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Refactor LSTM class: Improve test method and add comments

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“Shashank 2024-10-15 22:18:25 +05:30
parent b1e7e72524
commit 98332393b2
1 changed files with 10 additions and 14 deletions
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24
neural_network/lstm.py
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@ -346,18 +346,12 @@ class LongShortTermMemory:
self.backward_pass(errors, inputs) self.backward_pass(errors, inputs)
def test(self) -> None: def test(self):
""" """
Test the trained LSTM network on the input data and print the accuracy. Test the LSTM model.
>>> lstm = LongShortTermMemory("abcde" * 50, hidden_layer_size=10, Returns:
... training_epochs=5, learning_rate=0.01) str: The output predictions.
>>> lstm is not None
True
>>> lstm.train()
>>> output = lstm.test()
>>> output is not None
True
""" """
accuracy = 0 accuracy = 0
probabilities = self.forward_pass( probabilities = self.forward_pass(
@ -366,6 +360,7 @@ class LongShortTermMemory:
output = "" output = ""
for t in range(len(self.target_sequence)): for t in range(len(self.target_sequence)):
# Apply softmax to get probabilities for predictions
probs = self.softmax(probabilities[t].reshape(-1)) probs = self.softmax(probabilities[t].reshape(-1))
prediction_index = self.random_generator.choice( prediction_index = self.random_generator.choice(
self.vocabulary_size, p=probs self.vocabulary_size, p=probs
@ -374,17 +369,18 @@ class LongShortTermMemory:
output += prediction output += prediction
# Calculate accuracy
if prediction == self.target_sequence[t]: if prediction == self.target_sequence[t]:
accuracy += 1 accuracy += 1
# print(f"Ground Truth:\n{self.target_sequence}\n") print(f"Ground Truth:\n{self.target_sequence}\n")
# print(f"Predictions:\n{output}\n") print(f"Predictions:\n{output}\n")
print(f"Accuracy: {round(accuracy * 100 / len(self.input_sequence), 2)}%")
# print(f"Accuracy: {round(accuracy * 100 / len(self.input_sequence), 2)}%")
return output return output
if __name__ == "__main__": if __name__ == "__main__":
sample_data = """Long Short-Term Memory (LSTM) networks are a type sample_data = """Long Short-Term Memory (LSTM) networks are a type
of recurrent neural network (RNN) capable of learning " of recurrent neural network (RNN) capable of learning "