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{
"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",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 30\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: \tfc_layer_size: 128\n",
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"Epoch 10 avg loss: 0.7225580334663391 accuracy: 0.7866666666666666\n",
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"Epoch 14 avg loss: 0.6706524729728699 accuracy: 0.8433333333333334\n",
"Epoch 15 avg loss: 0.6072027683258057 accuracy: 0.79\n",
"Epoch 16 avg loss: 0.5744478046894074 accuracy: 0.8833333333333333\n",
"Epoch 17 avg loss: 0.5660895109176636 accuracy: 0.8733333333333333\n",
"Epoch 18 avg loss: 0.5563419878482818 accuracy: 0.8133333333333334\n",
"Epoch 19 avg loss: 0.5246312975883484 accuracy: 0.9066666666666666\n",
"Epoch 20 avg loss: 0.480787456035614 accuracy: 0.91\n",
"Epoch 21 avg loss: 0.4634073615074158 accuracy: 0.92\n",
"Epoch 22 avg loss: 0.44787479043006895 accuracy: 0.87\n",
"Epoch 23 avg loss: 0.4245096445083618 accuracy: 0.94\n",
"Epoch 24 avg loss: 0.41274533867836 accuracy: 0.9433333333333334\n",
"Epoch 25 avg loss: 0.40062321424484254 accuracy: 0.9466666666666667\n",
"Epoch 26 avg loss: 0.379450261592865 accuracy: 0.95\n",
"Epoch 27 avg loss: 0.34824095368385316 accuracy: 0.93\n",
"Epoch 28 avg loss: 0.3403467178344727 accuracy: 0.95\n",
"Epoch 29 avg loss: 0.34246460199356077 accuracy: 0.97\n"
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>▁▂▄▂▅▄▅▆▆▅▆▆▆▆▇▆▇▇▆▇▇▇▇███████</td></tr><tr><td>batch loss</td><td>█▄▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch</td><td>▁▁▁▂▂▂▂▃▃▃▃▄▄▄▄▅▅▅▅▆▆▆▆▇▇▇▇███</td></tr><tr><td>loss</td><td>█▅▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>0.97</td></tr><tr><td>batch loss</td><td>0.41384</td></tr><tr><td>epoch</td><td>29</td></tr><tr><td>loss</td><td>0.34246</td></tr></table><br/></div></div>"
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 30\n",
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"config {'batch_size': 136, 'epochs': 30, 'fc_layer_size': 512, 'learning_rate': 0.00036571020561291867, 'optimizer': 'sgd'}\n",
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"Epoch 20 avg loss: 0.6698652505874634 accuracy: 0.74\n",
"Epoch 21 avg loss: 0.6500520706176758 accuracy: 0.8033333333333333\n",
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"Epoch 24 avg loss: 0.5801876882712046 accuracy: 0.86\n",
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"Epoch 28 avg loss: 0.5300563474496206 accuracy: 0.8666666666666667\n",
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>▁▁▁▄▄▅▃▄▅▄▆▄▇▅▇▆▆▇▆▇▆▇▇▇█▇▇███</td></tr><tr><td>batch loss</td><td>▇█▇▇██▇▇▆▇▆▆▅▇▆▅▅▄▅▅▄▄▄▄▄▄▃▃▃▃▁▃▃▂▂▂▂▂▁▃</td></tr><tr><td>epoch</td><td>▁▁▁▂▂▂▂▃▃▃▃▄▄▄▄▅▅▅▅▆▆▆▆▇▇▇▇███</td></tr><tr><td>loss</td><td>██▇▇▇▇▇▆▆▆▆▅▅▄▄▄▄▃▃▃▃▂▂▂▂▂▂▂▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>0.89</td></tr><tr><td>batch loss</td><td>0.63121</td></tr><tr><td>epoch</td><td>29</td></tr><tr><td>loss</td><td>0.57246</td></tr></table><br/></div></div>"
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"Epoch 16 avg loss: 0.004403046448715031 accuracy: 1.0\n",
"Epoch 17 avg loss: 0.0026308108353987336 accuracy: 1.0\n",
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"Epoch 11 avg loss: 0.7380774787494114 accuracy: 0.59\n",
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"Epoch 16 avg loss: 0.45413038986069815 accuracy: 0.8633333333333333\n",
"Epoch 17 avg loss: 0.411521886076246 accuracy: 0.9233333333333333\n",
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"Epoch 2 avg loss: 1.0654590845108032 accuracy: 0.53\n",
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"Epoch 20 avg loss: 0.7074477910995484 accuracy: 0.76\n",
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>▁▂▃▃▄▅▃▇▆▆▅▆▆▇▆▆▆▆▆▆▆▆▆▇▆▇████</td></tr><tr><td>batch loss</td><td>████▇▇▇▇▇▇▆▆▆▅▆▆▅▅▅▅▅▅▅▅▄▄▅▄▄▄▃▄▃▃▃▃▃▃▂▁</td></tr><tr><td>epoch</td><td>▁▁▁▂▂▂▂▃▃▃▃▄▄▄▄▅▅▅▅▆▆▆▆▇▇▇▇███</td></tr><tr><td>loss</td><td>██▇▇▇▇▆▆▆▅▅▅▅▄▄▄▃▃▃▃▃▃▃▃▂▂▂▂▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>0.90333</td></tr><tr><td>batch loss</td><td>0.39859</td></tr><tr><td>epoch</td><td>29</td></tr><tr><td>loss</td><td>0.52641</td></tr></table><br/></div></div>"
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 20\n",
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"Epoch 9 avg loss: 0.019312835453699034 accuracy: 1.0\n",
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"Epoch 15 avg loss: 0.006185308254013459 accuracy: 1.0\n",
"Epoch 16 avg loss: 0.005990352093552549 accuracy: 1.0\n",
"Epoch 17 avg loss: 0.005950886756181717 accuracy: 1.0\n",
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"Epoch 0 avg loss: 40.78863833631788 accuracy: 0.39666666666666667\n",
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"Epoch 6 avg loss: 0.22699441867215292 accuracy: 0.97\n",
"Epoch 7 avg loss: 0.12237193062901497 accuracy: 0.98\n",
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"Epoch 10 avg loss: 0.04198069657598223 accuracy: 0.9966666666666667\n",
"Epoch 11 avg loss: 0.03529735774333988 accuracy: 1.0\n",
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"Epoch 13 avg loss: 0.024908980620758876 accuracy: 1.0\n",
"Epoch 14 avg loss: 0.02164408444826092 accuracy: 1.0\n",
"Epoch 15 avg loss: 0.017391732361699854 accuracy: 1.0\n",
"Epoch 16 avg loss: 0.015365603246859141 accuracy: 1.0\n",
"Epoch 17 avg loss: 0.012505612070006984 accuracy: 1.0\n",
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>▁▁▅▅▇▆██████████████</td></tr><tr><td>batch loss</td><td>▅█▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch</td><td>▁▁▂▂▂▃▃▄▄▄▅▅▅▆▆▇▇▇██</td></tr><tr><td>loss</td><td>█▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>1.0</td></tr><tr><td>batch loss</td><td>0.01591</td></tr><tr><td>epoch</td><td>19</td></tr><tr><td>loss</td><td>0.01182</td></tr></table><br/></div></div>"
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 20\n",
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"config {'batch_size': 56, 'epochs': 20, 'fc_layer_size': 512, 'learning_rate': 0.0009883923206319373, 'optimizer': 'sgd'}\n",
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>▁▃▅▄▅▆▆▇▇▇▇▇▇▇██████</td></tr><tr><td>batch loss</td><td>▇██▇▆▆▆▅▅▆▅▆▅▄▅▄▄▃▄▃▃▃▃▃▃▂▂▃▂▁▂▂▂▁▂▁▁▁▁▁</td></tr><tr><td>epoch</td><td>▁▁▂▂▂▃▃▄▄▄▅▅▅▆▆▇▇▇██</td></tr><tr><td>loss</td><td>█▇█▆▆▅▅▄▄▃▃▃▂▂▂▂▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>0.96333</td></tr><tr><td>batch loss</td><td>0.14189</td></tr><tr><td>epoch</td><td>19</td></tr><tr><td>loss</td><td>0.16302</td></tr></table><br/></div></div>"
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 20\n",
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"Epoch 9 avg loss: 0.00902516468028937 accuracy: 1.0\n",
"Epoch 10 avg loss: 0.005856897681951523 accuracy: 1.0\n",
"Epoch 11 avg loss: 0.004270538720967514 accuracy: 1.0\n",
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"Epoch 14 avg loss: 0.001740208693913051 accuracy: 1.0\n",
"Epoch 15 avg loss: 0.0014031159807927907 accuracy: 1.0\n",
"Epoch 16 avg loss: 0.0012772477680950292 accuracy: 1.0\n",
"Epoch 17 avg loss: 0.0010444886034487613 accuracy: 1.0\n",
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>▁▄▆▇████████████████</td></tr><tr><td>batch loss</td><td>█▅▃▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch</td><td>▁▁▂▂▂▃▃▄▄▄▅▅▅▆▆▇▇▇██</td></tr><tr><td>loss</td><td>█▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>1.0</td></tr><tr><td>batch loss</td><td>0.00121</td></tr><tr><td>epoch</td><td>19</td></tr><tr><td>loss</td><td>0.00084</td></tr></table><br/></div></div>"
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"config {'batch_size': 56, 'epochs': 30, 'fc_layer_size': 128, 'learning_rate': 0.00042389152465552673, 'optimizer': 'sgd'}\n",
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"Epoch 20 avg loss: 0.51861871778965 accuracy: 0.86\n",
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>▃▁▃▂▃▄▄▆▄▄▄▅▄▅▇▇▇▆▇█▇▇▇██▇▆▇██</td></tr><tr><td>batch loss</td><td>▇██▇▆▅▅▅▅▅▆▄▄▃▅▄▅▃▃▃▆▄▃▃▂▂▄▃▃▂▂▁▁▂▂▁▂▁▂▁</td></tr><tr><td>epoch</td><td>▁▁▁▂▂▂▂▃▃▃▃▄▄▄▄▅▅▅▅▆▆▆▆▇▇▇▇███</td></tr><tr><td>loss</td><td>██▇▆▆▆▅▆▅▅▄▄▄▄▅▄▃▂▂▂▃▂▂▂▂▂▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>0.94667</td></tr><tr><td>batch loss</td><td>0.29488</td></tr><tr><td>epoch</td><td>29</td></tr><tr><td>loss</td><td>0.30645</td></tr></table><br/></div></div>"
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"config {'batch_size': 56, 'epochs': 30, 'fc_layer_size': 128, 'learning_rate': 0.0003533606312141685, 'optimizer': 'adam'}\n",
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"Epoch 19 avg loss: 0.12752345825235048 accuracy: 0.9933333333333333\n",
"Epoch 20 avg loss: 0.10557452961802483 accuracy: 0.9933333333333333\n",
"Epoch 21 avg loss: 0.09049654006958008 accuracy: 0.9966666666666667\n",
"Epoch 22 avg loss: 0.07641533762216568 accuracy: 0.9966666666666667\n",
"Epoch 23 avg loss: 0.0705851074308157 accuracy: 1.0\n",
"Epoch 24 avg loss: 0.06503975080947082 accuracy: 1.0\n",
"Epoch 25 avg loss: 0.06235989493628343 accuracy: 1.0\n",
"Epoch 26 avg loss: 0.05170226159195105 accuracy: 1.0\n",
"Epoch 27 avg loss: 0.05299911399682363 accuracy: 1.0\n",
"Epoch 28 avg loss: 0.04441701558729013 accuracy: 1.0\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 20\n",
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"Epoch 4 avg loss: 0.3881495421131452 accuracy: 0.92\n",
"Epoch 5 avg loss: 0.19398833066225052 accuracy: 0.98\n",
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"Epoch 9 avg loss: 0.02202181549121936 accuracy: 1.0\n",
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"Epoch 11 avg loss: 0.01238373527303338 accuracy: 1.0\n",
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"Epoch 15 avg loss: 0.00601344268458585 accuracy: 1.0\n",
"Epoch 16 avg loss: 0.0048792791009570164 accuracy: 1.0\n",
"Epoch 17 avg loss: 0.004404674905041854 accuracy: 1.0\n",
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"Epoch 0 avg loss: 27.66898129383723 accuracy: 0.3333333333333333\n",
"Epoch 1 avg loss: 9.323363661766052 accuracy: 0.5\n",
"Epoch 2 avg loss: 2.2524943351745605 accuracy: 0.7766666666666666\n",
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"Epoch 9 avg loss: 0.0558641889753441 accuracy: 0.9966666666666667\n",
"Epoch 10 avg loss: 0.03121470706537366 accuracy: 1.0\n",
"Epoch 11 avg loss: 0.013721109523127476 accuracy: 1.0\n",
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"Epoch 13 avg loss: 0.007269753919293483 accuracy: 1.0\n",
"Epoch 14 avg loss: 0.004239639383740723 accuracy: 1.0\n",
"Epoch 15 avg loss: 0.0034417912053565183 accuracy: 1.0\n",
"Epoch 16 avg loss: 0.003068222004609803 accuracy: 1.0\n",
"Epoch 17 avg loss: 0.0026892528403550386 accuracy: 1.0\n",
"Epoch 18 avg loss: 0.0023329649896671376 accuracy: 1.0\n",
"Epoch 19 avg loss: 0.0021909799931260445 accuracy: 1.0\n"
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 20\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: \tfc_layer_size: 512\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0006575743797441523\n",
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"Epoch 0 avg loss: 9.903335551420847 accuracy: 0.5933333333333334\n",
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"Epoch 6 avg loss: 0.03616986842826009 accuracy: 1.0\n",
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"Epoch 11 avg loss: 0.004087410323942701 accuracy: 1.0\n",
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"Epoch 15 avg loss: 0.0013731500948779285 accuracy: 1.0\n",
"Epoch 16 avg loss: 0.0011467354197520763 accuracy: 1.0\n",
"Epoch 17 avg loss: 0.0008919178896273176 accuracy: 1.0\n",
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>▂▁▆▇████████████████</td></tr><tr><td>batch loss</td><td>▂█▆▃▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch</td><td>▁▁▂▂▂▃▃▄▄▄▅▅▅▆▆▇▇▇██</td></tr><tr><td>loss</td><td>█▄▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>1.0</td></tr><tr><td>batch loss</td><td>0.00024</td></tr><tr><td>epoch</td><td>19</td></tr><tr><td>loss</td><td>0.00057</td></tr></table><br/></div></div>"
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"\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 104\n",
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"config {'batch_size': 104, 'epochs': 30, 'fc_layer_size': 512, 'learning_rate': 0.0003980820663366078, 'optimizer': 'adam'}\n",
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>▁▅▄▇▆▇▇███████████████████████</td></tr><tr><td>batch loss</td><td>▂█▅▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch</td><td>▁▁▁▂▂▂▂▃▃▃▃▄▄▄▄▅▅▅▅▆▆▆▆▇▇▇▇███</td></tr><tr><td>loss</td><td>█▆▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>1.0</td></tr><tr><td>batch loss</td><td>0.00056</td></tr><tr><td>epoch</td><td>29</td></tr><tr><td>loss</td><td>0.00065</td></tr></table><br/></div></div>"
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"config {'batch_size': 48, 'epochs': 20, 'fc_layer_size': 512, 'learning_rate': 0.0009607460347692044, 'optimizer': 'adam'}\n",
"Epoch 0 avg loss: 25.255474090576172 accuracy: 0.5933333333333334\n",
"Epoch 1 avg loss: 5.102003284863064 accuracy: 0.5866666666666667\n",
"Epoch 2 avg loss: 0.8636805542877742 accuracy: 0.7366666666666667\n",
"Epoch 3 avg loss: 0.6188298123223441 accuracy: 0.8166666666666667\n",
"Epoch 4 avg loss: 0.27566932354654583 accuracy: 0.9366666666666666\n",
"Epoch 5 avg loss: 0.12154114033494677 accuracy: 0.9666666666666667\n",
"Epoch 6 avg loss: 0.05851750440030758 accuracy: 0.9966666666666667\n",
"Epoch 7 avg loss: 0.030152809540075914 accuracy: 1.0\n",
"Epoch 8 avg loss: 0.01572604657017759 accuracy: 1.0\n",
"Epoch 9 avg loss: 0.008001219414706742 accuracy: 1.0\n",
"Epoch 10 avg loss: 0.0068940040988049334 accuracy: 1.0\n",
"Epoch 11 avg loss: 0.0046819809358567 accuracy: 1.0\n",
"Epoch 12 avg loss: 0.003610321969193007 accuracy: 1.0\n",
"Epoch 13 avg loss: 0.0037284358737191985 accuracy: 1.0\n",
"Epoch 14 avg loss: 0.002716043115859585 accuracy: 1.0\n",
"Epoch 15 avg loss: 0.002497990316312228 accuracy: 1.0\n",
"Epoch 16 avg loss: 0.0021978149889037013 accuracy: 1.0\n",
"Epoch 17 avg loss: 0.0022922364961622016 accuracy: 1.0\n",
"Epoch 18 avg loss: 0.0021205566258036663 accuracy: 1.0\n",
"Epoch 19 avg loss: 0.001688087616847562 accuracy: 1.0\n"
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>▁▁▄▅▇▇██████████████</td></tr><tr><td>batch loss</td><td>█▇▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch</td><td>▁▁▂▂▂▃▃▄▄▄▅▅▅▆▆▇▇▇██</td></tr><tr><td>loss</td><td>█▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>1.0</td></tr><tr><td>batch loss</td><td>0.00085</td></tr><tr><td>epoch</td><td>19</td></tr><tr><td>loss</td><td>0.00169</td></tr></table><br/></div></div>"
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