模型訓練好后，就可以使用測試數據評估模型的性能。
score = model.evaluate(X_test, Y_test, verbose=0)
到此為止，我們已經完成了一個完整的Keras應用。進一步了解Keras，可參考更多Keras例子。
完整代碼
下面是本教程的完整代碼：
# Keras 導入庫與模塊 import numpy as np np.random.seed(123) # 種子相同，隨機數產生可以重現 from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Convolution2D, MaxPooling2D from keras.utils import np_utils from keras.datasets import mnist from keras import backend as K # 將預打亂的MNIST數據加載到培訓和測試集中 (X_train, y_train), (X_test, y_test) = mnist.load_data() # 預處理輸入數據 if K.image_data_format() == 'channels_first': X_train = X_train.reshape(X_train.shape[0], 1, 28, 28) X_test = X_test.reshape(X_test.shape[0], 1, 28, 28) input_shape = (1, 28, 28) else: X_train = X_train.reshape(X_train.shape[0], 28, 28, 1) X_test = X_test.reshape(X_test.shape[0], 28, 28, 1) input_shape = (28, 28, 1) X_train = X_train.astype('float32') X_test = X_test.astype('float32') X_train /= 255 X_test /= 255 # 預處理類標簽 Y_train = np_utils.to_categorical(y_train, 10) Y_test = np_utils.to_categorical(y_test, 10) # 定義模型架構 model = Sequential() model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=input_shape)) model.add(Convolution2D(32, 3, 3, activation='relu')) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(10, activation='softmax')) # 編譯模型 model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # 根據訓練數據擬合模型 model.fit(X_train, Y_train, batch_size=32, nb_epoch=10, verbose=1) # 根據測試數據評估模型 score = model.evaluate(X_test, Y_test, verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1])
運行輸出：
... 60000/60000 [==============================] - 148s 2ms/step - loss: 0.2055 - acc: 0.9372 Epoch 2/10 60000/60000 [==============================] - 131s 2ms/step - loss: 0.0857 - acc: 0.9746 Epoch 3/10 60000/60000 [==============================] - 128s 2ms/step - loss: 0.0661 - acc: 0.9802 Epoch 4/10 60000/60000 [==============================] - 120s 2ms/step - loss: 0.0551 - acc: 0.9831 Epoch 5/10 60000/60000 [==============================] - 124s 2ms/step - loss: 0.0469 - acc: 0.9856 Epoch 6/10 60000/60000 [==============================] - 134s 2ms/step - loss: 0.0411 - acc: 0.9875 Epoch 7/10 60000/60000 [==============================] - 120s 2ms/step - loss: 0.0350 - acc: 0.9890 Epoch 8/10 60000/60000 [==============================] - 117s 2ms/step - loss: 0.0321 - acc: 0.9898 Epoch 9/10 60000/60000 [==============================] - 123s 2ms/step - loss: 0.0317 - acc: 0.9898 Epoch 10/10 60000/60000 [==============================] - 122s 2ms/step - loss: 0.0281 - acc: 0.9913 Test loss: 0.024244179409698335 Test accuracy: 0.9925