質問編集履歴
2
エラーコードの更新
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@@ -109,10 +109,10 @@
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pred = model.predict(x_test, batch_size=1, verbose=0)
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y_pred2 = np.max(pred)
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```
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### 新
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### 最新のエラーコード
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ValueError Traceback (most recent call last)
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<ipython-input-
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<ipython-input-13-b74e493b703b> in <module>
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---> 72 print('confusion matrix 混合行列 =\n ', confusion_matrix(y_true=y_test2, y_pred=y_pred2))#混合行列
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82 type_pred = type_of_target(y_pred)
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~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_consistent_length(*arrays)
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206 """
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--> 208 lengths = [_num_samples(X) for X in arrays if X is not None]
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209 uniques = np.unique(lengths)
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210 if len(uniques) > 1:
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211 raise ValueError("Found input variables with inconsistent numbers of"
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--> 212 " samples: %r" % [int(l) for l in lengths])
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~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in <listcomp>(.0)
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206 """
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--> 208 lengths = [_num_samples(X) for X in arrays if X is not None]
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209 uniques = np.unique(lengths)
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210 if len(uniques) > 1:
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~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in _num_samples(x)
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150 if len(x.shape) == 0:
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151 raise TypeError("Singleton array %r cannot be considered"
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--> 152 " a valid collection." % x)
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153 # Check that shape is returning an integer or default to len
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154 # Dask dataframes may not return numeric shape[0] value
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ValueError: Found input variables with inconsistent numbers of samples: [1, 12000]
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```
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いただいた回答に修正した際のエラーコードを表示しました
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@@ -1,7 +1,7 @@
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1
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confusion_matrixを表示したいのですがエラーが出てしまいます。
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どうすれば表示できるのかお聞きしたいです。
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具体的なプログラムを教えてもらえると助かります。
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### ソースコード
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### 最初のソースコード
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```
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from __future__ import print_function
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92 # We can't have more than one value on y_type => The set is no more needed
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ValueError: Classification metrics can't handle a mix of binary and multiclass-multioutput targets
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```
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###修正後のy_pred2
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回答でいただいたものをもとにy_pred2を変えたらエラーが変わりました
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```
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pred = model.predict(x_test, batch_size=1, verbose=0)
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y_pred2 = np.max(pred)
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```
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### 新しいエラーコード
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```
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TypeError Traceback (most recent call last)
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<ipython-input-10-8349a4884248> in <module>
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---> 72 print('confusion matrix 混合行列 =\n ', confusion_matrix(y_true=y_test2, y_pred=y_pred2))#混合行列
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73 print('accuracy 正解率 = ', accuracy_score(y_true=y_test2, y_pred=y_pred2))#正解率（正しく分類されたデータ数の割合）
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74 print('precision 適合率 = ', precision_score(y_true=y_test2, y_pred=y_pred2))#適合率（Aに分類されたデータで実際にAであるデータ数の割合）
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~\Anaconda3\lib\site-packages\sklearn\metrics\_classification.py in confusion_matrix(y_true, y_pred, labels, sample_weight, normalize)
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267 """
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--> 268 y_type, y_true, y_pred = _check_targets(y_true, y_pred)
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269 if y_type not in ("binary", "multiclass"):
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270 raise ValueError("%s is not supported" % y_type)
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~\Anaconda3\lib\site-packages\sklearn\metrics\_classification.py in _check_targets(y_true, y_pred)
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78 y_pred : array or indicator matrix
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79 """
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---> 80 check_consistent_length(y_true, y_pred)
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81 type_true = type_of_target(y_true)
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82 type_pred = type_of_target(y_pred)
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~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_consistent_length(*arrays)
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206 """
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--> 208 lengths = [_num_samples(X) for X in arrays if X is not None]
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209 uniques = np.unique(lengths)
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210 if len(uniques) > 1:
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~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in <listcomp>(.0)
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206 """
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--> 208 lengths = [_num_samples(X) for X in arrays if X is not None]
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209 uniques = np.unique(lengths)
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210 if len(uniques) > 1:
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~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in _num_samples(x)
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150 if len(x.shape) == 0:
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151 raise TypeError("Singleton array %r cannot be considered"
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--> 152 " a valid collection." % x)
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153 # Check that shape is returning an integer or default to len
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154 # Dask dataframes may not return numeric shape[0] value
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TypeError: Singleton array 1.0 cannot be considered a valid collection.
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```
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