text: #%%
  # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  from tensorflow.examples.tutorials.mnist import input_data
  mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
  
  print(mnist.train.images.shape, mnist.train.labels.shape)
  print(mnist.test.images.shape, mnist.test.labels.shape)
  print(mnist.validation.images.shape, mnist.validation.labels.shape)
  """(55000, 784) (55000, 10)
     (10000, 784) (10000, 10)
     (5000, 784) (5000, 10)"""
  import tensorflow as tf
  sess = tf.InteractiveSession()
  x = tf.placeholder(tf.float32, [None, 784])
  # 数据类型，数据的格式，none表示不限制输入数据的条目，784表示每条输入是一个784维的数据
  #
  W = tf.Variable(tf.zeros([784, 10]))
  b = tf.Variable(tf.zeros([10]))
  y = tf.nn.softmax(tf.matmul(x, W) + b)  # 关于这里二维数组和一维数组相加的事情，我还纠结了半天
  
  
  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)
  
  tf.global_variables_initializer().run()
  
  for i in range(1000):
      batch_xs, batch_ys = mnist.train.next_batch(100)
      train_step.run({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(accuracy.eval({x: mnist.test.images, y_: mnist.test.labels}))
  