1 keras模型匯出成tf模型

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

pred_node_names 為keras模型輸出的名字，可用model.output.name拿到
graph_io.write_graph(constant_graph, output_fld
 
, output_graph_name, as_text=False)

函式定義：

write_graph(

graph_or_graph_def,

logdir,

name,

as_text=True

)

函式returns：the path of the output proto file

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

注意：

調整keras的learning_phase為0,表示為測試模式

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)

由此發現write_graph中model是由輸出決定的，即不同的輸出對應不同的model

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

2 pb模型匯出

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")

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

#和模型的輸出名字有關，output是模型的輸出tensor

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

        print "y_conv_2", y_conv_2