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MNIST 데이터 다운로드 및 읽어오기

import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz

텐서플로우 로딩

import tensorflow as tf
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)

트레이닝

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)


init = tf.initialize_all_variables()


sess = tf.Session()
sess.run(init)


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

Evaluation

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))


accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))


print sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})

0.9088
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