I use a small dataset(train:1000,test:1000) to test the code, and I meet a error when train the mode at the last batch.the batchsize of train and evaluation is 128
W tensorflow/core/framework/op_kernel.cc:1192] Out of range: RandomShuffleQueue '_3_prepared_batch_queue/random_shuffle_queue' is closed and has insufficient elements (requested 128, current size 8)
[[Node: prepared_batch_queue = QueueDequeueManyV2[component_types=[DT_STRING, DT_FLOAT, DT_INT32, DT_STRING], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](prepared_batch_queue/random_shuffle_queue, prepared_batch_queue/n)]]
and there is no any accuracy information output
if mode == tf.estimator.ModeKeys.EVAL:
print('start to evaluation')
with tf.name_scope('evaluation'):
CER = tf.metrics.mean(tf.edit_distance(sparse_code_pred, tf.cast(sparse_code_target, dtype=tf.int64)), name='CER')
# Convert label codes to decoding alphabet to compare predicted and groundtrouth words
target_chars = table_int2str.lookup(tf.cast(sparse_code_target, tf.int64))
target_words = get_words_from_chars(target_chars.values, seq_lengths_labels)
accuracy = tf.metrics.accuracy(target_words, predictions_dict['words'], name='accuracy')
eval_metric_ops = {
'eval/accuracy': accuracy,
'eval/CER': CER,
}
CER = tf.Print(CER, [CER], message='-- CER : ')
accuracy = tf.Print(accuracy, [accuracy], message='-- Accuracy : ')
tf.summary.scalar('accuracy/val_accuracy', accuracy)
print('end to evaluation')
I use a small dataset(train:1000,test:1000) to test the code, and I meet a error when train the mode at the last batch.the batchsize of train and evaluation is 128
and there is no any accuracy information output