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raspberry pi 1でtensorflow lite その10

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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。
kerasモデルから作ってみた。
データセットは、九九.

環境

tensorflow 1.12

kerasモデルを学習してセーブする。

import numpy as np
import tensorflow as tf
from tensorflow.contrib.keras.api.keras.models import Sequential
from tensorflow.contrib.keras.api.keras.layers import Dense, Dropout, Embedding, LSTM

def in_encode(i, j):
    k = j * 16 + i
    return np.array([k >> d & 1 for d in range(8)])

def out_encode(i, j):
    k = j * i
    return np.array([k >> d & 1 for d in range(7)])

def decode(p):
    f = 0
    if p[0] > 0.5:
        f += 1
    if p[1] > 0.5:
        f += 2
    if p[2] > 0.5:
        f += 4
    if p[3] > 0.5:
        f += 8
    if p[4] > 0.5:
        f += 16
    if p[5] > 0.5:
        f += 32
    if p[6] > 0.5:
        f += 64
    return f

trX = np.array([in_encode(i, j) for i in range(1, 10) for j in range(1, 10)])
trY = np.array([out_encode(i, j) for i in range(1, 10) for j in range(1, 10)])
model = Sequential()
model.add(Dense(40, activation = 'tanh', input_shape = (8, )))
model.add(Dense(40, activation = 'tanh'))
model.add(Dense(7, activation = 'linear'))
model.compile(loss = 'mean_squared_error', optimizer = 'adam')
model.fit(trX, trY, batch_size = 60, epochs = 1000, verbose = 1, validation_data = (trX, trY))
p = '    '
j = 1
for i in range(1, 10):
    p += '%3d ' % (i * j)
p += '\n'
for j in range(1, 10):
    p += '%3d ' % (j)
    for i in range(1, 10):
        x = np.array([in_encode(i, j)])
        pred = model.predict([x])
        k = decode(pred[0])
        p += '%3d ' % (k)
    p += '\n'
print (p)
model.save('kuku.h5')

kerasファイルからtfliteファイルを作る。

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

converter = lite.TFLiteConverter.from_keras_model_file("kuku.h5")
tflite_model = converter.convert()
open("kuku.tflite", "wb").write(tflite_model)
print ("ok")

tfliteファイルを検証する。

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

def in_encode(i, j):
    k = j * 16 + i
    return np.array([k >> d & 1 for d in range(8)])

def out_encode(i, j):
    k = j * i
    return np.array([k >> d & 1 for d in range(7)])

def decode(p):
    f = 0
    if p[0] > 0.5:
        f += 1
    if p[1] > 0.5:
        f += 2
    if p[2] > 0.5:
        f += 4
    if p[3] > 0.5:
        f += 8
    if p[4] > 0.5:
        f += 16
    if p[5] > 0.5:
        f += 32
    if p[6] > 0.5:
        f += 64
    return f

interpreter = lite.Interpreter(model_path = "kuku.tflite")
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
#print (input_details)
#print (output_details)
p = '    '
j = 1
for i in range(1, 10):
    p += '%3d ' % (i * j)
p += '\n'
for j in range(1, 10):
    p += '%3d ' % (j)
    for i in range(1, 10):
        x = np.array([in_encode(i, j)])
        input_data = np.array(x, dtype = np.float32)
        interpreter.set_tensor(input_details[0]['index'], input_data)
        interpreter.invoke()
        pred = interpreter.get_tensor(output_details[0]['index'])
        k = decode(pred[0])
        p += '%3d ' % (k)
    p += '\n'
print (p)

以上。

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