Most of the magic happens in the following code of # Read the filename: image_data = tf.gfile.
Fast GFile(filenames[i], 'r').read() height, width = image_reader.read_image_dims(sess, image_data) class_name = basename(dirname(filenames[i])) class_id = class_names_to_ids[class_name] example = image_to_tfexample( image_data, 'jpg', height, width, class_id) tfrecord_writer.write(example.
_convert_dataset('train', training_filenames, class_names_to_ids, dataset_dir = FLAGS.dataset_dir, tfrecord_filename = FLAGS.tfrecord_filename, _NUM_SHARDS = FLAGS.num_shards) _convert_dataset('validation', validation_filenames, class_names_to_ids, dataset_dir = FLAGS.dataset_dir, tfrecord_filename = FLAGS.tfrecord_filename, _NUM_SHARDS = FLAGS.num_shards) # Finally, write the labels file: labels_to_class_names = dict(zip(range(len(class_names)), class_names)) write_label_file(labels_to_class_names, FLAGS.dataset_dir) print '\n Finished converting the %s dataset!
However, this is not always possible if your dataset is too large to be held in your GPU memory for it to be trained.From CSV Files: Not as relevant for dealing with images.Yes, that annoying Validating Steam cache files message pop-ups up each time you launch Team Fortress 2 or Counter Strike Source from a cold boot (meaning you haven’t launched the game since your PC started up).Here’s a quick rundown of purported fixes for this recurring TF2 cache problem that MAY work for you.A large part of this post was inspired by their source code, and I highly recommend you to carefully study related material offered by them - after all, they are few of the best engineers in the world!
This plugin adds support for Hashi Corp Configuration Language (HCL) and Hashi Corp Interpolation Language (HIL), as well as their combination used in Terraform configuration files (.tf).
DEFINE_string('tfrecord_filename', None, 'String: The output filename to name your TFRecord file') FLAGS = flags.
FLAGS #Find the number of validation examples we need num_validation = int(FLAGS.validation_size * len(photo_filenames)) # Divide the training datasets into train and test: random.seed(FLAGS.random_seed) random.shuffle(photo_filenames) training_filenames = photo_filenames[num_validation:] validation_filenames = photo_filenames[:num_validation] # First, convert the training and validation sets.
To put the guide into concrete practice, we will use the standard Flowers dataset from Tensor Flow.
Note: A side benefit of using Tensor Flow-slim is that is you could use the official pre-trained models - including the inception-resnet-v2 model - from Google for performing transfer learning.
Serialize To String()) import random import tensorflow as tf from dataset_utils import _dataset_exists, _get_filenames_and_classes, write_label_file, _convert_dataset #===============DEFINE YOUR ARGUMENTS============== flags = flags #State your dataset directory flags.