Add RNN

code/artificial_intelligence/src/recurrent_neural_network/rnn.py

@@ -0,0 +1,40 @@
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import SimpleRNN, Dense
+
+# Generate dummy sequential data
+def generate_data(seq_length=50, total_sequences=1000):
+    X = []
+    y = []
+    for _ in range(total_sequences):
+        # Create random sequences of length 'seq_length'
+        sequence = np.random.rand(seq_length)
+        target = np.sum(sequence)  # The target could be the sum of the sequence
+        X.append(sequence)
+        y.append(target)
+    return np.array(X), np.array(y)
+
+# Prepare the dataset
+seq_length = 10
+X, y = generate_data(seq_length=seq_length)
+
+# Reshape input to be [samples, time steps, features] for RNN input
+X = X.reshape((X.shape[0], seq_length, 1))
+
+# Build the RNN model
+model = Sequential()
+model.add(SimpleRNN(50, activation='relu', input_shape=(seq_length, 1)))
+model.add(Dense(1))
+
+# Compile the model
+model.compile(optimizer='adam', loss='mse')
+
+# Train the model
+model.fit(X, y, epochs=10, batch_size=32)
+
+# Predict on new data
+test_data = np.random.rand(1, seq_length).reshape(1, seq_length, 1)
+prediction = model.predict(test_data)
+
+print(f"Prediction: {prediction}")