In [58]:
# importing scikit-learn datasets package and the MLP classifier package
from sklearn import datasets
from sklearn.neural_network import MLPClassifier
# setting the dataset to variable iris, of type bunch.
iris = datasets.load_iris()
# setting max_iter to 542 since lower values led to convergence warnings -
# ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached
# and the optimization hasn't converged yet.
m = MLPClassifier(verbose=False,max_iter=542)
In [59]:
#fitting the iris dataset against the MLP classifier.
m.fit(iris.data,iris.target)
Out[59]:
MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(100,), learning_rate='constant',
learning_rate_init=0.001, max_iter=542, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
In [60]:
#listing the parameters of the MLP classifier
m.get_params()
Out[60]:
{'activation': 'relu',
'alpha': 0.0001,
'batch_size': 'auto',
'beta_1': 0.9,
'beta_2': 0.999,
'early_stopping': False,
'epsilon': 1e-08,
'hidden_layer_sizes': (100,),
'learning_rate': 'constant',
'learning_rate_init': 0.001,
'max_iter': 542,
'momentum': 0.9,
'nesterovs_momentum': True,
'power_t': 0.5,
'random_state': None,
'shuffle': True,
'solver': 'adam',
'tol': 0.0001,
'validation_fraction': 0.1,
'verbose': False,
'warm_start': False}
In [61]:
#calling the predict function of the MLP classifier
m.predict(iris.data)
Out[61]:
array([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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
In [62]:
#returning the log of probability estimates
m.predict_log_proba(iris.data)
Out[62]:
array([[ -1.46603285e-03, -6.52592821e+00, -2.43505871e+01],
[ -6.34545567e-03, -5.06318749e+00, -2.15210784e+01],
[ -2.52159804e-03, -5.98412303e+00, -2.26286939e+01],
[ -6.21287056e-03, -5.08423721e+00, -2.08229326e+01],
[ -1.07567741e-03, -6.83534248e+00, -2.46301669e+01],
[ -1.67878160e-03, -6.39052628e+00, -2.40926535e+01],
[ -2.25339040e-03, -6.09644593e+00, -2.22608790e+01],
[ -2.65450208e-03, -5.93282517e+00, -2.31065222e+01],
[ -8.08441961e-03, -4.82185631e+00, -2.00107527e+01],
[ -5.32190781e-03, -5.23858325e+00, -2.20261460e+01],
[ -1.13310041e-03, -6.78336419e+00, -2.53918486e+01],
[ -3.54281508e-03, -5.64460460e+00, -2.21259769e+01],
[ -5.24665924e-03, -5.25278598e+00, -2.18745131e+01],
[ -2.04981910e-03, -6.19102856e+00, -2.24117929e+01],
[ -2.11199218e-04, -8.46281431e+00, -2.91203883e+01],
[ -2.18243915e-04, -8.43000637e+00, -2.82164753e+01],
[ -5.01312193e-04, -7.59853216e+00, -2.62935587e+01],
[ -1.76014505e-03, -6.34323903e+00, -2.37941020e+01],
[ -1.93322434e-03, -6.24953249e+00, -2.47930194e+01],
[ -9.82879624e-04, -6.92551532e+00, -2.46780538e+01],
[ -5.09851289e-03, -5.28135457e+00, -2.28135122e+01],
[ -1.59240612e-03, -6.44330526e+00, -2.36311378e+01],
[ -3.73607589e-04, -7.89249135e+00, -2.57099673e+01],
[ -1.15685286e-02, -4.46524580e+00, -1.99714425e+01],
[ -8.92589636e-03, -4.72325830e+00, -2.04356699e+01],
[ -1.17442268e-02, -4.45025996e+00, -2.06221425e+01],
[ -5.22303166e-03, -5.25728775e+00, -2.13908296e+01],
[ -2.01154395e-03, -6.20985834e+00, -2.40107273e+01],
[ -2.00435120e-03, -6.21343689e+00, -2.40685851e+01],
[ -6.34867102e-03, -5.06268256e+00, -2.09527072e+01],
[ -8.60453529e-03, -4.75976516e+00, -2.06770490e+01],
[ -3.97338827e-03, -5.53012215e+00, -2.28045216e+01],
[ -3.06353388e-04, -8.09092444e+00, -2.73580478e+01],
[ -2.05760391e-04, -8.48890110e+00, -2.84626912e+01],
[ -5.32190781e-03, -5.23858325e+00, -2.20261460e+01],
[ -1.99017063e-03, -6.22052985e+00, -2.37187447e+01],
[ -1.17353865e-03, -6.74831833e+00, -2.56406457e+01],
[ -5.32190781e-03, -5.23858325e+00, -2.20261460e+01],
[ -4.41886540e-03, -5.42408110e+00, -2.10447821e+01],
[ -2.67933490e-03, -5.92352608e+00, -2.33219230e+01],
[ -1.29393182e-03, -6.65071669e+00, -2.41210043e+01],
[ -4.22799657e-02, -3.18450816e+00, -1.73155159e+01],
[ -2.44137100e-03, -6.01641606e+00, -2.20028782e+01],
[ -5.61269013e-03, -5.18553036e+00, -2.07259660e+01],
[ -4.06356294e-03, -5.50772629e+00, -2.18527443e+01],
[ -7.49311351e-03, -4.89751522e+00, -2.07642713e+01],
[ -1.11237495e-03, -6.80181411e+00, -2.46825894e+01],
[ -3.39187991e-03, -5.68806652e+00, -2.18632312e+01],
[ -1.12168229e-03, -6.79348647e+00, -2.51819283e+01],
[ -2.62937367e-03, -5.94232404e+00, -2.31840592e+01],
[ -6.68334075e+00, -1.88009973e-03, -7.37497284e+00],
[ -6.07855183e+00, -5.53879869e-03, -5.73465565e+00],
[ -7.17957588e+00, -9.19026099e-03, -4.78117152e+00],
[ -6.15987891e+00, -4.41435744e-02, -3.19245501e+00],
[ -6.94740957e+00, -2.29489916e-02, -3.82922051e+00],
[ -6.19722699e+00, -4.71900543e-02, -3.12222909e+00],
[ -6.14812691e+00, -2.28842242e-02, -3.88797037e+00],
[ -3.94754020e+00, -2.04934928e-02, -6.92509922e+00],
[ -6.83953305e+00, -3.17960130e-03, -6.16393945e+00],
[ -5.16797612e+00, -3.30957170e-02, -3.61719302e+00],
[ -5.43296579e+00, -9.90433879e-03, -5.20567682e+00],
[ -5.63427575e+00, -1.13851405e-02, -4.86040949e+00],
[ -6.47872330e+00, -2.91607488e-03, -6.58853303e+00],
[ -6.62783791e+00, -5.08193344e-02, -3.03184432e+00],
[ -3.98862851e+00, -1.93387916e-02, -7.37314059e+00],
[ -6.07303478e+00, -2.89462358e-03, -7.44173310e+00],
[ -5.89287937e+00, -1.28249239e-01, -2.14040786e+00],
[ -5.55873435e+00, -4.57178482e-03, -7.25349110e+00],
[ -7.85674803e+00, -3.88352554e-01, -1.13494543e+00],
[ -5.63653079e+00, -5.29523585e-03, -6.36774450e+00],
[ -6.87042453e+00, -7.81832074e-01, -6.13606105e-01],
[ -5.51689603e+00, -4.86364513e-03, -7.08983432e+00],
[ -8.29274275e+00, -6.83079636e-01, -7.03823012e-01],
[ -6.74437557e+00, -1.72665819e-02, -4.13886808e+00],
[ -6.13027191e+00, -3.07168821e-03, -7.02318163e+00],
[ -6.31137556e+00, -2.84591732e-03, -6.88175874e+00],
[ -7.43065047e+00, -1.15916728e-02, -4.51606591e+00],
[ -7.50794846e+00, -2.11473265e-01, -1.66041350e+00],
[ -6.29250050e+00, -4.99957491e-02, -3.05938790e+00],
[ -4.03830698e+00, -1.79875844e-02, -8.51978215e+00],
[ -5.59261420e+00, -5.81002345e-03, -6.18122101e+00],
[ -5.19843470e+00, -6.35058920e-03, -7.12434468e+00],
[ -5.30643229e+00, -5.93969260e-03, -6.94597863e+00],
[ -9.58318139e+00, -2.28473277e+00, -1.07440612e-01],
[ -5.84778067e+00, -2.44856019e-01, -1.54039489e+00],
[ -5.43620431e+00, -1.68494048e-02, -4.39391611e+00],
[ -6.76007738e+00, -6.52100835e-03, -5.23240730e+00],
[ -7.29058685e+00, -2.50582335e-02, -3.72699868e+00],
[ -5.11198343e+00, -8.87608118e-03, -5.87361665e+00],
[ -5.83066582e+00, -2.10363858e-02, -4.02404214e+00],
[ -6.22470100e+00, -5.75527108e-02, -2.91973544e+00],
[ -6.31451146e+00, -1.98803876e-02, -4.02439549e+00],
[ -5.81804219e+00, -4.98054100e-03, -6.21724264e+00],
[ -4.24264220e+00, -1.54510851e-02, -6.94571148e+00],
[ -5.86012087e+00, -2.02445695e-02, -4.06342019e+00],
[ -5.28776792e+00, -6.80836176e-03, -6.35835668e+00],
[ -5.60231566e+00, -8.82844908e-03, -5.27845942e+00],
[ -6.09120588e+00, -3.84091427e-03, -6.45612653e+00],
[ -2.96905783e+00, -5.30681492e-02, -8.00752718e+00],
[ -5.63909001e+00, -7.96360389e-03, -5.43164868e+00],
[ -1.68887915e+01, -9.50161317e+00, -7.47802438e-05],
[ -1.23906390e+01, -5.26036237e+00, -5.21113415e-03],
[ -1.42693877e+01, -5.54540748e+00, -3.91363498e-03],
[ -1.25597611e+01, -4.74190164e+00, -8.76384520e-03],
[ -1.53266876e+01, -7.38960692e+00, -6.18050293e-04],
[ -1.69171746e+01, -7.25498035e+00, -7.06941065e-04],
[ -1.01636323e+01, -4.24207093e+00, -1.45212575e-02],
[ -1.45727636e+01, -5.20388376e+00, -5.51080658e-03],
[ -1.45700317e+01, -5.84351987e+00, -2.90330263e-03],
[ -1.51119262e+01, -6.91369814e+00, -9.94842961e-04],
[ -8.54843846e+00, -1.58658477e+00, -2.29183132e-01],
[ -1.21651904e+01, -4.28214042e+00, -1.39146344e-02],
[ -1.23335127e+01, -4.30629143e+00, -1.35796505e-02],
[ -1.34469308e+01, -6.35634188e+00, -1.73866080e-03],
[ -1.48124286e+01, -7.94273714e+00, -3.55665080e-04],
[ -1.24041710e+01, -5.26076253e+00, -5.20898936e-03],
[ -1.08202601e+01, -3.05184375e+00, -4.84464841e-02],
[ -1.44609492e+01, -5.42008029e+00, -4.43714521e-03],
[ -2.03052751e+01, -1.02513578e+01, -3.53116691e-05],
[ -1.07932900e+01, -2.96495860e+00, -5.29611483e-02],
[ -1.38762905e+01, -5.81692287e+00, -2.98213409e-03],
[ -1.17433826e+01, -5.15770886e+00, -5.77948060e-03],
[ -1.72951485e+01, -7.29226217e+00, -6.81048936e-04],
[ -9.00094246e+00, -1.69117207e+00, -2.03863945e-01],
[ -1.22676540e+01, -4.52882875e+00, -1.08567333e-02],
[ -1.13037283e+01, -2.63482934e+00, -7.44472233e-02],
[ -8.14258543e+00, -1.19428262e+00, -3.61274174e-01],
[ -7.93656629e+00, -1.24941720e+00, -3.38314887e-01],
[ -1.47298257e+01, -6.79922870e+00, -1.11565744e-03],
[ -9.80726719e+00, -1.08506618e+00, -4.12390744e-01],
[ -1.43052495e+01, -4.88466079e+00, -7.59104027e-03],
[ -1.04203558e+01, -1.59033555e+00, -2.28014162e-01],
[ -1.52725548e+01, -7.38719613e+00, -6.19554287e-04],
[ -8.04073613e+00, -6.33666410e-01, -7.57077349e-01],
[ -1.15526263e+01, -3.59562937e+00, -2.78368926e-02],
[ -1.54956232e+01, -6.06852997e+00, -2.31744276e-03],
[ -1.42571371e+01, -7.13223687e+00, -7.99893105e-04],
[ -1.05086626e+01, -2.97812511e+00, -5.22573940e-02],
[ -7.57003518e+00, -1.09166267e+00, -4.09734630e-01],
[ -1.07896047e+01, -2.96391982e+00, -5.30177345e-02],
[ -1.49078403e+01, -7.02397894e+00, -8.91008343e-04],
[ -1.02178010e+01, -2.77485768e+00, -6.44263842e-02],
[ -1.23906390e+01, -5.26036237e+00, -5.21113415e-03],
[ -1.52629204e+01, -7.11422978e+00, -8.14013404e-04],
[ -1.51554260e+01, -7.43711511e+00, -5.89417439e-04],
[ -1.20348629e+01, -4.46990174e+00, -1.15204811e-02],
[ -1.14646303e+01, -3.73489862e+00, -2.41759845e-02],
[ -1.02737090e+01, -2.87500412e+00, -5.81063838e-02],
[ -1.24858157e+01, -5.68422153e+00, -3.40876152e-03],
[ -9.63695962e+00, -2.86202389e+00, -5.89204578e-02]])
In [63]:
#calling the method for probability estimates
m.predict_proba(iris.data)
Out[63]:
array([[ 9.98535041e-01, 1.46495873e-03, 2.65873087e-11],
[ 9.93674634e-01, 6.32536533e-03, 4.50312939e-10],
[ 9.97481579e-01, 2.51842133e-03, 1.48758954e-10],
[ 9.93806389e-01, 6.19360969e-03, 9.05138954e-10],
[ 9.98924901e-01, 1.07509906e-03, 2.01027023e-11],
[ 9.98322627e-01, 1.67737320e-03, 3.44107022e-11],
[ 9.97749147e-01, 2.25085321e-03, 2.14893412e-10],
[ 9.97349018e-01, 2.65098192e-03, 9.22496877e-11],
[ 9.91948171e-01, 8.05182654e-03, 2.03910935e-09],
[ 9.94692228e-01, 5.30777128e-03, 2.71747985e-10],
[ 9.98867541e-01, 1.13245868e-03, 9.38556206e-12],
[ 9.96463453e-01, 3.53654647e-03, 2.45929325e-10],
[ 9.94767080e-01, 5.23291924e-03, 3.16242082e-10],
[ 9.97952280e-01, 2.04771947e-03, 1.84791501e-10],
[ 9.99788823e-01, 2.11176917e-04, 2.25515320e-13],
[ 9.99781780e-01, 2.18220101e-04, 5.56852916e-13],
[ 9.99498813e-01, 5.01186554e-04, 3.80936476e-12],
[ 9.98241403e-01, 1.75859686e-03, 4.63823550e-11],
[ 9.98068643e-01, 1.93135685e-03, 1.70815972e-11],
[ 9.99017603e-01, 9.82396737e-04, 1.91627312e-11],
[ 9.94914462e-01, 5.08553741e-03, 1.23656673e-10],
[ 9.98408861e-01, 1.59113886e-03, 5.45917820e-11],
[ 9.99626462e-01, 3.73537800e-04, 6.82815028e-12],
[ 9.88498130e-01, 1.15018683e-02, 2.12086352e-09],
[ 9.91113821e-01, 8.88617747e-03, 1.33321854e-09],
[ 9.88324467e-01, 1.16755314e-02, 1.10641309e-09],
[ 9.94790585e-01, 5.20941484e-03, 5.12956724e-10],
[ 9.97990478e-01, 2.00952212e-03, 3.73485386e-11],
[ 9.97997656e-01, 2.00234379e-03, 3.52489583e-11],
[ 9.93671439e-01, 6.32855999e-03, 7.94977572e-10],
[ 9.91432378e-01, 8.56762118e-03, 1.04730147e-09],
[ 9.96034495e-01, 3.96550468e-03, 1.24773427e-10],
[ 9.99693694e-01, 3.06306466e-04, 1.31386514e-12],
[ 9.99794261e-01, 2.05739224e-04, 4.35321672e-13],
[ 9.94692228e-01, 5.30777128e-03, 2.71747985e-10],
[ 9.98011808e-01, 1.98819150e-03, 5.00126735e-11],
[ 9.98827150e-01, 1.17285031e-03, 7.31828095e-12],
[ 9.94692228e-01, 5.30777128e-03, 2.71747985e-10],
[ 9.95590883e-01, 4.40911586e-03, 7.25048835e-10],
[ 9.97324251e-01, 2.67574861e-03, 7.43733814e-11],
[ 9.98706905e-01, 1.29309502e-03, 3.34488319e-11],
[ 9.58601368e-01, 4.13986023e-02, 3.01972229e-08],
[ 9.97561607e-01, 2.43839300e-03, 2.78145101e-10],
[ 9.94403032e-01, 5.59696742e-03, 9.97303485e-10],
[ 9.95944682e-01, 4.05531751e-03, 3.23201779e-10],
[ 9.92534890e-01, 7.46510916e-03, 9.59823901e-10],
[ 9.98888244e-01, 1.11175647e-03, 1.90760146e-11],
[ 9.96613866e-01, 3.38613366e-03, 3.19830107e-10],
[ 9.98878947e-01, 1.12105343e-03, 1.15778385e-11],
[ 9.97374080e-01, 2.62591981e-03, 8.53671927e-11],
[ 1.25158973e-03, 9.98121667e-01, 6.26743718e-04],
[ 2.29149271e-03, 9.94476512e-01, 3.23199511e-03],
[ 7.61990948e-04, 9.90851840e-01, 8.38616867e-03],
[ 2.11250905e-03, 9.56816573e-01, 4.10709177e-02],
[ 9.61121654e-04, 9.77312334e-01, 2.17265447e-02],
[ 2.03506607e-03, 9.53906086e-01, 4.40588476e-02],
[ 2.13748171e-03, 9.77375634e-01, 2.04868846e-02],
[ 1.93021228e-02, 9.79715072e-01, 9.82805600e-04],
[ 1.07060320e-03, 9.96825448e-01, 2.10394852e-03],
[ 5.69608533e-03, 9.67445954e-01, 2.68579606e-02],
[ 4.37011573e-03, 9.90144548e-01, 5.48533662e-03],
[ 3.57326424e-03, 9.88679425e-01, 7.74731078e-03],
[ 1.53577014e-03, 9.97088173e-01, 1.37605711e-03],
[ 1.32302049e-03, 9.50450369e-01, 4.82266108e-02],
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In [64]:
#calling the method that returns the mean accuracy on the given
#test data and labels
m.score(iris.data,iris.target)
Out[64]:
0.97999999999999998
Content source: harishkrao/Machine-Learning
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