import tensorflow.compat.v1 as tf1
 
tf1.reset_default_graph()
tf1.keras.backend.set_learning_phase(0)  # 调用模型前一定要执行该命令
tf1.disable_v2_behavior()  # 禁止tensorflow2.0的行为

hdf5_pb_model = tf1.keras.models.load_model('mnist_test_cnn.h5')
def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
    graph = session.graph
    with graph.as_default():
        #         freeze_var_names = list(set(v.op.name for v in tf1.global_variables()).difference(keep_var_names or []))
        output_names = output_names or []
        #         output_names += [v.op.name for v in tf1.global_variables()]
        print("output_names", output_names)
        input_graph_def = graph.as_graph_def()
        #         for node in input_graph_def.node:
        #             print('node:', node.name)
        print("len node1", len(input_graph_def.node))
        if clear_devices:
            for node in input_graph_def.node:
                node.device = ""
        frozen_graph = tf1.graph_util.convert_variables_to_constants(session, input_graph_def,
                                                                     output_names)
 
        outgraph = tf1.graph_util.remove_training_nodes(frozen_graph)  # 云掉与推理无关的内容
        print("##################################################################")
        for node in outgraph.node:
            print('node:', node.name)
        print("len node1", len(outgraph.node))
        return outgraph

output_folder2 = 'keras_model'
 
frozen_graph = freeze_session(tf1.compat.v1.keras.backend.get_session(),
                              output_names=[out.op.name for out in hdf5_pb_model.outputs])
tf1.train.write_graph(frozen_graph, output_folder2, "classify.pb", as_text=False)