pyspark/bigdl/util/tf_utils.py
def export_checkpoint(checkpoint_path):
    """
    Export variable tensors from the checkpoint files.

    :param checkpoint_path: tensorflow checkpoint path
    :return: dictionary of tensor. The key is the variable name and the value is the numpy
    """
    reader = tf.train.NewCheckpointReader(checkpoint_path)

    # Get tensor name list
    tensor_names = filter(lambda n: n!='global_step',
                          reader.get_variable_to_shape_map().keys())
    # Prepare key-value dictionary
    tensors = {}
    for tn in tensor_names:
        tensors[tn] = reader.get_tensor(tn)

    return tensors
def export_checkpoint(checkpoint_path):
    reader = tf.train.NewCheckpointReader(checkpoint_path)

    # Get tensor name list
    tensor_names = filter(lambda n: n!='global_step',
                          reader.get_variable_to_shape_map().keys())
    # Prepare key-value dictionary
    tensors = {}
    for tn in tensor_names:
        tensors[tn] = reader.get_tensor(tn)

    return tensors
def export_checkpoint ( checkpoint_path ) : reader = tf . train . NewCheckpointReader ( checkpoint_path ) # Get tensor name list tensor_names = filter ( lambda n : n != 'global_step' , reader . get_variable_to_shape_map ( ) . keys ( ) ) # Prepare key-value dictionary tensors = { } for tn in tensor_names : tensors [ tn ] = reader . get_tensor ( tn ) return tensors
Export variable tensors from the checkpoint files.

    :param checkpoint_path: tensorflow checkpoint path
    :return: dictionary of tensor. The key is the variable name and the value is the numpy
Export variable tensors from the checkpoint files .