rnn.py

#import neccesary libraries
import tensorflow as tf
from keras import layers, models
from keras.datasets import imdb
from keras.utils import pad_sequences
from keras.preprocessing import sequence

#load and preprocess the IMDB dataset
max_features = 10000
max_len = 500

(x_train,y_train),(x_test,y_test) = imdb.load_data(num_words=max_features)
x_train =  sequence.pad_sequences(x_train, maxlen = max_len)
x_test =  sequence.pad_sequences(x_test, maxlen = max_len)

#Define the RNN 
model = models.Sequential([
    layers.Embedding(max_features, 32, input_length=max_len),
    layers.SimpleRNN(32),
    layers.Dense(1, activation='sigmoid')
])

#compile the model
model.compile(optimizer='adam',loss = 'binary_crossentropy', metrics=['accuracy'])

#train the model 
model.fit(x_train, y_train, epochs=5, batch_size=64,validation_split=0.2)

#evaluate the model
test_loss, test_acc = model.evaluate(x_test, y_test)
print("Test Accuracy:", test_acc)