Python 学习神经网络问题

我正在做一些神经网络的练习，但有一个问题让我陷入了困境。我的网络没有预测正确的结果，尽管它说训练分数是97%

这是我的密码：

# Import `datasets` from `sklearn`
from sklearn import datasets
import pandas as pd
from sklearn.model_selection import train_test_split
# Import `train_test_split`
from sklearn.model_selection  import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier


# Load in the `digits` data
from sklearn.preprocessing import scale

iris = datasets.load_iris()

# split the data up - 3/4 for training, 1/4 for testing
data_train, data_test, name_train, name_test = train_test_split(iris.data, 
iris.target, test_size=0.25, random_state=0)

# Number of training features
# n_samples, n_features = data_train.shape

scaler = StandardScaler()
scaler.fit(data_train)
params_train_scaled = scaler.transform(data_train)
params_test_scaled = scaler.transform(data_test)

# 1 hidden layer, same size as the input layer
mlp = MLPClassifier(
    solver='lbfgs',
    hidden_layer_sizes=(iris.data.shape[1], ),
    random_state=0)
mlp.fit(params_train_scaled, name_train)

print(name_train)
print('Train score: %.3g' % mlp.score(params_train_scaled, name_train))
print('Test Score: %.3g' % mlp.score(params_test_scaled, name_test))
print

test_val = [[5.1, 3.5, 1.4, 0.2]]

print(mlp.predict(test_val))

我的想法是，这是我如何衡量训练和测试数据，但我不确定

我得到的结果是：

火车成绩：1分

考试分数：0.974

但是，预测值应为0，而不是1

---

感谢您的帮助。

既然您缩放了培训数据，您也应该缩放测试数据：

print(mlp.predict(scaler.transform(test_val)))