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Returns input function that would feed dict of numpy arrays into the model.

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


age = np.arange(4) * 1.0
height = np.arange(32, 36)
x = {'age': age, 'height': height}
y = np.arange(-32, -28)

with tf.Session() as session:
  input_fn = numpy_io.numpy_input_fn(
      x, y, batch_size=2, shuffle=False, num_epochs=1)

x numpy array object or dict of numpy array objects. If an array, the array will be treated as a single feature.
y numpy array object or dict of numpy array object. None if absent.
batch_size Integer, size of batches to return.
num_epochs Integer, number of epochs to iterate over data. If None 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.

Function, that has signature of ()->(dict of features, targets)

ValueError if the shape of y mismatches the shape of values in x (i.e., values in x have same shape).
ValueError if duplicate keys are in both x and y when y is a dict.
ValueError if x or y is an empty dict.
TypeError x is not a dict or array.
ValueError if 'shuffle' is not provided or a bool.