Python MLPClassifier 值错误
Python MLPClassifier Value Error
我目前正在尝试训练在 sklearn 中实现的 MLPClassifier...
当我尝试用给定的值训练它时，我得到这个错误：
ValueError: 使用序列设置数组元素。
feature_vector 的格式是
[[one_hot_encoded 品牌名称]，[不同的应用程序按均值 0 和方差 1 缩放]]
有人知道我做错了什么吗？
谢谢！
feature_vector秒：
[
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., 1., 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.]),
数组([ 0.82211852, -0.22976818, -0.22976818, -0.22976818, -0.22976818,
-0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,
-0.22976818, -0.22976818, 4.45590895, -0.22976818, -0.22976818,
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-0.22976818, 0.3439882 , -0.22976818, -0.22976818, -0.22976818,
4.93403927, -0.22976818, -0.22976818, -0.22976818, 0.63086639,
1.10899671, -0.22976818, -0.22976818, -0.22976818, -0.22976818,
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-0.22976818, -0.22976818, -0.22976818, 1.58712703, -0.22976818,
1.77837916, -0.22976818, -0.22976818, -0.22976818, -0.22976818,
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-0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,
0.91774459, -0.22976818, -0.22976818, 4.16903076, -0.22976818,
-0.22976818, -0.22976818, -0.22976818, -0.22976818, 2.44776161,
-0.22976818, -0.22976818, -0.22976818, 1.96963129, 1.96963129,
1.96963129, -0.22976818, -0.22976818, -0.22976818, -0.22976818,
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-0.22976818, -0.22976818, -0.22976818, -0.22976818, 7.13343874,
5.98592598, -0.22976818, -0.22976818, -0.22976818, -0.22976818,
-0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,
-0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,
3.02151799, 4.26465682, -0.22976818, -0.22976818, -0.22976818,
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-0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,
-0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,
-0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,
-0.22976818, -0.22976818, -0.22976818, -0.22976818, 0.3439882 ,
-0.22976818, 0.3439882 , -0.22976818, -0.22976818, -0.22976818,
-0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,
-0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,
-0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,
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-0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,
-0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,
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-0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,
-0.22976818, -0.22976818, -0.22976818, -0.22976818, 3.49964831,
-0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818])
]
g_a_group:
[0.0.0.0.0.0.0.0.0.0.1.0.]
MLP:
从 sklearn.neural_network 导入 MLPClassifier
clf = MLPClassifier(求解器='lbfgs', alpha=1e-5,
hidden_layer_sizes=(5, 2), random_state=1)
clf.fit(feature_vectors, g_a_group)
从 scikit-learn 的角度来看，您的数据对于 .fit 调用中的预期内容没有任何意义。特征向量应该是大小为 N x d 的矩阵，其中 N - 数据点的数量 和 d 的数量features，你的第二个变量应该包含标签，因此它应该是长度为 N 的向量（或 N x k，其中 k 是每个点的 outputs/labels 的数量).无论您的变量中代表什么 - 它们的大小与它们应该代表的不匹配。
我目前正在尝试训练在 sklearn 中实现的 MLPClassifier... 当我尝试用给定的值训练它时，我得到这个错误：
ValueError: 使用序列设置数组元素。
feature_vector 的格式是
[[one_hot_encoded 品牌名称]，[不同的应用程序按均值 0 和方差 1 缩放]]
有人知道我做错了什么吗？
谢谢！
feature_vector秒：
[
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., 1., 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.]),
数组([ 0.82211852, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, 4.45590895, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, 0.3439882 , -0.22976818, -0.22976818, -0.22976818, 4.93403927, -0.22976818, -0.22976818, -0.22976818, 0.63086639, 1.10899671, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, 1.58712703, -0.22976818, 1.77837916, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, 2.16088342, -0.22976818, 2.16088342, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, 9.42846428, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, 0.91774459, -0.22976818, -0.22976818, 4.16903076, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, 2.44776161, -0.22976818, -0.22976818, -0.22976818, 1.96963129, 1.96963129, 1.96963129, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, 7.13343874, 5.98592598, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, 3.02151799, 4.26465682, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, 2.25650948, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, 1.30024884, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, 4.74278714, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, 0.3439882 , -0.22976818, 0.3439882 , -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, 0.53524033, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818, 3.49964831, -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818])
]
g_a_group:
[0.0.0.0.0.0.0.0.0.0.1.0.]
MLP:
从 sklearn.neural_network 导入 MLPClassifier
clf = MLPClassifier(求解器='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)
clf.fit(feature_vectors, g_a_group)
从 scikit-learn 的角度来看，您的数据对于 .fit 调用中的预期内容没有任何意义。特征向量应该是大小为 N x d 的矩阵，其中 N - 数据点的数量 和 d 的数量features，你的第二个变量应该包含标签，因此它应该是长度为 N 的向量（或 N x k，其中 k 是每个点的 outputs/labels 的数量).无论您的变量中代表什么 - 它们的大小与它们应该代表的不匹配。