Keras模型转成tensorflow中.pb的方法-创新互联

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Keras的.h6模型转成tensorflow的.pb格式模型，方便后期的前端部署。直接上代码

from keras.models import Model
from keras.layers import Dense, Dropout
from keras.applications.mobilenet import MobileNet
from keras.applications.mobilenet import preprocess_input
from keras.preprocessing.image import load_img, img_to_array
import tensorflow as tf
from keras import backend as K
import os
 
base_model = MobileNet((None, None, 3), alpha=1, include_top=False, pooling='avg', weights=None)
x = Dropout(0.75)(base_model.output)
x = Dense(10, activation='softmax')(x)
 
model = Model(base_model.input, x)
model.load_weights('mobilenet_weights.h6')
 
def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
 from tensorflow.python.framework.graph_util import convert_variables_to_constants
 graph = session.graph
 with graph.as_default():
  freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
  output_names = output_names or []
  output_names += [v.op.name for v in tf.global_variables()]
  input_graph_def = graph.as_graph_def()
  if clear_devices:
   for node in input_graph_def.node:
    node.device = ""
  frozen_graph = convert_variables_to_constants(session, input_graph_def,
             output_names, freeze_var_names)
  return frozen_graph
 
output_graph_name = 'NIMA.pb'
output_fld = ''
#K.set_learning_phase(0)
 
print('input is :', model.input.name)
print ('output is:', model.output.name)
 
sess = K.get_session()
frozen_graph = freeze_session(K.get_session(), output_names=[model.output.op.name])
 
from tensorflow.python.framework import graph_io
graph_io.write_graph(frozen_graph, output_fld, output_graph_name, as_text=False)
print('saved the constant graph (ready for inference) at: ', os.path.join(output_fld, output_graph_name))

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