59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
import numpy as np
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class LSTM:
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def __init__(self, input_dim, hidden_dim):
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# Initialize weights and biases
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self.Wf = np.random.rand(hidden_dim, hidden_dim + input_dim)
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self.bf = np.random.rand(hidden_dim, 1)
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self.Wi = np.random.rand(hidden_dim, hidden_dim + input_dim)
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self.bi = np.random.rand(hidden_dim, 1)
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self.WC = np.random.rand(hidden_dim, hidden_dim + input_dim)
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self.bC = np.random.rand(hidden_dim, 1)
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self.Wo = np.random.rand(hidden_dim, hidden_dim + input_dim)
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self.bo = np.random.rand(hidden_dim, 1)
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def sigmoid(self, x):
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return 1 / (1 + np.exp(-x))
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def tanh(self, x):
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return np.tanh(x)
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def forward(self, x_t, h_prev, C_prev):
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# Combine previous hidden state and current input
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combined = np.vstack((h_prev, x_t))
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# Forget gate
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f_t = self.sigmoid(np.dot(self.Wf, combined) + self.bf)
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# Input gate
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i_t = self.sigmoid(np.dot(self.Wi, combined) + self.bi)
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C_tilde = self.tanh(np.dot(self.WC, combined) + self.bC)
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# Cell state
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C_t = f_t * C_prev + i_t * C_tilde
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# Output gate
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o_t = self.sigmoid(np.dot(self.Wo, combined) + self.bo)
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h_t = o_t * self.tanh(C_t)
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return h_t, C_t
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# Example usage
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input_dim = 5 # Input feature size
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hidden_dim = 3 # Number of hidden units
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lstm = LSTM(input_dim, hidden_dim)
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# Sample inputs
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h_prev = np.zeros((hidden_dim, 1)) # Previous hidden state
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C_prev = np.zeros((hidden_dim, 1)) # Previous cell state
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x_t = np.random.rand(input_dim, 1) # Current input
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# Forward pass
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h_t, C_t = lstm.forward(x_t, h_prev, C_prev)
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print("Current hidden state:", h_t)
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print("Current cell state:", C_t)
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