def restore_from_checkpoint(self, session, inception_checkpoint_file,
                              trained_checkpoint_file):
    """To restore model variables from the checkpoint file.

       The graph is assumed to consist of an inception model and other
       layers including a softmax and a fully connected layer. The former is
       pre-trained and the latter is trained using the pre-processed data. So
       we restore this from two checkpoint files.
    Args:
      session: The session to be used for restoring from checkpoint.
      inception_checkpoint_file: Path to the checkpoint file for the Inception
                                 graph.
      trained_checkpoint_file: path to the trained checkpoint for the other
                               layers.
    """
    inception_exclude_scopes = [
        'InceptionV3/AuxLogits', 'InceptionV3/Logits', 'global_step',
        'final_ops'
    ]
    reader = tf.train.NewCheckpointReader(inception_checkpoint_file)
    var_to_shape_map = reader.get_variable_to_shape_map()

    # Get all variables to restore. Exclude Logits and AuxLogits because they
    # depend on the input data and we do not need to intialize them.
    all_vars = tf.contrib.slim.get_variables_to_restore(
        exclude=inception_exclude_scopes)
    # Remove variables that do not exist in the inception checkpoint (for
    # example the final softmax and fully-connected layers).
    inception_vars = {
        var.op.name: var
        for var in all_vars if var.op.name in var_to_shape_map
    }
    inception_saver = tf.train.Saver(inception_vars)
    inception_saver.restore(session, inception_checkpoint_file)

    # Restore the rest of the variables from the trained checkpoint.
    trained_vars = tf.contrib.slim.get_variables_to_restore(
        exclude=inception_exclude_scopes + inception_vars.keys())
    trained_saver = tf.train.Saver(trained_vars)
    trained_saver.restore(session, trained_checkpoint_file)
def restore_from_checkpoint ( self , session , inception_checkpoint_file , trained_checkpoint_file ) : inception_exclude_scopes = [ 'InceptionV3/AuxLogits' , 'InceptionV3/Logits' , 'global_step' , 'final_ops' ] reader = tf . train . NewCheckpointReader ( inception_checkpoint_file ) var_to_shape_map = reader . get_variable_to_shape_map ( ) all_vars = tf . contrib . slim . get_variables_to_restore ( exclude = inception_exclude_scopes ) inception_vars = { var . op . name : var for var in all_vars if var . op . name in var_to_shape_map } inception_saver = tf . train . Saver ( inception_vars ) inception_saver . restore ( session , inception_checkpoint_file ) trained_vars = tf . contrib . slim . get_variables_to_restore ( exclude = inception_exclude_scopes + inception_vars . keys ( ) ) trained_saver = tf . train . Saver ( trained_vars ) trained_saver . restore ( session , trained_checkpoint_file )
To restore model variables from the checkpoint file.

       The graph is assumed to consist of an inception model and other
       layers including a softmax and a fully connected layer. The former is
       pre-trained and the latter is trained using the pre-processed data. So
       we restore this from two checkpoint files.
    Args:
      session: The session to be used for restoring from checkpoint.
      inception_checkpoint_file: Path to the checkpoint file for the Inception
                                 graph.
      trained_checkpoint_file: path to the trained checkpoint for the other
                               layers.