def main(_):
if not FLAGS.dataset_name:
raise ValueError('You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'chair':
download_and_convert_chair.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'stand':
download_and_convert_stand.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'desk':
download_and_convert_desk.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'sofa':
download_and_convert_sofa.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'bed':
download_and_convert_bed.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'wardrobe_cabinet':
download_and_convert_wardrobe_cabinet.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'tv_stand':
download_and_convert_tv_stand.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'table':
download_and_convert_table.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'television':
download_and_convert_television.run(FLAGS.dataset_dir)
else:
raise ValueError(
'dataset_name [%s] was not recognized.' % FLAGS.dataset_name)
def main(_):
if not FLAGS.dataset_name:
raise ValueError(
'You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError(
'You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'smgo':
download_and_convert_smgo.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'smgo6':
download_and_convert_smgo6.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist_eo':
download_and_convert_mnist_eo.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist_11':
download_and_convert_mnist_11.run(FLAGS.dataset_dir)
else:
raise ValueError('dataset_name [%s] was not recognized.' %
FLAGS.dataset_dir)
def main(_):
if not FLAGS.dataset_name:
raise ValueError(
'You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError(
'You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'wikiart':
if not FLAGS.input_dataset_dir is None:
convert_wikiart.run(FLAGS.input_dataset_dir, FLAGS.dataset_dir)
else:
raise ValueError(
"For wikiart, you must supply a valid input directory with --input_dataset_dir"
)
else:
raise ValueError('dataset_name [%s] was not recognized.' %
FLAGS.dataset_name)
def main(_):
if not FLAGS.dataset_name:
raise ValueError(
'You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError(
'You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'visualwakewords':
download_and_convert_visualwakewords.run(
FLAGS.dataset_dir, FLAGS.small_object_area_threshold,
FLAGS.foreground_class_of_interest)
elif FLAGS.dataset_name == 'generic':
download_and_convert_generic.run(FLAGS.dataset_dir,
perc_validation=FLAGS.perc_validation,
labels_filename=FLAGS.labels_filename,
num_shards=FLAGS.num_shards)
else:
raise ValueError('dataset_name [%s] was not recognized.' %
FLAGS.dataset_name)
def main(_):
if not FLAGS.dataset_name:
raise ValueError('You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
else:
raise ValueError(
'dataset_name [%s] was not recognized.' % FLAGS.dataset_dir)
def main(_):
if not FLAGS.dataset_name:
raise ValueError('You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'SignLanguage1':
download_and_convert_SignLanguage1.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
else:
raise ValueError(
'dataset_name [%s] was not recognized.' % FLAGS.dataset_name)
def main(_):
if not FLAGS.dataset_name:
raise ValueError('You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'cifar10_val':
download_and_convert_cifar10_val.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == ''google_speech':
download_and_convert_google_speech.run(FLAGS.dataset_dir)
def main(_):
if not FLAGS.dataset_name:
raise ValueError('You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'expand':
convert_foldered_images.run()
else:
raise ValueError(
'dataset_name [%s] was not recognized.' % FLAGS.dataset_dir)
def main(_):
if not FLAGS.dataset_name: #supply v. 补给, 提供, 供给; 替代他人职务, 替代
raise ValueError(
'You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError(
'You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
else:
raise ValueError( #recognized v. 认出, 识别; 正式承认; 认识; 认可, 认定
'dataset_name [%s] was not recognized.' % FLAGS.dataset_name)
def main(_):
if not FLAGS.dataset_name:
raise ValueError(
'You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError(
'You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10': #提供的三个数据集,[cifar10],[flowers],[mnist]
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
else:
raise ValueError('dataset_name [%s] was not recognized.' %
FLAGS.dataset_name) #数据经名字不属于上述三个
def main(_):
if not FLAGS.dataset_name:
raise ValueError('You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'casia':
download_and_convert_casia.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'wvu_face_2013':
download_and_convert_wvu_face_2013.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'wvu_face_overlap_2013':
download_and_convert_wvu_face_overlap_2013.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'wvu_face_overlap_2012':
download_and_convert_wvu_face_overlap_2012.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'wvu_face_overlap_frontal_2012':
download_and_convert_wvu_face_overlap_frontal_2012.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'wvu_joint_face_overlap_2012_no_repeat':
download_and_convert_wvu_joint_face_overlap_2012_no_repeat.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'wvu_joint_iris_overlap_2012_no_repeat':
download_and_convert_wvu_joint_iris_overlap_2012_no_repeat.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'wvu_joint_face_overlap_2013_no_repeat':
download_and_convert_wvu_joint_face_overlap_2013_no_repeat.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'wvu_joint_iris_overlap_2013_no_repeat':
download_and_convert_wvu_joint_iris_overlap_2013_no_repeat.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'wvu_joint_iris_and_face_overlap_2013_no_repeat':
download_and_convert_wvu_joint_iris_and_face_overlap_2013_no_repeat.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'wvu_joint_iris_and_face_overlap_2012_no_repeat':
download_and_convert_wvu_joint_iris_and_face_overlap_2012_no_repeat.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'wvu_iris_2013':
download_and_convert_wvu_iris_2013.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'wvu_iris_overlap_2013':
download_and_convert_wvu_iris_overlap_2013.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'wvu_iris_overlap_2012':
download_and_convert_wvu_iris_overlap_2012.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'casia_ndiris':
download_and_convert_casia_ndiris.run(FLAGS.dataset_dir)
else:
raise ValueError(
'dataset_name [%s] was not recognized.' % FLAGS.dataset_dir)
def main(_):
if not FLAGS.dataset_name:
raise ValueError(
'You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError(
'You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
else:
print(
'dataset_name [%s] was not official support, please make sure having this folder in data dir.'
% FLAGS.dataset_name)
img2tfrecord.run(FLAGS.dataset_dir)
def main(_):
if not FLAGS.dataset_name:
raise ValueError('You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'quiz':
convert_quiz.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'pj_vehicle':
convert_pj_vehicle.run(FLAGS.dataset_dir)
else:
raise ValueError(
'dataset_name [%s] was not recognized.' % FLAGS.dataset_name)
def main(_):
FLAGS.dataset_name = 'cifar10'
FLAGS.dataset_dir = 'D:/pig_recognize/models/research/slim/cifar10'
if not FLAGS.dataset_name:
raise ValueError(
'You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError(
'You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
else:
raise ValueError('dataset_name [%s] was not recognized.' %
FLAGS.dataset_name)
def main(_):
if not FLAGS.dataset_name:
raise ValueError(
"You must supply the dataset name with --dataset_name")
if not FLAGS.dataset_dir:
raise ValueError(
"You must supply the dataset directory with --dataset_dir")
if FLAGS.dataset_name == "cifar10":
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == "flowers":
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == "mnist":
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == "custom":
download_and_convert_custom.run(FLAGS.dataset_dir)
else:
raise ValueError("dataset_name [%s] was not recognized." %
FLAGS.dataset_name)
def main(_):
if not FLAGS.dataset_name:
raise ValueError(
'You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError(
'You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'custom':
convert_custom_dataset.run(FLAGS.dataset_dir, FLAGS.num_validation,
FLAGS.num_shards)
else:
raise ValueError('dataset_name [%s] was not recognized.' %
FLAGS.dataset_name)
def main(_):
if not FLAGS.dataset_name:
raise ValueError(
'You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError(
'You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'custom':
convert_tfrecord.run(FLAGS.dataset_dir,
dataset_name=FLAGS.dataset_name)
else:
convert_tfrecord.run(FLAGS.dataset_dir,
dataset_name=FLAGS.dataset_name)
def main(_):
if not FLAGS.dataset_name:
raise ValueError(
'You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError(
'You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'nuswide':
convert_nuswide.run(FLAGS.dataset_dir, FLAGS.create_label_dict)
elif FLAGS.dataset_name == 'espgame':
convert_espgame.run(FLAGS.dataset_dir, FLAGS.create_label_dict)
else:
raise ValueError('dataset_name [%s] was not recognized.' %
FLAGS.dataset_dir)
def main(_):
if not FLAGS.dataset_name:
raise ValueError(
'You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError(
'You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'visualwakewords':
download_and_convert_visualwakewords.run(
FLAGS.dataset_dir, FLAGS.small_object_area_threshold,
FLAGS.foreground_class_of_interest, FLAGS.download, FLAGS.coco_dir)
else:
raise ValueError('dataset_name [%s] was not recognized.' %
FLAGS.dataset_name)
def main(_):
if not FLAGS.dataset_name:
raise ValueError('You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'cats_and_dogs':
convert_cats_and_dogs.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'svhn':
convert_svhn.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'gtsrb':
convert_gtsrb.run(FLAGS.dataset_dir)
else:
raise ValueError(
'dataset_name [%s] was not recognized.' % FLAGS.dataset_name)
def main(_):
if not FLAGS.dataset_name:
raise ValueError(
'You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError(
'You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
#added by chenww
elif FLAGS.dataset_name == 'maskCls':
convert_maskCls.run(FLAGS.dataset_dir, FLAGS.validation_or_train)
elif FLAGS.dataset_name == 'mura2Cls':
convert_maskCls.run(FLAGS.dataset_dir, FLAGS.validation_or_train)
else:
raise ValueError('dataset_name [%s] was not recognized.' %
FLAGS.dataset_name)
def prepare_data():
MNIST_DATA_DIR = '/tmp/mnist-data'
if not tf.gfile.Exists(MNIST_DATA_DIR):
tf.gfile.MakeDirs(MNIST_DATA_DIR)
download_and_convert_mnist.run(MNIST_DATA_DIR)
# FIXME why reset graph here?
tf.reset_default_graph()
# Define our input pipeline. Pin it to the CPU so that the GPU can be reserved
# for forward and backwards propogation.
batch_size = 32
with tf.device('/cpu:0'):
# data provider FIXME why returning three fields?
real_images, _, _ = data_provider.provide_data('train', batch_size,
MNIST_DATA_DIR)
# Sanity check that we're getting images.
check_real_digits = tfgan.eval.image_reshaper(real_images[:20, ...],
num_cols=10)
visualize_digits(check_real_digits)
# FIXME is this a tensor or an iterator?
return real_images
def main(_):
if not FLAGS.dataset_name:
raise ValueError(
'You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError(
'You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'plantVSnoplant':
download_and_convert_plantVSnoplant.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'plantclef':
if FLAGS.dataset == 'training_data_2014':
download_and_convert_plantclef.run(FLAGS.dataset_dir,
FLAGS.dataset)
elif FLAGS.dataset == 'training_data_2015':
download_and_convert_plantclef.run(FLAGS.dataset_dir,
FLAGS.dataset)
elif FLAGS.dataset == 'test_data_2015':
download_and_convert_plantclef.run(FLAGS.dataset_dir,
FLAGS.dataset)
elif FLAGS.dataset == 'whole_data_2015':
download_and_convert_plantclef.run(Flags.dataset_dir,
Frlags.dataset)
elif FLAGS.dataset == 'test_data_2016':
download_and_convert_plantclef_for_test.run(
FLAGS.dataset_dir, FLAGS.dataset)
else:
raise ValueError('dataset [%s] was not recognized.' %
FLAGS.dataset)
else:
raise ValueError('dataset_name [%s] was not recognized.' %
FLAGS.dataset_dir)
def main(_):
if not FLAGS.dataset_name:
raise ValueError('You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'fxtrade':
if FLAGS.sig2png:
download_and_convert_fxtrade.sig2png(FLAGS.signal_path, FLAGS.out_dir, FLAGS.draw)
else:
download_and_convert_fxtrade.run(FLAGS.signal_dir, FLAGS.label_dir, FLAGS.label_files,\
FLAGS.dataset_dir, FLAGS.symbol, FLAGS.periods, FLAGS.draw_signals, \
image_size = tuple([int(i) for i in FLAGS.image_size.split(",")]), \
label_offset=FLAGS.label_offset, interpolation=FLAGS.interpolation)
else:
raise ValueError(
'dataset_name [%s] was not recognized.' % FLAGS.dataset_name)
from mnist_train_util import infogan_discriminator
from mnist_train_util import save_image
slim = tf.contrib.slim
layers = tf.contrib.layers
ds = tf.contrib.distributions
batch_size = 64
test_size = 10
# noise_dims = 64
cat_dim, cont_dim, noise_dims = 10, 2, 64
MNIST_DATA_DIR = './mnist-data'
MNIST_IMAGE_DIR = './mnist-image'
if __name__ == '__main__':
if not tf.gfile.Exists(MNIST_DATA_DIR):
tf.gfile.MakeDirs(MNIST_DATA_DIR)
download_and_convert_mnist.run(MNIST_DATA_DIR)
if not tf.gfile.Exists(MNIST_IMAGE_DIR):
tf.gfile.MakeDirs(MNIST_IMAGE_DIR)
images, one_hot_labels, _ = data_provider.provide_data(
'train', batch_size, MNIST_DATA_DIR)
test_images, test_one_hot_labels, _ = data_provider.provide_data(
'test', test_size, MNIST_DATA_DIR)
true_labels = tf.argmax(one_hot_labels, axis=1)
generator_fn = functools.partial(infogan_generator,
categorical_dim=cat_dim)
discriminator_fn = functools.partial(infogan_discriminator,
categorical_dim=cat_dim,
continuous_dim=cont_dim)
# unstructured_inputs, structured_inputs = util.get_infogan_noise(
# batch_size, cat_dim, cont_dim, noise_dims)
# Creates a QueueRunner for the pre-fetching operation.
images, labels = tf.train.batch(
[image, label],
batch_size=batch_size,
num_threads=num_threads,
capacity=5 * batch_size)
one_hot_labels = tf.one_hot(labels, dataset.num_classes)
return images, one_hot_labels, dataset.num_samples
if __name__ == '__main__':
if not tf.gfile.Exists(FLAGS.dataset_dir):
tf.gfile.MakeDirs(FLAGS.dataset_dir)
download_and_convert_mnist.run(FLAGS.dataset_dir)
tf.reset_default_graph()
# Define our input pipeline. Pin it to the CPU so that the GPU can be reserved
# for forward and backwards propogation.
batch_size = 32
with tf.device('/cpu:0'):
images, _, _ = provide_data('train', batch_size, FLAGS.dataset_dir)
# Sanity check that we're getting images.
imgs_to_visualize = tf.contrib.gan.eval.image_reshaper(images[:20, ...], num_cols=10)
visualize_digits(imgs_to_visualize)
noise_dims = 64
gan_model = tf.contrib.gan.gan_model(
