major.py

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import VGG16
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Flatten, Dropout
from tensorflow.keras.optimizers import Adam

# Set the paths
train_dir = './dataset/train'
val_dir = './dataset/val'
test_dir = './dataset/test'

# ImageDataGenerator for data augmentation
train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=40,
    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)
test_datagen = ImageDataGenerator(rescale=1./255)

# Flow images from the directories
train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(224, 224),
    batch_size=32,
    class_mode='categorical'
)

val_generator = val_datagen.flow_from_directory(
    val_dir,
    target_size=(224, 224),
    batch_size=32,
    class_mode='categorical'
)

test_generator = test_datagen.flow_from_directory(
    test_dir,
    target_size=(224, 224),
    batch_size=32,
    class_mode='categorical'
)


base_model = VGG16(weights='imagenet', include_top=False,
                   input_shape=(224, 224, 3))

# Freeze the convolutional base
for layer in base_model.layers:
    layer.trainable = False

# Add custom layers on top
x = Flatten()(base_model.output)
x = Dense(512, activation='relu')(x)
x = Dropout(0.5)(x)
x = Dense(7, activation='softmax')(x)  # 7 categories

# Create the model
model = Model(inputs=base_model.input, outputs=x)


model.compile(
    optimizer=Adam(learning_rate=0.0001),
    loss='categorical_crossentropy',
    metrics=['accuracy']
)


history = model.fit(
    train_generator,
    steps_per_epoch=train_generator.samples // train_generator.batch_size,
    epochs=20,
    validation_data=val_generator,
    validation_steps=val_generator.samples // val_generator.batch_size
)


test_loss, test_acc = model.evaluate(
    test_generator, steps=test_generator.samples // test_generator.batch_size)
print(f'Test accuracy: {test_acc}')