深度学习系列--2.神经网络模型参数选择

from keras import models
from keras import layers
from keras.datasets import mnist
from keras.utils import to_categorical

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# build network
network = models.Sequential()
network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28, )))
network.add(layers.Dense(10, activation="softmax"))

network.compile(
    optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

train_images = train_images.reshape((train_images.shape[0], 28 * 28))
train_images = train_images.astype('float32') / 255

test_images = test_images.reshape((test_images.shape[0], 28 * 28))
test_images = test_images.astype('float32') / 255

train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

network.fit(train_images, train_labels, epochs=10, batch_size=128)

test_loss, test_acc = network.evaluate(test_images, test_labels)
print("test_acc:", test_acc)
f