dermy-models/binary-compile.py

import tensorflow as tf 
import os 
from safetensors.numpy import save_file
DIR = "data/binary-classification"

#Import Data
PATH = os.path.join(os.getcwd(), DIR)

training_path = os.path.join(PATH, "train")
test_path = os.path.join(PATH, "test")

BATCH_SIZE = 64
IMG_SIZE = (224,224)

#TODO: Import data from both directories, then resplit into test, train, and validation

training_data = tf.keras.utils.image_dataset_from_directory(training_path,
                                             shuffle=True,
                                             batch_size=BATCH_SIZE,
                                             image_size=IMG_SIZE,
                                             validation_split=0.2,
                                             subset="training",
                                             seed=1234)
validation_data = tf.keras.utils.image_dataset_from_directory(training_path,
                                               shuffle=True,
                                               batch_size=BATCH_SIZE,
                                               image_size=IMG_SIZE,
                                               validation_split=0.2,
                                               subset="validation",
                                               seed=1234)
test_data = tf.keras.utils.image_dataset_from_directory(test_path,
                                         shuffle=True,
                                         batch_size=BATCH_SIZE,
                                         image_size=IMG_SIZE)




#View Data



#Init Prefetching
AUTOTUNE = tf.data.AUTOTUNE
training_data = training_data.prefetch(buffer_size=AUTOTUNE)
validation_data = validation_data.prefetch(buffer_size=AUTOTUNE)
test_data = test_data.prefetch(buffer_size=AUTOTUNE)


#Data Augmentation
data_augmentation = tf.keras.Sequential([
    tf.keras.layers.RandomFlip('horizontal'),
    tf.keras.layers.RandomRotation(0.2)
    ])

#Create Base Model From MobileNetV3
IMG_SHAPE = IMG_SIZE + (3,)
base_model = tf.keras.applications.MobileNetV3Large(
        input_shape=IMG_SHAPE,
        include_top=False,
        weights="imagenet"
        )

image_batch, label_batch = next(iter(training_data))
feature_batch = base_model(image_batch)

base_model.trainable = False


#View Base Model
base_model.summary()

#Add Classification Header


global_avg_layer = tf.keras.layers.GlobalAveragePooling2D()
feature_batch_avg = global_avg_layer(feature_batch)

prediction_layer = tf.keras.layers.Dense(2, activation="softmax")
predication_batch = prediction_layer(feature_batch_avg)

inputs = tf.keras.Input(shape=IMG_SHAPE)
x = data_augmentation(inputs)
x = base_model(x, training=False)
x = global_avg_layer(x)
x = tf.keras.layers.Dropout(0.2)(x)
outputs = prediction_layer(x)

model = tf.keras.Model(inputs, outputs)

#View Model with Classification Head
model.summary()


#Compile the Model
base_learning_rate = 0.0001

training_data = training_data.map(lambda x,y: (x, tf.one_hot(y,2)))
validation_data = validation_data.map(lambda x,y: (x, tf.one_hot(y,2)))
test_data = test_data.map(lambda x,y: (x, tf.one_hot(y,2)))

optimizer = tf.keras.optimizers.Adam(learning_rate=base_learning_rate)
loss = tf.keras.losses.CategoricalCrossentropy()
metrics = [tf.keras.metrics.CategoricalAccuracy()]

model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

#Train the Model
initial_epochs = 50

loss0, accuracy0 = model.evaluate(validation_data)

print(f"initial loss: {loss0}")
print(f"initial accuracy: {accuracy0}")

lr_schedule = tf.keras.callbacks.ReduceLROnPlateau(
        monitor="val_loss",
        factor=0.1,
        patience=5,
        min_lr=1e-6
        )

early_stopping = tf.keras.callbacks.EarlyStopping(
        monitor="val_loss",
        patience=10,
        restore_best_weights=True
        )

history = model.fit(training_data,
                    epochs=initial_epochs,
                    validation_data=validation_data,
                    callbacks=[lr_schedule, early_stopping])


#Evaluate Model
results = model.evaluate(validation_data)
print(f"Validation Loss: {results[0]}")
print(f"Validation Accuracy: {results[1]}")

results = model.evaluate(test_data)
print(f"Test Loss: {results[0]}")
print(f"Test Accuracy: {results[1]}")

weights = model.get_weights()
weights_dict = {f"weight_{i}": w for i, w in enumerate(weights)}

save_file(weights_dict, "models/mobilenet_v3.safetensors")