wehub-resource-sync
590 lines
26 KiB
Plaintext
590 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": "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 1.0.0\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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"source": [
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"# Model Zoo -- Using PyTorch Dataset Loading Utilities for Custom Datasets (Cropped Street View Hous Numbers, SVHN)"
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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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"This notebook provides an example for how to load an image dataset, stored as individual PNG files, using PyTorch's data loading utilities. For a more in-depth discussion, please see the official\n",
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"\n",
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"- [Data Loading and Processing Tutorial](http://pytorch.org/tutorials/beginner/data_loading_tutorial.html)\n",
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"- [torch.utils.data](http://pytorch.org/docs/master/data.html) API documentation\n",
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"\n",
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"In this example, we are using the cropped version of the **Street View House Numbers (SVHN) Dataset**, which is available at http://ufldl.stanford.edu/housenumbers/. \n",
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"\n",
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"To execute the following examples, you need to download the 2 \".mat\" files \n",
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"\n",
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"- [train_32x32.mat](http://ufldl.stanford.edu/housenumbers/train_32x32.mat) (ca. 182 Mb, 73,257 images)\n",
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"- [test_32x32.mat](http://ufldl.stanford.edu/housenumbers/test_32x32.mat) (ca. 65 Mb, 26,032 images)\n",
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"\n",
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"\n"
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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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"## 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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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import os\n",
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"\n",
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"import torch\n",
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"from torch.utils.data import Dataset\n",
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"from torch.utils.data import DataLoader\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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"import scipy.io as sio\n",
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"import imageio"
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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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"## Dataset"
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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 function will convert the images from \".mat\" into individual \".png\" files. In addition, we will create CSV contained the image paths and 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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def make_pngs(main_dir, mat_file, label):\n",
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" \n",
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" if not os.path.exists(main_dir):\n",
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" os.mkdir(main_dir)\n",
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" \n",
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" sub_dir = os.path.join(main_dir, label)\n",
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" if not os.path.exists(sub_dir):\n",
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" os.mkdir(sub_dir)\n",
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"\n",
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" data = sio.loadmat(mat_file)\n",
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"\n",
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" X = np.transpose(data['X'], (3, 0, 1, 2))\n",
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" y = data['y'].flatten()\n",
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"\n",
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" with open(os.path.join(main_dir, '%s_labels.csv' % label), 'w') as out_f:\n",
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" for i, img in enumerate(X):\n",
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" file_path = os.path.join(sub_dir, str(i) + '.png')\n",
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" imageio.imwrite(os.path.join(file_path),\n",
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" img)\n",
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"\n",
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" out_f.write(\"%d.png,%d\\n\" % (i, y[i]))\n",
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"\n",
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" \n",
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"make_pngs(main_dir='svhn_cropped',\n",
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" mat_file='train_32x32.mat',\n",
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" label='train')\n",
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" \n",
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" \n",
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"make_pngs(main_dir='svhn_cropped',\n",
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" mat_file='test_32x32.mat',\n",
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" label='test')"
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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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"data": {
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" <th>1.png</th>\n",
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" <td>9</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2.png</th>\n",
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" <tr>\n",
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" <th>3.png</th>\n",
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" 1\n",
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"1.png 9\n",
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"3.png 3\n",
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"4.png 2"
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"metadata": {},
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}
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],
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"source": [
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"df = pd.read_csv('svhn_cropped/train_labels.csv', header=None, index_col=0)\n",
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"df.head()"
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]
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},
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{
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},
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df = pd.read_csv('svhn_cropped/test_labels.csv', header=None, index_col=0)\n",
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"df.head()"
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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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"## Implementing a Custom Dataset Class"
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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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"Now, we implement a custom `Dataset` for reading the images. The `__getitem__` method will\n",
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"\n",
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"1. read a single image from disk based on an `index` (more on batching later)\n",
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"2. perform a custom image transformation (if a `transform` argument is provided in the `__init__` construtor)\n",
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"3. return a single image and it's corresponding label"
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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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"outputs": [],
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"source": [
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"class SVHNDataset(Dataset):\n",
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" \"\"\"Custom Dataset for loading cropped SVHN images\"\"\"\n",
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" \n",
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" def __init__(self, csv_path, img_dir, transform=None):\n",
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" \n",
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" df = pd.read_csv(csv_path, index_col=0, header=None)\n",
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" self.img_dir = img_dir\n",
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" self.csv_path = csv_path\n",
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" self.img_names = df.index.values\n",
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" self.y = df[1].values\n",
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" self.transform = transform\n",
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"\n",
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" def __getitem__(self, index):\n",
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" img = Image.open(os.path.join(self.img_dir,\n",
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" self.img_names[index]))\n",
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" \n",
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" if self.transform is not None:\n",
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" img = self.transform(img)\n",
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" \n",
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" label = self.y[index]\n",
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" return img, label\n",
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"\n",
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" def __len__(self):\n",
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" return self.y.shape[0]"
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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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"Now that we have created our custom Dataset class, let us add some custom transformations via the `transforms` utilities from `torchvision`, we\n",
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"\n",
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"1. normalize the images (here: dividing by 255)\n",
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"2. converting the image arrays into PyTorch tensors\n",
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"\n",
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"Then, we initialize a Dataset instance for the training images using the 'quickdraw_png_set1_train.csv' label file (we omit the test set, but the same concepts apply).\n",
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"\n",
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"Finally, we initialize a `DataLoader` that allows us to read from 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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Note that transforms.ToTensor()\n",
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"# already divides pixels by 255. internally\n",
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"\n",
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"custom_transform = transforms.Compose([#transforms.Grayscale(), \n",
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" #transforms.Lambda(lambda x: x/255.),\n",
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" transforms.ToTensor()])\n",
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"\n",
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"train_dataset = SVHNDataset(csv_path='svhn_cropped/train_labels.csv',\n",
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" img_dir='svhn_cropped/train',\n",
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" transform=custom_transform)\n",
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"\n",
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"test_dataset = SVHNDataset(csv_path='svhn_cropped/test_labels.csv',\n",
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" img_dir='svhn_cropped/test',\n",
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" transform=custom_transform)\n",
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"\n",
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"BATCH_SIZE=128\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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" num_workers=4)\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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" num_workers=4)"
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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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"That's it, now we can iterate over an epoch using the train_loader as an iterator and use the features and labels from the training dataset for model training:"
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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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"## Iterating Through the Custom 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": 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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"Epoch: 1 | Batch index: 0 | Batch size: 128\n",
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"Epoch: 2 | Batch index: 0 | Batch size: 128\n"
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]
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}
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],
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"source": [
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"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
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"torch.manual_seed(0)\n",
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"\n",
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"num_epochs = 2\n",
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"for epoch in range(num_epochs):\n",
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"\n",
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" for batch_idx, (x, y) in enumerate(train_loader):\n",
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" \n",
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" print('Epoch:', epoch+1, end='')\n",
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" print(' | Batch index:', batch_idx, end='')\n",
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" print(' | Batch size:', y.size()[0])\n",
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" \n",
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" x = x.to(device)\n",
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" y = y.to(device)\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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"source": [
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"Just to make sure that the batches are being loaded correctly, let's print out the dimensions of the last batch:"
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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": 9,
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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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"torch.Size([128, 3, 32, 32])"
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]
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},
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"execution_count": 9,
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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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"x.shape"
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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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"As we can see, each batch consists of 128 images, just as specified. However, one thing to keep in mind though is that\n",
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"PyTorch uses a different image layout (which is more efficient when working with CUDA); here, the image axes are \"num_images x channels x height x width\" (NCHW) instead of \"num_images height x width x channels\" (NHWC):"
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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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"To visually check that the images that coming of the data loader are intact, let's swap the axes to NHWC and convert an image from a Torch Tensor to a NumPy array so that we can visualize the image via `imshow`:"
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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": 10,
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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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"torch.Size([32, 32, 3])"
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]
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},
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"execution_count": 10,
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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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"one_image = x[99].permute(1, 2, 0)\n",
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"one_image.shape"
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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": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# note that imshow also works fine with scaled\n",
|
|
"# images in [0, 1] range.\n",
|
|
"plt.imshow(one_image.to(torch.device('cpu')));"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"torch 1.0.0\n",
|
|
"pandas 0.23.4\n",
|
|
"imageio 2.4.1\n",
|
|
"numpy 1.15.4\n",
|
|
"torchvision 0.2.1\n",
|
|
"scipy 1.1.0\n",
|
|
"PIL.Image 5.3.0\n",
|
|
"matplotlib 3.0.2\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%watermark -iv"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
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"version": 3
|
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
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"version": "3.7.1"
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},
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"toc": {
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
|
|
"title_cell": "Table of Contents",
|
|
"title_sidebar": "Contents",
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|
"toc_cell": false,
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|
"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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