68 lines
1.8 KiB
Python
68 lines
1.8 KiB
Python
import jax
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import jax.numpy as jnp
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from utils import State
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from .. import BaseProblem
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class FuncFit(BaseProblem):
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jitable = True
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def __init__(self,
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error_method: str = 'mse'
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):
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super().__init__()
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assert error_method in {'mse', 'rmse', 'mae', 'mape'}
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self.error_method = error_method
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def setup(self, randkey, state: State = State()):
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return state
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def evaluate(self, randkey, state, act_func, params):
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predict = jax.vmap(act_func, in_axes=(0, None))(self.inputs, params)
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if self.error_method == 'mse':
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loss = jnp.mean((predict - self.targets) ** 2)
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elif self.error_method == 'rmse':
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loss = jnp.sqrt(jnp.mean((predict - self.targets) ** 2))
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elif self.error_method == 'mae':
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loss = jnp.mean(jnp.abs(predict - self.targets))
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elif self.error_method == 'mape':
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loss = jnp.mean(jnp.abs((predict - self.targets) / self.targets))
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else:
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raise NotImplementedError
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return -loss
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def show(self, randkey, state, act_func, params, *args, **kwargs):
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predict = jax.vmap(act_func, in_axes=(0, None))(self.inputs, params)
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inputs, target, predict = jax.device_get([self.inputs, self.targets, predict])
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loss = -self.evaluate(randkey, state, act_func, params)
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msg = ""
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for i in range(inputs.shape[0]):
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msg += f"input: {inputs[i]}, target: {target[i]}, predict: {predict[i]}\n"
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msg += f"loss: {loss}\n"
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print(msg)
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@property
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def inputs(self):
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raise NotImplementedError
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@property
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def targets(self):
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raise NotImplementedError
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@property
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def input_shape(self):
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raise NotImplementedError
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@property
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def output_shape(self):
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raise NotImplementedError
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