import tensorflow as tf
from nn.layers import conv2d, linear, flatten, nnupsampling, batchnorm, gaussnoise, pool
from nn.activations import lrelu
from op import log_sum_exp
from data_loader import train_loader, validation_loader
from neon.backends import gen_backend
import numpy as np
from utils import drawblock, createfolders, OneHot, image_reshape
from imageio import imsave
import os
# Create folders to store images
gen_dir, real_dir, gen_dir128 = createfolders("./genimgs/CUB128GANAE", "/gen",
"/real", "/gen128")
# Create folder to store models
dir_name = './models/CUB128GANAE'
if not os.path.exists(dir_name):
os.mkdir(dir_name)
# Parameters
init_iter, max_iter = 0, 30000
display_iter = 100
eval_iter = 100
store_img_iter = 100
save_iter = 1000
lr_init = 0.0002
batch_size = 100
zdim = 100
n_classes = 200
dropout = 0.2
import tensorflow as tf
from layers import conv2d, linear, nnupsampling, batchnorm, pool
from activations import lrelu
import numpy as np
from utils import drawblock, createfolders
from imageio import imsave
import os
# Create folders to store images
gen_dir, gen_dir128 = createfolders("./genimgs/CUB128GANAEsample", "/gen",
"/gen128")
# Parameters
batch_size = 100
zdim = 100
n_classes = 200
im_size = [64, 64]
gname = 'g_'
tf.set_random_seed(
5555) # use different seed to generate different set of images
# Graph input
z = tf.random_uniform([batch_size, zdim], -1, 1)
# iny = tf.constant(np.eye(n_classes, dtype=np.float32)[:batch_size, :]) # uncomment to generate first 100 classes
iny = tf.constant(np.eye(n_classes, dtype=np.float32)[
batch_size:, :]) # uncomment to generate second 100 classes
# Generator
def generator(inp_z, inp_y, reuse=False):
with tf.variable_scope('Generator', reuse=reuse):
import tensorflow as tf
from layers import conv2d, linear, flatten, nnupsampling, batchnorm, gaussnoise, pool
from activations import lrelu
from op import log_sum_exp
from data_loader import train_loader, validation_loader
from neon.backends import gen_backend
import numpy as np
from utils import drawblock, createfolders, OneHot, image_reshape
from scipy.misc import imsave
import os
# Create folders to store images
gen_dir, real_dir, gen_dir64 = createfolders("./genimgs/CIFAR64GANAE", "/gen",
"/real", "/gen64")
# Create folder to store models
dir_name = './models/CIFAR64GANAE'
if not os.path.exists(dir_name):
os.mkdir(dir_name)
# Parameters
init_iter, max_iter = 0, 70000
display_iter = 100
eval_iter = 100
store_img_iter = 100
save_iter = 1000
lr_init = 0.0002
batch_size = 100
zdim = 100
n_classes = 10
dropout = 0.2
import tensorflow as tf
from nn.layers import conv2d, linear, nnupsampling, batchnorm, pool
from nn.activations import lrelu
import numpy as np
from utils import drawblock, createfolders
from imageio import imsave
import os
# Create folders to store images
gen_dir, gen_dir128 = createfolders("./genimgs/CIFAR64GANAEsample", "/gen",
"/gen64")
# Parameters
batch_size = 100
zdim = 100
n_classes = 10
gname = 'g_'
tf.set_random_seed(
5555) # use different seed to generate different set of images
# Graph input
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, :])
# Generator
def generator(inp_z, inp_y, reuse=False):
with tf.variable_scope('Generator', reuse=reuse):
inp = tf.concat([inp_z, inp_y], 1)
