on_model_min.py

# This model was trained using Speech Commands Dataset licensed under the Creative Commons BY 4.0 license.
# Warden P. Speech Commands: A public dataset for single-word speech recognition, 2017. 
# Available from http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz
from ulab import numpy as np
def _relu(x):
	return np.maximum(0,x)
def _sigmoid(x):
	return 1/(1+np.exp(-x))
def _layer_zero(input_data):
	w0=np.array([[-1.32244080e-02,-6.78005144e-02,-3.52041006e-01,-1.05415083e-01,1.66751444e-01,6.56064227e-02,-3.78853157e-02,-9.89838913e-02,2.23399669e-01,2.85320848e-01,2.50445873e-01,6.92801028e-02],[-1.02191016e-01,-8.37643072e-02,-2.95916975e-01,-1.58257499e-01,1.16360307e-01,-1.07163645e-01,-1.65166065e-01,-9.08204019e-02,-3.66394334e-02,1.35140315e-01,2.45711394e-02,-1.69819936e-01],[-3.27901579e-02,-3.27543765e-02,-1.59817144e-01,2.66330779e-01,1.59238249e-01,1.53209850e-01,-1.67782530e-02,1.14733237e-03,-1.56439826e-01,-8.20656270e-02,3.57945450e-02,8.27838108e-02],[-1.91612113e-02,1.88947409e-01,-8.52150247e-02,1.84596434e-01,1.70900285e-01,-5.44336624e-03,-9.91927460e-02,-1.75941736e-01,1.89493001e-01,-1.58842519e-01,-1.96932420e-01,1.88692987e-01],[-3.28698158e-01,2.54173875e-01,1.13056697e-01,2.55430579e-01,1.52448490e-01,-1.48508623e-01,2.38398183e-02,-8.93116556e-03,1.38616040e-01,-3.08241785e-01,-1.85895354e-01,7.93865174e-02],[-7.91448727e-02,7.35702887e-02,-1.63453907e-01,-1.80625007e-01,9.63369608e-02,3.13525796e-02,-6.48703501e-02,-1.19550209e-02,3.94476950e-03,-5.09965494e-02,2.27988288e-02,-3.76177877e-02],[8.98591988e-03,-1.07855819e-01,1.42863672e-02,-6.31889403e-02,-7.83453658e-02,-7.91811571e-02,1.03313662e-01,2.32185367e-02,1.75183296e-01,-2.13968307e-01,-9.04838815e-02,1.82151422e-02],[7.05777034e-02,-8.12802762e-02,-6.34063827e-03,-9.03418735e-02,1.68128774e-01,3.04880679e-01,4.09542359e-02,-8.16709548e-02,3.49298954e-01,2.52717584e-01,3.04751217e-01,-3.37342210e-02],[1.43065080e-01,-1.76382944e-01,-8.34526867e-02,1.60091788e-01,7.40477117e-03,3.41890864e-02,1.15982972e-01,1.51298210e-01,-1.05994932e-01,8.11275914e-02,-2.18080059e-01,8.54972154e-02],[2.13800997e-01,5.90478778e-02,-1.93640649e-01,-1.95329636e-01,-1.61638319e-01,1.68548644e-01,-1.39853925e-01,-8.07100981e-02,2.21201539e-01,1.68414757e-01,9.16122422e-02,-5.12663797e-02],[-7.08403513e-02,1.23568662e-01,-2.98107713e-02,-1.40601158e-01,-1.40044048e-01,-1.21196985e-01,-8.36995095e-02,-1.10475071e-01,1.48155287e-01,9.63921100e-02,-1.41534150e-01,-1.76726624e-01],[-2.64013946e-01,2.49536127e-01,-2.59356815e-02,-1.71600226e-02,-2.39763826e-01,-2.45325893e-01,5.80013636e-03,5.33876382e-02,-3.15957010e-01,1.36667818e-01,-5.56372255e-02,-9.71929915e-03],[1.18512243e-01,2.81017143e-02,-1.37581319e-01,1.88746139e-01,1.11700282e-01,-1.25679240e-01,8.36948454e-02,-2.06330895e-01,-8.81667808e-03,-1.00550074e-02,-2.32849851e-01,1.50931761e-01],[-9.43301097e-02,-6.52459934e-02,1.04215011e-01,-1.59915403e-01,-9.17221904e-02,6.56031221e-02,2.24770848e-02,-9.45116580e-02,-1.68898240e-01,6.46733330e-04,3.41005735e-02,-3.69044282e-02],[-1.71580657e-01,2.71902811e-02,1.39253750e-01,2.07824558e-02,-6.03623204e-02,-3.71620394e-02,6.49003088e-02,1.46792635e-01,1.49517268e-01,3.48790083e-04,-1.52225494e-01,-1.56370342e-01],[-1.11687988e-01,3.94560732e-02,1.50652424e-01,7.08949938e-02,3.06374077e-02,-5.12072816e-02,-5.63110597e-02,-1.01562582e-01,-1.56741261e-01,-7.98315406e-02,-3.73390578e-02,-2.87534669e-02],[-1.60809830e-01,-3.76055799e-02,4.27146070e-02,1.27584457e-01,2.92279065e-01,1.42452434e-01,-4.05023731e-02,-5.77891879e-02,1.64859831e-01,1.91967756e-01,-4.31217887e-02,-2.47262362e-02],[-5.01388963e-03,-1.27224252e-02,-2.88949549e-01,5.49950525e-02,2.65345305e-01,5.81302270e-02,2.98849382e-02,-1.11626983e-01,2.99642235e-01,2.38203228e-01,1.46586418e-01,-3.61043364e-02],[-2.34767292e-02,-1.91411465e-01,-8.37070644e-02,5.91226146e-02,1.88354760e-01,-1.42283663e-01,-4.10425544e-01,-1.39151126e-01,9.89680961e-02,2.16977689e-02,-8.32162648e-02,3.81928794e-02],[5.82948998e-02,1.97043523e-01,1.14847139e-01,6.03816584e-02,1.77838892e-01,1.55997083e-01,-2.71386206e-01,-8.29244964e-04,-5.80402091e-02,-2.55567431e-01,-1.14436127e-01,1.24639384e-01],[2.07560971e-01,1.32818535e-01,8.99974629e-02,2.53873795e-01,-1.40130445e-01,1.23976931e-01,-2.22839817e-01,3.65489684e-02,-6.87289611e-02,-2.17062041e-01,-2.82775789e-01,2.89404213e-01],[7.98908696e-02,-2.15768948e-01,-5.96046150e-02,-2.42270082e-01,-2.21022576e-01,-6.03738204e-02,-7.75424466e-02,2.46320534e-02,-5.96070513e-02,3.96337435e-02,8.65682811e-02,-5.89475408e-02],[2.00787887e-01,-3.08682680e-01,4.30393703e-02,-1.82202742e-01,3.68903391e-02,1.40846968e-01,-2.77767535e-02,-1.59952238e-01,4.57009859e-02,2.39123538e-01,2.04657346e-01,-1.07783005e-01],[3.59377563e-02,5.91453649e-02,9.82947946e-02,-3.02233428e-01,2.08113357e-01,1.75296843e-01,-2.56990939e-01,-1.59379095e-01,1.79920997e-02,3.17556322e-01,1.04646273e-02,-5.35206497e-02],[-9.99894962e-02,-4.16329764e-02,-6.18162304e-02,-5.57094812e-02,-4.01244015e-02,-1.39697090e-01,-9.00903866e-02,4.25830521e-02,2.11000770e-01,7.56493285e-02,1.31634220e-01,1.69434808e-02],[8.69879574e-02,1.68373007e-02,-2.25141421e-01,-3.30366828e-02,-1.30728081e-01,6.15885779e-02,-1.46299034e-01,1.57850951e-01,9.32278782e-02,3.62421796e-02,1.82705298e-01,1.05304265e-04],[6.54712319e-02,8.03612247e-02,-1.02100901e-01,-8.97241011e-02,-2.24052574e-02,2.05874071e-01,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01,1.66876361e-01,1.29547799e-02,1.61370814e-01,-1.54694617e-01,-3.00166057e-03],[1.90355942e-01,1.33289069e-01,-1.64813355e-01,-1.44941077e-01,2.41434872e-01,1.44326806e-01,-1.95227712e-01,1.16989255e-01,1.73638716e-01,-2.83718705e-01,-1.52360067e-01,-9.72280875e-02],[2.05092177e-01,1.00138582e-01,3.73223163e-02,7.10899010e-02,-5.13966084e-02,1.71260074e-01,-1.57255605e-01,-7.31929615e-02,1.85417935e-01,-1.15986265e-01,-3.78419608e-02,-5.21868356e-02],[-1.41747594e-01,1.01268843e-01,-9.35627744e-02,1.04786031e-01,-2.90073901e-01,-3.86830457e-02,-9.20206774e-03,-1.88380778e-01,-1.38789536e-02,1.62056834e-01,1.28520846e-01,1.46924764e-01],[9.64030400e-02,8.52443837e-03,-5.26412688e-02,-2.68287733e-02,4.43439707e-02,-1.93562417e-04,4.54956219e-02,-1.62427723e-01,1.45279795e-01,-2.24292263e-01,1.21207155e-01,1.26109973e-01],[8.57952312e-02,-1.62392646e-01,-2.54531018e-02,1.34275500e-02,-6.49445355e-02,-9.89314355e-03,-3.36394906e-02,2.61975676e-01,6.99261948e-02,-8.16385150e-02,-5.31573258e-02,-2.00114757e-01],[6.93330541e-02,2.55142570e-01,-1.06508434e-01,-4.85399306e-01,3.53967726e-01,1.97720721e-01,-5.35792150e-02,1.03842102e-01,-5.50794750e-02,3.58418338e-02,1.67985931e-01,-5.55812158e-02],[-1.87405214e-01,-1.49159655e-02,1.37586877e-01,-5.21194488e-02,1.30518481e-01,-1.10049739e-01,-8.48518685e-02,1.05462700e-01,-1.01069622e-01,2.01244026e-01,-1.44542426e-01,5.53654414e-03],[6.49743229e-02,-1.73481070e-02,-8.75578448e-02,8.40502046e-03,-8.55949223e-02,-9.35607255e-02,1.35292888e-01,5.01889028e-02,1.25616416e-01,-2.65425414e-01,8.57237428e-02,1.86133966e-01],[-4.94422615e-02,-2.27939263e-02,-2.62498260e-01,-1.20198037e-02,2.73603767e-01,-7.73352012e-02,-1.32786304e-01,9.92481248e-04,1.50989309e-01,-1.06613204e-01,3.88869494e-02,1.95009664e-01],[-1.66665718e-01,-8.81840810e-02,1.62480280e-01,8.65027383e-02,-4.50421944e-02,-2.85644904e-02,-9.14004967e-02,-9.18589067e-03,1.50019363e-01,-1.72894880e-01,-4.27378751e-02,1.95941895e-01],[-2.34617338e-01,3.07096392e-01,1.41527012e-01,-1.92694172e-01,1.69928759e-01,-1.96825504e-01,-1.35291025e-01,9.68835801e-02,1.18455365e-02,-4.34103981e-03,-3.30909640e-02,2.05484644e-01],[-8.99882838e-02,-1.08562194e-01,1.26326069e-01,-5.49939983e-02,1.80840999e-01,-7.45705366e-02,6.24163076e-03,1.93330675e-01,-8.26668441e-02,-8.44193250e-02,-6.19009137e-02,-1.56444106e-02],[1.49838805e-01,1.32693931e-01,7.97536373e-02,1.07611805e-01,8.62790048e-02,-1.05899230e-01,1.95404571e-02,1.79186791e-01,1.74422398e-01,6.35861009e-02,5.77469394e-02,-7.94934332e-02],[-2.53515691e-01,-1.22422062e-01,7.17289597e-02,1.26479164e-01,1.51392981e-01,1.03230532e-02,1.13166690e-01,2.33414606e-03,-1.24750189e-01,-6.71586394e-02,-1.00599537e-02,-4.82318690e-03],[6.55065775e-02,-2.66906079e-02,2.02062115e-01,3.04926276e-01,-1.39545932e-01,-7.18946010e-02,8.43387395e-02,-1.75457016e-01,1.28893316e-01,-6.62579015e-02,-2.46970043e-01,-5.30063361e-02],[-1.15126617e-01,4.61021587e-02,-1.98678747e-02,-1.73599273e-01,9.47329924e-02,1.71157375e-01,2.17010319e-01,1.00586444e-01,-4.16345857e-02,-1.60076410e-01,-1.13931790e-01,-2.06630483e-01],[1.24264009e-01,-9.28403139e-02,-1.53818727e-01,1.23330697e-01,6.59830570e-02,1.78052764e-02,-3.14918943e-02,-1.07281789e-01,3.48022506e-02,-1.89361379e-01,-3.81753705e-02,3.05433571e-03],[-2.23246560e-01,1.08738877e-01,-6.09561615e-02,9.84927043e-02,6.37344569e-02,-6.47336841e-02,1.27327368e-01,9.01958048e-02,1.90512910e-01,-1.33864716e-01,-1.54844876e-02,2.22840399e-01],[-5.84119046e-03,-1.08419947e-01,-7.67595023e-02,1.27619907e-01,8.30787886e-03,-6.48344904e-02,5.15327156e-02,-1.90994650e-01,1.22413924e-02,-1.72597602e-01,-1.78825736e-01,-3.69884111e-02],[-5.97957484e-02,-5.16469497e-03,4.61087860e-02,1.63839474e-01,8.74527395e-02,-1.08088635e-01,-1.16200872e-01,-9.98395309e-02,1.47109896e-01,1.75345764e-01,9.54224840e-02,2.62334459e-02],[2.49743044e-01,5.23341075e-02,9.72894579e-02,6.65683523e-02,3.04385424e-02,6.05203919e-02,-1.10029511e-01,-4.79650730e-03,5.83557449e-02,-2.66606301e-01,-1.16553977e-02,-6.30287901e-02]],dtype=np.float)
	b0=np.array([0.20976096,-0.04962333,-0.02581057,-0.05174313,-0.08515619,0.25901756,0.17089307,0.02367241,0.18702284,0.17247067,0.12180006,0.0110681],dtype=np.float)
	z1=np.dot(input_data,w0)+b0
	a1=_relu(z1)
	return a1
def _layer_one(input):
	w1=np.array([[-0.2869829,0.49936584,-0.3304323,0.06629702,-0.20466682,0.91446155,0.5552365,-0.7370109],[0.18920976,-0.5092444,-0.28084636,-0.44575545,-0.40435132,-0.14687157,-0.20119005,0.62166077],[0.06386591,-0.07859889,-0.22007199,-0.08100863,-0.33824995,-0.6337715,-0.23346993,0.96303356],[0.8126793,-0.31568807,-0.20590363,-0.14630575,0.15897414,-0.695171,-0.7473887,0.58570516],[0.15695478,-0.74753106,-0.3281877,-0.7391342,0.40028682,0.15088986,0.09051634,-0.26052514],[0.26351067,0.45630106,-0.4931773,0.0554491,-0.6418681,0.606632,0.50009197,0.3457684],[-0.4217268,0.37106997,-0.00244503,0.30324182,-0.05322583,0.06064797,0.7162733,-0.12078427],[-0.18803366,-0.24210905,-0.29715398,-0.01992992,0.19337863,0.51569986,0.31774595,-0.5008903],[0.09155359,0.07087095,-0.5513608,0.23573382,-0.4828722,0.5701719,0.4060024,0.54517764],[-0.55468464,1.063522,0.39539593,0.8171174,0.15701714,0.23522702,0.97429836,-0.07571346],[-0.40735996,0.30021647,-0.34817776,-0.01962602,-0.48373425,0.33069637,0.24029556,0.22653876],[0.74467254,-0.22513321,0.11010721,0.32339144,0.40810695,-0.61086184,-0.5148591,0.74665636]],dtype=np.float)
	b1=np.array([0.28241503,0.24006824,-0.02922342,-0.01879645,-0.05264836,0.09707203,0.14983825,0.17849602],dtype=np.float)
	z2=np.dot(input,w1)+b1
	a2=_relu(z2)
	return a2
def _layer_three(input):
	w2=np.array([[0.8823661],[-1.1978763],[0.74761945],[-0.8012815],[-0.24907705],[-1.0715413],[-0.73516214],[0.44261497]],dtype=np.float)
	b2=np.array([0.15408769],dtype=np.float)
	z3=np.dot(input,w2)+b2
	return _sigmoid(z3)
def predict(input_data):
	a=_layer_zero(input_data)
	b=_layer_one(a)
	return _layer_three(b)