from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

# prepare data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

xs = tf.placeholder(tf.float32, [None, 784])
ys = tf.placeholder(tf.float32, [None, 10])

# the model of the fully-connected network
weights = tf.Variable(tf.random_normal([784, 10]))
biases = tf.Variable(tf.zeros([1, 10]) + 0.1)
outputs = tf.matmul(xs, weights) + biases
predictions = tf.nn.softmax(outputs)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(predictions),
            reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

# compute the accuracy
correct_predictions = tf.equal(tf.argmax(predictions, 1), tf.argmax(ys, 1))
accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))

with tf.Session() as sess:
 init = tf.global_variables_initializer()
 sess.run(init)
 for i in range(1000):
  batch_xs, batch_ys = mnist.train.next_batch(100)
  sess.run(train_step, feed_dict={
   xs: batch_xs,
   ys: batch_ys
  })
  if i % 50 == 0:
   print(sess.run(accuracy, feed_dict={
    xs: mnist.test.images,
    ys: mnist.test.labels
   }))

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

xs = tf.placeholder(tf.float32, [None, 784])
ys = tf.placeholder(tf.float32, [None, 10])

weights = tf.Variable(tf.random_normal([784, 10]))


biases = tf.Variable(tf.zeros([1, 10]) + 0.1)
outputs = tf.matmul(xs, weights) + biases
predictions = tf.nn.softmax(outputs)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(predictions),
            reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

correct_predictions = tf.equal(tf.argmax(predictions, 1), tf.argmax(ys, 1))
accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))

with tf.Session() as sess:
 init = tf.global_variables_initializer()
 sess.run(init)
 for i in range(1000):
  batch_xs, batch_ys = mnist.train.next_batch(100)
  sess.run(train_step, feed_dict={
   xs: batch_xs,
   ys: batch_ys
  })
  if i % 50 == 0:
   print(sess.run(accuracy, feed_dict={
    xs: mnist.test.images,
    ys: mnist.test.labels
   }))

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
0.1041
0.632
0.7357
0.7837
0.7971
0.8147
0.8283
0.8376
0.8423
0.8501
0.8501
0.8533
0.8567
0.8597
0.8552
0.8647
0.8654
0.8701
0.8712
0.8712