Я использую керасы с тензорным потоком. Я правильно экспортирую модель, но не могу создать прото-файлы.
Проблема в том, что я получаю сообщение об ошибке:
Файл "keras_indians.py", строка 103, в файле minimal_graph = convert_variables_to_constants (sessions, sessions.graph_def, ['Dense3 / output_node']) "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ framework / graph_util.py ", строка 234, в convert_variables_to_constants inference_graph = extract_sub_graph (input_graph_def, output_node_names) Файл" /usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/graph_util.py ", line 158 , в extract_sub_graph assert d в name_to_node_map, "% s не в графике"% d AssertionError: Dense3 / output_node не в графике
Я пробовал любые возможные варианты имени последнего узла, и это не работает. Любая помощь приветствуется!
Ниже приведен код:
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid', name='output_node'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, nb_epoch=80, batch_size=10)
# evaluate the model
scores = model.evaluate(X, Y)
print("RESULT IS %s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
## Exporting the model ##
print ("Exporting ")
# THIS WORKS !!
#from keras import backend as K
#sess = K.get_session()
#and go about exporting the model as in the tutorial (Note that import for exporter has changed)
#from tensorflow.contrib.session_bundle import exporter
#K.set_learning_phase(0)
#export_path = "./"
#export_version = 1
#saver = tf.train.Saver(sharded=True)
#model_exporter = exporter.Exporter(saver)
#signature = exporter.classification_signature(input_tensor=model.input,
# scores_tensor=model.output)
#model_exporter.init(sess.graph.as_graph_def(),
# default_graph_signature=signature)
#model_exporter.export(export_path, tf.constant(export_version), sess)
# END THIS WORKS!
#OPTION 2 FROM: https://blog.keras.io/keras-as-a-simplified-interface-to-tensorflow-tutorial.html
from keras import backend as K
K.set_learning_phase(0) # all new operations will be in test mode from now on
# added by nir
sess = K.get_session()
# serialize the model and get its weights, for quick re-building
config = model.get_config()
weights = 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)
#from tensorflow_serving.session_bundle import exporter
from tensorflow.contrib.session_bundle import exporter
export_path = "./" # where to save the exported graph
export_version = 18 # version number (integer)
saver = tf.train.Saver(sharded=True)
model_exporter = exporter.Exporter(saver)
signature = exporter.classification_signature(input_tensor=new_model.input,
scores_tensor=new_model.output)
model_exporter.init(sess.graph.as_graph_def(),
default_graph_signature=signature)
model_exporter.export(export_path, tf.constant(export_version), sess)
#print sess.graph.as_graph_def();
print ("Export Done")
## Create proto files for grpc ##
from tensorflow.python.framework.graph_util import convert_variables_to_constants
minimal_graph = convert_variables_to_constants(sess, sess.graph_def, ['Dense3/output_node'])
tf.train.write_graph(minimal_graph, '.', 'minimal_graph.proto', as_text=False)
tf.train.write_graph(minimal_graph, '.', 'minimal_graph.txt', as_text=True)
