Keras 2 : examples : Transformer によるテキスト分類 (翻訳/解説)

翻訳 : (株)クラスキャット セールスインフォメーション
作成日時 : 05/25/2022 (keras 2.9.0)

* 本ページは、Keras の以下のドキュメントを翻訳した上で適宜、補足説明したものです：

• Code examples : Natural Language Processing : Text classification with Transformer (Author: Apoorv Nandan)

* サンプルコードの動作確認はしておりますが、必要な場合には適宜、追加改変しています。
* ご自由にリンクを張って頂いてかまいませんが、sales-info@classcat.com までご一報いただけると嬉しいです。

Keras 2 : examples : 自然言語処理 – Transformer によるテキスト分類

Description : Transformer ブロックを Keras 層として実装してそれをテキスト分類のために使用します。

セットアップ

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

Transformer ブロックを層として実装する

class TransformerBlock(layers.Layer):
    def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):
        super(TransformerBlock, self).__init__()
        self.att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
        self.ffn = keras.Sequential(
            [layers.Dense(ff_dim, activation="relu"), layers.Dense(embed_dim),]
        )
        self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)
        self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)
        self.dropout1 = layers.Dropout(rate)
        self.dropout2 = layers.Dropout(rate)

    def call(self, inputs, training):
        attn_output = self.att(inputs, inputs)
        attn_output = self.dropout1(attn_output, training=training)
        out1 = self.layernorm1(inputs + attn_output)
        ffn_output = self.ffn(out1)
        ffn_output = self.dropout2(ffn_output, training=training)
        return self.layernorm2(out1 + ffn_output)

埋め込み層の実装

2 つの別個の埋め込み層があります、一つはトークンのため、一つはトークンインデックス (位置) のためです。

class TokenAndPositionEmbedding(layers.Layer):
    def __init__(self, maxlen, vocab_size, embed_dim):
        super(TokenAndPositionEmbedding, self).__init__()
        self.token_emb = layers.Embedding(input_dim=vocab_size, output_dim=embed_dim)
        self.pos_emb = layers.Embedding(input_dim=maxlen, output_dim=embed_dim)

    def call(self, x):
        maxlen = tf.shape(x)[-1]
        positions = tf.range(start=0, limit=maxlen, delta=1)
        positions = self.pos_emb(positions)
        x = self.token_emb(x)
        return x + positions

データセットのダウンロードと準備

vocab_size = 20000  # Only consider the top 20k words
maxlen = 200  # Only consider the first 200 words of each movie review
(x_train, y_train), (x_val, y_val) = keras.datasets.imdb.load_data(num_words=vocab_size)
print(len(x_train), "Training sequences")
print(len(x_val), "Validation sequences")
x_train = keras.preprocessing.sequence.pad_sequences(x_train, maxlen=maxlen)
x_val = keras.preprocessing.sequence.pad_sequences(x_val, maxlen=maxlen)

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz
17465344/17464789 [==============================] - 0s 0us/step

:6: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/datasets/imdb.py:159: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/datasets/imdb.py:160: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])

25000 Training sequences
25000 Validation sequences

transformer 層を使用して分類器モデルを作成する

Transformer 層は入力シークエンスの各時間ステップに対して 1 つのベクトルを出力します。ここでは、総ての時間ステップに対する平均を取り、そしてテキストを分類するためにその上で順伝播ネットワークを使用します。

embed_dim = 32  # Embedding size for each token
num_heads = 2  # Number of attention heads
ff_dim = 32  # Hidden layer size in feed forward network inside transformer

inputs = layers.Input(shape=(maxlen,))
embedding_layer = TokenAndPositionEmbedding(maxlen, vocab_size, embed_dim)
x = embedding_layer(inputs)
transformer_block = TransformerBlock(embed_dim, num_heads, ff_dim)
x = transformer_block(x)
x = layers.GlobalAveragePooling1D()(x)
x = layers.Dropout(0.1)(x)
x = layers.Dense(20, activation="relu")(x)
x = layers.Dropout(0.1)(x)
outputs = layers.Dense(2, activation="softmax")(x)

model = keras.Model(inputs=inputs, outputs=outputs)

訓練と評価

model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
history = model.fit(
    x_train, y_train, batch_size=32, epochs=2, validation_data=(x_val, y_val)
)

Epoch 1/2
782/782 [==============================] - 15s 18ms/step - loss: 0.5112 - accuracy: 0.7070 - val_loss: 0.3598 - val_accuracy: 0.8444
Epoch 2/2
782/782 [==============================] - 13s 17ms/step - loss: 0.1942 - accuracy: 0.9297 - val_loss: 0.2977 - val_accuracy: 0.8745

以上