Loading deepchem/models/tensorflow_models/.model.py.swp 0 → 100644 +16 KiB File added.No diff preview for this file type. View file deepchem/models/tensorflow_models/__init__.py 0 → 100644 +0 −0 Empty file added. deepchem/models/tensorflow_models/icml/icml_eval.py 0 → 100644 +78 −0 Original line number Diff line number Diff line #!/usr/bin/python # # Copyright 2015 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Evaluate a model from the ICML-2015 paper. This script requires a trained model with its associated config and checkpoint. If you don't have a trained model, run icml_train.py first. """ # pylint: disable=line-too-long # pylint: enable=line-too-long from nowhere.research.biology.collaborations.pande.py import utils from tensorflow.python.platform import app from tensorflow.python.platform import flags from tensorflow.python.platform import gfile from biology import model_config from biology.icml import icml_models flags.DEFINE_string('config', None, 'Serialized ModelConfig proto.') flags.DEFINE_string('checkpoint', None, 'Model checkpoint file. File can contain either an ' 'absolute checkpoint (e.g. model.ckpt-{step}) or a ' 'serialized CheckpointState proto.') flags.DEFINE_string('input_pattern', None, 'Input file pattern; ' 'It should include %d for fold index substitution.') flags.DEFINE_string('master', 'local', 'BNS name of the TensorFlow master.') flags.DEFINE_string('logdir', None, 'Directory for output files.') flags.DEFINE_integer('num_folds', 5, 'Number of cross-validation folds.') flags.DEFINE_integer('fold', None, 'Fold index for this model.') flags.DEFINE_enum('model_type', 'single', ['single', 'deep', 'deepaux', 'py', 'pydrop1', 'pydrop2'], 'Which model from the ICML paper should be trained/evaluated') FLAGS = flags.FLAGS def main(unused_argv=None): config = model_config.ModelConfig() config.ReadFromFile(FLAGS.config, overwrite='allowed') gfile.MakeDirs(FLAGS.logdir) model = icml_models.CONSTRUCTORS[FLAGS.model_type](config, train=False, logdir=FLAGS.logdir, master=FLAGS.master) if FLAGS.num_folds is not None and FLAGS.fold is not None: folds = utils.kfold_pattern(FLAGS.input_pattern, FLAGS.num_folds, FLAGS.fold) _, test_pattern = folds.next() test_pattern = ','.join(test_pattern) else: test_pattern = FLAGS.input_pattern with model.graph.as_default(): model.Eval(model.ReadInput(test_pattern), FLAGS.checkpoint) if __name__ == '__main__': flags.MarkFlagAsRequired('config') flags.MarkFlagAsRequired('checkpoint') flags.MarkFlagAsRequired('input_pattern') flags.MarkFlagAsRequired('logdir') app.run() deepchem/models/tensorflow_models/icml/icml_models.py 0 → 100644 +309 −0 Original line number Diff line number Diff line #!/usr/bin/python # # Copyright 2015 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """TensorFlow implementation of the models from the ICML-2015 paper. hyperparam_dict = { "single": Hyperparams(num_layers=1, num_hidden=1200, node_depth=1, nonlinearity=ACTIVATION_RECTIFIED_LINEAR, weight_init=GaussianWeightInit(0.01), bias_init=ConstantBiasInit(0.5), dropout=1.), "deep": Hyperparams(num_layers=4, num_hidden=1000, node_depth=1, nonlinearity=ACTIVATION_RECTIFIED_LINEAR, weight_init=GaussianWeightInit(0.01), bias_init=ConstantBiasInit(0.5), dropout=1.), "deepaux": Hyperparams(num_layers=4, num_hidden=1000, auxiliary_softmax_layers=[0, 1, 2], auxiliary_softmax_weight=0.3, node_depth=1, nonlinearity=ACTIVATION_RECTIFIED_LINEAR, weight_init=GaussianWeightInit(0.01), bias_init=ConstantBiasInit(0.5), dropout=1.), "py": Hyperparams(num_layers=2, num_hidden=[2000, 100], node_depth=1, nonlinearity=ACTIVATION_RECTIFIED_LINEAR, weight_init=[GaussianWeightInit(0.01), GaussianWeightInit(0.04)], bias_init=[ConstantBiasInit(0.5), ConstantBiasInit(3.0)], dropout=1.), "pydrop1": Hyperparams(num_layers=2, num_hidden=[2000, 100], node_depth=1, nonlinearity=ACTIVATION_RECTIFIED_LINEAR, weight_init=[GaussianWeightInit(0.01), GaussianWeightInit(0.04)], bias_init=[ConstantBiasInit(0.5), ConstantBiasInit(3.0)], dropout=[0.75, 1.]), "pydrop2": Hyperparams(num_layers=2, num_hidden=[2000, 100], node_depth=1, nonlinearity=ACTIVATION_RECTIFIED_LINEAR, weight_init=[GaussianWeightInit(0.01), GaussianWeightInit(0.04)], bias_init=[ConstantBiasInit(0.5), ConstantBiasInit(3.0)], dropout=[0.75, 0.75])} """ import numpy as np import tensorflow.google as tf from tensorflow.python.platform import logging from biology import model from biology import model_ops from nowhere.mustreimplement import input_ops from nowhere.mustreimplement import label_ops from nowhere.learning.dist_belief import input_example_pb2 from nowhere.learning.dist_belief import types_pb2 as legacy_types_pb2 class UnreplicatedIcmlModel(model.Classifier): """Implements an icml model as configured in a model_config.proto.""" def Build(self): """Constructs the graph architecture as specified in its config. This method creates the following Placeholders: mol_features: Molecule descriptor (e.g. fingerprint) tensor with shape batch_size x num_features. """ with tf.name_scope(self.placeholder_scope): self.mol_features = tf.placeholder( tf.float32, shape=[self.config.batch_size, self.config.num_features], name='mol_features') layer_sizes = self.config.layer_sizes weight_init_stddevs = self.config.weight_init_stddevs bias_init_consts = self.config.bias_init_consts dropouts = self.config.dropouts lengths_set = { len(layer_sizes), len(weight_init_stddevs), len(bias_init_consts), len(dropouts), } assert len(lengths_set) == 1, 'All layer params must have same length.' num_layers = lengths_set.pop() assert num_layers > 0, 'Must have some layers defined.' prev_layer = self.mol_features prev_layer_size = self.config.num_features for i in xrange(num_layers): layer = tf.nn.relu(model_ops.FullyConnectedLayer( tensor=prev_layer, size=layer_sizes[i], weight_init=tf.truncated_normal( shape=[prev_layer_size, layer_sizes[i]], stddev=weight_init_stddevs[i]), bias_init=tf.constant(value=bias_init_consts[i], shape=[layer_sizes[i]]))) layer = model_ops.Dropout(layer, dropouts[i]) prev_layer = layer prev_layer_size = layer_sizes[i] self.output = model_ops.MultitaskLogits( layer, self.config.num_classification_tasks) def LabelsAndWeights(self): """Parse Label protos and create tensors for labels and weights. This method creates the following Placeholders in the graph: labels: Tensor with shape batch_size x num_tasks containing serialized Label protos. """ config = self.config with tf.name_scope(self.placeholder_scope): labels = tf.placeholder( tf.string, shape=[config.batch_size, config.num_classification_tasks], name='labels') self.labels = label_ops.MultitaskLabelClasses(labels, config.num_classes) self.weights = label_ops.MultitaskLabelWeights(labels) def ReadInput(self, input_pattern, input_data_types=None): """Read input data and return a generator for minibatches. Args: input_pattern: Input file pattern. input_data_types: List of legacy_types_pb2 constants matching the number of and data types present in the sstables. If not specified, defaults to full ICML 259-task types, but can be specified for unittests or other datasets with consistent types. Returns: A generator that yields a dict for feeding a single batch to Placeholders in the graph. Raises: AssertionError: If no default session is available. """ if model_ops.IsTraining(): randomize = True num_iterations = None else: randomize = False num_iterations = 1 num_tasks = self.config.num_classification_tasks tasks_in_input = self.config.tasks_in_input if input_data_types is None: input_data_types = ([legacy_types_pb2.DF_FLOAT] + [legacy_types_pb2.DF_LABEL_PROTO] * tasks_in_input) features, labels = input_ops.InputExampleInputReader( input_pattern=input_pattern, batch_size=self.config.batch_size, num_tasks=num_tasks, input_data_types=input_data_types, num_features=self.config.num_features, randomize=randomize, shuffling=randomize, num_iterations=num_iterations) return self._ReadInputGenerator(features, labels[:, :num_tasks]) def _GetFeedDict(self, named_values): feed_dict = {} for name, value in named_values.iteritems(): feed_dict['{}/{}:0'.format(self.placeholder_root, name)] = value return feed_dict def EvalBatch(self, input_batch): """Runs inference on the provided batch of input. Args: input_batch: iterator of input with len self.config.batch_size. Returns: Tuple of three numpy arrays with shape num_examples x num_tasks (x ...): output: Model predictions. labels: True labels. numpy array values are scalars, not 1-hot classes vector. weights: Example weights. """ output, labels, weights = super(UnreplicatedIcmlModel, self).EvalBatch( input_batch) # Converts labels from 1-hot to float. labels = labels[:, :, 1] # Whole batch, all tasks, 1-hot positive index. return output, labels, weights def BatchInputGenerator(self, serialized_batch): """Returns a generator that iterates over the provided batch of input. TODO(user): This is similar to input_ops.InputExampleInputReader(), but doesn't need to be executed as part of the TensorFlow graph. Consider refactoring so these can share code somehow. Args: serialized_batch: List of tuples: (_, value) where value is a serialized InputExample proto. Must have self.config.batch_size length or smaller. If smaller, we'll pad up to batch_size and mark the padding as invalid so it's ignored in eval metrics. Yields: Dict of model inputs for use as a feed_dict. Raises: ValueError: If the batch is larger than the batch_size. """ if len(serialized_batch) > self.config.batch_size: raise ValueError( 'serialized_batch length {} must be <= batch_size {}'.format( len(serialized_batch), self.config.batch_size)) for _ in xrange(self.config.batch_size - len(serialized_batch)): serialized_batch.append((None, '')) features = [] labels = [] for _, serialized_proto in serialized_batch: if serialized_proto: input_example = input_example_pb2.InputExample() input_example.ParseFromString(serialized_proto) features.append([f for f in input_example.endpoint[0].float_value]) label_protos = [endpoint.label for endpoint in input_example.endpoint[1:]] assert len(label_protos) == self.config.num_classification_tasks labels.append([l.SerializeToString() for l in label_protos]) else: # This was a padded value to reach the batch size. features.append([0.0 for _ in xrange(self.config.num_features)]) labels.append( ['' for _ in xrange(self.config.num_classification_tasks)]) valid = np.asarray([(np.sum(f) > 0) for f in features]) assert len(features) == self.config.batch_size assert len(labels) == self.config.batch_size assert len(valid) == self.config.batch_size yield self._GetFeedDict({ 'mol_features': features, 'labels': labels, 'valid': valid }) def _ReadInputGenerator(self, features_tensor, labels_tensor): """Generator that constructs feed_dict for minibatches. Args: features_tensor: Tensor of batch_size x molecule features. labels_tensor: Tensor of batch_size x label protos. Yields: A dict for feeding a single batch to Placeholders in the graph. Raises: AssertionError: If no default session is available. """ sess = tf.get_default_session() if sess is None: raise AssertionError('No default session') while True: try: logging.vlog(1, 'Starting session execution to get input data') features, labels = sess.run([features_tensor, labels_tensor]) logging.vlog(1, 'Done with session execution to get input data') # TODO(user): check if the below axis=1 needs to change to axis=0, # because cl/105081140. valid = np.sum(features, axis=1) > 0 yield self._GetFeedDict({ 'mol_features': features, 'labels': labels, 'valid': valid }) except tf.OpError as e: # InputExampleInput op raises OpError when it has hit num_iterations # or its input file is exhausted. However it may also be raised # if the input sstable isn't what we expect. if 'Invalid InputExample' in e.message: raise e else: break deepchem/models/tensorflow_models/icml/icml_train.py 0 → 100644 +133 −0 Original line number Diff line number Diff line #!/usr/bin/python # # Copyright 2015 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Train a model from the ICML-2015 paper. """ # pylint: disable=line-too-long # pylint: enable=line-too-long import os from tensorflow.python.platform import app from tensorflow.python.platform import flags from tensorflow.python.platform import gfile from biology import model_config from biology.icml import icml_models flags.DEFINE_string('config', None, 'Serialized ModelConfig proto.') flags.DEFINE_string('master', '', 'BNS name of the TensorFlow master.') flags.DEFINE_string('logdir', None, 'Directory for output files.') flags.DEFINE_integer('replica_id', 0, 'Task ID of this replica.') flags.DEFINE_integer('ps_tasks', 0, 'Number of parameter server tasks.') flags.DEFINE_integer('num_folds', 5, 'Number of cross-validation folds.') flags.DEFINE_integer('fold', None, 'Fold index for this model.') FLAGS = flags.FLAGS def kfold_pattern(input_pattern, num_folds, fold=None): """Generator for train/test filename splits. The pattern is not expanded except for the %d being replaced by the fold index. Args: input_pattern: Input filename pattern. Should contain %d for fold index. num_folds: Number of folds. fold: If not None, the generator only yields the train/test split for the given fold. Yields: train_filenames: A list of file patterns in training set. test_filenames: A list of file patterns in test set. """ # get filenames associated with each fold fold_filepatterns = [input_pattern % i for i in range(num_folds)] # create train/test splits for i in range(num_folds): if fold is not None and i != fold: continue train = fold_filepatterns[:i] + fold_filepatterns[i+1:] test = [fold_filepatterns[i]] if any([f in test for f in train]): logging.fatal('Train/test split is not complete.') if set(train + test) != set(fold_filepatterns): logging.fatal('Not all input files are accounted for.') yield train, test def Run(input_data_types=None): """Trains the model with specified parameters. Args: input_data_types: List of legacy_types_pb2 constants or None. """ config = model_config.ModelConfig({ 'input_pattern': '', # Should have %d for fold index substitution. 'num_classification_tasks': 259, 'tasks_in_input': 259, # Dimensionality of sstables 'max_steps': 50000000, 'summaries': False, 'batch_size': 128, 'learning_rate': 0.0003, 'num_classes': 2, 'optimizer': 'sgd', 'penalty': 0.0, 'num_features': 1024, 'layer_sizes': [1200], 'weight_init_stddevs': [0.01], 'bias_init_consts': [0.5], 'dropouts': [0.0], }) config.ReadFromFile(FLAGS.config, overwrite='required') if FLAGS.replica_id == 0: gfile.MakeDirs(FLAGS.logdir) config.WriteToFile(os.path.join(FLAGS.logdir, 'config.pbtxt')) model = icml_models.IcmlModel(config, train=True, logdir=FLAGS.logdir, master=FLAGS.master) if FLAGS.num_folds is not None and FLAGS.fold is not None: folds = kfold_pattern(config.input_pattern, FLAGS.num_folds, FLAGS.fold) train_pattern, _ = folds.next() train_pattern = ','.join(train_pattern) else: train_pattern = config.input_pattern with model.graph.as_default(): model.Train(model.ReadInput(train_pattern, input_data_types=input_data_types), max_steps=config.max_steps, summaries=config.summaries, replica_id=FLAGS.replica_id, ps_tasks=FLAGS.ps_tasks) def main(unused_argv=None): Run() if __name__ == '__main__': flags.MarkFlagAsRequired('config') flags.MarkFlagAsRequired('logdir') flags.MarkFlagAsRequired('fold') app.run() Loading
deepchem/models/tensorflow_models/.model.py.swp 0 → 100644 +16 KiB File added.No diff preview for this file type. View file
deepchem/models/tensorflow_models/icml/icml_eval.py 0 → 100644 +78 −0 Original line number Diff line number Diff line #!/usr/bin/python # # Copyright 2015 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Evaluate a model from the ICML-2015 paper. This script requires a trained model with its associated config and checkpoint. If you don't have a trained model, run icml_train.py first. """ # pylint: disable=line-too-long # pylint: enable=line-too-long from nowhere.research.biology.collaborations.pande.py import utils from tensorflow.python.platform import app from tensorflow.python.platform import flags from tensorflow.python.platform import gfile from biology import model_config from biology.icml import icml_models flags.DEFINE_string('config', None, 'Serialized ModelConfig proto.') flags.DEFINE_string('checkpoint', None, 'Model checkpoint file. File can contain either an ' 'absolute checkpoint (e.g. model.ckpt-{step}) or a ' 'serialized CheckpointState proto.') flags.DEFINE_string('input_pattern', None, 'Input file pattern; ' 'It should include %d for fold index substitution.') flags.DEFINE_string('master', 'local', 'BNS name of the TensorFlow master.') flags.DEFINE_string('logdir', None, 'Directory for output files.') flags.DEFINE_integer('num_folds', 5, 'Number of cross-validation folds.') flags.DEFINE_integer('fold', None, 'Fold index for this model.') flags.DEFINE_enum('model_type', 'single', ['single', 'deep', 'deepaux', 'py', 'pydrop1', 'pydrop2'], 'Which model from the ICML paper should be trained/evaluated') FLAGS = flags.FLAGS def main(unused_argv=None): config = model_config.ModelConfig() config.ReadFromFile(FLAGS.config, overwrite='allowed') gfile.MakeDirs(FLAGS.logdir) model = icml_models.CONSTRUCTORS[FLAGS.model_type](config, train=False, logdir=FLAGS.logdir, master=FLAGS.master) if FLAGS.num_folds is not None and FLAGS.fold is not None: folds = utils.kfold_pattern(FLAGS.input_pattern, FLAGS.num_folds, FLAGS.fold) _, test_pattern = folds.next() test_pattern = ','.join(test_pattern) else: test_pattern = FLAGS.input_pattern with model.graph.as_default(): model.Eval(model.ReadInput(test_pattern), FLAGS.checkpoint) if __name__ == '__main__': flags.MarkFlagAsRequired('config') flags.MarkFlagAsRequired('checkpoint') flags.MarkFlagAsRequired('input_pattern') flags.MarkFlagAsRequired('logdir') app.run()
deepchem/models/tensorflow_models/icml/icml_models.py 0 → 100644 +309 −0 Original line number Diff line number Diff line #!/usr/bin/python # # Copyright 2015 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """TensorFlow implementation of the models from the ICML-2015 paper. hyperparam_dict = { "single": Hyperparams(num_layers=1, num_hidden=1200, node_depth=1, nonlinearity=ACTIVATION_RECTIFIED_LINEAR, weight_init=GaussianWeightInit(0.01), bias_init=ConstantBiasInit(0.5), dropout=1.), "deep": Hyperparams(num_layers=4, num_hidden=1000, node_depth=1, nonlinearity=ACTIVATION_RECTIFIED_LINEAR, weight_init=GaussianWeightInit(0.01), bias_init=ConstantBiasInit(0.5), dropout=1.), "deepaux": Hyperparams(num_layers=4, num_hidden=1000, auxiliary_softmax_layers=[0, 1, 2], auxiliary_softmax_weight=0.3, node_depth=1, nonlinearity=ACTIVATION_RECTIFIED_LINEAR, weight_init=GaussianWeightInit(0.01), bias_init=ConstantBiasInit(0.5), dropout=1.), "py": Hyperparams(num_layers=2, num_hidden=[2000, 100], node_depth=1, nonlinearity=ACTIVATION_RECTIFIED_LINEAR, weight_init=[GaussianWeightInit(0.01), GaussianWeightInit(0.04)], bias_init=[ConstantBiasInit(0.5), ConstantBiasInit(3.0)], dropout=1.), "pydrop1": Hyperparams(num_layers=2, num_hidden=[2000, 100], node_depth=1, nonlinearity=ACTIVATION_RECTIFIED_LINEAR, weight_init=[GaussianWeightInit(0.01), GaussianWeightInit(0.04)], bias_init=[ConstantBiasInit(0.5), ConstantBiasInit(3.0)], dropout=[0.75, 1.]), "pydrop2": Hyperparams(num_layers=2, num_hidden=[2000, 100], node_depth=1, nonlinearity=ACTIVATION_RECTIFIED_LINEAR, weight_init=[GaussianWeightInit(0.01), GaussianWeightInit(0.04)], bias_init=[ConstantBiasInit(0.5), ConstantBiasInit(3.0)], dropout=[0.75, 0.75])} """ import numpy as np import tensorflow.google as tf from tensorflow.python.platform import logging from biology import model from biology import model_ops from nowhere.mustreimplement import input_ops from nowhere.mustreimplement import label_ops from nowhere.learning.dist_belief import input_example_pb2 from nowhere.learning.dist_belief import types_pb2 as legacy_types_pb2 class UnreplicatedIcmlModel(model.Classifier): """Implements an icml model as configured in a model_config.proto.""" def Build(self): """Constructs the graph architecture as specified in its config. This method creates the following Placeholders: mol_features: Molecule descriptor (e.g. fingerprint) tensor with shape batch_size x num_features. """ with tf.name_scope(self.placeholder_scope): self.mol_features = tf.placeholder( tf.float32, shape=[self.config.batch_size, self.config.num_features], name='mol_features') layer_sizes = self.config.layer_sizes weight_init_stddevs = self.config.weight_init_stddevs bias_init_consts = self.config.bias_init_consts dropouts = self.config.dropouts lengths_set = { len(layer_sizes), len(weight_init_stddevs), len(bias_init_consts), len(dropouts), } assert len(lengths_set) == 1, 'All layer params must have same length.' num_layers = lengths_set.pop() assert num_layers > 0, 'Must have some layers defined.' prev_layer = self.mol_features prev_layer_size = self.config.num_features for i in xrange(num_layers): layer = tf.nn.relu(model_ops.FullyConnectedLayer( tensor=prev_layer, size=layer_sizes[i], weight_init=tf.truncated_normal( shape=[prev_layer_size, layer_sizes[i]], stddev=weight_init_stddevs[i]), bias_init=tf.constant(value=bias_init_consts[i], shape=[layer_sizes[i]]))) layer = model_ops.Dropout(layer, dropouts[i]) prev_layer = layer prev_layer_size = layer_sizes[i] self.output = model_ops.MultitaskLogits( layer, self.config.num_classification_tasks) def LabelsAndWeights(self): """Parse Label protos and create tensors for labels and weights. This method creates the following Placeholders in the graph: labels: Tensor with shape batch_size x num_tasks containing serialized Label protos. """ config = self.config with tf.name_scope(self.placeholder_scope): labels = tf.placeholder( tf.string, shape=[config.batch_size, config.num_classification_tasks], name='labels') self.labels = label_ops.MultitaskLabelClasses(labels, config.num_classes) self.weights = label_ops.MultitaskLabelWeights(labels) def ReadInput(self, input_pattern, input_data_types=None): """Read input data and return a generator for minibatches. Args: input_pattern: Input file pattern. input_data_types: List of legacy_types_pb2 constants matching the number of and data types present in the sstables. If not specified, defaults to full ICML 259-task types, but can be specified for unittests or other datasets with consistent types. Returns: A generator that yields a dict for feeding a single batch to Placeholders in the graph. Raises: AssertionError: If no default session is available. """ if model_ops.IsTraining(): randomize = True num_iterations = None else: randomize = False num_iterations = 1 num_tasks = self.config.num_classification_tasks tasks_in_input = self.config.tasks_in_input if input_data_types is None: input_data_types = ([legacy_types_pb2.DF_FLOAT] + [legacy_types_pb2.DF_LABEL_PROTO] * tasks_in_input) features, labels = input_ops.InputExampleInputReader( input_pattern=input_pattern, batch_size=self.config.batch_size, num_tasks=num_tasks, input_data_types=input_data_types, num_features=self.config.num_features, randomize=randomize, shuffling=randomize, num_iterations=num_iterations) return self._ReadInputGenerator(features, labels[:, :num_tasks]) def _GetFeedDict(self, named_values): feed_dict = {} for name, value in named_values.iteritems(): feed_dict['{}/{}:0'.format(self.placeholder_root, name)] = value return feed_dict def EvalBatch(self, input_batch): """Runs inference on the provided batch of input. Args: input_batch: iterator of input with len self.config.batch_size. Returns: Tuple of three numpy arrays with shape num_examples x num_tasks (x ...): output: Model predictions. labels: True labels. numpy array values are scalars, not 1-hot classes vector. weights: Example weights. """ output, labels, weights = super(UnreplicatedIcmlModel, self).EvalBatch( input_batch) # Converts labels from 1-hot to float. labels = labels[:, :, 1] # Whole batch, all tasks, 1-hot positive index. return output, labels, weights def BatchInputGenerator(self, serialized_batch): """Returns a generator that iterates over the provided batch of input. TODO(user): This is similar to input_ops.InputExampleInputReader(), but doesn't need to be executed as part of the TensorFlow graph. Consider refactoring so these can share code somehow. Args: serialized_batch: List of tuples: (_, value) where value is a serialized InputExample proto. Must have self.config.batch_size length or smaller. If smaller, we'll pad up to batch_size and mark the padding as invalid so it's ignored in eval metrics. Yields: Dict of model inputs for use as a feed_dict. Raises: ValueError: If the batch is larger than the batch_size. """ if len(serialized_batch) > self.config.batch_size: raise ValueError( 'serialized_batch length {} must be <= batch_size {}'.format( len(serialized_batch), self.config.batch_size)) for _ in xrange(self.config.batch_size - len(serialized_batch)): serialized_batch.append((None, '')) features = [] labels = [] for _, serialized_proto in serialized_batch: if serialized_proto: input_example = input_example_pb2.InputExample() input_example.ParseFromString(serialized_proto) features.append([f for f in input_example.endpoint[0].float_value]) label_protos = [endpoint.label for endpoint in input_example.endpoint[1:]] assert len(label_protos) == self.config.num_classification_tasks labels.append([l.SerializeToString() for l in label_protos]) else: # This was a padded value to reach the batch size. features.append([0.0 for _ in xrange(self.config.num_features)]) labels.append( ['' for _ in xrange(self.config.num_classification_tasks)]) valid = np.asarray([(np.sum(f) > 0) for f in features]) assert len(features) == self.config.batch_size assert len(labels) == self.config.batch_size assert len(valid) == self.config.batch_size yield self._GetFeedDict({ 'mol_features': features, 'labels': labels, 'valid': valid }) def _ReadInputGenerator(self, features_tensor, labels_tensor): """Generator that constructs feed_dict for minibatches. Args: features_tensor: Tensor of batch_size x molecule features. labels_tensor: Tensor of batch_size x label protos. Yields: A dict for feeding a single batch to Placeholders in the graph. Raises: AssertionError: If no default session is available. """ sess = tf.get_default_session() if sess is None: raise AssertionError('No default session') while True: try: logging.vlog(1, 'Starting session execution to get input data') features, labels = sess.run([features_tensor, labels_tensor]) logging.vlog(1, 'Done with session execution to get input data') # TODO(user): check if the below axis=1 needs to change to axis=0, # because cl/105081140. valid = np.sum(features, axis=1) > 0 yield self._GetFeedDict({ 'mol_features': features, 'labels': labels, 'valid': valid }) except tf.OpError as e: # InputExampleInput op raises OpError when it has hit num_iterations # or its input file is exhausted. However it may also be raised # if the input sstable isn't what we expect. if 'Invalid InputExample' in e.message: raise e else: break
deepchem/models/tensorflow_models/icml/icml_train.py 0 → 100644 +133 −0 Original line number Diff line number Diff line #!/usr/bin/python # # Copyright 2015 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Train a model from the ICML-2015 paper. """ # pylint: disable=line-too-long # pylint: enable=line-too-long import os from tensorflow.python.platform import app from tensorflow.python.platform import flags from tensorflow.python.platform import gfile from biology import model_config from biology.icml import icml_models flags.DEFINE_string('config', None, 'Serialized ModelConfig proto.') flags.DEFINE_string('master', '', 'BNS name of the TensorFlow master.') flags.DEFINE_string('logdir', None, 'Directory for output files.') flags.DEFINE_integer('replica_id', 0, 'Task ID of this replica.') flags.DEFINE_integer('ps_tasks', 0, 'Number of parameter server tasks.') flags.DEFINE_integer('num_folds', 5, 'Number of cross-validation folds.') flags.DEFINE_integer('fold', None, 'Fold index for this model.') FLAGS = flags.FLAGS def kfold_pattern(input_pattern, num_folds, fold=None): """Generator for train/test filename splits. The pattern is not expanded except for the %d being replaced by the fold index. Args: input_pattern: Input filename pattern. Should contain %d for fold index. num_folds: Number of folds. fold: If not None, the generator only yields the train/test split for the given fold. Yields: train_filenames: A list of file patterns in training set. test_filenames: A list of file patterns in test set. """ # get filenames associated with each fold fold_filepatterns = [input_pattern % i for i in range(num_folds)] # create train/test splits for i in range(num_folds): if fold is not None and i != fold: continue train = fold_filepatterns[:i] + fold_filepatterns[i+1:] test = [fold_filepatterns[i]] if any([f in test for f in train]): logging.fatal('Train/test split is not complete.') if set(train + test) != set(fold_filepatterns): logging.fatal('Not all input files are accounted for.') yield train, test def Run(input_data_types=None): """Trains the model with specified parameters. Args: input_data_types: List of legacy_types_pb2 constants or None. """ config = model_config.ModelConfig({ 'input_pattern': '', # Should have %d for fold index substitution. 'num_classification_tasks': 259, 'tasks_in_input': 259, # Dimensionality of sstables 'max_steps': 50000000, 'summaries': False, 'batch_size': 128, 'learning_rate': 0.0003, 'num_classes': 2, 'optimizer': 'sgd', 'penalty': 0.0, 'num_features': 1024, 'layer_sizes': [1200], 'weight_init_stddevs': [0.01], 'bias_init_consts': [0.5], 'dropouts': [0.0], }) config.ReadFromFile(FLAGS.config, overwrite='required') if FLAGS.replica_id == 0: gfile.MakeDirs(FLAGS.logdir) config.WriteToFile(os.path.join(FLAGS.logdir, 'config.pbtxt')) model = icml_models.IcmlModel(config, train=True, logdir=FLAGS.logdir, master=FLAGS.master) if FLAGS.num_folds is not None and FLAGS.fold is not None: folds = kfold_pattern(config.input_pattern, FLAGS.num_folds, FLAGS.fold) train_pattern, _ = folds.next() train_pattern = ','.join(train_pattern) else: train_pattern = config.input_pattern with model.graph.as_default(): model.Train(model.ReadInput(train_pattern, input_data_types=input_data_types), max_steps=config.max_steps, summaries=config.summaries, replica_id=FLAGS.replica_id, ps_tasks=FLAGS.ps_tasks) def main(unused_argv=None): Run() if __name__ == '__main__': flags.MarkFlagAsRequired('config') flags.MarkFlagAsRequired('logdir') flags.MarkFlagAsRequired('fold') app.run()
