How can I use tensorboard with tf.estimator.Estimator

python-3.x, tensorboard, tensorflow

Solution

EDIT: Upon testing (in v1.1.0, and probably in later versions as well), it is apparent that `tf.estimator.Estimator` will automatically write summaries for you. I confirmed this using OP's code and tensorboard.

(Some poking around r1.4 leads me to conclude that this automatic summary writing occurs due to `tf.train.MonitoredTrainingSession`.)

Ultimately, the automatic summarizing is accomplished with the use of hooks, so if you wanted to customize the Estimator's default summarizing, you could do so using hooks. Below are the (edited) details from the original answer.

You'll want to use hooks, formerly known as monitors. (Linked is a conceptual/quickstart guide; the short of it is that the notion of hooking into / monitoring training is built into the Estimator API. A bit confusingly, though, it doesn't seem like the deprecation of monitors for hooks is really documented except in a deprecation annotation in the actual source code...)

Based on your usage, it looks like r1.2's `SummarySaverHook` fits your bill.

summary_hook = tf.train.SummarySaverHook(
    SAVE_EVERY_N_STEPS,
    output_dir='/tmp/tf',
    summary_op=tf.summary.merge_all())

You may want to customize the hook's initialization parameters, as by providing an explicity SummaryWriter or writing every N seconds instead of N steps.

If you pass this into the `EstimatorSpec`, you'll get your customized Summary behavior:

return tf.estimator.EstimatorSpec(mode=mode, predictions=y,loss=loss,
                                  train_op=train,
                                  training_hooks=[summary_hook])

EDIT NOTE: A previous version of this answer suggested passing the `summary_hook` into `estimator.train(input_fn=input_fn, steps=5, hooks=[summary_hook])`. This does not work because `tf.summary.merge_all()` has to be called in the same context as your model graph.

Problem

I am considering to move my code base to tf.estimator.Estimator, but I cannot find an example on how to use it in combination with tensorboard summaries. MWE: ``` import numpy as np import tensorflow as tf tf.logging.set_verbosity(tf.logging.INFO) # Declare list of features, we only have one real-valued feature def model(features, labels, mode): # Build a linear model and predict values W = tf.get_variable("W", [1], dtype=tf.float64) b = tf.get_variable("b", [1], dtype=tf.float64) y = W*features['x'] + b loss = tf.reduce_sum(tf.square(y - labels)) # Summaries to display for TRAINING and TESTING tf.summary.scalar("loss", loss) tf.summary.image("X", tf.reshape(tf.random_normal([10, 10]), [-1, 10, 10, 1])) # dummy, my inputs are images # Training sub-graph global_step = tf.train.get_global_step() optimizer = tf.train.GradientDescentOptimizer(0.01) train = tf.group(optimizer.minimize(loss), tf.assign_add(global_step, 1)) return tf.estimator.EstimatorSpec(mode=mode, predictions=y,loss= loss,train_op=train) estimator = tf.estimator.Estimator(model_fn=model, model_dir='/tmp/tf') # define our data set x=np.array([1., 2., 3., 4.]) y=np.array([0., -1., -2., -3.]) input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x}, y, 4, num_epochs=1000) for epoch in range(10): # train estimator.train(input_fn=input_fn, steps=100) # evaluate our model estimator.evaluate(input_fn=input_fn, steps=10) ``` How can I display my two summaries in tensorboard? Do I have to register a hook in which I use a `tf.summary.FileWriter` or something else?

Original source