{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-keras","slug":"pypi-keras","name":"keras","type":"framework","url":"https://pypi.org/project/keras/","page_url":"https://unfragile.ai/pypi-keras","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-keras__cap_0","uri":"capability://code.generation.editing.multi.backend.neural.network.computation.with.unified.api","name":"multi-backend neural network computation with unified api","description":"Provides a single high-level API for defining models and layers that transparently dispatches numerical computation to JAX, TensorFlow, PyTorch, or OpenVINO backends selected at import time via KERAS_BACKEND environment variable or ~/.keras/keras.json. The framework maintains a backend-agnostic source of truth in keras/src/ with generated public API surface in keras/api/, enabling seamless backend switching without code changes. Runtime dispatch follows two paths: symbolic execution during model construction (shape/dtype inference via compute_output_spec on KerasTensor objects) and eager execution during training/inference (forwarded to active backend implementation).","intents":["I want to write a model once and run it on JAX for research, TensorFlow for production, and PyTorch for team collaboration without rewriting code","I need to benchmark the same architecture across multiple backends to optimize for my hardware","I want to migrate from TensorFlow to PyTorch without rewriting my entire training pipeline"],"best_for":["research teams evaluating multiple frameworks","production teams needing framework flexibility","developers building framework-agnostic ML libraries"],"limitations":["Backend must be selected at import time and cannot be changed within a single Python session","OpenVINO backend supports inference only, not training","Backend-specific optimizations and features may not be fully exposed through the unified API","Performance overhead from abstraction layer adds latency compared to native framework usage"],"requires":["Python 3.9+","One of: TensorFlow 2.16.1+, JAX 0.4.20+, PyTorch 2.1.0+, or OpenVINO 2025.3.0+","KERAS_BACKEND environment variable or ~/.keras/keras.json configuration file"],"input_types":["Python model definitions","NumPy arrays","Backend-native tensors (tf.Tensor, torch.Tensor, jax.Array)"],"output_types":["Backend-native tensors","NumPy arrays","Model predictions"],"categories":["code-generation-editing","deep-learning-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-keras__cap_1","uri":"capability://code.generation.editing.backend.agnostic.layer.and.operation.definitions","name":"backend-agnostic layer and operation definitions","description":"Defines neural network layers (Dense, Conv2D, LSTM, etc.) and operations (numpy-compatible ops, neural network ops, core backend ops) in keras/src/ that are completely decoupled from backend implementation. Each layer inherits from a base Layer class that implements compute_output_spec() for symbolic shape/dtype inference and call() for eager execution. Backend-specific implementations are injected at runtime through the active backend module, allowing the same layer code to execute on JAX, TensorFlow, PyTorch, or OpenVINO without modification.","intents":["I want to define a custom layer once that works identically across all supported backends","I need to understand what operations are available and how they map to different backends","I want to implement a new layer type that automatically gains multi-backend support"],"best_for":["framework developers extending Keras with custom layers","researchers implementing novel architectures","teams building backend-agnostic ML libraries on top of Keras"],"limitations":["Custom layers must use only Keras ops or backend-agnostic operations; direct backend API calls break portability","Layer implementations cannot access backend-specific optimizations or features not exposed through Keras ops","DType policies and quantization behavior may differ slightly across backends due to numerical precision differences"],"requires":["Python 3.9+","Keras 3.0+","Understanding of Layer base class and compute_output_spec() pattern"],"input_types":["Python class definitions inheriting from keras.layers.Layer","Keras tensor specifications (shape, dtype)"],"output_types":["Layer instances","Output tensor specifications","Serialized layer configurations"],"categories":["code-generation-editing","deep-learning-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-keras__cap_10","uri":"capability://automation.workflow.callback.system.for.training.monitoring.and.control","name":"callback system for training monitoring and control","description":"Provides a callback system (keras/src/callbacks/) that enables monitoring and controlling training through hooks at various training stages: on_epoch_begin, on_epoch_end, on_batch_begin, on_batch_end, on_train_begin, on_train_end. Built-in callbacks include EarlyStopping (stop training when validation metric plateaus), ModelCheckpoint (save best model), ReduceLROnPlateau (reduce learning rate), TensorBoard (visualization), and CSVLogger (log metrics). Callbacks are executed synchronously during training and have access to training state (epoch, batch, metrics, model weights).","intents":["I want to stop training early when validation loss stops improving","I need to save the best model during training based on validation metrics","I want to visualize training progress in TensorBoard","I need to reduce learning rate when validation metric plateaus"],"best_for":["practitioners monitoring training progress","teams implementing early stopping and model checkpointing","researchers visualizing training dynamics"],"limitations":["Callbacks are executed synchronously; asynchronous callbacks may block training","Callback access to model state is read-only; modifying weights during callbacks is not supported","Some callbacks (e.g., TensorBoard) require additional dependencies","Callback overhead can be significant for frequent callbacks or large models","Custom callbacks must implement specific methods; incorrect implementation may break training"],"requires":["Python 3.9+","Keras 3.0+","Compiled model","Optional: TensorBoard for visualization"],"input_types":["Callback instances","Training state (epoch, batch, metrics)","Model weights"],"output_types":["Callback outputs (logs, checkpoints, visualizations)","Training control signals (stop training, reduce learning rate)"],"categories":["automation-workflow","deep-learning-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-keras__cap_11","uri":"capability://data.processing.analysis.metric.computation.and.tracking.during.training","name":"metric computation and tracking during training","description":"Provides a metric system (keras/src/metrics/) for computing and tracking statistics during training and evaluation. Metrics are stateful objects that accumulate values across batches and compute aggregate statistics (accuracy, AUC, precision, recall, etc.). Metrics are compiled into models via model.compile(metrics=[...]) and automatically computed during training/evaluation. The framework provides built-in metrics for classification, regression, and ranking tasks. Metrics support both eager and graph execution modes and work identically across all backends.","intents":["I want to track accuracy, precision, and recall during training","I need to compute custom metrics that aggregate across batches","I want to monitor multiple metrics simultaneously during training"],"best_for":["practitioners monitoring model performance","teams implementing custom metrics","researchers tracking training dynamics"],"limitations":["Metrics are stateful and must be reset between training/evaluation phases","Some metrics (e.g., AUC) require additional computation and may slow training","Custom metrics must implement update() and result() methods correctly","Metric computation overhead can be significant for large models or frequent metric updates","Some metrics may not be numerically stable across backends due to precision differences"],"requires":["Python 3.9+","Keras 3.0+","Compiled model with metrics specified"],"input_types":["Predictions (model outputs)","Ground truth labels","Sample weights"],"output_types":["Metric values (scalars)","Aggregated statistics","Training logs"],"categories":["data-processing-analysis","deep-learning-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-keras__cap_12","uri":"capability://automation.workflow.optimizer.implementations.with.learning.rate.scheduling","name":"optimizer implementations with learning rate scheduling","description":"Provides optimizer implementations (keras/src/optimizers/) including SGD, Adam, RMSprop, and others that update model weights based on gradients. Optimizers are backend-agnostic and delegate gradient updates to backend-specific implementations. Learning rate scheduling is supported through LearningRateSchedule objects that adjust learning rate during training based on epoch or batch number. Optimizers support momentum, weight decay, gradient clipping, and other advanced features. All optimizers work identically across backends.","intents":["I want to use Adam optimizer with a learning rate schedule that decays over time","I need to apply gradient clipping to prevent exploding gradients","I want to use momentum-based optimization with weight decay for regularization"],"best_for":["practitioners training neural networks","teams implementing custom optimizers","researchers exploring optimization algorithms"],"limitations":["Some advanced optimizer features (e.g., gradient accumulation) may require custom implementation","Learning rate scheduling requires careful tuning; incorrect schedules may hurt convergence","Optimizer state (momentum, adaptive learning rates) adds memory overhead","Some optimizers may not converge well for certain problem types","Optimizer behavior may differ slightly across backends due to numerical precision"],"requires":["Python 3.9+","Keras 3.0+","Compiled model with optimizer specified"],"input_types":["Model weights","Gradients","Learning rate schedules"],"output_types":["Updated model weights","Optimizer state","Training logs"],"categories":["automation-workflow","deep-learning-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-keras__cap_13","uri":"capability://data.processing.analysis.loss.function.computation.and.gradient.backpropagation","name":"loss function computation and gradient backpropagation","description":"Provides loss functions (keras/src/losses/) for training objectives including classification losses (categorical_crossentropy, sparse_categorical_crossentropy), regression losses (mean_squared_error, mean_absolute_error), and ranking losses. Loss functions are compiled into models via model.compile(loss=...) and automatically computed during training. The framework automatically computes gradients with respect to loss using the active backend's autodiff system (JAX's jax.grad, PyTorch's autograd, TensorFlow's GradientTape). Loss computation and gradient backpropagation are handled transparently without user code.","intents":["I want to use categorical cross-entropy loss for multi-class classification","I need to compute gradients with respect to loss for custom training loops","I want to use custom loss functions that combine multiple objectives"],"best_for":["practitioners training neural networks","teams implementing custom loss functions","researchers exploring training objectives"],"limitations":["Some loss functions may be numerically unstable for certain input ranges","Custom loss functions must be differentiable; non-differentiable functions will fail during backpropagation","Loss computation overhead can be significant for complex loss functions","Some loss functions may not be numerically stable across backends due to precision differences","Loss function behavior may differ slightly between backends due to implementation differences"],"requires":["Python 3.9+","Keras 3.0+","Compiled model with loss specified","Differentiable loss function"],"input_types":["Model predictions","Ground truth labels","Sample weights"],"output_types":["Loss values (scalars)","Gradients with respect to loss","Training logs"],"categories":["data-processing-analysis","deep-learning-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-keras__cap_14","uri":"capability://data.processing.analysis.model.introspection.and.weight.access","name":"model introspection and weight access","description":"Provides APIs for inspecting model structure and accessing weights: model.summary() displays layer structure and parameter counts, model.get_weights() returns all weights as NumPy arrays, model.set_weights() updates weights, model.get_config() returns model configuration as JSON, model.get_layer() retrieves specific layers by name. These APIs work identically across all backends and enable model analysis, weight manipulation, and configuration serialization without backend-specific code.","intents":["I want to print a summary of my model showing layer types and parameter counts","I need to extract weights from a trained model for analysis or transfer learning","I want to save and load model configuration as JSON","I need to access specific layers by name for fine-tuning or analysis"],"best_for":["practitioners analyzing model structure","teams implementing transfer learning","researchers analyzing learned weights"],"limitations":["model.summary() may be verbose for very large models","Weight extraction as NumPy arrays requires converting from backend-native tensors, which may be slow for large models","Model configuration serialization may not capture all custom logic in subclassed models","Weight access is read-only for safety; modifying weights requires model.set_weights()","Some layer properties may not be accessible through the public API"],"requires":["Python 3.9+","Keras 3.0+","Compiled or built model"],"input_types":["Model instances","Layer names","Weight arrays"],"output_types":["Model summaries (text)","Weight arrays (NumPy)","Model configurations (JSON)","Layer instances"],"categories":["data-processing-analysis","deep-learning-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-keras__cap_2","uri":"capability://data.processing.analysis.numpy.compatible.operation.api.with.backend.dispatch","name":"numpy-compatible operation api with backend dispatch","description":"Exposes a NumPy-compatible operation API (keras.ops.numpy.*) that mirrors NumPy's function signatures and behavior while dispatching to backend-specific implementations. Operations include array manipulation (reshape, concatenate, transpose), mathematical functions (sin, exp, matmul), and linear algebra (linalg.solve, linalg.eigh). The dispatch mechanism routes each operation call to the active backend's implementation in keras/src/backend/{backend}/numpy.py, ensuring numerical consistency across backends while leveraging backend-specific optimizations.","intents":["I want to write numerical code using familiar NumPy syntax that runs on any Keras backend","I need to implement custom loss functions or metrics that work across all backends","I want to use standard array operations without learning backend-specific APIs"],"best_for":["data scientists familiar with NumPy transitioning to deep learning","researchers implementing custom algorithms","developers building backend-agnostic numerical code"],"limitations":["Not all NumPy functions are implemented; coverage varies by backend","Numerical precision and behavior may differ slightly across backends (e.g., floating-point rounding)","Performance characteristics differ significantly between backends (JAX may JIT-compile, PyTorch uses autograd, TensorFlow uses graph mode)","Some advanced NumPy features (structured arrays, object arrays) are not supported"],"requires":["Python 3.9+","Keras 3.0+","Familiarity with NumPy API"],"input_types":["Keras tensors","NumPy arrays","Python scalars","Lists/tuples of numeric values"],"output_types":["Keras tensors (backend-native)","NumPy arrays"],"categories":["data-processing-analysis","deep-learning-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-keras__cap_3","uri":"capability://code.generation.editing.neural.network.operation.primitives.with.automatic.differentiation","name":"neural network operation primitives with automatic differentiation","description":"Provides a comprehensive set of neural network operations (keras.ops.nn.*) including activations (relu, sigmoid, softmax), normalization (batch_norm, layer_norm), convolution, pooling, and attention mechanisms. These operations are implemented in keras/src/ops/nn.py with backend-specific implementations in keras/src/backend/{backend}/nn.py. Each operation supports automatic differentiation through the active backend's autodiff system (JAX's jax.grad, PyTorch's autograd, TensorFlow's GradientTape), enabling gradient computation for training without explicit implementation.","intents":["I want to use standard neural network operations (conv, pooling, attention) that automatically compute gradients","I need to implement custom models using primitive operations that work across backends","I want to compose neural network building blocks without writing backend-specific code"],"best_for":["researchers implementing novel architectures","developers building custom models","teams needing framework-agnostic neural network primitives"],"limitations":["Some advanced operations (e.g., sparse operations, custom CUDA kernels) may not be available across all backends","Gradient computation behavior and numerical stability may differ between backends","Performance characteristics vary significantly (JAX may require explicit jit compilation, PyTorch uses eager mode, TensorFlow uses graph mode)","Memory usage patterns differ across backends, affecting large model training"],"requires":["Python 3.9+","Keras 3.0+","Active backend with autodiff support (all backends except OpenVINO support training)"],"input_types":["Keras tensors","Backend-native tensors","NumPy arrays"],"output_types":["Keras tensors","Gradient tensors (via autodiff)","Activation outputs"],"categories":["code-generation-editing","deep-learning-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-keras__cap_4","uri":"capability://automation.workflow.model.training.loop.with.distributed.training.support","name":"model training loop with distributed training support","description":"Provides a high-level training API (model.fit(), model.train_on_batch()) that abstracts away backend-specific training mechanics. The training loop handles gradient computation, optimizer updates, metric tracking, and callback execution in a backend-agnostic manner. Distributed training is supported through backend-specific mechanisms: JAX uses jax.experimental.multihost_utils, PyTorch uses torch.distributed, TensorFlow uses tf.distribute.Strategy. The framework automatically detects available devices and distributes computation across them without requiring user code changes.","intents":["I want to train a model on multiple GPUs/TPUs without writing distributed training code","I need a simple fit() API that works identically across all backends","I want to use callbacks and metrics without backend-specific implementation"],"best_for":["practitioners training standard models","teams scaling training across multiple devices","researchers prototyping models quickly"],"limitations":["Distributed training configuration is backend-specific; some strategies available in TensorFlow may not be available in PyTorch","Custom training loops require manual gradient computation and optimizer updates","Synchronization overhead in distributed training varies significantly by backend and hardware","Some advanced distributed features (e.g., gradient accumulation, mixed precision) may require backend-specific configuration"],"requires":["Python 3.9+","Keras 3.0+","Compiled model with loss and optimizer","Training data as NumPy arrays, tf.data.Dataset, or torch.DataLoader"],"input_types":["Compiled Keras models","Training data (arrays, datasets, or dataloaders)","Validation data","Callbacks"],"output_types":["Training history (loss, metrics per epoch)","Trained model weights","Callback outputs"],"categories":["automation-workflow","deep-learning-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-keras__cap_5","uri":"capability://automation.workflow.model.serialization.and.export.to.multiple.formats","name":"model serialization and export to multiple formats","description":"Provides model serialization to multiple deployment formats: SavedModel (TensorFlow format), ONNX (framework-agnostic), LiteRT (mobile), and OpenVINO (edge inference). Export is handled through keras/src/saving/ with format-specific implementations. SavedModel export preserves the full model graph and weights; ONNX export converts the model to ONNX intermediate representation for cross-framework compatibility; LiteRT export optimizes for mobile devices; OpenVINO export targets edge devices. Each format supports different deployment scenarios and optimization levels.","intents":["I want to export my Keras model to ONNX for deployment on non-TensorFlow infrastructure","I need to deploy a model to mobile devices using LiteRT","I want to save a model in SavedModel format for TensorFlow Serving","I need to optimize a model for edge inference using OpenVINO"],"best_for":["teams deploying models to production","mobile developers targeting iOS/Android","edge computing teams","cross-framework deployment scenarios"],"limitations":["ONNX export may not support all Keras operations; unsupported ops require custom implementations","LiteRT export requires quantization and may lose precision","OpenVINO export is inference-only and may not support all layer types","SavedModel export is TensorFlow-specific and requires TensorFlow for loading","Export process can be slow for very large models"],"requires":["Python 3.9+","Keras 3.0+","Trained and compiled model","Format-specific dependencies: onnx for ONNX export, tensorflow for SavedModel, etc."],"input_types":["Trained Keras models","Model weights","Quantization configurations"],"output_types":["SavedModel directory","ONNX model files","LiteRT .tflite files","OpenVINO .xml/.bin files"],"categories":["automation-workflow","deep-learning-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-keras__cap_6","uri":"capability://data.processing.analysis.quantization.and.model.compression","name":"quantization and model compression","description":"Provides quantization capabilities (keras/src/quantization/) for reducing model size and inference latency through reduced precision (int8, float16). Quantization is applied through quantization policies that specify precision for weights, activations, and computations. The framework supports post-training quantization and quantization-aware training (QAT). Quantization is implemented in a backend-agnostic manner, with backend-specific optimizations for each framework (JAX uses jax.numpy operations, PyTorch uses torch.quantization, TensorFlow uses tf.quantization).","intents":["I want to reduce my model size by 4x using int8 quantization without significant accuracy loss","I need to optimize inference latency on edge devices through quantization","I want to apply quantization-aware training to maintain accuracy during quantization","I need to quantize specific layers while keeping others at full precision"],"best_for":["mobile and edge deployment teams","practitioners optimizing inference latency","teams with strict model size constraints"],"limitations":["Quantization may reduce model accuracy; accuracy loss varies by model architecture and quantization scheme","Not all operations support quantization; some layers may remain at full precision","Quantization-aware training requires additional training time","Quantized models may not be portable across backends; quantization format is backend-specific","Numerical behavior of quantized operations differs from full-precision operations"],"requires":["Python 3.9+","Keras 3.0+","Trained model","Calibration data for post-training quantization"],"input_types":["Trained Keras models","Quantization policies","Calibration datasets"],"output_types":["Quantized models","Quantization metadata","Reduced-precision weights"],"categories":["data-processing-analysis","deep-learning-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-keras__cap_7","uri":"capability://data.processing.analysis.dtype.policies.for.mixed.precision.training.and.inference","name":"dtype policies for mixed-precision training and inference","description":"Provides DType (data type) policies that specify precision for layer computations, weights, and outputs. Policies enable mixed-precision training where computations use float32 for numerical stability but weights are stored in float16 for memory efficiency. DType policies are defined in keras/src/layers/layer.py and applied through layer.dtype_policy. The framework automatically handles precision conversion during forward/backward passes, leveraging backend-specific mixed-precision support (JAX's automatic mixed precision, PyTorch's autocast, TensorFlow's mixed_float16 policy).","intents":["I want to use mixed-precision training to reduce memory usage and increase training speed","I need to specify different precisions for different layers in my model","I want to use float16 weights with float32 computations for numerical stability"],"best_for":["teams training large models with memory constraints","practitioners optimizing training speed","researchers exploring precision-accuracy tradeoffs"],"limitations":["Mixed-precision training may reduce numerical stability; requires careful loss scaling","Not all operations support all precisions; some operations may be forced to float32","Precision conversion overhead can negate performance gains on some hardware","Gradient underflow/overflow is more likely with low-precision training; requires loss scaling","DType policy behavior may differ slightly across backends due to numerical precision differences"],"requires":["Python 3.9+","Keras 3.0+","GPU/TPU with mixed-precision support","Optimizer with loss scaling support"],"input_types":["Layer definitions","DType policy specifications","Training data"],"output_types":["Layers with specified precision","Mixed-precision model weights","Training metrics"],"categories":["data-processing-analysis","deep-learning-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-keras__cap_8","uri":"capability://code.generation.editing.functional.and.sequential.model.apis.for.rapid.prototyping","name":"functional and sequential model apis for rapid prototyping","description":"Provides two high-level model definition APIs: Sequential API for simple linear stacks of layers, and Functional API for complex architectures with multiple inputs/outputs and skip connections. Both APIs are defined in keras/src/models/ and compile to the same underlying Model class. Sequential API uses list-based layer stacking; Functional API uses symbolic tensor composition where layer calls return KerasTensor objects that represent computation graph structure. Both APIs support the same training, evaluation, and export capabilities.","intents":["I want to quickly prototype a simple model using a list of layers","I need to build a complex model with multiple inputs, outputs, and skip connections","I want to define a model using symbolic tensor composition without eager execution"],"best_for":["practitioners prototyping models quickly","researchers implementing standard architectures","teams building models with simple linear structures"],"limitations":["Sequential API only supports linear layer stacking; cannot represent branching or skip connections","Functional API requires understanding symbolic tensor composition","Both APIs generate static computation graphs; dynamic control flow requires Subclassing API","Model inspection and modification after creation is limited"],"requires":["Python 3.9+","Keras 3.0+","Knowledge of layer types and their signatures"],"input_types":["Layer instances","Input specifications (shape, dtype)","KerasTensor objects (Functional API)"],"output_types":["Compiled Model instances","Model summaries","Computation graphs"],"categories":["code-generation-editing","deep-learning-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-keras__cap_9","uri":"capability://code.generation.editing.custom.layer.and.model.subclassing.for.advanced.architectures","name":"custom layer and model subclassing for advanced architectures","description":"Provides a Subclassing API where developers inherit from keras.layers.Layer or keras.Model to implement custom layers and models with arbitrary Python logic. Subclassed layers override build() to create weights and call() to define forward computation. The framework automatically tracks weights, handles gradient computation, and manages state. Subclassing enables dynamic control flow (if/while statements), custom gradient computation (via @tf.custom_gradient), and arbitrary Python logic that cannot be expressed through Sequential or Functional APIs.","intents":["I want to implement a custom layer with dynamic control flow that depends on input values","I need to define custom gradients for a layer using @tf.custom_gradient","I want to implement a model with arbitrary Python logic and state management"],"best_for":["researchers implementing novel architectures","developers needing dynamic control flow","teams implementing custom gradient computation"],"limitations":["Subclassed models are harder to inspect and serialize than Functional models","Custom gradient computation is backend-specific; @tf.custom_gradient only works with TensorFlow backend","Dynamic control flow may not be compatible with graph compilation (JAX jit, TensorFlow graph mode)","Debugging subclassed models is more difficult due to arbitrary Python logic","Export to ONNX and other formats may not be supported for subclassed models with custom logic"],"requires":["Python 3.9+","Keras 3.0+","Understanding of Layer/Model base classes","Knowledge of build() and call() patterns"],"input_types":["Python class definitions","Input tensors","Custom gradient functions"],"output_types":["Layer/Model instances","Output tensors","Gradient computations"],"categories":["code-generation-editing","deep-learning-framework"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"low","permissions":["Python 3.9+","One of: TensorFlow 2.16.1+, JAX 0.4.20+, PyTorch 2.1.0+, or OpenVINO 2025.3.0+","KERAS_BACKEND environment variable or ~/.keras/keras.json configuration file","Keras 3.0+","Understanding of Layer base class and compute_output_spec() pattern","Compiled model","Optional: TensorBoard for visualization","Compiled model with metrics specified","Compiled model with optimizer specified","Compiled model with loss specified"],"failure_modes":["Backend must be selected at import time and cannot be changed within a single Python session","OpenVINO backend supports inference only, not training","Backend-specific optimizations and features may not be fully exposed through the unified API","Performance overhead from abstraction layer adds latency compared to native framework usage","Custom layers must use only Keras ops or backend-agnostic operations; direct backend API calls break portability","Layer implementations cannot access backend-specific optimizations or features not exposed through Keras ops","DType policies and quantization behavior may differ slightly across backends due to numerical precision differences","Callbacks are executed synchronously; asynchronous callbacks may block training","Callback access to model state is read-only; modifying weights during callbacks is not supported","Some callbacks (e.g., TensorBoard) require additional dependencies","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:15.343Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-keras","compare_url":"https://unfragile.ai/compare?artifact=pypi-keras"}},"signature":"aZgYdMlyTvZYKdGpb4ij+V5ZUce9bmw3ozVLa4WWFnYWqxetsvMf+1Eo2zUJyZM97awbL1FKlXC+EFN6QhoDCA==","signedAt":"2026-06-21T22:58:49.793Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-keras","artifact":"https://unfragile.ai/pypi-keras","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-keras","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}