Keras
코드 예제
자연어 처리
양방향 LSTM

IMDB에 대한 양방향 LSTM

저자 : fchollet
생성일 : 2020/05/03
최종 편집일 : 2020/05/03
설명 : Train a 2-layer bidirectional LSTM on the IMDB movie review sentiment classification dataset.

ⓘ 이 예제는 Keras 3를 사용합니다.
Colab
Colab
GitHub
GitHub

Setup 

import numpy as np
import keras
from keras import layers

max_features = 20000  # Only consider the top 20k words
maxlen = 200  # Only consider the first 200 words of each movie review

Build the model 

# Input for variable-length sequences of integers
inputs = keras.Input(shape=(None,), dtype="int32")
# Embed each integer in a 128-dimensional vector
x = layers.Embedding(max_features, 128)(inputs)
# Add 2 bidirectional LSTMs
x = layers.Bidirectional(layers.LSTM(64, return_sequences=True))(x)
x = layers.Bidirectional(layers.LSTM(64))(x)
# Add a classifier
outputs = layers.Dense(1, activation="sigmoid")(x)
model = keras.Model(inputs, outputs)
model.summary()
결과
Model: "functional_1"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape              ┃    Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ input_layer (InputLayer)        │ (None, None)              │          0 │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ embedding (Embedding)           │ (None, None, 128)         │  2,560,000 │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ bidirectional (Bidirectional)   │ (None, None, 128)         │     98,816 │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ bidirectional_1 (Bidirectional) │ (None, 128)               │     98,816 │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ dense (Dense)                   │ (None, 1)                 │        129 │
└─────────────────────────────────┴───────────────────────────┴────────────┘
 Total params: 2,757,761 (10.52 MB)
 Trainable params: 2,757,761 (10.52 MB)
 Non-trainable params: 0 (0.00 B)

Load the IMDB movie review sentiment data 

(x_train, y_train), (x_val, y_val) = keras.datasets.imdb.load_data(
    num_words=max_features
)
print(len(x_train), "Training sequences")
print(len(x_val), "Validation sequences")
# Use pad_sequence to standardize sequence length:
# this will truncate sequences longer than 200 words and zero-pad sequences shorter than 200 words.
x_train = keras.utils.pad_sequences(x_train, maxlen=maxlen)
x_val = keras.utils.pad_sequences(x_val, maxlen=maxlen)
결과
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz
 17464789/17464789 ━━━━━━━━━━━━━━━━━━━━ 0s 0us/step
25000 Training sequences
25000 Validation sequences

Train and evaluate the model

You can use the trained model hosted on Hugging Face Hub and try the demo on Hugging Face Spaces.

model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
model.fit(x_train, y_train, batch_size=32, epochs=2, validation_data=(x_val, y_val))
결과
Epoch 1/2
 782/782 ━━━━━━━━━━━━━━━━━━━━ 61s 75ms/step - accuracy: 0.7540 - loss: 0.4697 - val_accuracy: 0.8269 - val_loss: 0.4202
Epoch 2/2
 782/782 ━━━━━━━━━━━━━━━━━━━━ 54s 69ms/step - accuracy: 0.9151 - loss: 0.2263 - val_accuracy: 0.8428 - val_loss: 0.3650

<keras.src.callbacks.history.History at 0x7f3efd663850>