Python的MLPClassifier值錯誤

Question

我目前正試圖訓練MLPClassifier在sklearn實現... 當我嘗試使用給定值我得到這個錯誤訓練它：

ValueError異常：設置有一個數組元素序列。

的feature_vector的格式

[one_hot_encoded名優產品]，[不同的應用程序擴展到均值爲0，方差爲1]

有誰知道我做錯了嗎？

謝謝！

feature_vectors：

[

陣列（[0，0.1，0.1，0.1，0.1，0.1，0.1，0。 ，0.0,0.0,0.0,0.1,0.3,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0， 0。，0，0 ，0.1，0.1，0.1，0.1，0.1，0.1，0.1，0.1，0.1，0.1，0.1，0.1，0.1 ， 0.,0,0,0,0,0,0,0,0,1,0,0,0,01,35,164,106,1745,0。，0，0，0，0，0，0，0，0，0，0，0，0，0 ，0.1，0.1， 0。，0，0，0，0，0，0，0，0，0，0 ，0.1，0.1，0.1，0.1，0.1，0 。，0。，0，0，0]），

陣列（[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 ，-0.22976818,2.444776161, -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，-0.22976 818，-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，-0.22 976818，-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：

[0.0.0.0.0.0.0.0.0.0.0。]

MLP：

從sklearn.neural_network進口MLPClassifier

CLF = MLPClassifier（解算器= 'lbfgs'，α-= 1E-5， hidden_​​layer_sizes =（5,2 ），random_state = 1）

clf.fit（feature_vectors，g_a_group）

Answer 1

您的數據對於調用.fit調用中期望得到的結果沒有任何意義。特徵向量被認爲是大小N x d，其中N的矩陣 - 數的數據點和d數目的特徵，和第二個變量應該保持的標籤，因此它應該是長度N（或N x k其中k的矢量是每個點的輸出/標籤數量）。無論在變量中表示什麼 - 它們的大小與他們應該表示的大小不匹配。