{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from src.data.data_loader import get_train_dataset\n", "from torchvision import datasets, transforms\n", "import torch.nn as nn\n", "import pprint\n", "import torch\n", "import torch.optim as optim\n", "import torch.nn.functional as F\n", "from src.models.model import ShapeClassifier\n", "\n", "from src.configs.model_config import ModelConfig\n", "from src.data.data_loader import train_loader, num_classes\n", "from src.utils.logs import writer\n", "from src.utils.train import train\n", "from src.utils.test import test\n", "import wandb\n", "import json" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "wandb.login()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sweep_config = {\n", " 'method': 'bayes',\n", " \"metric\": {\n", " \"name\": 'accuracy',\n", " \"goal\": 'maximize'\n", " },\n", " \"parameters\": {\n", " 'learning_rate': {\n", " # a flat distribution between 0 and 0.1\n", " 'distribution': 'uniform',\n", " 'min': 0,\n", " 'max': 0.001\n", " },\n", " 'batch_size': {\n", " # integers between 32 and 256\n", " # with evenly-distributed logarithms\n", " 'distribution': 'q_log_uniform_values',\n", " 'q': 8,\n", " 'min': 32,\n", " 'max': 256,\n", " },\n", " 'epochs': {\n", " 'values': [15, 20, 30]\n", " },\n", " 'optimizer': {\n", " 'values': ['adam', 'sgd']\n", " },\n", " 'fc_layer_size': {\n", " 'values': [128, 256, 512]\n", " },\n", " }\n", "}\n", "\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def build_dataset(batch_size):\n", "\n", " return get_train_dataset(batch_size)\n", "\n", "\n", "def build_network(fc_layer_size):\n", " network = ShapeClassifier(num_classes=3,hidden_size=fc_layer_size)\n", "\n", " return network.to(device)\n", "\n", "\n", "def build_optimizer(network, optimizer, learning_rate):\n", " if optimizer == \"sgd\":\n", " optimizer = optim.SGD(network.parameters(),\n", " lr=learning_rate, momentum=0.9)\n", " elif optimizer == \"adam\":\n", " optimizer = optim.Adam(network.parameters(),\n", " lr=learning_rate)\n", " return optimizer\n", "\n", "\n", "def train_epoch(network, loader, optimizer):\n", " cumu_loss = 0\n", " for _, (data, target) in enumerate(loader):\n", " data, target = data.to(device), target.to(device)\n", " optimizer.zero_grad()\n", "\n", " # ➡ Forward pass\n", " loss = F.cross_entropy(network(data), target)\n", " cumu_loss += loss.item()\n", "\n", " # ⬅ Backward pass + weight update\n", " loss.backward()\n", " optimizer.step()\n", "\n", " wandb.log({\"batch loss\": loss.item()})\n", "\n", " return cumu_loss / len(loader)\n", "\n", "def validate(network, loader):\n", " network.eval()\n", " correct = 0\n", " with torch.no_grad():\n", " for _, (data, target) in enumerate(loader):\n", " data, target = data.to(device), target.to(device)\n", " output = network(data)\n", " pred = output.argmax(dim=1, keepdim=True)\n", " correct += pred.eq(target.view_as(pred)).sum().item()\n", "\n", " return correct / len(loader.dataset)\n", " \n", "def train(config=None):\n", " # Initialize a new wandb run \n", "\n", " with wandb.init():\n", " config = wandb.config\n", " # If called by wandb.agent, as below,\n", " # this config will be set by Sweep Controller\n", " print(\"config\" , config)\n", " \n", " loader = build_dataset(config.batch_size)\n", " network = build_network(config.fc_layer_size)\n", " optimizer = build_optimizer(\n", " network, config.optimizer, config.learning_rate)\n", "\n", "\n", " for epoch in range(config.epochs):\n", " avg_loss = train_epoch(network, loader, optimizer)\n", " acc = validate(network, loader)\n", " print(f\"Epoch {epoch} avg loss: {avg_loss} accuracy: {acc}\")\n", " wandb.log({\"loss\": avg_loss, \"epoch\": epoch, \"accuracy\": acc})" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Create sweep with ID: 04b419v1\n", "Sweep URL: http://localhost:8080/nguyen/pytorch-sweeps-demo/sweeps/04b419v1\n" ] } ], "source": [ "sweep_id = wandb.sweep(sweep_config, project=\"pytorch-sweeps-demo\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: dmjiyx7j with config:\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 48\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 20\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tfc_layer_size: 256\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 9.930082550267504e-06\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n", "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.16.0" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in d:\\pnstack\\template-pytorch-model\\wandb\\run-20231119_123119-dmjiyx7j" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run desert-sweep-6 to Weights & Biases (docs)
Sweep page: https://wandb.ai/nguyen/pytorch-sweeps-demo/sweeps/04b419v1" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/nguyen/pytorch-sweeps-demo" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/nguyen/pytorch-sweeps-demo/sweeps/04b419v1" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/nguyen/pytorch-sweeps-demo/runs/dmjiyx7j" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "config {'batch_size': 48, 'epochs': 20, 'fc_layer_size': 256, 'learning_rate': 9.930082550267504e-06, 'optimizer': 'adam'}\n", "Epoch 0 avg loss: 1.1227594017982483 accuracy: 0.51\n", "Epoch 1 avg loss: 1.0263650843075343 accuracy: 0.5933333333333334\n", "Epoch 2 avg loss: 0.9231991171836853 accuracy: 0.49\n", "Epoch 3 avg loss: 0.8516879166875567 accuracy: 0.62\n", "Epoch 4 avg loss: 0.8165683661188398 accuracy: 0.81\n", "Epoch 5 avg loss: 0.7675495403153556 accuracy: 0.6733333333333333\n", "Epoch 6 avg loss: 0.7831985950469971 accuracy: 0.7166666666666667\n", "Epoch 7 avg loss: 0.6932210155895778 accuracy: 0.8733333333333333\n", "Epoch 8 avg loss: 0.6585122261728559 accuracy: 0.7333333333333333\n", "Epoch 9 avg loss: 0.6423001033919198 accuracy: 0.8766666666666667\n", "Epoch 10 avg loss: 0.6038253137043544 accuracy: 0.7933333333333333\n", "Epoch 11 avg loss: 0.5986335618155343 accuracy: 0.78\n", "Epoch 12 avg loss: 0.5852176036153521 accuracy: 0.7766666666666666\n", "Epoch 13 avg loss: 0.5776768922805786 accuracy: 0.7733333333333333\n", "Epoch 14 avg loss: 0.572343749659402 accuracy: 0.79\n", "Epoch 15 avg loss: 0.5162468552589417 accuracy: 0.9166666666666666\n", "Epoch 16 avg loss: 0.48254855615752085 accuracy: 0.9066666666666666\n", "Epoch 17 avg loss: 0.45612808636256624 accuracy: 0.9\n", "Epoch 18 avg loss: 0.4477146693638393 accuracy: 0.9066666666666666\n", "Epoch 19 avg loss: 0.42570933273860384 accuracy: 0.9233333333333333\n" ] }, { "data": { "text/html": [ "\n", "

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accuracy0.92333
batch loss0.38374
epoch19
loss0.42571

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run desert-sweep-6 at: https://wandb.ai/nguyen/pytorch-sweeps-demo/runs/dmjiyx7j
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Sweep page: https://wandb.ai/nguyen/pytorch-sweeps-demo/sweeps/04b419v1" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/nguyen/pytorch-sweeps-demo" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/nguyen/pytorch-sweeps-demo/sweeps/04b419v1" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/nguyen/pytorch-sweeps-demo/runs/c61bls20" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "config {'batch_size': 120, 'epochs': 30, 'fc_layer_size': 512, 'learning_rate': 0.00040770515026138955, 'optimizer': 'adam'}\n", "Epoch 0 avg loss: 12.669246673583984 accuracy: 0.3333333333333333\n", "Epoch 1 avg loss: 9.699127753575643 accuracy: 0.3933333333333333\n", "Epoch 2 avg loss: 5.997192939122518 accuracy: 0.5666666666666667\n", "Epoch 3 avg loss: 5.1969099044799805 accuracy: 0.5933333333333334\n", "Epoch 4 avg loss: 1.2817224860191345 accuracy: 0.5533333333333333\n", "Epoch 5 avg loss: 2.5963169733683267 accuracy: 0.57\n", "Epoch 6 avg loss: 0.8057341774304708 accuracy: 0.69\n", "Epoch 7 avg loss: 1.1785928010940552 accuracy: 0.6966666666666667\n", "Epoch 8 avg loss: 0.7000896036624908 accuracy: 0.9033333333333333\n", "Epoch 9 avg loss: 0.3257632553577423 accuracy: 0.8033333333333333\n", "Epoch 10 avg loss: 0.42895398537317914 accuracy: 0.9366666666666666\n", "Epoch 11 avg loss: 0.19974619646867117 accuracy: 0.9566666666666667\n", "Epoch 12 avg loss: 0.18941768010457358 accuracy: 0.9533333333333334\n", "Epoch 13 avg loss: 0.13353885461886725 accuracy: 0.9766666666666667\n", "Epoch 14 avg loss: 0.12185853471358617 accuracy: 0.9733333333333334\n", "Epoch 15 avg loss: 0.10533156494299571 accuracy: 0.9933333333333333\n", "Epoch 16 avg loss: 0.07629338279366493 accuracy: 0.9833333333333333\n", "Epoch 17 avg loss: 0.06959461669127147 accuracy: 0.9933333333333333\n", "Epoch 18 avg loss: 0.05993655820687612 accuracy: 1.0\n", "Epoch 19 avg loss: 0.048177012552817665 accuracy: 1.0\n", "Epoch 20 avg loss: 0.043316529442866646 accuracy: 1.0\n", "Epoch 21 avg loss: 0.033304951464136444 accuracy: 1.0\n", "Epoch 22 avg loss: 0.032316296050945915 accuracy: 1.0\n", "Epoch 23 avg loss: 0.026140497997403145 accuracy: 1.0\n", "Epoch 24 avg loss: 0.025618030379215877 accuracy: 1.0\n", "Epoch 25 avg loss: 0.023070624719063442 accuracy: 1.0\n", "Epoch 26 avg loss: 0.020422006646792095 accuracy: 1.0\n", "Epoch 27 avg loss: 0.01966176989177863 accuracy: 1.0\n", "Epoch 28 avg loss: 0.017348712620635826 accuracy: 1.0\n", "Epoch 29 avg loss: 0.016349364692966144 accuracy: 1.0\n" ] }, { "data": { "text/html": [ "\n", "

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accuracy1.0
batch loss0.01435
epoch29
loss0.01635

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run hopeful-sweep-7 at: https://wandb.ai/nguyen/pytorch-sweeps-demo/runs/c61bls20
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Sweep page: https://wandb.ai/nguyen/pytorch-sweeps-demo/sweeps/04b419v1" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/nguyen/pytorch-sweeps-demo" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View sweep at https://wandb.ai/nguyen/pytorch-sweeps-demo/sweeps/04b419v1" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/nguyen/pytorch-sweeps-demo/runs/suqa7b6e" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "config {'batch_size': 72, 'epochs': 30, 'fc_layer_size': 128, 'learning_rate': 0.00014834775601176841, 'optimizer': 'adam'}\n", "Epoch 0 avg loss: 3.2243733406066895 accuracy: 0.3333333333333333\n", "Epoch 1 avg loss: 1.8535244941711426 accuracy: 0.3333333333333333\n", "Epoch 2 avg loss: 1.3806214809417725 accuracy: 0.43333333333333335\n", "Epoch 3 avg loss: 1.1464012622833253 accuracy: 0.51\n", "Epoch 4 avg loss: 0.8064233601093292 accuracy: 0.5933333333333334\n", "Epoch 5 avg loss: 0.6862831830978393 accuracy: 0.6933333333333334\n", "Epoch 6 avg loss: 0.6002423763275146 accuracy: 0.7766666666666666\n", "Epoch 7 avg loss: 0.49208289980888364 accuracy: 0.7766666666666666\n", "Epoch 8 avg loss: 0.4717391610145569 accuracy: 0.8733333333333333\n", "Epoch 9 avg loss: 0.44093636274337766 accuracy: 0.8333333333333334\n", "Epoch 10 avg loss: 0.431243771314621 accuracy: 0.8\n", "Epoch 11 avg loss: 0.42343916893005373 accuracy: 0.8333333333333334\n", "Epoch 12 avg loss: 0.37901818156242373 accuracy: 0.7733333333333333\n", "Epoch 13 avg loss: 0.44130058884620665 accuracy: 0.8933333333333333\n", "Epoch 14 avg loss: 0.3290287971496582 accuracy: 0.95\n", "Epoch 15 avg loss: 0.3358880043029785 accuracy: 0.9433333333333334\n", "Epoch 16 avg loss: 0.3037585198879242 accuracy: 0.91\n", "Epoch 17 avg loss: 0.24435886293649672 accuracy: 0.8966666666666666\n", "Epoch 18 avg loss: 0.24720126688480376 accuracy: 0.9166666666666666\n", "Epoch 19 avg loss: 0.24351224899291993 accuracy: 0.9133333333333333\n", "Epoch 20 avg loss: 0.21602382361888886 accuracy: 0.9266666666666666\n", "Epoch 21 avg loss: 0.209903547167778 accuracy: 0.9633333333333334\n", "Epoch 22 avg loss: 0.19107576906681062 accuracy: 0.9866666666666667\n", "Epoch 23 avg loss: 0.1764080971479416 accuracy: 0.9833333333333333\n", "Epoch 24 avg loss: 0.15718264430761336 accuracy: 0.9666666666666667\n", "Epoch 25 avg loss: 0.1513497233390808 accuracy: 0.9966666666666667\n", "Epoch 26 avg loss: 0.1373470574617386 accuracy: 1.0\n", "Epoch 27 avg loss: 0.11222847327589988 accuracy: 0.9933333333333333\n", "Epoch 28 avg loss: 0.11980711072683334 accuracy: 1.0\n", "Epoch 29 avg loss: 0.10200107395648957 accuracy: 1.0\n" ] }, { "data": { "text/html": [ "\n", "

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Run summary:


accuracy1.0
batch loss0.08601
epoch29
loss0.102

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Run summary:


accuracy0.97
batch loss0.41384
epoch29
loss0.34246

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Run summary:


accuracy0.89
batch loss0.63121
epoch29
loss0.57246

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Run summary:


accuracy1.0
batch loss0.00201
epoch19
loss0.00227

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Run summary:


accuracy0.94333
batch loss0.31127
epoch19
loss0.31874

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Run summary:


accuracy0.90333
batch loss0.39859
epoch29
loss0.52641

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Run summary:


accuracy1.0
batch loss0.00618
epoch19
loss0.00469

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Run summary:


accuracy1.0
batch loss0.01591
epoch19
loss0.01182

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Run summary:


accuracy0.96333
batch loss0.14189
epoch19
loss0.16302

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accuracy1.0
batch loss0.00121
epoch19
loss0.00084

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Run summary:


accuracy0.94667
batch loss0.29488
epoch29
loss0.30645

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Run summary:


accuracy1.0
batch loss0.03568
epoch29
loss0.03911

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accuracy1.0
batch loss0.00167
epoch19
loss0.00348

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accuracy1.0
batch loss0.01911
epoch29
loss0.01923

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accuracy1.0
batch loss0.00236
epoch19
loss0.00219

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Run summary:


accuracy1.0
batch loss0.00024
epoch19
loss0.00057

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accuracy1.0
batch loss0.00056
epoch29
loss0.00065

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Run summary:


accuracy1.0
batch loss0.00085
epoch19
loss0.00169

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