Notebook

RNN、LSTM、GRUの実装演習¶

LSTM、GRUについては、コードを書き替えて動かしてみたが、行列サイズがなぜか合わず、エラーの対処が間に合わず、動作させることができなかった。 RNNについては、最後に動作結果と考察を記述した。

準備¶

In [1]:

%tensorflow_version 1.x

TensorFlow 1.x selected.

Googleドライブのマウント¶

In [2]:

from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

ディレクトリを移動¶

以下では，Googleドライブのマイドライブ直下にDNN_codeフォルダを置くことを仮定しています．必要に応じて，パスを変更してください．

In [3]:

import os
# os.chdir('drive/My Drive/DNN_code/lesson_3/3_2_tf_languagemodel/')
# sys.path.append('/content/drive/My Drive/Colab Notebooks/rabbit_challenge/04_深層学習_後半/DNN_code_colab_lesson_3_4/lesson_3/3_2_tf_languagemodel')
os.chdir('/content/drive/My Drive/Colab Notebooks/rabbit_challenge/04_深層学習_後半/DNN_code_colab_lesson_3_4/lesson_3/3_2_tf_languagemodel')

predict_word¶

In [4]:

import tensorflow as tf
import numpy as np
import re
import glob
import collections
import random
import pickle
import time
import datetime
import os

# logging levelを変更
tf.logging.set_verbosity(tf.logging.ERROR)

class Corpus:
    def __init__(self):
        self.unknown_word_symbol = "<???>" # 出現回数の少ない単語は未知語として定義しておく
        self.unknown_word_threshold = 3 # 未知語と定義する単語の出現回数の閾値
        self.corpus_file = "./corpus/**/*.txt"
        self.corpus_encoding = "utf-8"
        self.dictionary_filename = "./data_for_predict/word_dict.dic"
        self.chunk_size = 5
        self.load_dict()

        words = []
        for filename in glob.glob(self.corpus_file, recursive=True):
            with open(filename, "r", encoding=self.corpus_encoding) as f:

                # word breaking
                text = f.read()
                # 全ての文字を小文字に統一し、改行をスペースに変換
                text = text.lower().replace("\n", " ")
                # 特定の文字以外の文字を空文字に置換する
                text = re.sub(r"[^a-z '\-]", "", text)
                # 複数のスペースはスペース一文字に変換
                text = re.sub(r"[ ]+", " ", text)

                # 前処理： '-' で始まる単語は無視する
                words = [ word for word in text.split() if not word.startswith("-")]


        self.data_n = len(words) - self.chunk_size
        self.data = self.seq_to_matrix(words)

    def prepare_data(self):
        """
        訓練データとテストデータを準備する。
        data_n = ( text データの総単語数 ) - chunk_size
        input: (data_n, chunk_size, vocabulary_size)
        output: (data_n, vocabulary_size)
        """

        # 入力と出力の次元テンソルを準備
        all_input = np.zeros([self.chunk_size, self.vocabulary_size, self.data_n])
        all_output = np.zeros([self.vocabulary_size, self.data_n])

        # 準備したテンソルに、コーパスの one-hot 表現(self.data) のデータを埋めていく
        # i 番目から ( i + chunk_size - 1 ) 番目までの単語が１組の入力となる
        # このときの出力は ( i + chunk_size ) 番目の単語
        for i in range(self.data_n):
            all_output[:, i] = self.data[:, i + self.chunk_size] # (i + chunk_size) 番目の単語の one-hot ベクトル
            for j in range(self.chunk_size):
                all_input[j, :, i] = self.data[:, i + self.chunk_size - j - 1]

        # 後に使うデータ形式に合わせるために転置を取る
        all_input = all_input.transpose([2, 0, 1])
        all_output = all_output.transpose()

        # 訓練データ：テストデータを 4 : 1 に分割する
        training_num = ( self.data_n * 4 ) // 5
        return all_input[:training_num], all_output[:training_num], all_input[training_num:], all_output[training_num:]


    def build_dict(self):
        # コーパス全体を見て、単語の出現回数をカウントする
        counter = collections.Counter()
        for filename in glob.glob(self.corpus_file, recursive=True):
            with open(filename, "r", encoding=self.corpus_encoding) as f:

                # word breaking
                text = f.read()
                # 全ての文字を小文字に統一し、改行をスペースに変換
                text = text.lower().replace("\n", " ")
                # 特定の文字以外の文字を空文字に置換する
                text = re.sub(r"[^a-z '\-]", "", text)
                # 複数のスペースはスペース一文字に変換
                text = re.sub(r"[ ]+", " ", text)

                # 前処理： '-' で始まる単語は無視する
                words = [word for word in text.split() if not word.startswith("-")]

                counter.update(words)

        # 出現頻度の低い単語を一つの記号にまとめる
        word_id = 0
        dictionary = {}
        for word, count in counter.items():
            if count <= self.unknown_word_threshold:
                continue

            dictionary[word] = word_id
            word_id += 1
        dictionary[self.unknown_word_symbol] = word_id

        print("総単語数：", len(dictionary))

        # 辞書を pickle を使って保存しておく
        with open(self.dictionary_filename, "wb") as f:
            pickle.dump(dictionary, f)
            print("Dictionary is saved to", self.dictionary_filename)

        self.dictionary = dictionary

        print(self.dictionary)

    def load_dict(self):
        with open(self.dictionary_filename, "rb") as f:
            self.dictionary = pickle.load(f)
            self.vocabulary_size = len(self.dictionary)
            self.input_layer_size = len(self.dictionary)
            self.output_layer_size = len(self.dictionary)
            print("総単語数: ", self.input_layer_size)

    def get_word_id(self, word):
        # print(word)
        # print(self.dictionary)
        # print(self.unknown_word_symbol)
        # print(self.dictionary[self.unknown_word_symbol])
        # print(self.dictionary.get(word, self.dictionary[self.unknown_word_symbol]))
        return self.dictionary.get(word, self.dictionary[self.unknown_word_symbol])

    # 入力された単語を one-hot ベクトルにする
    def to_one_hot(self, word):
        index = self.get_word_id(word)
        data = np.zeros(self.vocabulary_size)
        data[index] = 1
        return data

    def seq_to_matrix(self, seq):
        print(seq)
        data = np.array([self.to_one_hot(word) for word in seq]) # (data_n, vocabulary_size)
        return data.transpose() # (vocabulary_size, data_n)

class Language:
    """
    input layer: self.vocabulary_size
    hidden layer: rnn_size = 30
    output layer: self.vocabulary_size
    """

    def __init__(self):
        self.corpus = Corpus()
        self.dictionary = self.corpus.dictionary
        self.vocabulary_size = len(self.dictionary) # 単語数
        self.input_layer_size = self.vocabulary_size # 入力層の数
        self.hidden_layer_size = 30 # 隠れ層の RNN ユニットの数
        self.output_layer_size = self.vocabulary_size # 出力層の数
        self.batch_size = 128 # バッチサイズ
        self.chunk_size = 5 # 展開するシーケンスの数。c_0, c_1, ..., c_(chunk_size - 1) を入力し、c_(chunk_size) 番目の単語の確率が出力される。
        self.learning_rate = 0.005 # 学習率
        self.epochs = 1000 # 学習するエポック数
        self.forget_bias = 1.0 # LSTM における忘却ゲートのバイアス
        self.model_filename = "./data_for_predict/predict_model.ckpt"
        self.unknown_word_symbol = self.corpus.unknown_word_symbol

    def inference(self, input_data, initial_state):
        """
        :param input_data: (batch_size, chunk_size, vocabulary_size) 次元のテンソル
        :param initial_state: (batch_size, hidden_layer_size) 次元の行列
        :return:
        """
        # 重みとバイアスの初期化
        hidden_w = tf.Variable(tf.truncated_normal([self.input_layer_size, self.hidden_layer_size], stddev=0.01))
        hidden_b = tf.Variable(tf.ones([self.hidden_layer_size]))
        output_w = tf.Variable(tf.truncated_normal([self.hidden_layer_size, self.output_layer_size], stddev=0.01))
        output_b = tf.Variable(tf.ones([self.output_layer_size]))

        # BasicLSTMCell, BasicRNNCell は (batch_size, hidden_layer_size) が chunk_size 数ぶんつながったリストを入力とする。
        # 現時点での入力データは (batch_size, chunk_size, input_layer_size) という３次元のテンソルなので
        # tf.transpose や tf.reshape などを駆使してテンソルのサイズを調整する。

        input_data = tf.transpose(input_data, [1, 0, 2]) # 転置。(chunk_size, batch_size, vocabulary_size)
        input_data = tf.reshape(input_data, [-1, self.input_layer_size]) # 変形。(chunk_size * batch_size, input_layer_size)
        input_data = tf.matmul(input_data, hidden_w) + hidden_b # 重みWとバイアスBを適用。 (chunk_size, batch_size, hidden_layer_size)
        input_data = tf.split(input_data, self.chunk_size, 0) # リストに分割。chunk_size * (batch_size, hidden_layer_size)

        # RNN のセルを定義する。RNN Cell の他に LSTM のセルや GRU のセルなどが利用できる。
        # cell = tf.nn.rnn_cell.BasicLSTMCell(self.hidden_layer_size)
        # outputs, states = tf.nn.BasicLSTMCell(cell, input_data, initial_state=initial_state)
        cell = tf.nn.rnn_cell.BasicRNNCell(self.hidden_layer_size)
        outputs, states = tf.nn.static_rnn(cell, input_data, initial_state=initial_state)
        
        # 最後に隠れ層から出力層につながる重みとバイアスを処理する
        # 最終的に softmax 関数で処理し、確率として解釈される。
        # softmax 関数はこの関数の外で定義する。
        output = tf.matmul(outputs[-1], output_w) + output_b

        return output

    def loss(self, logits, labels):
        cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))

        return cost

    def training(self, cost):
        # 今回は最適化手法として Adam を選択する。
        # ここの AdamOptimizer の部分を変えることで、Adagrad, Adadelta などの他の最適化手法を選択することができる
        optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(cost)

        return optimizer

    def train(self):
        # 変数などの用意
        input_data = tf.placeholder("float", [None, self.chunk_size, self.input_layer_size])
        actual_labels = tf.placeholder("float", [None, self.output_layer_size])
        initial_state = tf.placeholder("float", [None, self.hidden_layer_size])

        prediction = self.inference(input_data, initial_state)
        cost = self.loss(prediction, actual_labels)
        optimizer = self.training(cost)
        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(actual_labels, 1))
        accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))

        # TensorBoard で可視化するため、クロスエントロピーをサマリーに追加
        tf.summary.scalar("Cross entropy: ", cost)
        summary = tf.summary.merge_all()

        # 訓練・テストデータの用意
        # corpus = Corpus()
        trX, trY, teX, teY = self.corpus.prepare_data()
        training_num = trX.shape[0]

        # ログを保存するためのディレクトリ
        timestamp = time.time()
        dirname = datetime.datetime.fromtimestamp(timestamp).strftime("%Y%m%d%H%M%S")

        # ここから実際に学習を走らせる
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            summary_writer = tf.summary.FileWriter("./log/" + dirname, sess.graph)

            # エポックを回す
            for epoch in range(self.epochs):
                step = 0
                epoch_loss = 0
                epoch_acc = 0

                # 訓練データをバッチサイズごとに分けて学習させる (= optimizer を走らせる)
                # エポックごとの損失関数の合計値や（訓練データに対する）精度も計算しておく
                while (step + 1) * self.batch_size < training_num:
                    start_idx = step * self.batch_size
                    end_idx = (step + 1) * self.batch_size

                    batch_xs = trX[start_idx:end_idx, :, :]
                    batch_ys = trY[start_idx:end_idx, :]

                    _, c, a = sess.run([optimizer, cost, accuracy],
                                       feed_dict={input_data: batch_xs,
                                                  actual_labels: batch_ys,
                                                  initial_state: np.zeros([self.batch_size, self.hidden_layer_size])
                                                  }
                                       )
                    epoch_loss += c
                    epoch_acc += a
                    step += 1

                # コンソールに損失関数の値や精度を出力しておく
                print("Epoch", epoch, "completed ouf of", self.epochs, "-- loss:", epoch_loss, " -- accuracy:",
                      epoch_acc / step)

                # Epochが終わるごとにTensorBoard用に値を保存
                summary_str = sess.run(summary, feed_dict={input_data: trX,
                                                           actual_labels: trY,
                                                           initial_state: np.zeros(
                                                               [trX.shape[0],
                                                                self.hidden_layer_size]
                                                           )
                                                           }
                                       )
                summary_writer.add_summary(summary_str, epoch)
                summary_writer.flush()

            # 学習したモデルも保存しておく
            saver = tf.train.Saver()
            saver.save(sess, self.model_filename)

            # 最後にテストデータでの精度を計算して表示する
            a = sess.run(accuracy, feed_dict={input_data: teX, actual_labels: teY,
                                              initial_state: np.zeros([teX.shape[0], self.hidden_layer_size])})
            print("Accuracy on test:", a)


    def predict(self, seq):
        """
        文章を入力したときに次に来る単語を予測する
        :param seq: 予測したい単語の直前の文字列。chunk_size 以上の単語数が必要。
        :return:
        """

        # 最初に復元したい変数をすべて定義してしまいます
        tf.reset_default_graph()
        input_data = tf.placeholder("float", [None, self.chunk_size, self.input_layer_size])
        initial_state = tf.placeholder("float", [None, self.hidden_layer_size])
        prediction = tf.nn.softmax(self.inference(input_data, initial_state))
        predicted_labels = tf.argmax(prediction, 1)

        # 入力データの作成
        # seq を one-hot 表現に変換する。
        words = [word for word in seq.split() if not word.startswith("-")]
        x = np.zeros([1, self.chunk_size, self.input_layer_size])
        for i in range(self.chunk_size):
            word = seq[len(words) - self.chunk_size + i]
            index = self.dictionary.get(word, self.dictionary[self.unknown_word_symbol])
            x[0][i][index] = 1
        feed_dict = {
            input_data: x, # (1, chunk_size, vocabulary_size)
            initial_state: np.zeros([1, self.hidden_layer_size])
        }

        # tf.Session()を用意
        with tf.Session() as sess:
            # 保存したモデルをロードする。ロード前にすべての変数を用意しておく必要がある。
            saver = tf.train.Saver()
            saver.restore(sess, self.model_filename)

            # ロードしたモデルを使って予測結果を計算
            u, v = sess.run([prediction, predicted_labels], feed_dict=feed_dict)

            keys = list(self.dictionary.keys())


            # コンソールに文字ごとの確率を表示
            for i in range(self.vocabulary_size):
                c = self.unknown_word_symbol if i == (self.vocabulary_size - 1) else keys[i]
                print(c, ":", u[0][i])

            print("Prediction:", seq + " " + ("<???>" if v[0] == (self.vocabulary_size - 1) else keys[v[0]]))

        return u[0]


def build_dict():
    cp = Corpus()
    cp.build_dict()

if __name__ == "__main__":
    #build_dict()

    ln = Language()

    # 学習するときに呼び出す
    #ln.train()

    # 保存したモデルを使って単語の予測をする
    # ln.predict("some of them looks like")
    ln.predict("twinkle twinkle little")

ストリーミング出力は最後の 5000 行に切り捨てられました。
beating : 3.7869772e-12
authentic : 4.227564e-12
glow : 4.3319476e-12
oy : 4.083499e-12
emotion : 4.34117e-12
delight : 3.926613e-12
nuclear : 4.1721795e-12
dropped : 4.273866e-12
hiroshima : 3.9479782e-12
beings : 4.37224e-12
tens : 4.313298e-12
burned : 3.946119e-12
homeless : 4.505887e-12
albert : 3.9105884e-12
initiated : 3.6656927e-12
bomb : 3.684767e-12
theoretical : 3.9410797e-12
radio : 3.9993495e-12
traditionally : 3.855457e-12
oi : 4.2345363e-12
souls : 4.100631e-12
assigned : 4.0247475e-12
gallery : 4.4200676e-12
easily : 4.343017e-12
coins : 4.041394e-12
timeless : 4.107676e-12
inevitably : 3.973111e-12
trips : 4.6628617e-12
expressions : 4.1775147e-12
raw : 3.5415312e-12
idioms : 3.5980637e-12
magic : 4.0865688e-12
handed : 3.747358e-12
blonde : 3.649048e-12
diamond : 4.3191674e-12
charity : 4.128168e-12
ball : 4.3117103e-12
lipschitz : 3.906481e-12
stone : 3.802749e-12
sighed : 3.836853e-12
feminist : 4.936635e-12
britain : 3.682933e-12
movement : 4.097871e-12
largely : 3.951429e-12
cruise : 3.8862446e-12
missiles : 4.190155e-12
village : 4.0148184e-12
elsewhere : 5.650756e-15
ignorant : 4.1914896e-12
jacket : 4.0411394e-12
centre : 4.0089035e-12
studies : 1.881175e-10
bear : 3.622005e-12
consists : 4.43307e-12
rewriting : 4.271935e-12
dawn : 4.6239818e-12
demonstrating : 3.784147e-12
themes : 4.270045e-12
advancement : 4.5030863e-12
agriculture : 4.101546e-12
despite : 3.9585423e-12
stupidity : 4.0587594e-12
domination : 4.517892e-12
superiority : 4.3237913e-12
primitive : 3.799972e-12
religions : 4.185195e-12
egyptian : 3.939907e-12
rulers : 3.9536525e-12
thesis : 4.0332004e-12
counting : 4.329337e-12
track : 4.4470343e-12
accepts : 3.989955e-12
speculation : 4.2292736e-12
laborers : 4.009699e-12
greek : 3.716565e-12
historian : 4.0984182e-12
labour : 3.826475e-12
blows : 3.9497633e-12
modern : 3.8994463e-12
researcher : 3.4483451e-12
commanded : 4.0801668e-12
sum : 3.8178727e-12
sixth : 3.855723e-19
bc : 3.792883e-12
pan : 3.887801e-12
flourished : 3.9279313e-12
welfare : 4.0213557e-12
roman : 4.1832076e-12
nurse : 3.9927107e-12
fourth : 3.9973892e-12
cultures : 3.873885e-12
instrument : 4.0474646e-12
blood : 3.139829e-10
bones : 4.4269605e-12
preferred : 3.8644097e-12
wild : 3.5028957e-12
destiny : 4.3401845e-12
paul : 3.8719206e-12
seller : 3.7036997e-12
purple : 3.7778006e-12
ibid : 4.655379e-12
array : 4.2876076e-12
brilliant : 3.694239e-12
unusual : 3.7003248e-12
inspiring : 3.8628697e-12
preceding : 3.8986947e-12
taste : 4.0778327e-12
stroke : 4.217875e-12
grim : 4.5163847e-12
clothing : 3.947896e-12
campbell : 3.7885667e-12
analyses : 1.8551052e-08
emerging : 4.3160462e-12
islamic : 4.3576366e-12
connected : 3.888632e-12
virgin : 3.6532405e-12
ancient : 4.5812117e-12
treatment : 3.6029776e-10
notably : 4.1648963e-12
etc : 4.068735e-12
unlikely : 4.107128e-12
ignored : 4.3968605e-12
gods : 3.805238e-12
identity : 4.007405e-12
meaningful : 4.2903563e-12
roots : 3.620458e-12
wildly : 3.8970667e-12
disease : 1.2795969e-14
investigate : 3.723376e-12
cure : 4.0400526e-12
blasts : 4.155105e-12
symptoms : 3.4510296e-12
characterized : 6.0084884e-12
concludes : 3.888958e-12
engage : 4.0748004e-12
laurence : 3.647893e-12
urdang : 3.909127e-12
archaeology : 4.0023635e-12
fragile : 3.9154847e-12
turns : 4.2275883e-12
earliest : 4.3902312e-12
stages : 4.249262e-12
destroyed : 4.0799334e-12
countless : 3.7370935e-12
generations : 3.6057101e-12
erosion : 3.5487461e-12
prehistoric : 3.862722e-12
developers : 3.9967795e-12
fashionable : 4.284828e-12
discover : 3.837863e-12
greeks : 3.9958193e-12
romans : 4.004456e-12
indians : 3.931274e-12
ruins : 4.0399836e-12
followed : 3.0966722e-11
troy : 4.1424286e-12
artifacts : 4.1111875e-12
dating : 3.8184703e-12
pursuit : 3.6204718e-12
museums : 3.9088958e-12
classical : 4.0641045e-12
documented : 4.1703737e-12
tablets : 3.8419797e-12
barely : 3.688022e-12
identify : 3.8219675e-12
conclusions : 1.3987679e-10
deep : 3.6808963e-12
countryside : 4.0403765e-12
precious : 3.8815774e-12
clever : 3.885481e-12
linguists : 4.0398986e-12
germany : 3.6810507e-12
ancestors : 3.847303e-12
jean : 3.9506376e-12
myriad : 3.727441e-12
revealed : 4.2768753e-12
crete : 4.1235643e-12
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placodes : 4.006312e-12
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proteins : 3.9195344e-12
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nucleotide : 4.3631257e-12
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ipf : 3.7250103e-12
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collagen : 3.951165e-12
fibroblast : 3.7182805e-12
fibrotic : 4.186017e-12
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txa : 3.956761e-12
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cox- : 4.11818e-12
hf-ipf : 4.33241e-12
hf-nl : 3.816752e-12
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serum : 7.456542e-14
dmem : 3.9983654e-12
txb : 3.9423876e-12
pgf : 4.2813153e-12
il- : 3.7139857e-12
incubation : 4.1321226e-12
trimethylsilyl : 3.9638006e-12
buffer : 3.7400742e-12
gel : 4.145076e-12
membrane : 3.584508e-12
tgf : 4.2248718e-12
vascular : 1.06318e-12
cardiac : 4.2573744e-12
hydrolyze : 3.712413e-12
divalent : 4.237243e-12
cations : 4.0575984e-12
e-ntpdase : 3.907681e-12
ntpdases : 4.142444e-12
acr : 4.3065564e-12
residues : 4.2257742e-12
phosphate : 4.166533e-12
vanadyl : 4.454938e-12
cofactor : 3.9965813e-12
equatorial : 4.021632e-12
tensors : 4.1418045e-12
epr : 3.9883266e-12
vo : 3.7841037e-12
soluble : 4.418668e-12
atp : 3.8800968e-12
adp : 4.103643e-12
purified : 3.9193774e-12
nucleotidase : 4.043537e-12
scd : 3.9245837e-12
adpnp : 3.8064356e-12
spectra : 3.8484407e-12
mhz : 4.1297903e-12
carboxyl : 4.243673e-12
oxygens : 3.9598867e-12
hydroxyl : 4.003226e-12
hydrolysis : 3.938254e-12
phosphates : 4.379902e-12
conformations : 4.2486865e-12
conformation : 3.8443103e-12
annan : 4.1170335e-12
unesco : 3.9825647e-12
sci : 3.936557e-12
ricyt : 4.2040846e-12
averaged : 2.193132e-11
ecological : 4.087083e-12
statins : 4.057831e-12
coronary : 3.4246046e-12
myocardial : 3.848022e-12
infarction : 4.0982695e-12
revascularization : 3.891392e-12
lipid-lowering : 4.139727e-12
incremental : 3.8115865e-12
ischemia : 4.0354243e-12
miracl : 4.3203297e-12
atorvastatin : 3.7590118e-12
mgdl : 4.084301e-12
rehospitalization : 4.010846e-12
worsening : 3.8978694e-12
endpoints : 4.0456818e-12
lipid : 4.285645e-12
lipoprotein : 3.7549417e-12
ldl : 4.0101577e-12
statin : 3.8286505e-12
a--z : 3.809457e-12
simvastatin : 3.9818504e-12
bmi : 0.004092469
covariates : 0.00018938891
yhl : 0.019706052
yol : 3.4098795e-09
obesity : 5.903488e-05
evgfp : 4.3666273e-06
unintended : 9.50353e-05
sizes : 9.390891e-13
obese : 0.010435885
<???> : 0.00016205752
Prediction: twinkle twinkle little or

In [ ]:

# BiDirectionalRNNのサンプル

# サンプルがなさそうなので独自作成

class BiDirectionalLSTM:
    def __init__(self, Wx1, Wh1, b1,
                 Wx2, Wh2, b2, stateful=False):
        self.lstmforward_lstm = LSTM(Wx1, Wh1, b1, stateful)
        self.lstmbackward_lstm = LSTM(Wx2, Wh2, b2, stateful)
        self.params = self.forward_lstm.params + self.lstmbackward.params
        self.grads = self.forward_lstm.grads + self.lstmbackward.grads

    def forward(self, xs):
        o1 = self.lstmforward.forward(xs)
        o2 = self.lstmbackward.forward(xs[:, ::-1])
        o2 = o2[:, ::-1]

        out = np.concatenate((o1, o2), axis=2)
        return out

    def backward(self, dhs):
        H = dhs.shape[2] // 2
        do1 = dhs[:, :, :H]
        do2 = dhs[:, :, H:]

        dxs1 = self.forwardm.lstmbackward(do1)
        do2 = do2[:, ::-1]
        dxs2 = self.backward.lstmbackward(do2)
        dxs2 = dxs2[:, ::-1]
        dxs = dxs1 + dxs2
        return dxs

In [4]:

考察¶

et、the、or しか予測しないのでうまく学習できていないように思う。
長い時系列は苦手という話で、短い時系列ならうまくいくのかと思っていたが、あまり性能がよくないようである。
今回のモデルの改良については、今後の課題としたい。

Prediction: some of them looks like et
Prediction: we will rock et
Prediction: i like butter the
Prediction: do you like this the
Prediction: please help the
Prediction: cat eats the
Prediction: twinkle twinkle little or