lstm.py

import tensorflow as tf
from keras import layers,models
from keras.datasets import imdb
from keras.preprocessing import sequence

#load and process 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 LSTM model 
model = models.Sequential([
    layers.Embedding(max_features, 32, input_length=max_len),
    layers.LSTM(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)