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neural_network/lstm.py
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@ -11,45 +11,47 @@ link : https://www.kaggle.com/code/navjindervirdee/lstm-neural-network-from-scra
"""
##### Explanation #####
# This script implements a Long Short-Term Memory (LSTM) network to learn
# This script implements a Long Short-Term Memory (LSTM) network to learn
# and predict sequences of characters.
# It uses numpy for numerical operations and tqdm for progress visualization.
# The data is a paragraph about LSTM, converted to lowercase and split into
# The data is a paragraph about LSTM, converted to lowercase and split into
# characters. Each character is one-hot encoded for training.
# The LSTM class initializes weights and biases for the forget, input, candidate,
# The LSTM class initializes weights and biases for the forget, input, candidate,
# and output gates. It also initializes weights and biases for the final output layer.
# The forward method performs forward propagation through the LSTM network,
# computing hidden and cell states. It uses sigmoid and tanh activation
# The forward method performs forward propagation through the LSTM network,
# computing hidden and cell states. It uses sigmoid and tanh activation
# functions for the gates and cell states.
# The backward method performs backpropagation through time, computing gradients
# for the weights and biases. It updates the weights and biases using
# The backward method performs backpropagation through time, computing gradients
# for the weights and biases. It updates the weights and biases using
# the computed gradients and the learning rate.
# The train method trains the LSTM network on the input data for a specified
# number of epochs. It uses one-hot encoded inputs and computes errors
# The train method trains the LSTM network on the input data for a specified
# number of epochs. It uses one-hot encoded inputs and computes errors
# using the softmax function.
# The test method evaluates the trained LSTM network on the input data,
# The test method evaluates the trained LSTM network on the input data,
# computing accuracy based on predictions.
# The script initializes the LSTM network with specified hyperparameters
# and trains it on the input data. Finally, it tests the trained network
# The script initializes the LSTM network with specified hyperparameters
# and trains it on the input data. Finally, it tests the trained network
# and prints the accuracy of the predictions.
##### Imports #####
from tqdm import tqdm
import numpy as np
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.
:param data: The input data as a string.
:param hidden_dim: The number of hidden units in the LSTM layer.
:param epochs: The number of training epochs.
@ -63,7 +65,7 @@ class LSTM:
self.chars = set(self.data)
self.data_size, self.char_size = len(self.data), len(self.chars)
print(f'Data size: {self.data_size}, Char Size: {self.char_size}')
print(f"Data size: {self.data_size}, Char Size: {self.char_size}")
self.char_to_idx = {c: i for i, c in enumerate(self.chars)}
self.idx_to_char = {i: c for i, c in enumerate(self.chars)}
@ -76,7 +78,7 @@ class LSTM:
def one_hot_encode(self, char: str) -> np.ndarray:
"""
One-hot encode a character.
:param char: The character to encode.
:return: A one-hot encoded vector.
"""
@ -88,20 +90,16 @@ 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)
@ -110,19 +108,20 @@ class LSTM:
def init_weights(self, input_dim: int, output_dim: int) -> np.ndarray:
"""
Initialize weights with random values.
:param input_dim: The input dimension.
:param output_dim: The output dimension.
:return: A matrix of initialized weights.
"""
return np.random.uniform(-1, 1, (output_dim, input_dim)) * \
np.sqrt(6 / (input_dim + output_dim))
return np.random.uniform(-1, 1, (output_dim, input_dim)) * np.sqrt(
6 / (input_dim + output_dim)
)
##### Activation Functions #####
def sigmoid(self, x: np.ndarray, derivative: bool = False) -> np.ndarray:
"""
Sigmoid activation function.
:param x: The input array.
:param derivative: Whether to compute the derivative.
:return: The sigmoid activation or its derivative.
@ -134,19 +133,19 @@ class LSTM:
def tanh(self, x: np.ndarray, derivative: bool = False) -> np.ndarray:
"""
Tanh activation function.
:param x: The input array.
:param derivative: Whether to compute the derivative.
:return: The tanh activation or its derivative.
"""
if derivative:
return 1 - x ** 2
return 1 - x**2
return np.tanh(x)
def softmax(self, x: np.ndarray) -> np.ndarray:
"""
Softmax activation function.
:param x: The input array.
:return: The softmax activation.
"""
@ -173,7 +172,7 @@ class LSTM:
def forward(self, inputs: list) -> list:
"""
Perform forward propagation through the LSTM network.
:param inputs: The input data as a list of one-hot encoded vectors.
:return: The outputs of the network.
"""
@ -182,21 +181,29 @@ class LSTM:
outputs = []
for t in range(len(inputs)):
self.concat_inputs[t] = np.concatenate(
(self.hidden_states[t - 1], inputs[t]))
(self.hidden_states[t - 1], inputs[t])
)
self.forget_gates[t] = self.sigmoid(np.dot(self.wf,
self.concat_inputs[t]) + self.bf)
self.input_gates[t] = self.sigmoid(np.dot(self.wi,
self.concat_inputs[t]) + self.bi)
self.candidate_gates[t] = self.tanh(np.dot(self.wc,
self.concat_inputs[t]) + self.bc)
self.output_gates[t] = self.sigmoid(np.dot(self.wo,
self.concat_inputs[t]) + self.bo)
self.forget_gates[t] = self.sigmoid(
np.dot(self.wf, self.concat_inputs[t]) + self.bf
)
self.input_gates[t] = self.sigmoid(
np.dot(self.wi, self.concat_inputs[t]) + self.bi
)
self.candidate_gates[t] = self.tanh(
np.dot(self.wc, self.concat_inputs[t]) + self.bc
)
self.output_gates[t] = self.sigmoid(
np.dot(self.wo, self.concat_inputs[t]) + self.bo
)
self.cell_states[t] = self.forget_gates[t] * self.cell_states[t - 1] + \
self.input_gates[t] * self.candidate_gates[t]
self.hidden_states[t] = self.output_gates[t] * \
self.tanh(self.cell_states[t])
self.cell_states[t] = (
self.forget_gates[t] * self.cell_states[t - 1]
+ self.input_gates[t] * self.candidate_gates[t]
)
self.hidden_states[t] = self.output_gates[t] * self.tanh(
self.cell_states[t]
)
outputs.append(np.dot(self.wy, self.hidden_states[t]) + self.by)
@ -205,7 +212,7 @@ class LSTM:
def backward(self, errors: list, inputs: list) -> None:
"""
Perform backpropagation through time to compute gradients and update weights.
:param errors: The errors at each time step.
:param inputs: The input data as a list of one-hot encoded vectors.
"""
@ -215,8 +222,10 @@ class LSTM:
d_wo, d_bo = 0, 0
d_wy, d_by = 0, 0
dh_next, dc_next = np.zeros_like(self.hidden_states[0]), \
np.zeros_like(self.cell_states[0])
dh_next, dc_next = (
np.zeros_like(self.hidden_states[0]),
np.zeros_like(self.cell_states[0]),
)
for t in reversed(range(len(inputs))):
error = errors[t]
@ -228,45 +237,69 @@ class LSTM:
d_hs = np.dot(self.wy.T, error) + dh_next
# Output Gate Weights and Biases Errors
d_o = self.tanh(self.cell_states[t]) * d_hs * \
self.sigmoid(self.output_gates[t], derivative=True)
d_o = (
self.tanh(self.cell_states[t])
* d_hs
* self.sigmoid(self.output_gates[t], derivative=True)
)
d_wo += np.dot(d_o, inputs[t].T)
d_bo += d_o
# Cell State Error
d_cs = self.tanh(self.tanh(self.cell_states[t]),
derivative=True) * self.output_gates[t] * d_hs + dc_next
d_cs = (
self.tanh(self.tanh(self.cell_states[t]), derivative=True)
* self.output_gates[t]
* d_hs
+ dc_next
)
# Forget Gate Weights and Biases Errors
d_f = d_cs * self.cell_states[t - 1] * \
self.sigmoid(self.forget_gates[t], derivative=True)
d_f = (
d_cs
* self.cell_states[t - 1]
* self.sigmoid(self.forget_gates[t], derivative=True)
)
d_wf += np.dot(d_f, inputs[t].T)
d_bf += d_f
# Input Gate Weights and Biases Errors
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_bi += d_i
# 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_bc += d_c
# Update the next hidden and cell state errors
dh_next = np.dot(self.wf.T, d_f) + np.dot(self.wi.T, d_i) + \
np.dot(self.wo.T, d_o) + np.dot(self.wc.T, d_c)
dh_next = (
np.dot(self.wf.T, d_f)
+ np.dot(self.wi.T, d_i)
+ np.dot(self.wo.T, d_o)
+ np.dot(self.wc.T, d_c)
)
dc_next = d_cs * self.forget_gates[t]
# Apply gradients to weights and biases
for param, grad in zip([self.wf, self.wi, self.wc, self.wo, self.wy],
[d_wf, d_wi, d_wc, d_wo, d_wy]):
for param, grad in zip(
[self.wf, self.wi, self.wc, self.wo, self.wy],
[d_wf, d_wi, d_wc, d_wo, d_wy],
):
param -= self.lr * grad
for param, grad in zip([self.bf, self.bi, self.bc, self.bo, self.by],
[d_bf, d_bi, d_bc, d_bo, d_by]):
for param, grad in zip(
[self.bf, self.bi, self.bc, self.bo, self.by],
[d_bf, d_bi, d_bc, d_bo, d_by],
):
param -= self.lr * grad
def train(self) -> None:
@ -289,7 +322,7 @@ class LSTM:
def predict(self, inputs: list) -> str:
"""
Predict the next character in the sequence.
:param inputs: The input data as a list of one-hot encoded vectors.
:return: The predicted character.
"""
@ -301,11 +334,13 @@ class LSTM:
Test the LSTM network on the input data and compute accuracy.
"""
inputs = [self.one_hot_encode(char) for char in self.train_X]
correct_predictions = sum(self.idx_to_char[np.argmax(self.softmax(output))] == target
for output, target in zip(self.forward(inputs), self.train_y))
correct_predictions = sum(
self.idx_to_char[np.argmax(self.softmax(output))] == target
for output, target in zip(self.forward(inputs), self.train_y)
)
accuracy = (correct_predictions / len(self.train_y)) * 100
print(f'Accuracy: {accuracy:.2f}%')
print(f"Accuracy: {accuracy:.2f}%")
if __name__ == "__main__":