Hi!
I've build a tensorflow model consisting of two convolutional and two fully connected layers. Then I initialize it with random variables, evaluate it on a np.ones vector and save the model.
input = tf.placeholder("float16", [1, 1604,9,1], name="x")
output = fc_gest(conv(input)) #output from my model
print(output)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
inp = np.ones((1,1604,9,1),dtype ="float16")
print('eval: ',np.float16(output.eval({input:inp})))
saver.save(sess, "./saved_sess/model.ckpts")
this code gives me ''add_3' as the name of my output vector and [[ 3.90820312 5.2890625 6.06640625 3.36328125 -4.046875
-6.26171875 10.546875 ]] a result of passing np.ones to my model.
I then generate a graph file with the command mvNCCompile model.ckpts.meta -in=x -on=add_3
After I get the graph file I load it to the stick and pass the same np.ones vector:
print("allocating graph")
graph = device.AllocateGraph(graphfile)
print("loading tensor")
inp = np.ones(([1,1604,9,1]),dtype ="float16")
graph.LoadTensor(inp, 'x')
print("computing result")
output, userobj = graph.GetResult()
print(np.float16(output))
print(output.shape)
the output of this is
allocating graph
loading tensor
computing result
[ nan nan nan nan nan nan nan]
(7,)
This time it's just nan's. If I make the model smaller it outputs numbers but they still don't match the TF output.
WHY?
