Tensorflow入門【MNIST編】【情報保存編】

mnist編で学習パラメタを保存する場合はこちら。
tensorflowというフォルダに保存する設定になっています。
##save dataのところが、今回追加した部分。

【mnist.py】
import tensorflow as tf
import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

sess = tf.InteractiveSession()

# create the model
x = tf.placeholder("float", [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y= tf.nn.softmax(tf.matmul(x, W) + b)

##save data
saver = tf.train.Saver()


# define loss and optimizer
y_ = tf.placeholder("float", [None, 10])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

# train
tf.global_variables_initializer().run()

for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    train_step.run({x: batch_xs, y_:batch_ys})

# test trained model
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuray = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print(accuray.eval({x: mnist.test.images, y_: mnist.test.labels}))

##save data
save_path = saver.save(sess,"/tensorflow/model.ckpt")
print("Model saved in file: %s" % save_path)