Microsoft Chatbot AI +
class RNN: # ... def step(self, x): # update the hidden state self.h = np.tanh(np.dot(self.W_hh, self.h) + np.dot(self.W_xh, x)) # compute the output vector y = np.dot(self.W_hy, self.h) return y
The np.tanh (hyperbolic tangent) function implements a non-linearity that squashes the activations to the range [-1, 1]. The input, x, is combined with the xh matrix via the numpy dot product vector operation. It is added to the dot product of the internal state and the hh matrix, then squashed to produce a new internal state. Finally, the output is processed through the hy matrix and returned.
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<A HREF="http://www.massmind.org/techref/method/ai/natural_language.htm"> Natural Language processing.</A>
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