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This notebook classifies movie reviews as positive or negative using the text of the review. This is an example of binary—or two-class—classification, an important and widely applicable kind of machine learning problem.
The tutorial demonstrates the basic application of transfer learning with TensorFlow Hub and Keras.
We'll use the IMDB dataset that contains the text of 50,000 movie reviews from the Internet Movie Database. These are split into 25,000 reviews for training and 25,000 reviews for testing. The training and testing sets are balanced, meaning they contain an equal number of positive and negative reviews.
This notebook uses tf.keras, a high-level API to build and train models in TensorFlow, and TensorFlow Hub, a library and platform for transfer learning. For a more advanced text classification tutorial using tf.keras, see the MLCC Text Classification Guide.
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import numpy as np
import tensorflow as tf
!pip install tensorflow-hub
!pip install tfds-nightly
import tensorflow_hub as hub
import tensorflow_datasets as tfds
print("Version: ", tf.__version__)
print("Eager mode: ", tf.executing_eagerly())
print("Hub version: ", hub.__version__)
print("GPU is", "available" if tf.config.experimental.list_physical_devices("GPU") else "NOT AVAILABLE")
The IMDB dataset is available on imdb reviews or on TensorFlow datasets. The following code downloads the IMDB dataset to your machine (or the colab runtime):
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# Split the training set into 60% and 40%, so we'll end up with 15,000 examples
# for training, 10,000 examples for validation and 25,000 examples for testing.
train_data, validation_data, test_data = tfds.load(
name="imdb_reviews",
split=('train[:60%]', 'train[60%:]', 'test'),
as_supervised=True)
Let's take a moment to understand the format of the data. Each example is a sentence representing the movie review and a corresponding label. The sentence is not preprocessed in any way. The label is an integer value of either 0 or 1, where 0 is a negative review, and 1 is a positive review.
Let's print first 10 examples.
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train_examples_batch, train_labels_batch = next(iter(train_data.batch(10)))
train_examples_batch
Let's also print the first 10 labels.
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train_labels_batch
The neural network is created by stacking layers—this requires three main architectural decisions:
In this example, the input data consists of sentences. The labels to predict are either 0 or 1.
One way to represent the text is to convert sentences into embeddings vectors. We can use a pre-trained text embedding as the first layer, which will have three advantages:
For this example we will use a pre-trained text embedding model from TensorFlow Hub called google/tf2-preview/gnews-swivel-20dim/1.
There are three other pre-trained models to test for the sake of this tutorial:
Let's first create a Keras layer that uses a TensorFlow Hub model to embed the sentences, and try it out on a couple of input examples. Note that no matter the length of the input text, the output shape of the embeddings is: (num_examples, embedding_dimension).
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embedding = "https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1"
hub_layer = hub.KerasLayer(embedding, input_shape=[],
dtype=tf.string, trainable=True)
hub_layer(train_examples_batch[:3])
Let's now build the full model:
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model = tf.keras.Sequential()
model.add(hub_layer)
model.add(tf.keras.layers.Dense(16, activation='relu'))
model.add(tf.keras.layers.Dense(1))
model.summary()
The layers are stacked sequentially to build the classifier:
(num_examples, embedding_dimension).Dense) layer with 16 hidden units.Let's compile the model.
A model needs a loss function and an optimizer for training. Since this is a binary classification problem and the model outputs logits (a single-unit layer with a linear activation), we'll use the binary_crossentropy loss function.
This isn't the only choice for a loss function, you could, for instance, choose mean_squared_error. But, generally, binary_crossentropy is better for dealing with probabilities—it measures the "distance" between probability distributions, or in our case, between the ground-truth distribution and the predictions.
Later, when we are exploring regression problems (say, to predict the price of a house), we will see how to use another loss function called mean squared error.
Now, configure the model to use an optimizer and a loss function:
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model.compile(optimizer='adam',
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=['accuracy'])
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history = model.fit(train_data.shuffle(10000).batch(512),
epochs=20,
validation_data=validation_data.batch(512),
verbose=1)
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results = model.evaluate(test_data.batch(512), verbose=2)
for name, value in zip(model.metrics_names, results):
print("%s: %.3f" % (name, value))
This fairly naive approach achieves an accuracy of about 87%. With more advanced approaches, the model should get closer to 95%.
For a more general way to work with string inputs and for a more detailed analysis of the progress of accuracy and loss during training, see the Text classification with preprocessed text tutorial.