Matlab - Codificacion Neuronal

import tensorflow as tf

from tensorflow.keras import layers, models

from tensorflow.keras.datasets import mnist

from tensorflow.keras.utils import to_categorical

# Cargar y preprocesar el conjunto de datos MNIST

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255

test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255

train_labels = to_categorical(train_labels)

test_labels = to_categorical(test_labels)

# Construir el modelo de la red neuronal

model = models.Sequential()

model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))

model.add(layers.MaxPooling2D((2, 2)))

model.add(layers.Conv2D(64, (3, 3), activation='relu'))

model.add(layers.MaxPooling2D((2, 2)))

model.add(layers.Conv2D(64, (3, 3), activation='relu'))

model.add(layers.Flatten())

model.add(layers.Dense(64, activation='relu'))

model.add(layers.Dense(10, activation='softmax'))

# Compilar el modelo

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Entrenar el modelo

model.fit(train_images, train_labels, epochs=5, batch_size=64, validation_data=(test_images, test_labels))