Package: keras3 1.5.1.9000

Introduction | Setup | A first simple example | Going lower-level | Supporting sample_weight & class_weight | Providing your own evaluation step | Wrapping up: an end-to-end GAN example

Introduction | How it works | Setup | DeviceMesh and TensorLayout | Distribution | DataParallel | ModelParallel and LayoutMap | Further reading

Overview | Installation | MNIST Example | Preparing the Data | Defining the Model | Training and Evaluation | Deep Learning with R Book | Why this name, Keras?

Introduction | Setup | A first example: A MNIST convnet | Writing cross-framework custom components | Training models on arbitrary data sources | Further reading | How to customize what happens in fit() | How to write custom training loops | How to distribute training

Introduction | Setup | The Layer class: the combination of state (weights) and some computation | Layers can have non-trainable weights | Best practice: deferring weight creation until the shape of the inputs is known | Layers are recursively composable | Backend-agnostic layers and backend-specific layers | The add_loss() method | You can optionally enable serialization on your layers | Privileged training argument in the call() method | Privileged mask argument in the call() method | The Model class | Putting it all together: an end-to-end example

Introduction | Setup | Single-host, multi-device synchronous training | Using callbacks to ensure fault tolerance | tf$data performance tips

Setup | Introduction | Training, evaluation, and inference | Save and serialize | Use the same graph of layers to define multiple models | All models are callable, just like layers | Manipulate complex graph topologies | Models with multiple inputs and outputs | A toy ResNet model | Shared layers | Extract and reuse nodes in the graph of layers | Extend the API using custom layers | When to use the functional API | Functional API strengths: | Less verbose | Model validation while defining its connectivity graph | A functional model is plottable and inspectable | A functional model can be serialized or cloned | Functional API weakness: | It does not support dynamic architectures | Mix-and-match API styles

Setup | Introduction | API overview: a first end-to-end example | The compile() method: specifying a loss, metrics, and an optimizer | Many built-in optimizers, losses, and metrics are available | Custom losses | Custom metrics | Handling losses and metrics that don't fit the standard signature | Automatically setting apart a validation holdout set | Training & evaluation using TF Dataset objects | Using sample weighting and class weighting | Class weights | Sample weights | Passing data to multi-input, multi-output models | Using callbacks | Many built-in callbacks are available | Writing your own callback | Checkpointing models | Using learning rate schedules | Passing a schedule to an optimizer | Using callbacks to implement a dynamic learning rate schedule | Visualizing loss and metrics during training with TensorBoard | Using the TensorBoard callback

Setup | Introduction | Freezing layers: understanding the trainable attribute | Recursive setting of the trainable attribute | The typical transfer-learning workflow | Fine-tuning | An end-to-end example: fine-tuning an image classification model on a cats vs. dogs dataset | Getting the data | Standardizing the data | Using random data augmentation | Build a model | Train the top layer | Do a round of fine-tuning of the entire model

Setup | Introduction | Using the GradientTape: a first end-to-end example | Low-level handling of metrics | Speeding-up your training step with tf_function() | Low-level handling of losses tracked by the model | Summary | End-to-end example: a GAN training loop from scratch

Introduction | How to save and load a model | Setup | Saving | APIs | Custom objects | Registering custom objects (preferred) | Passing custom objects to load_model() | Using a custom object scope | Model serialization | In-memory model cloning | get_config() and from_config() | save_model_config() and load_model_config() | Arbitrary object serialization and deserialization | Model weights saving | APIs for in-memory weight transfer | APIs for saving weights to disk & loading them back | Transfer learning example | Appendix: Handling custom objects | Defining the config methods | How custom objects are serialized

Introduction | Setup | Keras callbacks overview | An overview of callback methods | Global methods | on_(train|test|predict)_begin(logs = NULL) | on_(train|test|predict)_end(logs = NULL) | Batch-level methods for training/testing/predicting | on_(train|test|predict)_batch_begin(batch, logs = NULL) | on_(train|test|predict)_batch_end(batch, logs = NULL) | Epoch-level methods (training only) | on_epoch_begin(epoch, logs = NULL) | on_epoch_end(epoch, logs = NULL) | A basic example | Usage of logs list | Usage of self$model attribute | Examples of Keras callback applications | Early stopping at minimum loss | Learning rate scheduling | Built-in Keras callbacks

Setup | When to use a Sequential model | Creating a Sequential model | Specifying the input shape in advance | A common debugging workflow: add layers + summary() | What to do once you have a model | Feature extraction with a Sequential model | Transfer learning with a Sequential model

Setup | Introduction | Padding sequence data | Masking | Mask-generating layers: Embedding and Masking | Mask propagation in the Functional API and Sequential API | Passing mask tensors directly to layers | Supporting masking in your custom layers | Opting-in to mask propagation on compatible layers | Writing layers that need mask information | Summary

Setup | Introduction | Tensors | Variables | Doing math in TensorFlow | Gradients | Keras layers | Layer weight creation in build(input_shape) | Layer gradients | Trainable and non-trainable weights | Layers that own layers | Tracking losses created by layers | Keeping track of training metrics | Compiled functions | Training mode & inference mode | The Functional API for model-building | End-to-end experiment example 1: variational autoencoders. | End-to-end experiment example 2: hypernetworks.