text: import tensorflow.examples.tutorials.mnist.input_data as input_data
  mnist = input_data.read_data_sets("FMNIST_data/" , one_hot=True)
  # from tensorflow.examples.tutorials.mnist import input_data
  # mnist = input_data.read_data_sets('data/fashion')
  
  import tensorflow as tf
  import numpy as np
  
  x = tf.placeholder(tf.float32, [None, 784])
  W = tf.Variable(tf.zeros([784, 10]))
  b = tf.Variable(tf.zeros([10]))
  y = tf.nn.softmax(tf.matmul(x,W) + b)
  y_ = tf.placeholder("float", [None, 10])
  cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
  train_step = tf.train.GradientDescentOptimizer(0.003).minimize(cross_entropy)
  
  init = tf.initialize_all_variables()
  
  sess = tf.Session()
  sess.run(init)
  
  for i in range(2000) :
      batch_xs, batch_ys = mnist.train.next_batch(100)
      # batch_xs = np.reshape(batch_xs, [1,-1])
      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, "float"))
  print(sess.run(accuracy, feed_dict = {x:mnist.test.images, y_:mnist.test.labels}))
  