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.
View in Colab โข
GitHub source
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
max_features = 20000 # Only consider the top 20k words
maxlen = 200 # Only consider the first 200 words of each movie review
# 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: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, None)] 0
_________________________________________________________________
embedding (Embedding) (None, None, 128) 2560000
_________________________________________________________________
bidirectional (Bidirectional (None, None, 128) 98816
_________________________________________________________________
bidirectional_1 (Bidirection (None, 128) 98816
_________________________________________________________________
dense (Dense) (None, 1) 129
=================================================================
Total params: 2,757,761
Trainable params: 2,757,761
Non-trainable params: 0
_________________________________________________________________
(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")
x_train = keras.utils.pad_sequences(x_train, maxlen=maxlen)
x_val = keras.utils.pad_sequences(x_val, maxlen=maxlen)
25000 Training sequences
25000 Validation sequences
You can use the trained model hosted on Hugging Face Hub and try the demo on Hugging Face Spaces.
model.compile("adam", "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 [==============================] - 220s 281ms/step - loss: 0.4117 - accuracy: 0.8083 - val_loss: 0.6497 - val_accuracy: 0.6983
Epoch 2/2
726/782 [==========================>...] - ETA: 11s - loss: 0.3170 - accuracy: 0.8683