<h3 dir="auto">System information</h3>
<ul dir="auto">
<li><strong>Have I written custom code (as opposed to using a stock example script provided in TensorFlow)</strong>: Yes</li>
<li><strong>OS Platform and Distribution (e.g., Linux Ubuntu 16.04)</strong>: Linux</li>
<li><strong>TensorFlow installed from (source or binary)</strong>: Binary</li>
<li><strong>TensorFlow version (use command below)</strong>: 1.8</li>
<li><strong>Python version</strong>: 2.7</li>
<li><strong>Bazel version (if compiling from source)</strong>: N/A</li>
<li><strong>GCC/Compiler version (if compiling from source)</strong>: N/A</li>
<li><strong>CUDA/cuDNN version</strong>: N/A</li>
<li><strong>GPU model and memory</strong>: N/A</li>
<li><strong>Exact command to reproduce</strong>: N/A</li>
</ul>
<h3 dir="auto">Describe the problem</h3>
<p dir="auto">I can create an estimator either direct or from a keras model using the initialisers:</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="tf.estimator.estimator(
    model_fn,
    model_dir=None,
    config=None,
    params=None,
    warm_start_from=None
)"><pre class="notranslate"><code class="notranslate">tf.estimator.estimator(
    model_fn,
    model_dir=None,
    config=None,
    params=None,
    warm_start_from=None
)
</code></pre></div>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="tf.keras.estimator.model_to_estimator(
    keras_model=None,
    keras_model_path=None,
    custom_objects=None,
    model_dir=None,
    config=None
)"><pre class="notranslate"><code class="notranslate">tf.keras.estimator.model_to_estimator(
    keras_model=None,
    keras_model_path=None,
    custom_objects=None,
    model_dir=None,
    config=None
)
</code></pre></div>
<p dir="auto">However with the keras approach there is no option to warm start the model from a previously saved checkpoint. I am requesting a feature that makes it possible to do this with a call such as:</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="tf.keras.estimator.model_to_estimator(
    keras_model=None,
    keras_model_path=None,
    custom_objects=None,
    model_dir=None,
    config=None,
    warm_start_from=None
)"><pre class="notranslate"><code class="notranslate">tf.keras.estimator.model_to_estimator(
    keras_model=None,
    keras_model_path=None,
    custom_objects=None,
    model_dir=None,
    config=None,
    warm_start_from=None
)
</code></pre></div>
<p dir="auto">Without this I cannot see how to start an estimator in a pre-trained state.</p>
<p dir="auto">I have also tried to do this by just calling an untrained estimator to do .predict which causes it to attempt to load from a checkpoint. When I provide the checkpoint from previous estimator training I get an error as not all data seems to be available. This is described in more detail at stackoverflow here <a href="https://stackoverflow.com/questions/50855256/keras-estimator-model-to-estimator-cannot-warm-start-or-load-previous-checkpoi" rel="nofollow">https://stackoverflow.com/questions/50855256/keras-estimator-model-to-estimator-cannot-warm-start-or-load-previous-checkpoi</a></p>
<h3 dir="auto">System information</h3>
<ul dir="auto">
<li><strong>Have I written custom code (as opposed to using a stock example script provided in TensorFlow)</strong>: N/A</li>
<li><strong>OS Platform and Distribution (e.g., Linux Ubuntu 16.04)</strong>: Docker nightly</li>
<li><strong>TensorFlow installed from (source or binary)</strong>: Docker nightly</li>
<li><strong>TensorFlow version (use command below)</strong>: Docker nightly</li>
<li><strong>Python version</strong>: Docker nightly</li>
<li><strong>Bazel version (if compiling from source)</strong>: Docker nightly</li>
<li><strong>GCC/Compiler version (if compiling from source)</strong>: Docker nightly</li>
<li><strong>CUDA/cuDNN version</strong>: Docker nightly</li>
<li><strong>GPU model and memory</strong>:  N/A</li>
<li><strong>Exact command to reproduce</strong>: N/A</li>
</ul>
<h3 dir="auto">Describe the problem</h3>
<p dir="auto">Please add <code class="notranslate">warm_start_from</code> parameter in <code class="notranslate">tf.model_to_estimator</code></p>
<h3 dir="auto">Source code / logs</h3>
<p dir="auto">N/A</p>