From 636337fd06cbac939e774cee26c6647fc4823e6c Mon Sep 17 00:00:00 2001
From: Ramesh Sampath <1437573+sampathweb@users.noreply.github.com>
Date: Fri, 9 Jun 2023 05:28:30 +0530
Subject: [PATCH] Add example - bidir-lstm-imdb (#305)

---
 .../keras_io/nlp/bidirectional_lstm_imdb.py   | 58 +++++++++++++++++++
 1 file changed, 58 insertions(+)
 create mode 100644 examples/keras_io/nlp/bidirectional_lstm_imdb.py

diff --git a/examples/keras_io/nlp/bidirectional_lstm_imdb.py b/examples/keras_io/nlp/bidirectional_lstm_imdb.py
new file mode 100644
index 000000000..57c786083
--- /dev/null
+++ b/examples/keras_io/nlp/bidirectional_lstm_imdb.py
@@ -0,0 +1,58 @@
+"""
+Title: Bidirectional LSTM on IMDB
+Author: [fchollet](https://twitter.com/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.
+Accelerator: GPU
+"""
+"""
+## Setup
+"""
+
+import numpy as np
+import keras_core as keras
+from keras_core 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()
+
+"""
+## 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)
+
+"""
+## Train and evaluate the model
+
+You can use the trained model hosted on [Hugging Face Hub](https://huggingface.co/keras-io/bidirectional-lstm-imdb)
+and try the demo on [Hugging Face Spaces](https://huggingface.co/spaces/keras-io/bidirectional_lstm_imdb).
+"""
+
+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))