-
Notifications
You must be signed in to change notification settings - Fork 4
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Transition model creation to tf.estimator.Estimator #3
Labels
enhancement
New feature or request
Milestone
Comments
yweweler
changed the title
Transition model creating to tf.estimator.Estimator
Transition model creation to tf.estimator.Estimator
Nov 16, 2018
yweweler
added a commit
that referenced
this issue
Nov 17, 2018
WIP (#3) * The model_fn is not finished yet. * The model_fn is still stateful, as it saves the input placeholders and all intermediate tensors and summaries in the Model object. This state should be removed to enable reuse of a created model. Otherwise multiple calls to the model_fn will corrupt the internal state. * Both the inference and the evaluate code are broken in the current state. * The model_fn does not return EstimatorSpecs right now. Before this is possible, I have to move the gradient clipping and the batch normalization code from `train.py` into the model_fn.
yweweler
added a commit
that referenced
this issue
Nov 17, 2018
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
WIP (#3) * The NanTensorHook is still missing.
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
WIP (#3) * Add todos. * Remove unused code. * Writing of the summaries does not work currently. * Evaluation is extremely slow for some reason.
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
WIP (#3) * Training summaries are now written during training. * Removed training_summary from the Model constructor.
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
WIP (#3) * Remove the unnecessary write summary flag from the model constructor. * NanTensorHook is now attached in the model fn via the EstimatorSpec object (only for training)
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
WIP (#3) * The correct checkpoints are now loaded for evaluation. * Evaluation summaries are now written. * For some reason the mean loss is wrong however. * The evaluation metrics still pollute tensorboard.
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
WIP (#3) * The wrong summary was used in the eval metrics.
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
WIP (#3) * The estimator adds a NanTensorHook itself.
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
WIP (#3) * The estimator has an hardcoded 'loss' summary. In case the is no loss summary with exactly this name, or there is a scope with this name, the estimator will create a new duplicate summary or will alter the scope name.
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
WIP (#3) * Implement dataset loading for the inference/predict code. * Rewrite the model for prediction using the estimator. * Rewrite the inference code to use the estimator.
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
WIP (#3) * Inference does now wotk with the estimator API. * Pre-processing and multi-threading are currently a bit hacky.
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
yweweler
added a commit
that referenced
this issue
Nov 30, 2018
Fixed by #6 |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Instead of relying on the manual creation and implementation of training, evaluation and prediction we should rewrite the code to build the model using an
Estimator
.This should substantially reduce the effort necessary to export, serve, optimize, train, evaluate and predict using the architecture. As a side effect the codebase required can also be minimized.
The text was updated successfully, but these errors were encountered: