create_model.py

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot = True)
import tensorflow as tf

if __name__ == '__main__':
    
    x = tf.placeholder(tf.float32, [None,784], name="x")

    W = tf.Variable(tf.zeros([784,10]))
    b = tf.Variable(tf.zeros([10]))

    y = tf.nn.softmax(tf.matmul(x,W) + b, name="y")

    y_ = tf.placeholder(tf.float32, [None, 10])


    cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))

    train_step = tf.train.GradientDescentOptimizer(0.5).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})

    correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print(sess.run(accuracy, feed_dict = {x: mnist.test.images, y_:mnist.test.labels}))

    saver = tf.train.Saver()

    saver.save(sess, "/data/deep_mnist_model")