hsmnzaydn

sudo pip install tensorflow
sudo pip install numpy
sudo pip install sklearn
sudo pip install keras

hsmnzaydn

seed = 7
numpy.random.seed(seed)
dataset = numpy.loadtxt("./pima-indians-diabetes.data", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
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'))

hsmnzaydn

from tensorflow.python.tools import freeze_graph, optimize_for_inference_lib
from keras import backend as K
from sklearn.cross_validation import train_test_split
from keras.wrappers.scikit_learn import KerasRegressor
import tensorflow as tf

hsmnzaydn

def export_model(saver, model, input_node_names, output_node_name,MODEL_NAME):
    tf.train.write_graph(K.get_session().graph_def, 'out', \
                         MODEL_NAME + '_graph.pbtxt')
    saver.save(K.get_session(), 'out/' + MODEL_NAME + '.chkp')
    freeze_graph.freeze_graph('out/' + MODEL_NAME + '_graph.pbtxt', None, \
                              False, 'out/' + MODEL_NAME + '.chkp', output_node_name, \
                              "save/restore_all", "save/Const:0", \
                              'out/modelim' + MODEL_NAME + '.pb', True, "")
    input_graph_def = tf.GraphDef()
    with tf.gfile.Open('out/modelim' + MODEL_NAME + '.pb', "rb") as f:

hsmnzaydn

X_train, X_test, y_train, y_test = train_test_split(X, Y)
estimators = []
estimator = KerasRegressor(build_fn=model, epochs=20, batch_size=50, verbose=2)
export_model(tf.train.Saver(), estimator, ["input name"], "output name","test")

hsmnzaydn

export_model(tf.train.Saver(), estimator, [inputName], outPutName,"test")

hsmnzaydn

inputName=model.input.name[:-2]
outPutName=model.output.name[:-2]

hsmnzaydn

from keras.models import Sequential
from keras.layers import Dense
import numpy

hsmnzaydn

predict=numpy.array([1,89,66,23,94,28.1,0.167,21]).reshape(1,8)
print(model.predict_classes(predict))

hsmnzaydn

bazel-bin/tensorflow/contrib/lite/toco/toco \
--input_file=modelimtest.pb \
--input_format=TENSORFLOW_GRAPHDEF \
--output_format=TFLITE \
--output_file=modelim.lite \
--inference_type=FLOAT \
--input_type=FLOAT \
--input_arrays=dense_1_input \
--output_arrays=dense_3/Sigmoid \
--input_shapes=1,8