Recurrent Neural Networks (RNN)

import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt

# Generate sample sequential data
np.random.seed(42)
X = np.random.randn(1000, 50, 10)  # 1000 sequences, 50 time steps, 10 features
y = np.random.randint(0, 5, 1000)  # 5 classes

# Convert to one-hot encoding
y = tf.keras.utils.to_categorical(y, 5)

# Split the data
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create RNN model
model = keras.Sequential([
    keras.layers.LSTM(64, return_sequences=True, input_shape=(50, 10)),
    keras.layers.LSTM(32),
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dropout(0.2),
    keras.layers.Dense(5, activation='softmax')
])

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

# Train the model
history = model.fit(X_train, y_train,
                    epochs=10,
                    batch_size=32,
                    validation_split=0.2,
                    verbose=1)

# Evaluate the model
test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=0)
print(f"Test accuracy: {test_accuracy:.4f}")