TensorFlow基礎及MNIST資料集邏輯迴歸應用實踐-大資料ML樣本集案例實戰

摘要： 版權宣告：本套技術專欄是作者（秦凱新）平時工作的總結和昇華，通過從真實商業環境抽取案例進行總結和分享，並給出商業應用的調優建議和叢集環境容量規劃等內容，請持續關注本套部落格。QQ郵箱地址：[email protected]，如有任何學術交流，可隨時聯絡。 TensorFlow基本使用操作 ...

版權宣告：本套技術專欄是作者（秦凱新）平時工作的總結和昇華，通過從真實商業環境抽取案例進行總結和分享，並給出商業應用的調優建議和叢集環境容量規劃等內容，請持續關注本套部落格。QQ郵箱地址：[email protected]，如有任何學術交流，可隨時聯絡。

TensorFlow基本使用操作

• TensorFlow基本模型

  import tensorflow as tf
  a = 3
  # Create a variable.
  w = tf.Variable([[0.5,1.0]])
  x = tf.Variable([[2.0],[1.0]]) 
  
  y = tf.matmul(w, x)
  
  #variables have to be explicitly initialized before you can run Ops
  init_op = tf.global_variables_initializer()
  with tf.Session() as sess:
  sess.run(init_op)
  print (y.eval())
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• TensorFlow基本資料型別

  # float32
  tf.zeros([3, 4], int32) ==> [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
  
  # 'tensor' is [[1, 2, 3], [4, 5, 6]]
  tf.zeros_like(tensor) ==> [[0, 0, 0], [0, 0, 0]]
  tf.ones([2, 3], int32) ==> [[1, 1, 1], [1, 1, 1]]
  
  # 'tensor' is [[1, 2, 3], [4, 5, 6]]
  tf.ones_like(tensor) ==> [[1, 1, 1], [1, 1, 1]]
  
  # Constant 1-D Tensor populated with value list.
  tensor = tf.constant([1, 2, 3, 4, 5, 6, 7]) => [1 2 3 4 5 6 7]
  
  # Constant 2-D tensor populated with scalar value -1.
  tensor = tf.constant(-1.0, shape=[2, 3]) => [[-1. -1. -1.]
  [-1. -1. -1.]]
  
  tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.011.012.0]
  
  # 'start' is 3
  # 'limit' is 18
  # 'delta' is 3
  tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]
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• random_shuffle運算元及random_normal運算元

  norm = tf.random_normal([2, 3], mean=-1, stddev=4)
  
  # Shuffle the first dimension of a tensor
  c = tf.constant([[1, 2], [3, 4], [5, 6]])
  shuff = tf.random_shuffle(c)
  
  # Each time we run these ops, different results are generated
  sess = tf.Session()
  print (sess.run(norm))
  print (sess.run(shuff))
  
  [[-0.308862923.118096833.29861784]
  [-7.09597015 -1.898118021.75282788]]
  
  [[3 4]
  [5 6]
  [1 2]]
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• 簡單操作的複雜性

  state = tf.Variable(0)
  new_value = tf.add(state, tf.constant(1))
  update = tf.assign(state, new_value)
  
  with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
  print(sess.run(state))
  for _ in range(3):
  sess.run(update)
  print(sess.run(state))
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• 模型的儲存與載入

  #tf.train.Saver
  w = tf.Variable([[0.5,1.0]])
  x = tf.Variable([[2.0],[1.0]])
  y = tf.matmul(w, x)
  init_op = tf.global_variables_initializer()
  saver = tf.train.Saver()
  with tf.Session() as sess:
  sess.run(init_op)
  # Do some work with the model.
  # Save the variables to disk.
  save_path = saver.save(sess, "C://tensorflow//model//test")
  print ("Model saved in file: ", save_path)
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• numpy與TensorFlow互轉

  import numpy as np
  a = np.zeros((3,3))
  ta = tf.convert_to_tensor(a)
  with tf.Session() as sess:
  print(sess.run(ta))
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• TensorFlow佔坑操作

  input1 = tf.placeholder(tf.float32)
  input2 = tf.placeholder(tf.float32)
  output = tf.mul(input1, input2)
  with tf.Session() as sess:
  print(sess.run([output], feed_dict={input1:[7.], input2:[2.]}))
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