Python saving.load_weights_from_hdf5_group方法代碼示例

# 需要導入模塊: from keras.engine import saving [as 別名]
# 或者: from keras.engine.saving import load_weights_from_hdf5_group [as 別名]
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) 

# 需要導入模塊: from keras.engine import saving [as 別名]
# 或者: from keras.engine.saving import load_weights_from_hdf5_group [as 別名]
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() 

# 需要導入模塊: from keras.engine import saving [as 別名]
# 或者: from keras.engine.saving import load_weights_from_hdf5_group [as 別名]
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) 

# 需要導入模塊: from keras.engine import saving [as 別名]
# 或者: from keras.engine.saving import load_weights_from_hdf5_group [as 別名]
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) 

# 需要導入模塊: from keras.engine import saving [as 別名]
# 或者: from keras.engine.saving import load_weights_from_hdf5_group [as 別名]
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) 

# 需要導入模塊: from keras.engine import saving [as 別名]
# 或者: from keras.engine.saving import load_weights_from_hdf5_group [as 別名]
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)