Jax.value_and_grad
jax.value_and_grad is a JAX transformation that creates a function returning both a scalar function’s value and its gradient.
Deep Learning with R, Third Edition
jax.value_and_grad is a JAX transformation that constructs a function returning both a function's value and its gradient.
- How it is used
- Supplies loss values and gradients together for stateless JAX training steps.
- How deep it goes
- Works from a single-array loss through nested weight state and auxiliary outputs to complete training steps, without deriving gradient internals.
- Examples
- Uses a squared-input loss, a linear classifier with mean squared error, and a Keras custom training step.
- What you can do after
- Build it from scratch in a JAX classifier loop or apply it via Keras stateless training methods.
Read how Deep Learning with R, Third Edition explains jax.value_and_grad →
Deep Learning with Python, Third Edition
A JAX function that returns both a function value and its gradient in one call.
- How it is used
- Computes loss values and gradients together for efficient JAX optimization and custom training loops.
- How deep it goes
- Explains single arguments, nested state trees, auxiliary outputs, and stateless updates, but does not derive automatic differentiation internals.
- Examples
- Uses a squared scalar loss, a multi-variable state example, auxiliary outputs, and a linear classifier trained by gradient descent.
- What you can do after
- Build it from scratch by wrapping a loss function, extracting gradients, and applying stateless parameter updates.
Read how Deep Learning with Python, Third Edition explains jax.value_and_grad →
Deep Learning with JAX
The JAX function that returns both a function value and its gradient in one transformation.
- How it is used
- Tracks training loss while computing gradients for a parameter update.
- How deep it goes
- Explains tuple returns and auxiliary data, but defers autodiff implementation internals.
- Examples
- Demonstrates a loss update with parameter lists and an auxiliary prediction output.
- What you can do after
- Apply it in a JAX training step to return updated parameters, loss values, gradients, and optional auxiliary data.
Read how Deep Learning with JAX explains jax.value_and_grad →
Related concepts
- gradientproduces · A gradient is the vector of partial derivatives of a scalar function with respect to its variables, indicating its local rate and direction of change.
- JAXused by · JAX is a Python library for high-performance array-based numerical computing with composable transformations including automatic differentiation, compilation, vectorization, and parallelization.
- jax.gradalternative to · jax.grad is a JAX transformation that creates a function computing derivatives, typically gradients of a scalar-output function with respect to specified inputs.
- automatic differentiationimplements · Automatic differentiation is a computational technique that evaluates derivatives of a program by systematically applying the chain rule to its elementary operations.
- pytreeproduces · A recursive tree-like structure built from Python container nodes and leaf values, used in JAX to organize nested function arguments and results.
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