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02_bilstm.py
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02_bilstm.py
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import tensorflow_datasets as tfds
import tensorflow as tf
import matplotlib.pyplot as plt
BUFFER_SIZE = 10000
BATCH_SIZE = 64
HIDDEN_SIZE = 64
LR = 1e-4
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()
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
train_dataset = train_dataset.shuffle(BUFFER_SIZE)
train_dataset = train_dataset.padded_batch(BATCH_SIZE)
test_dataset = test_dataset.padded_batch(BATCH_SIZE)
model = tf.keras.Sequential([
tf.keras.layers.Embedding(encoder.vocab_size, HIDDEN_SIZE),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(HIDDEN_SIZE)),
tf.keras.layers.Dense(HIDDEN_SIZE, activation='relu'),
tf.keras.layers.Dense(1)
])
model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
optimizer=tf.keras.optimizers.Adam(LR),
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))