Using tf.data.Dataset API to build a data pipeline, I encountered an error in training:
pure virtual method called terminate
called without an active exception
Aborted (core dumped)
What's worth noting is that the error did not occur at the beginning but actually the middle of the training process(after 4800 epochs). Here is how I build the pipeline:
import tensorflow as tf
epochs = 30000
eval_step = 400
sess = tf.InteractiveSession()
# code to read and process data
features_placeholder = tf.placeholder(train_features.dtype, train_features.shape)
labels_placeholder = tf.placeholder(train_labels.dtype, train_labels.shape)
train_dataset = tf.data.Dataset.from_tensor_slices((features_placeholder, labels_placeholder, file_list_input))
train_dataset = train_dataset.repeat()
train_dataset = train_dataset.batch(batch_size_placeholder)
train_dataset = train_dataset.prefetch(buffer_size=1)
val_dataset = tf.data.Dataset.from_tensor_slices((features_placeholder, labels_placeholder, file_list_input))
val_dataset = val_dataset.repeat()
val_dataset = val_dataset.batch(batch_size_placeholder)
iterator = tf.data.Iterator.from_structure(train_dataset.output_types, train_dataset.output_shapes)
features, labels = iterator.get_next()
train_iterator = iterator.make_initializer(train_dataset)
val_iterator = iterator.make_initializer(val_dataset)
# code to build model
for i in range(epochs):
sess.run([train_iterator, ... # other ops],
feed_dict = {[features_placeholder: train_features,
labels_placeholder: train_labels]})
if (i%eval_step)==0:
sess.run([val_iterator, ... # other ops],
feed_dict = {[features_placeholder: val_features,
labels_placeholder: val_labels]})
Has anyone had the same issue? How do I solve it?
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