no_model_min.py

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([[-4.97591466e-01,-3.48154902e-01,-9.64563787e-02,-4.40266915e-02,5.13613582e-01,-6.10522591e-02,-3.47615294e-02,2.88594782e-01,-8.85496065e-02,2.12527990e-01,3.30001622e-01,7.58453384e-02],[-1.27492115e-01,4.95373383e-02,-1.57608762e-01,-4.92550097e-02,-7.41389468e-02,1.77854940e-01,1.98510706e-01,2.09193066e-01,-7.28763118e-02,9.65496376e-02,-7.77478367e-02,-9.66457278e-02],[1.06221072e-01,7.24562407e-02,2.04165325e-01,-7.58996159e-02,4.90117222e-02,-1.36507004e-01,-6.86904788e-02,1.85969453e-02,-1.41062304e-01,7.45521039e-02,1.33563578e-01,3.82802659e-03],[-8.52456763e-02,2.53689200e-01,-2.39008814e-02,1.94278255e-01,-2.69632757e-01,1.31557286e-01,-2.69171060e-03,1.76334307e-01,-1.33269429e-01,-2.20754936e-01,1.06023289e-01,2.43034512e-01],[2.28796661e-01,8.57962593e-02,-6.07645214e-02,-1.36777923e-01,-1.02011338e-01,-2.44704083e-01,2.28717878e-01,-2.07664315e-02,6.01623394e-02,-1.09244689e-01,1.13376960e-01,5.31305224e-02],[-1.49923503e-01,5.40319309e-02,2.76568979e-01,6.85752034e-02,1.37822792e-01,-1.36446223e-01,2.75661089e-02,-6.96043745e-02,8.00784677e-02,4.72860597e-02,-5.72931115e-03,-1.09331116e-01],[7.63551071e-02,-2.01310366e-01,6.90016709e-03,2.96932578e-01,9.26339552e-02,3.50874662e-02,1.24513488e-02,2.53022045e-01,-1.45443067e-01,-1.11637279e-01,-2.93283956e-05,-1.28838167e-01],[2.08078343e-02,-1.27906993e-01,1.40025541e-01,8.70746523e-02,-3.86649603e-03,2.75576245e-02,1.29678071e-01,7.90816918e-02,1.48384586e-01,-1.12928234e-01,2.14731600e-02,2.46492818e-01],[-2.68742368e-02,-7.25817233e-02,1.70269638e-01,-6.74946606e-02,-9.64430273e-02,1.97632000e-01,4.37725373e-02,2.24634647e-01,-9.60620418e-02,-1.10878691e-01,3.12824361e-02,8.61454979e-02],[2.13755235e-01,1.99725121e-01,-1.48769785e-02,-1.62346587e-01,-6.99169338e-02,6.64472058e-02,-1.06776608e-02,1.26448184e-01,-3.65157157e-01,6.87637031e-02,-1.51696905e-01,4.44301218e-02],[2.21566811e-01,-9.31609496e-02,9.17831659e-02,2.08272249e-01,4.88626212e-02,-1.04722701e-01,-1.05342060e-01,-5.49319871e-02,-1.22663617e-01,-1.30428895e-01,-3.10298800e-02,1.23200379e-01],[-1.33388475e-01,1.69885203e-01,-3.63785960e-02,7.98837617e-02,1.74949411e-03,1.46997184e-01,-3.92615974e-01,6.48709387e-02,-2.63804287e-01,7.34840930e-02,-1.53784797e-01,3.17909628e-01],[-1.54809460e-01,2.68280655e-01,1.37583271e-01,1.03114493e-01,-2.63132509e-02,1.78944036e-01,4.48270403e-02,2.13879585e-01,-1.73378274e-01,-6.08024448e-02,-1.40879855e-01,-4.25300561e-02],[-1.34938553e-01,-4.84419502e-02,3.40939648e-02,1.84744149e-01,-2.72330254e-01,-1.78654537e-01,5.73407188e-02,1.29591092e-01,-2.15156525e-01,-1.17826439e-01,-2.19659060e-01,-1.20152488e-01],[6.06940761e-02,-2.03321408e-02,-7.27882385e-02,2.08977237e-01,-1.88799575e-02,7.17384145e-02,1.78676009e-01,-5.66789657e-02,9.82583910e-02,-2.26157069e-01,-7.77398497e-02,-2.33672872e-01],[2.68916875e-01,7.65278637e-02,8.93844739e-02,1.47371888e-01,3.05777848e-01,-8.22771788e-02,-9.87015292e-02,2.83623725e-01,-1.25272602e-01,8.54255334e-02,-1.82457268e-01,2.00247794e-01],[-1.50934026e-01,-1.25254720e-01,-4.88218009e-01,-3.23920488e-01,2.38912597e-01,3.21868271e-01,-5.81769608e-02,6.74387962e-02,-1.21145807e-01,3.78832877e-01,1.60731912e-01,-1.28496408e-01],[3.66435617e-01,1.38609931e-01,-1.40166044e-01,-1.75830096e-01,2.67599165e-01,-4.49557081e-02,1.88275307e-01,4.64328341e-02,3.01892310e-01,7.79502443e-06,-1.07656427e-01,2.73387805e-02],[3.96858864e-02,-1.95369553e-02,-3.78224373e-01,-1.10172950e-01,1.37235045e-01,-1.10199889e-02,1.22827195e-01,2.51732431e-02,2.88845092e-01,1.54470786e-01,4.95812520e-02,9.68530402e-02],[-1.77407920e-01,3.35215300e-01,-6.64436966e-02,-5.28201982e-02,-3.11636388e-01,-1.18662659e-02,-9.22466069e-02,1.55326203e-01,-3.74499671e-02,6.63819611e-02,1.35921091e-02,-1.67969137e-01],[-3.00518781e-01,-4.08031270e-02,5.08502834e-02,-5.46384007e-02,5.07085994e-02,1.75255701e-01,1.00928307e-01,8.81417170e-02,2.52746623e-02,-1.55042544e-01,6.17961287e-02,-6.74463287e-02],[-2.73041666e-01,5.31119518e-02,1.44233312e-02,-3.12938280e-02,-1.41446277e-01,-6.11292310e-02,-1.63170025e-01,1.32249966e-01,-1.97827015e-02,4.34016399e-02,-1.63343892e-01,4.95858043e-02],[1.80105120e-01,-1.38527021e-01,1.33175910e-01,-6.06347434e-02,9.63696167e-02,-4.45689783e-02,-1.48434371e-01,1.94733709e-01,1.53649688e-01,1.09688520e-01,-6.07350208e-02,-1.23088874e-01],[-1.39569581e-01,-2.99566947e-02,2.36185282e-01,1.59565046e-01,1.32984832e-01,-9.71393660e-02,1.81482449e-01,1.22638695e-01,-2.71068662e-01,-1.07634492e-01,-9.59608480e-02,-2.07147390e-01],[2.39953306e-03,4.84044068e-02,-9.32793394e-02,1.01557665e-01,-7.95148984e-02,-1.89224467e-01,6.40991852e-02,-1.87834546e-01,1.97486326e-01,-3.25080343e-02,-2.95529455e-01,-1.60612255e-01],[-6.92793503e-02,1.89327472e-03,-1.32652104e-01,3.92031902e-03,1.27854139e-01,1.03263207e-01,-1.43438801e-01,1.46077663e-01,-2.47993499e-01,-2.36500278e-02,1.42356426e-01,-1.49148302e-02],[-1.63655102e-01,-6.05586506e-02,1.85286850e-02,-1.35026127e-01,-1.98037595e-01,-1.37843952e-01,-2.22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	b0=np.array([0.22709577,0.19766952,0.01995138,-0.04603582,0.01704955,0.01437583,0.05359448,-0.02731087,0.21360077,-0.07868154,-0.07776031,-0.16930926],dtype=np.float)
	z1=np.dot(input_data,w0)+b0
	a1=_relu(z1)
	return a1
def _layer_one(input):
	w1=np.array([[-1.0203481,0.46559307,-0.90648866,0.38084486,0.63899696,-0.9992195,0.8293284,0.27000228],[-0.2910284,-0.52721786,-0.40077013,0.16102973,0.7513776,-0.9326905,0.8123616,0.9563922],[0.76624835,0.48214787,0.81875545,-0.6023574,-0.02267493,0.5544211,-0.63002443,-0.41798884],[0.14604479,-0.23465909,0.34627366,-0.56753683,-0.8076582,0.16675556,-0.27811536,-0.36773536],[0.70223564,0.40773264,0.7151527,-0.53273094,-0.7913494,0.7881167,-0.3787351,-0.43144867],[-0.03614731,0.21656777,-0.759399,0.42939952,0.06886981,-0.60933244,-0.49799868,0.15505937],[0.02860112,0.43704703,-0.672733,-0.43158615,0.8740345,0.20003332,0.45194054,0.3210791],[0.15656586,0.26368082,-0.1948867,-0.26360726,-0.36085114,0.3431014,-0.29323974,0.42509705],[0.09939747,-0.20705642,0.51196814,0.05691895,0.34692687,0.49042138,0.41174233,0.2256699],[0.32137677,-0.87251765,-0.0379138,-0.08180265,-0.00335148,0.5709534,-0.5237682,-0.57087135],[-0.8040256,0.1928722,0.11546769,0.16279563,0.856959,-0.63919276,0.34508547,0.32145658],[-0.528815,-0.31074256,-0.04968386,0.32441455,-0.7268404,-0.2714887,-0.4780787,-0.3288983]],dtype=np.float)
	b1=np.array([0.14329214,0.02244809,0.07480378,-0.00108605,0.10871821,0.00806387,0.11875808,0.0619337],dtype=np.float)
	z2=np.dot(input,w1)+b1
	a2=_relu(z2)
	return a2
def _layer_three(input):
	w2=np.array([[0.48954403],[-1.2882802],[0.7204739],[0.31496158],[-1.4293383],[1.2237549],[-1.4405941],[-1.1687659]],dtype=np.float)
	b2=np.array([-0.07027833],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)