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TensorFlow-1 入门

TensorFlow-1 入门

作者: danyue_smile | 来源:发表于2018-06-28 16:02 被阅读0次

    1. Graph is Template; Session is the actual realization;

    **Computation Paths** 
    
    
    import tensorflow as tf
    
    two_node = tf.constant(2)
    
    three_node = tf.constant(3)
    
    sum_node = two_node + three_node ## equivalent to tf.add(two_node, three_node)
    
    input_placeholder = tf.placeholder(tf.int32)
    
    sess = tf.Session()
    
    print sess.run(input_placeholder, feed_dict={input_placeholder: 2})
    
    

    如上,分别展示了 tf 中的 常量,占位符的应用;需要注意的是:

    In general, sess.run() calls tend to be one of the biggest TensorFlow bottlenecks, so the fewer times you call it, the better. Whenever possible, return multiple items in a single sess.run() call instead of making multiple calls.

    也就是,sess.run() 不应该多次调用,应尽可能地在单次调用时,返回所需要的多个目标

    2. 上面介绍了 常量,占位符 ,常用的还有 变量

    
    import tensorflow as tf
    
    count_variable = tf.get_variable("count", []) # []给定标量,[3,8] 给定 3*8 矩阵
    
    zero_node = tf.constant(0.)
    
    assign_node = tf.assign(count_variable, zero_node)
    
    sess = tf.Session()
    
    sess.run(assign_node)
    
    print sess.run(count_variable)
    
    

    输出:

    0

    • tf.assign(target, value) 将值分配到目标变量上

    • side effects 指对 graph 中节点的影响

    • 节点间的依赖性

    [图片上传失败...(image-ed562b-1530172910213)]

    3. 关于初始化

    
    import tensorflow as tf
    
    const_init_node = tf.constant_initializer(0.)
    
    count_variable = tf.get_variable("count", [], initializer=const_init_node)
    
    init = tf.global_variables_initializer()
    
    sess = tf.Session()
    
    sess.run(init)
    
    print sess.run(count_variable)
    
    

    输出:

    0

    • tf.global_variables_initializer() 对 graph 中的 tf.initializer 进行初始化,
      并在 sess 运行时,被实现

    4. 变量共享

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