feature/basic-model-setup #3
|
|
@ -1,6 +1,6 @@
|
|||
.ipynb_checkpoints
|
||||
data/
|
||||
*.zip
|
||||
|
||||
# https://github.com/microsoft/vscode-python/blob/main/.gitignore
|
||||
.DS_Store
|
||||
.huskyrc.json
|
||||
|
|
@ -51,4 +51,4 @@ dist/**
|
|||
# translation files
|
||||
*.xlf
|
||||
package.nls.*.json
|
||||
l10n/
|
||||
l10n/
|
||||
|
|
|
|||
|
|
@ -0,0 +1,36 @@
|
|||
FROM ubuntu:22.04
|
||||
|
||||
# Packages
|
||||
RUN apt-get update && apt-get upgrade && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
|
||||
curl liblzma-dev python-tk python3-tk tk-dev libssl-dev libffi-dev libncurses5-dev zlib1g zlib1g-dev \
|
||||
libreadline-dev libbz2-dev libsqlite3-dev make gcc curl git-all wget python3-openssl gnupg2
|
||||
|
||||
# Setup CUDA
|
||||
RUN apt-key del 7fa2af80 && \
|
||||
wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-wsl-ubuntu.pin && \
|
||||
mv cuda-wsl-ubuntu.pin /etc/apt/preferences.d/cuda-repository-pin-600 && \
|
||||
wget https://developer.download.nvidia.com/compute/cuda/12.2.2/local_installers/cuda-repo-wsl-ubuntu-12-2-local_12.2.2-1_amd64.deb && \
|
||||
dpkg -i cuda-repo-wsl-ubuntu-12-2-local_12.2.2-1_amd64.deb && \
|
||||
cp /var/cuda-repo-wsl-ubuntu-12-2-local/cuda-*-keyring.gpg /usr/share/keyrings/ && \
|
||||
apt-get update && \
|
||||
apt-get -y install cuda-toolkit-12-2
|
||||
|
||||
# Pyenv
|
||||
ENV PYENV_ROOT="$HOME/.pyenv"
|
||||
ENV PATH="$PYENV_ROOT/bin:$PYENV_ROOT/versions/3.10.12/bin:$PATH"
|
||||
|
||||
RUN curl https://pyenv.run | bash
|
||||
RUN pyenv install 3.10.12 && \
|
||||
pyenv global 3.10.12 && \
|
||||
echo 'eval "$(pyenv init --path)"' >> ~/.bashrc && \
|
||||
echo 'eval "$(pyenv virtualenv-init -)"' >> ~/.bashrc
|
||||
|
||||
SHELL ["/bin/bash", "-c"]
|
||||
|
||||
WORKDIR /app
|
||||
ADD ./requirements.txt /app/requirements.txt
|
||||
RUN pip install -r requirements.txt
|
||||
|
||||
ENV CUDNN_PATH="/.pyenv/versions/3.10.12/lib/python3.10/site-packages/nvidia/cudnn/"
|
||||
ENV LD_LIBRARY_PATH="$CUDNN_PATH/lib":"/usr/local/cuda-12.2/lib64"
|
||||
ENV PATH="$PATH":"/usr/local/cuda-12.2/bin"
|
||||
16
Makefile
16
Makefile
|
|
@ -1,5 +1,6 @@
|
|||
.PHONY: download-dataset resize-dataset sobel-dataset
|
||||
|
||||
# Use inside docker container
|
||||
download-dataset:
|
||||
python3 ./file_manager/data_manager.py --download
|
||||
|
||||
|
|
@ -7,4 +8,17 @@ resize-dataset:
|
|||
python3 ./file_manager/data_manager.py --resize --shape 64 64 --source "original_dataset"
|
||||
|
||||
sobel-dataset:
|
||||
python3 ./file_manager/data_manager.py --sobel --source "resized_dataset"
|
||||
python3 ./file_manager/data_manager.py --sobel --source "resized_dataset"
|
||||
|
||||
login:
|
||||
wandb login $$(cat "$$API_KEY_SECRET")
|
||||
|
||||
# Outside docker
|
||||
docker-run:
|
||||
docker-compose run --entrypoint=/bin/bash gpu
|
||||
|
||||
docker-build:
|
||||
docker-compose build
|
||||
|
||||
check-gpu:
|
||||
python3 ./gpu_check.py
|
||||
|
|
|
|||
99
README.md
99
README.md
|
|
@ -12,85 +12,26 @@
|
|||
| 15.06.2024 | Prezentacja działania systemu
|
||||
| | Prezentacja wyników i skuteczności wybranego modelu
|
||||
|
||||
# Szczegółowy harmonogram
|
||||
# Dokumentacja
|
||||
|
||||
Spotkania dot. progresu prac - każda niedziela, godzina 18:00-20:00.
|
||||
Poniżej, kolumna "działanie" jest w formacie `<osoba/osoby> (<numer_zadania>)`.
|
||||
Brak osoby oznacza, że zadanie nie zostało jeszcze przypisane.
|
||||
[Link do dokumentacji](https://uam-my.sharepoint.com/personal/krzboj_st_amu_edu_pl/_layouts/15/doc.aspx?sourcedoc={dc695bbe-68d1-4947-8c29-1d008f252a3b}&action=edit)
|
||||
|
||||
| Data | Działanie
|
||||
|----------------------------:|:------------------------------------------------------------|
|
||||
| 05.05.2024 | Sergiusz (1), Mateusz (3), Krzysztof (2)
|
||||
| 12.05.2024 | Wszyscy (5), (4), (6), (7.1)
|
||||
| 19.05.2024 | Wszyscy (5), (7.2)
|
||||
| 26.05.2024 | Wszyscy (5), (7.3), (9)
|
||||
| 02.06.2024 | (8)
|
||||
| 09.06.2024 | Feedback, ewentualne poprawki
|
||||
| 15.06.2024 | Finalna prezentacja
|
||||
|
||||
Szczegóły działań:
|
||||
|
||||
1) Przygotowanie danych i modułu do ich przetwarzania
|
||||
- Napisanie skryptu, który pobiera dane oraz rozpakowuje je lokalnie.
|
||||
- Napisanie szablonu skryptu do przetwarzania danych. Skrypt powinien tworzyć katalogi (struktura katalogowa) z danymi po transformacji. Każda transformacja na oryginalnych danych będzie commit'owana do repozytorium, tak aby reszta zeszpołu mogła ją uruchomić.
|
||||
- Napisać jedną przykładową transformację, np. resize i kontury, korzystając z szablonu.
|
||||
- Utworzyć README.md z instrukcją tworzenia nowego modułu do przetwarzania.
|
||||
|
||||
2) Modele do przygotowania:
|
||||
- Przygotować wstępnie 3 modele w formacie WanDB, np. MobileNet, ResNet, ew. custom CNN z klasyfikacją wielozadaniową.
|
||||
- Uruchomić modele na WanDB żeby zobaczyć czy się uruchamiają i generują poprawne wykresy.
|
||||
- Utworzyć README.md z instrukcją tworzenia nowych modeli.
|
||||
|
||||
3) Moduł do ładowania plików
|
||||
- Napisać moduł, który ładuje dane po transformacji. Dane będą wykorzystywane do uczenia i walidacji modelu.
|
||||
- Moduł powinien dokonywać podziału zbioru danych na 3 czesci - train, valid, test.
|
||||
- Powinno być możliwe zdefiniowanie rozmiaru batch'a, rozmiaru validation set'a, scieżki skąd załadować dane.
|
||||
- Dodać możliwość definiowania seed'a, tak aby każdy mógł uzyskać podobne rezultaty w razie potrzeby. Seed powinien być przekierowany na stdout podczas uruchamiania skryptu.
|
||||
- Dodać możliwość wyboru rozkładu danych.
|
||||
- Dane wyjściowe powinny być w formacie pozwalającym na załadowanie ich bezpośrednio do modelu (binarne, tf record, lub inne).
|
||||
- README.md opisujący w jaki sposób parametryzować moduł.
|
||||
|
||||
4) Moduł do obslugi i uruchaminia WanDB Job's
|
||||
- Napisać skrypt do ściągania danych z kolejki aby obejść problem uruchamiania agenta na Colab/Kaggle.
|
||||
- Napisać skrypt, który uruchamia job'y i wysyła go na kolejkę. Powinien obsługiwać przyjmowanie hiperparamterów, oraz nazwę kolejki, do której zostanie job przesłany.
|
||||
- Napisać skrypt, który uruchamia agenta na danej maszynie.
|
||||
- Napisać skrypt do tworzenia jobów - powinna być sprecyzowana struktura katalogowa, pozwalająca na zarządzanie nimi i obsługę różnych modeli. Ewentualnie synchronizacja job'ów, między WanDB i środowiskiem lokalnym.
|
||||
- README.md opisujący powyższe.
|
||||
|
||||
5) Eksperymenty, dobieranie hiperparametrów, rozkładu danych, testowanie różnych strategii. Jeżeli konieczne, dodanie nowych modeli.
|
||||
|
||||
6) Dodać Heatmap'ę do modelu (CAM).
|
||||
|
||||
7) Przygotowanie frontu do projektu (https://www.gradio.app/)
|
||||
1. Uruchomienie lokalne Frontu do testów.
|
||||
2. Obsługa wyświetlania Heatmap.
|
||||
3. Deploy frontu na środowisko (lokalne/zdalne, do wyboru).
|
||||
|
||||
8) Wybór najlepszego modelu.
|
||||
|
||||
9) Modul do obslugi Sweeps - automatycznego dobierania hiperparametrów (opcjonalnie).
|
||||
|
||||
# Źródło danych
|
||||
|
||||
https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset
|
||||
|
||||
# Technologie
|
||||
|
||||
## WanDB
|
||||
|
||||
WanDB built-in features:
|
||||
|
||||
- Experiments Tracking
|
||||
- Predictions Visualization
|
||||
- Scheduling runs through queues & connected agents
|
||||
- Model Registry
|
||||
- Hyperparamter optimization via Sweeps
|
||||
|
||||
## Moc obliczeniowa
|
||||
|
||||
- Radeon 7800XT
|
||||
- GeForce RTX 3060TI
|
||||
- GeForce RTX 3070
|
||||
- GeForce RTX 4050M
|
||||
- [zasoby uczelniane](https://laboratoria.wmi.amu.edu.pl/uslugi/zasoby-dla-projektow/maszyna-gpu/)
|
||||
# Setup
|
||||
|
||||
1. Install Docker on your local system.
|
||||
2. To build docker image and run the shell, use Makefile
|
||||
```bash
|
||||
make docker-build
|
||||
make docker-run
|
||||
```
|
||||
3. Get your API key from https://wandb.ai/settings#api, and add the key to **secrets.txt** file.
|
||||
4. After running the container
|
||||
```bash
|
||||
make login # to login to WanDB.
|
||||
make check-gpu # to verify if GPU works
|
||||
```
|
||||
5. If needed, to manually run containers, run:
|
||||
```bash
|
||||
docker build -t gpu api_key="<wandb_api_key>" .
|
||||
docker run --rm -it --gpus all --entrypoint /bin/bash gpu
|
||||
```
|
||||
|
|
|
|||
|
|
@ -0,0 +1,23 @@
|
|||
services:
|
||||
gpu:
|
||||
image: gpu
|
||||
volumes:
|
||||
- .:/app
|
||||
command: nvidia-smi
|
||||
build:
|
||||
context: .
|
||||
dockerfile: Dockerfile
|
||||
environment:
|
||||
API_KEY_SECRET: /run/secrets/api_key_secret
|
||||
secrets:
|
||||
- api_key_secret
|
||||
deploy:
|
||||
resources:
|
||||
reservations:
|
||||
devices:
|
||||
- driver: nvidia
|
||||
count: 1
|
||||
capabilities: [gpu]
|
||||
secrets:
|
||||
api_key_secret:
|
||||
file: ./secrets.txt
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
try:
|
||||
import tensorflow
|
||||
except ImportError:
|
||||
print("Tensorflow is not installed, install requied packages from requirements.txt")
|
||||
exit(1)
|
||||
|
||||
import tensorflow
|
||||
|
||||
print("If you see the tensor result, then the Tensorflow is available.")
|
||||
rs = tensorflow.reduce_sum(tensorflow.random.normal([1000, 1000]))
|
||||
print(rs)
|
||||
|
||||
gpus = tensorflow.config.list_physical_devices('GPU')
|
||||
if len(gpus) == 0:
|
||||
print("No GPU available.")
|
||||
else:
|
||||
print(f"GPUs available: {len(gpus)}")
|
||||
print(gpus)
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
max_jobs: 1
|
||||
|
||||
entity: uczenie-maszynowe-projekt
|
||||
|
||||
queues:
|
||||
- GPU queue 1
|
||||
- GPU queue 2
|
||||
|
||||
builder:
|
||||
type: docker
|
||||
|
|
@ -0,0 +1,15 @@
|
|||
from model.test_model import TestModel
|
||||
from pathlib import Path
|
||||
from dataset.dataset import Dataset
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Loading dataset
|
||||
train_dataset = Dataset(Path('data/resized_dataset/train'))
|
||||
valid_dataset = Dataset(Path('data/resized_dataset/valid'))
|
||||
for i in train_dataset.take(1):
|
||||
print(i)
|
||||
|
||||
# Training model
|
||||
model = TestModel()
|
||||
history = model.fit()
|
||||
model.save("./src/model/test_model_final.keras")
|
||||
|
|
@ -0,0 +1,55 @@
|
|||
import tensorflow as tf
|
||||
|
||||
from wandb_utils.config import Config
|
||||
from wandb.keras import WandbMetricsLogger
|
||||
|
||||
|
||||
class Resnet50Model:
|
||||
def __init__(self):
|
||||
self.config = Config(epoch=8, batch_size=64).config()
|
||||
self.config.learning_rate = 0.01
|
||||
# Define specific configuration below, they will be visible in the W&B interface
|
||||
# Start of config
|
||||
self.config.optimizer = "sgd"
|
||||
self.config.loss = "sparse_categorical_crossentropy"
|
||||
self.config.metrics = ["accuracy"]
|
||||
# End
|
||||
self.model = self.__build_model()
|
||||
self.__compile()
|
||||
self.__load_dataset()
|
||||
|
||||
def __build_model(self):
|
||||
return tf.keras.applications.ResNet50(
|
||||
input_shape=(224, 224, 3), include_top=False, weights='imagenet'
|
||||
# output - odblokować ostatnią warstwę freeze
|
||||
# zobaczyc czy dziala to by default, czy wewn. warstwy są frozen, i czy ost. jest dynamiczna
|
||||
)
|
||||
|
||||
def __compile(self):
|
||||
self.model.compile(
|
||||
optimizer=self.config.optimizer,
|
||||
loss=self.config.loss,
|
||||
metrics=self.config.metrics,
|
||||
)
|
||||
|
||||
def __load_dataset(self):
|
||||
(self.x_train, self.y_train), (self.x_test, self.y_test) = tf.keras.datasets.cifar10.load_data()
|
||||
self.x_train = self.x_train.astype('float32') / 255.0
|
||||
self.x_test = self.x_test.astype('float32') / 255.0
|
||||
|
||||
def fit(self):
|
||||
wandb_callbacks = [
|
||||
WandbMetricsLogger(log_freq=5),
|
||||
# Not supported with Keras >= 3.0.0
|
||||
# WandbModelCheckpoint(filepath="models"),
|
||||
]
|
||||
return self.model.fit(
|
||||
x=self.x_train,
|
||||
y=self.y_train,
|
||||
epochs=self.config.epoch,
|
||||
batch_size=self.config.batch_size,
|
||||
callbacks=wandb_callbacks
|
||||
)
|
||||
|
||||
def save(self, filepath):
|
||||
self.model.save(filepath)
|
||||
|
|
@ -0,0 +1,65 @@
|
|||
import random
|
||||
import tensorflow as tf
|
||||
|
||||
from wandb_utils.config import Config
|
||||
from wandb.keras import WandbMetricsLogger
|
||||
|
||||
|
||||
class TestModel:
|
||||
def __init__(self):
|
||||
self.config = Config(epoch=8, batch_size=256).config()
|
||||
self.config.learning_rate = 0.01
|
||||
# Define specific configuration below, they will be visible in the W&B interface
|
||||
# Start of config
|
||||
self.config.layer_1 = 512
|
||||
self.config.activation_1 = "relu"
|
||||
self.config.dropout = random.uniform(0.01, 0.80)
|
||||
self.config.layer_2 = 10
|
||||
self.config.activation_2 = "softmax"
|
||||
self.config.optimizer = "sgd"
|
||||
self.config.loss = "sparse_categorical_crossentropy"
|
||||
self.config.metrics = ["accuracy"]
|
||||
# End
|
||||
self.model = self.__build_model()
|
||||
self.__compile()
|
||||
self.__load_dataset()
|
||||
|
||||
def __build_model(self):
|
||||
return tf.keras.models.Sequential([
|
||||
tf.keras.layers.Flatten(input_shape=(28, 28)),
|
||||
tf.keras.layers.Dense(self.config.layer_1, activation=self.config.activation_1),
|
||||
tf.keras.layers.Dropout(self.config.dropout),
|
||||
tf.keras.layers.Dense(self.config.layer_2, activation=self.config.activation_2)
|
||||
])
|
||||
|
||||
def __compile(self):
|
||||
self.model.compile(
|
||||
optimizer=self.config.optimizer,
|
||||
loss=self.config.loss,
|
||||
metrics=self.config.metrics,
|
||||
)
|
||||
def __load_dataset(self):
|
||||
mnist = tf.keras.datasets.mnist
|
||||
(self.x_train, self.y_train), (self.x_test, self.y_test) = mnist.load_data()
|
||||
self.x_train, self.x_test = self.x_train / 255.0, self.x_test / 255.0
|
||||
self.x_train, self.y_train = self.x_train[::5], self.y_train[::5]
|
||||
self.x_test, self.y_test = self.x_test[::20], self.y_test[::20]
|
||||
|
||||
def fit(self):
|
||||
wandb_callbacks = [
|
||||
WandbMetricsLogger(log_freq=5),
|
||||
# Not supported with Keras >= 3.0.0
|
||||
# WandbModelCheckpoint(filepath="models"),
|
||||
]
|
||||
return self.model.fit(
|
||||
x=self.x_train,
|
||||
y=self.y_train,
|
||||
epochs=self.config.epoch,
|
||||
batch_size=self.config.batch_size,
|
||||
validation_data=(self.x_test, self.y_test),
|
||||
callbacks=wandb_callbacks
|
||||
)
|
||||
|
||||
def save(self, filepath):
|
||||
self.model.save(filepath)
|
||||
|
||||
Binary file not shown.
|
|
@ -1,4 +1,7 @@
|
|||
tensorflow==2.16.1
|
||||
tensorflow[and-cuda]==2.16.1
|
||||
tensorflow-io==0.37.0
|
||||
numpy==1.26.4
|
||||
opencv-python==4.9.0.80
|
||||
numpy==1.26.4
|
||||
wget==3.2
|
||||
wandb==0.16.6
|
||||
|
|
@ -0,0 +1 @@
|
|||
FILL IN
|
||||
10
test.py
10
test.py
|
|
@ -1,10 +0,0 @@
|
|||
|
||||
from pathlib import Path
|
||||
|
||||
from dataset.dataset import Dataset
|
||||
|
||||
train_dataset = Dataset(Path('data/resized_dataset/train'))
|
||||
valid_dataset = Dataset(Path('data/resized_dataset/valid'))
|
||||
|
||||
for i in train_dataset.take(1):
|
||||
print(i)
|
||||
363
testing.ipynb
363
testing.ipynb
|
|
@ -1,363 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
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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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"Epoch 1/8\n",
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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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"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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"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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"47/47 [==============================] - 2s 32ms/step - loss: 1.2162 - accuracy: 0.7390 - val_loss: 1.0886 - val_accuracy: 0.7880\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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"47/47 [==============================] - 1s 28ms/step - loss: 1.0519 - accuracy: 0.7711 - val_loss: 0.9497 - val_accuracy: 0.8020\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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"47/47 [==============================] - 1s 27ms/step - loss: 0.8496 - accuracy: 0.8043 - val_loss: 0.7735 - val_accuracy: 0.8220\n",
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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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" .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
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|
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|
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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": {
|
||||
"text/html": [
|
||||
" 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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|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"Find logs at: <code>./wandb/run-20240416_232247-bfji8amn/logs</code>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
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|
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|
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|
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],
|
||||
"source": [
|
||||
"# This script needs these libraries to be installed:\n",
|
||||
"# tensorflow, numpy\n",
|
||||
"\n",
|
||||
"import wandb\n",
|
||||
"from wandb.keras import WandbMetricsLogger, WandbModelCheckpoint\n",
|
||||
"\n",
|
||||
"import random\n",
|
||||
"import numpy as np\n",
|
||||
"import tensorflow as tf\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Start a run, tracking hyperparameters\n",
|
||||
"wandb.init(\n",
|
||||
" # set the wandb project where this run will be logged\n",
|
||||
" project=\"Detection of plant diseases\",\n",
|
||||
"\n",
|
||||
" # track hyperparameters and run metadata with wandb.config\n",
|
||||
" config={\n",
|
||||
" \"layer_1\": 512,\n",
|
||||
" \"activation_1\": \"relu\",\n",
|
||||
" \"dropout\": random.uniform(0.01, 0.80),\n",
|
||||
" \"layer_2\": 10,\n",
|
||||
" \"activation_2\": \"softmax\",\n",
|
||||
" \"optimizer\": \"sgd\",\n",
|
||||
" \"loss\": \"sparse_categorical_crossentropy\",\n",
|
||||
" \"metric\": \"accuracy\",\n",
|
||||
" \"epoch\": 8,\n",
|
||||
" \"batch_size\": 256\n",
|
||||
" }\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# [optional] use wandb.config as your config\n",
|
||||
"config = wandb.config\n",
|
||||
"\n",
|
||||
"# get the data\n",
|
||||
"mnist = tf.keras.datasets.mnist\n",
|
||||
"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
|
||||
"x_train, x_test = x_train / 255.0, x_test / 255.0\n",
|
||||
"x_train, y_train = x_train[::5], y_train[::5]\n",
|
||||
"x_test, y_test = x_test[::20], y_test[::20]\n",
|
||||
"labels = [str(digit) for digit in range(np.max(y_train) + 1)]\n",
|
||||
"\n",
|
||||
"# build a model\n",
|
||||
"model = tf.keras.models.Sequential([\n",
|
||||
" tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
|
||||
" tf.keras.layers.Dense(config.layer_1, activation=config.activation_1),\n",
|
||||
" tf.keras.layers.Dropout(config.dropout),\n",
|
||||
" tf.keras.layers.Dense(config.layer_2, activation=config.activation_2)\n",
|
||||
" ])\n",
|
||||
"\n",
|
||||
"# compile the model\n",
|
||||
"model.compile(optimizer=config.optimizer,\n",
|
||||
" loss=config.loss,\n",
|
||||
" metrics=[config.metric]\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
"# WandbMetricsLogger will log train and validation metrics to wandb\n",
|
||||
"# WandbModelCheckpoint will upload model checkpoints to wandb\n",
|
||||
"history = model.fit(x=x_train, y=y_train,\n",
|
||||
" epochs=config.epoch,\n",
|
||||
" batch_size=config.batch_size,\n",
|
||||
" validation_data=(x_test, y_test),\n",
|
||||
" callbacks=[\n",
|
||||
" WandbMetricsLogger(log_freq=5),\n",
|
||||
" WandbModelCheckpoint(\"models\")\n",
|
||||
" ])\n",
|
||||
"\n",
|
||||
"# [optional] finish the wandb run, necessary in notebooks\n",
|
||||
"wandb.finish()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"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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|
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"name": "python3"
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|
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"language_info": {
|
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|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
@ -0,0 +1,22 @@
|
|||
import wandb
|
||||
|
||||
class Config:
|
||||
def __init__(self, epoch, batch_size):
|
||||
self.epoch = epoch
|
||||
self.batch_size = batch_size
|
||||
|
||||
self.run = wandb.init(
|
||||
project="Detection of plant diseases",
|
||||
config={
|
||||
"epoch": epoch,
|
||||
"batch_size": batch_size,
|
||||
}
|
||||
)
|
||||
|
||||
def config(self):
|
||||
return self.run.config
|
||||
|
||||
def finish(self):
|
||||
self.run.config.finish()
|
||||
|
||||
|
||||
Loading…
Reference in New Issue