How to average summaries over multiple batches? - tensorflow

Question

Assuming I have a bunch of summaries defined like:
loss = ...
tf.scalar_summary("loss", loss)
# ...
summaries = tf.merge_all_summaries()
I can evaluate the summaries tensor every few steps on the training data and pass the result to a SummaryWriter.
The result will be noisy summaries, because they're only computed on one batch.
However, I would like to compute the summaries on the entire validation dataset.
Of course, I can't pass the validation dataset as a single batch, because it would be too big.
So, I'll get summary outputs for each validation batch.
Is there a way to average those summaries so that it appears as if the summaries have been computed on the entire validation set?

Answer 1

Do the averaging of your measure in Python and create a new Summary object for each mean. Here is what I do:
accuracies = []
# Calculate your measure over as many batches as you need
for batch in validation_set:
accuracies.append(sess.run([training_op]))
# Take the mean of you measure
accuracy = np.mean(accuracies)
# Create a new Summary object with your measure
summary = tf.Summary()
summary.value.add(tag="%sAccuracy" % prefix, simple_value=accuracy)
# Add it to the Tensorboard summary writer
# Make sure to specify a step parameter to get nice graphs over time
summary_writer.add_summary(summary, global_step)

Answer 2

I would avoid calculating the average outside the graph.
You can use tf.train.ExponentialMovingAverage:
ema = tf.train.ExponentialMovingAverage(decay=my_decay_value, zero_debias=True)
maintain_ema_op = ema.apply(your_losses_list)
# Create an op that will update the moving averages after each training step.
with tf.control_dependencies([your_original_train_op]):
train_op = tf.group(maintain_ema_op)
Then, use:
sess.run(train_op)
That will call maintain_ema_op because it is defined as a control dependency.
In order to get your exponential moving averages, use:
moving_average = ema.average(an_item_from_your_losses_list_above)
And retrieve its value using:
value = sess.run(moving_average)
This calculates the moving average within your calculation graph.

Answer 3

I think it's always better to let tensorflow do the calculations.
Have a look at the streaming metrics. They have an update function to feed the information of your current batch and a function to get the averaged summary.
It's going to look somewhat like this:
accuracy = ...
streaming_accuracy, streaming_accuracy_update = tf.contrib.metrics.streaming_mean(accuracy)
streaming_accuracy_scalar = tf.summary.scalar('streaming_accuracy', streaming_accuracy)
# set up your session etc.
for i in iterations:
for b in batches:
sess.run([streaming_accuracy_update], feed_dict={...})
streaming_summ = sess.run(streaming_accuracy_scalar)
writer.add_summary(streaming_summary, i)
Also see the tensorflow documentation: https://www.tensorflow.org/versions/master/api_guides/python/contrib.metrics
and this question:
How to accumulate summary statistics in tensorflow

Answer 4

You can average store the current sum and recalculate the average after each batch, like:
loss_sum = tf.Variable(0.)
inc_op = tf.assign_add(loss_sum, loss)
clear_op = tf.assign(loss_sum, 0.)
average = loss_sum / batches
tf.scalar_summary("average_loss", average)
sess.run(clear_op)
for i in range(batches):
sess.run([loss, inc_op])
sess.run(average)

Answer 5

For future reference, the TensorFlow metrics API now supports this by default. For example, take a look at tf.mean_squared_error:
For estimation of the metric over a stream of data, the function creates an update_op operation that updates these variables and returns the mean_squared_error. Internally, a squared_error operation computes the element-wise square of the difference between predictions and labels. Then update_op increments total with the reduced sum of the product of weights and squared_error, and it increments count with the reduced sum of weights.
These total and count variables are added to the set of metric variables, so in practice what you would do is something like:
x_batch = tf.placeholder(...)
y_batch = tf.placeholder(...)
model_output = ...
mse, mse_update = tf.metrics.mean_squared_error(y_batch, model_output)
# This operation resets the metric internal variables to zero
metrics_init = tf.variables_initializer(
tf.get_default_graph().get_collection(tf.GraphKeys.METRIC_VARIABLES))
with tf.Session() as sess:
# Train...
# On evaluation step
sess.run(metrics_init)
for x_eval_batch, y_eval_batch in ...:
mse = sess.run(mse_update, feed_dict={x_batch: x_eval_batch, y_batch: y_eval_batch})
print('Evaluation MSE:', mse)

Answer 6

I found one solution myself. I think it's kind of hacky and I hope there is a more elegant solution.
During setup:
valid_loss_placeholder = tf.placeholder(dtype=tf.float32, shape=[])
valid_loss_summary = tf.scalar_summary("valid loss", valid_loss_placeholder)
Or for tensorflow versions after 0.12 (change in name for tf.scalar_summary):
valid_loss_placeholder = tf.placeholder(dtype=tf.float32, shape=[])
valid_loss_summary = tf.summary.scalar("valid loss", valid_loss_placeholder)
Within training loop:
# Compute valid loss in python by doing sess.run() for each batch
# and averaging
valid_loss = ...
summary = sess.run(valid_loss_summary, {valid_loss_placeholder: valid_loss})
summary_writer.add_summary(summary, step)

Answer 7

As of August 2018, streaming metrics have been depreciated. However, unintuitively, all metrics are streaming. So, use tf.metrics.accuracy.
However, if you want accuracy (or another metric) over only a subset of batches, then you can use Exponential Moving Average, as in the answer by #MZHm or reset any of the the tf.metric's by following this very informative blog post

Answer 8

For quite some time I'm only saving the summary once per epoch. I never knew that TensorFlows summary would then only save the summary for the last run batch.
Shocked I looked into this problem. This is the solution I came up with (using the dataset API):
loss = ...
train_op = ...
loss_metric, loss_metric_update = tf.metrics.mean(ae_loss)
tf.summary.scalar('loss', loss_metric)
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(os.path.join(res_dir, 'train'))
test_writer = tf.summary.FileWriter(os.path.join(res_dir, 'test'))
init_local = tf.initializers.local_variables()
init_global = tf.initializers.global_variables()
sess.run(init_global)
def train_run(epoch):
sess.run([dataset.train_init_op, init_local]) # test_init_op is the operation that switches to test data
for i in range(dataset.num_train_batches): # num_test_batches is the number of batches that should be run for the test set
sess.run([train_op, loss_metric_update])
summary, cur_loss = sess.run([merged, loss_metric])
train_writer.add_summary(summary, epoch)
return cur_loss
def test_run(epoch):
sess.run([dataset.test_init_op, init_local]) # test_init_op is the operation that switches to test data
for i in range(dataset.num_test_batches): # num_test_batches is the number of batches that should be run for the test set
sess.run(loss_metric_update)
summary, cur_loss = sess.run([merged, loss_metric])
test_writer.add_summary(summary, epoch)
return cur_loss
for epoch in range(epochs):
train_loss = train_run(epoch+1)
test_loss = test_run(epoch+1)
print("Epoch: {0:3}, loss: (train: {1:10.10f}, test: {2:10.10f})".format(epoch+1, train_loss, test_loss))
For the summary I'm just wrapping the tensor I'm interested in into tf.metrics.mean(). For each batch run I call the metrics update operation. At the end of every epoch the metrics tensor will return the correct mean of all batch results.
Don't forget to initialize local variables every time you switch between training and test data. Otherwise your train and test metrics will be near identical.

Answer 9

I had the same problem when I realized I had to iterate over my validation data when the memory space cramped up and the OOM errors flooding.
As multiple of these answers say, the tf.metrics have this built in, but I'm not using tf.metrics in my project. So inspired by that, I made this:
import tensorflow as tf
import numpy as np
def batch_persistent_mean(tensor):
# Make a variable that keeps track of the sum
accumulator = tf.Variable(initial_value=tf.zeros_like(tensor), dtype=tf.float32)
# Keep count of batches in accumulator (needed to estimate mean)
batch_nums = tf.Variable(initial_value=tf.zeros_like(tensor), dtype=tf.float32)
# Make an operation for accumulating, increasing batch count
accumulate_op = tf.assign_add(accumulator, tensor)
step_batch = tf.assign_add(batch_nums, 1)
update_op = tf.group([step_batch, accumulate_op])
eps = 1e-5
output_tensor = accumulator / (tf.nn.relu(batch_nums - eps) + eps)
# In regards to the tf.nn.relu, it's a hacky zero_guard:
# if batch_nums are zero then return eps, else it'll be batch_nums
# Make an operation to reset
flush_op = tf.group([tf.assign(accumulator, 0), tf.assign(batch_nums, 0)])
return output_tensor, update_op, flush_op
# Make a variable that we want to accumulate
X = tf.Variable(0., dtype=tf.float32)
# Make our persistant mean operations
Xbar, upd, flush = batch_persistent_mean(X)
Now you send Xbar to your summary e.g. tf.scalar_summary("mean_of_x", Xbar), and where you'd do sess.run(X) before, you'll do sess.run(upd). And between epochs you'd do sess.run(flush).
Testing behaviour:
### INSERT ABOVE CODE CHUNK IN S.O. ANSWER HERE ###
sess = tf.InteractiveSession()
with tf.Session() as sess:
sess.run([tf.global_variables_initializer(), tf.local_variables_initializer()])
# Calculate the mean of 1+2+...+20
for i in range(20):
sess.run(upd, {X: i})
print(sess.run(Xbar), "=", np.mean(np.arange(20)))
for i in range(40):
sess.run(upd, {X: i})
# Now Xbar is the mean of (1+2+...+20+1+2+...+40):
print(sess.run(Xbar), "=", np.mean(np.concatenate([np.arange(20), np.arange(40)])))
# Now flush it
sess.run(flush)
print("flushed. Xbar=", sess.run(Xbar))
for i in range(40):
sess.run(upd, {X: i})
print(sess.run(Xbar), "=", np.mean(np.arange(40)))