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pytorch-study/15.ipynb

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{
"cells": [
{
"metadata": {},
"cell_type": "code",
"source": "!pip install tensorboard",
"id": "add52c783768e27a",
"outputs": [],
"execution_count": null
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-20T09:23:44.953818Z",
"start_time": "2025-06-20T09:23:44.941696Z"
}
},
"cell_type": "code",
"source": [
"%load_ext tensorboard\n",
"%tensorboard --logdir runs --host 0.0.0.0 --port 6006"
],
"id": "6d148c29e2c0fbbe",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The tensorboard extension is already loaded. To reload it, use:\n",
" %reload_ext tensorboard\n"
]
},
{
"data": {
"text/plain": [
"Reusing TensorBoard on port 6006 (pid 414), started 0:24:42 ago. (Use '!kill 414' to kill it.)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <iframe id=\"tensorboard-frame-42c02cf9641fd4a7\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
" </iframe>\n",
" <script>\n",
" (function() {\n",
" const frame = document.getElementById(\"tensorboard-frame-42c02cf9641fd4a7\");\n",
" const url = new URL(\"/\", window.location);\n",
" const port = 6006;\n",
" if (port) {\n",
" url.port = port;\n",
" }\n",
" frame.src = url;\n",
" })();\n",
" </script>\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 12
},
{
"cell_type": "code",
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2025-06-20T09:23:36.713794Z",
"start_time": "2025-06-20T09:23:32.977971Z"
}
},
"source": [
"from torch.utils.tensorboard import SummaryWriter\n",
"import numpy as np\n",
"\n",
"# 创建一个SummaryWriter对象\n",
"writer = SummaryWriter()\n",
"\n",
"for n_iter in range(100):\n",
" writer.add_scalar('Loss/train', np.random.random(), n_iter)\n",
" writer.add_scalar('Loss/test', np.random.random(), n_iter)\n",
" writer.add_scalar('Accuracy/train', np.random.random(), n_iter)\n",
" writer.add_scalar('Accuracy/test', np.random.random(), n_iter)\n",
"\n",
"img = np.zeros((3, 100, 100))\n",
"img[0] = np.arange(0, 10000).reshape(100, 100) / 10000\n",
"img[1] = np.arange(0, 10000).reshape(100, 100) / 10000\n",
"\n",
"writer.add_image('my_image', img, 0)\n",
"writer.close()"
],
"outputs": [],
"execution_count": 11
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}