参考：
TensorFlow运作方式入门http://www.tensorfly.cn/tfdoc/tutorials/mnist_tf.html

注意以下代码仅为示例

Step1准备数据输入

按需制作训练集

Step2 构造图表(Build the Graph)

####2.1定义占位符

在创建session的时候数据才真正流入神经网络

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images_placeholder = tf.placeholder(tf.float32, shape=(batch_size,
                                                       IMAGE_PIXELS))
labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size))

####2.2构造inference()
占位符为输入，使数据经过神经网络向前反馈输出预测结果
每一层都创建于一个唯一的tf.name_scope之下，创建于该作用域之下的所有元素都将带有其前缀

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def inference(images, hidden1_units, hidden2_units):
  # Hidden 1
  with tf.name_scope('hidden1'):
    weights = tf.Variable(
        tf.truncated_normal([IMAGE_PIXELS, hidden1_units],
                            stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))),
        name='weights')
    biases = tf.Variable(tf.zeros([hidden1_units]),
                         name='biases')
    hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
  # Hidden 2
  with tf.name_scope('hidden2'):
    weights = tf.Variable(
        tf.truncated_normal([hidden1_units, hidden2_units],
                            stddev=1.0 / math.sqrt(float(hidden1_units))),
        name='weights')
    biases = tf.Variable(tf.zeros([hidden2_units]),
                         name='biases')
    hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
  # Linear
  with tf.name_scope('softmax_linear'):
    weights = tf.Variable(
        tf.truncated_normal([hidden2_units, NUM_CLASSES],
                            stddev=1.0 / math.sqrt(float(hidden2_units))),
        name='weights')
    biases = tf.Variable(tf.zeros([NUM_CLASSES]),
                         name='biases')
    logits = tf.matmul(hidden2, weights) + biases
  return logits

####2.3损失(Loss)

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cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits,
                                                        onehot_labels,
                                                        name='entropy')
loss = tf.reduce_mean(cross_entropy, name='entropy_mean')

####2.4训练(training)
#####2.4.1将损失最小化

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optimizer = tf.train.GradientDescentOptimizer(learning_rate)
train_op = optimizer.minimize(loss, global_step=global_step)

#####2.4.2生成一个变量用于保存全局训练步骤（global training step）的数值

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global_step = tf.Variable(0, name='global_step', trainable=False)

##step3 启动会话并执行图表

####3.1关联图表构建会话

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saver = tf.train.Saver() #保存模型：定义一个saver
with tf.Graph().as_default():
    with tf.Session() as sess:
        init = tf.initialize_all_variables()
        sess.run(init)

####3.2 feed_dict参数传入sess.run()，真正训练模型，保存模型

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for step in xrange(FLAGS.max_steps):
    feed_dict = {
        images_placeholder: images_feed,
        labels_placeholder: labels_feed,
    }
    _, loss_value = sess.run([train_op, loss],
                     feed_dict=feed_dict)
    
    saver.save(sess, FLAGS.train_dir, global_step=step)
    #保存模型：检查点保存到FLAGS.train_dir,

Step4 恢复并评估模型

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with tf.Session() as sess:
    model_dir=tf.train.latest_checkpoint('ckpt/')
    saver.restore(sess,model_dir)
    ***=sess.run(***, feed_dict={))