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Add a Kaggle tutorial
2021-08-24 02:59:53 +00:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### This is the tutorial of deep learning on FashionMNIST dataset using Pytorch. We will build a Convolutional Neural Network for predicting the classes of Dataset. I am assuming you know the basics of deep leanrning like layer architecture... convolution concepts. Without further ado... Lets start the tutorial."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "1rm65OztSCpF"
},
"source": [
"# **Importing Important Libraries**"
]
},
{
"cell_type": "code",
Add latest code
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"execution_count": 1,
Add a Kaggle tutorial
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"metadata": {
"colab": {},
"colab_type": "code",
"id": "-1_8VZgpEtea"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"from torch.autograd import Variable\n",
"\n",
"import torchvision\n",
"import torchvision.transforms as transforms\n",
"from torch.utils.data import Dataset, DataLoader\n",
"from sklearn.metrics import confusion_matrix"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "s6bjL6wESfoV"
},
"source": [
"### If the GPU is available use it for the computation otherwise use the CPU."
]
},
{
"cell_type": "code",
Add latest code
2021-08-25 01:27:14 +00:00
"execution_count": 2,
Add a Kaggle tutorial
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"metadata": {
"colab": {},
"colab_type": "code",
"id": "q6btjJ9YTXXw"
},
"outputs": [],
"source": [
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "CGIhqokgSv1W"
},
"source": [
"There are 2 ways to load the Fashion MNIST dataset. \n",
"\n",
"\n",
" 1. Load csv and then inherite Pytorch Dataset class .\n",
" 2. Use Pytorch module torchvision.datasets. It has many popular datasets like MNIST, FashionMNIST, CIFAR10 e.t.c.\n",
" \n",
" \n",
"\n",
"* We use DataLoader class from torch.utils.data to load data in batches in both method.\n",
"* Comment out the code of a method which you are not using. \n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "4jJh32jCT_J0"
},
"source": [
"### 1. Using a Dataset class.\n",
" \n",
" * First load the data from the disk using pandas read_csv() method.\n",
"\n",
" * Now inherit Dataset class in your own class that you are building, lets say FashionData.\n",
"\n",
" * It has 2 methods: __get_item__( ) and __len__().\n",
" * __get_item__( ) return the images and labels and __len__( ) returns the number of items in a dataset."
]
},
{
"cell_type": "code",
Continue working on model.py
2021-08-25 00:49:49 +00:00
"execution_count": 7,
Add a Kaggle tutorial
2021-08-24 02:59:53 +00:00
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 413
},
"colab_type": "code",
"id": "PV2g1_8qUDvA",
"outputId": "b429a123-7574-413d-d15a-92f0481e6753"
},
Continue working on model.py
2021-08-25 00:49:49 +00:00
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: '../input/fashion-mnist_train.csv'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_45959/798855398.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_csv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"../input/fashion-mnist_train.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mtest_csv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"../input/fashion-mnist_test.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/PyTorch/.venv/lib/python3.9/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 310\u001b[0m )\n\u001b[0;32m--> 311\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/PyTorch/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/PyTorch/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 481\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/PyTorch/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 809\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 811\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 813\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/PyTorch/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1038\u001b[0m )\n\u001b[1;32m 1039\u001b[0m \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1042\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/PyTorch/.venv/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 52\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/PyTorch/.venv/lib/python3.9/site-packages/pandas/io/parsers/base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 221\u001b[0m \"\"\"\n\u001b[0;32m--> 222\u001b[0;31m self.handles = get_handle(\n\u001b[0m\u001b[1;32m 223\u001b[0m \u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/PyTorch/.venv/lib/python3.9/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 699\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 700\u001b[0m \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 701\u001b[0;31m handle = open(\n\u001b[0m\u001b[1;32m 702\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 703\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../input/fashion-mnist_train.csv'"
]
}
],
Add a Kaggle tutorial
2021-08-24 02:59:53 +00:00
"source": [
"train_csv = pd.read_csv(\"../input/fashion-mnist_train.csv\")\n",
"test_csv = pd.read_csv(\"../input/fashion-mnist_test.csv\")"
]
},
{
"cell_type": "code",
Continue working on model.py
2021-08-25 00:49:49 +00:00
"execution_count": 3,
Add a Kaggle tutorial
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"metadata": {
"colab": {},
"colab_type": "code",
"id": "3Q36xjPfeo0a"
},
"outputs": [],
"source": [
"class FashionDataset(Dataset):\n",
" \"\"\"User defined class to build a datset using Pytorch class Dataset.\"\"\"\n",
" \n",
" def __init__(self, data, transform = None):\n",
" \"\"\"Method to initilaize variables.\"\"\" \n",
" self.fashion_MNIST = list(data.values)\n",
" self.transform = transform\n",
" \n",
" label = []\n",
" image = []\n",
" \n",
" for i in self.fashion_MNIST:\n",
" # first column is of labels.\n",
" label.append(i[0])\n",
" image.append(i[1:])\n",
" self.labels = np.asarray(label)\n",
" # Dimension of Images = 28 * 28 * 1. where height = width = 28 and color_channels = 1.\n",
" self.images = np.asarray(image).reshape(-1, 28, 28, 1).astype('float32')\n",
"\n",
" def __getitem__(self, index):\n",
" label = self.labels[index]\n",
" image = self.images[index]\n",
" \n",
" if self.transform is not None:\n",
" image = self.transform(image)\n",
"\n",
" return image, label\n",
"\n",
" def __len__(self):\n",
" return len(self.images)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "uLeNwQWJkFkQ"
},
"outputs": [],
"source": [
"# Transform data into Tensor that has a range from 0 to 1\n",
"train_set = FashionDataset(train_csv, transform=transforms.Compose([transforms.ToTensor()]))\n",
"test_set = FashionDataset(test_csv, transform=transforms.Compose([transforms.ToTensor()]))\n",
"\n",
"train_loader = DataLoader(train_set, batch_size=100)\n",
"test_loader = DataLoader(train_set, batch_size=100)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "VePPK186m6iS"
},
"source": [
"### 2. Using FashionMNIST class from torchvision module.\n",
"\n",
"\n",
"* It will download the dataset first time.\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
Add latest code
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"execution_count": 4,
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"metadata": {
"colab": {},
"colab_type": "code",
"id": "zM618_wYGM0n"
},
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"outputs": [],
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"source": [
"\n",
"train_set = torchvision.datasets.FashionMNIST(\"./data\", download=True, transform=\n",
" transforms.Compose([transforms.ToTensor()]))\n",
"test_set = torchvision.datasets.FashionMNIST(\"./data\", download=True, train=False, transform=\n",
" transforms.Compose([transforms.ToTensor()])) \n",
" "
]
},
{
"cell_type": "code",
Add latest code
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"execution_count": 5,
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"metadata": {
"colab": {},
"colab_type": "code",
"id": "8s2uhlGJZOOP"
},
"outputs": [],
"source": [
"\n",
"train_loader = torch.utils.data.DataLoader(train_set, \n",
" batch_size=100)\n",
"test_loader = torch.utils.data.DataLoader(test_set,\n",
" batch_size=100)\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "4SfsxOX4peHU"
},
"source": [
"### We have 10 types of clothes in FashionMNIST dataset.\n",
"\n",
"\n",
"> Making a method that return the name of class for the label number.\n",
"ex. if the label is 5, we return Sandal.\n",
"\n"
]
},
{
"cell_type": "code",
Add latest code
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"execution_count": 6,
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"metadata": {
"colab": {},
"colab_type": "code",
"id": "uWIE3hVqOlMi"
},
"outputs": [],
"source": [
"def output_label(label):\n",
" output_mapping = {\n",
" 0: \"T-shirt/Top\",\n",
" 1: \"Trouser\",\n",
" 2: \"Pullover\",\n",
" 3: \"Dress\",\n",
" 4: \"Coat\", \n",
" 5: \"Sandal\", \n",
" 6: \"Shirt\",\n",
" 7: \"Sneaker\",\n",
" 8: \"Bag\",\n",
" 9: \"Ankle Boot\"\n",
" }\n",
" input = (label.item() if type(label) == torch.Tensor else label)\n",
" return output_mapping[input]"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "yDH7i5UFo7w3"
},
"source": [
"### Playing with data and displaying some images using matplotlib imshow() method.\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
Continue working on model.py
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"execution_count": 11,
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"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"colab_type": "code",
"id": "RB9jenaDYZmt",
"outputId": "3d8212d3-3cbf-4e30-b369-e6ede4ad6350"
},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([100, 1, 28, 28])"
]
},
Continue working on model.py
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"execution_count": 11,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = next(iter(train_loader))\n",
"a[0].size()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"colab_type": "code",
"id": "D-PnIoRpjuZW",
"outputId": "cff25b7c-a4c6-42ea-81d1-43a4789959bc"
},
"outputs": [
{
"data": {
"text/plain": [
"60000"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(train_set)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 287
},
"colab_type": "code",
"id": "2kC6CrJrlbf_",
"outputId": "2eefede6-0e54-4512-b6a8-1c17eb42286a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"image, label = next(iter(train_set))\n",
"plt.imshow(image.squeeze(), cmap=\"gray\")\n",
"print(label)"
]
},
{
"cell_type": "code",
Add latest code
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"execution_count": 7,
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"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"colab_type": "code",
"id": "sgpYsgh0PY09",
"outputId": "4d00bf1d-fb85-4e2a-bb21-067e75360637"
},
"outputs": [
{
Add latest code
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"name": "stdout",
Add latest versions of all files
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"output_type": "stream",
Add a Kaggle tutorial
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"text": [
Add latest versions of all files
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"<class 'torch.Tensor'> <class 'torch.Tensor'>\n",
"torch.Size([10, 1, 28, 28]) torch.Size([10])\n"
Add a Kaggle tutorial
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]
}
],
"source": [
"demo_loader = torch.utils.data.DataLoader(train_set, batch_size=10)\n",
"\n",
"batch = next(iter(demo_loader))\n",
"images, labels = batch\n",
"print(type(images), type(labels))\n",
"print(images.shape, labels.shape)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 158
},
"colab_type": "code",
"id": "2Z0D4BgQRW8e",
"outputId": "c33042d3-017f-4dcb-8bf9-244910e2b49e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"labels: Ankle Boot, T-shirt/Top, T-shirt/Top, Dress, T-shirt/Top, Pullover, Sneaker, Pullover, Sandal, Sandal, "
]
},
{
"data": {
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"text/plain": [
"<Figure size 1080x1440 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"grid = torchvision.utils.make_grid(images, nrow=10)\n",
"\n",
"plt.figure(figsize=(15, 20))\n",
"plt.imshow(np.transpose(grid, (1, 2, 0)))\n",
"print(\"labels: \", end=\" \")\n",
"for i, label in enumerate(labels):\n",
" print(output_label(label), end=\", \")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "YM2u0Riup9mx"
},
"source": [
"## Building a CNN \n",
"\n",
"\n",
"* Make a model class (FashionCNN in our case)\n",
" * It inherit nn.Module class that is a super class for all the neural networks in Pytorch.\n",
"* Our Neural Net has following layers:\n",
" * Two Sequential layers each consists of following layers-\n",
" * Convolution layer that has kernel size of 3 * 3, padding = 1 (zero_padding) in 1st layer and padding = 0 in second one. Stride of 1 in both layer.\n",
" * Batch Normalization layer.\n",
" * Acitvation function: ReLU.\n",
" * Max Pooling layer with kernel size of 2 * 2 and stride 2.\n",
" * Flatten out the output for dense layer(a.k.a. fully connected layer).\n",
" * 3 Fully connected layer with different in/out features.\n",
" * 1 Dropout layer that has class probability p = 0.25.\n",
" \n",
" * All the functionaltiy is given in forward method that defines the forward pass of CNN.\n",
" * Our input image is changing in a following way:\n",
" * First Convulation layer : input: 28 \\* 28 \\* 3, output: 28 \\* 28 \\* 32\n",
" * First Max Pooling layer : input: 28 \\* 28 \\* 32, output: 14 \\* 14 \\* 32\n",
" * Second Conv layer : input : 14 \\* 14 \\* 32, output: 12 \\* 12 \\* 64\n",
" * Second Max Pooling layer : 12 \\* 12 \\* 64, output: 6 \\* 6 \\* 64\n",
" * Final fully connected layer has 10 output features for 10 types of clothes.\n",
"\n",
"> Lets implementing the network...\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
Add latest code
2021-08-25 01:27:14 +00:00
"execution_count": 8,
Add a Kaggle tutorial
2021-08-24 02:59:53 +00:00
"metadata": {
"colab": {},
"colab_type": "code",
"id": "hyCH0Q4hSgFB"
},
"outputs": [],
"source": [
"class FashionCNN(nn.Module):\n",
" \n",
" def __init__(self):\n",
" super(FashionCNN, self).__init__()\n",
" \n",
" self.layer1 = nn.Sequential(\n",
" nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, padding=1),\n",
" nn.BatchNorm2d(32),\n",
" nn.ReLU(),\n",
" nn.MaxPool2d(kernel_size=2, stride=2)\n",
" )\n",
" \n",
" self.layer2 = nn.Sequential(\n",
" nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3),\n",
" nn.BatchNorm2d(64),\n",
" nn.ReLU(),\n",
" nn.MaxPool2d(2)\n",
" )\n",
" \n",
" self.fc1 = nn.Linear(in_features=64*6*6, out_features=600)\n",
" self.drop = nn.Dropout2d(0.25)\n",
" self.fc2 = nn.Linear(in_features=600, out_features=120)\n",
" self.fc3 = nn.Linear(in_features=120, out_features=10)\n",
" \n",
" def forward(self, x):\n",
" out = self.layer1(x)\n",
" out = self.layer2(out)\n",
" out = out.view(out.size(0), -1)\n",
" out = self.fc1(out)\n",
" out = self.drop(out)\n",
" out = self.fc2(out)\n",
" out = self.fc3(out)\n",
" \n",
" return out\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "0mMVaW_PvCC2"
},
"source": [
"### Making a model of our CNN class\n",
"\n",
"* Creating a object(model in the code)\n",
"* Transfering it into GPU if available.\n",
"* Defining a Loss function. we're using CrossEntropyLoss() here.\n",
"* Using Adam algorithm for optimization purpose.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 345
},
"colab_type": "code",
"id": "NILDHzNgQ1Gt",
"outputId": "d16327ae-e7d2-4c46-bffe-e9724272c51d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"FashionCNN(\n",
" (layer1): Sequential(\n",
" (0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" )\n",
" (layer2): Sequential(\n",
" (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
" (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" )\n",
" (fc1): Linear(in_features=2304, out_features=600, bias=True)\n",
" (drop): Dropout2d(p=0.25, inplace=False)\n",
" (fc2): Linear(in_features=600, out_features=120, bias=True)\n",
" (fc3): Linear(in_features=120, out_features=10, bias=True)\n",
")\n"
]
}
],
"source": [
"model = FashionCNN()\n",
"model.to(device)\n",
"\n",
"error = nn.CrossEntropyLoss()\n",
"\n",
"learning_rate = 0.001\n",
"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
"print(model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "im58ZlvMvkty"
},
"source": [
"## Training a network and Testing it on test dataset"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 126
},
"colab_type": "code",
"id": "SYh_6HtpUlNl",
"outputId": "b92aa41b-4cbf-4ceb-c63c-7d2a3999df87"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Iteration: 500, Loss: 0.19216448068618774, Accuracy: 90.94000244140625%\n",
"Iteration: 1000, Loss: 0.2398412674665451, Accuracy: 90.38999938964844%\n",
"Iteration: 1500, Loss: 0.14675875008106232, Accuracy: 89.44999694824219%\n",
"Iteration: 2000, Loss: 0.2056727409362793, Accuracy: 90.43000030517578%\n",
"Iteration: 2500, Loss: 0.08225765824317932, Accuracy: 90.12999725341797%\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_190212/1265142370.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;31m# Forward pass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 22\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/PyTorch/.venv/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1049\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m 1050\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1051\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1052\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1053\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/tmp/ipykernel_190212/2706098268.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 31\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/PyTorch/.venv/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1049\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m 1050\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1051\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1052\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1053\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/PyTorch/.venv/lib/python3.9/site-packages/torch/nn/modules/dropout.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/PyTorch/.venv/lib/python3.9/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mdropout2d\u001b[0;34m(input, p, training, inplace)\u001b[0m\n\u001b[1;32m 1200\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mp\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1201\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"dropout probability has to be between 0 and 1, \"\u001b[0m \u001b[0;34m\"but got {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1202\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_VF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_dropout_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0m_VF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_dropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"num_epochs = 5\n",
"count = 0\n",
"# Lists for visualization of loss and accuracy \n",
"loss_list = []\n",
"iteration_list = []\n",
"accuracy_list = []\n",
"\n",
"# Lists for knowing classwise accuracy\n",
"predictions_list = []\n",
"labels_list = []\n",
"\n",
"for epoch in range(num_epochs):\n",
" for images, labels in train_loader:\n",
" # Transfering images and labels to GPU if available\n",
" images, labels = images.to(device), labels.to(device)\n",
" \n",
" train = Variable(images.view(100, 1, 28, 28))\n",
" labels = Variable(labels)\n",
" \n",
" # Forward pass \n",
" outputs = model(train)\n",
" loss = error(outputs, labels)\n",
" \n",
" # Initializing a gradient as 0 so there is no mixing of gradient among the batches\n",
" optimizer.zero_grad()\n",
" \n",
" #Propagating the error backward\n",
" loss.backward()\n",
" \n",
" # Optimizing the parameters\n",
" optimizer.step()\n",
" \n",
" count += 1\n",
" \n",
" # Testing the model\n",
" \n",
" if not (count % 50): # It's same as \"if count % 50 == 0\"\n",
" total = 0\n",
" correct = 0\n",
" \n",
" for images, labels in test_loader:\n",
" images, labels = images.to(device), labels.to(device)\n",
" labels_list.append(labels)\n",
" \n",
" test = Variable(images.view(100, 1, 28, 28))\n",
" \n",
" outputs = model(test)\n",
" \n",
" predictions = torch.max(outputs, 1)[1].to(device)\n",
" predictions_list.append(predictions)\n",
" correct += (predictions == labels).sum()\n",
" \n",
" total += len(labels)\n",
" \n",
" accuracy = correct * 100 / total\n",
" loss_list.append(loss.data)\n",
" iteration_list.append(count)\n",
" accuracy_list.append(accuracy)\n",
" \n",
" if not (count % 500):\n",
" print(\"Iteration: {}, Loss: {}, Accuracy: {}%\".format(count, loss.data, accuracy))\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "1xm9MZ6O0irC"
},
"source": [
"### Visualizing the Loss and Accuracy with Iterations\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"colab_type": "code",
"id": "4s4cGX4Hanyz",
"outputId": "4f19caa4-327f-4cca-bd9f-726774b9ddfd"
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(iteration_list, loss_list)\n",
"plt.xlabel(\"No. of Iteration\")\n",
"plt.ylabel(\"Loss\")\n",
"plt.title(\"Iterations vs Loss\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"colab_type": "code",
"id": "TtsjkmY8qo_t",
"outputId": "18a10340-f465-4c80-db26-cdce0201e347"
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(iteration_list, accuracy_list)\n",
"plt.xlabel(\"No. of Iteration\")\n",
"plt.ylabel(\"Accuracy\")\n",
"plt.title(\"Iterations vs Accuracy\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "mZTkzUKn0vq3"
},
"source": [
"### Looking the Accuracy in each class of FashionMNIST dataset"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 199
},
"colab_type": "code",
"id": "Mq9_qes8Qg6h",
"outputId": "fd7e3194-d5ab-4c3d-8693-ced0369df983"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy of T-shirt/Top: 83.60%\n",
"Accuracy of Trouser: 98.70%\n",
"Accuracy of Pullover: 87.40%\n",
"Accuracy of Dress: 92.00%\n",
"Accuracy of Coat: 78.70%\n",
"Accuracy of Sandal: 97.20%\n",
"Accuracy of Shirt: 79.30%\n",
"Accuracy of Sneaker: 98.40%\n",
"Accuracy of Bag: 97.60%\n",
"Accuracy of Ankle Boot: 94.50%\n"
]
}
],
"source": [
"class_correct = [0. for _ in range(10)]\n",
"total_correct = [0. for _ in range(10)]\n",
"\n",
"with torch.no_grad():\n",
" for images, labels in test_loader:\n",
" images, labels = images.to(device), labels.to(device)\n",
" test = Variable(images)\n",
" outputs = model(test)\n",
" predicted = torch.max(outputs, 1)[1]\n",
" c = (predicted == labels).squeeze()\n",
" \n",
" for i in range(100):\n",
" label = labels[i]\n",
" class_correct[label] += c[i].item()\n",
" total_correct[label] += 1\n",
" \n",
"for i in range(10):\n",
" print(\"Accuracy of {}: {:.2f}%\".format(output_label(i), class_correct[i] * 100 / total_correct[i]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ZxZhix0v0_KZ"
},
"source": [
"### Printing the Confusion Matrix "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "GXme4c22cii2"
},
"outputs": [],
"source": [
"from itertools import chain \n",
"\n",
"predictions_l = [predictions_list[i].tolist() for i in range(len(predictions_list))]\n",
"labels_l = [labels_list[i].tolist() for i in range(len(labels_list))]\n",
"predictions_l = list(chain.from_iterable(predictions_l))\n",
"labels_l = list(chain.from_iterable(labels_l))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 363
},
"colab_type": "code",
"id": "ft-Qlbb5bl0A",
"outputId": "5665491d-d6ee-4891-95ff-2749de05a110"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Classification report for CNN :\n",
" precision recall f1-score support\n",
"\n",
" 0 0.86 0.83 0.84 55000\n",
" 1 0.99 0.98 0.98 55000\n",
" 2 0.86 0.86 0.86 55000\n",
" 3 0.91 0.89 0.90 55000\n",
" 4 0.82 0.89 0.85 55000\n",
" 5 0.98 0.97 0.97 55000\n",
" 6 0.74 0.72 0.73 55000\n",
" 7 0.94 0.96 0.95 55000\n",
" 8 0.97 0.98 0.97 55000\n",
" 9 0.96 0.96 0.96 55000\n",
"\n",
" accuracy 0.90 550000\n",
" macro avg 0.90 0.90 0.90 550000\n",
"weighted avg 0.90 0.90 0.90 550000\n",
"\n",
"\n"
]
}
],
"source": [
"import sklearn.metrics as metrics\n",
"\n",
"confusion_matrix(labels_l, predictions_l)\n",
"print(\"Classification report for CNN :\\n%s\\n\"\n",
" % (metrics.classification_report(labels_l, predictions_l)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "4Y9pGNv64UYl"
},
"source": [
"### This is my implementation of deep learning in FashionMNIST dataset using Pytorch. I've achieved 93% test accuracy. Change those layer architecture or parameters to make it better. \n",
"***I hope you like it. Give your feedback. It helps me to a lot. Thank you. :)***"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "fashion_MNIST.ipynb",
"provenance": [],
"version": "0.3.2"
},
"kernelspec": {
"display_name": "PyTorch",
"language": "python",
"name": "pytorch"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
Add latest versions of all files
2021-08-27 02:51:03 +00:00
"version": "3.9.6"
Add a Kaggle tutorial
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}
},
"nbformat": 4,
"nbformat_minor": 4
Add latest versions of all files
2021-08-27 02:51:03 +00:00
}
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