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model.py
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#! /usr/bin/python
# -*- coding: utf8 -*-
import tensorflow as tf
import tensorlayer as tl
from tensorlayer.layers import *
# from tensorflow.python.ops import variable_scope as vs
# from tensorflow.python.ops import math_ops, init_ops, array_ops, nn
# from tensorflow.python.util import nest
# from tensorflow.contrib.rnn.python.ops import core_rnn_cell
# https://github.com/david-gpu/srez/blob/master/srez_model.py
def SRGAN_g(t_image, is_train=False, reuse=False):
""" Generator in Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
feature maps (n) and stride (s) feature maps (n) and stride (s)
"""
w_init = tf.random_normal_initializer(stddev=0.02)
b_init = None # tf.constant_initializer(value=0.0)
g_init = tf.random_normal_initializer(1., 0.02)
with tf.variable_scope("SRGAN_g", reuse=reuse) as vs:
tl.layers.set_name_reuse(reuse)
n = InputLayer(t_image, name='in')
n = Conv2d(n, 64, (9, 9), (1, 1), act=tf.nn.relu, padding='SAME', W_init=w_init, name='n64s1/c')
# temp = n
# Artifact Reduction Block, add by huzhihao, reference: http://arxiv.org/abs/1608.02778
#n = Conv2d(n, 32, (1,1), (1, 1), act = tf.nn.relu, padding='SAME', W_init=w_init, name='n32s1/c0/1')
# n = Conv2d(n, 32, (3,3), (1, 1), act = tf.nn.relu, padding='SAME', W_init=w_init, name='n32s1/c0/2')
#n = Conv2d(n, 64, (3,3), (1, 1), act = tf.nn.relu, padding='SAME', W_init=w_init, name='n32s1/c0/3')
# n = Conv2d(n, 64, (7,7), (1, 1), act = tf.nn.relu, padding='SAME', W_init=w_init, name='n32s1/c0/2')
temp = n
# B residual blocks
for i in range(16):
nn = Conv2d(n, 64, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, b_init=b_init, name='n64s1/c1/%s' % i)
nn = BatchNormLayer(nn, act=tf.nn.relu, is_train=is_train, gamma_init=g_init, name='n64s1/b1/%s' % i)
nn = Conv2d(nn, 64, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, b_init=b_init, name='n64s1/c2/%s' % i)
nn = BatchNormLayer(nn, is_train=is_train, gamma_init=g_init, name='n64s1/b2/%s' % i)
nn = ElementwiseLayer([n, nn], tf.add, 'b_residual_add/%s' % i)
n = nn
n = Conv2d(n, 64, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, b_init=b_init, name='n64s1/c/m')
n = BatchNormLayer(n, is_train=is_train, gamma_init=g_init, name='n64s1/b/m')
n = ElementwiseLayer([n, temp], tf.add, 'add3')
# B residual blacks end
n = Conv2d(n, 256, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, name='n256s1/1')
n = SubpixelConv2d(n, scale=2, n_out_channel=None, act=tf.nn.relu, name='pixelshufflerx2/1')
n = Conv2d(n, 256, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, name='n256s1/2')
n = SubpixelConv2d(n, scale=2, n_out_channel=None, act=tf.nn.relu, name='pixelshufflerx2/2')
n = Conv2d(n, 3, (9, 9), (1, 1), act=tf.nn.tanh, padding='SAME', W_init=w_init, name='out')
return n
def SRGAN_g2(t_image, is_train=False, reuse=False):
""" Generator in Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
feature maps (n) and stride (s) feature maps (n) and stride (s)
96x96 --> 384x384
Use Resize Conv
"""
w_init = tf.random_normal_initializer(stddev=0.02)
b_init = None # tf.constant_initializer(value=0.0)
g_init = tf.random_normal_initializer(1., 0.02)
size = t_image.get_shape().as_list()
with tf.variable_scope("SRGAN_g", reuse=reuse) as vs:
tl.layers.set_name_reuse(reuse)
n = InputLayer(t_image, name='in')
n = Conv2d(n, 64, (3, 3), (1, 1), act=tf.nn.relu, padding='SAME', W_init=w_init, name='n64s1/c')
temp = n
# B residual blocks
for i in range(16):
nn = Conv2d(n, 64, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, b_init=b_init, name='n64s1/c1/%s' % i)
nn = BatchNormLayer(nn, act=tf.nn.relu, is_train=is_train, gamma_init=g_init, name='n64s1/b1/%s' % i)
nn = Conv2d(nn, 64, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, b_init=b_init, name='n64s1/c2/%s' % i)
nn = BatchNormLayer(nn, is_train=is_train, gamma_init=g_init, name='n64s1/b2/%s' % i)
nn = ElementwiseLayer([n, nn], tf.add, 'b_residual_add/%s' % i)
n = nn
n = Conv2d(n, 64, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, b_init=b_init, name='n64s1/c/m')
n = BatchNormLayer(n, is_train=is_train, gamma_init=g_init, name='n64s1/b/m')
n = ElementwiseLayer([n, temp], tf.add, 'add3')
# B residual blacks end
# n = Conv2d(n, 256, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, name='n256s1/1')
# n = SubpixelConv2d(n, scale=2, n_out_channel=None, act=tf.nn.relu, name='pixelshufflerx2/1')
#
# n = Conv2d(n, 256, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, name='n256s1/2')
# n = SubpixelConv2d(n, scale=2, n_out_channel=None, act=tf.nn.relu, name='pixelshufflerx2/2')
## 0, 1, 2, 3 BILINEAR NEAREST BICUBIC AREA
n = UpSampling2dLayer(n, size=[size[1]*2, size[2]*2], is_scale=False, method=1, align_corners=False, name='up1/upsample2d')
n = Conv2d(n, 64, (3, 3), (1, 1),
padding='SAME', W_init=w_init, b_init=b_init, name='up1/conv2d') # <-- may need to increase n_filter
n = BatchNormLayer(n, act=tf.nn.relu,
is_train=is_train, gamma_init=g_init, name='up1/batch_norm')
n = UpSampling2dLayer(n, size=[size[1]*4, size[2]*4], is_scale=False, method=1, align_corners=False, name='up2/upsample2d')
n = Conv2d(n, 32, (3, 3), (1, 1),
padding='SAME', W_init=w_init, b_init=b_init, name='up2/conv2d') # <-- may need to increase n_filter
n = BatchNormLayer(n, act=tf.nn.relu,
is_train=is_train, gamma_init=g_init, name='up2/batch_norm')
n = Conv2d(n, 3, (1, 1), (1, 1), act=tf.nn.tanh, padding='SAME', W_init=w_init, name='out')
return n
def SRGAN_d2(t_image, is_train=False, reuse=False):
""" Discriminator in Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
feature maps (n) and stride (s) feature maps (n) and stride (s)
"""
w_init = tf.random_normal_initializer(stddev=0.02)
b_init = None
g_init = tf.random_normal_initializer(1., 0.02)
lrelu = lambda x : tl.act.lrelu(x, 0.2)
with tf.variable_scope("SRGAN_d", reuse=reuse) as vs:
tl.layers.set_name_reuse(reuse)
n = InputLayer(t_image, name='in')
n = Conv2d(n, 64, (3, 3), (1, 1), act=lrelu, padding='SAME', W_init=w_init, name='n64s1/c')
n = Conv2d(n, 64, (3, 3), (2, 2), act=lrelu, padding='SAME', W_init=w_init, b_init=b_init, name='n64s2/c')
n = BatchNormLayer(n, is_train=is_train, gamma_init=g_init, name='n64s2/b')
n = Conv2d(n, 128, (3, 3), (1, 1), act=lrelu, padding='SAME', W_init=w_init, b_init=b_init, name='n128s1/c')
n = BatchNormLayer(n, is_train=is_train, gamma_init=g_init, name='n128s1/b')
n = Conv2d(n, 128, (3, 3), (2, 2), act=lrelu, padding='SAME', W_init=w_init, b_init=b_init, name='n128s2/c')
n = BatchNormLayer(n, is_train=is_train, gamma_init=g_init, name='n128s2/b')
n = Conv2d(n, 256, (3, 3), (1, 1), act=lrelu, padding='SAME', W_init=w_init, b_init=b_init, name='n256s1/c')
n = BatchNormLayer(n, is_train=is_train, gamma_init=g_init, name='n256s1/b')
n = Conv2d(n, 256, (3, 3), (2, 2), act=lrelu, padding='SAME', W_init=w_init, b_init=b_init, name='n256s2/c')
n = BatchNormLayer(n, is_train=is_train, gamma_init=g_init, name='n256s2/b')
n = Conv2d(n, 512, (3, 3), (1, 1), act=lrelu, padding='SAME', W_init=w_init, b_init=b_init, name='n512s1/c')
n = BatchNormLayer(n, is_train=is_train, gamma_init=g_init, name='n512s1/b')
n = Conv2d(n, 512, (3, 3), (2, 2), act=lrelu, padding='SAME', W_init=w_init, b_init=b_init, name='n512s2/c')
n = BatchNormLayer(n, is_train=is_train, gamma_init=g_init, name='n512s2/b')
n = FlattenLayer(n, name='f')
n = DenseLayer(n, n_units=1024, act=lrelu, name='d1024')
n = DenseLayer(n, n_units=1, name='out')
logits = n.outputs
n.outputs = tf.nn.sigmoid(n.outputs)
return n, logits
def SRGAN_d(input_images, is_train=True, reuse=False):
w_init = tf.random_normal_initializer(stddev=0.02)
b_init = None # tf.constant_initializer(value=0.0)
gamma_init=tf.random_normal_initializer(1., 0.02)
df_dim = 64
lrelu = lambda x: tl.act.lrelu(x, 0.2)
with tf.variable_scope("SRGAN_d", reuse=reuse):
tl.layers.set_name_reuse(reuse)
net_in = InputLayer(input_images, name='input/images')
net_h0 = Conv2d(net_in, df_dim, (4, 4), (2, 2), act=lrelu,
padding='SAME', W_init=w_init, name='h0/c')
net_h1 = Conv2d(net_h0, df_dim*2, (4, 4), (2, 2), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='h1/c')
net_h1 = BatchNormLayer(net_h1, act=lrelu, is_train=is_train,
gamma_init=gamma_init, name='h1/bn')
net_h2 = Conv2d(net_h1, df_dim*4, (4, 4), (2, 2), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='h2/c')
net_h2 = BatchNormLayer(net_h2, act=lrelu, is_train=is_train,
gamma_init=gamma_init, name='h2/bn')
net_h3 = Conv2d(net_h2, df_dim*8, (4, 4), (2, 2), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='h3/c')
net_h3 = BatchNormLayer(net_h3, act=lrelu, is_train=is_train,
gamma_init=gamma_init, name='h3/bn')
net_h4 = Conv2d(net_h3, df_dim*16, (4, 4), (2, 2), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='h4/c')
net_h4 = BatchNormLayer(net_h4, act=lrelu, is_train=is_train,
gamma_init=gamma_init, name='h4/bn')
net_h5 = Conv2d(net_h4, df_dim*32, (4, 4), (2, 2), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='h5/c')
net_h5 = BatchNormLayer(net_h5, act=lrelu, is_train=is_train,
gamma_init=gamma_init, name='h5/bn')
net_h6 = Conv2d(net_h5, df_dim*16, (1, 1), (1, 1), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='h6/c')
net_h6 = BatchNormLayer(net_h6, act=lrelu, is_train=is_train,
gamma_init=gamma_init, name='h6/bn')
net_h7 = Conv2d(net_h6, df_dim*8, (1, 1), (1, 1), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='h7/c')
net_h7 = BatchNormLayer(net_h7, is_train=is_train,
gamma_init=gamma_init, name='h7/bn')
net = Conv2d(net_h7, df_dim*2, (1, 1), (1, 1), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='res/c')
net = BatchNormLayer(net, act=lrelu, is_train=is_train,
gamma_init=gamma_init, name='res/bn')
net = Conv2d(net, df_dim*2, (3, 3), (1, 1), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='res/c2')
net = BatchNormLayer(net, act=lrelu, is_train=is_train,
gamma_init=gamma_init, name='res/bn2')
net = Conv2d(net, df_dim*8, (3, 3), (1, 1), act=None,
padding='SAME', W_init=w_init, b_init=b_init, name='res/c3')
net = BatchNormLayer(net, is_train=is_train,
gamma_init=gamma_init, name='res/bn3')
net_h8 = ElementwiseLayer(layer=[net_h7, net],
combine_fn=tf.add, name='res/add')
net_h8.outputs = tl.act.lrelu(net_h8.outputs, 0.2)
net_ho = FlattenLayer(net_h8, name='ho/flatten')
net_ho = DenseLayer(net_ho, n_units=1, act=tf.identity,
W_init = w_init, name='ho/dense')
logits = net_ho.outputs
# Wasserstein GAN doesn't need the sigmoid output
# net_ho.outputs = tf.nn.sigmoid(net_ho.outputs)
return net_ho, logits
def Vgg19_simple_api(rgb, reuse):
"""
Build the VGG 19 Model
Parameters
-----------
rgb : rgb image placeholder [batch, height, width, 3] values scaled [0, 1]
"""
VGG_MEAN = [103.939, 116.779, 123.68]
with tf.variable_scope("VGG19", reuse=reuse) as vs:
start_time = time.time()
print("build model started")
rgb_scaled = rgb * 255.0
# Convert RGB to BGR
if tf.__version__ <= '0.11':
red, green, blue = tf.split(3, 3, rgb_scaled)
else: # TF 1.0
# print(rgb_scaled)
red, green, blue = tf.split(rgb_scaled, 3, 3)
assert red.get_shape().as_list()[1:] == [224, 224, 1]
assert green.get_shape().as_list()[1:] == [224, 224, 1]
assert blue.get_shape().as_list()[1:] == [224, 224, 1]
if tf.__version__ <= '0.11':
bgr = tf.concat(3, [
blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2],
])
else:
bgr = tf.concat([
blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2],
], axis=3)
assert bgr.get_shape().as_list()[1:] == [224, 224, 3]
""" input layer """
net_in = InputLayer(bgr, name='input')
""" conv1 """
network = Conv2d(net_in, n_filter=64, filter_size=(3, 3),
strides=(1, 1), act=tf.nn.relu,padding='SAME', name='conv1_1')
network = Conv2d(network, n_filter=64, filter_size=(3, 3),
strides=(1, 1), act=tf.nn.relu,padding='SAME', name='conv1_2')
network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2),
padding='SAME', name='pool1')
""" conv2 """
network = Conv2d(network, n_filter=128, filter_size=(3, 3),
strides=(1, 1), act=tf.nn.relu,padding='SAME', name='conv2_1')
network = Conv2d(network, n_filter=128, filter_size=(3, 3),
strides=(1, 1), act=tf.nn.relu,padding='SAME', name='conv2_2')
network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2),
padding='SAME', name='pool2')
""" conv3 """
network = Conv2d(network, n_filter=256, filter_size=(3, 3),
strides=(1, 1), act=tf.nn.relu,padding='SAME', name='conv3_1')
network = Conv2d(network, n_filter=256, filter_size=(3, 3),
strides=(1, 1), act=tf.nn.relu,padding='SAME', name='conv3_2')
network = Conv2d(network, n_filter=256, filter_size=(3, 3),
strides=(1, 1), act=tf.nn.relu,padding='SAME', name='conv3_3')
network = Conv2d(network, n_filter=256, filter_size=(3, 3),
strides=(1, 1), act=tf.nn.relu,padding='SAME', name='conv3_4')
network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2),
padding='SAME', name='pool3')
""" conv4 """
network = Conv2d(network, n_filter=512, filter_size=(3, 3),
strides=(1, 1), act=tf.nn.relu,padding='SAME', name='conv4_1')
network = Conv2d(network, n_filter=512, filter_size=(3, 3),
strides=(1, 1), act=tf.nn.relu,padding='SAME', name='conv4_2')
network = Conv2d(network, n_filter=512, filter_size=(3, 3),
strides=(1, 1), act=tf.nn.relu,padding='SAME', name='conv4_3')
network = Conv2d(network, n_filter=512, filter_size=(3, 3),
strides=(1, 1), act=tf.nn.relu,padding='SAME', name='conv4_4')
network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2),
padding='SAME', name='pool4') # (batch_size, 14, 14, 512)
conv = network
""" conv5 """
network = Conv2d(network, n_filter=512, filter_size=(3, 3),
strides=(1, 1), act=tf.nn.relu,padding='SAME', name='conv5_1')
network = Conv2d(network, n_filter=512, filter_size=(3, 3),
strides=(1, 1), act=tf.nn.relu,padding='SAME', name='conv5_2')
network = Conv2d(network, n_filter=512, filter_size=(3, 3),
strides=(1, 1), act=tf.nn.relu,padding='SAME', name='conv5_3')
network = Conv2d(network, n_filter=512, filter_size=(3, 3),
strides=(1, 1), act=tf.nn.relu,padding='SAME', name='conv5_4')
network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2),
padding='SAME', name='pool5') # (batch_size, 7, 7, 512)
""" fc 6~8 """
network = FlattenLayer(network, name='flatten')
network = DenseLayer(network, n_units=4096, act=tf.nn.relu, name='fc6')
network = DenseLayer(network, n_units=4096, act=tf.nn.relu, name='fc7')
network = DenseLayer(network, n_units=1000, act=tf.identity, name='fc8')
print("build model finished: %fs" % (time.time() - start_time))
return network, conv
# def vgg16_cnn_emb(t_image, reuse=False):
# """ t_image = 244x244 [0~255] """
# with tf.variable_scope("vgg16_cnn", reuse=reuse) as vs:
# tl.layers.set_name_reuse(reuse)
#
# mean = tf.constant([123.68, 116.779, 103.939], dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean')
# net_in = InputLayer(t_image - mean, name='vgg_input_im')
# """ conv1 """
# network = tl.layers.Conv2dLayer(net_in,
# act = tf.nn.relu,
# shape = [3, 3, 3, 64], # 64 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv1_1')
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 64, 64], # 64 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv1_2')
# network = tl.layers.PoolLayer(network,
# ksize=[1, 2, 2, 1],
# strides=[1, 2, 2, 1],
# padding='SAME',
# pool = tf.nn.max_pool,
# name ='vgg_pool1')
# """ conv2 """
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 64, 128], # 128 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv2_1')
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 128, 128], # 128 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv2_2')
# network = tl.layers.PoolLayer(network,
# ksize=[1, 2, 2, 1],
# strides=[1, 2, 2, 1],
# padding='SAME',
# pool = tf.nn.max_pool,
# name ='vgg_pool2')
# """ conv3 """
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 128, 256], # 256 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv3_1')
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 256, 256], # 256 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv3_2')
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 256, 256], # 256 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv3_3')
# network = tl.layers.PoolLayer(network,
# ksize=[1, 2, 2, 1],
# strides=[1, 2, 2, 1],
# padding='SAME',
# pool = tf.nn.max_pool,
# name ='vgg_pool3')
# """ conv4 """
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 256, 512], # 512 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv4_1')
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 512, 512], # 512 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv4_2')
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 512, 512], # 512 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv4_3')
#
# network = tl.layers.PoolLayer(network,
# ksize=[1, 2, 2, 1],
# strides=[1, 2, 2, 1],
# padding='SAME',
# pool = tf.nn.max_pool,
# name ='vgg_pool4')
# conv4 = network
#
# """ conv5 """
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 512, 512], # 512 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv5_1')
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 512, 512], # 512 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv5_2')
# network = tl.layers.Conv2dLayer(network,
# act = tf.nn.relu,
# shape = [3, 3, 512, 512], # 512 features for each 3x3 patch
# strides = [1, 1, 1, 1],
# padding='SAME',
# name ='vgg_conv5_3')
# network = tl.layers.PoolLayer(network,
# ksize=[1, 2, 2, 1],
# strides=[1, 2, 2, 1],
# padding='SAME',
# pool = tf.nn.max_pool,
# name ='vgg_pool5')
#
# network = FlattenLayer(network, name='vgg_flatten')
#
# # # network = DropoutLayer(network, keep=0.6, is_fix=True, is_train=is_train, name='vgg_out/drop1')
# # new_network = tl.layers.DenseLayer(network, n_units=4096,
# # act = tf.nn.relu,
# # name = 'vgg_out/dense')
# #
# # # new_network = DropoutLayer(new_network, keep=0.8, is_fix=True, is_train=is_train, name='vgg_out/drop2')
# # new_network = DenseLayer(new_network, z_dim, #num_lstm_units,
# # b_init=None, name='vgg_out/out')
# return conv4, network