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[pre-commit.ci] auto fixes from pre-commit.com hooks

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pre-commit-ci[bot] 2024-10-15 06:58:13 +00:00
parent 3d9b893ee0
commit 5c186b16e8
1 changed files with 5 additions and 16 deletions
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21
neural_network/lstm.py
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@ -97,32 +97,22 @@ class LSTM:
Initialize the weights and biases for the LSTM network.
"""
self.wf = self.init_weights(
self.char_size + self.hidden_dim, self.hidden_dim
)
self.wf = self.init_weights(self.char_size + self.hidden_dim, self.hidden_dim)
self.bf = np.zeros((self.hidden_dim, 1))
self.wi = self.init_weights(
self.char_size + self.hidden_dim, self.hidden_dim
)
self.wi = self.init_weights(self.char_size + self.hidden_dim, self.hidden_dim)
self.bi = np.zeros((self.hidden_dim, 1))
self.wc = self.init_weights(
self.char_size + self.hidden_dim, self.hidden_dim
)
self.wc = self.init_weights(self.char_size + self.hidden_dim, self.hidden_dim)
self.bc = np.zeros((self.hidden_dim, 1))
self.wo = self.init_weights(
self.char_size + self.hidden_dim, self.hidden_dim
)
self.wo = self.init_weights(self.char_size + self.hidden_dim, self.hidden_dim)
self.bo = np.zeros((self.hidden_dim, 1))
self.wy = self.init_weights(self.hidden_dim, self.char_size)
self.by = np.zeros((self.char_size, 1))
def init_weights(
self, input_dim: int, output_dim: int
) -> np.ndarray:
def init_weights(self, input_dim: int, output_dim: int) -> np.ndarray:
"""
Initialize weights with random values.
@ -367,7 +357,6 @@ class LSTM:
print(f"Accuracy: {round(accuracy * 100 / len(self.train_X), 2)}%")
if __name__ == "__main__":
data = """Long Short-Term Memory (LSTM) networks are a type
of recurrent neural network (RNN) capable of learning "