tutorial39_TextClassificationWithRnn.py

# https://www.tensorflow.org/tutorials/text/text_classification_rnn

import tensorflow_datasets as tfds
import tensorflow as tf

import matplotlib.pyplot as plt

# 그래프를 플롯하는 helper 함수
def plot_graphs(history, metric):
    plt.plot(history.history[metric])
    plt.plot(history.history['val_' + metric], '')
    plt.xlabel("Epochs")
    plt.ylabel(metric)
    plt.legend([metric, 'val_' + metric])
    plt.show()

# 입력 파이프라인 설정
dataset, info = tfds.load('imdb_reviews/subwords8k', with_info = True, as_supervised = True)
train_dataset, test_dataset = dataset['train'], dataset['test']
encoder = info.features['text'].encoder
print('Vocabulary size: {}'.format(encoder.vocab_size))

sample_string = 'Hello Tensorflow.'
encoded_string = encoder.encode(sample_string)
print('Encoded string is {}'.format(encoded_string))

original_string = encoder.decode(encoded_string)
print('The original string: "{}"'.format(original_string))
assert original_string == sample_string

for index in encoded_string:
    print('{} ---> {}'.format(index, encoder.decode([index])))

# 훈련을 위한 데이터 준비
BUFFER_SIZE = 10000
BATCH_SIZE = 64

train_dataset = train_dataset.shuffle(BUFFER_SIZE)
train_dataset = train_dataset.padded_batch(BATCH_SIZE)

test_dataset = test_dataset.padded_batch(BATCH_SIZE)

# 모델 만들기
model = tf.keras.Sequential([
    tf.keras.layers.Embedding(encoder.vocab_size, 64),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)),
    tf.keras.layers.Dense(64, activation = 'relu'),
    tf.keras.layers.Dense(1)
])
model.compile(loss = tf.keras.losses.BinaryCrossentropy(from_logits = True), 
        optimizer = tf.keras.optimizers.Adam(1e-4), metrics = ['accuracy'])

# 모델 훈련하기
history = model.fit(train_dataset, epochs = 10, validation_data = test_dataset, 
        validation_steps = 30)

test_loss, test_acc = model.evaluate(test_dataset)
print("Test Loss: {}".format(test_loss))
print("Test Accuracy: {}".format(test_acc))

def pad_to_size(vec, size):
    zeros = [0] * (size - len(vec))
    vec.extend(zeros)
    return vec

def sample_predict(sample_pred_text, pad):
    encoded_sample_pred_text = encoder.encode(sample_pred_text)
    
    if pad:
        encoded_sample_pred_text = pad_to_size(encoded_sample_pred_text, 64)
    
    encoded_sample_pred_text = tf.cast(encoded_sample_pred_text, tf.float32)
    predictions = model.predict(tf.expand_dims(encoded_sample_pred_text, 0))
    return (predictions)

# predict on a sample text without padding.
sample_pred_text = ('The movie was cool. The animation and the graphics were out of this world.'
        'I would recommend this movie.')
predictions = sample_predict(sample_pred_text, pad = False)
print(predictions)

# predict on a sample text with padding.
sample_pred_text = ('The movie was cool. The animation and the graphics were out of this world.'
        'I would recommend this movie.')
predictions = sample_predict(sample_pred_text, pad = True)
print(predictions)

plot_graphs(history, 'accuracy')
plot_graphs(history, 'loss')

# 두 개 이상의 LSTM 레이어 쌓기
# 두 가지 사용 가능한 모드 : 각 타임스텝에 대한 전체 연속 출력 스퀀스 반환, 
#                            각 입력 시퀀스에 대한 마지막 출력만 반환

model = tf.keras.Sequential([
    tf.keras.layers.Embedding(encoder.vocab_size, 64),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences = True)),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),
    tf.keras.layers.Dense(64, activation = 'relu'),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(1)
])

model.compile(loss = tf.keras.losses.BinaryCrossentropy(from_logits = True),
        optimizer = tf.keras.optimizers.Adam(1e-4), metrics = ['accuracy'])
history = model.fit(train_dataset, epochs = 10, validation_data = test_dataset, 
        validation_steps = 30)

test_loss, test_acc = model.evaluate(test_dataset)
print("Test Loss: {}".format(test_loss))
print("Test Accuracy: {}".format(test_acc))

# predict on a sample text without padding.
sample_pred_text = ('The movie was cool. The animation and the graphics were out of this world.'
        'I would recommend this movie.')
predictions = sample_predict(sample_pred_text, pad = False)
print(predictions)

# predict on a sample text with padding.
sample_pred_text = ('The movie was cool. The animation and the graphics were out of this world.'
        'I would recommend this movie.')
predictions = sample_predict(sample_pred_text, pad = True)
print(predictions)

plot_graphs(history, 'accuracy')
plot_graphs(history, 'loss')