Loading deepchem/models/deep.py +43 −39 Original line number Diff line number Diff line Loading @@ -8,9 +8,50 @@ from keras.layers.core import Dense, Dropout from keras.optimizers import SGD from deepchem.models import Model class MultiTaskDNN(Model): class KerasModel(Model) """ Abstract base class for different ML models. Abstract base class shared across all Keras models. """ def save(self, out_dir): """ Saves underlying keras model to disk. """ super(MultiTaskDNN, self).save(out_dir) model = self.get_raw_model() filename, _ = os.path.splitext(self.get_model_filename(out_dir)) # Note that keras requires the model architecture and weights to be stored # separately. A json file is generated that specifies the model architecture. # The weights will be stored in an h5 file. The pkl.gz file with store the # target name. json_filename = "%s.%s" % (filename, "json") h5_filename = "%s.%s" % (filename, "h5") # Save architecture json_string = model.to_json() with open(json_filename, "wb") as file_obj: file_obj.write(json_string) model.save_weights(h5_filename, overwrite=True) def load(self, model_dir): """ Load keras multitask DNN from disk. """ super(MultiTaskDNN, self).load(model_dir) filename = self.get_Model_filename(model_dir) filename, _ = os.path.splitext(filename) json_filename = "%s.%s" % (filename, "json") h5_filename = "%s.%s" % (filename, "h5") with open(json_filename) as file_obj: model = model_from_json(file_obj.read()) model.load_weights(h5_filename) self.raw_model = model class MultiTaskDNN(KerasModel): """ Model for multitask MLP in keras. """ def __init__(self, task_types, model_params, initialize_raw_model=True): super(MultiTaskDNN, self).__init__(model_type, task_types, model_params, Loading Loading @@ -99,43 +140,6 @@ class MultiTaskDNN(Model): y_pred = np.squeeze(y_pred) return y_pred def save(self, out_dir): """ Saves underlying keras model to disk. """ super(MultiTaskDNN, self).save(out_dir) model = self.get_raw_model() filename, _ = os.path.splitext(self.get_model_filename(out_dir)) # Note that keras requires the model architecture and weights to be stored # separately. A json file is generated that specifies the model architecture. # The weights will be stored in an h5 file. The pkl.gz file with store the # target name. json_filename = "%s.%s" % (filename, "json") h5_filename = "%s.%s" % (filename, "h5") # Save architecture json_string = model.to_json() with open(json_filename, "wb") as file_obj: file_obj.write(json_string) model.save_weights(h5_filename, overwrite=True) def load(self, model_dir): """ Load keras multitask DNN from disk. """ super(MultiTaskDNN, self).load(model_dir) filename = self.get_Model_filename(model_dir) filename, _ = os.path.splitext(filename) json_filename = "%s.%s" % (filename, "json") h5_filename = "%s.%s" % (filename, "h5") with open(json_filename) as file_obj: model = model_from_json(file_obj.read()) model.load_weights(h5_filename) self.raw_model = model Model.register_model_type("multitask_deep_regressor", MultiTaskDNN) Model.register_model_type("multitask_deep_classifier", MultiTaskDNN) Loading deepchem/models/deep3d.py +1 −1 Original line number Diff line number Diff line Loading @@ -24,7 +24,7 @@ def shuffle_data(X): return X class DockingDNN(Model): class DockingDNN(KerasModel): """ Wrapper class for fitting 3D convolutional networks for deep docking. """ Loading deepchem/models/model.pydeleted 100644 → 0 +0 −30 Original line number Diff line number Diff line """ Factory function to construct models. """ from __future__ import print_function from __future__ import division from __future__ import unicode_literals from deepchem.models.deep import SingleTaskDNN #from deepchem.models.deep import MultiTaskDNN #from deepchem.models.deep3d import DockingDNN #from deepchem.models.standard import SklearnModel #def model_builder(model_type, task_types, model_params, # initialize_raw_model=True): # """ # Factory function to construct model. # """ # if model_type == "singletask_deep_network": # model = SingleTaskDNN(task_types, model_params, # initialize_raw_model) # elif model_type == "multitask_deep_network": # model = MultiTaskDNN(task_types, model_params, # initialize_raw_model) # elif model_type == "convolutional_3D_regressor": # model = DockingDNN(task_types, model_params, # initialize_raw_model) # else: # model = SklearnModel(task_types, model_params) # return model Loading
deepchem/models/deep.py +43 −39 Original line number Diff line number Diff line Loading @@ -8,9 +8,50 @@ from keras.layers.core import Dense, Dropout from keras.optimizers import SGD from deepchem.models import Model class MultiTaskDNN(Model): class KerasModel(Model) """ Abstract base class for different ML models. Abstract base class shared across all Keras models. """ def save(self, out_dir): """ Saves underlying keras model to disk. """ super(MultiTaskDNN, self).save(out_dir) model = self.get_raw_model() filename, _ = os.path.splitext(self.get_model_filename(out_dir)) # Note that keras requires the model architecture and weights to be stored # separately. A json file is generated that specifies the model architecture. # The weights will be stored in an h5 file. The pkl.gz file with store the # target name. json_filename = "%s.%s" % (filename, "json") h5_filename = "%s.%s" % (filename, "h5") # Save architecture json_string = model.to_json() with open(json_filename, "wb") as file_obj: file_obj.write(json_string) model.save_weights(h5_filename, overwrite=True) def load(self, model_dir): """ Load keras multitask DNN from disk. """ super(MultiTaskDNN, self).load(model_dir) filename = self.get_Model_filename(model_dir) filename, _ = os.path.splitext(filename) json_filename = "%s.%s" % (filename, "json") h5_filename = "%s.%s" % (filename, "h5") with open(json_filename) as file_obj: model = model_from_json(file_obj.read()) model.load_weights(h5_filename) self.raw_model = model class MultiTaskDNN(KerasModel): """ Model for multitask MLP in keras. """ def __init__(self, task_types, model_params, initialize_raw_model=True): super(MultiTaskDNN, self).__init__(model_type, task_types, model_params, Loading Loading @@ -99,43 +140,6 @@ class MultiTaskDNN(Model): y_pred = np.squeeze(y_pred) return y_pred def save(self, out_dir): """ Saves underlying keras model to disk. """ super(MultiTaskDNN, self).save(out_dir) model = self.get_raw_model() filename, _ = os.path.splitext(self.get_model_filename(out_dir)) # Note that keras requires the model architecture and weights to be stored # separately. A json file is generated that specifies the model architecture. # The weights will be stored in an h5 file. The pkl.gz file with store the # target name. json_filename = "%s.%s" % (filename, "json") h5_filename = "%s.%s" % (filename, "h5") # Save architecture json_string = model.to_json() with open(json_filename, "wb") as file_obj: file_obj.write(json_string) model.save_weights(h5_filename, overwrite=True) def load(self, model_dir): """ Load keras multitask DNN from disk. """ super(MultiTaskDNN, self).load(model_dir) filename = self.get_Model_filename(model_dir) filename, _ = os.path.splitext(filename) json_filename = "%s.%s" % (filename, "json") h5_filename = "%s.%s" % (filename, "h5") with open(json_filename) as file_obj: model = model_from_json(file_obj.read()) model.load_weights(h5_filename) self.raw_model = model Model.register_model_type("multitask_deep_regressor", MultiTaskDNN) Model.register_model_type("multitask_deep_classifier", MultiTaskDNN) Loading
deepchem/models/deep3d.py +1 −1 Original line number Diff line number Diff line Loading @@ -24,7 +24,7 @@ def shuffle_data(X): return X class DockingDNN(Model): class DockingDNN(KerasModel): """ Wrapper class for fitting 3D convolutional networks for deep docking. """ Loading
deepchem/models/model.pydeleted 100644 → 0 +0 −30 Original line number Diff line number Diff line """ Factory function to construct models. """ from __future__ import print_function from __future__ import division from __future__ import unicode_literals from deepchem.models.deep import SingleTaskDNN #from deepchem.models.deep import MultiTaskDNN #from deepchem.models.deep3d import DockingDNN #from deepchem.models.standard import SklearnModel #def model_builder(model_type, task_types, model_params, # initialize_raw_model=True): # """ # Factory function to construct model. # """ # if model_type == "singletask_deep_network": # model = SingleTaskDNN(task_types, model_params, # initialize_raw_model) # elif model_type == "multitask_deep_network": # model = MultiTaskDNN(task_types, model_params, # initialize_raw_model) # elif model_type == "convolutional_3D_regressor": # model = DockingDNN(task_types, model_params, # initialize_raw_model) # else: # model = SklearnModel(task_types, model_params) # return model
