Python keras.engine.saving.load_weights_from_hdf5_group() Examples
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Example #1
Source File: model.py From DeepTL-Lane-Change-Classification with MIT License | 5 votes |
def load_weights(self, filepath, by_name=False, exclude=None): """Modified version of the correspoding Keras function with the addition of multi-GPU support and the ability to exclude some layers from loading. exlude: list of layer names to excluce """ import h5py from keras.engine import saving if exclude: by_name = True if h5py is None: raise ImportError('`load_weights` requires h5py.') f = h5py.File(filepath, mode='r') if 'layer_names' not in f.attrs and 'model_weights' in f: f = f['model_weights'] # In multi-GPU training, we wrap the model. Get layers # of the inner model because they have the weights. keras_model = self.keras_model layers = keras_model.inner_model.layers if hasattr(keras_model, "inner_model")\ else keras_model.layers # Exclude some layers if exclude: layers = filter(lambda l: l.name not in exclude, layers) if by_name: saving.load_weights_from_hdf5_group_by_name(f, layers) else: saving.load_weights_from_hdf5_group(f, layers) if hasattr(f, 'close'): f.close() # Update the log directory self.set_log_dir(filepath)
Example #2
Source File: keras_model_wrapper.py From image-segmentation with MIT License | 5 votes |
def load_weights(self, model_path, by_name=True, exclude=None): '''Modified version of the corresponding Keras function with the addition of multi-GPU support and the ability to exclude some layers from loading. exclude: list of layer names to exclude ''' import h5py from keras.engine import saving if exclude: by_name = True if h5py is None: raise ImportError('`load_weights` requires h5py.') f = h5py.File(model_path, mode='r') if 'layer_names' not in f.attrs and 'model_weights' in f: f = f['model_weights'] # In multi-GPU training, we wrap the model. Get layers # of the inner model because they have the weights. layers = self.model.inner_model.layers if hasattr(self.model, 'inner_model') \ else self.model.layers # Exclude some layers if exclude: layers = filter(lambda l: l.name not in exclude, layers) if by_name: saving.load_weights_from_hdf5_group_by_name(f, layers) else: saving.load_weights_from_hdf5_group(f, layers) if hasattr(f, 'close'): f.close()
Example #3
Source File: save_load_utils.py From keras-contrib with MIT License | 5 votes |
def load_all_weights(model, filepath, include_optimizer=True): """Loads the weights of a model saved via `save_all_weights`. If model has been compiled, optionally load its optimizer's weights. # Arguments model: instantiated model with architecture matching the saved model. Compile the model beforehand if you want to load optimizer weights. filepath: String, path to the saved model. # Returns None. The model will have its weights updated. # Raises ImportError: if h5py is not available. ValueError: In case of an invalid savefile. """ if h5py is None: raise ImportError('`load_all_weights` requires h5py.') with h5py.File(filepath, mode='r') as f: # set weights saving.load_weights_from_hdf5_group(f['model_weights'], model.layers) # Set optimizer weights. if (include_optimizer and 'optimizer_weights' in f and hasattr(model, 'optimizer') and model.optimizer): optimizer_weights_group = f['optimizer_weights'] optimizer_weight_names = [n.decode('utf8') for n in optimizer_weights_group.attrs['weight_names']] optimizer_weight_values = [optimizer_weights_group[n] for n in optimizer_weight_names] model.optimizer.set_weights(optimizer_weight_values)
Example #4
Source File: model.py From latte with Apache License 2.0 | 5 votes |
def load_weights(self, filepath, by_name=False, exclude=None): """Modified version of the correspoding Keras function with the addition of multi-GPU support and the ability to exclude some layers from loading. exlude: list of layer names to excluce """ import h5py from keras.engine import saving if exclude: by_name = True if h5py is None: raise ImportError('`load_weights` requires h5py.') f = h5py.File(filepath, mode='r') if 'layer_names' not in f.attrs and 'model_weights' in f: f = f['model_weights'] # In multi-GPU training, we wrap the model. Get layers # of the inner model because they have the weights. keras_model = self.keras_model layers = keras_model.inner_model.layers if hasattr(keras_model, "inner_model")\ else keras_model.layers # Exclude some layers if exclude: layers = filter(lambda l: l.name not in exclude, layers) if by_name: saving.load_weights_from_hdf5_group_by_name(f, layers) else: saving.load_weights_from_hdf5_group(f, layers) if hasattr(f, 'close'): f.close() # Update the log directory self.set_log_dir(filepath)
Example #5
Source File: model.py From dataiku-contrib with Apache License 2.0 | 4 votes |
def load_weights(self, filepath, by_name=False, exclude=None): """Modified version of the corresponding Keras function with the addition of multi-GPU support and the ability to exclude some layers from loading. exclude: list of layer names to exclude """ import h5py # Conditional import to support versions of Keras before 2.2 # TODO: remove in about 6 months (end of 2018) try: from keras.engine import saving except ImportError: # Keras before 2.2 used the 'topology' namespace. from keras.engine import topology as saving if exclude: by_name = True if h5py is None: raise ImportError('`load_weights` requires h5py.') f = h5py.File(filepath, mode='r') if 'layer_names' not in f.attrs and 'model_weights' in f: f = f['model_weights'] # In multi-GPU training, we wrap the model. Get layers # of the inner model because they have the weights. keras_model = self.keras_model layers = keras_model.inner_model.layers if hasattr(keras_model, "inner_model")\ else keras_model.layers # Exclude some layers if exclude: layers = filter(lambda l: l.name not in exclude, layers) if by_name: saving.load_weights_from_hdf5_group_by_name(f, layers) else: saving.load_weights_from_hdf5_group(f, layers) if hasattr(f, 'close'): f.close() # Update the log directory self.set_log_dir(filepath)
Example #6
Source File: model.py From PanopticSegmentation with MIT License | 4 votes |
def load_weights(self, filepath, by_name=False, exclude=None): """Modified version of the corresponding Keras function with the addition of multi-GPU support and the ability to exclude some layers from loading. exclude: list of layer names to exclude """ import h5py # Conditional import to support versions of Keras before 2.2 # TODO: remove in about 6 months (end of 2018) try: from keras.engine import saving except ImportError: # Keras before 2.2 used the 'topology' namespace. from keras.engine import topology as saving if exclude: by_name = True if h5py is None: raise ImportError('`load_weights` requires h5py.') f = h5py.File(filepath, mode='r') if 'layer_names' not in f.attrs and 'model_weights' in f: f = f['model_weights'] # In multi-GPU training, we wrap the model. Get layers # of the inner model because they have the weights. keras_model = self.keras_model layers = keras_model.inner_model.layers if hasattr(keras_model, "inner_model")\ else keras_model.layers # Exclude some layers if exclude: layers = filter(lambda l: l.name not in exclude, layers) if by_name: saving.load_weights_from_hdf5_group_by_name(f, layers) else: saving.load_weights_from_hdf5_group(f, layers) if hasattr(f, 'close'): f.close() # Update the log directory self.set_log_dir(filepath)
Example #7
Source File: model.py From raster-deep-learning with Apache License 2.0 | 4 votes |
def load_weights(self, filepath, by_name=False, exclude=None): """Modified version of the corresponding Keras function with the addition of multi-GPU support and the ability to exclude some layers from loading. exclude: list of layer names to exclude """ import h5py # Conditional import to support versions of Keras before 2.2 # TODO: remove in about 6 months (end of 2018) try: from keras.engine import saving except ImportError: # Keras before 2.2 used the 'topology' namespace. from keras.engine import topology as saving if exclude: by_name = True if h5py is None: raise ImportError('`load_weights` requires h5py.') f = h5py.File(filepath, mode='r') if 'layer_names' not in f.attrs and 'model_weights' in f: f = f['model_weights'] # In multi-GPU training, we wrap the model. Get layers # of the inner model because they have the weights. keras_model = self.keras_model layers = keras_model.inner_model.layers if hasattr(keras_model, "inner_model")\ else keras_model.layers # Exclude some layers if exclude: layers = filter(lambda l: l.name not in exclude, layers) if by_name: saving.load_weights_from_hdf5_group_by_name(f, layers) else: saving.load_weights_from_hdf5_group(f, layers) if hasattr(f, 'close'): f.close() # Update the log directory if self.mode == 'training': self.set_log_dir(filepath)
Example #8
Source File: model.py From bird_species_classification with MIT License | 4 votes |
def load_weights(self, filepath, by_name=False, exclude=None): """Modified version of the corresponding Keras function with the addition of multi-GPU support and the ability to exclude some layers from loading. exclude: list of layer names to exclude """ import h5py # Conditional import to support versions of Keras before 2.2 # TODO: remove in about 6 months (end of 2018) try: from keras.engine import saving except ImportError: # Keras before 2.2 used the 'topology' namespace. from keras.engine import topology as saving if exclude: by_name = True if h5py is None: raise ImportError('`load_weights` requires h5py.') f = h5py.File(filepath, mode='r') if 'layer_names' not in f.attrs and 'model_weights' in f: f = f['model_weights'] # In multi-GPU training, we wrap the model. Get layers # of the inner model because they have the weights. keras_model = self.keras_model layers = keras_model.inner_model.layers if hasattr(keras_model, "inner_model")\ else keras_model.layers # Exclude some layers if exclude: layers = filter(lambda l: l.name not in exclude, layers) if by_name: saving.load_weights_from_hdf5_group_by_name(f, layers) else: saving.load_weights_from_hdf5_group(f, layers) if hasattr(f, 'close'): f.close() # Update the log directory self.set_log_dir(filepath)
Example #9
Source File: model.py From i.ann.maskrcnn with GNU General Public License v2.0 | 4 votes |
def load_weights(self, filepath, by_name=False, exclude=None): """Modified version of the corresponding Keras function with the addition of multi-GPU support and the ability to exclude some layers from loading. exclude: list of layer names to exclude """ import h5py # Conditional import to support versions of Keras before 2.2 # TODO: remove in about 6 months (end of 2018) try: from keras.engine import saving except ImportError: # Keras before 2.2 used the 'topology' namespace. from keras.engine import topology as saving if exclude: by_name = True if h5py is None: raise ImportError('`load_weights` requires h5py.') f = h5py.File(filepath, mode='r') if 'layer_names' not in f.attrs and 'model_weights' in f: f = f['model_weights'] # In multi-GPU training, we wrap the model. Get layers # of the inner model because they have the weights. keras_model = self.keras_model layers = keras_model.inner_model.layers if hasattr(keras_model, "inner_model")\ else keras_model.layers # Exclude some layers if exclude: layers = filter(lambda l: l.name not in exclude, layers) if by_name: saving.load_weights_from_hdf5_group_by_name(f, layers) else: saving.load_weights_from_hdf5_group(f, layers) if hasattr(f, 'close'): f.close() # Update the log directory self.set_log_dir(filepath)
Example #10
Source File: model.py From Skin-Cancer-Segmentation with MIT License | 4 votes |
def load_weights(self, filepath, by_name=False, exclude=None): """Modified version of the corresponding Keras function with the addition of multi-GPU support and the ability to exclude some layers from loading. exclude: list of layer names to exclude """ import h5py # Conditional import to support versions of Keras before 2.2 # TODO: remove in about 6 months (end of 2018) try: from keras.engine import saving except ImportError: # Keras before 2.2 used the 'topology' namespace. from keras.engine import topology as saving if exclude: by_name = True if h5py is None: raise ImportError('`load_weights` requires h5py.') f = h5py.File(filepath, mode='r') if 'layer_names' not in f.attrs and 'model_weights' in f: f = f['model_weights'] # In multi-GPU training, we wrap the model. Get layers # of the inner model because they have the weights. keras_model = self.keras_model layers = keras_model.inner_model.layers if hasattr(keras_model, "inner_model")\ else keras_model.layers # Exclude some layers if exclude: layers = filter(lambda l: l.name not in exclude, layers) if by_name: saving.load_weights_from_hdf5_group_by_name(f, layers) else: saving.load_weights_from_hdf5_group(f, layers) if hasattr(f, 'close'): f.close() # Update the log directory self.set_log_dir(filepath)
Example #11
Source File: model.py From deepdiy with MIT License | 4 votes |
def load_weights(self, filepath, by_name=False, exclude=None): """Modified version of the corresponding Keras function with the addition of multi-GPU support and the ability to exclude some layers from loading. exclude: list of layer names to exclude """ import h5py # Conditional import to support versions of Keras before 2.2 # TODO: remove in about 6 months (end of 2018) try: from keras.engine import saving except ImportError: # Keras before 2.2 used the 'topology' namespace. from keras.engine import topology as saving if exclude: by_name = True if h5py is None: raise ImportError('`load_weights` requires h5py.') f = h5py.File(filepath, mode='r') if 'layer_names' not in f.attrs and 'model_weights' in f: f = f['model_weights'] # In multi-GPU training, we wrap the model. Get layers # of the inner model because they have the weights. keras_model = self.keras_model layers = keras_model.inner_model.layers if hasattr(keras_model, "inner_model")\ else keras_model.layers # Exclude some layers if exclude: layers = filter(lambda l: l.name not in exclude, layers) if by_name: saving.load_weights_from_hdf5_group_by_name(f, layers) else: saving.load_weights_from_hdf5_group(f, layers) if hasattr(f, 'close'): f.close() # Update the log directory self.set_log_dir(filepath)
Example #12
Source File: BaseModel.py From Keras-RFCN with MIT License | 4 votes |
def load_weights(self, filepath, by_name=False, exclude=None): """Modified version of the correspoding Keras function with the addition of multi-GPU support and the ability to exclude some layers from loading. exlude: list of layer names to excluce """ import h5py # Keras 2.2 use saving try: from keras.engine import saving except ImportError: # Keras before 2.2 used the 'topology' namespace. from keras.engine import topology as saving if exclude: by_name = True if h5py is None: raise ImportError('`load_weights` requires h5py.') f = h5py.File(filepath, mode='r') if 'layer_names' not in f.attrs and 'model_weights' in f: f = f['model_weights'] # In multi-GPU training, we wrap the model. Get layers # of the inner model because they have the weights. keras_model = self.keras_model layers = keras_model.inner_model.layers if hasattr(keras_model, "inner_model")\ else keras_model.layers # Exclude some layers if exclude: layers = filter(lambda l: l.name not in exclude, layers) if by_name: saving.load_weights_from_hdf5_group_by_name(f, layers) else: saving.load_weights_from_hdf5_group(f, layers) if hasattr(f, 'close'): f.close() # Update the log directory self.set_log_dir(filepath)
Example #13
Source File: model.py From ocrd_anybaseocr with Apache License 2.0 | 4 votes |
def load_weights(self, filepath, by_name=False, exclude=None): """Modified version of the corresponding Keras function with the addition of multi-GPU support and the ability to exclude some layers from loading. exclude: list of layer names to exclude """ import h5py # Conditional import to support versions of Keras before 2.2 # TODO: remove in about 6 months (end of 2018) try: from keras.engine import saving except ImportError: # Keras before 2.2 used the 'topology' namespace. from keras.engine import topology as saving if exclude: by_name = True if h5py is None: raise ImportError('`load_weights` requires h5py.') f = h5py.File(filepath, mode='r') if 'layer_names' not in f.attrs and 'model_weights' in f: f = f['model_weights'] # In multi-GPU training, we wrap the model. Get layers # of the inner model because they have the weights. keras_model = self.keras_model layers = keras_model.inner_model.layers if hasattr(keras_model, "inner_model")\ else keras_model.layers # Exclude some layers if exclude: layers = filter(lambda l: l.name not in exclude, layers) if by_name: saving.load_weights_from_hdf5_group_by_name(f, layers) else: saving.load_weights_from_hdf5_group(f, layers) if hasattr(f, 'close'): f.close() # Update the log directory self.set_log_dir(filepath)