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Refactor tqdm import in LSTM class
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neural_network/lstm.py

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@@ -14,7 +14,7 @@
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import numpy as np
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from numpy.random import Generator
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from tqdm import tqdm
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# from tqdm import tqdm
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class LSTM:
@@ -285,7 +285,7 @@ def train(self) -> None:
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"""
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inputs = [self.one_hot_encode(char) for char in self.train_X]
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for _ in tqdm(range(self.epochs)):
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for _ in range(self.epochs):
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predictions = self.forward(inputs)
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errors = []

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