python - Tensorflow 中的冻结图

Question

我已经训练了一个神经网络，并从中生成了一个包含 、 等文件的模型文件.ckpt夹.index。以下是我用来生成.pb文件的代码；但是，我收到以下错误：

AttributeError：“NoneType”对象没有属性“model_checkpoint_path”

有没有人帮助我解决问题？

dir = os.path.dirname(os.path.realpath(__file__))

def freeze_graph(model_dir, output_node_names):
"""Extract the sub graph defined by the output nodes and convert 
all its variables into constant 
Args:
    model_dir: the root folder containing the checkpoint state file
    output_node_names: a string, containing all the output node's names, 
                        comma separated
"""
if not tf.gfile.Exists(model_dir):
    raise AssertionError(
        "Export directory doesn't exists. Please specify an export "
        "directory: %s" % model_dir)

if not output_node_names:
    print("You need to supply the name of a node to --output_node_names.")
    return -1

# We retrieve our checkpoint fullpath
checkpoint = tf.train.get_checkpoint_state(model_dir)
input_checkpoint = checkpoint.model_checkpoint_path

# We precise the file fullname of our freezed graph
absolute_model_dir = "/".join(input_checkpoint.split('/')[:-1])
output_graph = absolute_model_dir + "/frozen_model.pb"

# We clear devices to allow TensorFlow to control on which device it will load operations
clear_devices = True

# We start a session using a temporary fresh Graph
with tf.Session(graph=tf.Graph()) as sess:
    # We import the meta graph in the current default Graph
    saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=clear_devices)

    # We restore the weights
    saver.restore(sess, input_checkpoint)

    # We use a built-in TF helper to export variables to constants
    output_graph_def = tf.graph_util.convert_variables_to_constants(
        sess, # The session is used to retrieve the weights
        tf.get_default_graph().as_graph_def(), # The graph_def is used to retrieve the nodes 
        output_node_names.split(",") # The output node names are used to select the usefull nodes
    ) 

    # Finally we serialize and dump the output graph to the filesystem
    with tf.gfile.GFile(output_graph, "wb") as f:
        f.write(output_graph_def.SerializeToString())
    print("%d ops in the final graph." % len(output_graph_def.node))

return output_graph_def

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_dir", type=str, default="", help="Model folder to export")
    parser.add_argument("--output_node_names", type=str, default="", help="The name of the output nodes, comma separated.")
   args = parser.parse_args()

   freeze_graph(args.model_dir, args.output_node_names)

标签： pythontensorflowneural-network