from keras import layers from keras import models model = models.Sequential() #首层接收2维输入 model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1))) model.add(layers.MaxPooling2D(2,2)) model.add(layers.Conv2D(64, (3,3), activation='relu')) model.add(layers.MaxPooling2D((2,2))) model.add(layers.Conv2D(64, (3,3), activation='relu')) model.add(layers.Flatten()) model.add(layers.Dense(64, activation='relu')) model.add(layers.Dense(10, activation='softmax')) model.summary()
from keras.datasets import mnist from keras.utils import to_categorical (train_images, train_labels), (test_images, test_labels) = mnist.load_data() train_images = train_images.reshape((60000, 28, 28, 1)) train_images = train_images.astype('float32') / 255 test_images = test_images.reshape((10000, 28, 28, 1)) test_images = test_images.astype('float32') / 255 train_labels = to_categorical(train_labels) test_labels = to_categorical(test_labels) model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) model.fit(train_images, train_labels, epochs = 5, batch_size=64) test_loss, test_acc = model.evaluate(test_images, test_labels) print(test_acc)
