含有Lambda自定义层keras模型，保存遇到的问题及解决方案

一，许多应用，keras含有的层已经不能满足要求，需要透过Lambda自定义层来实现一些layer，这个情况下，只能保存模型的权重，无法使用model.save来保存模型。保存时会报
TypeError: can’t pickle _thread.RLock objects

二，解决方案，为了便于后续的部署，可以转成tensorflow的PB进行部署。

from keras.models import load_model
import tensorflow as tf
import os, sys
from keras import backend as K
from tensorflow.python.framework import graph_util, graph_io

def h5_to_pb(h5_weight_path, output_dir, out_prefix="output_", log_tensorboard=True):
    if not os.path.exists(output_dir):
        os.mkdir(output_dir)
    h5_model = build_model()
    h5_model.load_weights(h5_weight_path)
    out_nodes = []
    for i in range(len(h5_model.outputs)):
        out_nodes.append(out_prefix + str(i + 1))
        tf.identity(h5_model.output[i], out_prefix + str(i + 1))
    model_name = os.path.splitext(os.path.split(h5_weight_path)[-1])[0] + '.pb'
    sess = K.get_session()
    init_graph = sess.graph.as_graph_def()
    main_graph = graph_util.convert_variables_to_constants(sess, init_graph, out_nodes)
    graph_io.write_graph(main_graph, output_dir, name=model_name, as_text=False)
    if log_tensorboard:
        from tensorflow.python.tools import import_pb_to_tensorboard
        import_pb_to_tensorboard.import_to_tensorboard(os.path.join(output_dir, model_name), output_dir)

def build_model():
    inputs = Input(shape=(784,), name='input_img')
    x = Dense(64, activation='relu')(inputs)
    x = Dense(64, activation='relu')(x)
    y = Dense(10, activation='softmax')(x)
    h5_model = Model(inputs=inputs, outputs=y)
    return h5_model

if __name__ == '__main__':
    if len(sys.argv) == 3:
        # usage: python3 h5_to_pb.py h5_weight_path output_dir
        h5_to_pb(h5_weight_path=sys.argv[1], output_dir=sys.argv[2])