dataset, info = tfds.load('imdb_reviews', with_info=True,
                          as_supervised=True)
train_dataset, test_dataset = dataset['train'], dataset['test']
 
BUFFER_SIZE = 10000
BATCH_SIZE = 64
 
train_dataset = train_dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)
test_dataset = test_dataset.batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)
 
# Text Encoder
VOCAB_SIZE = 1000
encoder = tf.keras.layers.TextVectorization(
    max_tokens=VOCAB_SIZE)
encoder.adapt(train_dataset.map(lambda text, label: text))
 
model = tf.keras.Sequential([
    encoder,
    tf.keras.layers.Embedding(
        input_dim=len(encoder.get_vocabulary()),
        output_dim=64,
        mask_zero=True),
    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'])
 
model.fit(train_dataset, epochs=10,
                    validation_data=test_dataset,
                    validation_steps=30)
 
test_loss, test_acc = model.evaluate(test_dataset)
 
print('Test Loss:', test_loss)
print('Test Accuracy:', test_acc)
 
sample_text = ('The movie was cool. The animation and the graphics '
               'were out of this world. I would recommend this movie.')
print(model.predict(np.array([sample_text])))

import numpy as np
import tensorflow_datasets as tfds
import tensorflow as tf