Returns input function that would feed dict of numpy arrays into the model.
tf.estimator.inputs.numpy_input_fn(
x, y=None, batch_size=128, num_epochs=1, shuffle=None, queue_capacity=1000,
num_threads=1
)
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.
Example:
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)
Args |
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.
|
Returns |
Function, that has signature of ()->(dict of features , targets )
|
Raises |
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.
|