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# Define the training inputs
def get_train_inputs(batch_size, mnist_data):
    """Return the input function to get the training data.

    Args:
        batch_size (int): Batch size of training iterator that is returned
                          by the input function.
        mnist_data (Object): Object holding the loaded mnist data.

    Returns:

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class IteratorInitializerHook(tf.train.SessionRunHook):
    """Hook to initialise data iterator after Session is created."""

    def __init__(self):
        super(IteratorInitializerHook, self).__init__()
        self.iterator_initializer_func = None

    def after_create_session(self, session, coord):
        """Initialise the iterator after the session has been created."""
        self.iterator_initializer_func(session)

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"""Script to illustrate usage of tf.estimator.Estimator in TF v1.3"""
import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data as mnist_data
from tensorflow.contrib import slim
from tensorflow.contrib.learn import ModeKeys
from tensorflow.contrib.learn import learn_runner


# Show debugging output

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"""Script to illustrate inference of a trained tf.estimator.Estimator.

NOTE: This is dependent on mnist_estimator.py which defines the model.
mnist_estimator.py can be found at:
https://gist.github.com/peterroelants/9956ec93a07ca4e9ba5bc415b014bcca
"""
import numpy as np
import skimage.io
import tensorflow as tf

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return tf.estimator.Estimator(
    model_fn=model_fn,
    config=config,
    params=params,
)

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tf.estimator.train_and_evaluate(model_estimator, train_spec, eval_spec)

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dataset = tf.data.Dataset.from_tensor_slices(mnist_data)
dataset = dataset.shuffle(
    buffer_size=1000, reshuffle_each_iteration=True
).repeat(count=None).batch(batch_size)

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2.0.0-dev20190205
2019-02-05 18:09:28.956373: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-02-05 18:09:28.959894: I tensorflow/stream_executor/platform/default/dso_loader.cc:161] successfully opened CUDA library libcuda.so.1 locally
2019-02-05 18:09:29.161041: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1010] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-02-05 18:09:29.168088: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1010] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-02-05 18:09:29.168686: I tensorflow/compiler/xla/service/service.cc:162] XLA service 0x1a03ef0 executing computations on platform CUDA. Devices:
2019-02-05 18:09:29.168697: I tensorflow/compiler/xla/service/service.cc:169]   StreamExecuto

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