Adding LSTM algorithm from scratch in neural network algorithm sections

neural_network/lstm.py

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+"""
+Name - - LSTM - Long Short-Term Memory Network For Sequence Prediction
+Goal - - Predict sequences of data
+Detail: Total 3 layers neural network
+* Input layer
+* LSTM layer
+* Output layer
+Author: Shashank Tyagi
+Github: LEVII007
+link : https://www.kaggle.com/code/navjindervirdee/lstm-neural-network-from-scratch
+"""
+
+##### Explanation #####
+# 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 
+# characters. Each character is one-hot encoded for training.
+
+# 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 
+# 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 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 
+# using the softmax function.
+
+# 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 
+# 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:
+        """
+        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.
+        :param lr: The learning rate.
+        """
+        self.data = data.lower()
+        self.hidden_dim = hidden_dim
+        self.epochs = epochs
+        self.lr = lr
+
+        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}')
+
+        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)}
+
+        self.train_X, self.train_y = self.data[:-1], self.data[1:]
+
+        self.initialize_weights()
+
+    ##### Helper Functions #####
+    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.
+        """
+        vector = np.zeros((self.char_size, 1))
+        vector[self.char_to_idx[char]] = 1
+        return vector
+
+    def initialize_weights(self) -> None:
+        """
+        Initialize the weights and biases for the LSTM network.
+        """
+        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.bi = np.zeros((self.hidden_dim, 1))
+
+        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.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:
+        """
+        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))
+
+    ##### 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.
+        """
+        if derivative:
+            return x * (1 - x)
+        return 1 / (1 + np.exp(-x))
+
+    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 np.tanh(x)
+
+    def softmax(self, x: np.ndarray) -> np.ndarray:
+        """
+        Softmax activation function.
+
+        :param x: The input array.
+        :return: The softmax activation.
+        """
+        exp_x = np.exp(x - np.max(x))
+        return exp_x / exp_x.sum(axis=0)
+
+    ##### LSTM Network Methods #####
+    def reset(self) -> None:
+        """
+        Reset the LSTM network states.
+        """
+        self.concat_inputs = {}
+
+        self.hidden_states = {-1: np.zeros((self.hidden_dim, 1))}
+        self.cell_states = {-1: np.zeros((self.hidden_dim, 1))}
+
+        self.activation_outputs = {}
+        self.candidate_gates = {}
+        self.output_gates = {}
+        self.forget_gates = {}
+        self.input_gates = {}
+        self.outputs = {}
+
+    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.
+        """
+        self.reset()
+
+        outputs = []
+        for t in range(len(inputs)):
+            self.concat_inputs[t] = np.concatenate(
+                (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.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)
+
+        return outputs
+
+    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.
+        """
+        d_wf, d_bf = 0, 0
+        d_wi, d_bi = 0, 0
+        d_wc, d_bc = 0, 0
+        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])
+        for t in reversed(range(len(inputs))):
+            error = errors[t]
+
+            # Final Gate Weights and Biases Errors
+            d_wy += np.dot(error, self.hidden_states[t].T)
+            d_by += error
+
+            # Hidden State Error
+            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_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
+
+            # Forget Gate Weights and Biases Errors
+            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_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_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)
+            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]):
+            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]):
+            param -= self.lr * grad
+
+    def train(self) -> None:
+        """
+        Train the LSTM network on the input data for a specified number of epochs.
+        """
+        for epoch in tqdm(range(self.epochs)):
+            inputs = [self.one_hot_encode(char) for char in self.train_X]
+            targets = [self.one_hot_encode(char) for char in self.train_y]
+
+            # Forward pass
+            outputs = self.forward(inputs)
+
+            # Compute error at each time step
+            errors = [output - target for output, target in zip(outputs, targets)]
+
+            # Backward pass and weight updates
+            self.backward(errors, inputs)
+
+    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.
+        """
+        output = self.forward(inputs)[-1]
+        return self.idx_to_char[np.argmax(self.softmax(output))]
+
+    def test(self) -> None:
+        """
+        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))
+
+        accuracy = (correct_predictions / len(self.train_y)) * 100
+        print(f'Accuracy: {accuracy:.2f}%')
+
+
+if __name__ == "__main__":
+    # Define the input data and hyperparameters
+    data = "LSTM Neural Networks are designed to handle sequences of data."
+    hidden_dim = 50
+    epochs = 1000
+    lr = 0.01
+
+    # Initialize and train the LSTM network
+    lstm = LSTM(data, hidden_dim, epochs, lr)
+    lstm.train()
+
+    # Test the LSTM network and compute accuracy
+    lstm.test()