Project Name: rushter+MLAlgorithms
Language: Python
Statement Type: API

Source Code:
def clasifier(optimizer):
    X, y = make_classification(
        n_samples=1000, n_features=100, n_informative=75, random_state=1111, n_classes=2, class_sep=2.5
    )
    y = one_hot(y)

    X -= np.mean(X, axis=0)
    X /= np.std(X, axis=0)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=1111)

    model = NeuralNet(
        layers=[
            Dense(128, Parameters(init="uniform")),
            Activation("relu"),
            Dropout(0.5),
            Dense(64, Parameters(init="normal")),
            Activation("relu"),
            Dense(2),
            Activation("softmax"),
        ],
        loss="categorical_crossentropy",
        optimizer=optimizer,
        metric="accuracy",
        batch_size=64,
        max_epochs=10,
    )
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    return roc_auc_score(y_test[:, 0], predictions[:, 0])

clasifier(optimizer={rho=0.95, eps=1e-08, lr=1.0})

Selected Statement: predictions = model.predict(X_test)

Function Input:
optimizer: {rho=0.95, eps=1e-08, lr=1.0}

Variable Values Before Statement:
X_test: array([[-0.56004849,  0.34018962,  1.01535167, ..., -0.84997967,         2.09726856, -0.93988689],       [ 0.27941223,  0.49234387,  0.22399796, ...,  1.26554326,         0.00246477, -0.44601728],       [ 0.31621209,  0.07147743, -0.76119601, ...,  0.89775686,         0.76871988,  1.87124925],       ...,       [-0.14644015, -0.945898  , -1.7625167 , ...,  1.07582814,        -1.2681324 ,  0.89124254],       [ 2.11886151, -0.81978169,  0.9298373 , ..., -0.14101579,        -0.75876976, -1.79802492],       [-2.15698547, -0.11529033, -0.13576962, ...,  0.58981803,        -1.0836584 ,  0.09128275]])

Value After Statement Execution: array([[1.35235888e-02, 9.86476411e-01],       [1.66726720e-02, 9.83327328e-01],       [9.99999994e-01, 5.90208114e-09],       [9.98244393e-01, 1.75560676e-03],       [7.40176791e-01, 2.59823209e-01],       [3.71724770e-05, 9.99962828e-01],       [9.99713566e-01, 2.86434297e-04],       [9.18564614e-01, 8.14353861e-02],       [8.86055945e-01, 1.13944055e-01],       [9.99811079e-01, 1.88920895e-04],       [4.07889501e-03, 9.95921105e-01],       [9.97496720e-01, 2.50328006e-03],       [8.84405282e-01, 1.15594718e-01],       [8.87751398e-01, 1.12248602e-01],       [9.99998858e-01, 1.14232539e-06],       [1.48689622e-02, 9.85131038e-01],       [1.39868786e-01, 8.60131214e-01],       [4.59735284e-04, 9.99540265e-01],       [8.10085291e-01, 1.89914709e-01],       [4.53483673e-04, 9.99546516e-01],       [2.54536929e-05, 9.99974546e-01],       [1.00000000e+00, 2.95234428e-10],       [9.99999994e-01, 5.67815508e-09],       [3.58133366e-04, 9.99641867e-01],       [9.99841916e-01, 1.58084197e-04],       [9.99916089e-01, 8.39110353e-05],       [2.87906452e-01, 7.12093548e-01],       [2.94657253e-03, 9.97053427e-01],       [1.29650123e-05, 9.99987035e-01],       [5.50214872e-05, 9.99944979e-01],       [9.95252480e-05, 9.99900475e-01],       [7.23698056e-05, 9.99927630e-01],       [9.96809259e-01, 3.19074119e-03],       [1.07959433e-06, 9.99998920e-01],       [9.88249217e-01, 1.17507825e-02],       [2.97550757e-05, 9.99970245e-01],       [8.57948547e-08, 9.99999914e-01],       [8.01493506e-01, 1.98506494e-01],       [1.09917929e-01, 8.90082071e-01],       [9.21897555e-04, 9.99078102e-01],       [9.99999987e-01, 1.25416177e-08],       [9.99999998e-01, 2.12806072e-09],       [8.99300702e-01, 1.00699298e-01],       [8.24316542e-02, 9.17568346e-01],       [5.67859947e-10, 9.99999999e-01],       [9.79143160e-01, 2.08568400e-02],       [1.08458105e-02, 9.89154189e-01],       [8.11828923e-01, 1.88171077e-01],       [9.99999997e-01, 3.48283036e-09],       [3.63447084e-03, 9.96365529e-01],       [9.42074990e-01, 5.79250098e-02],       [8.03211756e-04, 9.99196788e-01],       [9.99988213e-01, 1.17866694e-05],       [9.99998358e-01, 1.64225648e-06],       [9.72622763e-02, 9.02737724e-01],       [2.11943736e-01, 7.88056264e-01],       [1.62273056e-08, 9.99999984e-01],       [1.62848262e-06, 9.99998372e-01],       [1.44160831e-04, 9.99855839e-01],       [9.13952621e-01, 8.60473792e-02],       [9.99812916e-01, 1.87083698e-04],       [9.52723540e-01, 4.72764602e-02],       [9.99988339e-01, 1.16607282e-05],       [9.86051929e-01, 1.39480708e-02],       [9.99865345e-01, 1.34654685e-04],       [9.13378149e-03, 9.90866219e-01],       [9.97440609e-01, 2.55939080e-03],       [9.63875042e-01, 3.61249578e-02],       [1.48619796e-04, 9.99851380e-01],       [5.28682244e-01, 4.71317756e-01],       [9.99718892e-01, 2.81108349e-04],       [9.94642754e-01, 5.35724604e-03],       [6.75869852e-01, 3.24130148e-01],       [5.28322536e-01, 4.71677464e-01],       [2.30777115e-02, 9.76922289e-01],       [6.54557813e-01, 3.45442187e-01],       [9.93999608e-01, 6.00039239e-03],       [3.57095748e-01, 6.42904252e-01],       [8.79406665e-02, 9.12059333e-01],       [3.85768901e-01, 6.14231099e-01],       [9.98609603e-01, 1.39039719e-03],       [3.34849170e-02, 9.66515083e-01],       [3.98038731e-03, 9.96019613e-01],       [1.74548424e-02, 9.82545158e-01],       [9.00595452e-01, 9.94045476e-02],       [4.81287456e-02, 9.51871254e-01],       [8.31075240e-01, 1.68924760e-01],       [2.61540350e-02, 9.73845965e-01],       [1.86037392e-01, 8.13962608e-01],       [1.74708127e-04, 9.99825292e-01],       [1.02657966e-03, 9.98973420e-01],       [1.18972214e-06, 9.99998810e-01],       [5.85900774e-03, 9.94140992e-01],       [1.07627613e-06, 9.99998924e-01],       [3.40652630e-01, 6.59347370e-01],       [9.83588586e-01, 1.64114140e-02],       [9.97918350e-01, 2.08165016e-03],       [9.72075551e-01, 2.79244493e-02],       [8.92673320e-03, 9.91073267e-01],       [9.99997418e-01, 2.58224639e-06],       [9.36717962e-03, 9.90632820e-01],       [9.99845583e-01, 1.54416829e-04],       [7.07307425e-03, 9.92926926e-01],       [9.95053730e-01, 4.94627014e-03],       [1.41115970e-02, 9.85888403e-01],       [3.25889450e-04, 9.99674111e-01],       [9.81332495e-04, 9.99018668e-01],       [9.60401982e-01, 3.95980180e-02],       [9.99952843e-01, 4.71566138e-05],       [9.99999947e-01, 5.34044573e-08],       [6.00106898e-07, 9.99999400e-01],       [9.99129133e-01, 8.70867188e-04],       [9.94286308e-01, 5.71369203e-03],       [9.99981113e-01, 1.88874698e-05],       [7.92336020e-02, 9.20766398e-01],       [9.95091548e-01, 4.90845229e-03],       [2.88767388e-05, 9.99971123e-01],       [2.07836444e-01, 7.92163556e-01],       [9.92386903e-01, 7.61309722e-03],       [5.25658178e-06, 9.99994743e-01],       [1.88273783e-04, 9.99811726e-01],       [9.97402679e-01, 2.59732143e-03],       [1.33733905e-04, 9.99866266e-01],       [9.99999885e-01, 1.15261224e-07],       [5.70741250e-05, 9.99942926e-01],       [2.62490629e-03, 9.97375094e-01],       [8.76013445e-01, 1.23986555e-01],       [2.61776408e-04, 9.99738224e-01],       [2.14964444e-02, 9.78503556e-01],       [8.69490648e-01, 1.30509352e-01],       [3.07479580e-05, 9.99969252e-01],       [8.02048076e-02, 9.19795192e-01],       [9.99889581e-01, 1.10418878e-04],       [7.93254407e-03, 9.92067456e-01],       [1.92176072e-01, 8.07823928e-01],       [1.71791819e-03, 9.98282082e-01],       [7.20407537e-07, 9.99999280e-01],       [9.97076828e-01, 2.92317157e-03],       [2.17317964e-07, 9.99999783e-01],       [2.00199637e-02, 9.79980036e-01],       [7.18304676e-04, 9.99281695e-01],       [9.86816819e-01, 1.31831806e-02],       [9.99991335e-01, 8.66460511e-06],       [9.81203923e-01, 1.87960770e-02],       [9.99999989e-01, 1.09012882e-08],       [2.16560536e-01, 7.83439464e-01],       [9.97841732e-01, 2.15826773e-03],       [9.98825819e-01, 1.17418094e-03],       [9.99999183e-01, 8.17282702e-07],       [8.47976914e-01, 1.52023086e-01]])

Variable States During Runtime (line, value):
optimizer: 1
{rho=0.95, eps=1e-08, lr=1.0}
27
{rho=0.95, eps=1e-08, lr=1.0, accu=defaultdict(<class 'dict'>, {0: {'W': array([[1.02189175, 2.3577819 , 1.51764535, ..., 1.23169299, 3.07867511,        1.19097088],       [1.79837931, 2.15850133, 1.2952417 , ..., 1.41157237, 3.53508698,        0.8306613 ],       [1.00176246, 1.54970328, 1.14835514, ..., 0.73988777, 1.25295484,        1.04732108],       ...,       [0.79315582, 3.22064122, 1.98634498, ..., 1.07047564, 3.86998562,        1.81093538],       [1.33685355, 3.58025666, 1.43485979, ..., 1.33567161, 3.23535237,        1.16139226],       [1.24049738, 1.51520559, 1.38728968, ..., 1.28467756, 2.21665017,        0.67544222]]), 'b': array([1.24701152, 1.07898568, 1.26096616, 0.66303548, 1.09491793,       0.54777342, 0.82933226, 1.75515187, 0.8513086 , 2.26192259,       2.72603508, 1.57643474, 1.2351254 , 0.75682709, 3.12175382,       0.77849592, 1.53979515, 1.4790363 , 0.38903597, 1.87158323,       0.58569118, 4.60743107, 1.69568258, 0.90741705, 2.06461702,       2.76816931, 7.2805068 , 0.55199615, 1.2509025 , 2.41554144,       1.34352201, 2.37986267, 0.951207  , 1.47112046, 1.68694724,       1.04600856, 1.29258354, 3.88751626, 1.28381194, 1.26565436,       0.75323325, 2.31465107, 2.97211929, 3.72470318, 2.21997122,       1.88024124, 1.8445207 , 1.44853005, 6.43703821, 0.76357415,       0.78398435, 0.92437006, 2.01051995, 0.92685549, 0.95741625,       2.8152029 , 1.65657328, 2.51121866, 0.76184199, 3.03258657,       0.82002141, 1.683818  , 1.16362865, 0.85883003, 1.67069222,       1.43642043, 1.42787622, 1.64186725, 2.87486294, 0.60191759,       1.04036702, 0.56923805, 0.72624993, 0.70411294, 0.86654488,       0.85618806, 1.2281543 , 3.17203227, 1.50408587, 5.44372238,       5.41533707, 2.6395759 , 0.76130033, 0.6537063 , 0.81457075,       1.52755936, 1.35928077, 2.51945851, 1.39940071, 1.03681584,       0.62675816, 1.10998426, 6.40240473, 3.09963192, 1.1592539 ,       1.65955604, 1.0511133 , 1.36365397, 7.82102101, 0.98689887,       1.68746735, 1.21605589, 3.51047096, 1.98145413, 4.11055627,       1.72070386, 0.91946021, 0.74963646, 0.86630747, 4.49418519,       1.44151991, 0.79929557, 1.46259457, 6.83458282, 2.15505599,       2.70513408, 0.58877512, 1.35986934, 0.5661188 , 0.72856421,       1.82468196, 0.67120413, 5.35308901, 1.12292561, 0.72621126,       1.13444081, 2.16178393, 1.24055574])}, 1: {'W': array([[5.45915482e-03, 3.81760910e+00, 6.68676861e-01, ...,        1.62601232e+00, 2.37464367e+00, 7.82253766e+00],       [2.80223833e-03, 2.64129159e+00, 9.75193808e-01, ...,        2.82929786e+00, 4.66502013e+00, 1.00737594e+01],       [4.18466499e-03, 2.41653114e+00, 5.32879422e-01, ...,        1.31616525e+00, 1.60943697e+00, 5.59325401e+00],       ...,       [7.50449189e-03, 3.08985722e+00, 6.08991330e-01, ...,        1.25975188e+00, 2.28575080e+00, 5.10920038e+00],       [1.02523637e-02, 2.80714882e+00, 7.59856834e-01, ...,        1.58756775e+00, 3.46716116e+00, 6.43149821e+00],       [5.19958341e-03, 1.81792849e+00, 7.96020678e-01, ...,        2.10411693e+00, 2.89889679e+00, 9.25088996e+00]]), 'b': array([1.72037866e-03, 8.52788541e-01, 2.03837881e-01, 2.70120698e-03,       2.94283040e-01, 1.42170655e-01, 1.99078716e-01, 8.47160049e-03,       1.75972220e-01, 2.96368082e-01, 2.51724630e-02, 2.55886116e-01,       1.01855555e-01, 1.71679531e-01, 1.78645767e+00, 6.73317331e-03,       7.25939768e-01, 4.81054772e-04, 2.76793720e-01, 1.70717882e-01,       3.79590767e-02, 6.15495787e-02, 1.93435756e-02, 1.17812435e-02,       4.74578506e-02, 2.14499691e-01, 2.99670015e-02, 3.14822188e-03,       4.81733071e-02, 3.79465426e-01, 2.90130739e-01, 9.16535823e-01,       2.88197944e-01, 7.11741172e-01, 9.73335398e-01, 2.07337546e-01,       5.75103522e-02, 3.37785925e-02, 4.13675660e-01, 9.47900102e-01,       5.86283077e-01, 5.08640921e-01, 2.09856011e-01, 3.38311887e-01,       1.29053101e+00, 2.77363522e-03, 2.70998310e-03, 2.46111881e-02,       8.54615474e-02, 6.25665813e-01, 4.91633314e-02, 3.08387019e-01,       2.92374899e-01, 4.91648012e-01, 1.32740282e-02, 2.80214646e-01,       5.02608457e-01, 1.07545128e+00, 1.32504353e...  [1.41998934e-08, 4.12162325e-08, 3.72964210e-08, ...,        2.58398721e-08, 2.77508189e-08, 2.89419047e-08],       [1.49778686e-08, 3.66735246e-08, 5.15109023e-08, ...,        8.23728033e-08, 4.11718362e-08, 4.31269308e-08],       [3.30872245e-08, 5.72617422e-08, 5.33931305e-08, ...,        7.32284551e-08, 5.40883796e-08, 8.58946913e-08]]), 'b': array([1.17888086e-08, 4.18146562e-08, 3.60881249e-08, 5.18557473e-08,       2.91998964e-08, 3.42123777e-08, 3.18925816e-08, 9.25540109e-09,       3.06530327e-08, 1.28270243e-08, 1.70761826e-07, 1.51483393e-08,       2.36069742e-08, 2.14715175e-08, 2.24245352e-08, 1.47149097e-08,       1.66178146e-08, 2.83299615e-08, 1.57570188e-08, 3.48429714e-08,       3.90471086e-08, 2.06303777e-08, 1.43108136e-08, 4.60835373e-07,       4.64843554e-08, 1.31061724e-08, 1.35750428e-08, 5.47091382e-08,       2.95749720e-08, 2.03399523e-08, 4.24908236e-08, 4.30961153e-08,       2.81917199e-08, 3.73151130e-08, 2.55720749e-08, 1.98067123e-08,       1.93753723e-08, 9.80365144e-08, 3.27053239e-08, 1.48941176e-08,       3.33476933e-08, 2.66562346e-08, 2.47807546e-08, 2.85980386e-08,       2.54295013e-08, 1.12543136e-07, 1.32799243e-08, 2.02992493e-08,       2.54887755e-08, 2.83518944e-08, 2.87884961e-08, 1.83564032e-08,       1.52904998e-08, 1.49334920e-08, 6.33896441e-08, 1.57731193e-08,       2.29820217e-08, 2.67215806e-08, 2.54295085e-08, 9.53098238e-09,       4.31910696e-08, 2.29492198e-08, 2.44853474e-08, 3.93343049e-08])}, 2: {'W': array([[3.10732781e-08, 3.10732781e-08],       [6.19962700e-08, 6.19962700e-08],       [7.73977157e-08, 7.73977157e-08],       [4.83289756e-08, 4.83289756e-08],       [3.78004173e-08, 3.78004173e-08],       [4.84615343e-08, 4.84615343e-08],       [5.74666461e-08, 5.74666461e-08],       [1.53238393e-08, 1.53238393e-08],       [4.36786598e-08, 4.36786598e-08],       [1.99940867e-08, 1.99940867e-08],       [7.68494943e-08, 7.68494943e-08],       [2.07098289e-08, 2.07098289e-08],       [3.01331056e-08, 3.01331056e-08],       [2.91989326e-08, 2.91989326e-08],       [2.67265242e-08, 2.67265242e-08],       [2.36300030e-08, 2.36300030e-08],       [2.58964847e-08, 2.58964847e-08],       [3.99345822e-08, 3.99345822e-08],       [2.01764981e-08, 2.01764981e-08],       [3.67492839e-08, 3.67492839e-08],       [2.83350533e-08, 2.83350533e-08],       [4.39402672e-08, 4.39402672e-08],       [2.34148753e-08, 2.34148753e-08],       [5.33241909e-08, 5.33241909e-08],       [7.61694040e-08, 7.61694040e-08],       [1.84145885e-08, 1.84145885e-08],       [1.54214201e-08, 1.54214201e-08],       [5.89606035e-08, 5.89606035e-08],       [1.89748688e-08, 1.89748688e-08],       [2.69561781e-08, 2.69561781e-08],       [4.49638283e-08, 4.49638283e-08],       [8.57936955e-08, 8.57936955e-08],       [4.51625998e-08, 4.51625998e-08],       [4.59436138e-08, 4.59436138e-08],       [1.94678893e-08, 1.94678893e-08],       [3.60432758e-08, 3.60432758e-08],       [2.46571720e-08, 2.46571720e-08],       [4.15556922e-08, 4.15556922e-08],       [3.98907824e-08, 3.98907824e-08],       [2.52991261e-08, 2.52991261e-08],       [3.52945761e-08, 3.52945761e-08],       [3.05025137e-08, 3.05025137e-08],       [3.18059511e-08, 3.18059511e-08],       [3.00007683e-08, 3.00007683e-08],       [4.50742973e-08, 4.50742973e-08],       [2.11498438e-08, 2.11498438e-08],       [3.52900462e-08, 3.52900462e-08],       [5.08926177e-08, 5.08926177e-08],       [2.76334304e-08, 2.76334304e-08],       [4.72636263e-08, 4.72636263e-08],       [3.55281110e-08, 3.55281110e-08],       [2.45108154e-08, 2.45108154e-08],       [3.18376221e-08, 3.18376221e-08],       [1.96264395e-08, 1.96264395e-08],       [8.07458957e-08, 8.07458957e-08],       [2.01687708e-08, 2.01687708e-08],       [2.99927221e-08, 2.99927221e-08],       [3.63644472e-08, 3.63644472e-08],       [3.40271673e-08, 3.40271673e-08],       [4.44372350e-08, 4.44372350e-08],       [3.60026618e-08, 3.60026618e-08],       [3.96703308e-08, 3.96703308e-08],       [3.92369616e-08, 3.92369616e-08],       [3.40291803e-08, 3.40291803e-08]]), 'b': array([1.84589942e-08, 1.84589942e-08])}})}
X: 2
array([[  7.80780096,   0.40627657,  -1.48027083, ...,  -6.41386247,         -8.43409921,   0.5602089 ],       [  1.19388878,  -1.47559934,   6.08370626, ...,  -0.97569605,         10.78028251,  -1.31354468],       [ -6.1613578 ,  -0.43920608,  -1.28034138, ...,   4.39277501,          0.02982879,   0.06733291],       ...,       [ -5.51466423, -13.26000331,  10.36091139, ...,   1.89534754,         -3.35155079,  -1.19447879],       [  9.13942123,  -1.21728958,   1.84978723, ...,  -1.75954701,          7.32589326,   3.2035972 ],       [-10.31153907,  -5.04412657,  -0.1021587 , ...,  10.74854013,         -0.48404546,   4.45441204]])
7
array([[  8.25628211,   2.00699085,  -0.11353115, ...,  -6.52294682,         -7.31335416,   0.61603347],       [  1.64236993,   0.12511495,   7.45044595, ...,  -1.0847804 ,         11.90102756,  -1.25772012],       [ -5.71287665,   1.1615082 ,   0.0863983 , ...,   4.28369067,          1.15057384,   0.12315747],       ...,       [ -5.06618308, -11.65928903,  11.72765107, ...,   1.78626319,         -2.23080574,  -1.13865422],       [  9.58790238,   0.3834247 ,   3.21652692, ...,  -1.86863135,          8.44663831,   3.25942177],       [ -9.86305793,  -3.44341228,   1.26458099, ...,  10.63945579,          0.63669959,   4.5102366 ]])
8
array([[ 1.41230729,  0.34829269, -0.02025944, ..., -1.16753788,        -1.35048696,  0.11621826],       [ 0.28094135,  0.02171242,  1.32951954, ..., -0.19416412,         2.1976486 , -0.23727615],       [-0.97723615,  0.20156784,  0.01541763, ...,  0.76673492,         0.21246543,  0.02323437],       ...,       [-0.86661371, -2.0233501 ,  2.09277959, ...,  0.31972205,        -0.4119415 , -0.21481368],       [ 1.64009226,  0.06653943,  0.57398381, ..., -0.33446507,         1.55975967,  0.61490871],       [-1.68715996, -0.59756891,  0.22566235, ...,  1.90434906,         0.11757321,  0.85088213]])
y: 2
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5
array([[0., 1.],       [1., 0.],       [1., 0.],       ...,       [1., 0.],       [0., 1.],       [1., 0.]])
X_train: 9
array([[ 0.52035826, -1.70073552, -0.50697874, ..., -0.74790479,         0.16039734,  1.0323546 ],       [ 0.96925333, -0.29561268,  0.86400006, ...,  1.4457464 ,        -1.32196293, -0.0653791 ],       [ 0.24562753,  0.62176281,  0.95963704, ..., -0.16747796,        -0.60033504,  1.64606491],       ...,       [ 1.14501028,  1.07221843,  1.2764346 , ..., -0.83624018,        -1.87719572,  0.57666069],       [ 1.98506244, -0.27254282,  1.58679531, ..., -0.73272139,         2.03923433, -1.41719355],       [-0.82634613,  1.99698567,  0.0889722 , ...,  0.83719252,        -0.88980401,  0.81743782]])
X_test: 9
array([[-0.56004849,  0.34018962,  1.01535167, ..., -0.84997967,         2.09726856, -0.93988689],       [ 0.27941223,  0.49234387,  0.22399796, ...,  1.26554326,         0.00246477, -0.44601728],       [ 0.31621209,  0.07147743, -0.76119601, ...,  0.89775686,         0.76871988,  1.87124925],       ...,       [-0.14644015, -0.945898  , -1.7625167 , ...,  1.07582814,        -1.2681324 ,  0.89124254],       [ 2.11886151, -0.81978169,  0.9298373 , ..., -0.14101579,        -0.75876976, -1.79802492],       [-2.15698547, -0.11529033, -0.13576962, ...,  0.58981803,        -1.0836584 ,  0.09128275]])
y_train: 9
array([[1., 0.],       [1., 0.],       [1., 0.],       ...,       [0., 1.],       [1., 0.],       [1., 0.]])
y_test: 9
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model: 11
{verbose=True, shuffle=False, optimizer=<mla.neuralnet.optimizers.Adadelta object at 0x7f43904a38e0>, layers=[<mla.neuralnet.layers.basic.Dense object at 0x7f43904a3610>, <mla.neuralnet.layers.basic.Activation object at 0x7f43904a33d0>, <mla.neuralnet.layers.basic.Dropout object at 0x7f43904a32b0>, <mla.neuralnet.layers.basic.Dense object at 0x7f43904a3c70>, <mla.neuralnet.layers.basic.Activation object at 0x7f43904a3550>, <mla.neuralnet.layers.basic.Dense object at 0x7f43904a3820>, <mla.neuralnet.layers.basic.Activation object at 0x7f43904a3730>], batch_size=64, max_epochs=10, _n_layers=0, log_metric=True, metric_name='accuracy', bprop_entry=-1, training=False, _initialized=False}
27
{verbose=True, shuffle=False, optimizer=<mla.neuralnet.optimizers.Adadelta object at 0x7f43904a38e0>, layers=[<mla.neuralnet.layers.basic.Dense object at 0x7f43904a3610>, <mla.neuralnet.layers.basic.Activation object at 0x7f43904a33d0>, <mla.neuralnet.layers.basic.Dropout object at 0x7f43904a32b0>, <mla.neuralnet.layers.basic.Dense object at 0x7f43904a3c70>, <mla.neuralnet.layers.basic.Activation object at 0x7f43904a3550>, <mla.neuralnet.layers.basic.Dense object at 0x7f43904a3820>, <mla.neuralnet.layers.basic.Activation object at 0x7f43904a3730>], batch_size=64, max_epochs=10, _n_layers=7, log_metric=True, metric_name='accuracy', bprop_entry=-1, training=False, _initialized=True, n_samples=850, n_features=100, X=array([[ 0.52035826, -1.70073552, -0.50697874, ..., -0.74790479,         0.16039734,  1.0323546 ],       [ 0.96925333, -0.29561268,  0.86400006, ...,  1.4457464 ,        -1.32196293, -0.0653791 ],       [ 0.24562753,  0.62176281,  0.95963704, ..., -0.16747796,        -0.60033504,  1.64606491],       ...,       [ 1.14501028,  1.07221843,  1.2764346 , ..., -0.83624018,        -1.87719572,  0.57666069],       [ 1.98506244, -0.27254282,  1.58679531, ..., -0.73272139,         2.03923433, -1.41719355],       [-0.82634613,  1.99698567,  0.0889722 , ...,  0.83719252,        -0.88980401,  0.81743782]]), y=array([[1., 0.],       [1., 0.],       [1., 0.],       ...,       [0., 1.],       [1., 0.],       [1., 0.]])}
predictions: 28
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