I used a simple
Keras example as a starting point.
Develop Your First Neural Network in Python With Keras
Step-By-Step
I took the first half of this example:
# MLP for
Pima Indians Dataset Serialize to JSON and HDF5
from
keras.models import Sequential
from
keras.layers import Dense
from
keras.models import model_from_json
import numpy
import os
# fix random
seed for reproducibility
numpy.random.seed(7)
# 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, kernel_initializer='uniform', activation='relu'))
model.add(Dense(8,
kernel_initializer='uniform', activation='relu'))
model.add(Dense(1,
kernel_initializer='uniform', activation='sigmoid'))
# Compile
model
model.compile(loss='binary_crossentropy',
optimizer='adam', metrics=['accuracy'])
# Fit the
model
model.fit(X,
Y, epochs=150, batch_size=10, verbose=0)
x=numpy.array([[0.42030057,
0.78084083, 0.76165254, 0.19794683, 0.78010274, 0.24512312, 0.17131911,
0.03071891]])
print(x)
yy=model.predict(x,
batch_size=None, verbose=0, steps=None)
print(yy)
and I added prediction stuff:
x=numpy.array([[0.42030057,
0.78084083, 0.76165254, 0.19794683, 0.78010274, 0.24512312, 0.17131911,
0.03071891]])
print(x)
yy=model.predict(x,
batch_size=None, verbose=0, steps=None)
print(yy)
the output follows:
runfile('C:/Users/ars/.spyder-py3/ars-test/keras_first_network.py',
wdir='C:/Users/ars/.spyder-py3/ars-test')
768/768
[==============================] - 0s 13us/step
acc: 79.30%
[[0.42030057
0.78084083 0.76165254 0.19794683 0.78010274 0.24512312
0.17131911 0.03071891]]
[[0.00283898]]
This was all done with Anaconda using Keras. I put this to make a comparison later on.
pimasARS.py is the same as the Keras version.
# MLP for
Pima Indians Dataset Serialize to JSON and HDF5
from
keras.models import Sequential
from
keras.layers import Dense
from
keras.models import model_from_json
import numpy
import os
# fix random
seed for reproducibility
numpy.random.seed(7)
# 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, kernel_initializer='uniform', activation='relu'))
model.add(Dense(8,
kernel_initializer='uniform', activation='relu'))
model.add(Dense(1,
kernel_initializer='uniform', activation='sigmoid'))
# Compile
model
model.compile(loss='binary_crossentropy',
optimizer='adam', metrics=['accuracy'])
# Fit the
model
model.fit(X,
Y, epochs=150, batch_size=10, verbose=0)
# evaluate
the model
scores =
model.evaluate(X, Y, verbose=0)
print("%s:
%.2f%%" % (model.metrics_names[1], scores[1]*100))
but it saves the model to Keras model files JSON and HDF5.
# serialize
model to JSON
model_json =
model.to_json()
with
open("model.json", "w") as json_file:
json_file.write(model_json)
# serialize
weights to HDF5
model.save_weights("model.h5")
print("Saved
model to disk")
Movidius needs to work with TensorFlow graph files.
So I used
Convert-pimas file from
How to run Keras model on Movidius neural compute stick
convert-pimas.py
from
keras.models import model_from_json
from keras
import backend as K
import
tensorflow as tf
from
keras.models import model_from_json
from keras
import backend as K
import
tensorflow as tf
model_file =
"model.json"
weights_file
= "model.h5"
with
open(model_file, "r") as file:
config = file.read()
K.set_learning_phase(0)
model =
model_from_json(config)
model.load_weights(weights_file)
saver =
tf.train.Saver()
sess =
K.get_session()
saver.save(sess,
"./TF_Model/tf_model")
fw =
tf.summary.FileWriter('logs', sess.graph)
fw.close()
model_file =
"model.json"
weights_file
= "model.h5"
with
open(model_file, "r") as file:
config = file.read()
K.set_learning_phase(0)
model =
model_from_json(config)
model.load_weights(weights_file)
saver =
tf.train.Saver()
sess =
K.get_session()
saver.save(sess,
"./TF_Model/tf_model")
fw =
tf.summary.FileWriter('logs', sess.graph)
fw.close()
if you notice, this program does not produce a TensorFlow
graph file. This job
is done with the statement below
which you can find in the Makefile.
.PHONY:
compile
compile:
weights
test -f graph || ${NCCOMPILE} -s
12 ${MODEL_FILENAME} ${INPUT_NODE_FLAG} ${OUTPUT_NODE_FLAG}
mvNCCompile -s 12 TF_Model/tf_model.meta -in=dense_1_input
-on=dense_3/Sigmoid
-in and –on parameters can be found in the logs/events.out.tfevents.1549033801.ars
files
After this we have to prepare the Movidius stick, open, load
etc.
İn the predict-pimas.py.
#!/usr/bin/env
python3.5
# [NCSDK2
API](https://movidius.github.io/ncsdk/ncapi/ncapi2/py_api/readme.html)
from mvnc
import mvncapi as mvnc
from keras
import backend as K
import numpy
import cv2
# Using NCS
Predict
# set the
logging level for the NC API
#
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 0)
# get a list
of names for all the devices plugged into the system
devices =
mvnc.enumerate_devices()
if
len(devices) == 0:
print('No devices found')
quit()
# get the
first NCS device by its name. For this
program we will always open the first NCS device.
dev =
mvnc.Device(devices[0])
# try to
open the device. this will throw an
exception if someone else has it open already
try:
dev.open()
except:
print("Error - Could not open NCS
device.")
quit()
Once Movidius stick is ready we have to load the graph to it
# Read a
compiled network graph from file (set the graph_filepath correctly for your
graph file)
with
open("graph", mode='rb') as f:
graphFileBuff = f.read()
graph =
mvnc.Graph('graph1')
then we have to put in the test data (the same as the complete
Keras version)
# Allocate the
graph on the device and create input and output Fifos
in_fifo,
out_fifo = graph.allocate_with_fifos(dev, graphFileBuff)
testInput=numpy.array([[0.42030057,
0.78084083, 0.76165254, 0.19794683, 0.78010274, 0.24512312, 0.17131911,
0.03071891]])
# Write the
input to the input_fifo buffer and queue an inference in one call
#graph.queue_inference_with_fifo_elem(in_fifo,
out_fifo, testInput.astype('float32'), 'user object')
graph.queue_inference_with_fifo_elem(in_fifo,
out_fifo, testInput.astype('float32'),'object1')
read the output and print
# Read the
result to the output Fifo
output,
userobj = out_fifo.read_elem()
# Deallocate
and destroy the fifo and graph handles, close the device, and destroy the
device handle
try:
in_fifo.destroy()
out_fifo.destroy()
graph.destroy()
dev.close()
dev.destroy()
except:
print("Error - could not close/destroy
Graph/NCS device.")
quit()
#print("NCS
\r\n", output, '\r\nPredicted:',output.argmax())
print(output)
The output :
arsaral@ars:~/ncsdk/ncappzoo/apps/pimas$
python3 predict-pimas.py
Using
TensorFlow backend.
/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py:936:
DeprecationWarning: builtin type EagerTensor has no __module__ attribute
EagerTensor = c_api.TFE_Py_InitEagerTensor(_EagerTensorBase)
/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/tf_inspect.py:75:
DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature()
instead
return _inspect.getargspec(target)
/usr/local/lib/python3.5/dist-packages/mvnc/mvncapi.py:416:
DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves
surprisingly on unicode inputs. Use frombuffer instead
tensor = numpy.fromstring(tensor.raw,
dtype=numpy.float32)
[0.00512314]
arsaral@ars:~/ncsdk/ncappzoo/apps/pimas$
A Keras only test output: [0.00283898]
A Movidius test output:
[0.00512314]
