My question is about how to get batch inputs from multiple (or sharded) tfrecords. I've read the example https://github.com/tensorflow/models/blob/master/inception/inception/image_processing.py#L410. The basic pipeline is, take the training set as as example, (1) first generate a series of tfrecords (e.g., train-000-of-005
, train-001-of-005
, ...), (2) from these filenames, generate a list and fed them into the tf.train.string_input_producer
to get a queue, (3) simultaneously generate a tf.RandomShuffleQueue
to do other stuff, (4) using tf.train.batch_join
to generate batch inputs.
I think this is complex, and I'm not sure the logic of this procedure. In my case, I have a list of .npy
files, and I want to generate sharded tfrecords(multiple seperated tfrecords, not just one single large file). Each of these .npy
files contains different number of positive and negative samples (2 classes). A basic method is to generate one single large tfrecord file. But the file is too large (~20Gb
). So I resort to sharded tfrecords. Are there any simpler way to do this? Thanks.
The whole process is simplied using the Dataset API
. Here are both the parts: (1): Convert numpy array to tfrecords
and (2,3,4): read the tfrecords to generate batches
.
def npy_to_tfrecords(...):
# write records to a tfrecords file
writer = tf.python_io.TFRecordWriter(output_file)
# Loop through all the features you want to write
for ... :
let say X is of np.array([[...][...]])
let say y is of np.array[[0/1]]
# Feature contains a map of string to feature proto objects
feature = {}
feature['X'] = tf.train.Feature(float_list=tf.train.FloatList(value=X.flatten()))
feature['y'] = tf.train.Feature(int64_list=tf.train.Int64List(value=y))
# Construct the Example proto object
example = tf.train.Example(features=tf.train.Features(feature=feature))
# Serialize the example to a string
serialized = example.SerializeToString()
# write the serialized objec to the disk
writer.write(serialized)
writer.close()
# Creates a dataset that reads all of the examples from filenames.
filenames = ["file1.tfrecord", "file2.tfrecord", ..."fileN.tfrecord"]
dataset = tf.contrib.data.TFRecordDataset(filenames)
# for version 1.5 and above use tf.data.TFRecordDataset
# example proto decode
def _parse_function(example_proto):
keys_to_features = {'X':tf.FixedLenFeature((shape_of_npy_array), tf.float32),
'y': tf.FixedLenFeature((), tf.int64, default_value=0)}
parsed_features = tf.parse_single_example(example_proto, keys_to_features)
return parsed_features['X'], parsed_features['y']
# Parse the record into tensors.
dataset = dataset.map(_parse_function)
# Shuffle the dataset
dataset = dataset.shuffle(buffer_size=10000)
# Repeat the input indefinitly
dataset = dataset.repeat()
# Generate batches
dataset = dataset.batch(batch_size)
# Create a one-shot iterator
iterator = dataset.make_one_shot_iterator()
# Get batch X and y
X, y = iterator.get_next()