383c8>"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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QhB8IivADQRF+ICjCDwT1f7GaMHSyoBEtAAAAAElFTkSuQmCC\n",
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
|
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"im = Image.open('mnist_train/1.png')\n",
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"plt.imshow(im)"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Array Dimensions (28, 28)\n",
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" 0 0 0 0 0 0 0 0 0 0]]\n"
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]
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|
}
|
|
],
|
|
"source": [
|
|
"import numpy as np\n",
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"\n",
|
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"im_array = np.array(im)\n",
|
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"print('Array Dimensions', im_array.shape)\n",
|
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"print()\n",
|
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"print(im_array)"
|
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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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"<br>\n",
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"<br>\n",
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"<br>"
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]
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"metadata": {},
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"source": [
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"<br>\n",
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"<br>\n",
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"<br>"
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{
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"cell_type": "markdown",
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"<br>\n",
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"<br>\n",
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"<br>"
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]
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},
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{
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|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
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"import pandas as pd"
|
|
]
|
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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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"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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"(256, 2)\n"
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]
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},
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{
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Class Label</th>\n",
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" <th>File Name</th>\n",
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" <th>1</th>\n",
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" <td>8</td>\n",
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" <td>1.png</td>\n",
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>2</th>\n",
|
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" <td>8</td>\n",
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" <td>2.png</td>\n",
|
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
|
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" <td>0</td>\n",
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" <td>3.png</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>4</th>\n",
|
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" <td>9</td>\n",
|
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" <td>4.png</td>\n",
|
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" </tr>\n",
|
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" </tbody>\n",
|
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Class Label File Name\n",
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"0 5 0.png\n",
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"1 8 1.png\n",
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"2 8 2.png\n",
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"3 0 3.png\n",
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"4 9 4.png"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"df_train = pd.read_csv('mnist_train.csv')\n",
|
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"print(df_train.shape)\n",
|
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"df_train.head()"
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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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"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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"(256, 2)\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Class Label</th>\n",
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" <th>File Name</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0</td>\n",
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" <td>256.png</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>8</td>\n",
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" <td>257.png</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>7</td>\n",
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" <td>258.png</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>4</td>\n",
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" <td>259.png</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>7</td>\n",
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" <td>260.png</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Class Label File Name\n",
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"0 0 256.png\n",
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
|
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"source": [
|
|
"df_valid = pd.read_csv('mnist_valid.csv')\n",
|
|
"print(df_valid.shape)\n",
|
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"df_valid.head()"
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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": 8,
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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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"(256, 2)\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
|
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" <th>Class Label</th>\n",
|
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" <th>File Name</th>\n",
|
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" </tr>\n",
|
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" </thead>\n",
|
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" <tbody>\n",
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" <tr>\n",
|
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" <th>0</th>\n",
|
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" <td>4</td>\n",
|
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" <td>512.png</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>1</th>\n",
|
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" <td>0</td>\n",
|
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" <td>513.png</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>2</th>\n",
|
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" <td>6</td>\n",
|
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" <td>514.png</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>3</th>\n",
|
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" <td>8</td>\n",
|
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" <td>515.png</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>4</th>\n",
|
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" <td>4</td>\n",
|
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" <td>516.png</td>\n",
|
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" </tr>\n",
|
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" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
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],
|
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"text/plain": [
|
|
" Class Label File Name\n",
|
|
"0 4 512.png\n",
|
|
"1 0 513.png\n",
|
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"2 6 514.png\n",
|
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"3 8 515.png\n",
|
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"4 4 516.png"
|
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]
|
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},
|
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"execution_count": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df_test = pd.read_csv('mnist_test.csv')\n",
|
|
"print(df_test.shape)\n",
|
|
"df_test.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<br>\n",
|
|
"<br>\n",
|
|
"<br>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<br>\n",
|
|
"<br>\n",
|
|
"<br>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 2) Custom Dataset Class"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import torch\n",
|
|
"from PIL import Image\n",
|
|
"from torch.utils.data import Dataset\n",
|
|
"import os\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"class MyDataset(Dataset):\n",
|
|
"\n",
|
|
" def __init__(self, csv_path, img_dir, transform=None):\n",
|
|
" \n",
|
|
" df = pd.read_csv(csv_path)\n",
|
|
" self.img_dir = img_dir\n",
|
|
" self.img_names = df['File Name']\n",
|
|
" self.y = df['Class Label']\n",
|
|
" self.transform = transform\n",
|
|
"\n",
|
|
" def __getitem__(self, index):\n",
|
|
" img = Image.open(os.path.join(self.img_dir,\n",
|
|
" self.img_names[index]))\n",
|
|
" \n",
|
|
" if self.transform is not None:\n",
|
|
" img = self.transform(img)\n",
|
|
" \n",
|
|
" label = self.y[index]\n",
|
|
" return img, label\n",
|
|
"\n",
|
|
" def __len__(self):\n",
|
|
" return self.y.shape[0]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<br>\n",
|
|
"<br>\n",
|
|
"<br>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<br>\n",
|
|
"<br>\n",
|
|
"<br>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 3) Custom Dataloader"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from torchvision import transforms\n",
|
|
"from torch.utils.data import DataLoader\n",
|
|
"\n",
|
|
"\n",
|
|
"# Note that transforms.ToTensor()\n",
|
|
"# already divides pixels by 255. internally\n",
|
|
"\n",
|
|
"custom_transform = transforms.Compose([#transforms.Lambda(lambda x: x/255.), # not necessary\n",
|
|
" transforms.ToTensor()\n",
|
|
" ])\n",
|
|
"\n",
|
|
"train_dataset = MyDataset(csv_path='mnist_train.csv',\n",
|
|
" img_dir='mnist_train',\n",
|
|
" transform=custom_transform)\n",
|
|
"\n",
|
|
"train_loader = DataLoader(dataset=train_dataset,\n",
|
|
" batch_size=32,\n",
|
|
" shuffle=True, # want to shuffle the dataset\n",
|
|
" num_workers=4) # number processes/CPUs to use"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<br>\n",
|
|
"<br>\n",
|
|
"<br>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 4) Iterating Through the Dataset"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch: 1 | Batch index: 0 | Batch size: 32\n",
|
|
"Epoch: 1 | Batch index: 1 | Batch size: 32\n",
|
|
"Epoch: 1 | Batch index: 2 | Batch size: 32\n",
|
|
"Epoch: 1 | Batch index: 3 | Batch size: 32\n",
|
|
"Epoch: 1 | Batch index: 4 | Batch size: 32\n",
|
|
"Epoch: 1 | Batch index: 5 | Batch size: 32\n",
|
|
"Epoch: 1 | Batch index: 6 | Batch size: 32\n",
|
|
"Epoch: 1 | Batch index: 7 | Batch size: 32\n",
|
|
"Epoch: 2 | Batch index: 0 | Batch size: 32\n",
|
|
"Epoch: 2 | Batch index: 1 | Batch size: 32\n",
|
|
"Epoch: 2 | Batch index: 2 | Batch size: 32\n",
|
|
"Epoch: 2 | Batch index: 3 | Batch size: 32\n",
|
|
"Epoch: 2 | Batch index: 4 | Batch size: 32\n",
|
|
"Epoch: 2 | Batch index: 5 | Batch size: 32\n",
|
|
"Epoch: 2 | Batch index: 6 | Batch size: 32\n",
|
|
"Epoch: 2 | Batch index: 7 | Batch size: 32\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
|
|
"torch.manual_seed(0)\n",
|
|
"\n",
|
|
"num_epochs = 2\n",
|
|
"for epoch in range(num_epochs):\n",
|
|
"\n",
|
|
" for batch_idx, (x, y) in enumerate(train_loader):\n",
|
|
" \n",
|
|
" print('Epoch:', epoch+1, end='')\n",
|
|
" print(' | Batch index:', batch_idx, end='')\n",
|
|
" print(' | Batch size:', y.size()[0])\n",
|
|
" \n",
|
|
" x = x.to(device)\n",
|
|
" y = y.to(device)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"torch.Size([32, 1, 28, 28])\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(x.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"torch.Size([32, 784])\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"x_image_as_vector = x.view(-1, 28*28)\n",
|
|
"print(x_image_as_vector.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"tensor([[[[0., 0., 0., ..., 0., 0., 0.],\n",
|
|
" [0., 0., 0., ..., 0., 0., 0.],\n",
|
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" [0., 0., 0., ..., 0., 0., 0.],\n",
|
|
" ...,\n",
|
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" [0., 0., 0., ..., 0., 0., 0.],\n",
|
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" [0., 0., 0., ..., 0., 0., 0.],\n",
|
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" [0., 0., 0., ..., 0., 0., 0.]]],\n",
|
|
"\n",
|
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"\n",
|
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" [[[0., 0., 0., ..., 0., 0., 0.],\n",
|
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" [0., 0., 0., ..., 0., 0., 0.],\n",
|
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" [0., 0., 0., ..., 0., 0., 0.],\n",
|
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" ...,\n",
|
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" [0., 0., 0., ..., 0., 0., 0.],\n",
|
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" [0., 0., 0., ..., 0., 0., 0.],\n",
|
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" [0., 0., 0., ..., 0., 0., 0.]]],\n",
|
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"\n",
|
|
"\n",
|
|
" [[[0., 0., 0., ..., 0., 0., 0.],\n",
|
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" [0., 0., 0., ..., 0., 0., 0.],\n",
|
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" [0., 0., 0., ..., 0., 0., 0.],\n",
|
|
" ...,\n",
|
|
" [0., 0., 0., ..., 0., 0., 0.],\n",
|
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" [0., 0., 0., ..., 0., 0., 0.],\n",
|
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" [0., 0., 0., ..., 0., 0., 0.]]],\n",
|
|
"\n",
|
|
"\n",
|
|
" ...,\n",
|
|
"\n",
|
|
"\n",
|
|
" [[[0., 0., 0., ..., 0., 0., 0.],\n",
|
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" [0., 0., 0., ..., 0., 0., 0.],\n",
|
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" [0., 0., 0., ..., 0., 0., 0.],\n",
|
|
" ...,\n",
|
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" [0., 0., 0., ..., 0., 0., 0.],\n",
|
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" [0., 0., 0., ..., 0., 0., 0.],\n",
|
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" [0., 0., 0., ..., 0., 0., 0.]]],\n",
|
|
"\n",
|
|
"\n",
|
|
" [[[0., 0., 0., ..., 0., 0., 0.],\n",
|
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" [0., 0., 0., ..., 0., 0., 0.],\n",
|
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" [0., 0., 0., ..., 0., 0., 0.],\n",
|
|
" ...,\n",
|
|
" [0., 0., 0., ..., 0., 0., 0.],\n",
|
|
" [0., 0., 0., ..., 0., 0., 0.],\n",
|
|
" [0., 0., 0., ..., 0., 0., 0.]]],\n",
|
|
"\n",
|
|
"\n",
|
|
" [[[0., 0., 0., ..., 0., 0., 0.],\n",
|
|
" [0., 0., 0., ..., 0., 0., 0.],\n",
|
|
" [0., 0., 0., ..., 0., 0., 0.],\n",
|
|
" ...,\n",
|
|
" [0., 0., 0., ..., 0., 0., 0.],\n",
|
|
" [0., 0., 0., ..., 0., 0., 0.],\n",
|
|
" [0., 0., 0., ..., 0., 0., 0.]]]])"
|
|
]
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"x"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"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"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|