TensorFlow / Python: Transferencia de Aprendizaje con Fine-Tuning de Modelos Pre-entrenados

by Beastieux, posted in Blog

Este enfoque de aprendizaje automático implica tomar un modelo de red neuronal pre-entrenado en un conjunto de datos grande y afinarlo para una tarea específica. Este proceso implica:

  1. Transferencia de Aprendizaje (Transfer Learning): En el aprendizaje profundo, se ha demostrado que las redes neuronales entrenadas en conjuntos de datos masivos, como ImageNet para imágenes o GPT-3 para procesamiento de lenguaje natural, aprenden representaciones útiles y generalizadas. En lugar de entrenar un modelo desde cero, la transferencia de aprendizaje aprovecha estas representaciones pre-entrenadas como punto de partida.
  2. Fine-Tuning (Afinamiento): El proceso de fine-tuning implica tomar un modelo pre-entrenado y ajustarlo para realizar una tarea específica. Esto se hace desbloqueando una o varias capas del modelo pre-entrenado y permitiendo que los pesos se actualicen durante el entrenamiento en un nuevo conjunto de datos más pequeño y específico.
  3. Ejemplo de Fine-Tuning:
    • Imagina que tienes un modelo de red neuronal convolucional (CNN) que fue entrenado en un gran conjunto de datos de imágenes para clasificar objetos generales, como perros y gatos.
    • Ahora, quieres usar ese modelo para una tarea específica, como clasificar diferentes razas de perros. En lugar de entrenar un modelo desde cero, puedes realizar fine-tuning en el modelo pre-entrenado.
    • Para hacerlo, desbloquearías una o más capas en el modelo (generalmente las capas más profundas) y las entrenarías en un conjunto de datos más pequeño de imágenes de diferentes razas de perros.
    • El modelo pre-entrenado ya ha aprendido características útiles, como bordes, texturas y formas, que son relevantes para la tarea de clasificación de razas de perros. Fine-tuning ajusta estas características para que sean específicas de las razas de perros en cuestión.
  4. Beneficios de Fine-Tuning:
    • El fine-tuning a menudo requiere menos datos de entrenamiento y tiempo de cómputo en comparación con el entrenamiento desde cero.
    • Permite aprovechar modelos de última generación pre-entrenados, lo que es especialmente útil en tareas de visión por computadora, procesamiento de lenguaje natural y más.

En el ejemplo proporcionado a continuación, estamos probando el modelo MobileNetV2 pre-entrenado sin realizar Fine-Tuning. No se están ajustando las capas superiores del modelo pre-entrenado en un nuevo conjunto de datos. En su lugar, simplemente estamos utilizando el modelo pre-entrenado para hacer predicciones en el conjunto de datos de flores, lo que nos permite ver cómo se desempeña en ese conjunto de datos sin realizar ningún ajuste adicional.

import tensorflow as tf
import tensorflow_hub as hub
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import numpy as np

# URL del modelo MobileNetV2 pre-entrenado
model_url = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4"

# Directorio de datos de flores
data_root = tf.keras.utils.get_file(
    'flower_photos',
    'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',
    untar=True)

# Crear un generador de datos de imágenes para el conjunto de datos de flores
image_data_generator = ImageDataGenerator(rescale=1/255)
image_data = image_data_generator.flow_from_directory(data_root, target_size=(224, 224), batch_size=1)

# Cargar el modelo MobileNetV2 desde TensorFlow Hub
model = hub.load(model_url)

# Contador para limitar a 10 imágenes
count = 0
num_images = 10  # Número de imágenes para mostrar

# Listas para almacenar las imágenes y las clasificaciones
images = []
classifications = []

# Procesar imágenes de flores y obtener las predicciones
for batch in image_data:
    image = batch[0]  # Obtener la imagen del lote
    predictions = model(image)  # Realizar predicciones
    
    # Obtener las etiquetas y probabilidades
    labels_url = "https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt"
    labels_path = tf.keras.utils.get_file("ImageNetLabels.txt", labels_url)
    with open(labels_path) as file:
        labels = file.read().splitlines()
    
    top_label_index = tf.argmax(predictions, axis=-1)
    top_label = labels[top_label_index[0]]
    
    images.append(image[0])
    classifications.append(top_label)
    
    count += 1
    if count >= num_images:
        break  # Detener después de procesar el número deseado de imágenes

# Crear una matriz de imágenes y clasificaciones
fig, axes = plt.subplots(2, 5, figsize=(10, 4))
axes = axes.ravel()

for i in range(num_images):
    axes[i].imshow(images[i])
    axes[i].set_title(classifications[i])
    axes[i].axis('off')

plt.tight_layout()
plt.show()

Aquí les muestro un ejemplo de cómo realizar fine-tuning de un modelo pre-entrenado en TensorFlow. En este caso, utilizaremos un modelo pre-entrenado de TensorFlow Hub para la clasificación de imágenes y lo afinaremos para una tarea específica de clasificación de flores:

import tensorflow as tf
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Descargar el conjunto de datos de flores
data_root = tf.keras.utils.get_file(
    'flower_photos',
    'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',
    untar=True)

# Crear un generador de datos de imágenes para el conjunto de datos de flores
image_data_generator = ImageDataGenerator(rescale=1/255, validation_split=0.2)
batch_size = 32

train_data = image_data_generator.flow_from_directory(
    data_root,
    target_size=(224, 224),
    batch_size=batch_size,
    subset='training'
)

validation_data = image_data_generator.flow_from_directory(
    data_root,
    target_size=(224, 224),
    batch_size=batch_size,
    subset='validation'
)

# Cargar la arquitectura MobileNetV2 pre-entrenada sin las capas superiores
base_model = MobileNetV2(weights='imagenet', include_top=False)

# Agregar capas de clasificación adicionales para flores
x = GlobalAveragePooling2D()(base_model.output)
x = Dense(128, activation='relu')(x)
output = Dense(5, activation='softmax')(x)  # 5 clases de flores en este caso

# Crear el nuevo modelo con las capas de clasificación
model = Model(inputs=base_model.input, outputs=output)

# Congelar las capas del modelo base
for layer in base_model.layers:
    layer.trainable = False

# Compilar el modelo
model.compile(optimizer=Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])

# Entrenar el modelo
model.fit(train_data, validation_data=validation_data, epochs=10)

Este ejemplo descarga el conjunto de datos de flores, carga la arquitectura de MobileNetV2 pre-entrenada y agrega capas de clasificación para clasificar flores específicas. Luego, realiza el fine-tuning en el conjunto de datos de flores.

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