109 lines
2.1 KiB
Plaintext
109 lines
2.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2025-06-25T06:29:17.879978Z",
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"start_time": "2025-06-25T06:29:16.365981Z"
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}
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},
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"source": [
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"import torch\n",
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"import torch.nn as nn\n",
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"\n",
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"y = torch.randn(2)\n",
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"print(y)\n",
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"\n",
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"m = nn.Softmax(dim=0)\n",
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"out = m(y)\n",
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"print(out)"
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],
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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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"tensor([-0.9296, -0.1961])\n",
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"tensor([0.3244, 0.6756])\n"
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]
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}
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],
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"execution_count": 1
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-25T06:31:29.348989Z",
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"start_time": "2025-06-25T06:31:29.328174Z"
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}
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},
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"cell_type": "code",
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"source": [
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"x = torch.randint(0, 255, (1, 128 * 128), dtype=torch.float32)\n",
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"fc = nn.Linear(128 * 128, 2)\n",
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"y = fc(x)\n",
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"print(y)"
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],
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"id": "7a0673da7cb52123",
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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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"tensor([[59.5330, 43.6760]], grad_fn=<AddmmBackward0>)\n"
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]
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}
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],
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"execution_count": 2
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-25T06:31:56.045399Z",
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"start_time": "2025-06-25T06:31:56.041215Z"
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}
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},
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"cell_type": "code",
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"source": [
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"output = nn.Softmax(dim=1)(y)\n",
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"print(output)"
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],
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"id": "a7135e5b93ea1669",
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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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"tensor([[1.0000e+00, 1.2984e-07]], grad_fn=<SoftmaxBackward0>)\n"
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]
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}
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],
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"execution_count": 3
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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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",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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