yes_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([[-7.61112124e-02,4.17372352e-03,-2.88709961e-02,-9.98222008e-02,3.57228398e-01,-1.97849110e-01,-1.95554182e-01,7.91458711e-02,-1.88918740e-01,-4.03471254e-02,-1.19863980e-01,-2.06972323e-02],[-2.81994492e-01,-2.37183925e-02,-3.83828163e-01,-1.68631092e-01,7.67200142e-02,-2.08643094e-01,-2.27881178e-01,-1.77109525e-01,-1.10722609e-01,-2.76989490e-01,-2.46479690e-01,-9.40366611e-02],[-1.07054561e-01,1.07524976e-01,7.70086348e-02,1.82658225e-01,4.74974997e-02,2.27093875e-01,-5.81278913e-02,-6.20693266e-02,3.06330482e-03,-1.12597365e-02,-5.97939007e-02,-1.03424676e-02],[2.24220246e-01,-9.32385027e-02,1.18582331e-01,3.40451270e-01,2.52770800e-02,1.96188733e-01,3.25094879e-01,1.43494442e-01,-1.46675795e-01,2.93649912e-01,7.67850429e-02,2.99175512e-02],[1.79142997e-01,7.51023665e-02,1.96316034e-01,2.84412742e-01,-7.90214315e-02,9.08477604e-03,2.37653017e-01,-1.97127834e-01,-9.48660672e-02,-3.01951934e-02,2.88289860e-02,4.83069755e-02],[1.69888109e-01,-8.37777928e-02,-2.40450546e-01,2.75522768e-01,1.51522055e-01,6.02699677e-03,1.12581288e-03,-1.57974631e-01,-3.46071497e-02,1.90543413e-01,1.20634668e-01,-9.98358279e-02],[-1.05921574e-01,-3.22985835e-02,1.31440535e-01,3.80012065e-01,-7.23386034e-02,-4.14493158e-02,-1.19337812e-01,-1.12640820e-01,4.11481187e-02,5.42160384e-02,1.34143636e-01,-1.34831011e-01],[4.92170975e-02,-3.73338386e-02,-2.29159191e-01,1.37712568e-01,2.73860376e-02,-5.58822714e-02,-1.52483165e-01,1.46920308e-02,-3.43149938e-02,-2.74824381e-01,-6.22484684e-02,-1.39968529e-01],[2.25974590e-01,8.39661434e-02,1.62548825e-01,1.16550989e-01,1.58254579e-01,-3.07007618e-02,6.70202896e-02,-3.85020375e-02,-1.97871447e-01,1.30261555e-01,-1.89028844e-01,1.49246678e-01],[2.20076993e-01,3.27484719e-02,8.01391453e-02,7.66726583e-03,-1.77275136e-01,1.81044564e-01,5.20728156e-02,-1.03145838e-01,2.88931131e-02,-1.66258737e-01,2.22069189e-01,1.08987413e-01],[3.12679619e-01,-9.46736410e-02,-9.41528454e-02,-1.53909743e-01,-5.94927929e-02,-5.17465584e-02,8.73784274e-02,-5.76447882e-02,5.42754568e-02,-1.81477126e-02,-1.35131078e-02,1.09054990e-01],[1.12475790e-01,-3.14105421e-01,6.27802163e-02,-2.65171379e-01,-1.38570853e-02,-6.72486946e-02,-1.36688545e-01,-2.13842437e-01,-7.01503307e-02,8.62760693e-02,-3.44477668e-02,-2.04076841e-01],[1.22340389e-01,8.56022984e-02,2.85106599e-01,7.77772889e-02,-1.18722944e-02,-1.93859622e-01,2.96205003e-02,-2.00524464e-01,-1.65430307e-02,-1.22511685e-01,1.05617613e-01,-7.00076669e-02],[-1.88491896e-01,6.49654195e-02,5.93844652e-02,-2.53018574e-03,7.67664611e-02,-1.70570835e-01,1.54208153e-01,-1.46032209e-02,1.16261624e-01,2.76371446e-02,1.75930664e-01,7.17002302e-02],[1.39465202e-02,-8.88184756e-02,6.63036704e-02,-1.61812473e-02,-3.99977230e-02,2.22101837e-01,-1.22070052e-02,5.09350002e-02,1.56464800e-01,-8.62007961e-02,-6.31061383e-04,-1.38900340e-01],[2.02813312e-01,-2.34112695e-01,-8.51828828e-02,1.01706512e-01,-6.91643432e-02,5.89524098e-02,-1.39665380e-01,5.68916388e-02,9.88352299e-02,-1.23415917e-01,1.51193812e-01,-1.58437192e-02],[3.90965305e-02,-3.09201151e-01,4.55075093e-02,9.65153873e-02,1.18663460e-01,2.23914877e-01,1.38225004e-01,-3.23791839e-02,-2.19965905e-01,-5.08512110e-02,-2.83849150e-01,4.02231738e-02],[-2.54610538e-01,-2.31531456e-01,-8.18004534e-02,9.39603802e-03,2.56131738e-01,9.59355235e-02,-3.95799309e-01,-3.25305432e-01,-6.98757023e-02,-2.16554657e-01,-4.78710920e-01,8.39276984e-02],[3.94125618e-02,-1.25634179e-01,2.83304960e-01,1.63177103e-01,2.01454654e-01,1.44521385e-01,1.45092621e-01,4.40532640e-02,-4.31873165e-02,1.65865526e-01,3.42457257e-02,-1.03795476e-01],[1.17009226e-02,-1.38185456e-01,1.12871282e-01,8.22629407e-02,2.36608922e-01,1.96900740e-01,-9.10000596e-03,-1.00929044e-01,2.54351348e-01,1.21791989e-01,1.64711978e-02,-1.46532550e-01],[2.70402431e-01,-1.76089257e-01,1.41783789e-01,4.25084442e-01,-1.95781663e-01,2.69114166e-01,2.32648090e-01,-1.71323828e-02,3.86673608e-03,3.00210044e-02,-6.10924996e-02,-1.35343015e-01],[-1.90804452e-01,-2.21093848e-01,-1.05846867e-01,4.96036053e-01,-1.49161398e-01,4.55948055e-01,-1.19975349e-02,5.99649251e-02,1.18908621e-01,4.29194778e-01,3.26609999e-01,-1.75665960e-01],[4.12890874e-02,-2.50952631e-01,-5.73883690e-02,1.56791747e-01,-1.55286938e-01,2.01298207e-01,2.93655753e-01,3.44949514e-01,-7.69721791e-02,2.39075422e-01,2.05830693e-01,1.43098295e-01],[-1.92030102e-01,-2.81373356e-02,-2.60894056e-02,-1.84004270e-02,2.37246215e-01,1.36882231e-01,-2.04719722e-01,4.19154130e-02,9.03274268e-02,-7.18544647e-02,6.48954883e-02,-1.45355657e-01],[2.10507233e-02,-4.82093496e-03,-1.92964464e-01,-2.17383564e-01,1.02183893e-01,-4.27378528e-02,-1.96374133e-01,1.82642251e-01,1.05334565e-01,-1.27819613e-01,9.35240313e-02,1.95216462e-01],[6.05606809e-02,1.51554152e-01,-1.91797409e-02,-2.19756886e-01,3.82057816e-01,-1.74988821e-01,1.48052484e-01,-2.35403225e-01,-2.39828750e-02,4.73245941e-02,1.50806010e-01,4.64486741e-02],[-1.07198186e-01,-6.91393018e-02,-6.34186938e-02,-1.08972147e-01,-4.55366448e-02,-2.38903865e-01,-6.46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	b0=np.array([-6.9703087e-02,-1.4515671e-04,-2.5080740e-02,-8.6653113e-02,3.1750247e-01,1.1621542e-01,5.3288072e-02,4.2801246e-02,4.1285906e-02,-5.3254841e-03,-6.5613031e-02,8.8801607e-03],dtype=np.float)
	z1=np.dot(input_data,w0)+b0
	a1=_relu(z1)
	return a1
def _layer_one(input):
	w1=np.array([[-0.5801398,0.37769204,0.37809405,-0.24717931,0.11563622,-0.25186858,-0.63919586,0.31576687],[-0.21666908,0.5580129,0.42070875,0.55167425,0.18183447,0.9385317,-0.65536326,-0.05458922],[-0.54512554,0.20783952,0.77867997,0.11388333,0.67721295,0.41365,-0.47313258,-0.48754346],[-0.84670264,0.9559192,0.7561184,1.0073193,0.57274634,0.50831443,-0.9461864,-1.0413024],[0.82013226,0.09020517,0.09679506,-0.20612788,-0.03363921,-0.00135415,0.20121408,0.6914809],[0.14529973,0.13964415,0.08696514,0.31186566,0.36819813,0.2352645,0.5457378,0.45537654],[-0.9747818,0.57234675,0.72931165,0.34739783,0.9525267,0.60410994,-0.8495043,-1.2008456],[0.2028882,-0.20789772,0.59835464,0.71254015,0.07562512,0.6823455,-0.7240966,-0.17397189],[0.96710247,-0.63472927,-0.60146123,-0.44773296,-0.9477111,-0.7385757,1.1143273,0.32958677],[-0.6401787,0.24426389,0.11188699,0.49860278,0.40873092,0.44324496,-0.5178973,0.2686834],[-0.8883932,0.23574542,0.8664524,0.9909575,0.24973644,1.1387851,-0.4700223,-1.0952587],[-0.8596748,0.42395616,0.88151014,0.51238924,0.75031215,0.59053,-0.4049496,-0.660567]],dtype=np.float)
	b1=np.array([0.4063363,-0.2084384,-0.223644,0.0244865,-0.13537137,-0.18678664,0.40600446,0.39345324],dtype=np.float)
	z2=np.dot(input,w1)+b1
	a2=_relu(z2)
	return a2
def _layer_three(input):
	w2=np.array([[0.8253177],[-1.2144302],[-0.96321017],[-0.9754526],[-1.0325733],[-0.591513],[0.6275896],[0.30383065]],dtype=np.float)
	b2=np.array([0.29505387],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)