# DataLoader
be = gen_backend(backend='cpu', batch_size=batch_size, datatype=np.float32)
root_files = './dataset/cub200'
manifestfile = os.path.join(root_files, 'train-index.csv')
testmanifest = os.path.join(root_files, 'val-index.csv')
train = train_loader(manifestfile,
root_files,
be,
h=im_size[0],
w=im_size[1],
scale=[0.875, 0.875])
test = validation_loader(testmanifest,
root_files,
be,
h=im_size[0],
w=im_size[1],
scale=[0.875, 0.875],
ncls=n_classes)
OneHot = OneHot(be, n_classes)
# Graph input
is_train = tf.placeholder(tf.bool)
keep_prob = tf.placeholder(tf.float32)
x_n = tf.placeholder(tf.float32, [batch_size, 3, im_size[0], im_size[1]])
y = tf.placeholder(tf.float32, [batch_size, n_classes])
lr_tf = tf.placeholder(tf.float32)
z = tf.random_uniform([batch_size, zdim], -1, 1)
iny = tf.placeholder(tf.float32, [batch_size, n_classes])
zdim = 100
n_classes = 10
dropout = 0.2
im_size = [32, 32]
dname, gname = 'd_', 'g_'
tf.set_random_seed(1234)
# DataLoader
be = gen_backend(backend='cpu', batch_size=batch_size, datatype=np.float32)
root_files = './dataset/Cifar10'
manifestfile = os.path.join(root_files, 'train-index.csv')
testmanifest = os.path.join(root_files, 'val-index.csv')
train = train_loader(manifestfile, root_files, be, h=im_size[0], w=im_size[1])
test = validation_loader(testmanifest,
root_files,
be,
h=im_size[0],
w=im_size[1])
OneHot = OneHot(be, n_classes)
# Graph input
is_train = tf.placeholder(tf.bool)
keep_prob = tf.placeholder(tf.float32)
x_n = tf.placeholder(tf.float32, [batch_size, 3, im_size[0], im_size[1]])
y = tf.placeholder(tf.float32, [batch_size, n_classes])
lr_tf = tf.placeholder(tf.float32)
z = tf.random_uniform([batch_size, zdim], -1, 1)
iny = tf.constant(
np.tile(np.eye(n_classes, dtype=np.float32),
[batch_size / n_classes + 1, 1])[:batch_size, :])