# tf.compat.v1.estimator.inputs.numpy_input_fn

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/python/estimator/inputs/numpy_io.py#L89-L224) |

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

    tf.compat.v1.estimator.inputs.numpy_input_fn(
        x,
        y=None,
        batch_size=128,
        num_epochs=1,
        shuffle=None,
        queue_capacity=1000,
        num_threads=1
    )

> [!WARNING]
> **Deprecated:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use tf.keras instead.

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)

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| ## 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. |

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| ## Returns ||
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| Function, that has signature of ()-\>(dict of `features`, `targets`) ||

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| ## 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. |

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