Loading deepchem/models/tensorflow_models/__init__.py +143 −8 Original line number Diff line number Diff line Loading @@ -18,6 +18,25 @@ from deepchem.models.tensorflow_models import model_ops from deepchem.models.tensorflow_models import utils as tf_utils from deepchem.utils.save import log def softmax(x): """Simple numpy softmax implementation """ # (n_samples, n_classes) if len(x.shape) == 2: row_max = np.max(x, axis = 1) x -= row_max.reshape((x.shape[0], 1)) x = np.exp(x) row_sum = np.sum(x, axis = 1) x /= row_sum.reshape((x.shape[0], 1)) # (n_samples, n_tasks, n_classes) elif len(x.shape) == 3: row_max = np.max(x, axis = 2) x -= row_max.reshape(x.shape[:2] + (1,)) x = np.exp(x) row_sum = np.sum(x, axis = 2) x /= row_sum.reshape(x.shape[:2] + (1,)) return x class TensorflowGraph(object): """Simple class that holds information needed to run Tensorflow graph.""" def __init__(self, graph, session, name_scopes, output, labels, weights, loss): Loading Loading @@ -160,6 +179,12 @@ class TensorflowGraphModel(object): self.train_graph = self.construct_graph(training=True) self.eval_graph = self.construct_graph(training=False) ######################################################## DEBUG print("self.train_graph.output") print(self.train_graph.output) print("self.eval_graph.output") print(self.eval_graph.output) ######################################################## DEBUG def construct_graph(self, training): Loading Loading @@ -188,8 +213,13 @@ class TensorflowGraphModel(object): else: loss = None output = self.add_output_ops(graph, output) # add softmax heads return TensorflowGraph(graph, shared_session, name_scopes, output, labels, weights, loss) return TensorflowGraph(graph=graph, session=shared_session, name_scopes=name_scopes, output=output, labels=labels, weights=weights, loss=loss) def add_training_cost(self, graph, name_scopes, output, labels, weights): with graph.as_default(): Loading Loading @@ -295,9 +325,6 @@ class TensorflowGraphModel(object): self.verbosity) ############################################################## TIMING #### TODO(rbharath): There's an almost identical copy of this method in #### fcnet. Should this version of the method be removed? An arbitrary #### TensorflowGraph can have structure different from the below.... def predict_on_batch(self, X): """Return model output for the provided input. Loading @@ -321,9 +348,7 @@ class TensorflowGraphModel(object): if not self._restored_model: self.restore() with self.graph.as_default(): assert not model_ops.is_training() self.require_attributes(['output']) with self.eval_graph.graph.as_default(): # run eval data through the model n_tasks = self.n_tasks Loading Loading @@ -358,6 +383,7 @@ class TensorflowGraphModel(object): for i, logits in enumerate(output): softmax.append(tf.nn.softmax(logits, name='softmax_%d' % i)) output = softmax return output def build(self, graph, name_scopes, training): """Define the core graph. Loading Loading @@ -536,6 +562,55 @@ class TensorflowClassifier(TensorflowGraphModel): name='labels_%d' % task))) return labels def predict_proba_on_batch(self, X): """Return model output for the provided input. Restore(checkpoint) must have previously been called on this object. Args: dataset: deepchem.datasets.dataset object. Returns: Tuple of three numpy arrays with shape n_examples x n_tasks (x ...): output: Model outputs. Note that the output arrays may be more than 2D, e.g. for classifier models that return class probabilities. Raises: AssertionError: If model is not in evaluation mode. ValueError: If output and labels are not both 3D or both 2D. """ if not self._restored_model: self.restore() with self.eval_graph.graph.as_default(): # run eval data through the model n_tasks = self.n_tasks outputs = [] with self._get_shared_session(train=False).as_default(): feed_dict = self.construct_feed_dict(X) ################################################### DEBUG print("feed_dict.keys()") print(feed_dict.keys()) ################################################### DEBUG data = self._get_shared_session(train=False).run( self.eval_graph.output, feed_dict=feed_dict) batch_outputs = np.asarray(data[:n_tasks], dtype=float) # reshape to batch_size x n_tasks x ... if batch_outputs.ndim == 3: batch_outputs = batch_outputs.transpose((1, 0, 2)) elif batch_outputs.ndim == 2: batch_outputs = batch_outputs.transpose((1, 0)) else: raise ValueError( 'Unrecognized rank combination for output: %s ' % (batch_outputs.shape,)) outputs.append(batch_outputs) # We apply softmax to predictions to get class probabilities. outputs = softmax(np.squeeze(np.hstack(outputs))) return np.copy(outputs) class TensorflowRegressor(TensorflowGraphModel): """Regression model. Loading Loading @@ -591,6 +666,66 @@ class TensorflowRegressor(TensorflowGraphModel): name='labels_%d' % task))) return labels def predict_on_batch(self, X): """Return model output for the provided input. Restore(checkpoint) must have previously been called on this object. Args: dataset: deepchem.datasets.dataset object. Returns: Tuple of three numpy arrays with shape n_examples x n_tasks (x ...): output: Model outputs. labels: True labels. weights: Example weights. Note that the output and labels arrays may be more than 2D, e.g. for classifier models that return class probabilities. Raises: AssertionError: If model is not in evaluation mode. ValueError: If output and labels are not both 3D or both 2D. """ if not self._restored_model: self.restore() with self.train_graph.graph.as_default(): # run eval data through the model n_tasks = self.n_tasks outputs = [] with self._get_shared_session(train=False).as_default(): n_samples = len(X) # Some tensorflow models can't handle variadic batches, # especially models using tf.pack, tf.split. Pad batch-size # to handle these cases. X = pad_features(self.batch_size, X) feed_dict = self.construct_feed_dict(X) data = self._get_shared_session(train=False).run( self.output, feed_dict=feed_dict) batch_outputs = np.asarray(data[:n_tasks], dtype=float) # reshape to batch_size x n_tasks x ... if batch_outputs.ndim == 3: batch_outputs = batch_outputs.transpose((1, 0, 2)) elif batch_outputs.ndim == 2: batch_outputs = batch_outputs.transpose((1, 0)) # Handle edge case when batch-size is 1. elif batch_outputs.ndim == 1: #print("X.shape, batch_outputs.shape") #print(X.shape, batch_outputs.shape) n_samples = len(X) batch_outputs = batch_outputs.reshape((n_samples, n_tasks)) else: raise ValueError( 'Unrecognized rank combination for output: %s' % (batch_outputs.shape)) # Prune away any padding that was added batch_outputs = batch_outputs[:n_samples] outputs.append(batch_outputs) outputs = np.squeeze(np.concatenate(outputs)) return np.copy(outputs) class TensorflowModel(Model): """ Abstract base class shared across all Tensorflow models. Loading deepchem/models/tensorflow_models/fcnet.py +0 −130 Original line number Diff line number Diff line Loading @@ -16,25 +16,6 @@ from deepchem.models.tensorflow_models import model_ops from deepchem.metrics import to_one_hot from deepchem.datasets import pad_features def softmax(x): """Simple numpy softmax implementation """ # (n_samples, n_classes) if len(x.shape) == 2: row_max = np.max(x, axis = 1) x -= row_max.reshape((x.shape[0], 1)) x = np.exp(x) row_sum = np.sum(x, axis = 1) x /= row_sum.reshape((x.shape[0], 1)) # (n_samples, n_tasks, n_classes) elif len(x.shape) == 3: row_max = np.max(x, axis = 2) x -= row_max.reshape(x.shape[:2] + (1,)) x = np.exp(x) row_sum = np.sum(x, axis = 2) x /= row_sum.reshape(x.shape[:2] + (1,)) return x class TensorflowMultiTaskClassifier(TensorflowClassifier): """Implements an icml model as configured in a model_config.proto.""" Loading @@ -54,10 +35,6 @@ class TensorflowMultiTaskClassifier(TensorflowClassifier): tf.float32, shape=[None, n_features], name='mol_features') ########################################################### DEBUG print("self.mol_features") print(self.mol_features) ########################################################### DEBUG layer_sizes = self.layer_sizes weight_init_stddevs = self.weight_init_stddevs Loading Loading @@ -120,51 +97,6 @@ class TensorflowMultiTaskClassifier(TensorflowClassifier): (self.batch_size,)) return TensorflowGraph.get_feed_dict(orig_dict) def predict_proba_on_batch(self, X): """Return model output for the provided input. Restore(checkpoint) must have previously been called on this object. Args: dataset: deepchem.datasets.dataset object. Returns: Tuple of three numpy arrays with shape n_examples x n_tasks (x ...): output: Model outputs. Note that the output arrays may be more than 2D, e.g. for classifier models that return class probabilities. Raises: AssertionError: If model is not in evaluation mode. ValueError: If output and labels are not both 3D or both 2D. """ if not self._restored_model: self.restore() with self.eval_graph.graph.as_default(): # run eval data through the model n_tasks = self.n_tasks outputs = [] with self._get_shared_session(train=False).as_default(): feed_dict = self.construct_feed_dict(X) data = self._get_shared_session(train=False).run( self.eval_graph.output, feed_dict=feed_dict) batch_outputs = np.asarray(data[:n_tasks], dtype=float) # reshape to batch_size x n_tasks x ... if batch_outputs.ndim == 3: batch_outputs = batch_outputs.transpose((1, 0, 2)) elif batch_outputs.ndim == 2: batch_outputs = batch_outputs.transpose((1, 0)) else: raise ValueError( 'Unrecognized rank combination for output: %s ' % (batch_outputs.shape,)) outputs.append(batch_outputs) # We apply softmax to predictions to get class probabilities. outputs = softmax(np.squeeze(np.hstack(outputs))) return np.copy(outputs) class TensorflowMultiTaskRegressor(TensorflowRegressor): """Implements an icml model as configured in a model_config.proto.""" Loading Loading @@ -255,65 +187,3 @@ class TensorflowMultiTaskRegressor(TensorflowRegressor): orig_dict["weights_%d" % task] = np.ones( (self.batch_size,)) return TensorflowGraph.get_feed_dict(orig_dict) def predict_on_batch(self, X): """Return model output for the provided input. Restore(checkpoint) must have previously been called on this object. Args: dataset: deepchem.datasets.dataset object. Returns: Tuple of three numpy arrays with shape n_examples x n_tasks (x ...): output: Model outputs. labels: True labels. weights: Example weights. Note that the output and labels arrays may be more than 2D, e.g. for classifier models that return class probabilities. Raises: AssertionError: If model is not in evaluation mode. ValueError: If output and labels are not both 3D or both 2D. """ if not self._restored_model: self.restore() with self.train_graph.graph.as_default(): assert not model_ops.is_training() self.require_attributes(['output']) # run eval data through the model n_tasks = self.n_tasks outputs = [] with self._get_shared_session(train=False).as_default(): n_samples = len(X) # Some tensorflow models can't handle variadic batches, # especially models using tf.pack, tf.split. Pad batch-size # to handle these cases. X = pad_features(self.batch_size, X) feed_dict = self.construct_feed_dict(X) data = self._get_shared_session(train=False).run( self.output, feed_dict=feed_dict) batch_outputs = np.asarray(data[:n_tasks], dtype=float) # reshape to batch_size x n_tasks x ... if batch_outputs.ndim == 3: batch_outputs = batch_outputs.transpose((1, 0, 2)) elif batch_outputs.ndim == 2: batch_outputs = batch_outputs.transpose((1, 0)) # Handle edge case when batch-size is 1. elif batch_outputs.ndim == 1: #print("X.shape, batch_outputs.shape") #print(X.shape, batch_outputs.shape) n_samples = len(X) batch_outputs = batch_outputs.reshape((n_samples, n_tasks)) else: raise ValueError( 'Unrecognized rank combination for output: %s' % (batch_outputs.shape)) # Prune away any padding that was added batch_outputs = batch_outputs[:n_samples] outputs.append(batch_outputs) outputs = np.squeeze(np.concatenate(outputs)) return np.copy(outputs) Loading
deepchem/models/tensorflow_models/__init__.py +143 −8 Original line number Diff line number Diff line Loading @@ -18,6 +18,25 @@ from deepchem.models.tensorflow_models import model_ops from deepchem.models.tensorflow_models import utils as tf_utils from deepchem.utils.save import log def softmax(x): """Simple numpy softmax implementation """ # (n_samples, n_classes) if len(x.shape) == 2: row_max = np.max(x, axis = 1) x -= row_max.reshape((x.shape[0], 1)) x = np.exp(x) row_sum = np.sum(x, axis = 1) x /= row_sum.reshape((x.shape[0], 1)) # (n_samples, n_tasks, n_classes) elif len(x.shape) == 3: row_max = np.max(x, axis = 2) x -= row_max.reshape(x.shape[:2] + (1,)) x = np.exp(x) row_sum = np.sum(x, axis = 2) x /= row_sum.reshape(x.shape[:2] + (1,)) return x class TensorflowGraph(object): """Simple class that holds information needed to run Tensorflow graph.""" def __init__(self, graph, session, name_scopes, output, labels, weights, loss): Loading Loading @@ -160,6 +179,12 @@ class TensorflowGraphModel(object): self.train_graph = self.construct_graph(training=True) self.eval_graph = self.construct_graph(training=False) ######################################################## DEBUG print("self.train_graph.output") print(self.train_graph.output) print("self.eval_graph.output") print(self.eval_graph.output) ######################################################## DEBUG def construct_graph(self, training): Loading Loading @@ -188,8 +213,13 @@ class TensorflowGraphModel(object): else: loss = None output = self.add_output_ops(graph, output) # add softmax heads return TensorflowGraph(graph, shared_session, name_scopes, output, labels, weights, loss) return TensorflowGraph(graph=graph, session=shared_session, name_scopes=name_scopes, output=output, labels=labels, weights=weights, loss=loss) def add_training_cost(self, graph, name_scopes, output, labels, weights): with graph.as_default(): Loading Loading @@ -295,9 +325,6 @@ class TensorflowGraphModel(object): self.verbosity) ############################################################## TIMING #### TODO(rbharath): There's an almost identical copy of this method in #### fcnet. Should this version of the method be removed? An arbitrary #### TensorflowGraph can have structure different from the below.... def predict_on_batch(self, X): """Return model output for the provided input. Loading @@ -321,9 +348,7 @@ class TensorflowGraphModel(object): if not self._restored_model: self.restore() with self.graph.as_default(): assert not model_ops.is_training() self.require_attributes(['output']) with self.eval_graph.graph.as_default(): # run eval data through the model n_tasks = self.n_tasks Loading Loading @@ -358,6 +383,7 @@ class TensorflowGraphModel(object): for i, logits in enumerate(output): softmax.append(tf.nn.softmax(logits, name='softmax_%d' % i)) output = softmax return output def build(self, graph, name_scopes, training): """Define the core graph. Loading Loading @@ -536,6 +562,55 @@ class TensorflowClassifier(TensorflowGraphModel): name='labels_%d' % task))) return labels def predict_proba_on_batch(self, X): """Return model output for the provided input. Restore(checkpoint) must have previously been called on this object. Args: dataset: deepchem.datasets.dataset object. Returns: Tuple of three numpy arrays with shape n_examples x n_tasks (x ...): output: Model outputs. Note that the output arrays may be more than 2D, e.g. for classifier models that return class probabilities. Raises: AssertionError: If model is not in evaluation mode. ValueError: If output and labels are not both 3D or both 2D. """ if not self._restored_model: self.restore() with self.eval_graph.graph.as_default(): # run eval data through the model n_tasks = self.n_tasks outputs = [] with self._get_shared_session(train=False).as_default(): feed_dict = self.construct_feed_dict(X) ################################################### DEBUG print("feed_dict.keys()") print(feed_dict.keys()) ################################################### DEBUG data = self._get_shared_session(train=False).run( self.eval_graph.output, feed_dict=feed_dict) batch_outputs = np.asarray(data[:n_tasks], dtype=float) # reshape to batch_size x n_tasks x ... if batch_outputs.ndim == 3: batch_outputs = batch_outputs.transpose((1, 0, 2)) elif batch_outputs.ndim == 2: batch_outputs = batch_outputs.transpose((1, 0)) else: raise ValueError( 'Unrecognized rank combination for output: %s ' % (batch_outputs.shape,)) outputs.append(batch_outputs) # We apply softmax to predictions to get class probabilities. outputs = softmax(np.squeeze(np.hstack(outputs))) return np.copy(outputs) class TensorflowRegressor(TensorflowGraphModel): """Regression model. Loading Loading @@ -591,6 +666,66 @@ class TensorflowRegressor(TensorflowGraphModel): name='labels_%d' % task))) return labels def predict_on_batch(self, X): """Return model output for the provided input. Restore(checkpoint) must have previously been called on this object. Args: dataset: deepchem.datasets.dataset object. Returns: Tuple of three numpy arrays with shape n_examples x n_tasks (x ...): output: Model outputs. labels: True labels. weights: Example weights. Note that the output and labels arrays may be more than 2D, e.g. for classifier models that return class probabilities. Raises: AssertionError: If model is not in evaluation mode. ValueError: If output and labels are not both 3D or both 2D. """ if not self._restored_model: self.restore() with self.train_graph.graph.as_default(): # run eval data through the model n_tasks = self.n_tasks outputs = [] with self._get_shared_session(train=False).as_default(): n_samples = len(X) # Some tensorflow models can't handle variadic batches, # especially models using tf.pack, tf.split. Pad batch-size # to handle these cases. X = pad_features(self.batch_size, X) feed_dict = self.construct_feed_dict(X) data = self._get_shared_session(train=False).run( self.output, feed_dict=feed_dict) batch_outputs = np.asarray(data[:n_tasks], dtype=float) # reshape to batch_size x n_tasks x ... if batch_outputs.ndim == 3: batch_outputs = batch_outputs.transpose((1, 0, 2)) elif batch_outputs.ndim == 2: batch_outputs = batch_outputs.transpose((1, 0)) # Handle edge case when batch-size is 1. elif batch_outputs.ndim == 1: #print("X.shape, batch_outputs.shape") #print(X.shape, batch_outputs.shape) n_samples = len(X) batch_outputs = batch_outputs.reshape((n_samples, n_tasks)) else: raise ValueError( 'Unrecognized rank combination for output: %s' % (batch_outputs.shape)) # Prune away any padding that was added batch_outputs = batch_outputs[:n_samples] outputs.append(batch_outputs) outputs = np.squeeze(np.concatenate(outputs)) return np.copy(outputs) class TensorflowModel(Model): """ Abstract base class shared across all Tensorflow models. Loading
deepchem/models/tensorflow_models/fcnet.py +0 −130 Original line number Diff line number Diff line Loading @@ -16,25 +16,6 @@ from deepchem.models.tensorflow_models import model_ops from deepchem.metrics import to_one_hot from deepchem.datasets import pad_features def softmax(x): """Simple numpy softmax implementation """ # (n_samples, n_classes) if len(x.shape) == 2: row_max = np.max(x, axis = 1) x -= row_max.reshape((x.shape[0], 1)) x = np.exp(x) row_sum = np.sum(x, axis = 1) x /= row_sum.reshape((x.shape[0], 1)) # (n_samples, n_tasks, n_classes) elif len(x.shape) == 3: row_max = np.max(x, axis = 2) x -= row_max.reshape(x.shape[:2] + (1,)) x = np.exp(x) row_sum = np.sum(x, axis = 2) x /= row_sum.reshape(x.shape[:2] + (1,)) return x class TensorflowMultiTaskClassifier(TensorflowClassifier): """Implements an icml model as configured in a model_config.proto.""" Loading @@ -54,10 +35,6 @@ class TensorflowMultiTaskClassifier(TensorflowClassifier): tf.float32, shape=[None, n_features], name='mol_features') ########################################################### DEBUG print("self.mol_features") print(self.mol_features) ########################################################### DEBUG layer_sizes = self.layer_sizes weight_init_stddevs = self.weight_init_stddevs Loading Loading @@ -120,51 +97,6 @@ class TensorflowMultiTaskClassifier(TensorflowClassifier): (self.batch_size,)) return TensorflowGraph.get_feed_dict(orig_dict) def predict_proba_on_batch(self, X): """Return model output for the provided input. Restore(checkpoint) must have previously been called on this object. Args: dataset: deepchem.datasets.dataset object. Returns: Tuple of three numpy arrays with shape n_examples x n_tasks (x ...): output: Model outputs. Note that the output arrays may be more than 2D, e.g. for classifier models that return class probabilities. Raises: AssertionError: If model is not in evaluation mode. ValueError: If output and labels are not both 3D or both 2D. """ if not self._restored_model: self.restore() with self.eval_graph.graph.as_default(): # run eval data through the model n_tasks = self.n_tasks outputs = [] with self._get_shared_session(train=False).as_default(): feed_dict = self.construct_feed_dict(X) data = self._get_shared_session(train=False).run( self.eval_graph.output, feed_dict=feed_dict) batch_outputs = np.asarray(data[:n_tasks], dtype=float) # reshape to batch_size x n_tasks x ... if batch_outputs.ndim == 3: batch_outputs = batch_outputs.transpose((1, 0, 2)) elif batch_outputs.ndim == 2: batch_outputs = batch_outputs.transpose((1, 0)) else: raise ValueError( 'Unrecognized rank combination for output: %s ' % (batch_outputs.shape,)) outputs.append(batch_outputs) # We apply softmax to predictions to get class probabilities. outputs = softmax(np.squeeze(np.hstack(outputs))) return np.copy(outputs) class TensorflowMultiTaskRegressor(TensorflowRegressor): """Implements an icml model as configured in a model_config.proto.""" Loading Loading @@ -255,65 +187,3 @@ class TensorflowMultiTaskRegressor(TensorflowRegressor): orig_dict["weights_%d" % task] = np.ones( (self.batch_size,)) return TensorflowGraph.get_feed_dict(orig_dict) def predict_on_batch(self, X): """Return model output for the provided input. Restore(checkpoint) must have previously been called on this object. Args: dataset: deepchem.datasets.dataset object. Returns: Tuple of three numpy arrays with shape n_examples x n_tasks (x ...): output: Model outputs. labels: True labels. weights: Example weights. Note that the output and labels arrays may be more than 2D, e.g. for classifier models that return class probabilities. Raises: AssertionError: If model is not in evaluation mode. ValueError: If output and labels are not both 3D or both 2D. """ if not self._restored_model: self.restore() with self.train_graph.graph.as_default(): assert not model_ops.is_training() self.require_attributes(['output']) # run eval data through the model n_tasks = self.n_tasks outputs = [] with self._get_shared_session(train=False).as_default(): n_samples = len(X) # Some tensorflow models can't handle variadic batches, # especially models using tf.pack, tf.split. Pad batch-size # to handle these cases. X = pad_features(self.batch_size, X) feed_dict = self.construct_feed_dict(X) data = self._get_shared_session(train=False).run( self.output, feed_dict=feed_dict) batch_outputs = np.asarray(data[:n_tasks], dtype=float) # reshape to batch_size x n_tasks x ... if batch_outputs.ndim == 3: batch_outputs = batch_outputs.transpose((1, 0, 2)) elif batch_outputs.ndim == 2: batch_outputs = batch_outputs.transpose((1, 0)) # Handle edge case when batch-size is 1. elif batch_outputs.ndim == 1: #print("X.shape, batch_outputs.shape") #print(X.shape, batch_outputs.shape) n_samples = len(X) batch_outputs = batch_outputs.reshape((n_samples, n_tasks)) else: raise ValueError( 'Unrecognized rank combination for output: %s' % (batch_outputs.shape)) # Prune away any padding that was added batch_outputs = batch_outputs[:n_samples] outputs.append(batch_outputs) outputs = np.squeeze(np.concatenate(outputs)) return np.copy(outputs)
