Fix to loss returns (ae9a19a0) · Commits · 钟慕尧 / deepchem

deepchem/models/keras_model.py

+19 −11

Original line number	Diff line number	Diff line
		@@ -272,7 +272,7 @@ class KerasModel(Model):
		restore: bool = False,
		variables: Optional[List[tf.Variable]] = None,
		loss: Optional[KerasLossFn] = None,
		callbacks: Union[Callable, List[Callable]] = []) -> float:
		callbacks: Union[Callable, List[Callable]] = []) -> List[float]:
		"""Train this model on a dataset.

		Parameters
		@@ -313,14 +313,15 @@ class KerasModel(Model):
		deterministic=deterministic), max_checkpoints_to_keep,
		checkpoint_interval, restore, variables, loss, callbacks)

		def fit_generator(self,
		def fit_generator(
		self,
		generator: Iterable[Tuple[Any, Any, Any]],
		max_checkpoints_to_keep: int = 5,
		checkpoint_interval: int = 1000,
		restore: bool = False,
		variables: Optional[List[tf.Variable]] = None,
		loss: Optional[KerasLossFn] = None,
		callbacks: Union[Callable, List[Callable]] = []) -> float:
		callbacks: Union[Callable, List[Callable]] = []) -> List[float]:
		"""Train this model on data from a generator.

		Parameters
		@@ -357,6 +358,7 @@ class KerasModel(Model):
		if checkpoint_interval > 0:
		manager = tf.train.CheckpointManager(self._checkpoint, self.model_dir,
		max_checkpoints_to_keep)
		avg_losses = []
		avg_loss = 0.0
		averaged_batches = 0
		train_op = None
		@@ -403,6 +405,7 @@ class KerasModel(Model):
		avg_loss = float(avg_loss) / averaged_batches
		logger.info(
		'Ending global_step %d: Average loss %g' % (current_step, avg_loss))
		avg_losses.append(avg_loss)
		avg_loss = 0.0
		averaged_batches = 0

		@@ -421,13 +424,14 @@ class KerasModel(Model):
		avg_loss = float(avg_loss) / averaged_batches
		logger.info(
		'Ending global_step %d: Average loss %g' % (current_step, avg_loss))
		avg_losses.append(avg_loss)

		if checkpoint_interval > 0:
		manager.save()

		time2 = time.time()
		logger.info("TIMING: model fitting took %0.3f s" % (time2 - time1))
		return avg_loss
		return avg_losses

		def _create_gradient_fn(self,
		variables: Optional[List[tf.Variable]]) -> Callable:
		@@ -496,7 +500,7 @@ class KerasModel(Model):
		"""
		self._ensure_built()
		dataset = NumpyDataset(X, y, w)
		return self.fit(
		losses = self.fit(
		dataset,
		nb_epoch=1,
		max_checkpoints_to_keep=max_checkpoints_to_keep,
		@@ -504,6 +508,10 @@ class KerasModel(Model):
		variables=variables,
		loss=loss,
		callbacks=callbacks)
		if len(losses) != 1:
		raise ValueError(
		"Each batch should take only one global step to fit. Unknown error.")
		return losses[0]

		def _predict(
		self, generator: Iterable[Tuple[Any, Any, Any]],

deepchem/models/models.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -127,7 +127,7 @@ class Model(BaseEstimator):
		"""
		raise NotImplementedError

		def fit(self, dataset: Dataset, nb_epoch: int = 10) -> float:
		def fit(self, dataset: Dataset, nb_epoch: int = 10) -> List[float]:
		"""
		Fits a model on data in a Dataset object.

		@@ -140,7 +140,7 @@ class Model(BaseEstimator):

		Returns
		-------
		the average loss over the most recent epoch
		The average losses over course of training.
		"""
		for epoch in range(nb_epoch):
		logger.info("Starting epoch %s" % str(epoch + 1))

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