diff --git a/neural_network/lstm.py b/neural_network/lstm.py
index 20df37d23..c642e6df1 100644
--- a/neural_network/lstm.py
+++ b/neural_network/lstm.py
@@ -28,6 +28,15 @@ class LSTM:
         :param epochs: The number of training epochs.
         :param lr: The learning rate.
         """
+        """
+        Test the LSTM model.
+
+        >>> lstm = LSTM(data="abcde" * 50, hidden_dim=10, epochs=5, lr=0.01)
+        >>> lstm.train()
+        >>> predictions = lstm.test()
+        >>> len(predictions) > 0
+        True
+        """
         self.data: str = data.lower()
         self.hidden_dim: int = hidden_dim
         self.epochs: int = epochs
@@ -157,6 +166,15 @@ class LSTM:
         :param inputs: The input data as a list of one-hot encoded vectors.
         :return: The outputs of the network.
         """
+        """
+        Forward pass through the LSTM network.
+
+        >>> lstm = LSTM(data="abcde", hidden_dim=10, epochs=1, lr=0.01)
+        >>> inputs = [lstm.one_hot_encode(char) for char in lstm.train_X]
+        >>> outputs = lstm.forward(inputs)
+        >>> len(outputs) == len(inputs)
+        True
+        """
         self.reset()
 
         outputs = []
@@ -282,6 +300,14 @@ class LSTM:
         """
         Train the LSTM network on the input data.
         """
+        """
+        Train the LSTM network on the input data.
+
+        >>> lstm = LSTM(data="abcde" * 50, hidden_dim=10, epochs=5, lr=0.01)
+        >>> lstm.train()
+        >>> lstm.losses[-1] < lstm.losses[0]
+        True
+        """
         inputs = [self.one_hot_encode(char) for char in self.train_X]
 
         for _ in range(self.epochs):
@@ -298,6 +324,15 @@ class LSTM:
         """
         Test the trained LSTM network on the input data and print the accuracy.
         """
+        """
+        Test the LSTM model.
+
+        >>> lstm = LSTM(data="abcde" * 50, hidden_dim=10, epochs=5, lr=0.01)
+        >>> lstm.train()
+        >>> predictions = lstm.test()
+        >>> len(predictions) > 0
+        True
+        """
         accuracy = 0
         probabilities = self.forward(
             [self.one_hot_encode(char) for char in self.train_X]
@@ -328,6 +363,8 @@ if __name__ == "__main__":
         "machine translation, speech recognition, and more.
         iter and Schmidhuber in 1997, and were refined and "
         "popularized by many people in following work."""
+    import doctest
+    doctest.testmod()
 
     # lstm = LSTM(data=data, hidden_dim=25, epochs=10, lr=0.05)