TensorFlow / Python: Clasificación de Imágenes con Redes Neuronales Convolutivas

by Beastieux, posted in Redes Neuronales, TensorFlow

Clasificar imágenes es un tema común en redes neuronales. Aquí tienes un ejemplo simple de cómo realizar esta tarea utilizando TensorFlow y la biblioteca Keras, vamos a realizar la clasificación de imágenes de gatos y perros:

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
import matplotlib.pyplot as plt

# Directorios de datos (ajusta las rutas a tu configuración)
train_dir = '/content/entrenamiento/'
test_dir = '/content/prueba/'

# Preprocesamiento de datos
train_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(150, 150),
    batch_size=20,
    class_mode='binary',
    classes=['gatos', 'perros']
)

test_generator = test_datagen.flow_from_directory(
    test_dir,
    target_size=(150, 150),
    batch_size=20,
    class_mode='binary',
    classes=['gatos', 'perros']
)

# Construir el modelo
model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),
    MaxPooling2D(2, 2),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D(2, 2),
    Conv2D(128, (3, 3), activation='relu'),
    MaxPooling2D(2, 2),
    Flatten(),
    Dense(512, activation='relu'),
    Dense(1, activation='sigmoid')
])

# Compilar el modelo
model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

# Entrenar el modelo
history = model.fit(train_generator, epochs=15, validation_data=test_generator)

# Evaluar el modelo
test_loss, test_acc = model.evaluate(test_generator)
print(f'Precisión en el conjunto de prueba: {test_acc * 100:.2f}%')


# Visualizar imágenes y sus predicciones
sample_images, sample_labels = next(test_generator)

predictions = model.predict(sample_images)

for i in range(len(sample_labels)):
    label = 'Gato' if sample_labels[i] == 0 else 'Perro'
    prediction = 'Gato' if predictions[i] < 0.5 else 'Perro'
    
    plt.imshow(sample_images[i])
    plt.title(f'Clase real: {label}\nPredicción: {prediction}')
    plt.show()


# Visualizar la pérdida y la precisión durante el entrenamiento
plt.plot(history.history['accuracy'], label='Precisión de entrenamiento')
plt.plot(history.history['val_accuracy'], label='Precisión de prueba')
plt.xlabel('Épocas')
plt.ylabel('Precisión')
plt.legend(loc='lower right')
plt.show()
  1. Importación de Bibliotecas: Se importan las bibliotecas necesarias, incluyendo TensorFlow (para el aprendizaje profundo), ImageDataGenerator (para el preprocesamiento de imágenes), Sequential (para la creación del modelo), Conv2D y MaxPooling2D (capas convolucionales), Flatten y Dense (capas completamente conectadas) y Matplotlib (para la visualización).
  2. Directorios de Datos: Se definen las rutas a los directorios que contienen los datos de entrenamiento y prueba. Asegúrate de ajustar estas rutas a tu propia configuración, de modo que los directorios de datos estén correctamente especificados, deben contener los directorios gatos y perros en los directorios de prueba y entrenamiento.
  3. Preprocesamiento de Datos: Se configuran generadores de datos para preprocesar las imágenes. El ImageDataGenerator se utiliza para escalar los valores de píxeles en el rango [0, 1] al dividirlos por 255. Esto normaliza las imágenes.
  4. Generadores de Datos de Entrenamiento y Prueba: Se utilizan los generadores de datos para cargar las imágenes desde los directorios de datos de entrenamiento y prueba. Además, se especifica que hay dos clases: «gatos» y «perros».
  5. Construcción del Modelo: Se crea un modelo de red neuronal secuencial utilizando Keras. El modelo consiste en capas convolucionales y capas completamente conectadas. El modelo se define para procesar imágenes de 150×150 píxeles con 3 canales de color (RGB).
  6. Compilación del Modelo: El modelo se compila especificando la función de pérdida (‘binary_crossentropy’ para clasificación binaria), el optimizador (‘adam’) y las métricas a seguir (‘accuracy’ para precisión).
  7. Entrenamiento del Modelo: El modelo se entrena con los datos de entrenamiento utilizando el generador de datos de entrenamiento. Se ejecutan 15 épocas y se utiliza el conjunto de prueba para la validación. El historial del entrenamiento se almacena en history.
  8. Evaluación del Modelo: Se evalúa el modelo en el conjunto de prueba para calcular la precisión en los datos de prueba.
  9. Visualización de Imágenes y Predicciones: Se seleccionan un conjunto de imágenes de prueba y se realizan predicciones en ellas. Luego, se muestra cada imagen junto con su etiqueta real y la etiqueta predicha por el modelo.
  10. Visualización de la Pérdida y la Precisión: Se visualiza cómo cambia la precisión en el conjunto de entrenamiento y prueba a lo largo de las épocas.

Precisión en el conjunto de prueba: 100.00%

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