 View on TensorFlow.org
|
 View source on GitHub
|
 Download notebook
|
This text classification tutorial trains a recurrent neural network on the IMDB large movie review dataset for sentiment analysis.
Setup
pip install -q tfds-nightly
import tensorflow_datasets as tfds
import tensorflow as tf
Import matplotlib and create a helper function to plot graphs:
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()
Setup input pipeline
The IMDB large movie review dataset is a binary classification dataset—all the reviews have either a positive or negative sentiment.
Download the dataset using TFDS.
dataset, info = tfds.load('imdb_reviews/subwords8k', with_info=True,
as_supervised=True)
train_dataset, test_dataset = dataset['train'], dataset['test']
WARNING:absl:TFDS datasets with text encoding are deprecated and will be removed in a future version. Instead, you should use the plain text version and tokenize the text using `tensorflow_text` (See: https://www.tensorflow.org/tutorials/tensorflow_text/intro#tfdata_example) Downloading and preparing dataset imdb_reviews/subwords8k/1.0.0 (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to /home/kbuilder/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0... Shuffling and writing examples to /home/kbuilder/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0.incompleteUNSS8I/imdb_reviews-train.tfrecord Shuffling and writing examples to /home/kbuilder/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0.incompleteUNSS8I/imdb_reviews-test.tfrecord Warning:absl:Dataset is using deprecated text encoder API which will be removed soon. Please use the plain_text version of the dataset and migrate to `tensorflow_text`. Shuffling and writing examples to /home/kbuilder/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0.incompleteUNSS8I/imdb_reviews-unsupervised.tfrecord Dataset imdb_reviews downloaded and prepared to /home/kbuilder/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0. Subsequent calls will reuse this data.
The dataset info includes the encoder (a tfds.deprecated.text.SubwordTextEncoder).
encoder = info.features['text'].encoder
print('Vocabulary size: {}'.format(encoder.vocab_size))
Vocabulary size: 8185
This text encoder will reversibly encode any string, falling back to byte-encoding if necessary.
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))
Encoded string is [4025, 222, 6307, 2327, 4043, 2120, 7975] The original string: "Hello TensorFlow."
assert original_string == sample_string
for index in encoded_string:
print('{} ----> {}'.format(index, encoder.decode([index])))
4025 ----> Hell 222 ----> o 6307 ----> Ten 2327 ----> sor 4043 ----> Fl 2120 ----> ow 7975 ----> .
Prepare the data for training
Next create batches of these encoded strings. Use the padded_batch method to zero-pad the sequences to the length of the longest string in the batch:
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)
Create the model
Build a tf.keras.Sequential model and start with an embedding layer. An embedding layer stores one vector per word. When called, it converts the sequences of word indices to sequences of vectors. These vectors are trainable. After training (on enough data), words with similar meanings often have similar vectors.
This index-lookup is much more efficient than the equivalent operation of passing a one-hot encoded vector through a tf.keras.layers.Dense layer.
A recurrent neural network (RNN) processes sequence input by iterating through the elements. RNNs pass the outputs from one timestep to their input—and then to the next.
The tf.keras.layers.Bidirectional wrapper can also be used with an RNN layer. This propagates the input forward and backwards through the RNN layer and then concatenates the output. This helps the RNN to learn long range dependencies.
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)
])
Please note that Keras sequential model is used here since all the layers in the model only have single input and produce single output. In case you want to use stateful RNN layer, you might want to build your model with Keras functional API or model subclassing so that you can retrieve and reuse the RNN layer states. Please check Keras RNN guide for more details.
Compile the Keras model to configure the training process:
model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
optimizer=tf.keras.optimizers.Adam(1e-4),
metrics=['accuracy'])
Train the model
history = model.fit(train_dataset, epochs=10,
validation_data=test_dataset,
validation_steps=30)
Epoch 1/10 391/391 [==============================] - 40s 103ms/step - loss: 0.6505 - accuracy: 0.5492 - val_loss: 0.4595 - val_accuracy: 0.7620 Epoch 2/10 391/391 [==============================] - 40s 104ms/step - loss: 0.3331 - accuracy: 0.8583 - val_loss: 0.3579 - val_accuracy: 0.8719 Epoch 3/10 391/391 [==============================] - 41s 104ms/step - loss: 0.2460 - accuracy: 0.9042 - val_loss: 0.3280 - val_accuracy: 0.8594 Epoch 4/10 391/391 [==============================] - 41s 104ms/step - loss: 0.2060 - accuracy: 0.9240 - val_loss: 0.3350 - val_accuracy: 0.8531 Epoch 5/10 391/391 [==============================] - 40s 103ms/step - loss: 0.1785 - accuracy: 0.9363 - val_loss: 0.3538 - val_accuracy: 0.8693 Epoch 6/10 391/391 [==============================] - 41s 104ms/step - loss: 0.1635 - accuracy: 0.9402 - val_loss: 0.3705 - val_accuracy: 0.8687 Epoch 7/10 391/391 [==============================] - 41s 104ms/step - loss: 0.1405 - accuracy: 0.9532 - val_loss: 0.4286 - val_accuracy: 0.8656 Epoch 8/10 391/391 [==============================] - 41s 104ms/step - loss: 0.1269 - accuracy: 0.9574 - val_loss: 0.3992 - val_accuracy: 0.8609 Epoch 9/10 391/391 [==============================] - 40s 104ms/step - loss: 0.1156 - accuracy: 0.9626 - val_loss: 0.4647 - val_accuracy: 0.8536 Epoch 10/10 391/391 [==============================] - 41s 105ms/step - loss: 0.1136 - accuracy: 0.9626 - val_loss: 0.4158 - val_accuracy: 0.8167
test_loss, test_acc = model.evaluate(test_dataset)
print('Test Loss: {}'.format(test_loss))
print('Test Accuracy: {}'.format(test_acc))
391/391 [==============================] - 16s 41ms/step - loss: 0.4312 - accuracy: 0.8018 Test Loss: 0.431190550327301 Test Accuracy: 0.801800012588501
The above model does not mask the padding applied to the sequences. This can lead to skew if trained on padded sequences and test on un-padded sequences. Ideally you would use masking to avoid this, but as you can see below it only have a small effect on the output.
If the prediction is >= 0.5, it is positive else it is negative.
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)
[[0.01823657]]
# 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)
[[-0.10063186]]
plot_graphs(history, 'accuracy')
plot_graphs(history, 'loss')
Stack two or more LSTM layers
Keras recurrent layers have two available modes that are controlled by the return_sequences constructor argument:
- Return either the full sequences of successive outputs for each timestep (a 3D tensor of shape
(batch_size, timesteps, output_features)). - Return only the last output for each input sequence (a 2D tensor of shape (batch_size, output_features)).
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)
Epoch 1/10 391/391 [==============================] - 74s 190ms/step - loss: 0.6606 - accuracy: 0.5480 - val_loss: 0.5329 - val_accuracy: 0.7203 Epoch 2/10 391/391 [==============================] - 75s 192ms/step - loss: 0.3841 - accuracy: 0.8429 - val_loss: 0.3507 - val_accuracy: 0.8599 Epoch 3/10 391/391 [==============================] - 75s 191ms/step - loss: 0.2715 - accuracy: 0.8994 - val_loss: 0.3651 - val_accuracy: 0.8714 Epoch 4/10 391/391 [==============================] - 75s 192ms/step - loss: 0.2295 - accuracy: 0.9172 - val_loss: 0.3576 - val_accuracy: 0.8635 Epoch 5/10 391/391 [==============================] - 75s 192ms/step - loss: 0.1906 - accuracy: 0.9353 - val_loss: 0.3610 - val_accuracy: 0.8651 Epoch 6/10 391/391 [==============================] - 75s 192ms/step - loss: 0.1652 - accuracy: 0.9475 - val_loss: 0.4046 - val_accuracy: 0.8677 Epoch 7/10 391/391 [==============================] - 75s 192ms/step - loss: 0.1447 - accuracy: 0.9573 - val_loss: 0.4394 - val_accuracy: 0.8568 Epoch 8/10 391/391 [==============================] - 76s 193ms/step - loss: 0.1243 - accuracy: 0.9657 - val_loss: 0.5010 - val_accuracy: 0.8406 Epoch 9/10 391/391 [==============================] - 75s 191ms/step - loss: 0.1127 - accuracy: 0.9692 - val_loss: 0.4892 - val_accuracy: 0.8531 Epoch 10/10 391/391 [==============================] - 75s 192ms/step - loss: 0.0948 - accuracy: 0.9764 - val_loss: 0.5652 - val_accuracy: 0.8505
test_loss, test_acc = model.evaluate(test_dataset)
print('Test Loss: {}'.format(test_loss))
print('Test Accuracy: {}'.format(test_acc))
391/391 [==============================] - 30s 76ms/step - loss: 0.5370 - accuracy: 0.8510 Test Loss: 0.5369970798492432 Test Accuracy: 0.8510400056838989
# predict on a sample text without padding.
sample_pred_text = ('The movie was not good. The animation and the graphics '
'were terrible. I would not recommend this movie.')
predictions = sample_predict(sample_pred_text, pad=False)
print(predictions)
[[-2.3630884]]
# predict on a sample text with padding
sample_pred_text = ('The movie was not good. The animation and the graphics '
'were terrible. I would not recommend this movie.')
predictions = sample_predict(sample_pred_text, pad=True)
print(predictions)
[[-2.690089]]
plot_graphs(history, 'accuracy')
plot_graphs(history, 'loss')
Check out other existing recurrent layers such as GRU layers.
If you're interestied in building custom RNNs, see the Keras RNN Guide.