Tensorflowを使用して、PythonでトレーニングするためのIMDBデータセットを準備するにはどうすればよいですか？

Tensorflowは、Googleが提供する機械学習フレームワークです。これは、Pythonと組み合わせて使用​​されるオープンソースのフレームワークであり、アルゴリズム、深層学習アプリケーションなどを実装します。それは研究および生産目的で使用されます。複雑な数学演算をすばやく実行するのに役立つ最適化手法があります。

「tensorflow」パッケージは、以下のコード行を使用してWindowsにインストールできます-

pip install tensorflow

Tensorは、TensorFlowで使用されるデータ構造です。フロー図のエッジを接続するのに役立ちます。このフロー図は「データフローグラフ」と呼ばれます。テンソルは多次元配列またはリストに他なりません。

「IMDB」データセットには、5万本を超える映画のレビューが含まれています。このデータセットは通常、自然言語処理に関連する操作で使用されます。

Google Colaboratoryを使用して、以下のコードを実行しています。 Google ColabまたはColaboratoryは、ブラウザー上でPythonコードを実行するのに役立ち、構成が不要で、GPU（グラフィックプロセッシングユニット）に無料でアクセスできます。 ColaboratoryはJupyterNotebookの上に構築されています。

以下は、IMDBデータセットのコードスニペットです-

例

def vectorize_text(text, label):
  text = tf.expand_dims(text, −1)
  return vectorize_layer(text), label

text_batch, label_batch = next(iter(raw_train_ds))
first_review, first_label = text_batch[0], label_batch[0]
print("Review is ", first_review)
print("Label is ", raw_train_ds.class_names[first_label])
print("Vectorized review is ", vectorize_text(first_review, first_label))

print("1222 −−−> ",vectorize_layer.get_vocabulary()[1222])
print(" 451 −−−> ",vectorize_layer.get_vocabulary()[451])
print('Vocabulary size: {}'.format(len(vectorize_layer.get_vocabulary())))

train_ds = raw_train_ds.map(vectorize_text)
val_ds = raw_val_ds.map(vectorize_text)
test_ds = raw_test_ds.map(vectorize_text)

コードクレジット − https://www.tensorflow.org/tutorials/keras/text_classification

出力

Review is tf.Tensor(b'Silent Night, Deadly Night 5 is the very last of the series, and like part 4, it\'s unrelated to the first three except by title and the fact that it\'s a Christmas-themed horror flick.<br /><br />Except to the oblivious, there\'s some obvious things going on here...Mickey Rooney plays a toymaker named Joe Petto and his creepy son\'s name is Pino. Ring a bell, anyone? Now, a little boy named Derek heard a knock at the door one evening, and opened it to find a present on the doorstep for him. Even though it said "don\'t open till Christmas", he begins to open it anyway but is stopped by his dad, who scolds him and sends him to bed, and opens the gift himself. Inside is a little red ball that sprouts Santa arms and a head, and proceeds to kill dad. Oops, maybe he should have left well-enough alone. Of course Derek is then traumatized by the incident since he watched it from the stairs, but he doesn\'t grow up to be some killer Santa, he just stops talking.<br /><br />There\'s a mysterious stranger lurking around, who seems very interested in the toys that Joe Petto makes. We even see him buying a bunch when Derek\'s mom takes him to the store to find a gift for him to bring him out of his trauma. And what exactly is this guy doing? Well, we\'re not sure but he does seem to be taking these toys apart to see what makes them tick. He does keep his landlord from evicting him by promising him to pay him in cash the next day and presents him with a "Larry the Larvae" toy for his kid, but of course "Larry" is not a good toy and gets out of the box in the car and of course, well, things aren\'t pretty.<br /><br />Anyway, eventually what\'s going on with Joe Petto and Pino is of course revealed, and as with the old story, Pino is not a "real boy". Pino is probably even more agitated and naughty because he suffers from "Kenitalia" (a smooth plastic crotch) so that could account for his evil ways. And the identity of the lurking stranger is revealed too, and there\'s even kind of a happy ending of sorts. Whee.<br /><br />A step up from part 4, but not much of one. Again, Brian Yuzna is involved, and Screaming Mad George, so some decent special effects, but not enough to make this great. A few leftovers from part 4 are hanging around too, like Clint Howard and Neith Hunter, but that doesn\'t really make any difference. Anyway, I now have seeing the whole series out of my system. Now if I could get some of it out of my brain. 4 out of 5.', shape=(), dtype=string)
Label is neg
Vectorized review is (<tf.Tensor: shape=(1, 250), dtype=int64, numpy=
array([[1287, 313, 2380, 313, 661, 7, 2, 52, 229, 5, 2,
200, 3, 38, 170, 669, 29, 5492, 6, 2, 83, 297,
549, 32, 410, 3, 2, 186, 12, 29, 4, 1, 191,
510, 549, 6, 2, 8229, 212, 46, 576, 175, 168, 20,
1, 5361, 290, 4, 1, 761, 969, 1, 3, 24, 935,
2271, 393, 7, 1, 1675, 4, 3747, 250, 148, 4, 112,
436, 761, 3529, 548, 4, 3633, 31, 2, 1331, 28, 2096,
3, 2912, 9, 6, 163, 4, 1006, 20, 2, 1, 15,
85, 53, 147, 9, 292, 89, 959, 2314, 984, 27, 762,
6, 959, 9, 564, 18, 7, 2140, 32, 24, 1254, 36,
1, 85, 3, 3298, 85, 6, 1410, 3, 1936, 2, 3408,
301, 965, 7, 4, 112, 740, 1977, 12, 1, 2014, 2772,
3, 4, 428, 3, 5177, 6, 512, 1254, 1, 278, 27,
139, 25, 308, 1, 579, 5, 259, 3529, 7, 92, 8981,
32, 2, 3842, 230, 27, 289, 9, 35, 2, 5712, 18,
27, 144, 2166, 56, 6, 26, 46, 466, 2014, 27, 40,
2745, 657, 212, 4, 1376, 3002, 7080, 183, 36, 180, 52,
920, 8, 2, 4028, 12, 969, 1, 158, 71, 53, 67,
85, 2754, 4, 734, 51, 1, 1611, 294, 85, 6, 2,
1164, 6, 163, 4, 3408, 15, 85, 6, 717, 85, 44,
5, 24, 7158, 3, 48, 604, 7, 11, 225, 384, 73,
65, 21, 242, 18, 27, 120, 295, 6, 26, 667, 129,
4028, 948, 6, 67, 48, 158, 93, 1]])>, <tf.Tensor: shape=(), dtype=int32, numpy=0>)
1222 ---> stick
451 ---> already
Vocabulary size: 10000

説明

• 「vectorize_text」という名前の関数が定義されています。この関数は、基本的に、指定されたテキストを数値に変換して、コンピューターが理解できるようにします。

• IMDBデータセットは、モデルのトレーニングに使用されます。

• レビュー、ラベル、およびベクトル化されたデータのサンプルがコンソールに表示されます。

• トレーニングデータ、テストデータ、検証データはすべてベクトル化されています。