我正在尝试开发MPLClassifier神经网络。当我在一些数据上测试我的模型时，我发现结果完全不是我所期望的，所以我在训练的数据上测试了模型。尽管如此，预测并不是很准确。以下是我的代码。
>>> mlp = MLPClassifier(solver='lbfgs', hidden_layer_sizes = (100,1000,100),max_iter=30000)
>>> mlp.fit(i, t)
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, 1000, 100), learning_rate='constant',
learning_rate_init=0.001, max_iter=30000, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
>>> mlp.predict(i)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
>>> t
array([0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
>>> i
array([[ 8.00000000e+00, 7.50000000e+04, 7.90000000e+01,
1.00000000e+00, 6.20000000e+01, 7.63000000e+02,
0.00000000e+00],
[ 7.75000000e+00, 1.23000000e+05, 8.00000000e+01,
1.00000000e+00, 2.80000000e+01, 7.50000000e+02,
0.00000000e+00],
[ 8.50000000e+00, 5.10000000e+04, 9.50000000e+01,
1.00000000e+00, 2.70000000e+01, 6.86000000e+02,
0.00000000e+00],
[ 8.75000000e+00, 2.42000000e+05, 9.50000000e+01,
1.00000000e+00, 4.70000000e+01, 7.06000000e+02,
0.00000000e+00],
[ 8.25000000e+00, 2.40000000e+05, 7.70000000e+01,
2.00000000e+00, 1.90000000e+01, 7.37000000e+02,
0.00000000e+00],
[ 7.62500000e+00, 2.25000000e+05, 6.40000000e+01,
2.00000000e+00, 2.10000000e+01, 7.93000000e+02,
0.00000000e+00],
[ 8.00000000e+00, 1.20000000e+05, 7.50000000e+01,
2.00000000e+00, 3.60000000e+01, 7.50000000e+02,
1.00000000e+00],
[ 8.00000000e+00, 1.30000000e+05, 6.10000000e+01,
2.00000000e+00, 3.40000000e+01, 6.46000000e+02,
0.00000000e+00],
[ 8.37500000e+00, 1.07000000e+05, 9.50000000e+01,
1.00000000e+00, 3.70000000e+01, 6.76000000e+02,
0.00000000e+00],
[ 7.87500000e+00, 6.00000000e+04, 7.40000000e+01,
2.00000000e+00, 1.70000000e+01, 7.81000000e+02,
0.00000000e+00],
[ 7.75000000e+00, 1.53000000e+05, 8.00000000e+01,
1.00000000e+00, 4.50000000e+01, 7.61000000e+02,
0.00000000e+00],
[ 8.25000000e+00, 1.37000000e+05, 8.00000000e+01,
1.00000000e+00, 3.40000000e+01, 7.47000000e+02,
0.00000000e+00],
[ 7.75000000e+00, 1.52000000e+05, 8.50000000e+01,
1.00000000e+00, 4.10000000e+01, 6.04000000e+02,
0.00000000e+00],
[ 8.00000000e+00, 8.40000000e+04, 7.50000000e+01,
2.00000000e+00, 5.70000000e+01, 6.76000000e+02,
0.00000000e+00],
[ 7.75000000e+00, 1.06000000e+05, 8.00000000e+01,
2.00000000e+00, 2.70000000e+01, 6.52000000e+02,
0.00000000e+00],
[ 8.12500000e+00, 1.29000000e+05, 9.30000000e+01,
2.00000000e+00, 3.60000000e+01, 6.68000000e+02,
1.00000000e+00],
[ 6.32000000e+00, 6.40000000e+04, 8.00000000e+01,
1.00000000e+00, 5.20000000e+01, 5.88000000e+02,
1.00000000e+00],
[ 8.12500000e+00, 2.35000000e+05, 5.80000000e+01,
2.00000000e+00, 4.70000000e+01, 7.16000000e+02,
0.00000000e+00],
[ 8.00000000e+00, 1.65000000e+05, 4.60000000e+01,
2.00000000e+00, 2.80000000e+01, 6.57000000e+02,
1.00000000e+00],
[ 7.87500000e+00, 4.00000000e+04, 6.60000000e+01,
2.00000000e+00, 2.50000000e+01, 7.97000000e+02,
0.00000000e+00],
[ 7.37500000e+00, 6.00000000e+04, 7.10000000e+01,
1.00000000e+00, 8.00000000e+00, 7.66000000e+02,
1.00000000e+00]])