In [1]:
# http://peterroelants.github.io/posts/rnn_implementation_part01/
In [1]:
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import LogNorm
%matplotlib inline
np.random.seed(seed=1)
In [2]:
# データセットを作成
# 01からなる長さ10の系列が20サンプル
nb_of_samples = 20
sequence_len = 10
X = np.zeros((nb_of_samples, sequence_len))
for row_idx in range(nb_of_samples):
X[row_idx, :] = np.around(np.random.rand(sequence_len))
X = X.astype(int)
print(X.shape)
# ターゲットは各サンプルの1の数（回帰問題）
t = np.sum(X, axis=1)
print(t.shape)
(20, 10)
(20,)
In [3]:
# forward処理の関数
def update_state(xk, sk, wx, wRec):
return sk * wRec + xk * wx
def forward_states(X, wx, wRec):
# すべての時系列サンプルの状態を記憶する行列 S[サンプル, 状態]
# unfoldすると時系列長の分だけ状態がある
# S[0]は初期状態とする（0で初期化）
# S[:, -1]が各時系列を入力したときの最終出力 = y
S = np.zeros((X.shape[0], X.shape[1] + 1))
for k in range(0, X.shape[1]):
S[:, k + 1] = update_state(X[:, k], S[:, k], wx, wRec)
return S
def cost(y, t):
return ((t - y) ** 2).sum() / nb_of_samples
In [5]:
def output_gradient(y, t):
return 2.0 * (y - t) / nb_of_samples
def backward_gradient(X, S, grad_out, wRec):
grad_over_time = np.zeros((X.shape[0], X.shape[1] + 1))
grad_over_time[:, -1] = grad_out
# 各レイヤでパラメータを共有しているため勾配を足し合わせる
wx_grad = 0
wRec_grad = 0
for k in range(X.shape[1], 0, -1):
wx_grad += np.sum(grad_over_time[:, k] * X[:, k-1])
wRec_grad += np.sum(grad_over_time[:, k] * S[:, k-1])
grad_over_time[:, k-1] = grad_over_time[:, k] * wRec
return (wx_grad, wRec_grad), grad_over_time
In [6]:
# gradient checking
params = [1.2, 1.2] # [wx, wRec]
eps = 1e-7
S = forward_states(X, params[0], params[1])
grad_out = output_gradient(S[:, -1], t)
backprop_grads, grad_over_time = backward_gradient(X, S, grad_out, params[1])
print(backprop_grads)
for p_idx, _ in enumerate(params):
grad_backprop = backprop_grads[p_idx]
params[p_idx] += eps
plus_cost = cost(forward_states(X, params[0], params[1])[:, -1], t)
params[p_idx] -= 2 * eps
min_cost = cost(forward_states(X, params[0], params[1])[:, -1], t)
params[p_idx] += eps
grad_num = (plus_cost - min_cost) / (2 * eps)
if not np.isclose(grad_num, grad_backprop):
raise ValueError('Numerical gradient of {:.6f} is not close to the backpropagation gradient of {:.6f}!'.format(float(grad_num), float(grad_backprop)))
print('No gradient errors found')
(274.59542913321798, 1593.2428444791924)
No gradient errors found
In [13]:
def update_rprop(X, t, W, W_prev_sign, W_delta, eta_p, eta_n):
# 各エポックで呼ばれる
# forward_states()の中で状態Sは毎回初期化している
S = forward_states(X, W[0], W[1])
grad_out = output_gradient(S[:, -1], t)
W_grads, _ = backward_gradient(X, S, grad_out, W[1])
W_sign = np.sign(W_grads)
for i, _ in enumerate(W):
if W_sign[i] == W_prev_sign[i]:
W_delta[i] *= eta_p
else:
W_delta[i] *= eta_n
return W_delta, W_sign
In [14]:
eta_p = 1.2
eta_n = 0.5
W = [-1.5, 2] # [wx, wRec]
W_delta = [0.001, 0.001]
W_sign = [0, 0]
ls_of_ws = [(W[0], W[1])]
for i in range(500):
W_delta, W_sign = update_rprop(X, t, W, W_sign, W_delta, eta_p, eta_n)
for i, _ in enumerate(W):
W[i] -= W_sign[i] * W_delta[i]
ls_of_ws.append((W[0], W[1]))
print('Final weights are: wx = {0}, wRec = {1}'.format(W[0], W[1]))
S: [[ 0. 0. -1.5 -3. -6. -12. -24. -48. -96.
-192. -385.5]
[ 0. 0. -1.5 -3. -7.5 -15. -31.5 -63. -127.5
-255. -510. ]
[ 0. -1.5 -4.5 -9. -19.5 -40.5 -82.5 -165. -330.
-660. -1321.5]
[ 0. 0. 0. -1.5 -4.5 -10.5 -21. -43.5 -88.5
-177. -355.5]
[ 0. -1.5 -4.5 -9. -19.5 -39. -78. -157.5 -315.
-630. -1260. ]
[ 0. 0. -1.5 -3. -6. -12. -24. -49.5 -99.
-199.5 -400.5]
[ 0. 0. 0. -1.5 -3. -6. -13.5 -28.5 -58.5
-118.5 -238.5]
[ 0. -1.5 -3. -6. -13.5 -27. -54. -109.5 -219.
-439.5 -880.5]
[ 0. -1.5 -4.5 -10.5 -21. -42. -85.5 -171. -343.5
-688.5 -1378.5]
[ 0. 0. -1.5 -3. -7.5 -15. -30. -61.5 -124.5
-249. -499.5]
[ 0. 0. -1.5 -4.5 -9. -19.5 -40.5 -81. -163.5
-328.5 -658.5]
[ 0. 0. 0. -1.5 -4.5 -9. -19.5 -40.5 -82.5
-166.5 -333. ]
[ 0. 0. 0. 0. 0. -1.5 -4.5 -10.5 -22.5
-45. -90. ]
[ 0. -1.5 -4.5 -10.5 -21. -43.5 -87. -175.5 -351.
-703.5 -1408.5]
[ 0. -1.5 -3. -6. -12. -24. -48. -96. -193.5
-388.5 -777. ]
[ 0. 0. -1.5 -4.5 -9. -18. -37.5 -75. -151.5
-304.5 -610.5]
[ 0. 0. 0. -1.5 -4.5 -9. -18. -36. -72.
-145.5 -292.5]
[ 0. 0. -1.5 -4.5 -9. -19.5 -40.5 -82.5 -165.
-330. -660. ]
[ 0. -1.5 -3. -7.5 -15. -30. -61.5 -123. -247.5
-496.5 -994.5]
[ 0. 0. 0. -1.5 -4.5 -10.5 -22.5 -45. -90.
-181.5 -364.5]]
S: [[ 0. 0. -1.4995 -2.99825025
-5.99500137 -11.98700525 -23.968017 -47.92404998
-95.82413794 -191.60036381 -384.60442744]
[ 0. 0. -1.4995 -2.99825025
-7.49450137 -14.9852555 -31.46251837 -62.90930548
-127.28665631 -254.50966929 -508.89208375]
[ 0. -1.4995 -4.49775025 -8.99325162
-19.48150662 -40.45277249 -82.3848186 -164.7284448 -329.37452537
-658.58436348 -1318.33893477]
[ 0. 0. 0. -1.4995 -4.49775025
-10.49275162 -20.98025687 -43.44952362 -88.37682248
-176.70945654 -354.83005836]
[ 0. -1.4995 -4.49775025 -8.99325162
-19.48150662 -38.95327249 -77.88706835 -157.23469317 -314.390769
-628.62434261 -1256.93437305]
[ 0. 0. -1.4995 -2.99825025
-5.99500137 -11.98700525 -23.968017 -49.42354998
-98.82238819 -199.09486519 -399.58968294]
[ 0. 0. 0. -1.4995 -2.99825025
-5.99500137 -13.48650525 -28.46576725 -58.41680161
-118.30389481 -238.04813768]
[ 0. -1.4995 -2.99825025 -5.99500137
-13.48650525 -26.96626725 -53.91905136 -109.31064319
-218.56663106 -438.5234788 -878.32719586]
[ 0. -1.4995 -4.49775025 -10.49275162
-20.98025687 -41.95002362 -85.37857223 -170.71445517
-342.84305311 -687.01418469 -1375.18436229]
[ 0. 0. -1.4995 -2.99825025
-7.49450137 -14.9852555 -29.96301837 -61.41055523
-124.28990519 -248.51766542 -498.410572 ]
[ 0. 0. -1.4995 -4.49775025
-8.99325162 -19.48150662 -40.45277249 -80.8853186 -163.22969455
-327.87727425 -657.09010985]
[ 0. 0. 0. -1.4995 -4.49775025
-8.99325162 -19.48150662 -40.45277249 -82.3848186 -166.2279448
-332.37277562]
[ 0. 0. 0. 0. 0.
-1.4995 -4.49775025 -10.49275162 -22.47975687
-44.94827387 -89.8740736 ]
[ 0. -1.4995 -4.49775025 -10.49275162
-20.98025687 -43.44952362 -86.87732248 -175.21070629
-350.33380723 -701.99194756 -1405.13239915]
[ 0. -1.4995 -2.99825025 -5.99500137
-11.98700525 -23.968017 -47.92404998 -95.82413794
-193.09986381 -387.60267769 -775.01155404]
[ 0. 0. -1.4995 -4.49775025
-8.99325162 -17.98200662 -37.45452224 -74.89031723 -151.2426893
-303.90925725 -609.16605987]
[ 0. 0. 0. -1.4995 -4.49775025
-8.99325162 -17.98200662 -35.95502224 -71.89206698
-145.24768792 -291.922252 ]
[ 0. 0. -1.4995 -4.49775025
-8.99325162 -19.48150662 -40.45277249 -82.3848186 -164.7284448
-329.37452537 -658.58436348]
[ 0. -1.4995 -2.99825025 -7.49450137 -14.9852555
-29.96301837 -61.41055523 -122.79040519 -247.01891517
-495.41382088 -992.07943485]
[ 0. 0. 0. -1.4995 -4.49775025
-10.49275162 -22.47975687 -44.94827387 -89.8740736 -181.20271017
-363.81431898]]
S: [[ 0. 0. -1.4989 -2.99615121
-5.98900665 -11.9714254 -23.92968223 -47.83304181
-95.61346728 -191.12175975 -383.53218556]
[ 0. 0. -1.4989 -2.99615121
-7.48790665 -14.96757661 -31.41758889 -62.80061842
-127.03105617 -253.92237817 -507.56544173]
[ 0. -1.4989 -4.49505121 -8.98515786
-19.45933205 -40.39615884 -82.24678191 -164.40309236
-328.62534132 -656.88919476 -1314.5547114 ]
[ 0. 0. 0. -1.4989 -4.49505121
-10.48405786 -20.95658326 -43.38901429 -88.22920066
-176.36134919 -354.0276009 ]
[ 0. -1.4989 -4.49505121 -8.98515786
-19.45933205 -38.89725884 -77.7517307 -156.9168345 -313.66106047
-626.97709378 -1253.26451275]
[ 0. 0. -1.4989 -2.99615121
-5.98900665 -11.9714254 -23.92968223 -49.33194181
-98.60961849 -198.6096664 -398.49976217]
[ 0. 0. 0. -1.4989 -2.99615121
-5.98900665 -13.4703254 -28.42473344 -58.31709968
-118.06895055 -237.50692524]
[ 0. -1.4989 -2.99615121 -5.98900665 -13.4703254
-26.92583344 -53.82204847 -109.08379268 -218.04759319
-437.35423403 -875.7262784 ]
[ 0. -1.4989 -4.49505121 -10.48405786
-20.95658326 -41.89011429 -85.23304945 -170.37234254 -342.0561755
-685.2349892 -1371.21511992]
[ 0. 0. -1.4989 -2.99615121
-7.48790665 -14.96757661 -29.91868889 -61.30336721
-124.03820072 -247.93995943 -497.1060849 ]
[ 0. 0. -1.4989 -4.49505121
-8.98515786 -19.45933205 -40.39615884 -80.74788191
-162.90584115 -327.13138587 -655.40182722]
[ 0. 0. 0. -1.4989 -4.49505121
-8.98515786 -19.45933205 -40.39615884 -82.24678191
-165.90199236 -331.62149253]
[ 0. 0. 0. 0. 0.
-1.4989 -4.49505121 -10.48405786 -22.45548326 -44.8862655
-89.7231561 ]
[ 0. -1.4989 -4.49505121 -10.48405786
-20.95658326 -43.38901429 -86.73030066 -174.86409798
-349.53584545 -700.18610148 -1401.10089825]
[ 0. -1.4989 -2.99615121 -5.98900665 -11.9714254
-23.92968223 -47.83304181 -95.61346728 -192.62065975
-386.52833677 -772.63149237]
[ 0. 0. -1.4989 -4.49505121
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-150.93441575 -303.20170364 -607.56878541]
[ 0. 0. 0. -1.4989 -4.49505121
-8.98515786 -17.96043205 -35.90110763 -71.76272405 -144.9454091
-291.23027824]
[ 0. 0. -1.4989 -4.49505121
-8.98515786 -19.45933205 -40.39615884 -82.24678191
-164.40309236 -328.62534132 -656.88919476]
[ 0. -1.4989 -2.99615121 -7.48790665
-14.96757661 -29.91868889 -61.30336721 -122.53930072
-246.44270822 -494.11322945 -989.18183436]
[ 0. 0. 0. -1.4989 -4.49505121
-10.48405786 -22.45548326 -44.8862655 -89.7231561 -180.84651673
-362.99300229]]
S: [[ 0. 0. -1.49818 -2.99363331
-5.98181821 -11.95274952 -23.88374503 -47.72402164
-95.36118555 -190.54881375 -382.24900866]
[ 0. 0. -1.49818 -2.99363331
-7.47999821 -14.94638283 -31.36374324 -62.67040446
-126.72492879 -253.21921821 -505.97757745]
[ 0. -1.49818 -4.49181331 -8.97545152
-19.43274773 -40.32830785 -82.08139819 -164.01340823
-327.72831206 -654.86015859 -1310.02665169]
[ 0. 0. 0. -1.49818 -4.49181331
-10.47363152 -20.92820104 -43.31649275 -88.05232949
-175.94440374 -353.06676867]
[ 0. -1.49818 -4.49181331 -8.97545152
-19.43274773 -38.83012785 -77.58958488 -156.53613671
-312.78737764 -625.00548226 -1248.87345454]
[ 0. 0. -1.49818 -2.99363331
-5.98181821 -11.95274952 -23.88374503 -49.22220164
-98.35481887 -198.02881196 -397.19539149]
[ 0. 0. 0. -1.49818 -2.99363331
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-117.78756659 -236.85893982]
[ 0. -1.49818 -2.99363331 -5.98181821
-13.45092952 -26.87737834 -53.70583985 -108.81211507
-217.42619209 -435.95484851 -872.61443919]
[ 0. -1.49818 -4.49181331 -10.47363152
-20.92820104 -41.81831275 -85.05869618 -169.96258553
-341.11401915 -683.10539079 -1366.46570977]
[ 0. 0. -1.49818 -2.99363331
-7.47999821 -14.94638283 -29.86556324 -61.17495115
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[ 0. 0. -1.49818 -4.49181331
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-162.51795492 -326.23830716 -653.3810406 ]
[ 0. 0. 0. -1.49818 -4.49181331
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[ 0. 0. 0. 0. 0.
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-44.81194607 -89.54233439]
[ 0. -1.49818 -4.49181331 -10.47363152
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[ 0. -1.49818 -2.99363331 -5.98181821
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[ 0. 0. 0. -1.49818 -4.49181331
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-144.58338719 -290.40181262]
[ 0. 0. -1.49818 -4.49181331
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[ 0. -1.49818 -2.99363331 -7.47999821
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[ 0. 0. 0. -1.49818 -4.49181331
-10.47363152 -22.42638104 -44.81194607 -89.54233439
-180.41988173 -362.00957928]]
S: [[ 0. 0. -1.497316 -2.9906132 -5.9731996
-11.93036714 -23.82871317 -47.59347007 -95.05919926
-189.86325963 -380.71424227]
[ 0. 0. -1.497316 -2.9906132 -7.4705156
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[ 0. -1.497316 -4.4879292 -8.96381281
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[ 0. 0. 0. -1.497316 -4.4879292
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-351.9168117 ]
[ 0. -1.497316 -4.4879292 -8.96381281
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[ 0. 0. -1.497316 -2.9906132 -5.9731996
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-10.46112881 -22.39149594 -44.72289311 -89.32574997
-179.90906563 -360.83257133]]
S: [[ 0. 0. -1.4962792 -2.98699104
-5.96286809 -11.90354955 -23.76280836 -47.43720007 -94.6978958
-189.04343968 -378.87976573]
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[ 0. 1.00147654 2.00260603 3.0033886 3.00234783 3.00130743
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[ 0. 0. 1.00147654 2.00260603 2.00191207 3.00269488
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[ 0. 0. 0. 1.00147654 2.00260603 2.00191207
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[ 0. 0. 0. 0. 0. 1.00147654
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[ 0. 1.00147654 2.00260603 3.0033886 3.00234783 4.00278396
4.00139687 5.0014868 4.99975363 5.99949759 6.99889512]
[ 0. 1.00147654 1.00112949 1.00078257 1.00043577 1.00008909
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[ 0. 0. 1.00147654 2.00260603 2.00191207 2.00121834
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[ 0. 0. 0. 1.00147654 2.00260603 2.00191207
2.00121834 2.00052486 1.99983161 3.00061514 4.00105187]
[ 0. 0. 1.00147654 2.00260603 2.00191207 3.00269488
4.00313089 5.00322021 5.00148644 4.99975327 4.9980207 ]
[ 0. 1.00147654 1.00112949 2.00225911 2.00156526 2.00087166
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[ 0. 0. 0. 1.00147654 2.00260603 3.0033886
4.00382437 4.00243692 4.00104995 5.00114 6.00088348]]
S: [[ 0. 0. 1.00146028 1.00111607 1.00077198 1.000428
1.00008415 0.99974041 0.99939679 0.99905329 2.00017018]
[ 0. 0. 1.00146028 1.00111607 2.00223225 2.00154407
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[ 0. 1.00146028 2.00257635 2.00188804 3.00266026 4.00308849
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[ 0. 0. 0. 1.00146028 2.00257635 3.00334832
3.00231605 4.0027444 5.0028289 5.00110939 6.00085074]
[ 0. 1.00146028 2.00257635 2.00188804 3.00266026 3.00162822
3.00059653 4.00102548 3.99965029 3.99827557 3.99690133]
[ 0. 0. 1.00146028 1.00111607 1.00077198 1.000428
1.00008415 2.00120069 2.00051286 3.00128554 4.00171425]
[ 0. 0. 0. 1.00146028 1.00111607 1.00077198
2.00188828 3.00266049 4.00308873 5.00317311 6.00291376]
[ 0. 1.00146028 1.00111607 1.00077198 2.00188828 2.00120021
2.00051238 3.00128507 3.0002535 4.00068257 5.00076778]
[ 0. 1.00146028 2.00257635 3.00334832 3.00231605 3.00128412
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[ 0. 0. 1.00146028 1.00111607 2.00223225 2.00154407
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[ 0. 0. 1.00146028 2.00257635 2.00188804 3.00266026
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[ 0. 0. 0. 1.00146028 2.00257635 2.00188804
3.00266026 4.00308849 5.00317288 6.00291352 6.00085027]
[ 0. 0. 0. 0. 0. 1.00146028
2.00257635 3.00334832 4.00377632 4.00240019 4.00102453]
[ 0. 1.00146028 2.00257635 3.00334832 3.00231605 4.0027444
4.00136862 5.0014536 4.99973455 5.99947638 6.99887459]
[ 0. 1.00146028 1.00111607 1.00077198 1.000428 1.00008415
0.99974041 0.99939679 2.00051357 3.00128625 3.00025468]
[ 0. 0. 1.00146028 2.00257635 2.00188804 2.00119998
3.00197243 3.00094062 4.00136945 5.00145442 6.00119566]
[ 0. 0. 0. 1.00146028 2.00257635 2.00188804
2.00119998 2.00051215 1.99982455 3.00059748 4.00102642]
[ 0. 0. 1.00146028 2.00257635 2.00188804 3.00266026
4.00308849 5.00317288 5.00145324 4.9997342 4.99801575]
[ 0. 1.00146028 1.00111607 2.00223225 2.00154407 2.00085612
3.00162869 3.000597 4.00102595 5.00111104 6.00085239]
[ 0. 0. 0. 1.00146028 2.00257635 3.00334832
4.00377632 4.00240019 4.00102453 5.00110962 6.00085098]]
S: [[ 0. 0. 1.00144077 1.00109996 1.00075926 1.00041868
1.00007822 0.99973787 0.99939764 0.99905752 2.00015829]
[ 0. 0. 1.00144077 1.00109996 2.00220003 2.00151864
3.00227825 3.00125651 4.00167588 4.00031403 3.99895263]
[ 0. 1.00144077 2.00254072 2.00185922 3.00261871 4.00303762
5.00311607 5.0014134 4.99971131 4.9980098 5.99774964]
[ 0. 0. 0. 1.00144077 2.00254072 3.00329999
3.0022779 4.00269693 5.00277549 5.00107294 6.00081173]
[ 0. 1.00144077 2.00254072 2.00185922 3.00261871 3.00159685
3.00057535 4.00099495 3.99963333 3.99827217 3.99691147]
[ 0. 0. 1.00144077 1.00109996 1.00075926 1.00041868
1.00007822 2.00117864 2.00049759 3.00125755 4.00167692]
[ 0. 0. 0. 1.00144077 1.00109996 1.00075926
2.00185945 3.00261894 4.00303785 5.0031163 6.0028544 ]
[ 0. 1.00144077 1.00109996 1.00075926 2.00185945 2.00117817
2.00049713 3.00125709 3.00023569 4.00065542 5.00073468]
[ 0. 1.00144077 2.00254072 3.00329999 3.0022779 3.00125616
4.00167553 4.00031368 5.00039305 6.00013208 6.99953087]
[ 0. 0. 1.00144077 1.00109996 2.00220003 2.00151864
2.00083748 3.00159732 4.00201658 4.00065461 5.00073386]
[ 0. 0. 1.00144077 2.00254072 2.00185922 3.00261871
4.00303762 4.0016753 5.00175421 6.00149278 7.00089111]
[ 0. 0. 0. 1.00144077 2.00254072 2.00185922
3.00261871 4.00303762 5.00311607 6.00285417 6.00081127]
[ 0. 0. 0. 0. 0. 1.00144077
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[ 0. 1.00144077 2.00254072 3.00329999 3.0022779 4.00269693
4.00133472 5.00141375 4.99971166 5.99945092 6.99884995]
[ 0. 1.00144077 1.00109996 1.00075926 1.00041868 1.00007822
0.99973787 0.99939764 2.00049829 3.00125824 3.00023685]
[ 0. 0. 1.00144077 2.00254072 2.00185922 2.00117794
3.00193767 3.00091604 4.00133553 5.00141456 6.00115324]
[ 0. 0. 0. 1.00144077 2.00254072 2.00185922
2.00117794 2.0004969 1.99981609 3.00057627 4.00099588]
[ 0. 0. 1.00144077 2.00254072 2.00185922 3.00261871
4.00303762 5.00311607 5.0014134 4.99971131 4.9980098 ]
[ 0. 1.00144077 1.00109996 2.00220003 2.00151864 2.00083748
3.00159732 3.00057581 4.00099542 5.00107456 6.00081336]
[ 0. 0. 0. 1.00144077 2.00254072 3.00329999
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S: [[ 0. 0. 1.00141736 1.00108062 1.000744 1.0004075
1.0000711 0.99973482 0.99939865 0.9990626 2.00014401]
[ 0. 0. 1.00141736 1.00108062 2.00216136 2.00148812
3.00223246 3.00122294 4.00163111 4.00028554 3.99894041]
[ 0. 1.00141736 2.00249798 2.00182462 3.00256885 4.00297657
5.0030479 5.00136559 4.99968385 4.99800267 5.99773941]
[ 0. 0. 0. 1.00141736 2.00249798 3.00324198
3.00223212 4.00263995 5.0027114 5.0010292 6.00076492]
[ 0. 1.00141736 2.00249798 2.00182462 3.00256885 3.00155922
3.00054992 4.00095832 3.99961297 3.99826808 3.99692363]
[ 0. 0. 1.00141736 1.00108062 1.000744 1.0004075
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[ 0. 0. 0. 1.00141736 1.00108062 1.000744
2.00182485 3.00256908 4.0029768 5.00304813 6.00278318]
[ 0. 1.00141736 1.00108062 1.000744 2.00182485 2.00115172
2.00047882 3.0012235 3.00021432 4.00062283 5.00069495]
[ 0. 1.00141736 2.00249798 3.00324198 3.00223212 3.0012226
4.00163077 4.0002852 5.00035743 6.00009338 6.99949316]
[ 0. 0. 1.00141736 1.00108062 2.00216136 2.00148812
2.0008151 3.00155967 4.00196773 4.00062204 5.00069416]
[ 0. 0. 1.00141736 2.00249798 2.00182462 3.00256885
4.00297657 4.00163055 5.00170233 6.00143783 7.00083716]
[ 0. 0. 0. 1.00141736 2.00249798 2.00182462
3.00256885 4.00297657 5.0030479 6.00278295 6.00076447]
[ 0. 0. 0. 0. 0. 1.00141736
2.00249798 3.00324198 4.00364948 4.00230322 4.00095742]
[ 0. 1.00141736 2.00249798 3.00324198 3.00223212 4.00263995
4.00129404 5.00136593 4.99968419 5.99942037 6.99882038]
[ 0. 1.00141736 1.00108062 1.000744 1.0004075 1.0000711
0.99973482 0.99939865 2.00047995 3.00122463 3.00021545]
[ 0. 0. 1.00141736 2.00249798 2.00182462 2.0011515
3.00189595 3.00088654 4.00129483 5.00136672 6.00110234]
[ 0. 0. 0. 1.00141736 2.00249798 2.00182462
2.0011515 2.0004786 1.99980592 3.00055083 4.00095923]
[ 0. 0. 1.00141736 2.00249798 2.00182462 3.00256885
4.00297657 5.0030479 5.00136559 4.99968385 4.99800267]
[ 0. 1.00141736 1.00108062 2.00216136 2.00148812 2.0008151
3.00155967 3.00055038 4.00095878 5.00103078 6.00076651]
[ 0. 0. 0. 1.00141736 2.00249798 3.00324198
4.00364948 4.00230322 4.00095742 5.00102943 6.00076515]]
S: [[ 0. 0. 1.00138926 1.00105742 1.00072569 1.00039407
1.00006256 0.99973116 0.99939987 0.99906869 2.00012688]
[ 0. 0. 1.00138926 1.00105742 2.00211495 2.00145149
3.00217751 3.00118266 4.00157739 4.00025135 3.99892575]
[ 0. 1.00138926 2.00244668 2.00178311 3.00250902 4.00290332
5.0029661 5.00130822 4.99965089 4.99799411 5.99772714]
[ 0. 0. 0. 1.00138926 2.00244668 3.00317237
3.00217718 4.00257159 5.00263448 5.00097671 6.00070875]
[ 0. 1.00138926 2.00244668 2.00178311 3.00250902 3.00151406
3.00051942 4.00091437 3.99958855 3.99826317 3.99693822]
[ 0. 0. 1.00138926 1.00105742 1.00072569 1.00039407
1.00006256 2.00112042 2.00045729 3.00118364 4.00157837]
[ 0. 0. 0. 1.00138926 1.00105742 1.00072569
2.00178333 3.00250924 4.00290354 5.00296632 6.0026977 ]
[ 0. 1.00138926 1.00105742 1.00072569 2.00178333 2.00111998
2.00045685 3.0011832 3.00018867 4.00058374 5.00064729]
[ 0. 1.00138926 2.00244668 3.00317237 3.00217718 3.00118233
4.00157706 4.00025102 5.00031468 6.00004694 6.99944791]
[ 0. 0. 1.00138926 1.00105742 2.00211495 2.00145149
2.00078825 3.00151449 4.00190912 4.00058297 5.00064652]
[ 0. 0. 1.00138926 2.00244668 2.00178311 3.00250902
4.00290332 4.00157684 5.00164006 6.00137188 7.00077241]
[ 0. 0. 0. 1.00138926 2.00244668 2.00178311
3.00250902 4.00290332 5.0029661 6.00269748 6.00070831]
[ 0. 0. 0. 0. 0. 1.00138926
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[ 0. 1.00138926 2.00244668 3.00317237 3.00217718 4.00257159
4.00124522 5.00130855 4.99965122 5.9993837 6.99878489]
[ 0. 1.00138926 1.00105742 1.00072569 1.00039407 1.00006256
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[ 0. 0. 1.00138926 2.00244668 2.00178311 2.00111976
3.00184589 3.00085115 4.00124599 5.00130932 6.00104125]
[ 0. 0. 0. 1.00138926 2.00244668 2.00178311
2.00111976 2.00045663 1.99979372 3.00052029 4.00091525]
[ 0. 0. 1.00138926 2.00244668 2.00178311 3.00250902
4.00290332 5.0029661 5.00130822 4.99965089 4.99799411]
[ 0. 1.00138926 1.00105742 2.00211495 2.00145149 2.00078825
3.00151449 3.00051986 4.00091481 5.00097825 6.00071029]
[ 0. 0. 0. 1.00138926 2.00244668 3.00317237
4.00356645 4.00223975 4.00091349 5.00097693 6.00070897]]
Final weights are: wx = 1.001355547207059, wRec = 0.9996744737846419
In [9]:
# 入力は任意の長さでOK
test_input = np.asmatrix([[0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ,1]])
test_output = forward_states(test_input, W[0], W[1])
In [10]:
test_input.sum()
Out[10]:
29
In [11]:
test_output.shape
Out[11]:
(1, 37)
In [12]:
test_output[:, -1][0]
Out[12]:
28.902835057275531
In [ ]:
Content source: aidiary/notebooks
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