python - Using two different models in tensorflow -


im trying use 2 different mobilenet models. following code of how initialize model.

def initialsetup():     os.environ['tf_cpp_min_log_level'] = '2'     start_time = timeit.default_timer()      # takes 2-5 seconds run     # unpersists graph file     tf.gfile.fastgfile('age/output_graph.pb', 'rb') f:         age_graph_def = tf.graphdef()         age_graph_def.parsefromstring(f.read())         tf.import_graph_def(age_graph_def, name='')      tf.gfile.fastgfile('output_graph.pb', 'rb') f:         gender_graph_def = tf.graphdef()         gender_graph_def.parsefromstring(f.read())         tf.import_graph_def(gender_graph_def, name='')      print ('took {} seconds unpersist graph'.format(timeit.default_timer() - start_time))

since both 2 different models, how use predictions?

update

initialsetup()  age_session = tf.session(graph=age_graph_def) gender_session = tf.session(graph=gender_graph_def)  tf.session() sess:     start_time = timeit.default_timer()      # feed image_data input graph , first prediction     softmax_tensor = age_session.graph.get_tensor_by_name('final_result:0')      print ('took {} seconds feed data graph'.format(timeit.default_timer() - start_time))      while true:         # capture frame-by-frame         ret, frame = video_capture.read()

error

traceback (most recent call last): file "c:/users/desktop/untitled/testimg/testimg/combo.py", line 48, in age_session = tf.session(graph=age_graph_def) file "c:\program files\anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1292, in init super(session, self).init(target, graph, config=config) file "c:\program files\anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 529, in init raise typeerror('graph must tf.graph, got %s' % type(graph)) typeerror: graph must tf.graph, got exception ignored in: > traceback (most recent call last): file "c:\program files\anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 587, in del if self._session not none: attributeerror: 'session' object has no attribute '_session'

when working multiple models in same graph, use name scoping give individual tensors predictable names. example, rewrite initial_setup() follows:

def initialsetup():     os.environ['tf_cpp_min_log_level'] = '2'     start_time = timeit.default_timer()      # takes 2-5 seconds run     # unpersists graph file     tf.gfile.fastgfile('age/output_graph.pb', 'rb') f:         age_graph_def = tf.graphdef()         age_graph_def.parsefromstring(f.read())         tf.import_graph_def(age_graph_def, name='age_model')      tf.gfile.fastgfile('output_graph.pb', 'rb') f:         gender_graph_def = tf.graphdef()         gender_graph_def.parsefromstring(f.read())         tf.import_graph_def(gender_graph_def, name='gender_model')      print ('took {} seconds unpersist graph'.format(timeit.default_timer() - start_time))

now names of of nodes age_graph_def prefixed "age_model/" , names of of nodes gender_graph_def prefixed "gender_model/". part of same default graph, can use single tf.session no graph argument access either model.

initialsetup()  tf.session() sess:     start_time = timeit.default_timer()      # feed image_data input graph , first prediction     softmax_tensor = sess.graph.get_tensor_by_name('age_model/final_result:0')      # alternatively, tensor gender model:     # tensor = sess.graph.get_tensor_by_name('gender_model/...')      print ('took {} seconds feed data graph'.format(timeit.default_timer() - start_time))      while true:         # capture frame-by-frame         ret, frame = video_capture.read()

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