def train_step(self, states, initializing): return elegy.TrainStep( logs=dict(loss=2.0), states=elegy.States( optimizer_states=0) if initializing else elegy.States( optimizer_states=states.optimizer_states + 1), )
def train_step(self, x, optimizer_states, initializing): if initializing: states = elegy.States(optimizer_states=0) else: states = elegy.States(optimizer_states=optimizer_states + 1) return elegy.TrainStep( logs=dict(loss=jnp.sum(x)), states=states, )
def train_step(self): _, logs, states = self.test_step() return elegy.TrainStep(logs, states.update(optimizer_states=4))