def feedforward(self, s_inst, s_trans):
y_r = act.sigmoid(s_inst, self.V_r)
g = act.hard_sigmoid(s_inst, self.W_g, self.g_strength)
f = act.hard_sigmoid(s_inst, self.W_f, self.f_strength)
y_m, self.cumulative_memory = act.sigmoid_gated(
s_trans, self.V_m, self.cumulative_memory, f, g)
Q = act.linear(y_r, self.W_r) + act.linear(y_m, self.W_m)
return y_r, y_m, g, f, Q
def feedforward(self,s_inst,s_trans): g_strength = 3 f_strength = 3 y_r = act.sigmoid(s_inst, self.V_r) g = act.softmax(s_inst,self.W_g, g_strength) g = np.transpose(g) f = act.hard_sigmoid(s_inst,self.W_f, f_strength) f = np.transpose(f)
def feedforward(self, s_inst, s_trans):
g = act.hard_sigmoid(s_inst, self.W_g, self.g_strength)
g = np.transpose(g)
#g = np.ones((self.L,1))
y_m = 1e-6 * np.ones((self.L, 1, self.M))
inp_r = 1e-6 * np.ones((self.L, 1, self.R))
for l in np.arange(self.L):
y_m[l, :, :], self.memory_content[l, :, :] = act.sigmoid_acc_leaky(
s_trans, self.V_m[l, :, :], self.memory_leak, g[l, 0])
inp_r[l, :, :] = act.linear(y_m[l, :, :], self.W_m[l, :, :])
#print('\t MEM STATE ',str(l),':', y_m[l,:,:],'\t alpha=',self.ALPHA[l,0,0],'\t gate=',g[l,:])
inp_r = np.sum(inp_r, axis=0)
inp_r += act.linear(s_inst, self.V_r)
y_r = act.sigmoidal(inp_r)
#print(y_r)
Q = act.linear(y_r, self.W_r)
return y_r, y_m, g, Q
def feedforward(self, s_inst, s_trans):
g_strength = 4
y_r = act.sigmoid(s_inst, self.V_r)
g = act.hard_sigmoid(s_inst, self.W_g, g_strength)
g = np.transpose(g)
y_m = 1e-6 * np.ones((self.L, 1, self.M))
Q = act.linear(y_r, self.W_r)
for l in np.arange(self.L):
y_m[l, :, :], self.cumulative_memory[
l, :, :] = act.sigmoid_acc_leaky(
s_trans, self.V_m[l, :, :],
self.cumulative_memory[l, :, :], self.LEAK[l, 0, 0], g[l,
0])
Q += act.linear(y_m[l, :, :], self.W_m[l, :, :])
print('\t MEM STATE ', str(l), ':', y_m[l, :, :], '\t gate=',
g[l, :], '\t alpha=', self.ALPHA[l, 0, 0], '\t leak=',
self.LEAK[l, 0, 0])
return y_r, y_m, g, Q