This Keras workflow trains a binary sentiment classifier on the pre-indexed IMDB movie-review dataset. It caps the vocabulary at 20,000 words, truncates or pads each review to 200 tokens, and feeds the resulting sequences through two bidirectional LSTM layers. The code below follows Keras’s official example; its reported accuracy is specific to that example run, not a guaranteed result.
What the model does
Keras’s built-in IMDB dataset contains integer-encoded movie reviews and positive-or-negative labels; it does not provide raw review text to this model. The classifier learns a sentiment score from those sequences. Its input is a variable-length sequence of token IDs, while preprocessing below makes every sequence exactly 200 positions long.
The Functional model maps each token ID to a 128-dimensional embedding. A first bidirectional LSTM with 64 units returns an output for every time step, allowing a second bidirectional LSTM with 64 units to process that sequence. The second layer returns a final representation, which a one-unit sigmoid Dense layer converts to a score between 0 and 1. For this binary task, higher scores represent the positive class.
The official example’s model summary reports 2,757,761 total parameters. See the Keras Bidirectional LSTM example and the Bidirectional layer API.
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Load and prepare the IMDB sequences
The example sets a 20,000-word vocabulary cap and a maximum sequence length of 200. load_data(num_words=max_features) loads the dataset’s pre-indexed sequences, and pad_sequences pads or truncates both splits to the fixed length.
import keras
from keras import layers
max_features = 20000
maxlen = 200
(x_train, y_train), (x_val, y_val) = keras.datasets.imdb.load_data(
num_words=max_features
)
x_train = keras.utils.pad_sequences(x_train, maxlen=maxlen)
x_val = keras.utils.pad_sequences(x_val, maxlen=maxlen)The example reports 25,000 training sequences and 25,000 validation sequences. Padding shorter reviews makes their arrays uniform in length; truncation discards tokens beyond the chosen maximum. Those choices affect which sequence information reaches the model, so change them deliberately when adapting the example.
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The dataset API documents additional controls, including truncation with maxlen, a shuffle seed, and the start, out-of-vocabulary, and index-offset settings. Zero is reserved for padding by convention. The values are word indexes rather than readable text: decoding them requires the corresponding word-index mapping and attention to the configured special tokens and offsets. Consult the Keras IMDB dataset API before changing those settings.
Build the two-layer bidirectional model
inputs = keras.Input(shape=(None,), dtype="int32")
x = layers.Embedding(max_features, 128)(inputs)
x = layers.Bidirectional(layers.LSTM(64, return_sequences=True))(x)
x = layers.Bidirectional(layers.LSTM(64))(x)
outputs = layers.Dense(1, activation="sigmoid")(x)
model = keras.Model(inputs, outputs)The input shape leaves the sequence dimension variable; the padded arrays supplied for this run happen to have 200 positions. return_sequences=True on the first LSTM is essential here: the next recurrent layer needs a sequence of outputs, not only one final output. The second LSTM can use its default final-output behavior because it feeds the Dense classifier.
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Bidirectional wraps a compatible sequence-processing RNN such as LSTM. Keras documents that the wrapped layer must provide the recurrent attributes and serialization behavior the wrapper requires. Also, wrapping an already-created RNN instance does not carry over that instance’s weights: the wrapper initializes fresh weights.
Compile, train, and evaluate
Compile with Adam, binary cross-entropy, and accuracy, then train for two epochs with batches of 32 as in the displayed Keras run:
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model.compile(
optimizer="adam",
loss="binary_crossentropy",
metrics=["accuracy"],
)
model.fit(
x_train,
y_train,
batch_size=32,
epochs=2,
validation_data=(x_val, y_val),
)To evaluate the trained model separately, use the same validation split:
loss, accuracy = model.evaluate(x_val, y_val)
print("Validation loss:", loss)
print("Validation accuracy:", accuracy)The official example page, created and last modified 2020-05-03, displays validation accuracy of 0.8269 and loss of 0.4202 after epoch 1, followed by accuracy of 0.8428 and loss of 0.3650 after epoch 2. These are the figures reported for that example run. Different Keras versions, hardware, random seeds, or reruns need not produce the same values; treat them as an illustration of the workflow, not a benchmark or promise.
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Adapting the workflow safely
- Keep the task in scope. This dataset and setup address positive-versus-negative sentiment in IMDB movie reviews, not sentiment classification in general.
- Revisit sequence handling. The 200-token limit means longer reviews lose tokens and shorter ones receive padding. If the length or vocabulary cap changes, reassess the preprocessing and model input accordingly.
- Keep validation data separate. The example supplies an explicit validation split. If you instead follow Keras’s raw-text workflow and use
validation_splitwith a subset, the Keras text classification from scratch example advises setting a seed or usingshuffle=Falseso training and validation do not overlap.
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