Improving types and simplifying return (4a51afbf) · Commits · 钟慕尧 / deepchem

deepchem/models/keras_model.py

+14 −13

Original line number	Diff line number	Diff line
		@@ -273,7 +273,7 @@ class KerasModel(Model):
		variables: Optional[List[tf.Variable]] = None,
		loss: Optional[KerasLossFn] = None,
		callbacks: Union[Callable, List[Callable]] = [],
		all_losses: Optional[list] = None) -> float:
		all_losses: Optional[List[float]] = None) -> float:
		"""Train this model on a dataset.

		Parameters
		@@ -303,7 +303,7 @@ class KerasModel(Model):
		callbacks: function or list of functions
		one or more functions of the form f(model, step) that will be invoked after
		every step. This can be used to perform validation, logging, etc.
		all_losses: list, optional (default False)
		all_losses: Optional[List[float]], optional (default False)
		If specified, all logged losses are appended into this list. Note that
		you can call `fit()` repeatedly with the same list and losses will
		continue to be appended.
		@@ -326,7 +326,7 @@ class KerasModel(Model):
		variables: Optional[List[tf.Variable]] = None,
		loss: Optional[KerasLossFn] = None,
		callbacks: Union[Callable, List[Callable]] = [],
		all_losses: Optional[list] = None) -> float:
		all_losses: Optional[List[float]] = None) -> float:
		"""Train this model on data from a generator.

		Parameters
		@@ -352,7 +352,7 @@ class KerasModel(Model):
		callbacks: function or list of functions
		one or more functions of the form f(model, step) that will be invoked after
		every step. This can be used to perform validation, logging, etc.
		all_losses: list, optional (default False)
		all_losses: Optional[List[float]], optional (default False)
		If specified, all logged losses are appended into this list. Note that
		you can call `fit()` repeatedly with the same list and losses will
		continue to be appended.
		@@ -367,8 +367,8 @@ 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
		last_avg_loss = 0.0
		averaged_batches = 0
		train_op = None
		if loss is None:
		@@ -414,7 +414,11 @@ 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 all_losses is not None:
		all_losses.append(avg_loss)
		# Capture the last avg_loss in case of return since we're resetting to
		# 0 now
		last_avg_loss = avg_loss
		avg_loss = 0.0
		averaged_batches = 0

		@@ -433,19 +437,16 @@ 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 all_losses is not None:
		all_losses.append(avg_loss)
		last_avg_loss = avg_loss

		if checkpoint_interval > 0:
		manager.save()

		time2 = time.time()
		logger.info("TIMING: model fitting took %0.3f s" % (time2 - time1))
		if all_losses is not None:
		all_losses.extend(avg_losses)
		if len(avg_losses) > 0:
		return avg_losses[-1]
		else:
		return 0.0
		return last_avg_loss

		def _create_gradient_fn(self,
		variables: Optional[List[tf.Variable]]) -> Callable:

deepchem/models/tests/test_kerasmodel.py

+1 −0

Original line number	Diff line number	Diff line
		@@ -78,6 +78,7 @@ def test_fit_use_all_losses():
		model.fit(dataset, nb_epoch=1000, all_losses=losses)
		# Each epoch is a single step for this model
		assert len(losses) == 100
		assert np.count_nonzero(np.array(losses)) == 100


		def test_fit_on_batch():

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