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李弘洋
67 articles
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  • defines the namespace for tf.Operation objects
  • ensorFlow provides a "default graph" that is implicitly passed to all API functions in the same context.
  • use multiple graphs in the same process.
  • can either use completely separate Python processes to build and execute the graphs,
  • dropout and batch normalization use different operations in each case.
  • a separate graph for evaluating or performing inference with a trained model.
  • , a common way of organizing your code is to use one graph for training your model,
  • typical TensorFlow graph---especially training graphs with automatically computed gradients---has too many nodes to visualize at once. The graph visualizer makes use of name scopes to group related operations into "super" nodes.
  • Print the timings of each operation that executed
  • collect metadata about the execution
  • enables you to specify options about the call
  • be substituted for those tensors in the execution.
  • takes a dictionary of feeds, which is a mapping from tf.Tensor objects (typically tf.placeholder tensors) to values
  • requires you to specify a list of fetches
  • controls the behavior of the session
  • tf.estimator.Estimator will create and manage a tf.Session for you
  • Since a tf.Session owns physical resources (such as GPUs and network connections), it is typically used as a context manager (in a with block)
  • caches information about your tf.Graph
  • represent a connection between the client program
  • nodes and edges of the graph
  • it is easy for the system to identify operations that can execute in parallel
  • nodes represent units of computation, and the edges represent the data consumed or produced by a computation.
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