reptile_step#

ivy.reptile_step(batch, cost_fn, variables, inner_grad_steps, inner_learning_rate, /, *, inner_optimization_step=<function gradient_descent_update>, batched=True, return_inner_v=False, num_tasks=None, stop_gradients=True)[source]#

Perform a step of Reptile.

Parameters:

  • batch (Container) – The input batch.

  • cost_fn (Callable) – The cost function that receives the task-specific sub-batch and variables, and returns the cost.

  • variables (Container) – Variables to be optimized.

  • inner_grad_steps (int) – Number of gradient steps to perform during the inner loop.

  • inner_learning_rate (float) – The learning rate of the inner loop.

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f91af9b51b0>) – The function used for the inner loop optimization. It takes the learnable weights,the derivative of the cost with respect to the weights, and the learning rate as arguments, and returns the updated variables. Default is gradient_descent_update.

  • batched (bool, default: True) – Whether to batch along the time dimension and run the meta steps in batch. Default is True.

  • return_inner_v (Union[str, bool], default: False) – Either ‘first’, ‘all’, or False. If ‘first’, the variables for the first task inner loop will also be returned. If ‘all’, variables for all tasks will be returned. Default is False.

  • num_tasks (Optional[int], default: None) – Number of unique tasks to inner-loop optimize for the meta step. Determined from the batch by default.

  • stop_gradients (bool, default: True) – Whether to stop the gradients of the cost. Default is True.

Return type:

Tuple[Array, Container, Any]

Returns:

ret – The cost, the gradients with respect to the outer loop variables, and additional information from the inner loop optimization.

Examples

With ivy.Container input:

>>> from ivy.functional.ivy.gradients import gradient_descent_update
>>> import ivy
>>> from ivy.functional.ivy.gradients import _variable

>>> ivy.set_backend("torch")

>>> def inner_cost_fn(batch_in, v):
...     return batch_in.mean().x / v.mean().latent

>>> num_tasks = 2
>>> batch = ivy.Container({"x": ivy.arange(1, num_tasks + 1, dtype="float32")})
>>> variables = ivy.Container({
...     "latent": _variable(ivy.repeat(ivy.array([[1.0]]), num_tasks, axis=0))
... })

>>> cost, gradients = ivy.reptile_step(batch, inner_cost_fn, variables, 5, 0.01,
...                                    num_tasks=num_tasks)
>>> print(cost)
ivy.array(1.4485182)
>>> print(gradients)
{
    latent: ivy.array([-139.9569855])
}

>>> batch = ivy.Container({"x": ivy.arange(1, 4, dtype="float32")})
>>> variables = ivy.Container({
...     "latent": _variable(ivy.array([1.0, 2.0]))
... })

>>> cost, gradients, firsts = ivy.reptile_step(batch, inner_cost_fn, variables, 4,
...                                            0.025, batched=False, num_tasks=2,
...                                            return_inner_v='first')
>>> print(cost)
ivy.array(0.9880483)
>>> print(gradients)
{
    latent: ivy.array([-13.01766968, -13.01766968])
}
>>> print(firsts)
{
    latent: ivy.array([[1.02197957, 2.02197981]])
}