python - NP阵列的形状为(2186, 128)。我有兴趣将获得的数组应用到SVM

Question

我提取了 CNN 倒数第二层(#第 12 层)的输出。提取的 NP 数组的形状为 (2186, 128)。我有兴趣将获得的数组应用到 SVM 中。

提取特征的代码:

import numpy as np
X_train=np.array(get_activations(model=model,layer=12, X_batch=x_train)[0], dtype=np.float32)
print(X_train)

这给了我形状 (2186, 128) 的输出

将上述 np 数组应用到 SVM 的代码:

from sklearn.svm import SVC
clf = SVC()
clf.fit(X_train, y)
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
    decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
    max_iter=-1, probability=False, random_state=None, shrinking=True,
    tol=0.001, verbose=False)

这给出了错误:

ValueError                                Traceback (most recent call last)
<ipython-input-54-2d6b8b03f3c1> in <module>()
----> 1 clf.fit(X_train, y)
      2 SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
      3     decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
      4     max_iter=-1, probability=False, random_state=None, shrinking=True,
      5     tol=0.001, verbose=False)

~/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py in fit(self, X, y, sample_weight)
    147         self._sparse = sparse and not callable(self.kernel)
    148 
--> 149         X, y = check_X_y(X, y, dtype=np.float64, order='C', accept_sparse='csr')
    150         y = self._validate_targets(y)
    151 

~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py in check_X_y(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)
    576                         dtype=None)
    577     else:
--> 578         y = column_or_1d(y, warn=True)
    579         _assert_all_finite(y)
    580     if y_numeric and y.dtype.kind == 'O':

~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py in column_or_1d(y, warn)
    612         return np.ravel(y)
    613 
--> 614     raise ValueError("bad input shape {0}".format(shape))
    615 
    616 

ValueError: bad input shape (2186, 3)

Answer 1

看起来你的y有one-hot-encoding(3类)。将其转换为整数标签 (0, 1, 2) 就可以了。