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Hello I faced with the problem when trying to use Intel Movidius Neural Stick with tensorflow. I have keras model and I convert it to tensorflow model. When I convert it to Movidius graph I got error:

Traceback (most recent call last):
File "/usr/local/bin/mvNCCompile", line 118, in
create_graph(args.network, args.inputnode, args.outputnode, args.outfile, args.nshaves, args.inputsize, args.weights)
File "/usr/local/bin/mvNCCompile", line 104, in create_graph
net = parse_tensor(args, myriad_config)
File "/usr/local/bin/ncsdk/Controllers/TensorFlowParser.py", line 290, in parse_tensor
if have_first_input(strip_tensor_id(node.outputs[0].name)):
IndexError: list index out of range

Here is my code:

from keras.models import model_from_json
from keras.models import load_model
from keras import backend as K
import tensorflow as tf
import nn
import os

weights_file = "weights.h5"

sess = K.get_session()
K.set_learning_phase(0)
model = nn.alexnet_model() # get keras model
model.load_weights(weights_file)

saver = tf.train.Saver()
saver.save(sess, "./TF_Model/tf_model") # convert keras to tensorflow model

tf_model_path = "./TF_Model/tf_model"

fw = tf.summary.FileWriter('logs', sess.graph)
fw.close()

os.system('mvNCCompile TF_Model/tf_model.meta -in=conv2d_1_input -on=activation_7/Softmax') # get Movidius graph

Python version: 2.7
OS: Ubuntu 16.04
Tensorflow version: 1.12

As I know, the ncsdk compiler does not resolve every part of a normal tensorflow network, so you have to modify the network and re-save it in an NCS-friendly way in order to successfully make a Movidius graph.

For more information about how to modify tensorflow network, have a look at the official guidance.

Hope it will help you.