This method will by called the Controller to perform an evaluation. The
num_steps parameter specifies the number of steps of evaluation to run,
which is specified by the user when calling one of the Controller's
evaluation methods. A special sentinel value of -1 is reserved to indicate
evaluation should run until the underlying data source is exhausted.
The number of evaluation steps to run. Note that it is up to
the model what constitutes a "step". Evaluations may also want to
support "complete" evaluations when num_steps == -1, running until a
given data source is exhausted.
Either None, or a dictionary mapping names to Tensors or NumPy values.
If a dictionary is returned, it will be written to logs and as TensorBoard
summaries. The dictionary may also be nested, which will generate a
hierarchy of summary directories.
Decorator to automatically enter the module name scope.
class MyModule(tf.Module): @tf.Module.with_name_scope def __call__(self, x): if not hasattr(self, 'w'): self.w = tf.Variable(tf.random.normal([x.shape, 3])) return tf.matmul(x, self.w)
Using the above module would produce tf.Variables and tf.Tensors whose
names included the module name: