dnn_main.py

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

# Load the IMDb dataset
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)

# Pad sequences to a fixed length
max_length = 500
train_data = pad_sequences(train_data, maxlen=max_length)
test_data = pad_sequences(test_data, maxlen=max_length)

# Define the model
model = models.Sequential()
model.add(layers.Embedding(input_dim=10000, output_dim=16, input_length=max_length))
model.add(layers.Flatten())
model.add(layers.Dense(32, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

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

# Display the model summary
model.summary()

# Train the model
history = model.fit(train_data, train_labels, epochs=5, batch_size=32, validation_split=0.2)

# Evaluate the model on the test set
test_loss, test_accuracy = model.evaluate(test_data, test_labels)
print(f'Test Accuracy: {test_accuracy * 100:.2f}%')