Keras使用

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

digits = datasets.load_digits()
n_samples = len(digits.images)
data = digits.images.reshape((n_samples, -1))

train_images = data[:n_samples // 2]
train_images = train_images.astype('float32') / 255

test_images = data[n_samples // 2:]
test_images = test_images.astype('float32') / 255

train_labels = to_categorical(digits.target[:n_samples // 2])

test_labels = to_categorical(digits.target[n_samples // 2:])
network = models.Sequential()
network.add(layers.Dense(35, activation='relu', input_shape=(8 * 8,)))
network.add(layers.Dense(10, activation='softmax'))
network.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
network.fit(train_images, train_labels, epochs=300, batch_size=100,verbose=0)

# 保存模型
from aiworks_plugins.tf_plugins import save_keras_model_to_hdfs
save_keras_model_to_hdfs(network)