np可能存在问题。沿_轴应用_？

Question

我正在处理时尚NMIST数据，并尝试使用np.apply_沿_轴。我想这是我的问题。我用一个玩具数据集（6-（1,2）个训练点和2-（2,2）个测试点）以及一个100点的时装Mnist数据子集（1个测试点）进行了测试，它似乎起了作用。所以，当我尝试使用超过2个数据点时，它不起作用。我已经在下面发布了我的代码和错误。如果有人能给我指点，我将不胜感激。我删除了错误代码中矩阵的内部部分，以缩短这篇文章的篇幅，以防有人怀疑，只留下第一行和最后几行

谢谢， 奥贝罗伊

import tensorflow as tf
from tensorflow import keras

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy import io

from scipy.spatial import distance 

fashion_mnist = keras.datasets.fashion_mnist

from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix

from sklearn.neighbors import NearestNeighbors

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

X1=train_images.flatten().reshape((60000,784))
x1=test_images.flatten().reshape((10000,784))

# Subsetting fashion MNIST to work with

X=X1[0:100,:]
Y=train_labels[0:100]
x=x1[0:3,:]
y=test_labels[0:3]

def CLC(target):
    centroids=[]
    # Find k nearest neighbors by label
    for i in list(labels):
        X_i = X[Y==i]
#    print(X_i)
        Y_i = Y[Y==i]
        nbrs_i = NearestNeighbors(n_neighbors=1).fit(X_i)
        distances, indices = nbrs_i.kneighbors(target.reshape(1,-1))
    
        # Subsetting data for nearest neighbors
        xss=X_i[indices,:]
        yss=Y_i[indices]
        # Centroid of k nearest neighbors by label
#    print(xss)
#    print(yss)
        centroid = np.mean(xss, axis=1)
        print(centroid)
        centroids.append(centroid)
#centroids
    # find closest local centroid
    centroids=np.squeeze(centroids)
    clf = NearestCentroid()
    clf.fit(centroids, labels)
    return(clf.predict(target))

y_pred=np.apply_along_axis(CLC, axis=1, arr=x)
#acc=sum(y_pred==y)/len(y_pred)
#acc

Error Output:

[[  0.   0.   0.   0.   0.   0.   0.   0.   0.   0. 122. 101.  73.  59.
   39. 108.  89.  91.  83.   0.   0.   0.   0.   0.   0.   0.   0.   0.
    0.   0.   0.   0.   0.   0.   0.   0.   0.   3. 215. 232. 236. 223.


    0.   0.   0.  61.  81.  91.  56.  12.  99.  75.  78.  75.  99.  70.
   61.  70.  77.  77.  61.  58.  89.   7.  55. 188.  97.  78.   0.   0.
    0.   0.   0.   9.  28.  23.   4.   0.   0.   0.   0.   0.   0.   0.
    0.   0.   0.   0.   0.   0.   0.   0.   1.  66.  33.  11.   0.   0.]]
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-569-bda921e18e4e> in <module>
     26     return(clf.predict(target))
     27 
---> 28 y_pred=np.apply_along_axis(CLC, axis=1, arr=x)
     29 #acc=sum(y_pred==y)/len(y_pred)
     30 #acc

<__array_function__ internals> in apply_along_axis(*args, **kwargs)

~/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/shape_base.py in apply_along_axis(func1d, axis, arr, *args, **kwargs)
    377     except StopIteration:
    378         raise ValueError('Cannot apply_along_axis when any iteration dimensions are 0')
--> 379     res = asanyarray(func1d(inarr_view[ind0], *args, **kwargs))
    380 
    381     # build a buffer for storing evaluations of func1d.

<ipython-input-569-bda921e18e4e> in CLC(target)
     24     clf = NearestCentroid()
     25     clf.fit(centroids, labels)
---> 26     return(clf.predict(target))
     27 
     28 y_pred=np.apply_along_axis(CLC, axis=1, arr=x)

~/opt/anaconda3/lib/python3.7/site-packages/sklearn/neighbors/nearest_centroid.py in predict(self, X)
    190         check_is_fitted(self, 'centroids_')
    191 
--> 192         X = check_array(X, accept_sparse='csr')
    193         return self.classes_[pairwise_distances(
    194             X, self.centroids_, metric=self.metric).argmin(axis=1)]

~/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    519                     "Reshape your data either using array.reshape(-1, 1) if "
    520                     "your data has a single feature or array.reshape(1, -1) "
--> 521                     "if it contains a single sample.".format(array))
    522 
    523         # in the future np.flexible dtypes will be handled like object dtypes

ValueError: Expected 2D array, got 1D array instead:
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   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].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.