diff --git a/neural_network/lstm.py b/neural_network/lstm.py
index 5c7a1387c..ae834cdbe 100644
--- a/neural_network/lstm.py
+++ b/neural_network/lstm.py
@@ -7,42 +7,46 @@ Detail: Total 3 layers neural network
* Output layer
Author: Shashank Tyagi
Github: LEVII007
-link : https://www.kaggle.com/code/navjindervirdee/lstm-neural-network-from-scratch
+Date: [Current Date]
"""
-##### Explanation #####
-# This script implements a Long Short-Term Memory (LSTM) network to learn
-# and predict sequences of characters.
+#### 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 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 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 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 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 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 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.
+# 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
+from tqdm import tqdm
class LSTM:
@@ -68,7 +72,7 @@ class LSTM:
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.idx_to_char = dict(enumerate(self.chars))
self.train_X, self.train_y = self.data[:-1], self.data[1:]
@@ -90,30 +94,42 @@ 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)
+ rng = np.random.default_rng()
+ self.wf = self.init_weights(
+ self.char_size + self.hidden_dim, self.hidden_dim, rng
+ )
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, rng
+ )
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, rng
+ )
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, rng
+ )
self.bo = np.zeros((self.hidden_dim, 1))
- self.wy = self.init_weights(self.hidden_dim, self.char_size)
+ self.wy = self.init_weights(self.hidden_dim, self.char_size, rng)
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, rng: np.random.Generator
+ ) -> np.ndarray:
"""
Initialize weights with random values.
:param input_dim: The input dimension.
:param output_dim: The output dimension.
+ :param rng: The random number generator.
:return: A matrix of initialized weights.
"""
- return np.random.uniform(-1, 1, (output_dim, input_dim)) * np.sqrt(
+ return rng.uniform(-1, 1, (output_dim, input_dim)) * np.sqrt(
6 / (input_dim + output_dim)
)
@@ -280,79 +296,95 @@ class LSTM:
d_wc += np.dot(d_c, inputs[t].T)
d_bc += d_c
- # Update the next hidden and cell state errors
- dh_next = (
+ # Concatenated Input Error (Sum of Error at Each Gate!)
+ d_z = (
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)
+ + np.dot(self.wo.T, d_o)
)
- 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
+ # Error of Hidden State and Cell State at Next Time Step
+ dh_next = d_z[: self.hidden_dim, :]
+ dc_next = self.forget_gates[t] * d_cs
- 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
+ for d_ in (d_wf, d_bf, d_wi, d_bi, d_wc, d_bc, d_wo, d_bo, d_wy, d_by):
+ np.clip(d_, -1, 1, out=d_)
+
+ self.wf += d_wf * self.lr
+ self.bf += d_bf * self.lr
+
+ self.wi += d_wi * self.lr
+ self.bi += d_bi * self.lr
+
+ self.wc += d_wc * self.lr
+ self.bc += d_bc * self.lr
+
+ self.wo += d_wo * self.lr
+ self.bo += d_bo * self.lr
+
+ self.wy += d_wy * self.lr
+ self.by += d_by * self.lr
def train(self) -> None:
"""
- Train the LSTM network on the input data for a specified number of epochs.
+ Train the LSTM network on the input data.
"""
- 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]
+ inputs = [self.one_hot_encode(char) for char in self.train_X]
- # Forward pass
- outputs = self.forward(inputs)
+ for _ in tqdm(range(self.epochs)):
+ predictions = self.forward(inputs)
- # Compute error at each time step
- errors = [output - target for output, target in zip(outputs, targets)]
+ errors = []
+ for t in range(len(predictions)):
+ errors.append(-self.softmax(predictions[t]))
+ errors[-1][self.char_to_idx[self.train_y[t]]] += 1
- # 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))]
+ self.backward(errors, self.concat_inputs)
def test(self) -> None:
"""
- Test the LSTM network on the input data and compute accuracy.
+ Test the trained LSTM network on the input data and print the 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 = 0
+ probabilities = self.forward(
+ [self.one_hot_encode(char) for char in self.train_X]
)
- accuracy = (correct_predictions / len(self.train_y)) * 100
- print(f"Accuracy: {accuracy:.2f}%")
+ output = ""
+ for t in range(len(self.train_y)):
+ prediction = self.idx_to_char[
+ np.random.choice(
+ range(self.char_size), p=self.softmax(probabilities[t].reshape(-1))
+ )
+ ]
+
+ output += prediction
+
+ if prediction == self.train_y[t]:
+ accuracy += 1
+
+ print(f"Ground Truth:\n{self.train_y}\n")
+ print(f"Predictions:\n{output}\n")
+
+ print(f"Accuracy: {round(accuracy * 100 / len(self.train_X), 2)}%")
if __name__ == "__main__":
- # Define the input data and hyperparameters
- data = "LSTM Neural Networks are designed to handle sequences of data.This is just rantom test data"
- # hidden_dim = 50
- # epochs = 1000
- # lr = 0.01
+ data = """Long Short-Term Memory (LSTM) networks are a type
+ of recurrent neural network (RNN) capable of learning "
+ "order dependence in sequence prediction problems.
+ This behavior is required in complex problem domains like "
+ "machine translation, speech recognition, and more.
+ iter and Schmidhuber in 1997, and were refined and "
+ "popularized by many people in following work."""
- # # Initialize and train the LSTM network
- # lstm = LSTM(data, hidden_dim, epochs, lr)
- # lstm.train()
+ lstm = LSTM(data=data, hidden_dim=25, epochs=1000, lr=0.05)
- # # Test the LSTM network and compute accuracy
- # lstm.test()
+ ##### Training #####
+ lstm.train()
+
+ ##### Testing #####
+ lstm.test()
+
+# testing can be done by uncommenting the above lines of code.