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pre-commit-ci[bot] 2024-10-15 04:46:21 +00:00
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commit 91c8173691
1 changed files with 12 additions and 12 deletions
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neural_network/lstm.py
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@ -42,7 +42,7 @@ class LSTM:
def __init__(self, data: str, hidden_dim: int = 25, epochs: int = 1000, lr: float = 0.05) -> None: def __init__(self, data: str, hidden_dim: int = 25, epochs: int = 1000, lr: float = 0.05) -> None:
""" """
Initialize the LSTM network with the given data and hyperparameters. Initialize the LSTM network with the given data and hyperparameters.
:param data: The input data as a string. :param data: The input data as a string.
:param hidden_dim: The number of hidden units in the LSTM layer. :param hidden_dim: The number of hidden units in the LSTM layer.
:param epochs: The number of training epochs. :param epochs: The number of training epochs.
@ -69,7 +69,7 @@ class LSTM:
def one_hot_encode(self, char: str) -> np.ndarray: def one_hot_encode(self, char: str) -> np.ndarray:
""" """
One-hot encode a character. One-hot encode a character.
:param char: The character to encode. :param char: The character to encode.
:return: A one-hot encoded vector. :return: A one-hot encoded vector.
""" """
@ -99,7 +99,7 @@ class LSTM:
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. Initialize weights with random values.
:param input_dim: The input dimension. :param input_dim: The input dimension.
:param output_dim: The output dimension. :param output_dim: The output dimension.
:return: A matrix of initialized weights. :return: A matrix of initialized weights.
@ -110,7 +110,7 @@ class LSTM:
def sigmoid(self, x: np.ndarray, derivative: bool = False) -> np.ndarray: def sigmoid(self, x: np.ndarray, derivative: bool = False) -> np.ndarray:
""" """
Sigmoid activation function. Sigmoid activation function.
:param x: The input array. :param x: The input array.
:param derivative: Whether to compute the derivative. :param derivative: Whether to compute the derivative.
:return: The sigmoid activation or its derivative. :return: The sigmoid activation or its derivative.
@ -122,7 +122,7 @@ class LSTM:
def tanh(self, x: np.ndarray, derivative: bool = False) -> np.ndarray: def tanh(self, x: np.ndarray, derivative: bool = False) -> np.ndarray:
""" """
Tanh activation function. Tanh activation function.
:param x: The input array. :param x: The input array.
:param derivative: Whether to compute the derivative. :param derivative: Whether to compute the derivative.
:return: The tanh activation or its derivative. :return: The tanh activation or its derivative.
@ -134,7 +134,7 @@ class LSTM:
def softmax(self, x: np.ndarray) -> np.ndarray: def softmax(self, x: np.ndarray) -> np.ndarray:
""" """
Softmax activation function. Softmax activation function.
:param x: The input array. :param x: The input array.
:return: The softmax activation. :return: The softmax activation.
""" """
@ -161,7 +161,7 @@ class LSTM:
def forward(self, inputs: list) -> list: def forward(self, inputs: list) -> list:
""" """
Perform forward propagation through the LSTM network. Perform forward propagation through the LSTM network.
:param inputs: The input data as a list of one-hot encoded vectors. :param inputs: The input data as a list of one-hot encoded vectors.
:return: The outputs of the network. :return: The outputs of the network.
""" """
@ -186,7 +186,7 @@ class LSTM:
def backward(self, errors: list, inputs: list) -> None: def backward(self, errors: list, inputs: list) -> None:
""" """
Perform backpropagation through time to compute gradients and update weights. Perform backpropagation through time to compute gradients and update weights.
:param errors: The errors at each time step. :param errors: The errors at each time step.
:param inputs: The input data as a list of one-hot encoded vectors. :param inputs: The input data as a list of one-hot encoded vectors.
""" """
@ -224,7 +224,7 @@ class LSTM:
d_i = d_cs * self.candidate_gates[t] * self.sigmoid(self.input_gates[t], derivative=True) d_i = d_cs * self.candidate_gates[t] * self.sigmoid(self.input_gates[t], derivative=True)
d_wi += np.dot(d_i, inputs[t].T) d_wi += np.dot(d_i, inputs[t].T)
d_bi += d_i d_bi += d_i
# Candidate Gate Weights and Biases Errors # Candidate Gate Weights and Biases Errors
d_c = d_cs * self.input_gates[t] * self.tanh(self.candidate_gates[t], derivative=True) d_c = d_cs * self.input_gates[t] * self.tanh(self.candidate_gates[t], derivative=True)
d_wc += np.dot(d_c, inputs[t].T) d_wc += np.dot(d_c, inputs[t].T)
@ -270,7 +270,7 @@ class LSTM:
errors[-1][self.char_to_idx[self.train_y[t]]] += 1 errors[-1][self.char_to_idx[self.train_y[t]]] += 1
self.backward(errors, self.concat_inputs) self.backward(errors, self.concat_inputs)
def test(self) -> None: def test(self) -> None:
""" """
Test the trained LSTM network on the input data and print the accuracy. Test the trained LSTM network on the input data and print the accuracy.
@ -289,7 +289,7 @@ class LSTM:
print(f'Ground Truth:\n{self.train_y}\n') print(f'Ground Truth:\n{self.train_y}\n')
print(f'Predictions:\n{output}\n') print(f'Predictions:\n{output}\n')
print(f'Accuracy: {round(accuracy * 100 / len(self.train_X), 2)}%') print(f'Accuracy: {round(accuracy * 100 / len(self.train_X), 2)}%')
##### Data ##### ##### Data #####
@ -314,4 +314,4 @@ if __name__ == "__main__":
##### Testing ##### ##### Testing #####
# lstm.test() # lstm.test()
# testing can be done by uncommenting the above lines of code. # testing can be done by uncommenting the above lines of code.