Tensorflow Text Classification Tutorial

tf_text_classification.py

import tensorflow_datasets as tfds
import tensorflow as tf

import matplotlib.pyplot as plt

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
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])))

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)

for example_batch, label_batch in train_dataset.take(20):
    print("Batch shape:", example_batch.shape)
    print("label shape:", label_batch.shape)

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'])

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))