Training Notebook

Training Code Overview

This shows the key parts of the model training process from training/training.ipynb.

Basic Setup

import tensorflow as tf
from tensorflow.keras import models, layers
import matplotlib.pyplot as plt

IMAGE_SIZE = 256
BATCH_SIZE = 32
CHANNELS = 3
EPOCHS = 50

Load Dataset

dataset = tf.keras.preprocessing.image_dataset_from_directory(
    "PlantVillage",
    shuffle = True,
    image_size = (IMAGE_SIZE, IMAGE_SIZE),
    batch_size = BATCH_SIZE
)

class_names = dataset.class_names  # ["Early Blight", "Late Blight", "Healthy"]

Data Splitting

train_size = 0.8
train_ds = dataset.take(54)
test_ds = dataset.skip(54)

Model Architecture

model = models.Sequential([
    # Preprocessing
    layers.Rescaling(1./255),
    
    # CNN layers
    layers.Conv2D(32, (3,3), activation='relu'),
    layers.MaxPooling2D((2,2)),
    layers.Conv2D(64, (3,3), activation='relu'), 
    layers.MaxPooling2D((2,2)),
    layers.Conv2D(128, (3,3), activation='relu'),
    layers.MaxPooling2D((2,2)),
    
    # Classification layers
    layers.Flatten(),
    layers.Dropout(0.5),
    layers.Dense(128, activation='relu'),
    layers.Dense(len(class_names), activation='softmax')
])

The complete training code is in the Jupyter notebook at training/training.ipynb.