Graph Construction

The low level API can be used to define computations that will be executed at a later point, and potentially execute them. It can also be used to create custom layers for the Learn API. The main type of object underlying the low level API is the [Output][output], which represents the value of a [Tensor][tensor] that has not yet been computed. Its name comes from the fact that it represents the output of some computation. An [Output][output] object thus represents a partially defined computation that will eventually produce a value. Core TensorFlow programs work by first building a graph of [Output][output] objects, detailing how each output is computed based on the other available outputs, and then by running parts of this graph to achieve the desired results.

Similar to a [Tensor][tensor], each element in an [Output][output] has the same data type, and the data type is always known. However, the shape of an [Output][output] might be only partially known. Most operations produce tensors of fully-known shapes if the shapes of their inputs are also fully known, but in some cases it’s only possible to find the shape of a tensor at graph execution time.

It is important to understand the main concepts underlying the core API:

  • Tensor:
  • Output:
    • Sparse Output:
    • Placeholder:
    • Variable:
  • Graph:
  • Session:

With the exception of [Variable][variable]s, the value of outputs is immutable, which means that in the context of a single execution, outputs only have a single value. However, evaluating the same output twice can result in different values. For example, that tensor may be the result of reading data from disk, or generating a random number.


Working with Outputs

Evaluating Outputs

Printing Outputs


Logging in the native TensorFlow library can be controlled by setting the TF_CPP_MIN_LOG_LEVEL environment variable:

  • 0: Debug level (default).
  • 1: Warning level.
  • 2: Error level.
  • 3: Fatal level.