train_model.py

import os
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from model import create_model 

base_dir = 'data/chest_xray'
train_dir = os.path.join(base_dir, 'train')
val_dir = os.path.join(base_dir, 'val')

train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest'
)
val_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary'
)

val_generator = val_datagen.flow_from_directory(
    val_dir,
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary'
)

sample_images, _ = next(train_generator)
for i in range(5):
    plt.subplot(1, 5, i+1)
    plt.imshow(sample_images[i])
    plt.axis('off')
plt.show()

model = create_model()

history = model.fit(
    train_generator,
    steps_per_epoch=243,
    epochs=10,
    validation_data=val_generator,
    validation_steps=280,
    callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)]
)

model.save('xray_image_classifier_model.keras')