test_utils.py

import numpy as np

in_shape, out_shape = 64, 8
seed = 42


def get_inputs(dense_layers: int = 1):
    rng = np.random.default_rng(seed)
    input = rng.random(in_shape)
    if dense_layers > 1:
        weights = []
        weights.append(rng.uniform(-0.05, 0.05, (in_shape, out_shape)))
        for i in range(dense_layers - 1):
            weights.append(rng.uniform(-0.05, 0.05, (out_shape, out_shape)))
    else:
        weights = rng.uniform(-0.05, 0.05, (in_shape, out_shape))
    return input, weights


class Exp_Outputs_MSE:
    def get_exp_forward_out(self):
        return 0.30023515

    def get_exp_gradients(self):
        return np.array(
            [
                [
                    -0.00706387,
                    -0.01753505,
                    -0.00441881,
                    -0.00945725,
                    -0.02830696,
                    -0.00076180,
                    -0.00746438,
                    -0.00668549,
                    -0.02724645,
                    -0.01717544,
                    -0.01966256,
                    -0.00228859,
                    -0.01112922,
                    -0.00553870,
                    -0.01739331,
                    -0.02414879,
                    -0.01391922,
                    -0.02925571,
                    -0.00538653,
                    -0.01151049,
                    -0.00755976,
                    -0.02017106,
                    -0.00091569,
                    -0.00333996,
                    -0.00692552,
                    -0.02516754,
                    -0.01666497,
                    -0.02988113,
                    -0.02642845,
                    -0.00990472,
                    -0.00797618,
                    -0.00101532,
                    -0.02106796,
                    -0.01967314,
                    -0.01657638,
                    -0.02532902,
                    -0.02718995,
                    -0.01638422,
                    -0.02415908,
                    -0.01031831,
                    -0.01758900,
                    -0.00522881,
                    -0.00936672,
                    -0.02148854,
                    -0.00524188,
                    -0.00610111,
                    -0.01914130,
                    -0.02223975,
                    -0.00992202,
                    -0.02688273,
                    -0.02500287,
                    -0.03101993,
                    -0.00665861,
                    -0.01047341,
                    -0.00921358,
                    -0.00685222,
                    -0.01690888,
                    -0.01347684,
                    -0.02688134,
                    -0.02767093,
                    -0.01036241,
                    -0.01652824,
                    -0.01358637,
                    -0.00734379,
                ]
            ]
        )


class Exp_Outputs_CrossEntropy:
    def get_exp_forward_out(self):
        return 0.02467970

    def get_exp_gradients(self):
        return np.array(
            [
                [
                    0,
                    0,
                    0,
                    0,
                    0,
                    -1.02498677,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                    0,
                ]
            ]
        )


class Exp_Outputs_Dense_MSE:
    def get_exp_forward_out(self):
        return np.array([[-0.10312795, 0.2598164, -0.33941832, 0.04407372, -0.23454618, 0.13231218, 0.00909819, 0.00293661]])

    def get_exp_updated_bias(self):
        return np.array([0.00275782, 0.00185046, 0.00334855, 0.00238982, 0.00308637, 0.00216922, 0.00247725, 0.00249266])

    def get_exp_updated_weights(self):
        return np.array(
            [
                [1.56062627e-02, 6.79011410e-03, 8.51234328e-03, -1.77553780e-02, -4.45295051e-02, -4.64938022e-03, -2.66242474e-02, -7.21792737e-03],
                [3.65506560e-02, -2.57939249e-02, -4.27001193e-02, -2.08127722e-02, -1.92860849e-02, 1.71436761e-02, 6.79042842e-03, 2.94837952e-02],
                [1.87992118e-02, -7.77251367e-03, 3.42770964e-02, -3.12508196e-02, -4.50788438e-02, -3.91327254e-02, 2.43629012e-02, -1.67208561e-03],
                [-3.19496058e-02, 1.39492843e-03, -3.24336216e-02, 2.12986190e-02, -3.23203998e-03, -1.03851333e-02, -1.81212332e-02, 1.47665599e-02],
                [-1.35590145e-02, -4.10607345e-02, -3.78840528e-02, 4.64148335e-02, 4.11487333e-02, 2.01750044e-02, -2.31797025e-02, 4.71523888e-02],
                [3.05656809e-02, 2.34943666e-02, -1.79693406e-03, -2.04442851e-02, -3.73497792e-02, 4.23765779e-02, -2.00550584e-03, -2.73317695e-02],
                [-1.73052512e-02, 9.33041424e-03, -2.97740120e-02, 3.74804139e-02, 2.82011088e-02, 2.35973746e-02, -4.90515959e-03, 1.46281458e-02],
                [1.05776209e-02, 1.64392404e-02, -3.89233977e-02, -6.54071057e-03, -4.34125029e-02, 1.10422797e-03, -1.50665976e-02, -3.35881896e-02],
                [-3.93063910e-02, 9.00152605e-03, -3.25117111e-02, 4.28181775e-02, 8.50151945e-03, -1.50351133e-02, 9.40891914e-03, -4.74002697e-02],
                [4.70980033e-02, -9.36235650e-04, 2.97816601e-02, -4.06506583e-02, 5.58885513e-05, 4.76854621e-05, 4.48983647e-02, 8.29546340e-03],
                [-1.62846595e-03, -2.26162877e-02, -1.56014673e-02, 2.95337918e-03, -4.96443594e-03, -4.70344499e-02, 3.35477516e-02, 4.05403525e-02],
                [-3.34192403e-02, 7.11855479e-03, -3.60391103e-02, 1.94388069e-02, -1.90162864e-02, 1.79526210e-02, 2.49952953e-02, 2.91748568e-02],
                [-3.74502428e-02, 4.27926295e-02, -2.48225890e-02, -4.47200239e-02, 7.47244991e-03, -1.15110865e-02, 3.45739909e-02, 3.24300826e-02],
                [-1.60170831e-02, 4.68124263e-02, -1.81531608e-02, 3.47196148e-03, -2.18641479e-02, 4.53891046e-02, -3.15010287e-02, -4.34580743e-02],
                [-5.26743755e-03, 5.00580780e-02, 4.06525210e-02, 2.59204786e-02, 4.04477865e-02, 4.03065272e-02, 2.98428582e-03, -1.73018146e-02],
                [2.78279260e-02, 1.65866222e-02, -1.18733076e-02, -4.00102735e-02, 2.53803022e-02, -2.32610181e-02, 4.42442447e-02, -2.53365133e-02],
                [-3.61947641e-02, 3.41375023e-02, -3.28145139e-02, -3.07478141e-02, 1.16499308e-02, 3.86592187e-02, -2.89826859e-02, -1.75852422e-02],
                [2.79164799e-02, 4.73007336e-02, 2.87813600e-04, -3.54577415e-02, -4.84094061e-02, -2.68959627e-02, -3.66596878e-02, 1.79249421e-02],
                [-3.55342887e-02, 2.16449052e-03, 2.21976042e-02, 1.00895474e-02, -2.74680629e-02, 3.22077647e-02, 2.35909652e-02, 2.59614028e-02],
                [-3.51522081e-02, -3.64557505e-02, 4.48714122e-02, -8.73261970e-03, -1.79555845e-02, 2.28623278e-04, 1.78512149e-02, 4.71368507e-02],
                [-1.92647073e-02, 4.38836515e-02, -4.49755602e-02, 7.33149424e-03, 1.57372467e-02, -3.77658010e-02, -3.40880640e-02, -6.19891426e-03],
                [4.76009101e-02, 1.02602914e-02, 4.44894694e-02, 3.12833413e-02, -2.16764305e-03, 2.92453896e-02, -4.73380685e-02, -3.82018872e-02],
                [3.56198698e-02, 3.14779468e-02, -2.34855004e-02, 5.39674843e-03, 1.35975108e-02, 3.88795510e-02, 1.27153825e-02, -6.32322393e-03],
                [-1.01185283e-02, -5.75910741e-03, 1.81837603e-02, 3.88834588e-02, -1.85381365e-03, -2.32786667e-02, -2.41212752e-02, 2.68276744e-02],
                [3.38035189e-02, -3.80318277e-02, -4.07376625e-02, 1.13035589e-02, -3.29803005e-02, 3.41549031e-02, -1.70382783e-02, -3.36725637e-02],
                [4.26338241e-02, -3.30866538e-02, -2.08762363e-02, -3.41735110e-02, -3.78502682e-02, -4.74629849e-02, -4.39782925e-02, -3.20506878e-02],
                [-4.33746725e-02, 9.97802988e-03, 1.96342878e-02, -9.52157751e-03, -1.67604201e-02, 1.46504398e-03, 3.86566818e-02, 3.62765379e-02],
                [-4.55316901e-02, -3.17691006e-02, -2.61788331e-02, -2.49565598e-02, 7.25845946e-03, -8.27873778e-03, -4.49660756e-02, -1.25293983e-02],
                [2.80079758e-03, -3.95473018e-02, 3.38625014e-02, -4.44350913e-02, 4.29603793e-02, -3.97539996e-02, 3.47397104e-02, 4.06499058e-02],
                [4.98407930e-02, 3.14665437e-02, 3.02349739e-02, 1.58806890e-02, 3.00077740e-02, -3.50630954e-02, 5.29888971e-03, 3.12489481e-03],
                [3.78111340e-02, -2.34191166e-03, -8.99717957e-03, 1.57361720e-02, -2.10550595e-02, -3.44076045e-02, -3.67306988e-04, -6.45462517e-03],
                [-2.40747891e-02, -1.14584817e-02, -1.01210047e-02, -1.49382725e-02, -9.06750374e-03, 2.06730757e-02, -1.79155841e-02, 4.72974628e-02],
                [4.25333679e-02, -1.30603067e-03, -1.60728395e-02, 4.32214141e-03, 3.58616635e-02, 1.59655213e-02, 3.12463362e-02, 4.08439897e-03],
                [1.43134240e-02, -2.04989184e-02, 2.47298181e-02, -2.88742110e-02, 2.06231866e-02, 3.68755162e-02, -3.58719938e-02, 1.23614036e-02],
                [-3.91954742e-02, 2.34404579e-02, -3.99783477e-02, 4.47161347e-02, -3.48099880e-02, 4.69065942e-02, 3.12516280e-02, 1.05386432e-02],
                [2.87849382e-02, 2.98620928e-02, 4.52371612e-02, -2.42088623e-02, 9.59236734e-03, -4.00840752e-02, 1.20859388e-02, -3.23985815e-02],
                [6.85336161e-03, 7.48346560e-03, -2.96643679e-03, 2.57366616e-03, 2.67933235e-02, 3.02062985e-02, -4.62829805e-04, 1.02831936e-02],
                [4.44355346e-02, -3.71463709e-02, -3.66967246e-02, -4.00922522e-02, 1.72545277e-02, -7.10726157e-03, 2.86105834e-02, 1.83089115e-02],
                [-1.60104502e-02, 4.02565412e-02, 2.70130299e-02, -2.24042349e-02, -1.28804734e-02, -1.80637855e-02, -3.36767212e-02, -3.46560553e-02],
                [4.54599708e-02, -4.97013284e-03, -9.42511484e-03, 2.45693028e-02, 7.36659719e-03, 4.50669751e-02, 2.96894498e-02, -3.93426046e-04],
                [-1.11584663e-02, 4.94720861e-02, 2.32398473e-02, 4.61641774e-02, -3.68029326e-02, 3.60016488e-02, 1.47903245e-02, -3.67181636e-02],
                [1.11221764e-02, 2.01504733e-02, -4.59814705e-02, -2.57825642e-03, 3.51098999e-02, -1.86578371e-02, -2.08243611e-03, -3.69300484e-03],
                [-1.78760551e-02, 4.31400016e-02, 3.04742735e-02, -3.72676551e-02, 5.18647432e-02, 3.94390300e-02, -1.98744200e-02, 3.54351811e-02],
                [-3.84965949e-02, 5.04884943e-02, 1.76144484e-02, 1.57590006e-02, -3.99918482e-02, 4.03809324e-02, -4.63262387e-02, -2.51385719e-02],
                [-3.34025919e-02, 2.92168576e-02, -2.73927171e-02, 4.30527702e-02, 1.81955621e-02, -4.45783734e-02, -4.73952964e-02, -4.27596681e-02],
                [1.28119122e-02, 3.16373631e-02, -2.34499294e-02, 3.68641205e-02, -4.17930111e-02, 3.18420976e-02, 4.47731502e-02, 2.92168409e-02],
                [2.08806749e-02, 3.45150344e-02, -4.46873829e-02, -2.88957879e-02, -3.63117307e-02, 1.29362475e-03, 2.54786946e-02, 1.39670363e-02],
                [3.59082669e-02, -3.39451618e-02, 2.44275890e-02, -3.00067998e-02, -2.20342390e-02, 2.16159169e-02, 4.87347394e-02, 1.18730636e-02],
                [-4.26677689e-02, 1.28938276e-02, -4.34795767e-02, 4.00456116e-02, 2.30642594e-02, -3.12067345e-02, -3.91371846e-02, -2.99454480e-02],
                [4.83881272e-02, -3.88532947e-03, 2.88760625e-02, 1.39748165e-02, 7.67264236e-03, -3.51838209e-02, 4.49486487e-02, -1.95173807e-02],
                [8.35303217e-03, 2.03475151e-02, 1.55927176e-02, 4.45371829e-02, -3.45391110e-02, 1.26891863e-03, -9.10133775e-03, -2.08482426e-03],
                [-3.80579457e-02, -3.65769155e-02, -2.21677925e-02, -1.95119455e-02, -7.18695577e-03, 1.11147249e-02, 1.34811494e-02, -8.80055875e-03],
                [-6.95149321e-03, -2.67809760e-02, 1.14656771e-02, -1.64153036e-02, -4.39652801e-02, -6.45298418e-03, -6.37320452e-04, -2.54791807e-02],
                [9.07933246e-03, 7.80746900e-03, 2.24265028e-02, 1.63837187e-02, 1.72952786e-02, -1.69363786e-02, 3.03902272e-02, 6.57168450e-03],
                [-4.91346139e-03, 1.39061278e-02, -1.15729887e-02, 2.95913988e-03, 2.58469675e-02, 4.01699506e-02, 4.38525937e-02, 2.12102896e-03],
                [4.18062136e-03, 3.14317495e-02, -1.59406252e-02, 3.56040336e-02, 1.82377966e-03, -3.67207564e-02, -4.08600196e-02, 3.61454114e-02],
                [-4.31776010e-02, -2.10896526e-02, -1.50502957e-02, -3.16038318e-02, -1.71942823e-02, 2.52647456e-02, -4.73948643e-02, 3.38612646e-02],
                [3.72232869e-02, -1.17214099e-02, -3.27342562e-02, 1.14432275e-02, -3.62774022e-02, -1.22743389e-02, 4.72518355e-02, 5.09641245e-02],
                [2.75960229e-02, -1.86451618e-02, 1.92346219e-02, 2.08747257e-02, -1.07843652e-02, 1.43921142e-02, -4.85809222e-02, -2.87457686e-02],
                [2.82468367e-03, -3.34129371e-02, -3.30258049e-02, 3.39041352e-02, 4.92667817e-02, 5.84538374e-03, 3.41906957e-02, 4.93176505e-02],
                [-3.39970775e-02, -3.93858645e-03, -8.50455090e-03, -4.03977111e-02, 2.75959522e-02, -5.17218467e-03, -1.41150213e-03, -3.32666039e-02],
                [-3.06081362e-02, 4.15821075e-02, -4.39576060e-02, -2.55889390e-02, -1.93400923e-02, 4.16653929e-05, 9.81154293e-03, 5.03279502e-04],
                [-4.00296487e-02, -2.45873090e-02, 3.62515561e-02, 1.51096797e-02, 1.66594293e-02, 1.82464477e-02, 2.76905354e-02, -4.27802093e-02],
                [-1.12294331e-02, 5.36834262e-03, -1.35927191e-02, 3.62760909e-02, 6.18316815e-04, 2.85222102e-02, 3.70966494e-02, 2.38602911e-03],
            ]
        )


class Exp_Outputs_Dense_Softmax_CrossEntropy:
    def get_exp_forward_out(self):
        return np.array([[0.11417257, 0.16412945, 0.09014512, 0.13227892, 0.10011233, 0.14448147, 0.12773237, 0.12694776]])

    def get_exp_updated_bias(self):
        return np.array([0.00086619, -0.00313036, 0.00278839, -0.00058231, 0.00199101, -0.00155852, -0.00021859, -0.00015582])

    def get_exp_updated_weights(self):
        return np.array(
            [
                [0.01414223, 0.00293518, 0.00807881, -0.02005568, -0.04537726, -0.00753448, -0.02871071, -0.00926773],
                [0.03572046, -0.0279799, -0.04294596, -0.02211718, -0.01976681, 0.01550765, 0.00560728, 0.02832143],
                [0.01717507, -0.01204903, 0.03379615, -0.03380268, -0.04601931, -0.04233335, 0.02204825, -0.00394606],
                [-0.03326876, -0.00207853, -0.03282426, 0.01922595, -0.0039959, -0.01298474, -0.02000123, 0.01291959],
                [-0.01373716, -0.04152982, -0.03793681, 0.04613493, 0.04104558, 0.01982394, -0.02343359, 0.04690296],
                [0.02872017, 0.01863497, -0.00234343, -0.02334396, -0.03841843, 0.03873971, -0.00463563, -0.02991568],
                [-0.01874504, 0.00553932, -0.03020037, 0.03521821, 0.02736739, 0.02076005, -0.00695707, 0.01261228],
                [0.00909068, 0.012524, -0.03936372, -0.008877, -0.04427352, -0.00182601, -0.0171857, -0.03567006],
                [-0.03954873, 0.00836342, -0.03258348, 0.04243741, 0.00836119, -0.01551269, 0.00906354, -0.04773958],
                [0.04624604, -0.00317952, 0.02952937, -0.04198927, -0.00043744, -0.00163123, 0.0436842, 0.00710263],
                [-0.00232988, -0.02446316, -0.01580917, 0.00185132, -0.00537059, -0.04841669, 0.03254814, 0.0395583],
                [-0.03517233, 0.00250251, -0.03655824, 0.01668434, -0.02003142, 0.01449789, 0.02249688, 0.02672034],
                [-0.03866819, 0.03958566, -0.02518325, -0.04663368, 0.00676719, -0.01391125, 0.03283823, 0.03072482],
                [-0.01757344, 0.04271441, -0.01861404, 0.00102661, -0.02276536, 0.04232207, -0.03371907, -0.04563714],
                [-0.00610621, 0.04784951, 0.04040414, 0.02460259, 0.03996209, 0.0386536, 0.00178891, -0.01847619],
                [0.02739808, 0.01545479, -0.0120006, -0.04068566, 0.0251314, -0.0241081, 0.04363164, -0.02593835],
                [-0.03724383, 0.03137522, -0.03312517, -0.03239611, 0.01104246, 0.03659187, -0.03047776, -0.01905405],
                [0.02779576, 0.04698287, 0.00025207, -0.03564741, -0.04847931, -0.02713386, -0.03683173, 0.01775592],
                [-0.03709986, -0.00195779, 0.021734, 0.00762972, -0.02837461, 0.02912257, 0.0213598, 0.02376944],
                [-0.03634708, -0.03960196, 0.04451758, -0.01061001, -0.01864748, -0.00212606, 0.01614835, 0.0454639],
                [-0.02069873, 0.04010776, -0.04540021, 0.00507836, 0.01490687, -0.04059175, -0.03613175, -0.00820669],
                [0.04693028, 0.00849446, 0.04429088, 0.03022964, -0.00255597, 0.02792381, -0.04829382, -0.03914084],
                [0.03378367, 0.02664308, -0.02402924, 0.00251171, 0.01253425, 0.03526105, 0.01009853, -0.0088941],
                [-0.01180798, -0.01020758, 0.01768347, 0.03622899, -0.0028321, -0.02660799, -0.02652899, 0.02446226],
                [0.03233111, -0.04190881, -0.04117368, 0.0089901, -0.0338329, 0.03125329, -0.01913668, -0.03573409],
                [0.04226564, -0.03405612, -0.02098526, -0.034752, -0.03806347, -0.04818854, -0.04450301, -0.03256618],
                [-0.04425753, 0.00765338, 0.01937285, -0.01090873, -0.01727164, -0.00027477, 0.03739847, 0.03504044],
                [-0.04561455, -0.03198728, -0.02620337, -0.02508675, 0.00721048, -0.00844203, -0.04508416, -0.01264541],
                [0.00250894, -0.04031579, 0.03377607, -0.04489366, 0.04279138, -0.04032915, 0.03432377, 0.04024127],
                [0.04854872, 0.0280644, 0.02985236, 0.01385058, 0.0292596, -0.03760932, 0.0034575, 0.00131585],
                [0.03640232, -0.00605143, -0.00941436, 0.01352264, -0.02187084, -0.03718388, -0.00237507, -0.00842711],
                [-0.02590496, -0.01627747, -0.01066296, -0.01781384, -0.01012727, 0.01706645, -0.02052384, 0.04473503],
                [0.04191703, -0.00292891, -0.01625535, 0.00335375, 0.03550477, 0.01475093, 0.03036796, 0.00322146],
                [0.01361265, -0.02234411, 0.0245223, -0.02997527, 0.0202174, 0.03549454, -0.0368707, 0.01138025],
                [-0.0400837, 0.02110169, -0.04024137, 0.04332056, -0.03532432, 0.04515621, 0.02998578, 0.00929503],
                [0.02842653, 0.02891837, 0.04513103, -0.024772, 0.00938483, -0.04079037, 0.01157515, -0.03290039],
                [0.0066076, 0.00683635, -0.00303921, 0.00218752, 0.02665101, 0.02972199, -0.00081308, 0.0099391],
                [0.04353568, -0.03951577, -0.03696319, -0.04150611, 0.01673346, -0.00888056, 0.02732816, 0.01704902],
                [-0.01643968, 0.03912635, 0.02688593, -0.02307864, -0.01312902, -0.01890964, -0.03428843, -0.03525702],
                [0.04419293, -0.00830635, -0.00980032, 0.02257853, 0.00663292, 0.04257008, 0.02788373, -0.00216741],
                [-0.01198539, 0.04729471, 0.02299497, 0.0448649, -0.03728177, 0.03437206, 0.01361183, -0.03787595],
                [0.00954706, 0.01600306, -0.0464479, -0.00505308, 0.03419783, -0.02176184, -0.00432721, -0.00589834],
                [-0.0192007, 0.03965211, 0.03008202, -0.03934893, 0.05109771, 0.03682863, -0.02176223, 0.03358054],
                [-0.03908747, 0.04893266, 0.01743948, 0.01483061, -0.040334, 0.03921651, -0.04716833, -0.02596587],
                [-0.03497691, 0.02507153, -0.02785891, 0.04057918, 0.01728394, -0.04768082, -0.04963894, -0.04496389],
                [0.0112896, 0.02762898, -0.02390072, 0.03447226, -0.04267451, 0.02884215, 0.04260363, 0.02708544],
                [0.02014771, 0.03258508, -0.04490443, -0.03004742, -0.03673616, -0.00015079, 0.02443411, 0.01294081],
                [0.03536286, -0.03538127, 0.02426608, -0.03086375, -0.02235006, 0.02054111, 0.04795745, 0.01110943],
                [-0.04395879, 0.00949444, -0.04386188, 0.03801714, 0.02231669, -0.0337509, -0.04097709, -0.03175302],
                [0.04812377, -0.00458141, 0.02879778, 0.01355945, 0.00751956, -0.03570478, 0.0445719, -0.01988751],
                [0.00797488, 0.01935181, 0.01548074, 0.04394303, -0.03475808, 0.00052371, -0.00964026, -0.00261428],
                [-0.03807187, -0.03661359, -0.02217192, -0.01953383, -0.00719502, 0.01108728, 0.0134613, -0.00882006],
                [-0.00844006, -0.0307005, 0.01102488, -0.01875415, -0.04482724, -0.00938643, -0.00275875, -0.02756333],
                [0.00782168, 0.00449597, 0.02205408, 0.0144077, 0.01656703, -0.01941477, 0.02859789, 0.00481084],
                [-0.00624737, 0.01039383, -0.01196799, 0.0008633, 0.02507456, 0.03754128, 0.04195158, 0.00025341],
                [0.00270377, 0.02754308, -0.01637795, 0.03328361, 0.00096861, -0.03963111, -0.04296475, 0.03407767],
                [-0.0440457, -0.02337543, -0.01530736, -0.03296779, -0.01769696, 0.02355403, -0.04863203, 0.03264584],
                [0.03614744, -0.0145542, -0.03305284, 0.00975285, -0.03690037, -0.01439446, 0.0457186, 0.04945783],
                [0.02733158, -0.01934146, 0.01915631, 0.02045923, -0.01093749, 0.01387099, -0.04895779, -0.02911602],
                [0.00260804, -0.03398339, -0.03308996, 0.03356374, 0.04914133, 0.00541845, 0.03388194, 0.04901432],
                [-0.03526144, -0.00726778, -0.00887896, -0.04238429, 0.02686382, -0.00766381, -0.00321341, -0.03503685],
                [-0.03149927, 0.03923567, -0.04422149, -0.0269891, -0.01985611, -0.00171446, 0.00854154, -0.00074441],
                [-0.04109886, -0.02740264, 0.03593494, 0.01342972, 0.0160403, 0.0161394, 0.02616675, -0.04427723],
                [-0.01267652, 0.00155802, -0.01402124, 0.03400242, -0.00021963, 0.02567049, 0.03503433, 0.00035995],
            ]
        )


class Exp_Outputs_Dense_Sigmoid_Dense_Softmax_CrossEntropy:
    def get_exp_forward_out(self):
        return np.array([[0.12930149, 0.12288380, 0.11873319, 0.11343321, 0.12975556, 0.12713468, 0.13424416, 0.12451401]])

    def get_exp_updated_bias_1(self):
        return np.array([4.6173764e-06, -1.3567642e-05, -1.9950550e-05, -1.3050105e-06, -4.1163034e-07, -2.9609077e-05, -1.9764662e-05, -1.8533692e-05])

    def get_exp_updated_weights_1(self):
        return np.array(
            [
                [1.34754051e-02, 5.34743955e-03, 5.90527523e-03, -1.96059998e-02, -4.69185375e-02, -6.35117711e-03, -2.85568312e-02, -9.16147977e-03],
                [3.53423357e-02, -2.66120061e-02, -4.41784784e-02, -2.18621828e-02, -2.06408054e-02, 1.61786564e-02, 5.69454068e-03, 2.83816867e-02],
                [1.64353177e-02, -9.37296264e-03, 3.13849114e-02, -3.33038308e-02, -4.77291457e-02, -4.10206355e-02, 2.22189650e-02, -3.82819003e-03],
                [-3.38696018e-02, 9.50158719e-05, -3.47827040e-02, 1.96311269e-02, -5.38465939e-03, -1.19185261e-02, -1.98625755e-02, 1.30153345e-02],
                [-1.38183041e-02, -4.12362851e-02, -3.82012874e-02, 4.61896434e-02, 4.08580303e-02, 1.99679248e-02, -2.34148651e-02, 4.69158888e-02],
                [2.78795958e-02, 2.16757804e-02, -5.08331414e-03, -2.27771178e-02, -4.03613076e-02, 4.02313545e-02, -4.44165384e-03, -2.97817457e-02],
                [-1.94008220e-02, 7.91162997e-03, -3.23379077e-02, 3.56604345e-02, 2.58516409e-02, 2.19237600e-02, -6.80573983e-03, 1.27167776e-02],
                [8.41342658e-03, 1.49739953e-02, -4.15712520e-02, -8.42028484e-03, -4.58389074e-02, -6.24192704e-04, -1.70294158e-02, -3.55621502e-02],
                [-3.96591127e-02, 8.76271911e-03, -3.29432599e-02, 4.25118431e-02, 8.10606126e-03, -1.53168133e-02, 9.08901729e-03, -4.77219857e-02],
                [4.58579995e-02, -1.77576696e-03, 2.82645375e-02, -4.17275876e-02, -1.33435242e-03, -9.42636048e-04, 4.37737405e-02, 7.16445781e-03],
                [-2.64934800e-03, -2.33074650e-02, -1.68504994e-02, 2.06675637e-03, -6.10900670e-03, -4.78497706e-02, 3.26218642e-02, 3.96092050e-02],
                [-3.59708108e-02, 5.39104035e-03, -3.91609147e-02, 1.72228012e-02, -2.18770038e-02, 1.59148239e-02, 2.26811450e-02, 2.68475730e-02],
                [-3.92229334e-02, 4.15924489e-02, -2.69914456e-02, -4.62595858e-02, 5.48498193e-03, -1.29268356e-02, 3.29662487e-02, 3.08132134e-02],
                [-1.82823110e-02, 4.52787764e-02, -2.09246296e-02, 1.50463928e-03, -2.44038291e-02, 4.35799956e-02, -3.35554779e-02, -4.55241874e-02],
                [-6.48824638e-03, 4.92315404e-02, 3.91588807e-02, 2.48602219e-02, 3.90790664e-02, 3.93315367e-02, 1.87707203e-03, -1.84153132e-02],
                [2.72022914e-02, 1.61630437e-02, -1.26387607e-02, -4.05536294e-02, 2.46788673e-02, -2.37606764e-02, 4.36768234e-02, -2.59071533e-02],
                [-3.77216488e-02, 3.31037417e-02, -3.46826315e-02, -3.20738927e-02, 9.93805099e-03, 3.74397822e-02, -3.03674955e-02, -1.89779103e-02],
                [2.77407784e-02, 4.71817777e-02, 7.28454324e-05, -3.56103331e-02, -4.86063957e-02, -2.70362850e-02, -3.68190408e-02, 1.77646838e-02],
                [-3.78129259e-02, 6.21764164e-04, 1.94097310e-02, 8.11058097e-03, -3.00227758e-02, 3.03879473e-02, 2.15243548e-02, 2.38830615e-02],
                [-3.68913077e-02, -3.76331918e-02, 4.27436531e-02, -1.02430051e-02, -1.99053921e-02, -1.16029847e-03, 1.62739363e-02, 4.55506183e-02],
                [-2.13518776e-02, 4.24705558e-02, -4.75291759e-02, 5.51881455e-03, 1.33971991e-02, -3.94327044e-02, -3.59810218e-02, -8.10261816e-03],
                [4.66248281e-02, 9.59944539e-03, 4.32952493e-02, 3.04356292e-02, -3.26198572e-03, 2.84658466e-02, -4.82233278e-02, -3.90921719e-02],
                [3.29473428e-02, 2.96685379e-02, -2.67552920e-02, 3.07569234e-03, 1.06011825e-02, 3.67451534e-02, 1.02915308e-02, -8.76083318e-03],
                [-1.25774713e-02, -7.42390892e-03, 1.51752848e-02, 3.67478989e-02, -4.61067911e-03, -2.52424870e-02, -2.63514165e-02, 2.45848745e-02],
                [3.16604711e-02, -3.94827537e-02, -4.33596447e-02, 9.44234990e-03, -3.53829972e-02, 3.24433707e-02, -1.89819168e-02, -3.56272347e-02],
                [4.20979448e-02, -3.34494673e-02, -2.15318762e-02, -3.46389152e-02, -3.84510756e-02, -4.78909612e-02, -4.44643088e-02, -3.25394608e-02],
                [-4.46596518e-02, 9.10804980e-03, 1.80621408e-02, -1.06375637e-02, -1.82010829e-02, 4.38804534e-04, 3.74912694e-02, 3.51045132e-02],
                [-4.56522927e-02, -3.18507552e-02, -2.63263863e-02, -2.50613000e-02, 7.12324679e-03, -8.37505516e-03, -4.50754538e-02, -1.26393987e-02],
                [2.37600738e-03, -3.98349017e-02, 3.33427787e-02, -4.48040143e-02, 4.24841233e-02, -4.00932543e-02, 3.43544446e-02, 4.02624533e-02],
                [4.79602218e-02, 3.01933214e-02, 2.79341266e-02, 1.42474361e-02, 2.78993547e-02, -3.65650021e-02, 3.59330326e-03, 1.40962750e-03],
                [3.57606523e-02, -3.73016810e-03, -1.15059083e-02, 1.39553547e-02, -2.33539753e-02, -3.60452086e-02, -2.22699251e-03, -8.32486618e-03],
                [-2.67385393e-02, -1.32619459e-02, -1.33800572e-02, -1.72517058e-02, -1.20539907e-02, 1.85456872e-02, -2.03314740e-02, 4.48678620e-02],
                [4.16363068e-02, -1.91337778e-03, -1.71703808e-02, 3.54305375e-03, 3.48559134e-02, 1.52490865e-02, 3.04327440e-02, 3.26618901e-03],
                [1.32934740e-02, -2.11894643e-02, 2.34819259e-02, -2.97600254e-02, 1.94796603e-02, 3.60609405e-02, -3.67970355e-02, 1.14311082e-02],
                [-4.04882580e-02, 2.25651935e-02, -4.15600464e-02, 4.35933731e-02, -3.62594016e-02, 4.58741225e-02, 3.00791375e-02, 9.35949851e-03],
                [2.82632858e-02, 2.95089129e-02, 4.45989259e-02, -2.46619117e-02, 9.00751166e-03, -4.05006893e-02, 1.16128251e-02, -3.28743830e-02],
                [6.49566110e-03, 7.24128867e-03, -3.40407691e-03, 2.26300815e-03, 2.63922848e-02, 2.99206246e-02, -7.87246274e-04, 9.95693635e-03],
                [4.31258231e-02, -3.80330980e-02, -3.82991359e-02, -4.12297212e-02, 1.57861337e-02, -8.15325510e-03, 2.74227392e-02, 1.71143264e-02],
                [-1.66351777e-02, 3.98335792e-02, 2.62486879e-02, -2.29468029e-02, -1.35808913e-02, -1.85627211e-02, -3.42433192e-02, -3.52258682e-02],
                [4.36158367e-02, -6.21868391e-03, -1.16813816e-02, 2.29676981e-02, 5.29903080e-03, 4.35941666e-02, 2.80169118e-02, -2.07545771e-03],
                [-1.23620341e-02, 4.86572236e-02, 2.17673033e-02, 4.51188944e-02, -3.81523222e-02, 3.50404233e-02, 1.36987483e-02, -3.78159359e-02],
                [8.82964488e-03, 1.85983405e-02, -4.87863421e-02, -4.56929067e-03, 3.25396098e-02, -2.04887521e-02, -4.16164938e-03, -5.78401983e-03],
                [-1.98040269e-02, 4.18346897e-02, 2.81154346e-02, -3.89420725e-02, 4.97031808e-02, 3.78992669e-02, -2.16229968e-02, 3.36766802e-02],
                [-3.93566042e-02, 4.99062352e-02, 1.65622421e-02, 1.50120938e-02, -4.09560576e-02, 3.96940894e-02, -4.71062213e-02, -2.59229839e-02],
                [-3.56939696e-02, 2.76655033e-02, -3.01961806e-02, 4.10627350e-02, 1.56265609e-02, -4.64083701e-02, -4.94734682e-02, -4.48496342e-02],
                [1.05962334e-02, 3.01372614e-02, -2.61607748e-02, 3.49398330e-02, -4.42771390e-02, 3.00725568e-02, 4.27636355e-02, 2.71959230e-02],
                [1.98138691e-02, 3.37927639e-02, -4.59926017e-02, -2.98222937e-02, -3.75077911e-02, 4.41626151e-04, 2.45111529e-02, 1.29940035e-02],
                [3.51144411e-02, -3.44826132e-02, 2.34563574e-02, -3.06962263e-02, -2.29242444e-02, 2.09819339e-02, 4.80147786e-02, 1.11490162e-02],
                [-4.45468165e-02, 1.16216373e-02, -4.57785614e-02, 3.84136811e-02, 2.09575482e-02, -3.27074267e-02, -4.08413894e-02, -3.16593237e-02],
                [4.80033606e-02, -4.14583180e-03, 2.84053069e-02, 1.36406515e-02, 7.24125747e-03, -3.54911126e-02, 4.45996858e-02, -1.98683273e-02],
                [7.80264474e-03, 1.99748818e-02, 1.49193276e-02, 4.40591797e-02, -3.51561829e-02, 8.29354743e-04, -9.60051175e-03, -2.58683204e-03],
                [-3.80782150e-02, -3.65906395e-02, -2.21925918e-02, -1.95295494e-02, -7.20968191e-03, 1.10985367e-02, 1.34627661e-02, -8.81904736e-03],
                [-9.11805592e-03, -2.82478239e-02, 8.81492533e-03, -1.82969347e-02, -4.63943407e-02, -8.18329584e-03, -2.60228571e-03, -2.74552982e-02],
                [7.24886311e-03, 6.56816969e-03, 2.01869551e-02, 1.47939799e-02, 1.52430320e-02, -1.83982719e-02, 2.87300814e-02, 4.90211602e-03],
                [-6.85492437e-03, 1.25916805e-02, -1.39483353e-02, 1.27300422e-03, 2.36702785e-02, 3.86194102e-02, 4.20917831e-02, 3.50223214e-04],
                [2.03111628e-03, 2.99764480e-02, -1.85705069e-02, 3.37372199e-02, -5.86156733e-04, -3.84374447e-02, -4.28095162e-02, 3.41848508e-02],
                [-4.44410890e-02, -2.19450835e-02, -1.65961515e-02, -3.27011570e-02, -1.86108518e-02, 2.42556687e-02, -4.85407859e-02, 3.27088386e-02],
                [3.56574282e-02, -1.27815595e-02, -3.46500576e-02, 1.00832982e-02, -3.80329788e-02, -1.35249039e-02, 4.58316803e-02, 4.95359041e-02],
                [2.72111353e-02, -1.89057458e-02, 1.87637173e-02, 2.05404535e-02, -1.12158880e-02, 1.40847247e-02, -4.89300005e-02, -2.90968250e-02],
                [2.50935904e-03, -3.36264223e-02, -3.34115960e-02, 3.36302817e-02, 4.89132516e-02, 5.59355179e-03, 3.39047089e-02, 4.90300432e-02],
                [-3.58373262e-02, -5.18450746e-03, -1.07560633e-02, -4.19959426e-02, 2.55327411e-02, -6.64188806e-03, -3.08051729e-03, -3.49450894e-02],
                [-3.19051594e-02, 4.07039709e-02, -4.55444902e-02, -2.67153867e-02, -2.07942612e-02, -9.94194532e-04, 8.63520615e-03, -6.79733581e-04],
                [-4.15858552e-02, -2.56409235e-02, 3.43475603e-02, 1.37581322e-02, 1.49146719e-02, 1.70035902e-02, 2.62791291e-02, -4.41996269e-02],
                [-1.33356294e-02, 3.94236436e-03, -1.61696151e-02, 3.44468877e-02, -1.74306403e-03, 2.68401075e-02, 3.51864323e-02, 4.64970042e-04],
            ]
        )

    def get_exp_updated_bias_2(self):
        return np.array([-3.4411906e-04, 1.6929625e-04, 5.0134357e-04, 9.2534360e-04, -3.8044571e-04, -1.7077565e-04, -7.3953270e-04, 3.8879512e-05])

    def get_exp_updated_weights_2(self):
        return np.array(
            [
                [0.04079203, 0.00879268, 0.03526519, -0.01550208, -0.00029873, 0.00306012, -0.03985275, -0.01012631],
                [0.04153948, 0.01317881, -0.03196629, -0.01559199, -0.03105449, -0.04761411, 0.04232851, -0.00515732],
                [-0.01938963, 0.00991814, -0.04906002, -0.02181289, 0.0201451, 0.01330594, 0.04787299, 0.01205194],
                [-0.00242526, 0.02622977, 0.04058898, 0.02254246, 0.04612671, 0.02811325, 0.03630223, -0.03856973],
                [0.02308938, -0.00591636, 0.00553179, 0.0158189, 0.0468135, 0.04838239, -0.02150377, 0.02339252],
                [0.02481493, -0.01526047, -0.03734579, -0.04541207, 0.02753153, -0.00112105, 0.04815983, -0.00348193],
                [0.04761885, -0.00875737, 0.02962003, -0.0410533, 0.00535508, 0.0301202, 0.04209872, 0.03227784],
                [-0.04647524, -0.01264499, -0.04487911, -0.03860842, 0.01734006, 0.02124031, 0.02700176, 0.03656512],
            ]
        )


class Exp_Outputs_Dense_ReLU_Dense_Softmax_CrossEntropy:
    def get_exp_forward_out(self):
        return np.array([[0.12681605, 0.12530987, 0.12358681, 0.12379250, 0.12473451, 0.12365375, 0.12748529, 0.12462112]])

    def get_exp_updated_bias_1(self):
        return np.array([0, -2.6018699e-05, 0, 1.9286010e-06, 0, -2.1219770e-05, -1.1510074e-05, -3.4990592e-06])

    def get_exp_updated_weights_1(self):
        return np.array(
            [
                [1.34718316e-02, 5.33780269e-03, 5.92071610e-03, -1.96034983e-02, -4.69182171e-02, -6.34468393e-03, -2.85504423e-02, -9.14984383e-03],
                [3.53403091e-02, -2.66174711e-02, -4.41697240e-02, -2.18607653e-02, -2.06406247e-02, 1.61823388e-02, 5.69816353e-03, 2.83882860e-02],
                [1.64313540e-02, -9.38365329e-03, 3.14020403e-02, -3.33010517e-02, -4.77287918e-02, -4.10134345e-02, 2.22260524e-02, -3.81528120e-03],
                [-3.38728204e-02, 8.63329042e-05, -3.47687900e-02, 1.96333826e-02, -5.38437255e-03, -1.19126756e-02, -1.98568180e-02, 1.30258193e-02],
                [-1.38187390e-02, -4.12374586e-02, -3.81994098e-02, 4.61899489e-02, 4.08580676e-02, 1.99687146e-02, -2.34140884e-02, 4.69173081e-02],
                [2.78750900e-02, 2.16636341e-02, -5.06384997e-03, -2.27739625e-02, -4.03609052e-02, 4.02395390e-02, -4.43360023e-03, -2.97670774e-02],
                [-1.94043368e-02, 7.90215284e-03, -3.23227234e-02, 3.56628969e-02, 2.58519538e-02, 2.19301451e-02, -6.79945713e-03, 1.27282208e-02],
                [8.40979721e-03, 1.49642071e-02, -4.15555686e-02, -8.41774326e-03, -4.58385833e-02, -6.17598183e-04, -1.70229264e-02, -3.55503298e-02],
                [-3.96597050e-02, 8.76112375e-03, -3.29407044e-02, 4.25122567e-02, 8.10611434e-03, -1.53157385e-02, 9.09007527e-03, -4.77200598e-02],
                [4.58559208e-02, -1.78137480e-03, 2.82735229e-02, -4.17261310e-02, -1.33416697e-03, -9.38857615e-04, 4.37774584e-02, 7.17122946e-03],
                [-2.65106000e-03, -2.33120825e-02, -1.68431010e-02, 2.06795521e-03, -6.10885397e-03, -4.78466600e-02, 3.26249227e-02, 3.96147817e-02],
                [-3.59750912e-02, 5.37950080e-03, -3.91424261e-02, 1.72257982e-02, -2.18766220e-02, 1.59225985e-02, 2.26887949e-02, 2.68615056e-02],
                [-3.92259061e-02, 4.15844321e-02, -2.69786008e-02, -4.62575033e-02, 5.48524689e-03, -1.29214339e-02, 3.29715647e-02, 3.08228936e-02],
                [-1.82861108e-02, 4.52685356e-02, -2.09082160e-02, 1.50729972e-03, -2.44034901e-02, 4.35868949e-02, -3.35486867e-02, -4.55118194e-02],
                [-6.49029389e-03, 4.92260195e-02, 3.91677283e-02, 2.48616561e-02, 3.90792489e-02, 3.93352546e-02, 1.88073225e-03, -1.84086468e-02],
                [2.72012427e-02, 1.61602143e-02, -1.26342271e-02, -4.05528918e-02, 2.46789604e-02, -2.37587709e-02, 4.36787009e-02, -2.59037372e-02],
                [-3.77242081e-02, 3.30968387e-02, -3.46715674e-02, -3.20721008e-02, 9.93827917e-03, 3.74444351e-02, -3.03629171e-02, -1.89695731e-02],
                [2.77404841e-02, 4.71809804e-02, 7.41186232e-05, -3.56101282e-02, -4.86063696e-02, -2.70357504e-02, -3.68185118e-02, 1.77656431e-02],
                [-3.78167480e-02, 6.11459312e-04, 1.94262434e-02, 8.11325759e-03, -3.00224349e-02, 3.03948894e-02, 2.15311870e-02, 2.38955040e-02],
                [-3.68942246e-02, -3.76410559e-02, 4.27562557e-02, -1.02409627e-02, -1.99051313e-02, -1.15499925e-03, 1.62791517e-02, 4.55601178e-02],
                [-2.13553775e-02, 4.24611159e-02, -4.75140512e-02, 5.52126626e-03, 1.33975111e-02, -3.94263454e-02, -3.59747633e-02, -8.09122063e-03],
                [4.66231927e-02, 9.59503092e-03, 4.33023237e-02, 3.04367747e-02, -3.26183974e-03, 2.84688212e-02, -4.82203998e-02, -3.90868410e-02],
                [3.29428613e-02, 2.96564531e-02, -2.67359260e-02, 3.07883136e-03, 1.06015820e-02, 3.67532969e-02, 1.02995429e-02, -8.74623936e-03],
                [-1.25815952e-02, -7.43502937e-03, 1.51931029e-02, 3.67507860e-02, -4.61031171e-03, -2.52349954e-02, -2.63440441e-02, 2.45983023e-02],
                [3.16568762e-02, -3.94924432e-02, -4.33441140e-02, 9.44486726e-03, -3.53826769e-02, 3.24499011e-02, -1.89754926e-02, -3.56155299e-02],
                [4.20970470e-02, -3.34518887e-02, -2.15279926e-02, -3.46382856e-02, -3.84509936e-02, -4.78893295e-02, -4.44626994e-02, -3.25365365e-02],
                [-4.46618050e-02, 9.10223834e-03, 1.80714522e-02, -1.06360549e-02, -1.82008911e-02, 4.42719989e-04, 3.74951214e-02, 3.51115316e-02],
                [-4.56524938e-02, -3.18512991e-02, -2.63255127e-02, -2.50611585e-02, 7.12326495e-03, -8.37468728e-03, -4.50750925e-02, -1.26387402e-02],
                [2.37529492e-03, -3.98368239e-02, 3.33458558e-02, -4.48035151e-02, 4.24841866e-02, -4.00919616e-02, 3.43557186e-02, 4.02647741e-02],
                [4.79570664e-02, 3.01848166e-02, 2.79477537e-02, 1.42496442e-02, 2.78996360e-02, -3.65592726e-02, 3.59894149e-03, 1.41989696e-03],
                [3.57572138e-02, -3.73944128e-03, -1.14910500e-02, 1.39577631e-02, -2.33536679e-02, -3.60389613e-02, -2.22084485e-03, -8.31366889e-03],
                [-2.67430060e-02, -1.32739926e-02, -1.33607546e-02, -1.72485765e-02, -1.20535921e-02, 1.85538046e-02, -2.03234889e-02, 4.48824055e-02],
                [4.16348018e-02, -1.91743474e-03, -1.71638802e-02, 3.54410731e-03, 3.48560475e-02, 1.52518200e-02, 3.04354336e-02, 3.27108754e-03],
                [1.32917631e-02, -2.11940780e-02, 2.34893169e-02, -2.97588259e-02, 1.94798131e-02, 3.60640474e-02, -3.67939807e-02, 1.14366775e-02],
                [-4.04904261e-02, 2.25593466e-02, -4.15506773e-02, 4.35948893e-02, -3.62592079e-02, 4.58780602e-02, 3.00830118e-02, 9.36655793e-03],
                [2.82624103e-02, 2.95065530e-02, 4.46027070e-02, -2.46612988e-02, 9.00758989e-03, -4.04990986e-02, 1.16143888e-02, -3.28715332e-02],
                [6.49506133e-03, 7.23967096e-03, -3.40148481e-03, 2.26342818e-03, 2.63923388e-02, 2.99217142e-02, -7.86173856e-04, 9.95888934e-03],
                [4.31236252e-02, -3.80390175e-02, -3.82896438e-02, -4.12281826e-02, 1.57863293e-02, -8.14926438e-03, 2.74266656e-02, 1.71214771e-02],
                [-1.66362245e-02, 3.98307517e-02, 2.62532141e-02, -2.29460690e-02, -1.35807982e-02, -1.85608175e-02, -3.42414454e-02, -3.52224559e-02],
                [4.36127447e-02, -6.22702390e-03, -1.16680181e-02, 2.29698643e-02, 5.29930647e-03, 4.35997881e-02, 2.80224402e-02, -2.06538732e-03],
                [-1.23640522e-02, 4.86517809e-02, 2.17760243e-02, 4.51203063e-02, -3.81521434e-02, 3.50440927e-02, 1.37023563e-02, -3.78093645e-02],
                [8.82580038e-03, 1.85879730e-02, -4.87697311e-02, -4.56659775e-03, 3.25399525e-02, -2.04817671e-02, -4.15477622e-03, -5.77150099e-03],
                [-1.98072605e-02, 4.18259688e-02, 2.81294044e-02, -3.89398076e-02, 4.97034676e-02, 3.79051417e-02, -2.16172151e-02, 3.36872078e-02],
                [-3.93580459e-02, 4.99023460e-02, 1.65684745e-02, 1.50131043e-02, -4.09559272e-02, 3.96967120e-02, -4.71036434e-02, -2.59182882e-02],
                [-3.56978141e-02, 2.76551396e-02, -3.01795769e-02, 4.10654284e-02, 1.56269036e-02, -4.64013889e-02, -4.94665951e-02, -4.48371209e-02],
                [1.05925174e-02, 3.01272422e-02, -2.61447188e-02, 3.49424370e-02, -4.42768075e-02, 3.00793089e-02, 4.27702814e-02, 2.72080228e-02],
                [1.98120791e-02, 3.37879397e-02, -4.59848717e-02, -2.98210420e-02, -3.75076309e-02, 4.44876816e-04, 2.45143529e-02, 1.29998289e-02],
                [3.51131111e-02, -3.44862044e-02, 2.34621093e-02, -3.06952931e-02, -2.29241252e-02, 2.09843516e-02, 4.80171591e-02, 1.11533515e-02],
                [-4.45499681e-02, 1.16131399e-02, -4.57649454e-02, 3.84158865e-02, 2.09578294e-02, -3.27017009e-02, -4.08357568e-02, -3.16490643e-02],
                [4.80027162e-02, -4.14757198e-03, 2.84080952e-02, 1.36411032e-02, 7.24131521e-03, -3.54899392e-02, 4.46008369e-02, -1.98662262e-02],
                [7.80172180e-03, 1.99723933e-02, 1.49233155e-02, 4.40598242e-02, -3.51561010e-02, 8.31031823e-04, -9.59886238e-03, -2.58382643e-03],
                [-3.80782485e-02, -3.65907289e-02, -2.21924447e-02, -1.95295252e-02, -7.20967865e-03, 1.10985981e-02, 1.34628266e-02, -8.81893653e-03],
                [-9.12168901e-03, -2.82576215e-02, 8.83062463e-03, -1.82943903e-02, -4.63940166e-02, -8.17669462e-03, -2.59578996e-03, -2.74434667e-02],
                [7.24579347e-03, 6.55989163e-03, 2.02002190e-02, 1.47961304e-02, 1.52433058e-02, -1.83926933e-02, 2.87355706e-02, 4.91211191e-03],
                [-6.85818028e-03, 1.25829009e-02, -1.39342668e-02, 1.27528445e-03, 2.36705691e-02, 3.86253260e-02, 4.20976020e-02, 3.60825099e-04],
                [2.02751136e-03, 2.99667269e-02, -1.85549315e-02, 3.37397419e-02, -5.85835369e-04, -3.84308957e-02, -4.28030714e-02, 3.41965891e-02],
                [-4.44432087e-02, -2.19507962e-02, -1.65869966e-02, -3.26996706e-02, -1.86106637e-02, 2.42595188e-02, -4.85369973e-02, 3.27157378e-02],
                [3.56548019e-02, -1.27886403e-02, -3.46387103e-02, 1.00851376e-02, -3.80327441e-02, -1.35201328e-02, 4.58363742e-02, 4.95444573e-02],
                [2.72104889e-02, -1.89074874e-02, 1.87665056e-02, 2.05409061e-02, -1.12158302e-02, 1.40858972e-02, -4.89288457e-02, -2.90947240e-02],
                [2.50883028e-03, -3.36278491e-02, -3.34093124e-02, 3.36306505e-02, 4.89133000e-02, 5.59451245e-03, 3.39056551e-02, 4.90317643e-02],
                [-3.58404107e-02, -5.19282976e-03, -1.07427286e-02, -4.19937819e-02, 2.55330168e-02, -6.63628103e-03, -3.07499990e-03, -3.49350423e-02],
                [-3.19073349e-02, 4.06981073e-02, -4.55350913e-02, -2.67138630e-02, -2.07940675e-02, -9.90242348e-04, 8.63909535e-03, -6.72650815e-04],
                [-4.15884666e-02, -2.56479625e-02, 3.43588367e-02, 1.37599604e-02, 1.49149047e-02, 1.70083325e-02, 2.62837950e-02, -4.41911295e-02],
                [-1.33391619e-02, 3.93283926e-03, -1.61543526e-02, 3.44493613e-02, -1.74274913e-03, 2.68465262e-02, 3.51927467e-02, 4.76471527e-04],
            ]
        )

    def get_exp_updated_bias_2(self):
        return np.array([-1.4528632e-04, -2.4793148e-05, 1.1305332e-04, 9.6597672e-05, 2.1237134e-05, 1.0769844e-04, -1.9882440e-04, 3.0309557e-05])

    def get_exp_updated_weights_2(self):
        return np.array(
            [
                [0.04095522, 0.00871239, 0.03502743, -0.01594092, -0.0001183, 0.0031411, -0.03950203, -0.01014475],
                [0.04169602, 0.01307678, -0.03221997, -0.01608934, -0.03083418, -0.04748971, 0.04269439, -0.00517139],
                [-0.01924649, 0.00984772, -0.04926855, -0.02219779, 0.02030335, 0.01337698, 0.0481806, 0.01203577],
                [-0.00225582, 0.02614216, 0.04033777, 0.02207385, 0.04632206, 0.02820526, 0.03667138, -0.03858826],
                [0.02324135, -0.00599113, 0.00531038, 0.01541024, 0.04698151, 0.04845781, -0.02117717, 0.02337535],
                [0.02497913, -0.01535399, -0.03759807, -0.04589252, 0.02773712, -0.00101578, 0.04852771, -0.00349864],
                [0.04779037, -0.00884262, 0.02936924, -0.04151719, 0.00554636, 0.03020696, 0.04246836, 0.03225859],
                [-0.04630335, -0.01272984, -0.04512982, -0.03907149, 0.01753062, 0.02132614, 0.02737148, 0.03654575],
            ]
        )


class Exp_Outputs_Dense_Tanh_Dense_Softmax_CrossEntropy:
    def get_exp_forward_out(self):
        return np.array([[0.12696187, 0.12554525, 0.12560177, 0.12505510, 0.12317029, 0.12231171, 0.12717687, 0.12417720]])

    def get_exp_updated_bias_1(self):
        return np.array([-1.5378191e-06, -2.6206495e-05, 2.9573869e-06, 1.0886861e-06, 1.7816868e-05, -5.9833765e-06, -6.3101411e-06, 1.4958517e-05])

    def get_exp_updated_weights_1(self):
        return np.array(
            [
                [1.34706413e-02, 5.33765741e-03, 5.92300482e-03, -1.96041483e-02, -4.69044261e-02, -6.33289199e-03, -2.85464171e-02, -9.13555827e-03],
                [3.53396349e-02, -2.66175531e-02, -4.41684276e-02, -2.18611322e-02, -2.06328053e-02, 1.61890257e-02, 5.70044573e-03, 2.83963867e-02],
                [1.64300334e-02, -9.38381441e-03, 3.14045809e-02, -3.33017744e-02, -4.77134958e-02, -4.10003513e-02, 2.22305171e-02, -3.79943359e-03],
                [-3.38738933e-02, 8.62019369e-05, -3.47667262e-02, 1.96327977e-02, -5.37194777e-03, -1.19020501e-02, -1.98531915e-02, 1.30386911e-02],
                [-1.38188843e-02, -4.12374772e-02, -3.81991304e-02, 4.61898707e-02, 4.08597440e-02, 1.99701488e-02, -2.34135985e-02, 4.69190441e-02],
                [2.78735906e-02, 2.16634497e-02, -5.06096473e-03, -2.27747820e-02, -4.03435230e-02, 4.02544029e-02, -4.42852732e-03, -2.97490694e-02],
                [-1.94055066e-02, 7.90200941e-03, -3.23204733e-02, 3.56622562e-02, 2.58655157e-02, 2.19417419e-02, -6.79549901e-03, 1.27422698e-02],
                [8.40858836e-03, 1.49640599e-02, -4.15532440e-02, -8.41840357e-03, -4.58245799e-02, -6.05621375e-04, -1.70188397e-02, -3.55358236e-02],
                [-3.96599025e-02, 8.76109954e-03, -3.29403244e-02, 4.25121486e-02, 8.10839701e-03, -1.53137865e-02, 9.09074116e-03, -4.77176979e-02],
                [4.58552279e-02, -1.78145932e-03, 2.82748546e-02, -4.17265072e-02, -1.32614246e-03, -9.31995397e-04, 4.37798016e-02, 7.17954244e-03],
                [-2.65163020e-03, -2.33121514e-02, -1.68420039e-02, 2.06764392e-03, -6.10224763e-03, -4.78410125e-02, 3.26268524e-02, 3.96216251e-02],
                [-3.59765179e-02, 5.37932711e-03, -3.91396843e-02, 1.72250196e-02, -2.18601096e-02, 1.59367193e-02, 2.26936135e-02, 2.68786121e-02],
                [-3.92268971e-02, 4.15843129e-02, -2.69766971e-02, -4.62580435e-02, 5.49671846e-03, -1.29116243e-02, 3.29749100e-02, 3.08347791e-02],
                [-1.82873756e-02, 4.52683792e-02, -2.09057834e-02, 1.50660868e-03, -2.43888311e-02, 4.35994342e-02, -3.35444100e-02, -4.54966314e-02],
                [-6.49097562e-03, 4.92259376e-02, 3.91690396e-02, 2.48612836e-02, 3.90871502e-02, 3.93420123e-02, 1.88303797e-03, -1.84004623e-02],
                [2.72008926e-02, 1.61601715e-02, -1.26335546e-02, -4.05530855e-02, 2.46830098e-02, -2.37553082e-02, 4.36798818e-02, -2.58995425e-02],
                [-3.77250612e-02, 3.30967344e-02, -3.46699283e-02, -3.20725664e-02, 9.94816050e-03, 3.74528840e-02, -3.03600337e-02, -1.89593360e-02],
                [2.77403854e-02, 4.71809693e-02, 7.43073542e-05, -3.56101803e-02, -4.86052334e-02, -2.70347781e-02, -3.68181802e-02, 1.77668221e-02],
                [-3.78180221e-02, 6.11303840e-04, 1.94286909e-02, 8.11256189e-03, -3.00076883e-02, 3.04074995e-02, 2.15354897e-02, 2.39107814e-02],
                [-3.68951969e-02, -3.76411751e-02, 4.27581221e-02, -1.02414936e-02, -1.98938772e-02, -1.14537496e-03, 1.62824355e-02, 4.55717742e-02],
                [-2.13565435e-02, 4.24609743e-02, -4.75118086e-02, 5.52062923e-03, 1.34110181e-02, -3.94147970e-02, -3.59708220e-02, -8.07722844e-03],
                [4.66226488e-02, 9.59496479e-03, 4.33033705e-02, 3.04364767e-02, -3.25552328e-03, 2.84742229e-02, -4.82185595e-02, -3.90802957e-02],
                [3.29413675e-02, 2.96562705e-02, -2.67330557e-02, 3.07801599e-03, 1.06188767e-02, 3.67680863e-02, 1.03045907e-02, -8.72832257e-03],
                [-1.25829689e-02, -7.43519701e-03, 1.51957441e-02, 3.67500372e-02, -4.59439913e-03, -2.52213869e-02, -2.63394006e-02, 2.46147867e-02],
                [3.16556804e-02, -3.94925922e-02, -4.33418117e-02, 9.44421347e-03, -3.53688076e-02, 3.24617624e-02, -1.89714450e-02, -3.56011614e-02],
                [4.20967489e-02, -3.34519260e-02, -2.15274170e-02, -3.46384495e-02, -3.84475254e-02, -4.78863642e-02, -4.44616899e-02, -3.25329416e-02],
                [-4.46625240e-02, 9.10215080e-03, 1.80728324e-02, -1.06364461e-02, -1.81925762e-02, 4.49831132e-04, 3.74975465e-02, 3.51201445e-02],
                [-4.56525609e-02, -3.18513066e-02, -2.63253823e-02, -2.50611939e-02, 7.12404540e-03, -8.37401953e-03, -4.50748652e-02, -1.26379309e-02],
                [2.37505767e-03, -3.98368500e-02, 3.33463103e-02, -4.48036455e-02, 4.24869359e-02, -4.00896110e-02, 3.43565233e-02, 4.02676240e-02],
                [4.79560159e-02, 3.01846880e-02, 2.79497746e-02, 1.42490705e-02, 2.79118065e-02, -3.65488641e-02, 3.60249332e-03, 1.43250439e-03],
                [3.57560702e-02, -3.73958098e-03, -1.14888474e-02, 1.39571382e-02, -2.33403984e-02, -3.60276140e-02, -2.21697195e-03, -8.29992257e-03],
                [-2.67444942e-02, -1.32741742e-02, -1.33578936e-02, -1.72493905e-02, -1.20363543e-02, 1.85685456e-02, -2.03184579e-02, 4.49002646e-02],
                [4.16342989e-02, -1.91749586e-03, -1.71629172e-02, 3.54383374e-03, 3.48618515e-02, 1.52567849e-02, 3.04371268e-02, 3.27710155e-03],
                [1.32911932e-02, -2.11941469e-02, 2.34904122e-02, -2.97591370e-02, 1.94864143e-02, 3.60696912e-02, -3.67920548e-02, 1.14435153e-02],
                [-4.04911488e-02, 2.25592591e-02, -4.15492877e-02, 4.35944945e-02, -3.62508409e-02, 4.58852164e-02, 3.00854538e-02, 9.37522482e-03],
                [2.82621197e-02, 2.95065176e-02, 4.46032658e-02, -2.46614572e-02, 9.01096594e-03, -4.04962115e-02, 1.16153741e-02, -3.28680351e-02],
                [6.49486156e-03, 7.23964628e-03, -3.40110064e-03, 2.26331898e-03, 2.63946541e-02, 2.99236942e-02, -7.85498240e-04, 9.96128749e-03],
                [4.31228951e-02, -3.80391069e-02, -3.82882357e-02, -4.12285812e-02, 1.57948043e-02, -8.14201590e-03, 2.74291392e-02, 1.71302576e-02],
                [-1.66365728e-02, 3.98307107e-02, 2.62538847e-02, -2.29462590e-02, -1.35767553e-02, -1.85573604e-02, -3.42402644e-02, -3.52182686e-02],
                [4.36117128e-02, -6.22714963e-03, -1.16660371e-02, 2.29692999e-02, 5.31124044e-03, 4.36099917e-02, 2.80259233e-02, -2.05302425e-03],
                [-1.23647247e-02, 4.86516990e-02, 2.17773169e-02, 4.51199412e-02, -3.81443538e-02, 3.50507535e-02, 1.37046296e-02, -3.78012955e-02],
                [8.82451981e-03, 1.85878165e-02, -4.87672687e-02, -4.56729718e-03, 3.25547867e-02, -2.04690807e-02, -4.15044650e-03, -5.75613184e-03],
                [-1.98083371e-02, 4.18258384e-02, 2.81314757e-02, -3.89403962e-02, 4.97159436e-02, 3.79158109e-02, -2.16135737e-02, 3.37001346e-02],
                [-3.93585265e-02, 4.99022901e-02, 1.65693983e-02, 1.50128417e-02, -4.09503616e-02, 3.97014692e-02, -4.71020192e-02, -2.59125214e-02],
                [-3.56990956e-02, 2.76549850e-02, -3.01771164e-02, 4.10647281e-02, 1.56417321e-02, -4.63887081e-02, -4.94622700e-02, -4.48217578e-02],
                [1.05912797e-02, 3.01270895e-02, -2.61423383e-02, 3.49417590e-02, -4.42624688e-02, 3.00915707e-02, 4.27744649e-02, 2.72228774e-02],
                [1.98114831e-02, 3.37878652e-02, -4.59837243e-02, -2.98213679e-02, -3.75007279e-02, 4.50780586e-04, 2.45163664e-02, 1.30069815e-02],
                [3.51126678e-02, -3.44862565e-02, 2.34629624e-02, -3.06955352e-02, -2.29189880e-02, 2.09887456e-02, 4.80186604e-02, 1.11586731e-02],
                [-4.45510186e-02, 1.16130114e-02, -4.57629263e-02, 3.84153128e-02, 2.09699888e-02, -3.26912999e-02, -4.08322066e-02, -3.16364691e-02],
                [4.80025001e-02, -4.14759805e-03, 2.84085087e-02, 1.36409858e-02, 7.24380510e-03, -3.54878083e-02, 4.46015634e-02, -1.98636465e-02],
                [7.80141447e-03, 1.99723542e-02, 1.49239069e-02, 4.40596566e-02, -3.51525396e-02, 8.34077713e-04, -9.59782209e-03, -2.58013676e-03],
                [-3.80782597e-02, -3.65907326e-02, -2.21924223e-02, -1.95295326e-02, -7.20954733e-03, 1.10987108e-02, 1.34628648e-02, -8.81880056e-03],
                [-9.12289880e-03, -2.82577705e-02, 8.83295201e-03, -1.82950515e-02, -4.63799946e-02, -8.16470478e-03, -2.59169796e-03, -2.74289418e-02],
                [7.24477088e-03, 6.55976683e-03, 2.02021860e-02, 1.47955716e-02, 1.52551513e-02, -1.83825642e-02, 2.87390277e-02, 4.92438348e-03],
                [-6.85926480e-03, 1.25827678e-02, -1.39321815e-02, 1.27469224e-03, 2.36831326e-02, 3.86360697e-02, 4.21012715e-02, 3.73840739e-04],
                [2.02631066e-03, 2.99665816e-02, -1.85526218e-02, 3.37390862e-02, -5.71925193e-04, -3.84190008e-02, -4.27990109e-02, 3.42109986e-02],
                [-4.44439128e-02, -2.19508838e-02, -1.65856387e-02, -3.27000581e-02, -1.86024867e-02, 2.42665112e-02, -4.85346094e-02, 3.27242091e-02],
                [3.56539264e-02, -1.27887474e-02, -3.46370265e-02, 1.00846598e-02, -3.80226113e-02, -1.35114668e-02, 4.58393320e-02, 4.95549552e-02],
                [2.72102747e-02, -1.89075135e-02, 1.87669192e-02, 2.05407888e-02, -1.12133399e-02, 1.40880272e-02, -4.89281192e-02, -2.90921424e-02],
                [2.50865426e-03, -3.36278714e-02, -3.34089734e-02, 3.36305536e-02, 4.89153415e-02, 5.59625728e-03, 3.39062512e-02, 4.90338802e-02],
                [-3.58414389e-02, -5.19295502e-03, -1.07407523e-02, -4.19943444e-02, 2.55449265e-02, -6.62609655e-03, -3.07152420e-03, -3.49227041e-02],
                [-3.19080576e-02, 4.06980179e-02, -4.55336981e-02, -2.67142598e-02, -2.07856745e-02, -9.83064529e-04, 8.64154473e-03, -6.63955521e-04],
                [-4.15893346e-02, -2.56480686e-02, 3.43605094e-02, 1.37594854e-02, 1.49249751e-02, 1.70169435e-02, 2.62867343e-02, -4.41806950e-02],
                [-1.33403381e-02, 3.93269537e-03, -1.61520895e-02, 3.44487205e-02, -1.72911922e-03, 2.68581826e-02, 3.51967253e-02, 4.90591512e-04],
            ]
        )

    def get_exp_updated_bias_2(self):
        return np.array([-1.5694856e-04, -4.3621061e-05, -4.8142672e-05, -4.4095514e-06, 1.4637530e-04, 2.1506309e-04, -1.7414927e-04, 6.5824388e-05])

    def get_exp_updated_weights_2(self):
        return np.array(
            [
                [0.04097135, 0.00871688, 0.03503238, -0.01594047, -0.00013335, 0.003119, -0.03948413, -0.01015151],
                [0.04169388, 0.01307214, -0.03226158, -0.01611556, -0.0308025, -0.04746303, 0.04270179, -0.00516254],
                [-0.01919518, 0.00986198, -0.04925282, -0.02219635, 0.02025549, 0.01330666, 0.04823754, 0.01201425],
                [-0.00225633, 0.02614134, 0.04033067, 0.0220694, 0.04632757, 0.02820999, 0.03667247, -0.03858669],
                [0.0232775, -0.00598108, 0.00532147, 0.01541126, 0.0469478, 0.04840827, -0.02113706, 0.02336019],
                [0.02497771, -0.01535645, -0.03761936, -0.04590588, 0.02775357, -0.00100174, 0.04853111, -0.003494],
                [0.04779027, -0.0088428, 0.02936778, -0.04151811, 0.0055475, 0.03020793, 0.04246859, 0.03225891],
                [-0.04630339, -0.01272989, -0.04513029, -0.03907178, 0.01753099, 0.02132645, 0.02737156, 0.03654585],
            ]
        )