質問編集履歴
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全コードとエラーメッセージ、print結果を記載しました。
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（以下、全てのコードとエラーメッセージです。）
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```ここに言語を入力
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import keras
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from keras.datasets import mnist
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from keras.models import Sequential
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from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
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#from keras.optimizers import RMSprop
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#from keras.utils import np_utils
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#from sklearn.datasets import fetch_mldata
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# データを高速かつ効率的に使えるPandasをインポート
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import pandas as pd
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# 数値計算を効率的に行うNumpyをインポート
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import numpy as np
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# グラフが簡単に描写できるMatplotlibをインポート
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import matplotlib
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import matplotlib.pyplot as plt
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# MNISTデータを読込む
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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# MNISTデータを加工する
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# １次元に加工する場合（畳み込みは２次元）（デフォルトは2次元）
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#x_train = x_train.reshape(60000, 784)
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#x_test = x_test.reshape(10000, 784)
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# データを float 型に変換
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x_train = x_train.astype('float32')
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x_test = x_test.astype('float32')
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# 0〜255 までの範囲のデータを 0〜1 までの範囲に変更（正規化）
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x_train /= 255
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x_test /= 255
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# 分類するクラス数
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y_train = keras.utils.to_categorical(y_train, 10)
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y_test = keras.utils.to_categorical(y_test, 10)
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# 後の評価で使用？
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y_test_backup = y_test
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# 両方のサイズを確認
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print("x_train.shape(学習用の画像データ) : ", x_train.shape)
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print("y_train_shape(学習用の正解データ) : ", y_train.shape)
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print("x_test.shape(テスト用の画像データ) : ", x_test.shape)
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print("y_test.shape(テスト用の正解データ) : ", y_test.shape)
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x_train.shape(学習用の画像データ) : (60000, 28, 28)
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y_train_shape(学習用の正解データ) : (60000, 10)
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x_test.shape(テスト用の画像データ) : (10000, 28, 28)
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y_test.shape(テスト用の正解データ) : (10000, 10)
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# 28x28x1のサイズへ変換
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x_train = x_train.reshape(x_train.shape[0], 28, 28,1)
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x_test = x_test.reshape(x_test.shape[0], 28, 28,1)
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# モデルの宣言
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model = Sequential()
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# 先に作成したmodelへレイヤーを追加
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model.add(Conv2D(32, kernel_size=(3, 3),
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activation='relu',
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input_shape=(28,28,1)))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Dropout(0.25))
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model.add(Flatten())
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model.add(Dense(128, activation='relu'))
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model.add(Dense(10, activation='softmax'))
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# Learnig Processの設定
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model.compile(loss='categorical_crossentropy',
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optimizer='sgd',
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metrics=['accuracy'])
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# 注意 - 数分程度かかる
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# モデルの訓練（エポック数）
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model.fit(x_train, y_train, epochs=1)
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Epoch 1/1
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60000/60000 [==============================] - 19s 318us/step - loss: 0.4984 - acc: 0.8590
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<keras.callbacks.History at 0x1fa3aaad5c0>
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# テストデータを使ってモデルの評価
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loss_and_metrics = model.evaluate(x_test, y_test, batch_size=128)
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print(loss_and_metrics)
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10000/10000 [==============================] - 1s 54us/step
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[0.2483551863193512, 0.9253]
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# 評価の実行
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from sklearn.metrics import accuracy_score
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from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
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predict_classes = model.predict_classes(x_test)
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print('正確度(accuracy):', score[1])
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print(' ')
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#print('精度(正確度):{:.3f}'.format(accuracy_score(y_test, predict_classes)))
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#print('適合率:{:.3f}'.format(precision_score(y_test, predict_classes)))
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#print('再現率:{:.3f}'.format(recall_score(y_test, predict_classes)))
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#print('f-1値:{:.3f}'.format(f1_score(y_test, predict_classes)))
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# 混同行列（Confusion Matrix）
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from sklearn.metrics import confusion_matrix
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print(confusion_matrix(y_test, predict_classes))
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10000/10000 [==============================] - 1s 60us/step
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正確度(accuracy): 0.9253
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---------------------------------------------------------------------------
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ValueError Traceback (most recent call last)
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<ipython-input-7-0c069648309b> in <module>
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16 from sklearn.metrics import confusion_matrix
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---> 18 print(confusion_matrix(y_test, predict_classes))
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C:\python\anaconda\pgm\lib\site-packages\sklearn\metrics\classification.py in confusion_matrix(y_true, y_pred, labels, sample_weight)
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252 """
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--> 253 y_type, y_true, y_pred = _check_targets(y_true, y_pred)
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254 if y_type not in ("binary", "multiclass"):
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255 raise ValueError("%s is not supported" % y_type)
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C:\python\anaconda\pgm\lib\site-packages\sklearn\metrics\classification.py in _check_targets(y_true, y_pred)
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79 if len(y_type) > 1:
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80 raise ValueError("Classification metrics can't handle a mix of {0} "
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---> 81 "and {1} targets".format(type_true, type_pred))
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83 # 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 multilabel-indicator and multiclass targets
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```
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以下、printの結果です。
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```ここに言語を入力
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10000/10000 [==============================] - 1s 89us/step
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print(y_test)
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ValueError Traceback (most recent call last)
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<ipython-input-49-2f493ca036b8> in <module>
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print(predict_classes)
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[0. 1. 0. ... 0. 0. 0.]
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[0. 0. 0. ... 0. 0. 0.]
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[0. 0. 0. ... 0. 0. 0.]]
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---> 81 "and {1} targets".format(type_true, type_pred))
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ValueError: Classification metrics can't handle a mix of multilabel-indicator and multiclass targets
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```
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