Presentamos KerasHub: la tienda de modelos preentrenados

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value="/zh-hans/introducing-keras-hub-for-pretrained-models/" lang="zh-hans" > 简体中文 </option> </select> </div> <div class="backdrop"></div> <div class="blog-detail-container"> <section class="tags-container glue-page glue-spacer-5-top"> <div class="glue-eyebrow"><a href="/es/search/?product_categories=Gemma">Gemma</a></div> </section> <section class="heading-container glue-page glue-spacer-1-top"> <h1 class="glue-headline glue-headline--headline-1">Presentamos KerasHub: la tienda de modelos preentrenados</h1> </section> <section class="summary-container glue-page glue-spacer-4-top"> <div class="date-time"> <div class="published-date glue-font-weight-medium">OCT 22, 2024</div> </div> </section> <section class="glue-page glue-grid glue-spacer-1-top"> <section class="author-container glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-10-md"> <div class="author-obj"> <a class="glue-font-weight-medium" href="/es/search/?author=Divyashree+Sreepathihalli">Divyashree Sreepathihalli</a> <span class="glue-font-weight-medium role">Software Engineer</span> </div> <div class="author-obj"> <a class="glue-font-weight-medium" href="/es/search/?author=Luciano+Martins">Luciano Martins</a> <span class="glue-font-weight-medium role">Developer Advocate</span> <span class="glue-font-weight-medium team">Google AI</span> </div> </section> <section class="social-container glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-2-md"> <button id="social-button" class="glue-button glue-button--low-emphasis glue-button--icon" aria-haspopup="true" aria-expanded="false"> <svg role="presentation" aria-hidden="true" class="glue-icon"> <use href="/glue-icon/#share"></use> </svg> <span>Compartir</span> </button> <ul id="social-menu" class="glue-elevation-level-1" role="menu" aria-labelledby="social-button"> <li> <a href="https://www.facebook.com/sharer/sharer.php?u={url}" title="Share on Facebook" target="_blank" rel="noopener"> <svg role="presentation" 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href="/glue-icon/#email"></use> </svg> <span>Mail</span> </a> </li> <li> <a href="#" title="Get shareable link" data-link="" data-copy-text="Copiar vínculo" data-copied-text="¡Copiado!"> <svg role="presentation" aria-hidden="true" class="glue-icon glue-icon--social glue-icon--32px"> <use href="/glue-icon/#link"></use> </svg> <span></span> </a> </li> </ul> </section> </section> <section class="blocks-container glue-page glue-spacer-3-top"> <div class="block"> <img class="banner-image" src="https://storage.googleapis.com/gweb-developer-goog-blog-assets/images/Keras-GfD-Banner.original.png" alt="Keras on Google for Developers - banner" /> <div class="inner-block-content rich-content"> <p data-block-key="u50tg">El mundo del aprendizaje profundo está evolucionando con rapidez, y los modelos preentrenados son cada vez más importantes para realizar una amplia variedad de tareas. Keras, conocida por su API fácil de usar y su enfoque en la accesibilidad, siempre estuvo al frente de este movimiento ofreciendo bibliotecas especializadas, como KerasNLP para modelos basados en texto y KerasCV para modelos de visión artificial.</p><p data-block-key="as7cn">Sin embargo, a medida que los modelos desdibujan cada vez más las diferencias entre modalidades (piensa en los potentes LLM de chat con entrada de imágenes o las tareas de visión que aprovechan los codificadores de texto), mantener estos dominios separados es menos práctico. La división entre PLN y CV puede dificultar el desarrollo y la implementación de modelos verdaderamente multimodales, lo que lleva a esfuerzos redundantes y una experiencia de usuario fragmentada.</p> </div> <div class="inner-block-content"> <div class="image-wrapper"> <img class="regular-image" src="https://storage.googleapis.com/gweb-developer-goog-blog-assets/images/image3_ljbDif4.original.png" alt="keras-team/keras-hub, a unified, comprehensive library for pretrained models" /> </div> </div> <div class="inner-block-content rich-content"> <p data-block-key="u50tg">Para resolver este problema, nos complace anunciar una importante evolución en el ecosistema de Keras: <a href="https://keras.io/keras_hub/">KerasHub</a>, una biblioteca unificada y completa para modelos preentrenados, que agiliza el acceso a arquitecturas de PLN y CV de vanguardia. KerasHub es un repositorio central donde puedes explorar y utilizar sin problemas modelos de vanguardia como BERT para el análisis de texto junto con EfficientNet para la clasificación de imágenes, todo dentro de un marco de Keras consistente y familiar.</p><h3 data-block-key="620os"><br/><b>Una experiencia unificada para desarrolladores</b></h3><p data-block-key="fklj1">Esta unificación no solo simplifica el descubrimiento y el uso de modelos, sino que también fomenta un ecosistema más cohesivo. Con KerasHub, puedes aprovechar funciones avanzadas, como la publicación y el intercambio de modelos simplificados, el ajuste de LoRA para una adaptación eficiente de recursos, la cuantificación para un rendimiento optimizado y una sólida capacitación de múltiples hosts para abordar conjuntos de datos a gran escala, todo aplicable en diversas modalidades. Esto marca un paso significativo hacia la democratización del acceso a potentes herramientas de IA y la aceleración del desarrollo de aplicaciones multimodales innovadoras.</p><h3 data-block-key="6vq37"><br/><b>Cinco pasos con KerasHub</b></h3><p data-block-key="8n85p">Para empezar, instala KerasHub en tu sistema. A partir de ahí, puedes explorar la extensa colección de <a href="https://keras.io/api/keras_hub/models/">modelos ya disponibles</a> y diferentes implementaciones de arquitecturas populares. Luego, podrás cargar e incorporar fácilmente estos modelos preentrenados en tus propios proyectos y ajustarlos para un rendimiento óptimo en función de tus requisitos específicos.</p><h3 data-block-key="4cu9q"><br/><b>Cómo instalar KerasHub</b></h3><p data-block-key="8btr5">Para instalar la versión más reciente de KerasHub con Keras 3, ejecuta lo siguiente:</p> </div> <div class="inner-block-content code-block"> <div class="highlight"><pre class="shell"><span></span>$<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--upgrade<span class="w"> </span>keras-hub </pre></div> </div> <div class="inner-block-content rich-content"> <p data-block-key="u50tg">A continuación, podrás empezar a explorar los modelos disponibles. El entorno de configuración estándar para comenzar a trabajar con Keras 3 no cambia con KerasHub:</p> </div> <div class="inner-block-content code-block"> <div class="highlight"><pre class="python"><span></span><span class="kn">import</span> <span class="nn">os</span> <span class="c1"># Define el backend de Keras 3 que quieres usar - "jax", "tensorflow" o "torch"</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"KERAS_BACKEND"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"jax"</span> <span class="c1"># Importa los módulos de Keras 3 y KerasHub</span> <span class="kn">import</span> <span class="nn">keras</span> <span class="kn">import</span> <span class="nn">keras_hub</span> </pre></div> </div> <div class="inner-block-content rich-content"> <h2 data-block-key="u50tg">Cómo usar modelos de visión artificial y lenguaje natural con KerasHub</h2><p data-block-key="728q8">Ahora ya puedes comenzar a usar KerasHub para acceder a los modelos disponibles en el ecosistema de Keras 3 y utilizarlos. Estos son algunos ejemplos:</p><h3 data-block-key="6b0r3"><b><br/>Gemma</b></h3><p data-block-key="87ia"><a href="https://ai.google.dev/gemma/docs/base">Gemma</a> es una colección de modelos abiertos de vanguardia, pero accesibles, desarrollados por Google. Como aprovechan la misma investigación y tecnología que usan los modelos Gemini, los modelos base de Gemma se destacan en diversas tareas de generación de texto. Algunos ejemplos son responder preguntas, resumir información y hacer razonamientos lógicos. Además, se pueden personalizar para satisfacer necesidades específicas.</p><p data-block-key="fgnpl">En este ejemplo, usarás Keras y KerasHub para cargar e iniciar la generación de contenidos usando parámetros Gemma2-2B. Para obtener más información sobre las variantes de Gemma, consulta la <a href="https://www.kaggle.com/models/google/gemma/">tarjeta de modelo de Gemma 2</a> en Kaggle.</p> </div> <div class="inner-block-content code-block"> <div class="highlight"><pre class="python"><span></span><span class="c1"># Carga el parámetro 2-2B de los modelos de Kaggle </span> <span class="n">gemma_lm</span> <span class="o">=</span> <span class="n">keras_hub</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">GemmaCausalLM</span><span class="o">.</span><span class="n">from_preset</span><span class="p">(</span><span class="s2">"gemma_2b_en"</span><span class="p">)</span> <span class="c1"># Comienza a generar contenidos con Gemma 2-2B</span> <span class="n">gemma_lm</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="s2">"Keras is a"</span><span class="p">,</span> <span class="n">max_length</span><span class="o">=</span><span class="mi">32</span><span class="p">)</span> </pre></div> </div> <div class="inner-block-content rich-content"> <h3 data-block-key="u50tg"><b>PaliGemma</b></h3><p data-block-key="cj0hi"><a href="https://ai.google.dev/gemma/docs/paligemma">PaliGemma</a> es un modelo abierto y compacto que comprende tanto imágenes como texto. Como obtiene inspiración de <a href="https://arxiv.org/abs/2310.09199">PaLI-3</a> y está construido con componentes de código abierto, como el <a href="https://arxiv.org/abs/2303.15343">modelo de visión SigLIP</a> y el <a href="https://arxiv.org/abs/2403.08295">modelo de lenguaje Gemma</a>, PaliGemma puede brindar respuestas detalladas y relevantes para lograr una mejor comprensión del contenido visual, lo que posibilita funciones como la generación de subtítulos para imágenes y videos cortos, la identificación de objetos e incluso la lectura de texto dentro de imágenes.</p> </div> <div class="inner-block-content code-block"> <div class="highlight"><pre class="python"><span></span><span class="kn">import</span> <span class="nn">os</span> <span class="c1"># Define el backend de Keras 3 que quieras usar - "jax", "tensorflow" o "torch"</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"KERAS_BACKEND"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"jax"</span> <span class="c1"># Importa los módulos de Keras 3 y KerasHub</span> <span class="kn">import</span> <span class="nn">keras</span> <span class="kn">import</span> <span class="nn">keras_hub</span> <span class="kn">from</span> <span class="nn">keras.utils</span> <span class="kn">import</span> <span class="n">get_file</span><span class="p">,</span> <span class="n">load_img</span><span class="p">,</span> <span class="n">img_to_array</span> <span class="c1"># Importa PaliGemma 3B con ajuste de imágenes de 224x224</span> <span class="n">pali_gemma_lm</span> <span class="o">=</span> <span class="n">keras_hub</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">PaliGemmaCausalLM</span><span class="o">.</span><span class="n">from_preset</span><span class="p">(</span> <span class="s2">"pali_gemma_3b_mix_224"</span> <span class="p">)</span> <span class="c1"># Descarga una imagen de prueba y prepárala para su uso con KerasHub</span> <span class="n">url</span> <span class="o">=</span> <span class="s1">'https://storage.googleapis.com/keras-cv/models/paligemma/cow_beach_1.png'</span> <span class="n">img_path</span> <span class="o">=</span> <span class="n">get_file</span><span class="p">(</span><span class="n">origin</span><span class="o">=</span><span class="n">url</span><span class="p">)</span> <span class="n">img</span> <span class="o">=</span> <span class="n">img_to_array</span><span class="p">(</span><span class="n">load_img</span><span class="p">(</span><span class="n">image_path</span><span class="p">))</span> <span class="c1"># Crea la instrucción con la pregunta sobre la imagen</span> <span class="n">prompt</span> <span class="o">=</span> <span class="s1">'answer where is the cow standing?'</span> <span class="c1"># Genera los contenidos con PaliGemma</span> <span class="n">output</span> <span class="o">=</span> <span class="n">pali_gemma_lm</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span> <span class="n">inputs</span><span class="o">=</span><span class="p">{</span> <span class="s2">"images"</span><span class="p">:</span> <span class="n">img</span><span class="p">,</span> <span class="s2">"prompts"</span><span class="p">:</span> <span class="n">prompt</span><span class="p">,</span> <span class="p">}</span> <span class="p">)</span> </pre></div> </div> <div class="inner-block-content rich-content"> <p data-block-key="u50tg">Para obtener más detalles sobre los modelos preentrenados disponibles en Keras 3, consulta la <a href="https://www.kaggle.com/organizations/keras/models">lista de modelos en Keras</a> en Kaggle.</p><h3 data-block-key="fcv95"><br/><b>Stable Diffusion 3 de stability.ai</b></h3><p data-block-key="56ia0">También tienes a disposición modelos de visión artificial. Por ejemplo, puedes usar <a href="https://stability.ai/news/stable-diffusion-3">Stable Diffusion 3</a> de stability.ai con KerasHub:</p> </div> <div class="inner-block-content code-block"> <div class="highlight"><pre class="python"><span></span><span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span> <span class="kn">from</span> <span class="nn">keras.utils</span> <span class="kn">import</span> <span class="n">array_to_img</span> <span class="kn">from</span> <span class="nn">keras_hub.models</span> <span class="kn">import</span> <span class="n">StableDiffusion3TextToImage</span> <span class="n">text_to_image</span> <span class="o">=</span> <span class="n">StableDiffusion3TextToImage</span><span class="o">.</span><span class="n">from_preset</span><span class="p">(</span> <span class="s2">"stable_diffusion_3_medium"</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mi">1024</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1024</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">"float16"</span><span class="p">,</span> <span class="p">)</span> <span class="c1"># Genera imágenes con SD3</span> <span class="n">image</span> <span class="o">=</span> <span class="n">text_to_image</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span> <span class="s2">"photograph of an astronaut riding a horse, detailed, 8k"</span><span class="p">,</span> <span class="p">)</span> <span class="c1"># Muestra la imagen generada</span> <span class="n">img</span> <span class="o">=</span> <span class="n">array_to_img</span><span class="p">(</span><span class="n">image</span><span class="p">)</span> <span class="n">img</span> </pre></div> </div> <div class="inner-block-content rich-content"> <p data-block-key="u50tg">Para obtener más detalles sobre los modelos de visión artificial preentrenados disponibles en Keras 3, consulta la <a href="https://keras.io/api/keras_hub/models/">lista de modelos en Keras</a>.</p><h2 data-block-key="6bgmm"><br/>¿Qué cambia para los desarrolladores de KerasNLP?</h2><p data-block-key="b4vep">La transición de KerasNLP a KerasHub es un proceso directo. Solo requiere actualizar la instrucción de importación de <code>keras_nlp</code> a <code>keras_hub</code>.</p><p data-block-key="8mod1">Ejemplo: Anteriormente, si importabas keras_nlp para usar un modelo BERT como el siguiente</p> </div> <div class="inner-block-content code-block"> <div class="highlight"><pre class="python"><span></span><span class="kn">import</span> <span class="nn">keras_nlp</span> <span class="c1"># Carga un modelo de BERT </span> <span class="n">classifier</span> <span class="o">=</span> <span class="n">keras_nlp</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">BertClassifier</span><span class="o">.</span><span class="n">from_preset</span><span class="p">(</span> <span class="s2">"bert_base_en_uncased"</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="p">)</span> </pre></div> </div> <div class="inner-block-content rich-content"> <p data-block-key="u50tg">Ajusta la importación y ya podrás usar KerasHub:</p> </div> <div class="inner-block-content code-block"> <div class="highlight"><pre class="python"><span></span><span class="kn">import</span> <span class="nn">keras_hub</span> <span class="c1"># Carga un modelo de BERT </span> <span class="n">classifier</span> <span class="o">=</span> <span class="n">keras_hub</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">BertClassifier</span><span class="o">.</span><span class="n">from_preset</span><span class="p">(</span> <span class="s2">"bert_base_en_uncased"</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="p">)</span> </pre></div> </div> <div class="inner-block-content rich-content"> <h2 data-block-key="u50tg">¿Qué cambia para los desarrolladores de KerasCV?</h2><p data-block-key="deqts">Si usas KerasCV actualmente, actualizar a KerasHub te brindará estos beneficios:</p><p data-block-key="4blku"></p><ul><li data-block-key="8g3j5"><b>Carga de modelos simplificada:</b> KerasHub ofrece una API consistente para cargar modelos, que puede simplificar tu código si estás trabajando tanto con KerasCV como con KerasNLP.</li></ul><p data-block-key="8nte9"></p><ul><li data-block-key="f8le9"><b>Flexibilidad de marco de trabajo:</b> Si te interesa explorar diferentes marcos de trabajo, como JAX o PyTorch, KerasHub te permite usarlos fácilmente con los modelos KerasCV y KerasNLP.</li></ul><p data-block-key="4mdot"></p><ul><li data-block-key="76h9c"><b>Repositorio centralizado:</b> Encontrar y acceder a los modelos es más fácil con el repositorio de modelos unificado de KerasHub, ya que allí se agregarán nuevas arquitecturas en el futuro.</li></ul><h3 data-block-key="e5gb4"><br/><b>¿Cómo adapto mi código a KerasHub?</b></h3><h3 data-block-key="13otm">Modelos</h3><p data-block-key="107ui">Actualmente, se están portando los modelos de KerasCV a KerasHub. Si bien la mayoría ya están disponibles, algunos todavía están en progreso. Ten en cuenta que el modelo de <a href="https://www.kaggle.com/models/keras/centerpillar">Centerpillar</a> no se transferirá. Deberías poder usar cualquier modelo de visión en KerasHub con lo siguiente:</p> </div> <div class="inner-block-content code-block"> <div class="highlight"><pre class="python"><span></span><span class="kn">import</span> <span class="nn">keras_hub</span> <span class="c1"># Carga un modelo usando ajustes predeterminados</span> <span class="n">Model</span> <span class="o">=</span> <span class="n">keras_hub</span><span class="o">.</span><span class="n">models</span><span class="o">.<</span><span class="n">model_name</span><span class="o">>.</span><span class="n">from_preset</span><span class="p">(</span><span class="s1">'preset_name`)</span> <span class="c1"># o carga un modelo personalizado especificando el backbone y el preprocessor</span> <span class="n">Model</span> <span class="o">=</span> <span class="n">keras_hub</span><span class="o">.</span><span class="n">models</span><span class="o">.<</span><span class="n">model_name</span><span class="o">></span><span class="p">(</span><span class="n">backbone</span><span class="o">=</span><span class="n">backbone</span><span class="p">,</span> <span class="n">preprocessor</span><span class="o">=</span><span class="n">preprocessor</span><span class="p">)</span> </pre></div> </div> <div class="inner-block-content rich-content"> <p data-block-key="u50tg">KerasHub presenta nuevas e interesantes funciones para los desarrolladores de KerasCV, lo que ofrece una mayor flexibilidad y capacidades ampliadas. Incluye lo siguiente:</p><h3 data-block-key="fela8"><br/>Preprocesamiento integrado</h3><p data-block-key="6sjsv">Cada modelo está acompañado de un preprocesador personalizado que aborda las tareas rutinarias, como el cambio de tamaño o de escala, y mucho más, lo que optimiza el flujo de trabajo.</p><p data-block-key="ai30i">Antes de esto, el preprocesamiento de entrada se realiza manualmente antes de proporcionar las entradas al modelo.</p> </div> <div class="inner-block-content code-block"> <div class="highlight"><pre class="python"><span></span><span class="c1"># Preprocesa entradas de ejemplo</span> <span class="k">def</span> <span class="nf">preprocess_inputs</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">label</span><span class="p">):</span> <span class="c1"># Cambia el tamaño o haz más preprocesamiento en las entradas</span> <span class="k">return</span> <span class="n">preprocessed_inputs</span> <span class="n">backbone</span> <span class="o">=</span> <span class="n">keras_cv</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">ResNet50V2Backbone</span><span class="o">.</span><span class="n">from_preset</span><span class="p">(</span> <span class="s2">"resnet50_v2_imagenet"</span><span class="p">,</span> <span class="p">)</span> <span class="n">model</span> <span class="o">=</span> <span class="n">keras_cv</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">ImageClassifier</span><span class="p">(</span> <span class="n">backbone</span><span class="o">=</span><span class="n">backbone</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="p">)</span> <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">preprocessed_input</span><span class="p">)</span> </pre></div> </div> <div class="inner-block-content rich-content"> <p data-block-key="u50tg">Actualmente, el preprocesamiento de los modelos de tareas está integrado dentro de los preajustes establecidos. Las entradas se someten a preprocesamiento, donde las imágenes de muestra se someten a cambios de tamaño y escala dentro del preprocesador. Este es un componente intrínseco del modelo de tareas. No obstante, está la opción de utilizar uno personalizado.</p> </div> <div class="inner-block-content code-block"> <div class="highlight"><pre class="python"><span></span><span class="n">classifier</span> <span class="o">=</span> <span class="n">keras_hub</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">ImageClassifier</span><span class="o">.</span><span class="n">from_preset</span><span class="p">(</span><span class="s1">'resnet_18_imagenet'</span><span class="p">)</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> </pre></div> </div> <div class="inner-block-content rich-content"> <h3 data-block-key="u50tg">Funciones de pérdida</h3><p data-block-key="5qhd">Al igual que las capas de aumento, las funciones de pérdida de KerasCV ahora están disponibles en Keras a través de <code>keras.losses.<loss_function></code>. Por ejemplo, si actualmente usas la <a href="https://keras.io/api/keras_cv/losses/focal_loss/">función FocalLoss</a>:</p> </div> <div class="inner-block-content code-block"> <div class="highlight"><pre class="python"><span></span><span class="kn">import</span> <span class="nn">keras</span> <span class="kn">import</span> <span class="nn">keras_cv</span> <span class="n">keras_cv</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">FocalLoss</span><span class="p">(</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">from_logits</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">label_smoothing</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span> <span class="p">)</span> </pre></div> </div> <div class="inner-block-content rich-content"> <p data-block-key="u50tg">Solo debes ajustar tu código de definición de función de pérdida para usar <code>keras.losses</code> en lugar de <code>keras_cv.losses</code>:</p> </div> <div class="inner-block-content code-block"> <div class="highlight"><pre class="python"><span></span><span class="kn">import</span> <span class="nn">keras</span> <span class="n">keras</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">FocalLoss</span><span class="p">(</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">from_logits</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">label_smoothing</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span> <span class="p">)</span> </pre></div> </div> <div class="inner-block-content rich-content"> <h2 data-block-key="u50tg"><b>Comienza a usar KerasHub</b></h2><p data-block-key="e16fi">Descubre el mundo de KerasHub hoy mismo:</p><p data-block-key="e3thf"></p><ul><li data-block-key="22sc3"><b>Para empezar, consulta la documentación:</b><a href="https://keras.io/keras_hub/"> https://keras.io/keras_hub/</a></li></ul><p data-block-key="3nqt6"></p><ul><li data-block-key="bsh6s"><b>Consulta las guías de introducción de KerasHub:</b> <a href="https://keras.io/guides/keras_hub/">https://keras.io/guides/keras_hub/</a></li></ul><p data-block-key="56fd8"></p><ul><li data-block-key="atjro"><b>Experimenta con los modelos preentrenados:</b> <a href="https://keras.io/api/keras_hub/models/">https://keras.io/api/keras_hub/models/</a></li></ul><p data-block-key="dqjhr"></p><ul><li data-block-key="6vhaq"><b>Explora el código fuente y haz contribuciones:</b><a href="https://github.com/keras-team/keras-hub/"> https://github.com/keras-team/keras-hub/</a></li></ul><p data-block-key="elc5i"></p><ul><li data-block-key="19vfn"><b>Obtén más información sobre Keras en Kaggle:</b> <a href="https://www.kaggle.com/organizations/keras">https://www.kaggle.com/organizations/keras</a></li></ul><p data-block-key="3adku"><br/>Únete a la comunidad de Keras y aprovecha los modelos de aprendizaje profundo unificados, accesibles y eficientes. El futuro de la IA es multimodal, y KerasHub es tu forma de acceder a él.</p> </div> </div> </section> <section class="navigation-container glue-page glue-spacer-6-top"> <div class="posted-in-section"> <div class="posted-in-section__heading"> <span class="glue-caption"> publicado en: </span> </div> <div class="posted-in-section__tags"> <ul> <li> <a href="/es/search/?product_categories=Gemma" class="glue-caption">Gemma</a> </li> <li> <a href="/es/search/?technology_categories=AI" class="glue-caption">AI</a> </li> <li> <a href="/es/search/?content_type_categories=Announcements" class="glue-caption">Announcements</a> </li> <li> <a href="/es/search/?content_type_categories=Best+Practices" class="glue-caption">Best Practices</a> </li> <li> <a href="/es/search/?tag=Keras" class="glue-caption">Keras</a> </li> <li> <a href="/es/search/?tag=Learn" class="glue-caption">Learn</a> </li> <li> <a href="/es/search/?tag=multimodal" class="glue-caption">multimodal</a> </li> <li> <a 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