63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
"""
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Title: Bidirectional LSTM on IMDB
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Author: [fchollet](https://twitter.com/fchollet)
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Date created: 2020/05/03
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Last modified: 2020/05/03
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Description: Train a 2-layer bidirectional LSTM on the IMDB movie review sentiment classification dataset.
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Accelerator: GPU
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"""
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"""
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## Setup
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"""
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import numpy as np
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import keras_core as keras
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from keras_core import layers
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max_features = 20000 # Only consider the top 20k words
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maxlen = 200 # Only consider the first 200 words of each movie review
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"""
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## Build the model
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"""
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# Input for variable-length sequences of integers
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inputs = keras.Input(shape=(None,), dtype="int32")
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# Embed each integer in a 128-dimensional vector
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x = layers.Embedding(max_features, 128)(inputs)
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# Add 2 bidirectional LSTMs
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x = layers.Bidirectional(layers.LSTM(64, return_sequences=True))(x)
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x = layers.Bidirectional(layers.LSTM(64))(x)
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# Add a classifier
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outputs = layers.Dense(1, activation="sigmoid")(x)
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model = keras.Model(inputs, outputs)
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model.summary()
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"""
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## Load the IMDB movie review sentiment data
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"""
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(x_train, y_train), (x_val, y_val) = keras.datasets.imdb.load_data(
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num_words=max_features
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)
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print(len(x_train), "Training sequences")
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print(len(x_val), "Validation sequences")
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# Use pad_sequence to standardize sequence length:
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# this will truncate sequences longer than 200 words and zero-pad sequences shorter than 200 words.
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x_train = keras.utils.pad_sequences(x_train, maxlen=maxlen)
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x_val = keras.utils.pad_sequences(x_val, maxlen=maxlen)
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"""
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## Train and evaluate the model
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You can use the trained model hosted on [Hugging Face Hub](https://huggingface.co/keras-io/bidirectional-lstm-imdb)
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and try the demo on [Hugging Face Spaces](https://huggingface.co/spaces/keras-io/bidirectional_lstm_imdb).
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"""
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model.compile(
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optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]
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)
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model.fit(
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x_train, y_train, batch_size=32, epochs=2, validation_data=(x_val, y_val)
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)
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