train_model.py

import os
import time
import numpy as np
import pickle
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Activation, Dropout
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau

# ==============================
# CONFIG
# ==============================
MODEL_NAME = "Tigrigna_convnet"
EPOCHS = 100
BATCH_SIZE = 32
CHANNELS = 1
IMG_HEIGHT, IMG_WIDTH = 28, 28

# ==============================
# LOAD DATA
# ==============================
def load_data(dataset_path):
    with open(dataset_path, "rb") as f:
        data, labels = pickle.load(f)

    print(f"[INFO] dataset loaded. Shape: {data.shape}, Labels: {len(labels)}")
    print(f"[INFO] Unique labels: {len(np.unique(labels))}")

    # Our data is already normalized (0-1) and has the right shape
    # No need to reshape or normalize again
    
    # Convert labels to categorical
    num_classes = len(np.unique(labels))
    labels = to_categorical(labels, num_classes=num_classes)

    # Split the data
    X_train, X_test, y_train, y_test = train_test_split(
        data, labels, test_size=0.2, random_state=42, stratify=np.argmax(labels, axis=1)
    )

    return X_train, y_train, X_test, y_test, num_classes

# ==============================
# BUILD MODEL
# ==============================
def build_model(num_classes):
    model = Sequential()
    
    # First Convolutional Layer
    model.add(Conv2D(32, (3, 3), padding="same", input_shape=(IMG_HEIGHT, IMG_WIDTH, CHANNELS)))
    model.add(Activation("relu"))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    
    # Second Convolutional Layer
    model.add(Conv2D(64, (3, 3), padding="same"))
    model.add(Activation("relu"))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    
    # Fully connected layers
    model.add(Flatten())
    model.add(Dense(512, activation="relu"))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes, activation="softmax"))
    
    return model

# ==============================
# PLOT TRAINING HISTORY
# ==============================
def plot_model_history(history):
    fig, axs = plt.subplots(1, 2, figsize=(15, 5))

    # Accuracy
    axs[0].plot(history.history["accuracy"], label="train")
    if "val_accuracy" in history.history:
        axs[0].plot(history.history["val_accuracy"], label="val")
    axs[0].set_title("Model Accuracy")
    axs[0].set_xlabel("Epoch")
    axs[0].set_ylabel("Accuracy")
    axs[0].legend()

    # Loss
    axs[1].plot(history.history["loss"], label="train")
    if "val_loss" in history.history:
        axs[1].plot(history.history["val_loss"], label="val")
    axs[1].set_title("Model Loss")
    axs[1].set_xlabel("Epoch")
    axs[1].set_ylabel("Loss")
    axs[1].legend()

    plt.savefig('training_history.png')
    plt.show()

# ==============================
# TRAIN MODEL
# ==============================
def train_model(model, X_train, y_train, X_test, y_test):
    print("[INFO] training model...")
    model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

    # Data augmentation
    datagen = ImageDataGenerator(
        rotation_range=10,
        width_shift_range=0.1,
        height_shift_range=0.1,
        zoom_range=0.1
    )
    datagen.fit(X_train)

    # Callbacks
    early_stop = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
    reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.0001)

    start = time.time()
    history = model.fit(
        datagen.flow(X_train, y_train, batch_size=BATCH_SIZE),
        steps_per_epoch=len(X_train) // BATCH_SIZE,
        epochs=EPOCHS,
        validation_data=(X_test, y_test),
        callbacks=[early_stop, reduce_lr],
        verbose=1,
    )
    end = time.time()
    print(f"[INFO] Training finished in {end - start:.2f} seconds")

    plot_model_history(history)

    _, acc = model.evaluate(X_test, y_test, verbose=0)
    print(f"[INFO] Test Accuracy: {acc * 100:.2f}%")

    return model, history

# ==============================
# MAIN
# ==============================
def main():
    X_train, y_train, X_test, y_test, num_classes = load_data("dataset_pickles/tigrigna_dataset.pickle")
    
    print(f"[INFO] using {num_classes} classes")
    print(f"[INFO] Training data shape: {X_train.shape}")
    print(f"[INFO] Training labels shape: {y_train.shape}")

    model = build_model(num_classes)
    model.summary()
    
    model, history = train_model(model, X_train, y_train, X_test, y_test)

    os.makedirs("out", exist_ok=True)
    model.save("out/Tig_Model.h5")
    
    print("[INFO] Model saved at out/Tig_Model.h5")

if __name__ == "__main__":
    main()