Loading deepchem/models/keras_model.py +27 −1 Original line number Diff line number Diff line Loading @@ -76,6 +76,8 @@ class KerasModel(Model): model_dir=None, learning_rate=0.001, optimizer=None, tensorboard=False, tensorboard_log_frequency=100, **kwargs): """Create a new KerasModel. Loading @@ -99,6 +101,10 @@ class KerasModel(Model): optimizer: Optimizer the optimizer to use for fitting. If this is specified, learning_rate is ignored. tensorboard: bool whether to log progress to TensorBoard during training tensorboard_log_frequency: int the frequency at which to log data to TensorBoard, measured in batches """ super(KerasModel, self).__init__( model_instance=model, model_dir=model_dir, **kwargs) Loading @@ -112,6 +118,12 @@ class KerasModel(Model): self.optimizer = Adam(learning_rate=learning_rate) else: self.optimizer = optimizer self.tensorboard = tensorboard self.tensorboard_log_frequency = tensorboard_log_frequency self._tensorboard_step = 0 if tensorboard and tf.executing_eagerly(): raise ValueError( "Logging to TensorBoard is not currently supported in eager mode") if output_types is None: self._prediction_outputs = None self._loss_outputs = None Loading Loading @@ -226,6 +238,9 @@ class KerasModel(Model): except ValueError: # The loss doesn't depend on any variables. self._train_op = 0 if self.tensorboard: self._summary_ops = tf.summary.scalar('loss', self._loss_tensor) self._summary_writer = tf.summary.FileWriter(self.model_dir) self._init_new_vars() def _init_new_vars(self): Loading Loading @@ -308,6 +323,10 @@ class KerasModel(Model): for batch in generator: self._create_training_ops(batch) inputs, labels, weights = self._prepare_batch(batch) self._tensorboard_step += 1 should_log = ( self.tensorboard and self._tensorboard_step % self.tensorboard_log_frequency == 0) if tf.executing_eagerly(): # In eager mode we execute the loss function, accumulating the gradients. Loading @@ -329,12 +348,19 @@ class KerasModel(Model): # In graph mode we execute the training op. fetches = [self._train_op, self._loss_tensor, self._global_step] if should_log: fetches.append(self._summary_ops) feed_dict = dict(zip(self._input_placeholders, inputs)) feed_dict.update(dict(zip(self._label_placeholders, labels))) feed_dict.update(dict(zip(self._weights_placeholders, weights))) fetched_values = self.session.run(fetches, feed_dict=feed_dict) avg_loss += fetched_values[1] current_step = fetched_values[2] if should_log: self._summary_writer.reopen() self._summary_writer.add_summary( fetched_values[3], global_step=current_step) self._summary_writer.close() # Report progress and write checkpoints. Loading Loading @@ -629,7 +655,7 @@ class KerasModel(Model): dict Maps tasks to scores under metric. """ evaluator = GeneratorEvaluator(self, generator, transformers, labels=None) evaluator = GeneratorEvaluator(self, generator, transformers) return evaluator.compute_model_performance(metrics, per_task_metrics) def compute_saliency(self, X): Loading deepchem/models/tests/test_kerasmodel.py +23 −0 Original line number Diff line number Diff line import os import unittest import deepchem as dc import numpy as np Loading Loading @@ -189,3 +190,25 @@ class TestKerasModel(unittest.TestCase): """Test computing saliency maps for multiple outputs with multiple dimensions, in eager mode.""" with context.eager_mode(): self.test_saliency_shapes() def test_tensorboard(self): n_data_points = 20 n_features = 2 X = np.random.rand(n_data_points, n_features) y = [[0.0, 1.0] for x in range(n_data_points)] dataset = dc.data.NumpyDataset(X, y) keras_model = tf.keras.Sequential([ tf.keras.layers.Dense(2, activation='softmax'), ]) model = dc.models.KerasModel( keras_model, dc.models.losses.CategoricalCrossEntropy(), tensorboard=True, tensorboard_log_frequency=1) model.fit(dataset, nb_epoch=10) files_in_dir = os.listdir(model.model_dir) event_file = list(filter(lambda x: x.startswith("events"), files_in_dir)) assert len(event_file) > 0 event_file = os.path.join(model.model_dir, event_file[0]) file_size = os.stat(event_file).st_size assert file_size > 0 deepchem/utils/evaluate.py +4 −2 Original line number Diff line number Diff line Loading @@ -136,14 +136,16 @@ class GeneratorEvaluator(object): Evaluate a Metric over a model and Generator. """ def __init__(self, model, generator, transformers, labels, weights=list()): def __init__(self, model, generator, transformers, labels=None, weights=None): """ Parameters ---------- model: Model Model to evaluate generator: Generator Generator which yields {layer: numpyArray} to feed into model Generator which yields batches to feed into the model. For a TensorGraph, each batch should be a dict mapping Layers to NumPy arrays. For a KerasModel, it should be a tuple of the form (inputs, labels, weights). transformers: Tranformers to "undo" when applied to the models outputs labels: list of Layer Loading Loading
deepchem/models/keras_model.py +27 −1 Original line number Diff line number Diff line Loading @@ -76,6 +76,8 @@ class KerasModel(Model): model_dir=None, learning_rate=0.001, optimizer=None, tensorboard=False, tensorboard_log_frequency=100, **kwargs): """Create a new KerasModel. Loading @@ -99,6 +101,10 @@ class KerasModel(Model): optimizer: Optimizer the optimizer to use for fitting. If this is specified, learning_rate is ignored. tensorboard: bool whether to log progress to TensorBoard during training tensorboard_log_frequency: int the frequency at which to log data to TensorBoard, measured in batches """ super(KerasModel, self).__init__( model_instance=model, model_dir=model_dir, **kwargs) Loading @@ -112,6 +118,12 @@ class KerasModel(Model): self.optimizer = Adam(learning_rate=learning_rate) else: self.optimizer = optimizer self.tensorboard = tensorboard self.tensorboard_log_frequency = tensorboard_log_frequency self._tensorboard_step = 0 if tensorboard and tf.executing_eagerly(): raise ValueError( "Logging to TensorBoard is not currently supported in eager mode") if output_types is None: self._prediction_outputs = None self._loss_outputs = None Loading Loading @@ -226,6 +238,9 @@ class KerasModel(Model): except ValueError: # The loss doesn't depend on any variables. self._train_op = 0 if self.tensorboard: self._summary_ops = tf.summary.scalar('loss', self._loss_tensor) self._summary_writer = tf.summary.FileWriter(self.model_dir) self._init_new_vars() def _init_new_vars(self): Loading Loading @@ -308,6 +323,10 @@ class KerasModel(Model): for batch in generator: self._create_training_ops(batch) inputs, labels, weights = self._prepare_batch(batch) self._tensorboard_step += 1 should_log = ( self.tensorboard and self._tensorboard_step % self.tensorboard_log_frequency == 0) if tf.executing_eagerly(): # In eager mode we execute the loss function, accumulating the gradients. Loading @@ -329,12 +348,19 @@ class KerasModel(Model): # In graph mode we execute the training op. fetches = [self._train_op, self._loss_tensor, self._global_step] if should_log: fetches.append(self._summary_ops) feed_dict = dict(zip(self._input_placeholders, inputs)) feed_dict.update(dict(zip(self._label_placeholders, labels))) feed_dict.update(dict(zip(self._weights_placeholders, weights))) fetched_values = self.session.run(fetches, feed_dict=feed_dict) avg_loss += fetched_values[1] current_step = fetched_values[2] if should_log: self._summary_writer.reopen() self._summary_writer.add_summary( fetched_values[3], global_step=current_step) self._summary_writer.close() # Report progress and write checkpoints. Loading Loading @@ -629,7 +655,7 @@ class KerasModel(Model): dict Maps tasks to scores under metric. """ evaluator = GeneratorEvaluator(self, generator, transformers, labels=None) evaluator = GeneratorEvaluator(self, generator, transformers) return evaluator.compute_model_performance(metrics, per_task_metrics) def compute_saliency(self, X): Loading
deepchem/models/tests/test_kerasmodel.py +23 −0 Original line number Diff line number Diff line import os import unittest import deepchem as dc import numpy as np Loading Loading @@ -189,3 +190,25 @@ class TestKerasModel(unittest.TestCase): """Test computing saliency maps for multiple outputs with multiple dimensions, in eager mode.""" with context.eager_mode(): self.test_saliency_shapes() def test_tensorboard(self): n_data_points = 20 n_features = 2 X = np.random.rand(n_data_points, n_features) y = [[0.0, 1.0] for x in range(n_data_points)] dataset = dc.data.NumpyDataset(X, y) keras_model = tf.keras.Sequential([ tf.keras.layers.Dense(2, activation='softmax'), ]) model = dc.models.KerasModel( keras_model, dc.models.losses.CategoricalCrossEntropy(), tensorboard=True, tensorboard_log_frequency=1) model.fit(dataset, nb_epoch=10) files_in_dir = os.listdir(model.model_dir) event_file = list(filter(lambda x: x.startswith("events"), files_in_dir)) assert len(event_file) > 0 event_file = os.path.join(model.model_dir, event_file[0]) file_size = os.stat(event_file).st_size assert file_size > 0
deepchem/utils/evaluate.py +4 −2 Original line number Diff line number Diff line Loading @@ -136,14 +136,16 @@ class GeneratorEvaluator(object): Evaluate a Metric over a model and Generator. """ def __init__(self, model, generator, transformers, labels, weights=list()): def __init__(self, model, generator, transformers, labels=None, weights=None): """ Parameters ---------- model: Model Model to evaluate generator: Generator Generator which yields {layer: numpyArray} to feed into model Generator which yields batches to feed into the model. For a TensorGraph, each batch should be a dict mapping Layers to NumPy arrays. For a KerasModel, it should be a tuple of the form (inputs, labels, weights). transformers: Tranformers to "undo" when applied to the models outputs labels: list of Layer Loading
