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Tensorflow Estimator API save image summary in eval mode


Tensorflow Estimator API save image summary in eval mode

By : Tailians WWIPL
Date : January 12 2021, 08:33 AM
hope this fix your issue With TF1.4, you can pass tf.estimator.EstimatorSpec evaluation_hooks. The evaluation_hooks is a list of hooks, and you must add to it the following hook:
code :
# Create a SummarySaverHook
eval_summary_hook = tf.train.SummarySaverHook(
                                save_steps=1,
                                output_dir= self.job_dir + "/eval_core",
                                summary_op=tf.summary.merge_all())
# Add it to the evaluation_hook list
evaluation_hooks.append(eval_summary_hook)

#Now, return the estimator:
return tf.estimator.EstimatorSpec(
                mode=mode,
                predictions=predictions,
                loss=loss,
                train_op=train_op,
                training_hooks=training_hooks,
                eval_metric_ops=eval_metric_ops,
                evaluation_hooks=evaluation_hooks)


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Tensorflow - Using tf.summary with 1.2 Estimator API

Tensorflow - Using tf.summary with 1.2 Estimator API


By : Junu Gurung
Date : March 29 2020, 07:55 AM
it helps some times Just for whoever have this question in the future, the selected solution doesn't work for me (see my comments in the selected solution).
Actually, with TF 1.2 Estimator API, one doesn't need to have summary_hook. I just have tf.summary.scalar("loss", loss) in the model_fn, and run the code without summary_hook. The loss is recorded and shown in the tensorboard. I'm not sure if TF API was changed after this and similar questions.
Tensorflow Estimator - Periodic Evaluation on Eval Dataset

Tensorflow Estimator - Periodic Evaluation on Eval Dataset


By : Sneha Saxena
Date : March 29 2020, 07:55 AM
will help you Only a few parameters/options of Experiment are deprecated (what specific errors are you seeing?). If you create an Estimator that will do periodic checkpoints (using options in RunConfig) and an Experiment using it, you will get evaluation for each checkpoint by default when using train_and_evaluate method.
EDIT: As Maxime pointed out in the comments. He needed to add the following lines to get rid of his error:
code :
os.environ['TF_CONFIG'] = json.dumps({'environment': 'local'})
config = tf.contrib.learn.RunConfig()
Save tf.summary.image with Estimator API

Save tf.summary.image with Estimator API


By : juhjan
Date : October 17 2020, 03:08 PM
it should still fix some issue You shouldn't have to add a hook. Just add the tf.summary.image call anywhere in your model_fn/input_fn and the estimator should automatically add a summary hook for all summaries created.
Creating an image summary only for a subset of validation set images using Tensorflow Estimator API

Creating an image summary only for a subset of validation set images using Tensorflow Estimator API


By : lockheart
Date : March 29 2020, 07:55 AM
help you fix your problem I figured out a solution that uses Estimator/Experiment API.
First you need to modify your Dataset input to not only provide labels and features, but also some form of an identifier for each sample (in my case it was a filename). Then in the hyperparameters dictionary (params argument) you need to specify which of the validation samples you want to plot. You also will have to pass the model_dir in those parameters. For example:
code :
params = tf.contrib.training.HParams(
        model_dir=model_dir,
        images_to_plot=["100307_EMOTION.nii.gz", "100307_FACE-SHAPE.nii.gz",
                        "100307_GAMBLING.nii.gz", "100307_RELATIONAL.nii.gz",
                        "100307_SOCIAL.nii.gz"]
    )

learn_runner.run(
        experiment_fn=experiment_fn,
        run_config=run_config,
        schedule="train_and_evaluate",
        hparams=params
    )
if mode == tf.contrib.learn.ModeKeys.EVAL:
    summaries = []
    for image_to_plot in params.images_to_plot:
        is_to_plot = tf.equal(tf.squeeze(filenames), image_to_plot)

        summary = tf.cond(is_to_plot,
                          lambda: tf.summary.image('predicted', predictions),
                          lambda: tf.summary.histogram("ignore_me", [0]),
                          name="%s_predicted" % image_to_plot)
        summaries.append(summary)

    evaluation_hooks = [tf.train.SummarySaverHook(
        save_steps=1,
        output_dir=os.path.join(params.model_dir, "eval"),
        summary_op=tf.summary.merge(summaries))]
else:
    evaluation_hooks = None
return tf.estimator.EstimatorSpec(
    mode=mode,
    predictions=predictions,
    loss=loss,
    train_op=train_op,
    evaluation_hooks=evaluation_hooks
)
Tensorflow Estimator API :How to get weights of each node of a trained model using estimator api of tensorflow

Tensorflow Estimator API :How to get weights of each node of a trained model using estimator api of tensorflow


By : Jason w
Date : March 29 2020, 07:55 AM
Hope this helps Yes, it should be possible to do so. You can extract the trainable variables names with:
code :
train_var_names = [var.name for var in tf.trainable_variables()]
weights_0 = dnn_model.get_variable_value(train_var_names[0])
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