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トップ > 独自学習したモデルをTensorFlow.jsで使う

独自学習したモデルをTensorFlow.jsで使う

1. TensorFlow.js

Tensorflowをブラウザで動かす技術が登場した。その名もTensorFlow.jsだ。 js.tensorflow.org

この記事では、独自に学習したモデルをTensorflowで使うときの流れを紹介する。

2. 学習するモデル（デブ判定器）

身長と体重を入力すると、どのくらい太っているのかを返すモデルを作ろうと思う。 「どのくらい太っているのか」は、身長と体重から計算されるBMIという値を使って定義する。

www.jpm1960.org

以下のように定義した。

  • 18.5未満 やせ (クラス番号0)
  • 18.5～24.9 ふつう (クラス番号1)
  • 25.0～29.9 肥満1度 (クラス番号2)
  • 30.0～34.9 肥満2度 (クラス番号3)
  • 35.0～39.9 肥満3度 (クラス番号4)
  • 40.0以上 肥満4度 (クラス番号5)

学習にもちいるデータセットの形式はこんな感じだ。

177.5890062,64.71439732,20.51959369,1
164.8972845,63.23919414,23.25730132,1
184.4277219,61.55920972,18.09841875,0
154.4941492,80.30581669,33.64518567,3
158.6124011,73.35584165,29.15818083,2
・
・
・

左から順に、身長(cm), 体重(kg), BMI, クラス番号である。カンマで区切られている。 実際の学習データと評価データは記事の末尾に載せておくので参考にしていただきたい。

ここでは、身長と体重の情報だけを使って、クラス番号を予測するモデルを作る。

bmi_tf.py

from sklearn import preprocessing
import tensorflow as tf
import numpy as np

training_data = 'bmi_training.csv'
test_data = 'bmi_test.csv'

# dim次元のOne-hotベクトルを表すlistを返す関数
def one_hot(idx, dim):
    return [1 if i == idx else 0 for i in range(dim)]

# データを読み込む関数
def load_data(filename):
    with open(filename) as f:
        rows = [[float(elem.strip()) for elem in row.split(',')] for row in f.readlines() ]
        x = [row[0:2] for row in rows] # データセットの中から, 身長と体重だけを取り出す。
        t = [one_hot(row[3], 6) for row in rows] # データセットの中から、クラス番号だけを取り出す。分類先のクラス数は6個。

        x = preprocessing.normalize(np.array(x, dtype=np.float32)) # 正規化
        return x, t

x = tf.placeholder(tf.float32, [None, 2], name='x') # 身長と体重の2次元ベクトルを受け取れるようにする。
t = tf.placeholder(tf.float32, [None, 6], name='t') # 「やせ」「ふつう」「肥満1度」「肥満2度」「肥満3度」「肥満4度」の6次元ベクトルを受け取れるようにする。
keep_prob = tf.placeholder(tf.float32, name='keep_prob') # ドロップアウトのためのハイパーパラメータ

# ネットワーク定義
w1 = tf.Variable(tf.random_normal([2,30], mean=0.0, stddev=0.5))
b1 = tf.Variable(tf.random_normal([30], mean=0.0, stddev=0.5))
h1 = tf.matmul(x, w1) + b1
h1 = tf.nn.dropout(h1, keep_prob) # ドロップアウト。keep_probで指定した割合のノードが生き残り、それ以外のノードは値が0になる。
h1 = tf.nn.tanh(h1)

w2 = tf.Variable(tf.random_normal([30,30], mean=0.0, stddev=0.5))
b2 = tf.Variable(tf.random_normal([30], mean=0.0, stddev=0.5))
h2 = tf.matmul(h1, w2) + b2
h2 = tf.nn.dropout(h2, keep_prob) # ドロップアウト
h2 = tf.nn.tanh(h2)

w3 = tf.Variable(tf.random_normal([30,6], mean=0.0, stddev=0.5))
b3 = tf.Variable(tf.random_normal([6], mean=0.0, stddev=0.5))
y = tf.matmul(h2, w3) + b3

# 誤差関数の定義
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=t, logits=y))

# 正解率の定義
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(t, 1))
acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# 推論結果の定義
predict = tf.argmax(y, 1, name='predict') # 'predict'という名前をつけた。

# 学習アルゴリズム定義
train = tf.train.AdamOptimizer().minimize(loss)

# 初期化
init = tf.global_variables_initializer()

# データセットのロード
train_x, train_t = load_data(training_data)
test_x, test_t = load_data(test_data)

with tf.Session() as sess:
    sess.run(init)
    print('Epoch\tTraining loss\tTest loss\tTraining acc\tTest acc')
    for epoch in range(3000):
        sess.run(train, feed_dict={
            x: train_x,
            t: train_t,
            keep_prob: 0.5
        })
        if (epoch+1) % 5 == 0:
            print('{}\t{}\t{}\t{}\t{}'.format(
                str(epoch+1),
                str(sess.run(loss, feed_dict={x:train_x, t:train_t, keep_prob:1.0})),
                str(sess.run(loss, feed_dict={x:test_x, t:test_t, keep_prob:1.0})),
                str(sess.run(acc, feed_dict={x:train_x, t:train_t, keep_prob:1.0})),
                str(sess.run(acc, feed_dict={x:test_x, t:test_t, keep_prob:1.0})),
            ))

    tf.saved_model.simple_save(sess, 'saved_model_debu', inputs={'x': x, 'keep_prob': keep_prob}, outputs={'predict': predict})

以下のコマンドで学習を開始する。

python bmi_tf.py

学習した結果、こうなった↓

Epoch    Training loss   Test loss   Training acc    Test acc
5   3.1208303   3.2619486   0.3511111   0.36
10  2.9821303   3.114055    0.3511111   0.36
15  2.8266895   2.9496677   0.3511111   0.36
20  2.6841924   2.7964628   0.3511111   0.36
25  2.5317469   2.634027    0.3511111   0.36
30  2.3827975   2.4757023   0.3511111   0.36
35  2.2274723   2.3102322   0.3511111   0.36
40  2.0915022   2.1640873   0.3511111   0.36
・
・
（省略）
・
・
3000    0.58536345  0.5924296   0.7511111   0.68

図示するとこんな感じだ。だいたい精度70%であることがわかる。 f:id:twx:20180831153711p:plain

3. TensorFlow.js形式への変換

学習済のモデルは以下のディレクトリ階層で保存される。

saved_model_debu/
├── saved_model.pb
└── variables
    ├── variables.data-00000-of-00001
    └── variables.index

このモデルを、TensorFlow.jsで使える形に変換しよう。変換にはtensorflowjs_converterというツールを使う。tensorflowjs_converterは以下のコマンドでインストールできる。

pip install tensorflowjs

インストールできたら、早速変換してみよう。

tensorflowjs_converter --input_format=tf_saved_model --output_node_names='predict' --saved_model_tags=serve ./saved_model_debu ./web_model_debu

ここで、--output_node_namesには、学習済みモデルの出力層につけた名前を指定すること。2つの引数./saved_model_debuと./web_model_debuは、前者が学習済みモデルのディレクトリ、後者が変換後のモデル名（任意）だ。

変換後のモデルは以下のディレクトリ階層で保存される。

web_model_debu/
├── group1-shard1of1
├── tensorflowjs_model.pb
└── weights_manifest.json

4. TensorFlow.jsアプリの作り方

先程生成した変換後のモデルを全て、インターネット上にアップロードしよう。 筆者はAmazon AWSのS3にアップした。

https://s3-ap-northeast-1.amazonaws.com/********************/weights_manifest.json

のようなURLでアクセスできるように、AWS側でアクセス権の緩和と、CORSの設定をしておく。CORSの詳細は以下の記事を参考にした。

qiita.com

モデルをアップロードし、外からアクセス可能であることが確認できたら、次に以下のようなhtmlファイルを作ろう。

index.html

<html>
<head>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.12.5"></script>
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.2.1/jquery.min.js"></script>
</head>
<body>

<p>身長(cm)</p>
<input id="height">

<p>体重(kg)</p>
<input id="weight">

<button id="predict"/>推定</button>
<p id="result"></p>

<script type="text/javascript">

  const MODEL_URL = 'https://s3-ap-northeast-1.amazonaws.com/********************/tensorflowjs_model.pb';
  const WEIGHTS_URL = 'https://s3-ap-northeast-1.amazonaws.com/********************/weights_manifest.json';
  const debuNet = tf.loadFrozenModel(MODEL_URL, WEIGHTS_URL);

  $(function(){
    $('#predict').click(function(){
      var list = [
        parseFloat($('#height').val()),
        parseFloat($('#weight').val())
      ];

      // 正規化
      var norm = 0;
      for(var i=0; i<list.length; i++){
        var elem = list[i];
        norm += elem*elem;
      }
      norm = Math.sqrt(norm);
      for(var i=0; i<list.length; i++){
        list[i] = list[i]/norm;
      }

      var x = tf.tensor([list]);
      var keep_prob = tf.tensor(1);

      debuNet
      .then(function(model) {
          return model.predict({
            x: x,
            keep_prob :keep_prob
          }).data();
      })
      .then(function(result){
        if(result == 0) {
          $('#result').text('やせ')
        } else if(result ==1) {
          $('#result').text('ふつう')
        } else if(result ==2) {
          $('#result').text('肥満1度')
        } else if(result ==3) {
          $('#result').text('肥満2度')
        } else if(result ==4) {
          $('#result').text('肥満3度')
        } else if(result ==5) {
          $('#result').text('肥満4度')
        } else {

        }
      });
    });
  });

</script>
</body>
</html>

ブラウザでみると、こんな画面になる。

f:id:twx:20180901130157p:plain

特に難しい点はない。初心者が気をつけるポイントとしては以下が挙げられる。

  • tensorflow.jsをheadタグ内でロードする
  • S3にアップしたモデルを読み込む
  • scriptタグ内で、身長と体重を受け取り正規化したあと、その値をモデルに入力して推定(predict)する
  • モデルはPromiseの形式になっているので、predictを行うときや結果を取り出すときは then メソッドを使う

画面に試しに身長と体重を入力して「推定ボタン」を推してみると・・・

f:id:twx:20180901130206p:plain

ふぇぇ (´･ω･｀)

以上。TensorFlow.jsで自作モデルを動かしてみました。 いい記事だと思っていただければ、「スター☆」ボタンのクリックと、「読者になる」のボタンのクリックをお願いします！！ 最後に、学習と評価に使ったデータセットを以下においておきます。

bmi_training.csv

157.7155327,89.68050674,36.05364012,4
149.0121203,64.41123934,29.00804543,2
180.8459627,55.09253775,16.84516007,0
148.1409148,58.21515077,26.52686836,2
149.2502218,53.00152538,23.79350368,1
184.8827907,86.88905118,25.41979705,2
148.836688,71.11034448,32.10057323,3
167.876071,73.54321637,26.09546421,2
177.8102206,50.07939998,15.83965344,0
150.449792,63.65515523,28.12227159,2
149.8739218,80.70286729,35.92831268,4
149.6785531,68.83859997,30.72648466,3
173.5408125,57.99238695,19.25607336,1
146.4555397,69.72566599,32.50731235,3
177.0621681,75.50536945,24.0838704,1
151.9656812,89.98033185,38.96337319,4
173.9581647,61.11309788,20.19503755,1
161.9356154,51.38360914,19.59475571,1
183.6585108,63.37645285,18.7890928,1
149.2184957,86.90654869,39.03077622,4
177.408315,84.17821087,26.74556329,2
157.6604586,50.83106006,20.44954248,1
152.7515651,75.97009171,32.55901914,3
173.8752019,84.72934364,28.02583414,2
150.2700977,53.63825253,23.75360241,1
177.5344335,79.65344496,25.27198563,2
145.5893331,62.96287424,29.70471786,2
179.2373856,60.37189685,18.79219975,1
184.8458864,66.59516262,19.49050552,1
172.8220754,83.34467776,27.90483374,2
164.1720909,70.22624545,26.05558277,2
165.7871527,53.74288128,19.55326114,1
174.1596466,68.32979815,22.52760737,1
174.5970877,87.13738126,28.58449429,2
179.2134466,63.90002495,19.89572838,1
165.4931726,82.13920995,29.99095932,2
166.7130118,64.7062284,23.28129274,1
170.9222462,88.16626659,30.17902939,3
155.4404346,61.4285431,25.42390234,2
161.1070384,81.16486086,31.27080205,3
175.4938469,50.52338884,16.40471482,0
173.6004887,50.95757405,16.90856913,0
166.7265635,88.32838536,31.77537571,3
152.7375314,66.19004502,28.3727311,2
172.5711501,88.01113292,29.55297623,2
159.4249706,62.74471303,24.68677997,1
165.6377195,51.96867408,18.94188308,1
169.210192,62.37068417,21.78349055,1
152.0001686,50.79446732,21.98509116,1
168.0976016,81.25903212,28.75733329,2
170.1208967,78.28826519,27.05087658,2
178.3777173,69.50922592,21.84546272,1
145.3744007,62.87172388,29.74948785,2
167.6526607,64.36569707,22.89989951,1
177.8696125,62.96025472,19.90045282,1
182.9627614,54.05407072,16.14741447,0
170.4067472,82.56212355,28.43199156,2
173.50718,59.56403492,19.78559919,1
153.9613623,87.00660585,36.70529648,4
169.992288,64.99083206,22.49021767,1
171.8726179,67.62774648,22.89346511,1
149.1961418,82.31398231,36.97927806,4
148.5367581,53.84273354,24.40389899,1
164.4137849,50.3552604,18.62809276,1
183.2855421,72.62546189,21.61884514,1
149.6538687,63.18539533,28.21245053,2
150.6515524,80.87792831,35.63549499,4
150.0463927,57.66190822,25.61166972,2
179.390308,69.34457456,21.5483756,1
168.6063504,67.31180665,23.67791142,1
164.715269,75.71062652,27.90545577,2
157.6131655,88.96907978,35.81410607,4
180.6176576,80.64710438,24.72113278,1
155.7455737,67.45877817,27.81038782,2
169.5264842,50.5105705,17.57547872,0
183.376559,76.88244008,22.86333186,1
172.1076611,76.53237814,25.83716171,2
182.4525469,78.52615519,23.5892685,1
152.4416425,70.28994314,30.24725397,3
175.0827847,76.36906384,24.91326089,1
148.9325606,50.40144284,22.72289473,1
169.8609411,57.74390422,20.01331784,1
148.0900142,80.17583936,36.55880284,4
145.9226146,71.74693768,33.69443388,3
159.7269954,57.76592443,22.6420155,1
184.3214793,75.06659062,22.09504025,1
170.6399787,54.662222,18.77265528,1
148.4289187,55.17332476,25.04333185,2
184.5440594,52.19220818,15.32517818,0
162.1844097,57.65434794,21.91865272,1
161.9099959,89.63481422,34.19238531,3
181.0545639,81.81706614,24.9588717,1
172.9925781,59.32444994,19.82342893,1
153.5270401,75.09438561,31.85940894,3
168.0620045,55.64229267,19.69998693,1
159.0545163,68.76936709,27.18335249,2
182.2331352,59.02123891,17.7727089,0
161.2793743,85.68580379,32.94209499,3
152.6140681,61.52894501,26.41741571,2
156.2272884,51.67716953,21.17312339,1
148.6560908,80.69228625,36.5146048,4
172.8285406,66.76435121,22.35186201,1
148.1289169,75.62562337,34.46587218,3
145.5838673,74.24987407,35.03234571,4
176.4481979,88.13375372,28.30791047,2
166.3855633,58.06165102,20.97288129,1
159.7874675,85.86434008,33.63004199,3
158.6001423,83.0993259,33.03622045,3
155.6849648,86.72531051,35.78100361,4
182.0380679,81.83331672,24.69481168,1
151.0529471,57.73475946,25.30340432,2
183.8554464,55.81207933,16.51106943,0
146.0337252,53.14171151,24.91891964,1
175.3983385,57.83828842,18.80028714,1
184.9982516,63.22194314,18.47279419,0
159.3768643,84.30043029,33.18785893,3
145.9164074,53.89040686,25.31064458,2
156.9363765,60.67406266,24.63518533,1
155.4874739,81.30182693,33.62865429,3
155.4100094,74.98071056,31.04499388,3
153.8766917,57.49420776,24.28166219,1
182.1612558,81.83728795,24.66261966,1
171.4525982,78.5244881,26.71264989,2
178.9225733,65.39993003,20.42899549,1
180.2905305,88.71783925,27.29387024,2
167.4993216,83.59944472,29.7973257,2
178.3726076,61.48050138,19.32329028,1
153.594292,60.74658652,25.74967349,2
159.3989171,69.99456954,27.54822451,2
167.2124346,89.74665363,32.09823288,3
160.7251328,54.42312257,21.06763907,1
158.3090676,78.59805222,31.36174371,3
146.2168134,57.68851931,26.98328141,2
149.5912478,86.27764039,38.55546006,4
147.1582954,76.81850828,35.47287368,4
149.4123965,67.45203913,30.21494584,3
163.3200818,50.19543108,18.81850033,1
153.7814331,61.94603791,26.1942354,2
155.5895322,78.40873408,32.38945883,3
169.2698674,65.12670279,22.73001821,1
154.1585034,65.08940062,27.38894485,2
167.8883344,64.03186102,22.71721473,1
152.9047756,80.15882862,34.28540334,3
179.820623,54.4641647,16.84348102,0
163.5407149,72.08616904,26.95256873,2
162.7653018,64.14076394,24.21087429,1
176.945171,60.88480941,19.4460555,1
184.014674,86.65779044,25.59190992,2
156.5266603,56.08205813,22.89007971,1
169.5587512,58.51553614,20.35310953,1
175.3063965,89.09586175,28.99092067,2
148.9111549,81.24573858,36.63921224,4
151.6049608,78.86193774,34.31157224,3
164.6174398,62.04237867,22.89479473,1
157.9983343,79.91732085,32.01370211,3
172.441642,67.62475641,22.74162086,1
170.1356003,82.54416927,28.51648586,2
170.5969597,56.81603835,19.52218276,1
160.1619961,88.09289876,34.34171308,3
182.6271006,71.5375181,21.44882077,1
184.0416223,69.38383399,20.48453872,1
170.2841579,86.50081983,29.83126922,2
175.1466346,80.41120407,26.21277329,2
169.339176,74.30209615,25.91111975,2
160.9370743,77.67668167,29.99013773,2
182.3850435,79.8669827,24.0098162,1
146.7053531,73.04376137,33.93838774,3
168.0845942,71.47918406,25.30018904,2
170.6004502,69.61179557,23.9178694,1
178.3041772,71.91893524,22.62143827,1
162.3017291,53.32948874,20.24515596,1
172.4008266,51.39286873,17.29116174,0
149.4934094,90.99993305,40.71898834,5
147.6022913,69.52411995,31.91165306,3
151.9466991,52.3702797,22.6830972,1
156.7888829,59.06078923,24.0252942,1
172.4115667,78.54564514,26.42343745,2
148.651563,78.87955444,35.6964879,4
167.6532432,83.85123979,29.8322198,2
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bmi_test.csv

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Koma Hirokazu 's Hacklog ―― Copyright © 2018 Koma Hirokazu