Testowanie WanDB job agents
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"cell_type": "code",
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"execution_count": 4,
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"outputs": [
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"data": {
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"text/html": [
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"Tracking run with wandb version 0.16.6"
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"Run data is saved locally in <code>/mnt/c/Users/krzys/OneDrive/Studia/inz-uczenia-maszynowego/Detection-of-plant-diseases/wandb/run-20240416_232247-bfji8amn</code>"
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"data": {
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"text/html": [
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"Syncing run <strong><a href='https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases/runs/bfji8amn' target=\"_blank\">floral-energy-3</a></strong> to <a href='https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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"text/plain": [
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"<IPython.core.display.HTML object>"
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{
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"data": {
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"text/html": [
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" View project at <a href='https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases' target=\"_blank\">https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases</a>"
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"text/plain": [
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"<IPython.core.display.HTML object>"
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"metadata": {},
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{
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"data": {
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"text/html": [
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" View run at <a href='https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases/runs/bfji8amn' target=\"_blank\">https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases/runs/bfji8amn</a>"
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"output_type": "display_data"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/8\n",
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"44/47 [===========================>..] - ETA: 0s - loss: 2.1872 - accuracy: 0.2224INFO:tensorflow:Assets written to: models/assets\n"
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO:tensorflow:Assets written to: models/assets\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./models)... Done. 0.1s\n"
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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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"47/47 [==============================] - 2s 32ms/step - loss: 2.1734 - accuracy: 0.2344 - val_loss: 1.9111 - val_accuracy: 0.5380\n",
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"Epoch 2/8\n",
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"40/47 [========================>.....] - ETA: 0s - loss: 1.7703 - accuracy: 0.5437INFO:tensorflow:Assets written to: models/assets\n"
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO:tensorflow:Assets written to: models/assets\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./models)... Done. 0.1s\n"
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]
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"text": [
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"47/47 [==============================] - 1s 31ms/step - loss: 1.7483 - accuracy: 0.5527 - val_loss: 1.5486 - val_accuracy: 0.6880\n",
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"Epoch 3/8\n",
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"46/47 [============================>.] - ETA: 0s - loss: 1.4466 - accuracy: 0.6818INFO:tensorflow:Assets written to: models/assets\n"
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO:tensorflow:Assets written to: models/assets\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./models)... Done. 0.1s\n"
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]
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"47/47 [==============================] - 2s 33ms/step - loss: 1.4444 - accuracy: 0.6829 - val_loss: 1.2824 - val_accuracy: 0.7460\n",
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"Epoch 4/8\n",
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"44/47 [===========================>..] - ETA: 0s - loss: 1.2232 - accuracy: 0.7362INFO:tensorflow:Assets written to: models/assets\n"
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO:tensorflow:Assets written to: models/assets\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./models)... Done. 0.1s\n"
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]
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"47/47 [==============================] - 2s 32ms/step - loss: 1.2162 - accuracy: 0.7390 - val_loss: 1.0886 - val_accuracy: 0.7880\n",
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"Epoch 5/8\n",
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"44/47 [===========================>..] - ETA: 0s - loss: 1.0583 - accuracy: 0.7694INFO:tensorflow:Assets written to: models/assets\n"
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO:tensorflow:Assets written to: models/assets\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./models)... Done. 0.1s\n"
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"text": [
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"47/47 [==============================] - 1s 28ms/step - loss: 1.0519 - accuracy: 0.7711 - val_loss: 0.9497 - val_accuracy: 0.8020\n",
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"Epoch 6/8\n",
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"41/47 [=========================>....] - ETA: 0s - loss: 0.9382 - accuracy: 0.7897INFO:tensorflow:Assets written to: models/assets\n"
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"INFO:tensorflow:Assets written to: models/assets\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./models)... Done. 0.1s\n"
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"47/47 [==============================] - 1s 28ms/step - loss: 0.9339 - accuracy: 0.7902 - val_loss: 0.8484 - val_accuracy: 0.8180\n",
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"Epoch 7/8\n",
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"47/47 [==============================] - ETA: 0s - loss: 0.8496 - accuracy: 0.8043INFO:tensorflow:Assets written to: models/assets\n"
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO:tensorflow:Assets written to: models/assets\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./models)... Done. 0.1s\n"
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"47/47 [==============================] - 1s 27ms/step - loss: 0.8496 - accuracy: 0.8043 - val_loss: 0.7735 - val_accuracy: 0.8220\n",
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"Epoch 8/8\n",
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"44/47 [===========================>..] - ETA: 0s - loss: 0.7790 - accuracy: 0.8180INFO:tensorflow:Assets written to: models/assets\n"
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO:tensorflow:Assets written to: models/assets\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./models)... Done. 0.1s\n"
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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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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"47/47 [==============================] - 1s 29ms/step - loss: 0.7779 - accuracy: 0.8183 - val_loss: 0.7165 - val_accuracy: 0.8260\n"
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"data": {
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"model_id": "316da49b179f47019f8cf5c9c72353fe"
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"metadata": {},
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{
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"data": {
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"text/html": [
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"<style>\n",
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" table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
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" .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
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" .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
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" </style>\n",
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"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>batch/accuracy</td><td>▁▁▁▂▂▄▅▅▅▅▆▆▆▇▇▇▇▇▇▇▇▇▇▇████████████████</td></tr><tr><td>batch/batch_step</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇███</td></tr><tr><td>batch/learning_rate</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>batch/loss</td><td>███▇▇▆▆▆▅▅▅▄▄▄▄▄▃▃▃▃▃▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch/accuracy</td><td>▁▅▆▇▇███</td></tr><tr><td>epoch/epoch</td><td>▁▂▃▄▅▆▇█</td></tr><tr><td>epoch/learning_rate</td><td>▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch/loss</td><td>█▆▄▃▂▂▁▁</td></tr><tr><td>epoch/val_accuracy</td><td>▁▅▆▇▇███</td></tr><tr><td>epoch/val_loss</td><td>█▆▄▃▂▂▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>batch/accuracy</td><td>0.81726</td></tr><tr><td>batch/batch_step</td><td>395</td></tr><tr><td>batch/learning_rate</td><td>0.01</td></tr><tr><td>batch/loss</td><td>0.77969</td></tr><tr><td>epoch/accuracy</td><td>0.81825</td></tr><tr><td>epoch/epoch</td><td>7</td></tr><tr><td>epoch/learning_rate</td><td>0.01</td></tr><tr><td>epoch/loss</td><td>0.77791</td></tr><tr><td>epoch/val_accuracy</td><td>0.826</td></tr><tr><td>epoch/val_loss</td><td>0.71648</td></tr></table><br/></div></div>"
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{
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"data": {
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"text/html": [
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" View run <strong style=\"color:#cdcd00\">floral-energy-3</strong> at: <a href='https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases/runs/bfji8amn' target=\"_blank\">https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases/runs/bfji8amn</a><br/> View project at: <a href='https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases' target=\"_blank\">https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases</a><br/>Synced 5 W&B file(s), 0 media file(s), 42 artifact file(s) and 0 other file(s)"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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"metadata": {},
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},
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{
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"data": {
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"text/html": [
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"Find logs at: <code>./wandb/run-20240416_232247-bfji8amn/logs</code>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# This script needs these libraries to be installed:\n",
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"# tensorflow, numpy\n",
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"\n",
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"import wandb\n",
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"from wandb.keras import WandbMetricsLogger, WandbModelCheckpoint\n",
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"\n",
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"import random\n",
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"import numpy as np\n",
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"import tensorflow as tf\n",
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"\n",
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"\n",
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"# Start a run, tracking hyperparameters\n",
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"wandb.init(\n",
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" # set the wandb project where this run will be logged\n",
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" project=\"Detection of plant diseases\",\n",
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"\n",
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" # track hyperparameters and run metadata with wandb.config\n",
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" config={\n",
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" \"layer_1\": 512,\n",
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" \"activation_1\": \"relu\",\n",
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" \"dropout\": random.uniform(0.01, 0.80),\n",
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" \"layer_2\": 10,\n",
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" \"activation_2\": \"softmax\",\n",
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" \"optimizer\": \"sgd\",\n",
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" \"loss\": \"sparse_categorical_crossentropy\",\n",
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" \"metric\": \"accuracy\",\n",
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" \"epoch\": 8,\n",
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" \"batch_size\": 256\n",
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" }\n",
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")\n",
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"\n",
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"# [optional] use wandb.config as your config\n",
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"config = wandb.config\n",
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"\n",
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"# get the data\n",
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"mnist = tf.keras.datasets.mnist\n",
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"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
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"x_train, x_test = x_train / 255.0, x_test / 255.0\n",
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"x_train, y_train = x_train[::5], y_train[::5]\n",
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"x_test, y_test = x_test[::20], y_test[::20]\n",
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"labels = [str(digit) for digit in range(np.max(y_train) + 1)]\n",
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"\n",
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"# build a model\n",
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"model = tf.keras.models.Sequential([\n",
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" tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
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" tf.keras.layers.Dense(config.layer_1, activation=config.activation_1),\n",
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" tf.keras.layers.Dropout(config.dropout),\n",
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" tf.keras.layers.Dense(config.layer_2, activation=config.activation_2)\n",
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" ])\n",
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"\n",
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"# compile the model\n",
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"model.compile(optimizer=config.optimizer,\n",
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" loss=config.loss,\n",
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" metrics=[config.metric]\n",
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" )\n",
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"\n",
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"# WandbMetricsLogger will log train and validation metrics to wandb\n",
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"# WandbModelCheckpoint will upload model checkpoints to wandb\n",
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"history = model.fit(x=x_train, y=y_train,\n",
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" epochs=config.epoch,\n",
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" batch_size=config.batch_size,\n",
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" validation_data=(x_test, y_test),\n",
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" callbacks=[\n",
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" WandbMetricsLogger(log_freq=5),\n",
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" WandbModelCheckpoint(\"models\")\n",
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" ])\n",
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"\n",
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"# [optional] finish the wandb run, necessary in notebooks\n",
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"wandb.finish()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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 (ipykernel)",
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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
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user