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[Monkey2912332]

import tensorflow as tf
from tensorflow.keras import layers, models
import numpy as np

Cargar un conjunto de datos de ejemplo (MNIST)

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

Preprocesar los datos

x_train = x_train.reshape(-1, 28, 28, 1).astype("float32") / 255.0
x_test = x_test.reshape(-1, 28, 28, 1).astype("float32") / 255.0

Crear el modelo

model = models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(10, activation='softmax') # 10 clases para MNIST
])

Compilar el modelo

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

Entrenar el modelo

model.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test))

Evaluar el modelo

test_loss, test_acc = model.evaluate(x_test, y_test)
print(f"Precisión en el conjunto de prueba: {test_acc}")