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 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]
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• 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]]
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• 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))
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• 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)
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• 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))
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• 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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