{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"keras-3","slug":"keras-3","name":"Keras 3","type":"framework","url":"https://keras.io","page_url":"https://unfragile.ai/keras-3","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"keras-3__cap_0","uri":"capability://code.generation.editing.multi.backend.neural.network.compilation.and.execution","name":"multi-backend neural network compilation and execution","description":"Compiles a single Keras 3 model definition to execute identically across JAX, TensorFlow, or PyTorch backends via a unified intermediate representation. The framework translates high-level layer operations into backend-specific computation graphs at compile time, allowing developers to switch backends by changing a single configuration parameter without modifying model code. This is achieved through a backend abstraction layer that maps Keras operations (e.g., `keras.ops.conv2d`) to equivalent backend implementations, with automatic differentiation and gradient computation delegated to the underlying framework.","intents":["I want to train a model on TensorFlow but deploy it using JAX for inference without rewriting code","I need to benchmark the same architecture across multiple backends to find the fastest one for my hardware","I want to use PyTorch's ecosystem but leverage Keras's high-level API without lock-in"],"best_for":["ML researchers comparing framework performance on identical models","teams with heterogeneous infrastructure (some TPUs, some GPUs, some CPUs) needing portable code","developers building framework-agnostic model libraries"],"limitations":["Not all backend-specific features are exposed; advanced JAX transformations (jit, vmap) may require custom code","Performance overhead from abstraction layer is unquantified; native backend code may be faster","Some operations may have subtle numerical differences across backends due to implementation variations","Custom layers using backend-specific APIs (e.g., `torch.nn.Module` directly) break portability"],"requires":["Python 3.9+","At least one of: JAX 0.4.1+, TensorFlow 2.13+, or PyTorch 2.0+","Keras 3.0+"],"input_types":["model architecture (Functional API or subclassed Layer)","training data (NumPy arrays, tf.data.Dataset, or PyTorch DataLoader)","configuration string specifying backend (e.g., 'jax', 'tensorflow', 'torch')"],"output_types":["compiled model object executable on selected backend","training history with metrics","model predictions as NumPy arrays or backend tensors"],"categories":["code-generation-editing","framework-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras-3__cap_1","uri":"capability://code.generation.editing.functional.api.layer.composition.with.symbolic.tensor.chaining","name":"functional api layer composition with symbolic tensor chaining","description":"Enables declarative model construction by chaining layer calls on symbolic Input tensors, building an acyclic computation graph without executing any operations. Each layer call returns a symbolic tensor representing the output shape and type, allowing developers to compose complex architectures (CNNs, RNNs, Transformers) in a few lines by nesting layer calls. The framework defers actual computation until `model.fit()` or `model.predict()` is invoked, enabling graph-level optimizations and automatic differentiation setup.","intents":["I want to quickly prototype a CNN architecture without writing a custom training loop","I need to build a multi-input, multi-output model with branching and merging paths","I want to visualize my model architecture as a computation graph before training"],"best_for":["rapid prototyping and experimentation with standard architectures","teams preferring declarative over imperative code style","developers building reusable model templates"],"limitations":["Cannot express dynamic control flow (e.g., if statements based on tensor values); use subclassing API for that","Symbolic execution means shape mismatches are caught at model definition time, not data loading time, requiring explicit Input shape specification","Recurrent connections (feedback loops) require explicit layer instantiation; functional API alone cannot express stateful RNNs","Debugging symbolic graphs is harder than imperative code; errors reference symbolic tensor names, not line numbers"],"requires":["Python 3.9+","Keras 3.0+","understanding of tensor shapes and layer output dimensions"],"input_types":["keras.Input specification with shape tuple","layer instances (Conv2D, Dense, Dropout, etc.)","callable layer objects"],"output_types":["keras.Model object with defined input/output tensors","model.summary() text representation","keras.utils.plot_model() PNG/SVG visualization"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras-3__cap_10","uri":"capability://data.processing.analysis.automatic.differentiation.and.gradient.computation","name":"automatic differentiation and gradient computation","description":"Integrates with the underlying backend's autodiff system (JAX's `grad`, TensorFlow's `GradientTape`, PyTorch's `autograd`) to automatically compute gradients of the loss with respect to model parameters during backpropagation. Developers do not explicitly call gradient computation functions; the framework handles this transparently in `model.fit()` or custom training loops via `model.train_step()`. Gradients are computed using reverse-mode autodiff (backpropagation), enabling efficient gradient computation for deep networks.","intents":["I want to train a model using gradient descent without manually computing gradients","I need to implement a custom loss function and have gradients computed automatically","I want to access gradients for debugging or custom optimization (e.g., gradient clipping)"],"best_for":["practitioners training standard supervised learning models with automatic gradient computation","researchers implementing custom training algorithms that require gradient access","developers building models with custom loss functions that need automatic differentiation"],"limitations":["Gradient computation is automatic but not inspectable; no built-in tools to visualize gradient flow or detect vanishing/exploding gradients","Some operations may have undefined or numerically unstable gradients; no automatic detection of gradient issues","Custom layers must implement `call()` using differentiable operations; non-differentiable operations (e.g., argmax) break gradient flow","Gradient computation overhead is unquantified; no profiling tools to measure autodiff cost","Higher-order gradients (Hessian computation) are not explicitly supported; requires custom implementation using backend APIs"],"requires":["Python 3.9+","Keras 3.0+","compiled model with loss function specified","training data with labels"],"input_types":["model inputs (tensors)","model outputs (predictions)","loss function (string or callable)","target labels (tensors)"],"output_types":["gradients with respect to model parameters (tensors)","updated model parameters after optimizer step"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras-3__cap_11","uri":"capability://automation.workflow.optimizer.abstraction.with.multiple.algorithms.and.learning.rate.scheduling","name":"optimizer abstraction with multiple algorithms and learning rate scheduling","description":"Provides a unified optimizer interface supporting multiple algorithms (SGD, Adam, RMSprop, Adagrad, etc.) specified as strings (e.g., 'adam') or optimizer objects in `model.compile()`. Optimizers maintain internal state (momentum, adaptive learning rates) across training steps and apply parameter updates based on gradients. Learning rate scheduling is supported via `keras.optimizers.schedules.*` (e.g., `ExponentialDecay`, `CosineDecay`) or custom schedules, enabling dynamic learning rate adjustment during training without manual intervention.","intents":["I want to train a model using Adam optimizer with a learning rate of 0.001","I need to reduce the learning rate during training to fine-tune the model (learning rate scheduling)","I want to use SGD with momentum for better convergence on my dataset"],"best_for":["practitioners using standard optimizers (Adam, SGD, RMSprop) without custom optimization logic","teams using learning rate scheduling to improve convergence","developers experimenting with different optimizers to find the best one for their task"],"limitations":["String-based optimizer specification (e.g., 'adam') provides no type safety; typos cause runtime errors","Optimizer state is not easily inspectable; no built-in tools to monitor momentum, adaptive learning rates, or other internal state","Custom optimizers require subclassing `keras.optimizers.Optimizer` and implementing `build()` and `_resource_apply_dense()` methods, which is complex","Learning rate schedules are decoupled from optimizers; no unified interface for schedule + optimizer configuration","Gradient clipping and other advanced techniques require custom `train_step()` implementation"],"requires":["Python 3.9+","Keras 3.0+","optimizer name (string) or optimizer object in `model.compile()`","optional: learning rate value or schedule"],"input_types":["optimizer name (string: 'adam', 'sgd', 'rmsprop', etc.)","optimizer configuration (learning_rate, momentum, decay, etc.)","optional: learning rate schedule (keras.optimizers.schedules.*)"],"output_types":["optimizer object with internal state (momentum, adaptive learning rates)","updated model parameters after each training step"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras-3__cap_12","uri":"capability://data.processing.analysis.loss.function.abstraction.with.standard.and.custom.objectives","name":"loss function abstraction with standard and custom objectives","description":"Provides a library of loss functions (CrossEntropy, MeanSquaredError, BinaryCrossentropy, etc.) accessible via `keras.losses.*` or as strings (e.g., 'categorical_crossentropy') in `model.compile()`. Loss functions compute a scalar objective value from model predictions and target labels, guiding the optimization process. Custom loss functions can be implemented as Python functions or by subclassing `keras.losses.Loss`, enabling domain-specific objectives (e.g., contrastive loss, focal loss). Loss values are automatically differentiated to compute gradients.","intents":["I want to train a classification model using categorical cross-entropy loss","I need to implement a custom loss function for my specific task (e.g., ranking loss)","I want to use weighted loss to handle class imbalance in my dataset"],"best_for":["practitioners using standard loss functions (cross-entropy, MSE) for common tasks","researchers implementing custom loss functions for novel objectives","teams handling imbalanced datasets with weighted loss functions"],"limitations":["String-based loss specification (e.g., 'categorical_crossentropy') provides no type safety; typos cause runtime errors","Loss functions assume specific output shapes and label formats; mismatches cause runtime errors","Custom loss functions must be differentiable; non-differentiable operations break gradient computation","Loss values are scalars; no built-in support for multi-task learning with multiple loss terms","Loss weighting across samples is not built-in; requires custom implementation or sample_weight parameter"],"requires":["Python 3.9+","Keras 3.0+","loss function name (string) or loss object in `model.compile()`","model predictions and target labels with compatible shapes"],"input_types":["loss function name (string: 'categorical_crossentropy', 'mse', 'binary_crossentropy', etc.)","loss function object (keras.losses.Loss subclass)","model predictions (tensors)","target labels (tensors)","optional: sample_weight (per-sample loss weights)"],"output_types":["scalar loss value (float)","gradients with respect to model parameters (computed automatically)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras-3__cap_13","uri":"capability://code.generation.editing.batch.normalization.and.layer.normalization.with.training.inference.modes","name":"batch normalization and layer normalization with training/inference modes","description":"Provides `keras.layers.BatchNormalization` and `keras.layers.LayerNormalization` layers that normalize layer inputs to improve training stability and convergence. BatchNormalization maintains running statistics (mean, variance) computed during training and uses them during inference, requiring a `training` flag to distinguish modes. The framework automatically handles mode switching during `model.fit()` (training=True) and `model.predict()` (training=False), eliminating manual mode management.","intents":["I want to add batch normalization to my CNN to stabilize training and improve convergence","I need to use layer normalization in my Transformer model instead of batch normalization","I want the model to automatically use training statistics during training and running statistics during inference"],"best_for":["practitioners training deep networks that benefit from normalization (CNNs, Transformers)","teams building models with batch-dependent behavior (batch norm) or batch-independent behavior (layer norm)","developers avoiding manual mode switching between training and inference"],"limitations":["BatchNormalization behavior differs between training and inference; running statistics must be tracked and updated during training","Small batch sizes (< 16) can lead to unstable batch statistics; layer normalization is more stable for small batches","BatchNormalization adds computational overhead during training (statistics computation) and inference (normalization)","Running statistics are not easily inspectable; no built-in tools to monitor batch norm behavior","Batch norm statistics are not saved in model checkpoints by default; requires explicit configuration"],"requires":["Python 3.9+","Keras 3.0+","batch size >= 1 (though larger batches are recommended for stable statistics)","training data with sufficient samples"],"input_types":["layer input (tensor)","training flag (boolean, automatically set by model.fit() and model.predict())","optional: momentum for running statistics update (default 0.99)"],"output_types":["normalized layer output (tensor)","updated running statistics (mean, variance) during training"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras-3__cap_2","uri":"capability://code.generation.editing.subclassed.layer.and.model.customization.with.imperative.forward.passes","name":"subclassed layer and model customization with imperative forward passes","description":"Allows developers to define custom layers and models by subclassing `keras.layers.Layer` or `keras.Model`, implementing `__init__()` for layer composition and `call()` for the forward pass logic. This imperative approach enables dynamic control flow (conditionals, loops based on tensor values), stateful operations, and fine-grained control over computation that the functional API cannot express. Custom layers are automatically integrated into the training pipeline via `model.fit()` and support automatic differentiation through the backend's autodiff system.","intents":["I need to implement a custom layer with dynamic behavior (e.g., attention weights computed from input)","I want to build a model with recurrent connections or feedback loops","I need to add custom loss computation or metric calculation within the model"],"best_for":["researchers implementing novel architectures or custom training algorithms","teams building domain-specific layers (e.g., graph neural networks, custom attention mechanisms)","developers needing fine-grained control over forward and backward passes"],"limitations":["Subclassed models cannot be easily serialized to JSON/YAML; functional models are preferred for model persistence","Debugging custom `call()` methods requires understanding the backend's autodiff system; errors may surface during backprop, not forward pass","Performance may be slower than functional API due to lack of graph-level optimizations; the backend cannot fuse operations across custom layers","Type hints are optional; no static type checking for layer inputs/outputs, leading to runtime shape errors"],"requires":["Python 3.9+","Keras 3.0+","understanding of neural network forward/backward passes and autodiff","familiarity with the selected backend's tensor operations"],"input_types":["tensor inputs (NumPy arrays or backend tensors)","optional keyword arguments for layer configuration","training flag (boolean) to enable/disable dropout, batch norm, etc."],"output_types":["tensor outputs (backend-specific tensors)","custom loss values (scalars)","custom metric values (scalars)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras-3__cap_3","uri":"capability://automation.workflow.unified.training.loop.with.automatic.differentiation.and.gradient.descent","name":"unified training loop with automatic differentiation and gradient descent","description":"Provides a high-level `model.fit()` method that orchestrates the entire training process: forward pass, loss computation, backward pass (automatic differentiation), and optimizer step updates. Developers specify the optimizer (e.g., 'adam', 'rmsprop'), loss function (e.g., 'categorical_crossentropy'), and metrics (e.g., 'accuracy') as strings or objects, and the framework handles gradient computation via the backend's autodiff system, batching, validation, and metric aggregation. The method returns a `History` object with per-epoch metrics for analysis.","intents":["I want to train a model with standard SGD-based optimization without writing a custom training loop","I need to monitor validation metrics during training and save the best model checkpoint","I want to use learning rate scheduling or early stopping without manual epoch management"],"best_for":["practitioners training standard supervised learning models (classification, regression)","teams using common optimizers and loss functions without custom training logic","developers prototyping models quickly without infrastructure overhead"],"limitations":["String-based optimizer/loss specification (e.g., 'adam') provides no type safety; typos cause runtime errors","Limited customization of the training loop; custom loss weighting, gradient clipping, or multi-task learning require subclassing `keras.Model` and overriding `train_step()`","No built-in distributed training support shown in documentation; multi-GPU/multi-node training requires external tools (e.g., `tf.distribute`)","Batch size and epoch count are hyperparameters; no automatic tuning or adaptive scheduling","Validation data must fit in memory; no streaming validation for large datasets"],"requires":["Python 3.9+","Keras 3.0+","training data as NumPy arrays, tf.data.Dataset, or PyTorch DataLoader","compiled model with optimizer, loss, and metrics specified via `model.compile()`"],"input_types":["x: training input features (NumPy array or dataset)","y: training labels (NumPy array or dataset)","batch_size: integer (default 32)","epochs: integer (default 1)","validation_data: optional tuple (x_val, y_val) or dataset","callbacks: optional list of keras.callbacks.Callback objects"],"output_types":["keras.callbacks.History object with training/validation metrics per epoch","model weights updated in-place","saved checkpoints (if callbacks include ModelCheckpoint)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras-3__cap_4","uri":"capability://automation.workflow.model.serialization.and.checkpoint.management","name":"model serialization and checkpoint management","description":"Enables saving and loading trained models via `model.save()` and `keras.models.load_model()`, supporting both the native Keras format (.keras file containing architecture, weights, and optimizer state) and ONNX export for cross-framework compatibility. The framework also provides `keras.callbacks.ModelCheckpoint` for automatic checkpoint saving during training based on validation metrics, allowing recovery of the best model and resumption of training from a checkpoint.","intents":["I want to save a trained model and load it later for inference without retraining","I need to export a Keras model to ONNX format for deployment on non-Python runtimes","I want to automatically save model checkpoints during training and restore the best one based on validation loss"],"best_for":["production ML pipelines requiring model versioning and reproducibility","teams deploying models across heterogeneous inference environments (mobile, edge, cloud)","practitioners training long-running models with checkpointing for fault tolerance"],"limitations":["Functional models serialize cleanly to JSON architecture + HDF5 weights; subclassed models cannot be serialized to JSON and require custom `get_config()` implementation","ONNX export may lose backend-specific optimizations or custom operations; not all Keras layers have ONNX equivalents","Checkpoint files can be large (model weights + optimizer state); no built-in compression or incremental checkpointing","Loading a model requires the same Keras version and backend; version mismatches may cause deserialization errors","Custom layers require custom serialization logic via `get_config()` and `from_config()` methods"],"requires":["Python 3.9+","Keras 3.0+","trained model object","optional: ONNX library for export (onnx, onnx-simplifier)"],"input_types":["model object (keras.Model)","filepath string (e.g., 'model.keras')","optional: save_format ('keras' or 'onnx')","optional: include_optimizer boolean"],"output_types":[".keras file (Keras native format with architecture + weights + optimizer state)",".onnx file (ONNX interchange format)","loaded model object (keras.Model) with weights and optimizer state restored"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras-3__cap_5","uri":"capability://memory.knowledge.pretrained.model.loading.and.inference.via.kerashub","name":"pretrained model loading and inference via kerashub","description":"Provides access to pretrained model architectures and weights via KerasHub, a companion library offering task-specific APIs like `CausalLM.from_preset()` for language models and `TextToImage.from_preset()` for diffusion models. Developers specify a model preset name (e.g., 'gemma2_instruct_2b_en', 'stable_diffusion_3_medium') and the framework downloads the architecture and weights from Kaggle Models, instantiating a ready-to-use model for inference or fine-tuning. This abstracts away model architecture details and checkpoint management.","intents":["I want to use a pretrained language model (Gemma 2) for text generation without implementing the architecture from scratch","I need to generate images from text prompts using Stable Diffusion 3 without managing model weights","I want to fine-tune a pretrained model on my custom dataset with minimal setup"],"best_for":["practitioners building applications with pretrained models (LLMs, diffusion models)","teams without GPU resources for training, leveraging pretrained weights","developers prototyping with state-of-the-art models without architecture knowledge"],"limitations":["Limited model catalog; only specific presets are available (Gemma 2, Stable Diffusion 3 shown), not arbitrary HuggingFace models","Model weights are hosted on Kaggle Models; requires internet connectivity and Kaggle account for some models","Preset names are opaque strings; no programmatic discovery of available models or their capabilities","Fine-tuning APIs are not detailed in documentation; unclear if KerasHub provides task-specific fine-tuning methods","No version pinning for presets; model weights may change, affecting reproducibility","Inference is single-sample only; batch inference requires manual batching"],"requires":["Python 3.9+","Keras 3.0+","keras-hub library (separate package)","internet connectivity to download model weights from Kaggle Models","sufficient disk space for model weights (varies by model; Gemma 2 2B ~5GB, Stable Diffusion 3 ~10GB)"],"input_types":["preset name string (e.g., 'gemma2_instruct_2b_en')","optional: load_weights boolean (default True)","optional: backend specification"],"output_types":["task-specific model object (CausalLM, TextToImage, etc.)","generated text (for CausalLM.generate())","generated images (for TextToImage.generate())"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras-3__cap_6","uri":"capability://data.processing.analysis.metric.computation.and.monitoring.during.training","name":"metric computation and monitoring during training","description":"Provides a `metrics` parameter in `model.compile()` that accepts metric names (strings like 'accuracy', 'mse') or custom `keras.metrics.Metric` objects, computing and aggregating metrics across batches during training and validation. Metrics are evaluated on each batch and accumulated using a stateful object that maintains running averages, enabling per-epoch metric reporting without storing all predictions in memory. Custom metrics can be implemented by subclassing `keras.metrics.Metric` and overriding `update_state()` and `result()` methods.","intents":["I want to track accuracy, precision, and recall during training to monitor model performance","I need to implement a custom metric (e.g., F1 score) that is not built-in","I want to log metrics to a monitoring system (TensorBoard, Weights & Biases) during training"],"best_for":["practitioners monitoring model convergence and generalization during training","teams using standard metrics (accuracy, AUC, precision, recall) without custom logic","developers integrating Keras with experiment tracking platforms"],"limitations":["String-based metric specification (e.g., 'accuracy') provides no type safety; typos cause runtime errors","Built-in metrics assume standard task types (classification, regression); custom metrics required for domain-specific evaluation","Metrics are computed on training and validation data separately; no cross-validation or test set metrics without manual evaluation","Metric state is reset at the start of each epoch; no per-batch metric history without custom callbacks","Metrics are aggregated using simple averaging; weighted averaging or stratified metrics require custom implementation"],"requires":["Python 3.9+","Keras 3.0+","compiled model with metrics specified in `model.compile()`","training data with labels"],"input_types":["metric names (strings: 'accuracy', 'mse', 'mae', 'auc', etc.)","custom Metric objects (subclasses of keras.metrics.Metric)","optional: metric configuration dictionaries"],"output_types":["per-epoch metric values in History object","per-batch metric values (if accessed via callbacks)","final metric values after training"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras-3__cap_7","uri":"capability://automation.workflow.callback.based.training.hooks.and.custom.training.logic","name":"callback-based training hooks and custom training logic","description":"Provides a callback system via `keras.callbacks.Callback` that allows developers to inject custom logic at specific points in the training loop (epoch start/end, batch start/end, training start/end). Built-in callbacks include `ModelCheckpoint` (save best model), `EarlyStopping` (stop training if validation metric plateaus), `ReduceLROnPlateau` (reduce learning rate on metric plateau), and `TensorBoard` (log metrics to TensorBoard). Custom callbacks can be implemented by subclassing `Callback` and overriding hook methods, enabling integration with external systems (logging, hyperparameter tuning, model serving).","intents":["I want to save the best model based on validation loss and stop training if it stops improving","I need to log training metrics to TensorBoard or Weights & Biases for visualization","I want to reduce the learning rate when validation loss plateaus to fine-tune the model"],"best_for":["practitioners using standard training patterns (early stopping, checkpointing, learning rate scheduling)","teams integrating Keras with experiment tracking and monitoring platforms","developers building custom training workflows without modifying the core training loop"],"limitations":["Callbacks are called synchronously; no asynchronous callbacks for I/O-bound operations (e.g., logging to remote servers)","Callback execution order is determined by list order; no dependency management or conditional execution","Limited access to internal training state; callbacks receive only metrics and epoch number, not gradients or intermediate activations","Callbacks cannot modify training behavior (e.g., skip batches, modify loss); use custom `train_step()` for that","No built-in callback for hyperparameter tuning; integration with Optuna, Ray Tune requires custom implementation"],"requires":["Python 3.9+","Keras 3.0+","list of Callback objects passed to `model.fit(callbacks=...)`","optional: external libraries for specific callbacks (tensorboard, wandb)"],"input_types":["Callback subclass instances","optional: callback configuration (e.g., monitor='val_loss', patience=5 for EarlyStopping)","optional: filepath pattern for ModelCheckpoint (e.g., 'model-{epoch:02d}.keras')"],"output_types":["side effects: saved checkpoints, reduced learning rates, early stopping","logged metrics to external systems (TensorBoard, Weights & Biases)","custom outputs from callback.on_epoch_end() (e.g., hyperparameter updates)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras-3__cap_8","uri":"capability://code.generation.editing.layer.and.model.visualization.with.architecture.diagrams","name":"layer and model visualization with architecture diagrams","description":"Provides `keras.utils.plot_model()` to generate visual representations of model architecture as PNG or SVG diagrams, showing layers, connections, and tensor shapes. The function accepts a model object and optional parameters (show_shapes=True to display tensor dimensions, show_layer_names=True to label layers, rankdir='TB' for top-to-bottom layout). This enables quick verification of model structure before training and aids in documentation and communication of architecture to non-technical stakeholders.","intents":["I want to visualize my model architecture to verify it matches my design before training","I need to create a diagram of my model for documentation or presentation purposes","I want to debug shape mismatches by seeing the tensor dimensions flowing through each layer"],"best_for":["practitioners designing and verifying model architectures","teams documenting model designs for knowledge sharing","developers debugging shape mismatches and layer connectivity issues"],"limitations":["Visualization is static; does not show dynamic control flow or conditional execution","Large models with many layers produce cluttered diagrams; no automatic layout optimization or hierarchical grouping","Subclassed models may not visualize correctly if they use dynamic layer creation in `call()`","Requires graphviz library for PNG/SVG output; text-based visualization (model.summary()) is available without graphviz","No interactive visualization; cannot drill down into layer details or inspect intermediate activations"],"requires":["Python 3.9+","Keras 3.0+","graphviz library (optional, for PNG/SVG output; text summary works without it)","compiled model object"],"input_types":["model object (keras.Model)","optional: show_shapes boolean (default False)","optional: show_layer_names boolean (default True)","optional: rankdir string ('TB', 'LR', 'BT', 'RL' for layout direction)","optional: expand_nested boolean (default False) to show nested models"],"output_types":["PNG image file","SVG vector image file","text representation via model.summary()"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras-3__cap_9","uri":"capability://code.generation.editing.built.in.layer.library.with.100.standard.neural.network.components","name":"built-in layer library with 100+ standard neural network components","description":"Provides a comprehensive library of prebuilt layers (Conv2D, Dense, LSTM, Attention, Dropout, BatchNormalization, etc.) accessible via `keras.layers.*`, each implementing standard neural network operations with configurable parameters (filters, kernel size, activation, regularization). Layers are backend-agnostic, automatically compiled to the selected backend (JAX, TensorFlow, PyTorch) at model instantiation. The library covers convolutional, recurrent, attention-based, and normalization layers, enabling construction of most standard architectures without custom implementations.","intents":["I want to build a CNN for image classification using standard Conv2D and Dense layers","I need to implement an LSTM for sequence modeling without writing custom RNN logic","I want to add attention mechanisms to my model using built-in Attention or MultiHeadAttention layers"],"best_for":["practitioners building standard architectures (CNNs, RNNs, Transformers) without custom layers","teams prototyping quickly using well-tested, optimized layer implementations","developers learning neural networks through hands-on experimentation with standard components"],"limitations":["Layer implementations are generic; domain-specific optimizations (e.g., sparse convolutions, quantized layers) are not provided","Some advanced layers (e.g., custom attention variants) may not be available; custom layer subclassing required","Layer parameters are specified as Python arguments; 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