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OOM on GPU with Tensorboard enabled #4797 (problem2) #4834

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OOM on GPU with Tensorboard enabled #4797 (problem2) #4834

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16 changes: 13 additions & 3 deletions keras/callbacks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -545,19 +545,29 @@ def _set_model(self, model):

def on_epoch_end(self, epoch, logs={}):
import tensorflow as tf
from keras.engine.training import slice_X

if self.model.validation_data and self.histogram_freq:
if epoch % self.histogram_freq == 0:
# TODO: implement batched calls to sess.run
# (current call will likely go OOM on GPU)
if self.model.uses_learning_phase:
cut_v_data = len(self.model.inputs)
val_data = self.model.validation_data[:cut_v_data] + [0]
tensors = self.model.inputs + [K.learning_phase()]
else:
val_data = self.model.validation_data
tensors = self.model.inputs
feed_dict = dict(zip(tensors, val_data))
# Sample one batch of validation data to avoid OOM on GPU
if 'batch_size' in self.params:
index_array = np.arange(len(val_data[0]))
batch_ids = np.random.choice(index_array, self.params['batch_size'])
if self.model.uses_learning_phase:
ins_batch = slice_X(val_data[:-1], batch_ids) + [val_data[-1]]
else:
ins_batch = slice_X(val_data, batch_ids)
else:
# Generators yield one batch at a time and don't provide batch_size
ins_batch = val_data
feed_dict = dict(zip(tensors, ins_batch))
result = self.sess.run([self.merged], feed_dict=feed_dict)
summary_str = result[0]
self.writer.add_summary(summary_str, epoch)
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