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# 概要

raspberry pi 1でtensorflow liteやってみた。
tfliteファイルを作ってみた。
SavedModelから作ってみた。
データセットは、xor.

tensorflow 1.12

# モデルを学習してSavedModelを作る。

import tensorflow as tf
import tensorflow.contrib.lite as lite
import numpy as np
from tensorflow.python.framework import graph_util

X = [[0, 0], [0, 1], [1, 0], [1, 1]]
Y = [[1, 0], [0, 1], [0, 1], [1, 0]]
x = tf.placeholder(tf.float32, shape = [None, 2], name = "input")
y = tf.placeholder(tf.float32, shape = [None, 2])
w1 = tf.Variable(tf.random_uniform([2, 2], -1, 1, seed = 0))
w2 = tf.Variable(tf.random_uniform([2, 2], -1, 1, seed = 0))
b1 = tf.Variable(tf.zeros([2]))
b2 = tf.Variable(tf.zeros([2]))
h1 = tf.sigmoid(tf.matmul(x, w1) + b1)
h2 = tf.nn.softmax(tf.matmul(h1, w2) + b2, name = "output")
cost = -tf.reduce_sum(y * tf.log(h2))
graph = tf.get_default_graph()
graph_def = graph.as_graph_def()
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for i in range(10000):
sess.run(opti, feed_dict = {
x: X,
y: Y
})
for i in [[1, 1], [1, 0], [0, 1], [0, 0]]:
print (i, sess.run(h2, feed_dict = {
x: [i],
}))
signature_def = tf.saved_model.signature_def_utils.build_signature_def({
"input": tf.saved_model.utils.build_tensor_info(x),
}, {
"output": tf.saved_model.utils.build_tensor_info(h2)
}, tf.saved_model.signature_constants.REGRESS_METHOD_NAME)
builder = tf.saved_model.builder.SavedModelBuilder("./model")
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature_def
}, assets_collection = tf.get_collection(tf.GraphKeys.ASSET_FILEPATHS))
builder.save()

# SavedModelからtfliteを作る。

import tensorflow as tf
import tensorflow.contrib.lite as lite

converter = lite.TFLiteConverter.from_saved_model("./model")
tflite_model = converter.convert()
open("xor4_model.tflite", "wb").write(tflite_model)

# tfliteファイルを検証する。

import numpy as np
import tensorflow as tf
import tensorflow.contrib.lite as lite

interpreter = lite.Interpreter(model_path = "xor4_model.tflite")
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
print (input_details)
print (output_details)
input_shape = input_details[0]['shape']
input_data = np.array([[0.0, 0.0]], dtype = np.float32)
print(input_data)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
print (output_data)
input_data = np.array([[1.0, 0.0]], dtype = np.float32)
print(input_data)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
print (output_data)
input_data = np.array([[0.0, 1.0]], dtype = np.float32)
print(input_data)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
print (output_data)
input_data = np.array([[1.0, 1.0]], dtype = np.float32)
print(input_data)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
print (output_data)

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