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@arun477
arun477 committed Sep 13, 2023
1 parent 68c23a6 commit 5fe67f2
Showing 1 changed file with 20 additions and 182 deletions.
202 changes: 20 additions & 182 deletions mnist.ipynb
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"cells": [
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: 'dlopen(/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/torchvision/image.so, 0x0006): Symbol not found: __ZN3c106detail19maybe_wrap_dim_slowIxEET_S2_S2_b\n",
" Referenced from: /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/torchvision/image.so\n",
" Expected in: /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/torch/lib/libc10.dylib'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?\n",
" warn(\n"
]
}
],
"outputs": [],
"source": [
"import torch\n",
"from torch import nn\n",
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},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 144x144 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"def transform_ds(b):\n",
" b[x] = [TF.to_tensor(ele) for ele in b[x]]\n",
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},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(torch.Size([1024, 1, 28, 28]), torch.Size([1024]))"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"bs = 1024\n",
"class DataLoaders:\n",
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},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -119,7 +84,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -143,7 +108,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -160,26 +125,9 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train, epoch:1, loss: 0.4002, accuracy: 0.7806\n",
"eval, epoch:1, loss: 0.2896, accuracy: 0.9007\n",
"train, epoch:2, loss: 0.2815, accuracy: 0.9171\n",
"eval, epoch:2, loss: 0.2144, accuracy: 0.9318\n",
"train, epoch:3, loss: 0.2128, accuracy: 0.9370\n",
"eval, epoch:3, loss: 0.1721, accuracy: 0.9435\n",
"train, epoch:4, loss: 0.1453, accuracy: 0.9489\n",
"eval, epoch:4, loss: 0.1629, accuracy: 0.9590\n",
"train, epoch:5, loss: 0.1110, accuracy: 0.9565\n",
"eval, epoch:5, loss: 0.1162, accuracy: 0.9681\n"
]
}
],
"outputs": [],
"source": [
"model = linear_classifier()\n",
"lr = 0.1\n",
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},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# with open('./linear_classifier.pkl', 'wb') as model_file:\n",
"# pickle.dump(model, model_file)"
"with open('./linear_classifier.pkl', 'wb') as model_file:\n",
" pickle.dump(model, model_file)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 34,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>\n",
" #whiteboard {\n",
" border: 3px solid black;\n",
" border-radius: 6px; \n",
" background-color: #FFFFFF;\n",
" }\n",
" #capture-button {\n",
" background-color: #3F52D9; \n",
" color: white;\n",
" border: none;\n",
" padding: 10px 20px;\n",
" cursor: pointer;\n",
" font-size: 16px;\n",
" border-radius: 3px;\n",
" margin-top: 10px;\n",
" width: 190px;\n",
" margin-right: 20px;\n",
" }\n",
" #clear-button {\n",
" background-color: #FF0000,; \n",
" color: black;\n",
" border: none;\n",
" padding: 10px 20px;\n",
" cursor: pointer;\n",
" font-size: 16px;\n",
" border-radius: 3px;\n",
" margin-top: 10px;\n",
" width: 190px;\n",
" }\n",
" #container {\n",
" display: flex;\n",
" flex-direction: column; /* Arrange children vertically */\n",
" align-items: center; /* Center horizontally */\n",
" justify-content: center;\n",
" }\n",
" #btn-container {\n",
" display: flex;\n",
" flex-direction: row; /* Arrange children vertically */\n",
" align-items: center; /* Center horizontally */\n",
" }\n",
"\n",
"</style>\n",
"<div id='container'>\n",
"<canvas id=\"whiteboard\" width=\"400\" height=\"200\" fill_rect='white'></canvas>\n",
"<div id='btn-container'>\n",
"<button id=\"capture-button\">Predict</button>\n",
"<button id=\"clear-button\">Clear</button>\n",
"</div>\n",
"\n",
"</div>\n",
"<script>\n",
" var canvas = document.getElementById('whiteboard');\n",
" var context = canvas.getContext('2d');\n",
" var drawing = false;\n",
" canvas.addEventListener('mousedown', function (e) {\n",
" drawing = true;\n",
" context.beginPath();\n",
" context.moveTo(e.clientX - canvas.getBoundingClientRect().left, e.clientY - canvas.getBoundingClientRect().top);\n",
" });\n",
" canvas.addEventListener('mousemove', function (e) {\n",
" if (drawing) {\n",
" context.lineTo(e.clientX - canvas.getBoundingClientRect().left, e.clientY - canvas.getBoundingClientRect().top);\n",
" context.stroke();\n",
" }\n",
" });\n",
" canvas.addEventListener('mouseup', function () {\n",
" drawing = false;\n",
" });\n",
" canvas.addEventListener('mouseout', function () {\n",
" drawing = false;\n",
" });\n",
" \n",
" var clearButton = document.getElementById('clear-button');\n",
" clearButton.addEventListener('click', function () {\n",
" context.clearRect(0, 0, canvas.width, canvas.height);\n",
" });\n",
"\n",
" var captureButton = document.getElementById('capture-button');\n",
" captureButton.addEventListener('click', function () {\n",
" // Convert the canvas content to a data URL (image)\n",
" var imageData = canvas.toDataURL(\"image/png\");\n",
"\n",
" // Send the image data to the Jupyter kernel variable\n",
" IPython.notebook.kernel.execute('image_data = \"' + imageData + '\"');\n",
" });\n",
"</script>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"%%html\n",
"<style>\n",
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},
{
"cell_type": "code",
"execution_count": 36,
"execution_count": null,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'image_data' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/arun477/vae/mnist.ipynb Cell 13\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell://github/arun477/vae/mnist.ipynb#Y104sdnNjb2RlLXZmcw%3D%3D?line=5'>6</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtorch\u001b[39;00m\n\u001b[1;32m <a href='vscode-notebook-cell://github/arun477/vae/mnist.ipynb#Y104sdnNjb2RlLXZmcw%3D%3D?line=7'>8</a>\u001b[0m \u001b[39m# Extract the base64 portion of the data URL\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell://github/arun477/vae/mnist.ipynb#Y104sdnNjb2RlLXZmcw%3D%3D?line=8'>9</a>\u001b[0m image_data_base64 \u001b[39m=\u001b[39m re\u001b[39m.\u001b[39msub(\u001b[39m'\u001b[39m\u001b[39m^data:image/.+;base64,\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39m'\u001b[39m, image_data)\n\u001b[1;32m <a href='vscode-notebook-cell://github/arun477/vae/mnist.ipynb#Y104sdnNjb2RlLXZmcw%3D%3D?line=10'>11</a>\u001b[0m \u001b[39m# Decode the base64 string to bytes and create a PIL Image\u001b[39;00m\n\u001b[1;32m <a href='vscode-notebook-cell://github/arun477/vae/mnist.ipynb#Y104sdnNjb2RlLXZmcw%3D%3D?line=11'>12</a>\u001b[0m image_bytes \u001b[39m=\u001b[39m base64\u001b[39m.\u001b[39mb64decode(image_data_base64)\n",
"\u001b[0;31mNameError\u001b[0m: name 'image_data' is not defined"
]
}
],
"outputs": [],
"source": [
"import numpy as np\n",
"from PIL import Image\n",
Expand Down Expand Up @@ -487,7 +325,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.0"
"version": "3.9.7"
},
"orig_nbformat": 4
},
Expand Down

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