from tensorflow.keras.datasets import imdb
from tensorflow.keras.preprocessing.sequence import pad_sequences
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, SimpleRNN, Dense

vocab_size = 10000
max_len = 200

(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=vocab_size)

X_train = pad_sequences(X_train, maxlen = max_len, padding="post")
X_test = pad_sequences(X_test, maxlen = max_len, padding="post")

print(f"Training Data Shape: {X_train.shape}")
print(f"Test Data Shape: {X_test.shape}")

model = Sequential([
    Embedding(input_dim=vocab_size, output_dim=128),
    SimpleRNN(128, activation='tanh', return_sequences=False),
    Dense(1, activation='sigmoid')
])

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

model.summary()

history = model.fit(X_train, y_train, epochs=5, batch_size=32, validation_split=0.2)
loss, accuracy = model.evaluate(X_test, y_test)
print(f"Test Loss: {loss:.4f}, Test Accuracy: {accuracy:.4f}")