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TensorFlow基础及MNIST数据集逻辑回归应用实践-大数据ML样本集案例实战

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TensorFlow基本使用操作

 

TensorFlow基本模型

 

import  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())

 

TensorFlow基本数据类型

 

# 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") => [ 10.0  11.0  12.0]
  # 'start' is 3
  # 'limit' is 18
  # 'delta' is 3
  tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]

 

random_shuffle算子及random_normal算子

 

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))
  [[-0.30886292  3.11809683  3.29861784]
   [-7.09597015 -1.89811802  1.75282788]]
  [[3 4]
   [5 6]
   [1 2]]

 

简单操作的复杂性

 

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))

 

模型的保存与加载

 

#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)

 

numpy与TensorFlow互转

 

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

 

TensorFlow占坑操作

 

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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