84 lines
1.8 KiB
Plaintext
84 lines
1.8 KiB
Plaintext
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{
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"cells": [
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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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"source": [
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"import os\n",
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"#!pip install torch torchvision torchaudio\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.optim as optim\n",
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"from torchvision import datasets, transforms, models\n",
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"from torch.utils.data import DataLoader\n",
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"from sklearn.metrics import accuracy_score\n",
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"import matplotlib.pyplot as plt\n"
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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": 12,
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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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"CUDA não disponível. Treinando na CPU.\n"
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]
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}
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],
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"source": [
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"import torch\n",
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"if torch.cuda.is_available():\n",
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" print(\"CUDA disponível. Treinando na GPU.\")\n",
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" device = torch.device(\"cuda\")\n",
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"else:\n",
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" print(\"CUDA não disponível. Treinando na CPU.\")\n",
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" device = torch.device(\"cpu\")"
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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": 5,
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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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"Using device: cpu\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\" if torch.cuda.is_available() else \"cpu\")\n",
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"print(f\"Using device: {device}\")\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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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": 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",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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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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