Loading deepchem/models/__init__.py +0 −1 Original line number Diff line number Diff line Loading @@ -7,7 +7,6 @@ from __future__ import unicode_literals from deepchem.models.models import Model from deepchem.models.sklearn_models import SklearnModel from deepchem.models.keras_models import KerasModel from deepchem.models.tf_keras_models.multitask_classifier import MultitaskGraphClassifier from deepchem.models.tf_keras_models.support_classifier import SupportGraphClassifier from deepchem.models.multitask import SingletaskToMultitask Loading deepchem/models/keras_models/__init__.py +99 −99 Original line number Diff line number Diff line Loading @@ -7,102 +7,102 @@ from __future__ import unicode_literals import os import numpy as np from keras.models import Graph from keras.models import load_model from keras.models import model_from_json from keras.layers.core import Dense, Dropout, Activation from keras.layers.normalization import BatchNormalization from keras.optimizers import SGD from deepchem.models import Model class KerasModel(Model): """ Abstract base class shared across all Keras models. """ def save(self): """ Saves underlying keras model to disk. """ model = self.model_instance filename, _ = os.path.splitext(Model.get_model_filename(self.model_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") self.model_instance.save(h5_filename) # Save architecture json_string = model.to_json() with open(json_filename, "w") as file_obj: file_obj.write(json_string) model.save_weights(h5_filename, overwrite=True) def reload(self, custom_objects={}): """ Load keras multitask DNN from disk. """ filename = Model.get_model_filename(self.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(), custom_objects=custom_objects) model.load_weights(h5_filename) self.model_instance = model def predict_on_batch(self, X, pad_batch=False): """ Makes predictions on given batch of new data. Parameters ---------- X: np.ndarray Features pad_batch: bool, optional Used for Tensorflow models with rigid batch-size requirements. """ n_samples = len(X) n_tasks = self.get_num_tasks() if pad_batch: X = pad_features(self.batch_size, X) y_pred = self.model_instance.predict_on_batch(X) y_pred = np.reshape(y_pred, (n_samples, n_tasks)) return y_pred # TODO(rbharath): The methods below aren't extensible and depend on # implementation details of fcnet. Better way to expose this information? def fit_on_batch(self, X, y, w): """Fit model on batch of data.""" return self.model_instance.fit_on_batch(X, y, w) def get_num_tasks(self): return self.model_instance.n_tasks def predict_proba_on_batch(self, X, pad_batch=False, n_classes=2): """ Makes predictions of class probabilities on given batch of new data. Parameters ---------- X: np.ndarray Features pad_batch: bool, optional Ignored for Sklearn Model. Only used for Tensorflow models with rigid batch-size requirements. n_classes: int Number of classifier classes """ n_samples = len(X) n_tasks = self.get_num_tasks() if pad_batch: X = pad_features(self.batch_size, X) y_pred_proba = self.model_instance.predict_proba_on_batch(X, n_classes) y_pred_proba = np.reshape(y_pred_proba, (n_samples, n_tasks, n_classes)) return y_pred_proba #from keras.models import Graph #from keras.models import load_model #from keras.models import model_from_json #from keras.layers.core import Dense, Dropout, Activation #from keras.layers.normalization import BatchNormalization #from keras.optimizers import SGD #from deepchem.models import Model # #class KerasModel(Model): # """ # Abstract base class shared across all Keras models. # """ # # def save(self): # """ # Saves underlying keras model to disk. # """ # model = self.model_instance # filename, _ = os.path.splitext(Model.get_model_filename(self.model_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") # self.model_instance.save(h5_filename) # # Save architecture # json_string = model.to_json() # with open(json_filename, "w") as file_obj: # file_obj.write(json_string) # model.save_weights(h5_filename, overwrite=True) # # def reload(self, custom_objects={}): # """ # Load keras multitask DNN from disk. # """ # filename = Model.get_model_filename(self.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(), custom_objects=custom_objects) # model.load_weights(h5_filename) # self.model_instance = model # # def predict_on_batch(self, X, pad_batch=False): # """ # Makes predictions on given batch of new data. # # Parameters # ---------- # X: np.ndarray # Features # pad_batch: bool, optional # Used for Tensorflow models with rigid batch-size requirements. # """ # n_samples = len(X) # n_tasks = self.get_num_tasks() # if pad_batch: # X = pad_features(self.batch_size, X) # y_pred = self.model_instance.predict_on_batch(X) # y_pred = np.reshape(y_pred, (n_samples, n_tasks)) # return y_pred # # # TODO(rbharath): The methods below aren't extensible and depend on # # implementation details of fcnet. Better way to expose this information? # def fit_on_batch(self, X, y, w): # """Fit model on batch of data.""" # return self.model_instance.fit_on_batch(X, y, w) # # def get_num_tasks(self): # return self.model_instance.n_tasks # # def predict_proba_on_batch(self, X, pad_batch=False, n_classes=2): # """ # Makes predictions of class probabilities on given batch of new data. # # Parameters # ---------- # X: np.ndarray # Features # pad_batch: bool, optional # Ignored for Sklearn Model. Only used for Tensorflow models # with rigid batch-size requirements. # n_classes: int # Number of classifier classes # """ # n_samples = len(X) # n_tasks = self.get_num_tasks() # # if pad_batch: # X = pad_features(self.batch_size, X) # y_pred_proba = self.model_instance.predict_proba_on_batch(X, # n_classes) # y_pred_proba = np.reshape(y_pred_proba, (n_samples, n_tasks, n_classes)) # return y_pred_proba Loading
deepchem/models/__init__.py +0 −1 Original line number Diff line number Diff line Loading @@ -7,7 +7,6 @@ from __future__ import unicode_literals from deepchem.models.models import Model from deepchem.models.sklearn_models import SklearnModel from deepchem.models.keras_models import KerasModel from deepchem.models.tf_keras_models.multitask_classifier import MultitaskGraphClassifier from deepchem.models.tf_keras_models.support_classifier import SupportGraphClassifier from deepchem.models.multitask import SingletaskToMultitask Loading
deepchem/models/keras_models/__init__.py +99 −99 Original line number Diff line number Diff line Loading @@ -7,102 +7,102 @@ from __future__ import unicode_literals import os import numpy as np from keras.models import Graph from keras.models import load_model from keras.models import model_from_json from keras.layers.core import Dense, Dropout, Activation from keras.layers.normalization import BatchNormalization from keras.optimizers import SGD from deepchem.models import Model class KerasModel(Model): """ Abstract base class shared across all Keras models. """ def save(self): """ Saves underlying keras model to disk. """ model = self.model_instance filename, _ = os.path.splitext(Model.get_model_filename(self.model_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") self.model_instance.save(h5_filename) # Save architecture json_string = model.to_json() with open(json_filename, "w") as file_obj: file_obj.write(json_string) model.save_weights(h5_filename, overwrite=True) def reload(self, custom_objects={}): """ Load keras multitask DNN from disk. """ filename = Model.get_model_filename(self.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(), custom_objects=custom_objects) model.load_weights(h5_filename) self.model_instance = model def predict_on_batch(self, X, pad_batch=False): """ Makes predictions on given batch of new data. Parameters ---------- X: np.ndarray Features pad_batch: bool, optional Used for Tensorflow models with rigid batch-size requirements. """ n_samples = len(X) n_tasks = self.get_num_tasks() if pad_batch: X = pad_features(self.batch_size, X) y_pred = self.model_instance.predict_on_batch(X) y_pred = np.reshape(y_pred, (n_samples, n_tasks)) return y_pred # TODO(rbharath): The methods below aren't extensible and depend on # implementation details of fcnet. Better way to expose this information? def fit_on_batch(self, X, y, w): """Fit model on batch of data.""" return self.model_instance.fit_on_batch(X, y, w) def get_num_tasks(self): return self.model_instance.n_tasks def predict_proba_on_batch(self, X, pad_batch=False, n_classes=2): """ Makes predictions of class probabilities on given batch of new data. Parameters ---------- X: np.ndarray Features pad_batch: bool, optional Ignored for Sklearn Model. Only used for Tensorflow models with rigid batch-size requirements. n_classes: int Number of classifier classes """ n_samples = len(X) n_tasks = self.get_num_tasks() if pad_batch: X = pad_features(self.batch_size, X) y_pred_proba = self.model_instance.predict_proba_on_batch(X, n_classes) y_pred_proba = np.reshape(y_pred_proba, (n_samples, n_tasks, n_classes)) return y_pred_proba #from keras.models import Graph #from keras.models import load_model #from keras.models import model_from_json #from keras.layers.core import Dense, Dropout, Activation #from keras.layers.normalization import BatchNormalization #from keras.optimizers import SGD #from deepchem.models import Model # #class KerasModel(Model): # """ # Abstract base class shared across all Keras models. # """ # # def save(self): # """ # Saves underlying keras model to disk. # """ # model = self.model_instance # filename, _ = os.path.splitext(Model.get_model_filename(self.model_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") # self.model_instance.save(h5_filename) # # Save architecture # json_string = model.to_json() # with open(json_filename, "w") as file_obj: # file_obj.write(json_string) # model.save_weights(h5_filename, overwrite=True) # # def reload(self, custom_objects={}): # """ # Load keras multitask DNN from disk. # """ # filename = Model.get_model_filename(self.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(), custom_objects=custom_objects) # model.load_weights(h5_filename) # self.model_instance = model # # def predict_on_batch(self, X, pad_batch=False): # """ # Makes predictions on given batch of new data. # # Parameters # ---------- # X: np.ndarray # Features # pad_batch: bool, optional # Used for Tensorflow models with rigid batch-size requirements. # """ # n_samples = len(X) # n_tasks = self.get_num_tasks() # if pad_batch: # X = pad_features(self.batch_size, X) # y_pred = self.model_instance.predict_on_batch(X) # y_pred = np.reshape(y_pred, (n_samples, n_tasks)) # return y_pred # # # TODO(rbharath): The methods below aren't extensible and depend on # # implementation details of fcnet. Better way to expose this information? # def fit_on_batch(self, X, y, w): # """Fit model on batch of data.""" # return self.model_instance.fit_on_batch(X, y, w) # # def get_num_tasks(self): # return self.model_instance.n_tasks # # def predict_proba_on_batch(self, X, pad_batch=False, n_classes=2): # """ # Makes predictions of class probabilities on given batch of new data. # # Parameters # ---------- # X: np.ndarray # Features # pad_batch: bool, optional # Ignored for Sklearn Model. Only used for Tensorflow models # with rigid batch-size requirements. # n_classes: int # Number of classifier classes # """ # n_samples = len(X) # n_tasks = self.get_num_tasks() # # if pad_batch: # X = pad_features(self.batch_size, X) # y_pred_proba = self.model_instance.predict_proba_on_batch(X, # n_classes) # y_pred_proba = np.reshape(y_pred_proba, (n_samples, n_tasks, n_classes)) # return y_pred_proba
