循環(huán)神經(jīng)網(wǎng)絡(luò)代碼RNN-超全注釋 #inputs t時(shí)刻序列，也就是相當(dāng)于輸入 #targets t+1時(shí)刻序列，也就是相當(dāng)于輸出 #hprev t-1時(shí)刻的隱藏層神經(jīng)元激活值 def lossFun(inputs, targets, hprev): xs, hs, ys, ps = {}, {}, {}, {} hs[-1] = np.copy(hprev) print('hs=',hs) loss = 0 #前向傳導(dǎo) inputs 6xn for t in range(len(inputs)):