Charles Weill
Charles Weill
@tx2010011751 You should be able to use the [tf.contrib.estimator.RNNEstimator](https://www.tensorflow.org/api_docs/python/tf/contrib/estimator/RNNEstimator) with `adanet.AutoEnsembleEstimator` if you want to try ensembling RNN models. As a first step, try getting `tf.contrib.estimator.RNNEstimator` to train on its...
@jeffltc If you want to do multiple boosting rounds, make sure that `max_steps` > `max_iteration_steps`. For example if `max_steps` == `max_iteration_steps`, you will only do one round (no boosting). if...
@jeffltc I'm glad I could help! @zhiqwang: `AutoEnsembleEstimator` and `adanet.Estimator` are very similar. `AutoEnsembleEstimator` is a thin wrapper around the latter which converts `tf.estimator.Estimator` instances into `adanet.subnetwork.Builders` for `adanet.Estimator` to...
If you want to test predictions on a few batches or a `tf.data.Dataset` you can use [`Estimator.predict`](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator#predict). In order to serve for a live data feed, you first need to...
You need to call these methods on your `estimator = adanet.Estimator()` instance, not on the class. So change `final_prediction = adanet.Estimator.predict(input_fn)` to be `final_prediction = estimator.predict(input_fn)`
The following test goes through the full lifecycle of the `adanet.Estimator`: https://github.com/tensorflow/adanet/blob/v0.3.0/adanet/core/estimator_test.py#L555 You can use it as a reference.
You can also find the architectures in TensorBoard, if you go to the `Text` tab, you will find the architectures in real-time. However, this is less than ideal. If you...
@cicciobyte: FYI `tf.trainable_variables` may not return the full picture, because internally AdaNet modifies that collection.
The Source of Truth for the model architecture is the TensorFlow Graph in your exported `SavedModel`. You can visualize something similar in TensorBoard's "Graph" tab. I'm actively thinking about an...
Keras's `plot_model` function does not work with the current implementation of AdaNet, since we use the `tf.estimator.Estimator` API which does not support it.