Loading deepchem/models/keras_model.py +31 −36 Original line number Diff line number Diff line Loading @@ -273,7 +273,7 @@ class KerasModel(Model): variables: Optional[List[tf.Variable]] = None, loss: Optional[KerasLossFn] = None, callbacks: Union[Callable, List[Callable]] = [], return_loss_curve: bool = False) -> Union[float, List[float]]: all_losses: Optional[list] = None) -> float: """Train this model on a dataset. Parameters Loading Loading @@ -303,23 +303,22 @@ 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. return_loss_curve: bool, optional (default False) If `True` return the full set of average losses computed over the process of fitting. Else return the last computed average loss. all_losses: list, 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. Returns ------- Either the average loss over the most recent checkpoint interval or a list of all such average losses over the course of fitting. The average loss over the most recent checkpoint interval """ return self.fit_generator( self.default_generator( dataset, epochs=nb_epoch, deterministic=deterministic), max_checkpoints_to_keep, checkpoint_interval, restore, variables, loss, callbacks, return_loss_curve) dataset, epochs=nb_epoch, deterministic=deterministic), max_checkpoints_to_keep, checkpoint_interval, restore, variables, loss, callbacks, all_losses) def fit_generator( self, def fit_generator(self, generator: Iterable[Tuple[Any, Any, Any]], max_checkpoints_to_keep: int = 5, checkpoint_interval: int = 1000, Loading @@ -327,7 +326,7 @@ class KerasModel(Model): variables: Optional[List[tf.Variable]] = None, loss: Optional[KerasLossFn] = None, callbacks: Union[Callable, List[Callable]] = [], return_loss_curve: bool = False) -> Union[float, List[float]]: all_losses: Optional[list] = None) -> float: """Train this model on data from a generator. Parameters Loading @@ -353,14 +352,14 @@ 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. return_loss_curve: bool, optional (default False) If `True` return the full set of average losses computed over the process of fitting. Else return the last computed average loss. all_losses: list, 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. Returns ------- Either the average loss over the most recent checkpoint interval or a list of all such average losses over the course of fitting. The average loss over the most recent checkpoint interval """ if not isinstance(callbacks, SequenceCollection): callbacks = [callbacks] Loading Loading @@ -441,9 +440,8 @@ class KerasModel(Model): time2 = time.time() logger.info("TIMING: model fitting took %0.3f s" % (time2 - time1)) if return_loss_curve: return avg_losses else: if all_losses is not None: all_losses.extend(avg_losses) if len(avg_losses) > 0: return avg_losses[-1] else: Loading Loading @@ -516,17 +514,14 @@ class KerasModel(Model): """ self._ensure_built() dataset = NumpyDataset(X, y, w) # We set return_loss_curve=False, so we know this is a float, but mypy # can't automatically infer that. return self.fit( # type: ignore return self.fit( dataset, nb_epoch=1, max_checkpoints_to_keep=max_checkpoints_to_keep, checkpoint_interval=self._global_step.numpy() + 2 if checkpoint else 0, variables=variables, loss=loss, callbacks=callbacks, return_loss_curve=False) callbacks=callbacks) def _predict( self, generator: Iterable[Tuple[Any, Any, Any]], Loading deepchem/models/models.py +2 −3 Original line number Diff line number Diff line Loading @@ -21,7 +21,7 @@ from deepchem.utils.save import load_from_disk from deepchem.utils.save import save_to_disk from deepchem.utils.evaluate import Evaluator from typing import Any, Dict, List, Optional, Sequence, Union from typing import Any, Dict, List, Optional, Sequence from deepchem.utils.typing import OneOrMany logger = logging.getLogger(__name__) Loading Loading @@ -127,8 +127,7 @@ class Model(BaseEstimator): """ raise NotImplementedError def fit(self, dataset: Dataset, nb_epoch: int = 10) -> Union[float, List[float]]: def fit(self, dataset: Dataset, nb_epoch: int = 10) -> float: """ Fits a model on data in a Dataset object. Loading deepchem/models/tests/test_kerasmodel.py +3 −2 Original line number Diff line number Diff line Loading @@ -58,7 +58,7 @@ def test_overfit_sequential_model(): assert scores[metric.name] > 0.9 def test_fit_return_loss_curve(): def test_fit_use_all_losses(): """Test fitting a KerasModel and getting a loss curve back.""" n_data_points = 10 n_features = 2 Loading @@ -74,7 +74,8 @@ def test_fit_return_loss_curve(): dc.models.losses.BinaryCrossEntropy(), learning_rate=0.005, log_frequency=10) losses = model.fit(dataset, nb_epoch=1000, return_loss_curve=True) losses = [] model.fit(dataset, nb_epoch=1000, all_losses=losses) # Each epoch is a single step for this model assert len(losses) == 100 Loading Loading
deepchem/models/keras_model.py +31 −36 Original line number Diff line number Diff line Loading @@ -273,7 +273,7 @@ class KerasModel(Model): variables: Optional[List[tf.Variable]] = None, loss: Optional[KerasLossFn] = None, callbacks: Union[Callable, List[Callable]] = [], return_loss_curve: bool = False) -> Union[float, List[float]]: all_losses: Optional[list] = None) -> float: """Train this model on a dataset. Parameters Loading Loading @@ -303,23 +303,22 @@ 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. return_loss_curve: bool, optional (default False) If `True` return the full set of average losses computed over the process of fitting. Else return the last computed average loss. all_losses: list, 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. Returns ------- Either the average loss over the most recent checkpoint interval or a list of all such average losses over the course of fitting. The average loss over the most recent checkpoint interval """ return self.fit_generator( self.default_generator( dataset, epochs=nb_epoch, deterministic=deterministic), max_checkpoints_to_keep, checkpoint_interval, restore, variables, loss, callbacks, return_loss_curve) dataset, epochs=nb_epoch, deterministic=deterministic), max_checkpoints_to_keep, checkpoint_interval, restore, variables, loss, callbacks, all_losses) def fit_generator( self, def fit_generator(self, generator: Iterable[Tuple[Any, Any, Any]], max_checkpoints_to_keep: int = 5, checkpoint_interval: int = 1000, Loading @@ -327,7 +326,7 @@ class KerasModel(Model): variables: Optional[List[tf.Variable]] = None, loss: Optional[KerasLossFn] = None, callbacks: Union[Callable, List[Callable]] = [], return_loss_curve: bool = False) -> Union[float, List[float]]: all_losses: Optional[list] = None) -> float: """Train this model on data from a generator. Parameters Loading @@ -353,14 +352,14 @@ 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. return_loss_curve: bool, optional (default False) If `True` return the full set of average losses computed over the process of fitting. Else return the last computed average loss. all_losses: list, 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. Returns ------- Either the average loss over the most recent checkpoint interval or a list of all such average losses over the course of fitting. The average loss over the most recent checkpoint interval """ if not isinstance(callbacks, SequenceCollection): callbacks = [callbacks] Loading Loading @@ -441,9 +440,8 @@ class KerasModel(Model): time2 = time.time() logger.info("TIMING: model fitting took %0.3f s" % (time2 - time1)) if return_loss_curve: return avg_losses else: if all_losses is not None: all_losses.extend(avg_losses) if len(avg_losses) > 0: return avg_losses[-1] else: Loading Loading @@ -516,17 +514,14 @@ class KerasModel(Model): """ self._ensure_built() dataset = NumpyDataset(X, y, w) # We set return_loss_curve=False, so we know this is a float, but mypy # can't automatically infer that. return self.fit( # type: ignore return self.fit( dataset, nb_epoch=1, max_checkpoints_to_keep=max_checkpoints_to_keep, checkpoint_interval=self._global_step.numpy() + 2 if checkpoint else 0, variables=variables, loss=loss, callbacks=callbacks, return_loss_curve=False) callbacks=callbacks) def _predict( self, generator: Iterable[Tuple[Any, Any, Any]], Loading
deepchem/models/models.py +2 −3 Original line number Diff line number Diff line Loading @@ -21,7 +21,7 @@ from deepchem.utils.save import load_from_disk from deepchem.utils.save import save_to_disk from deepchem.utils.evaluate import Evaluator from typing import Any, Dict, List, Optional, Sequence, Union from typing import Any, Dict, List, Optional, Sequence from deepchem.utils.typing import OneOrMany logger = logging.getLogger(__name__) Loading Loading @@ -127,8 +127,7 @@ class Model(BaseEstimator): """ raise NotImplementedError def fit(self, dataset: Dataset, nb_epoch: int = 10) -> Union[float, List[float]]: def fit(self, dataset: Dataset, nb_epoch: int = 10) -> float: """ Fits a model on data in a Dataset object. Loading
deepchem/models/tests/test_kerasmodel.py +3 −2 Original line number Diff line number Diff line Loading @@ -58,7 +58,7 @@ def test_overfit_sequential_model(): assert scores[metric.name] > 0.9 def test_fit_return_loss_curve(): def test_fit_use_all_losses(): """Test fitting a KerasModel and getting a loss curve back.""" n_data_points = 10 n_features = 2 Loading @@ -74,7 +74,8 @@ def test_fit_return_loss_curve(): dc.models.losses.BinaryCrossEntropy(), learning_rate=0.005, log_frequency=10) losses = model.fit(dataset, nb_epoch=1000, return_loss_curve=True) losses = [] model.fit(dataset, nb_epoch=1000, all_losses=losses) # Each epoch is a single step for this model assert len(losses) == 100 Loading
