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.
code :

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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 API save image summary in eval mode

Tensorflow Estimator API save image summary in eval mode

By : Tailians WWIPL
Date : March 29 2020, 07:55 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(
                                output_dir= self.job_dir + "/eval_core",
# Add it to the evaluation_hook list

#Now, return the estimator:
return tf.estimator.EstimatorSpec(
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(
        images_to_plot=["100307_EMOTION.nii.gz", "100307_FACE-SHAPE.nii.gz",
                        "100307_GAMBLING.nii.gz", "100307_RELATIONAL.nii.gz",

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)

    evaluation_hooks = [tf.train.SummarySaverHook(
        output_dir=os.path.join(params.model_dir, "eval"),
    evaluation_hooks = None
return tf.estimator.EstimatorSpec(
tf.summary.image seems not work for estimator prediction

tf.summary.image seems not work for estimator prediction

By : Ivo Chaves Ríos
Date : March 29 2020, 07:55 AM
wish helps you I would assume that you need to put the summary ops (histogram/image) before calling merge_all so that merge_all actually has something to merge.
code :
tf.summary.image('feat', feat)
summary_hook = tf.train.SummarySaverHook(
if mode == tf.estimator.ModeKeys.PREDICT:
    return tf.estimator.EstimatorSpec(mode, predictions=preds, prediction_hooks=[summary_hook])
How to add an image to summary during evaluation when using Estimator?

How to add an image to summary during evaluation when using Estimator?

By : bram_de_jong
Date : March 29 2020, 07:55 AM
With these it helps The way I make it work is by passing a tf.train.SummarySaverHook under the evaluation mode and then declaring it to the tf.estimator.EstimatorSpec at evaluation_hooks=. images is a list of the desired tf.summary.image you want to print during evaluation. example:
code :
eval_summary_hook = tf.train.SummarySaverHook(output_dir=params['eval_save_path'], summary_op=images, save_secs=120)

spec = tf.estimator.EstimatorSpec(mode=mode, predictions=y_pred, loss=loss, eval_metric_ops=eval_metric_ops,
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