Tutorial by Examples

import tensorflow as tf filename_queue = tf.train.string_input_producer(["file.csv"], num_epochs=1) reader = tf.TextLineReader() key, value = reader.read(filename_queue) col1, col2 = tf.decode_csv(value, record_defaults=[[0], [0]]) with tf.Session() as sess: sess.run(tf.initializ...

TFRecord files is the native tensorflow binary format for storing data (tensors). To read the file you can use a code similar to the CSV example: import tensorflow as tf filename_queue = tf.train.string_input_producer(["file.tfrecord"], num_epochs=1) reader = tf.TFRecordReader() key, s...

To randomly shuffle the examples, you can use tf.train.shuffle_batch function instead of tf.train.batch, as follows: parsed_batch = tf.train.shuffle_batch([serialized_example], batch_size=100, capacity=1000, min_after_dequeue=200) tf.train.shuffle_batch (as well as tf.train.batch) crea...

Assume your data examples are already read to a python's variable and you would like to read it n times, in batches of given size: import numpy as np import tensorflow as tf data = np.array([1, 2, 3, 4, 5]) n = 4 To merge data in batches, possibly with random shuffling, you can use tf.train.b...

It has not been explained in the Tensorflow documentation how to load images and labels directly from a TXT file. The code below illustrates how I achieved it. However, it does not mean that is the best way to do it and that this way will help in further steps. For instance, I'm loading the labels ...