numpy_input_fn: Construct Input Function Containing Python Dictionaries of...
In rstudio/tflearn: Interface to 'TensorFlow' Estimators

Description Usage Arguments Details Raises See Also

This returns a function outputting features and target based on the dict of numpy arrays. The dict features has the same keys as the x.

numpy_input_fn(
  x,
  y = NULL,
  batch_size = 128,
  num_epochs = 1,
  shuffle = NULL,
  queue_capacity = 1000,
  num_threads = 1
)

`x`	dict of numpy array object.
`y`	numpy array object. `NULL` if absent.
`batch_size`	Integer, size of batches to return.
`num_epochs`	Integer, number of epochs to iterate over data. If `NULL` will run forever.
`shuffle`	Boolean, if `TRUE` shuffles the queue. Avoid shuffle at prediction time.
`queue_capacity`	Integer, size of queue to accumulate.
`num_threads`	Integer, number of threads used for reading and enqueueing. In order to have predicted and repeatable order of reading and enqueueing, such as in prediction and evaluation mode, `num_threads` should be 1. #'

Note that this function is still experimental and should only be used if necessary, e.g. feed in data that's dictionary of numpy arrays.

ValueError: if the shape of y mismatches the shape of values in x (i.e., values in x have same shape). TypeError: x is not a dict or shuffle is not bool.

Other input functions: input_fn()

rstudio/tflearn documentation built on Nov. 25, 2021, 2:45 a.m.