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Python topology.load_weights_from_hdf5_group方法代码示例

本文整理汇总了Python中keras.engine.topology.load_weights_from_hdf5_group方法的典型用法代码示例。如果您正苦于以下问题:Python topology.load_weights_from_hdf5_group方法的具体用法?Python topology.load_weights_from_hdf5_group怎么用?Python topology.load_weights_from_hdf5_group使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在keras.engine.topology的用法示例。


在下文中一共展示了topology.load_weights_from_hdf5_group方法的7个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: load_weights

# 需要导入模块: from keras.engine import topology [as 别名]
# 或者: from keras.engine.topology import load_weights_from_hdf5_group [as 别名]
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 topology

        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:
            topology.load_weights_from_hdf5_group_by_name(f, layers)
        else:
            topology.load_weights_from_hdf5_group(f, layers)
        if hasattr(f, 'close'):
            f.close()

        # Update the log directory
        self.set_log_dir(filepath) 
开发者ID:SunskyF,项目名称:EasyPR-python,代码行数:39,代码来源:model.py

示例2: load_weights

# 需要导入模块: from keras.engine import topology [as 别名]
# 或者: from keras.engine.topology import load_weights_from_hdf5_group [as 别名]
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 topology

        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:
            topology.load_weights_from_hdf5_group_by_name(f, layers)
        else:
            topology.load_weights_from_hdf5_group(f, layers)
        if hasattr(f, 'close'):
            f.close()

        # Update the log directory
        self.set_log_dir(filepath) 
开发者ID:olgaliak,项目名称:segmentation-unet-maskrcnn,代码行数:39,代码来源:model.py

示例3: load_weights

# 需要导入模块: from keras.engine import topology [as 别名]
# 或者: from keras.engine.topology import load_weights_from_hdf5_group [as 别名]
def load_weights(self, filepath, by_name=False, exclude=None, verbose=1):
        """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 topology
        
        if verbose==1:
            self.logger.log('loading weights form {}'.format(filepath))
        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:
            topology.load_weights_from_hdf5_group_by_name(f, layers)
        else:
            topology.load_weights_from_hdf5_group(f, layers)
        if hasattr(f, 'close'):
            f.close() 
开发者ID:jacobkie,项目名称:2018DSB,代码行数:38,代码来源:model_rcnn_weight.py

示例4: load_weights

# 需要导入模块: from keras.engine import topology [as 别名]
# 或者: from keras.engine.topology import load_weights_from_hdf5_group [as 别名]
def load_weights(filepath, model):
    if h5py is None:
        raise ImportError('`load_weights` requires h5py.')

    with h5py.File(filepath, mode='r') as f:
        # set weights
        topology.load_weights_from_hdf5_group(f['model_weights'], model.layers)

    return model 
开发者ID:jhartford,项目名称:DeepIV,代码行数:11,代码来源:models.py

示例5: load_weights

# 需要导入模块: from keras.engine import topology [as 别名]
# 或者: from keras.engine.topology import load_weights_from_hdf5_group [as 别名]
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) 
开发者ID:dataiku,项目名称:dataiku-contrib,代码行数:45,代码来源:model.py

示例6: load_weights

# 需要导入模块: from keras.engine import topology [as 别名]
# 或者: from keras.engine.topology import load_weights_from_hdf5_group [as 别名]
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) 
开发者ID:Esri,项目名称:raster-deep-learning,代码行数:46,代码来源:model.py

示例7: load_weights

# 需要导入模块: from keras.engine import topology [as 别名]
# 或者: from keras.engine.topology import load_weights_from_hdf5_group [as 别名]
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) 
开发者ID:parap1uie-s,项目名称:Keras-RFCN,代码行数:44,代码来源:BaseModel.py


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