| import numpy as np | |
| from math import exp | |
| np.random.seed(0) | |
| class VanillaRecurrentNetwork(object): | |
| def __init__(self): | |
| self.hidden_state = np.zeros((3, 3)) | |
| self.W_hh = np.random.randn(3, 3) | |
| self.W_xh = np.random.randn(3, 3) | |
| self.W_hy = np.random.randn(3, 3) | |
| self.Bh = np.random.randn(3,) | |
| self.By = np.random.rand(3,) | |
| self.hidden_state_activation_function = lambda x : np.tanh(x) | |
| self.y_activation_function = lambda x : x | |
| self.loss_funciton = lambda y : exp(y) / np.sum(exp(y)) | |
| def forward_prop(self, x): | |
| self.hidden_state = self.hidden_state_activation_function( | |
| np.dot(self.hidden_state, self.W_hh) + np.dot(x, self.W_xh) + self.Bh | |
| ) | |
| return self.y_activation_function(self.W_hy.dot(self.hidden_state) + self.By) | |