Loading deepchem/models/keras_model.py +25 −6 Original line number Diff line number Diff line Loading @@ -272,7 +272,8 @@ 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]] = [], all_losses: Optional[List[float]] = None) -> float: """Train this model on a dataset. Parameters Loading Loading @@ -302,16 +303,20 @@ 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: Optional[List[float]], optional (default None) 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 ------- the average loss over the most recent checkpoint interval 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) checkpoint_interval, restore, variables, loss, callbacks, all_losses) def fit_generator(self, generator: Iterable[Tuple[Any, Any, Any]], Loading @@ -320,7 +325,8 @@ 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]] = [], all_losses: Optional[List[float]] = None) -> float: """Train this model on data from a generator. Parameters Loading @@ -346,10 +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. all_losses: Optional[List[float]], optional (default None) 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 ------- the average loss over the most recent checkpoint interval The average loss over the most recent checkpoint interval """ if not isinstance(callbacks, SequenceCollection): callbacks = [callbacks] Loading @@ -358,6 +368,7 @@ class KerasModel(Model): manager = tf.train.CheckpointManager(self._checkpoint, self.model_dir, max_checkpoints_to_keep) avg_loss = 0.0 last_avg_loss = 0.0 averaged_batches = 0 train_op = None if loss is None: Loading Loading @@ -403,6 +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)) 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 Loading @@ -421,13 +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)) 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)) return avg_loss return last_avg_loss def _create_gradient_fn(self, variables: Optional[List[tf.Variable]]) -> Callable: Loading deepchem/models/models.py +1 −1 Original line number Diff line number Diff line Loading @@ -140,7 +140,7 @@ class Model(BaseEstimator): Returns ------- the average loss over the most recent epoch The average loss over the most recent checkpoint interval. """ for epoch in range(nb_epoch): logger.info("Starting epoch %s" % str(epoch + 1)) Loading deepchem/models/tests/test_kerasmodel.py +23 −0 Original line number Diff line number Diff line Loading @@ -58,6 +58,29 @@ def test_overfit_sequential_model(): assert scores[metric.name] > 0.9 def test_fit_use_all_losses(): """Test fitting a KerasModel and getting a loss curve back.""" n_data_points = 10 n_features = 2 X = np.random.rand(n_data_points, n_features) y = (X[:, 0] > X[:, 1]).astype(np.float32) dataset = dc.data.NumpyDataset(X, y) keras_model = tf.keras.Sequential([ tf.keras.layers.Dense(10, activation='relu'), tf.keras.layers.Dense(1, activation='sigmoid') ]) model = dc.models.KerasModel( keras_model, dc.models.losses.BinaryCrossEntropy(), learning_rate=0.005, log_frequency=10) 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(): """Test fitting a KerasModel to individual batches.""" n_data_points = 10 Loading Loading
deepchem/models/keras_model.py +25 −6 Original line number Diff line number Diff line Loading @@ -272,7 +272,8 @@ 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]] = [], all_losses: Optional[List[float]] = None) -> float: """Train this model on a dataset. Parameters Loading Loading @@ -302,16 +303,20 @@ 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: Optional[List[float]], optional (default None) 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 ------- the average loss over the most recent checkpoint interval 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) checkpoint_interval, restore, variables, loss, callbacks, all_losses) def fit_generator(self, generator: Iterable[Tuple[Any, Any, Any]], Loading @@ -320,7 +325,8 @@ 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]] = [], all_losses: Optional[List[float]] = None) -> float: """Train this model on data from a generator. Parameters Loading @@ -346,10 +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. all_losses: Optional[List[float]], optional (default None) 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 ------- the average loss over the most recent checkpoint interval The average loss over the most recent checkpoint interval """ if not isinstance(callbacks, SequenceCollection): callbacks = [callbacks] Loading @@ -358,6 +368,7 @@ class KerasModel(Model): manager = tf.train.CheckpointManager(self._checkpoint, self.model_dir, max_checkpoints_to_keep) avg_loss = 0.0 last_avg_loss = 0.0 averaged_batches = 0 train_op = None if loss is None: Loading Loading @@ -403,6 +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)) 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 Loading @@ -421,13 +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)) 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)) return avg_loss return last_avg_loss def _create_gradient_fn(self, variables: Optional[List[tf.Variable]]) -> Callable: Loading
deepchem/models/models.py +1 −1 Original line number Diff line number Diff line Loading @@ -140,7 +140,7 @@ class Model(BaseEstimator): Returns ------- the average loss over the most recent epoch The average loss over the most recent checkpoint interval. """ for epoch in range(nb_epoch): logger.info("Starting epoch %s" % str(epoch + 1)) Loading
deepchem/models/tests/test_kerasmodel.py +23 −0 Original line number Diff line number Diff line Loading @@ -58,6 +58,29 @@ def test_overfit_sequential_model(): assert scores[metric.name] > 0.9 def test_fit_use_all_losses(): """Test fitting a KerasModel and getting a loss curve back.""" n_data_points = 10 n_features = 2 X = np.random.rand(n_data_points, n_features) y = (X[:, 0] > X[:, 1]).astype(np.float32) dataset = dc.data.NumpyDataset(X, y) keras_model = tf.keras.Sequential([ tf.keras.layers.Dense(10, activation='relu'), tf.keras.layers.Dense(1, activation='sigmoid') ]) model = dc.models.KerasModel( keras_model, dc.models.losses.BinaryCrossEntropy(), learning_rate=0.005, log_frequency=10) 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(): """Test fitting a KerasModel to individual batches.""" n_data_points = 10 Loading
