keras模型导出到tensorflow，以及pb模型导出

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1 keras模型导出成tf模型

https://github.com/amir-abdi/keras_to_tensorflow/blob/master/keras_to_tensorflow.ipynb

constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), pred_node_names)

pred_node_names 为keras模型输出的名字，可用model.output.name拿到
graph_io.write_graph(constant_graph, output_fld, output_graph_name, as_text=False)

函数定义：

write_graph(

graph_or_graph_def,

logdir,

name,

as_text=True

)

函数returns：the path of the output proto file

The graph is written as a binary proto unless as_text is True

注意：

调整keras的learning_phase为0,表示为测试模式

from keras import backend as K

K.set_learning_phase(0)  # all new operations will be in test mode from now on# serialize the model and get its weights, for quick re-buildingconfig = previous_model.get_config()weights = previous_model.get_weights()# re-build a model where the learning phase is now hard-coded to 0

from keras.models import model_from_config

new_model = model_from_config(config)

new_model.set_weights(weights)

由此发现write_graph中model是由输出决定的，即不同的输出对应不同的model

graph_def = g_2.as_graph_def()
tf.train.write_graph(graph_def, export_dir, 'expert-graph.pb', as_text=False)

猜测：此种只保留了图的结构

2 pb模型导出

with open(output_graph_path, "rb") as f:

        output_graph_def = tf.GraphDef()
        output_graph_def.ParseFromString(f.read())
        _ = tf.import_graph_def(output_graph_def, name="")

with tf.Session() as sess:

        tf.initialize_all_variables().run()
        input_x = sess.graph.get_tensor_by_name("input:0")

#和模型的输入名字有关，input是模型的输入tensor
        print input_x
        output = sess.graph.get_tensor_by_name("output:0")
        print output

#和模型的输出名字有关，output是模型的输出tensor

        y_conv_2 = sess.run(output,{input_x:mnist.test.images})

        print "y_conv_2", y_conv_2