Source code for brainpy.train.base

# -*- coding: utf-8 -*-

from typing import Dict, Sequence, Any, Union

import jax.numpy as jnp

import brainpy.math as bm
from brainpy.dyn.base import DynamicalSystem
from brainpy.dyn.runners import DSRunner
from import check_dict_data
from brainpy.types import Array, Output
from ..running import constants as c

__all__ = [

[docs]class DSTrainer(DSRunner): """Structural Trainer for Dynamical Systems.""" target: DynamicalSystem train_nodes: Sequence[DynamicalSystem] # need to be initialized by subclass
[docs] def __init__( self, target: DynamicalSystem, **kwargs ): if not isinstance(target, (DynamicalSystem, DynamicalSystem)): raise TypeError(f'"target" must be an instance of {DynamicalSystem.__name__}, ' f'but we got {type(target)}: {target}') super(DSTrainer, self).__init__(target=target, **kwargs) # jit self.jit[c.PREDICT_PHASE] = self.jit.get(c.PREDICT_PHASE, True) self.jit[c.FIT_PHASE] = self.jit.get(c.FIT_PHASE, True)
def predict( self, inputs: Union[Array, Sequence[Array], Dict[str, Array]], reset_state: bool = False, shared_args: Dict = None, eval_time: bool = False ) -> Output: """Prediction function. What's different from `predict()` function in :py:class:`~.DynamicalSystem` is that the `inputs_are_batching` is default `True`. Parameters ---------- inputs: Array, sequence of Array, dict of Array The input values. reset_state: bool Reset the target state before running. shared_args: dict The shared arguments across nodes. eval_time: bool Whether we evaluate the running time or not? Returns ------- output: Array, sequence of Array, dict of Array The running output. """ return super(DSTrainer, self).predict(duration=None, inputs=inputs, inputs_are_batching=True, reset_state=reset_state, shared_args=shared_args, eval_time=eval_time) def fit( self, train_data: Any, reset_state: bool = False, shared_args: Dict = None ) -> Output: # need to be implemented by subclass raise NotImplementedError('Must implement the fit function. ') def _check_ys(self, ys, num_batch, num_step, move_axis=False): if isinstance(ys, (bm.ndarray, jnp.ndarray)): if len(self.train_nodes) == 1: ys = {self.train_nodes[0].name: ys} else: raise ValueError(f'The network\n {} \nhas {len(self.train_nodes)} ' f'training nodes, while we only got one target data.') check_dict_data(ys, key_type=str, val_type=(bm.ndarray, jnp.ndarray)) # check data path abs_node_names = [ for node in self.train_nodes] formatted_ys = {} ys_not_included = {} for k, v in ys.items(): if k in abs_node_names: formatted_ys[k] = v else: ys_not_included[k] = v if len(ys_not_included): rel_nodes ='relative', level=-1, include_self=True).subset(DynamicalSystem).unique() for k, v in ys_not_included.items(): if k in rel_nodes: formatted_ys[rel_nodes[k].name] = v else: raise ValueError(f'Unknown target "{k}" for fitting.') # check data shape for key, val in formatted_ys.items(): if val.ndim < 3: raise ValueError("Targets must be a tensor with shape of " "(num_sample, num_time, feature_dim, ...), " f"but we got {val.shape}") if val.shape[0] != num_batch: raise ValueError(f'Batch size of the target {key} does not match ' f'with the input data {val.shape[0]} != {num_batch}') if val.shape[1] != num_step: raise ValueError(f'The time step of the target {key} does not match ' f'with the input data {val.shape[1]} != {num_step})') if move_axis: # change shape to (num_time, num_sample, num_feature) formatted_ys = {k: bm.moveaxis(v, 0, 1) for k, v in formatted_ys.items()} return formatted_ys