Bidirectional LSTM on IMDB
Author: fchollet
Date created: 2020/05/03
Last modified: 2020/05/03
Description: Train a 2-layer bidirectional LSTM on the IMDB movie review sentiment classification dataset.
Setup
import numpy as np
import keras
from keras import layers
max_features = 20000 # Only consider the top 20k words
maxlen = 200 # Only consider the first 200 words of each movie review
Build the model
# Input for variable-length sequences of integers
inputs = keras.Input(shape=(None,), dtype="int32")
# Embed each integer in a 128-dimensional vector
x = layers.Embedding(max_features, 128)(inputs)
# Add 2 bidirectional LSTMs
x = layers.Bidirectional(layers.LSTM(64, return_sequences=True))(x)
x = layers.Bidirectional(layers.LSTM(64))(x)
# Add a classifier
outputs = layers.Dense(1, activation="sigmoid")(x)
model = keras.Model(inputs, outputs)
model.summary()
Model: "functional_1"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩ │ input_layer (InputLayer) │ (None, None) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ embedding (Embedding) │ (None, None, 128) │ 2,560,000 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ bidirectional (Bidirectional) │ (None, None, 128) │ 98,816 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ bidirectional_1 (Bidirectional) │ (None, 128) │ 98,816 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ dense (Dense) │ (None, 1) │ 129 │ └─────────────────────────────────┴───────────────────────────┴────────────┘
Total params: 2,757,761 (10.52 MB)
Trainable params: 2,757,761 (10.52 MB)
Non-trainable params: 0 (0.00 B)
Load the IMDB movie review sentiment data
(x_train, y_train), (x_val, y_val) = keras.datasets.imdb.load_data(
num_words=max_features
)
print(len(x_train), "Training sequences")
print(len(x_val), "Validation sequences")
# Use pad_sequence to standardize sequence length:
# this will truncate sequences longer than 200 words and zero-pad sequences shorter than 200 words.
x_train = keras.utils.pad_sequences(x_train, maxlen=maxlen)
x_val = keras.utils.pad_sequences(x_val, maxlen=maxlen)
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz
17464789/17464789 ━━━━━━━━━━━━━━━━━━━━ 0s 0us/step
25000 Training sequences
25000 Validation sequences
Train and evaluate the model
You can use the trained model hosted on Hugging Face Hub and try the demo on Hugging Face Spaces.
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))
Epoch 1/2
782/782 ━━━━━━━━━━━━━━━━━━━━ 61s 75ms/step - accuracy: 0.7540 - loss: 0.4697 - val_accuracy: 0.8269 - val_loss: 0.4202
Epoch 2/2
782/782 ━━━━━━━━━━━━━━━━━━━━ 54s 69ms/step - accuracy: 0.9151 - loss: 0.2263 - val_accuracy: 0.8428 - val_loss: 0.3650
<keras.src.callbacks.history.History at 0x7f3efd663850>