import input_data
import tensorflow as tf

EXPORT_DIR = './model'

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
sess = tf.InteractiveSession()
x = tf.placeholder("float", shape=[None, 784], name='input')
y_ = tf.placeholder("float", shape=[None, 10])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
#sess.run(tf.initialize_all_variables())
sess.run(tf.global_variables_initializer())
y = tf.nn.softmax(tf.matmul(x,W) + b,name='output')
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
for i in range(1000):
    batch = mnist.train.next_batch(50)
    train_step.run(feed_dict={x: batch[0], y_: batch[1]})

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


output_graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, output_node_names=['output'])
with tf.gfile.FastGFile('mnist_xu.pb', mode = 'wb') as f:
    f.write(output_graph_def.SerializeToString())

