def __denseBlock(self,
x,
growth_rate=16,
num_layers=8,
kernel_size=[3, 3],
layer=0):
dense_block_output = x
for i in range(num_layers):
'''
In Paper <Densely Connected Convolutional Networks>
each composite function contains three consecutive operations:
batch normalization(BN), followed by a rectified linear unit (ReLU) and a 3*3 convolution (Conv).
'''
if self.is_bn:
x = tl.BatchNormLayer(x, name='denseblock%d/BN%d' % (layer, i))
x = ReluLayer(x, name='denseblock%d/relu%d' % (layer, i))
x = tl.Conv2d(x,
growth_rate,
kernel_size,
name='denseblock%d/conv%d' % (layer, i))
# concat the output of layer
dense_block_output = tl.ConcatLayer([dense_block_output, x],
concat_dim=3,
name='denseblock%d/concat%d' %
(layer, i))
x = dense_block_output
return dense_block_output
def buildModel(self):
print("Building SRResNet...")
#input layer
x = tl.InputLayer(self.input, name='inputlayer')
x = tl.Conv2d(x, self.feature_size, [3, 3], name='c1')
conv_1 = x
# B residual blocks
for i in range(self.num_layers):
x = self.__resBlock(x, self.feature_size, layer=i)
x = tl.Conv2d(x, self.feature_size, [3, 3], name='c2')
x = tl.BatchNormLayer(x)
x = tl.ElementwiseLayer([x, conv_1],
tf.add,
name='res%d/res_add' % (layer))
# B residual blacks end
x = utils.SubpixelUpsample(x, self.feature_size, self.scale)
# One final convolution on the upsampling output
output = tl.Conv2d(x,
self.output_channels, [1, 1],
act=tf.nn.tanh,
name='lastLayer')
self.cacuLoss(output)
# Tensorflow graph setup... session, saver, etc.
self.sess = tf.Session()
self.saver = tf.train.Saver()
print("Done building!")
"""
def __resBlock(self, x, channels=64, kernel_size=[3, 3], scale=1, layer=0):
nn = tl.Conv2d(x,
channels,
kernel_size,
act=None,
name='res%d/c1' % (layer))
nn = tl.BatchNormLayer(nn, act=tf.nn.relu)
nn = tl.Conv2d(nn,
channels,
kernel_size,
act=None,
name='res%d/c2' % (layer))
nn = tl.BatchNormLayer(nn, act=tf.nn.relu)
nn = ScaleLayer(nn, scale, name='res%d/scale' % (layer))
n = tl.ElementwiseLayer([x, nn],
tf.add,
name='res%d/res_add' % (layer))
return n
def dense_block(self,
x,
growth_rate=16,
n_conv=8,
kernel_size=(3, 3),
layer=0):
dense_block_output = x
for i in range(n_conv):
x = tl.BatchNormLayer(x, name='dense_%d/bn_%d' % (layer, i))
x = ReluLayer(x, name='dense_%d/relu_%d' % (layer, i))
x = tl.Conv2d(x,
growth_rate,
kernel_size,
name='dense_%d/conv_%d' % (layer, i))
# concat the output of layer
dense_block_output = tl.ConcatLayer([dense_block_output, x],
concat_dim=3,
name='dense_%d/concat_%d' %
(layer, i))
x = dense_block_output
return x
def behaviour_net(self):
init = tf.truncated_normal_initializer(stddev=0.1)
zero_init = tf.zeros_initializer()
screen_input = layers.InputLayer(self.screen_in, name="screen_inputs")
screen_input_bn = layers.BatchNormLayer(screen_input, name="screen_bn")
conv_depth=64
conv1_scr = layers.Conv2d(screen_input_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv1_scr")
conv1_scr_bn = layers.BatchNormLayer(conv1_scr, name="screen_bn_1")
conv2_scr = layers.Conv2d(conv1_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv2_scr")
conv2_scr_bn = layers.BatchNormLayer(conv2_scr, name="screen_bn_2")
conv3_scr = layers.Conv2d(conv2_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv3_scr")
conv3_scr_bn = layers.BatchNormLayer(conv3_scr, name="screen_bn_3")
conv4_scr = layers.Conv2d(conv3_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv4_scr")
conv4_scr_bn = layers.BatchNormLayer(conv4_scr, name="screen_bn_4")
conv5_scr = layers.Conv2d(conv4_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv5_scr")
conv5_scr_bn = layers.BatchNormLayer(conv5_scr, name="screen_bn_5")
conv6_scr = layers.Conv2d(conv5_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv6_scr")
conv6_scr_bn = layers.BatchNormLayer(conv6_scr, name="screen_bn_6")
conv7_scr = layers.Conv2d(conv6_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv7_scr")
conv7_scr_bn = layers.BatchNormLayer(conv7_scr, name="screen_bn_7")
conv8_scr = layers.Conv2d(conv7_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv8_scr")
conv8_scr_bn = layers.BatchNormLayer(conv8_scr, name="screen_bn_8")
conv9_scr = layers.Conv2d(conv8_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv9_scr")
conv9_scr_bn = layers.BatchNormLayer(conv9_scr, name="screen_bn_9")
conv10_scr = layers.Conv2d(conv9_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv10_scr")
conv10_scr_bn = layers.BatchNormLayer(conv10_scr, name="screen_bn_10")
scr_GAP = layers.PoolLayer(conv10_scr_bn, ksize=[1, 84, 84, 1], padding="VALID", pool=tf.nn.avg_pool,
name="scr_GAP")
scr_info = layers.FlattenLayer(scr_GAP, name="scr_flattened")
minimap_input = layers.InputLayer(self.minimap_in, name="mini_in")
minimap_input_bn = layers.BatchNormLayer(minimap_input, name="minimap_bn")
conv1_mini = layers.Conv2d(minimap_input_bn, 32, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv1_mini")
conv1_mini_bn = layers.BatchNormLayer(conv1_mini, name="mini_bn_1")
conv2_mini = layers.Conv2d(conv1_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv2_mini")
conv2_mini_bn = layers.BatchNormLayer(conv2_mini, name="mini_bn_2")
conv3_mini = layers.Conv2d(conv2_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv3_mini")
conv3_mini_bn = layers.BatchNormLayer(conv3_mini, name="mini_bn_3")
conv4_mini = layers.Conv2d(conv3_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv4_mini")
conv4_mini_bn = layers.BatchNormLayer(conv4_mini, name="mini_bn_4")
conv5_mini = layers.Conv2d(conv4_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv5_mini")
conv5_mini_bn = layers.BatchNormLayer(conv5_mini, name="mini_bn_5")
conv6_mini = layers.Conv2d(conv5_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv6_mini")
conv6_mini_bn = layers.BatchNormLayer(conv6_mini, name="mini_bn_6")
conv7_mini = layers.Conv2d(conv6_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv7_mini")
conv7_mini_bn = layers.BatchNormLayer(conv7_mini, name="mini_bn_7")
conv8_mini = layers.Conv2d(conv7_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv8_mini")
conv8_mini_bn = layers.BatchNormLayer(conv8_mini, name="mini_bn_8")
conv9_mini = layers.Conv2d(conv8_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv9_mini")
conv9_mini_bn = layers.BatchNormLayer(conv9_mini, name="mini_bn_9")
conv10_mini = layers.Conv2d(conv9_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv10_mini")
conv10_mini_bn = layers.BatchNormLayer(conv10_mini, name="mini_bn_10")
mini_GAP = layers.PoolLayer(conv10_mini_bn, ksize=[1, 64, 64, 1], padding="VALID", pool=tf.nn.avg_pool,
name="mini_GAP")
mini_info = layers.FlattenLayer(mini_GAP, name="mini_flattened")
multi_select_in = layers.InputLayer(self.multi_select_in, name="multi_select")
select_info = layers.FlattenLayer(multi_select_in, name="select_flattened")
select_info_bn = layers.BatchNormLayer(select_info, name="select_bn")
info_combined = layers.ConcatLayer([scr_info, mini_info, select_info_bn], name="info_all")
dense1 = layers.DenseLayer(info_combined, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init, name="Dense1")
dense1_bn = layers.BatchNormLayer(dense1, name="dense1_bn")
dense2 = layers.DenseLayer(dense1_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense2")
dense2_bn = layers.BatchNormLayer(dense2, name="dense2_bn")
dense3 = layers.DenseLayer(dense2_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense3")
dense3_bn = layers.BatchNormLayer(dense3, name="dense3_bn")
dense4 = layers.DenseLayer(dense3_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense4")
dense4_bn = layers.BatchNormLayer(dense4, name="dense4_bn")
dense5 = layers.DenseLayer(dense4_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense5")
dense5_bn = layers.BatchNormLayer(dense5, name="dense5_bn")
dense6 = layers.DenseLayer(dense5_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense6")
dense6_bn = layers.BatchNormLayer(dense6, name="dense6_bn")
self.q=layers.DenseLayer(dense6_bn, n_units=7057, W_init=zero_init, b_init=zero_init,
name="q")
self.Q_behave=self.q.outputs
def target_net(self):
init = tf.truncated_normal_initializer(stddev=0.1)
zero_init = tf.zeros_initializer()
screen_input = layers.InputLayer(self.screen_in, name="screen_inputs_t")
screen_input_bn = layers.BatchNormLayer(screen_input, name="screen_bn_t")
conv_depth = 64
conv1_scr = layers.Conv2d(screen_input_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv1_scr_t")
conv1_scr_bn = layers.BatchNormLayer(conv1_scr, name="screen_bn_1_t")
conv2_scr = layers.Conv2d(conv1_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv2_scr_t")
conv2_scr_bn = layers.BatchNormLayer(conv2_scr, name="screen_bn_2_t")
conv3_scr = layers.Conv2d(conv2_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv3_scr_t")
conv3_scr_bn = layers.BatchNormLayer(conv3_scr, name="screen_bn_3_t")
conv4_scr = layers.Conv2d(conv3_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv4_scr_t")
conv4_scr_bn = layers.BatchNormLayer(conv4_scr, name="screen_bn_4_t")
conv5_scr = layers.Conv2d(conv4_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv5_scr_t")
conv5_scr_bn = layers.BatchNormLayer(conv5_scr, name="screen_bn_5_t")
conv6_scr = layers.Conv2d(conv5_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv6_scr_t")
conv6_scr_bn = layers.BatchNormLayer(conv6_scr, name="screen_bn_6_t")
conv7_scr = layers.Conv2d(conv6_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv7_scr_t")
conv7_scr_bn = layers.BatchNormLayer(conv7_scr, name="screen_bn_7_t")
conv8_scr = layers.Conv2d(conv7_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv8_scr_t")
conv8_scr_bn = layers.BatchNormLayer(conv8_scr, name="screen_bn_8_t")
conv9_scr = layers.Conv2d(conv8_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv9_scr_t")
conv9_scr_bn = layers.BatchNormLayer(conv9_scr, name="screen_bn_9_t")
conv10_scr = layers.Conv2d(conv9_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv10_scr_t")
conv10_scr_bn = layers.BatchNormLayer(conv10_scr, name="screen_bn_10_t")
scr_GAP = layers.PoolLayer(conv10_scr_bn, ksize=[1, 84, 84, 1], padding="VALID", pool=tf.nn.avg_pool,
name="scr_GAP_t")
scr_info = layers.FlattenLayer(scr_GAP, name="scr_flattened_t")
minimap_input = layers.InputLayer(self.minimap_in, name="mini_in_t")
minimap_input_bn = layers.BatchNormLayer(minimap_input, name="minimap_bn_t")
conv1_mini = layers.Conv2d(minimap_input_bn, 32, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv1_mini_t")
conv1_mini_bn = layers.BatchNormLayer(conv1_mini, name="mini_bn_1_t")
conv2_mini = layers.Conv2d(conv1_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv2_mini_t")
conv2_mini_bn = layers.BatchNormLayer(conv2_mini, name="mini_bn_2_t")
conv3_mini = layers.Conv2d(conv2_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv3_mini_t")
conv3_mini_bn = layers.BatchNormLayer(conv3_mini, name="mini_bn_3_t")
conv4_mini = layers.Conv2d(conv3_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv4_mini_t")
conv4_mini_bn = layers.BatchNormLayer(conv4_mini, name="mini_bn_4_t")
conv5_mini = layers.Conv2d(conv4_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv5_mini_t")
conv5_mini_bn = layers.BatchNormLayer(conv5_mini, name="mini_bn_5_t")
conv6_mini = layers.Conv2d(conv5_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv6_mini_t")
conv6_mini_bn = layers.BatchNormLayer(conv6_mini, name="mini_bn_6_t")
conv7_mini = layers.Conv2d(conv6_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv7_mini_t")
conv7_mini_bn = layers.BatchNormLayer(conv7_mini, name="mini_bn_7_t")
conv8_mini = layers.Conv2d(conv7_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv8_mini_t")
conv8_mini_bn = layers.BatchNormLayer(conv8_mini, name="mini_bn_8_t")
conv9_mini = layers.Conv2d(conv8_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv9_mini_t")
conv9_mini_bn = layers.BatchNormLayer(conv9_mini, name="mini_bn_9_t")
conv10_mini = layers.Conv2d(conv9_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv10_mini_t")
conv10_mini_bn = layers.BatchNormLayer(conv10_mini, name="mini_bn_10_t")
mini_GAP = layers.PoolLayer(conv10_mini_bn, ksize=[1, 64, 64, 1], padding="VALID", pool=tf.nn.avg_pool,
name="mini_GAP_t")
mini_info = layers.FlattenLayer(mini_GAP, name="mini_flattened_t")
multi_select_in = layers.InputLayer(self.multi_select_in, name="multi_select_t")
select_info = layers.FlattenLayer(multi_select_in, name="select_flattened_t")
select_info_bn = layers.BatchNormLayer(select_info, name="select_bn_t")
info_combined = layers.ConcatLayer([scr_info, mini_info, select_info_bn], name="info_all_t")
dense1 = layers.DenseLayer(info_combined, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense1_t")
dense1_bn = layers.BatchNormLayer(dense1, name="dense1_bn_t")
dense2 = layers.DenseLayer(dense1_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense2_t")
dense2_bn = layers.BatchNormLayer(dense2, name="dense2_bn_t")
dense3 = layers.DenseLayer(dense2_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense3_t")
dense3_bn = layers.BatchNormLayer(dense3, name="dense3_bn_t")
dense4 = layers.DenseLayer(dense3_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense4_t")
dense4_bn = layers.BatchNormLayer(dense4, name="dense4_bn_t")
dense5 = layers.DenseLayer(dense4_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense5_t")
dense5_bn = layers.BatchNormLayer(dense5, name="dense5_bn_t")
dense6 = layers.DenseLayer(dense5_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense6_t")
dense6_bn = layers.BatchNormLayer(dense6, name="dense6_bn_t")
self.q_target = layers.DenseLayer(dense6_bn, n_units=7057, W_init=zero_init, b_init=zero_init,
name="q_t")
self.target_params = self.q_target.all_params
self.Q_target = self.q_target.outputs
def encoder(self, input_imgs, is_train=True, reuse=False):
'''
input_imgs: the input images to be encoded into a vector as latent representation. size here is [b_size,64,64,3]
'''
z_dim = self.z_dim # 512 128???
ef_dim = 64 # encoder filter number
w_init = tf.random_normal_initializer(stddev=0.02)
gamma_init = tf.random_normal_initializer(1., 0.02)
with tf.variable_scope("encoder", reuse=reuse):
tl.layers.set_name_reuse(reuse)
net_in = l.InputLayer(input_imgs, name='en/in') # (b_size,64,64,3)
net_h0 = l.Conv2d(net_in,
ef_dim, (5, 5), (2, 2),
act=None,
padding='SAME',
W_init=w_init,
name='en/h0/conv2d')
net_h0 = l.BatchNormLayer(net_h0,
act=tf.nn.relu,
is_train=is_train,
gamma_init=gamma_init,
name='en/h0/batch_norm')
# net_h0.outputs._shape = (b_size,32,32,64)
net_h1 = l.Conv2d(net_h0,
ef_dim * 2, (5, 5), (2, 2),
act=None,
padding='SAME',
W_init=w_init,
name='en/h1/conv2d')
net_h1 = l.BatchNormLayer(net_h1,
act=tf.nn.relu,
is_train=is_train,
gamma_init=gamma_init,
name='en/h1/batch_norm')
# net_h1.outputs._shape = (b_size,16,16,64*2)
net_h2 = l.Conv2d(net_h1,
ef_dim * 4, (5, 5), (2, 2),
act=None,
padding='SAME',
W_init=w_init,
name='en/h2/conv2d')
net_h2 = l.BatchNormLayer(net_h2,
act=tf.nn.relu,
is_train=is_train,
gamma_init=gamma_init,
name='en/h2/batch_norm')
# net_h2.outputs._shape = (b_size,8,8,64*4)
net_h3 = l.Conv2d(net_h2,
ef_dim * 8, (5, 5), (2, 2),
act=None,
padding='SAME',
W_init=w_init,
name='en/h3/conv2d')
net_h3 = l.BatchNormLayer(net_h3,
act=tf.nn.relu,
is_train=is_train,
gamma_init=gamma_init,
name='en/h3/batch_norm')
# net_h2.outputs._shape = (b_size,4,4,64*8)
# mean of z
net_h4 = l.FlattenLayer(net_h3, name='en/h4/flatten')
# net_h4.outputs._shape = (b_size,8*8*64*4)
net_out1 = l.DenseLayer(net_h4,
n_units=z_dim,
act=tf.identity,
W_init=w_init,
name='en/h3/lin_sigmoid')
net_out1 = l.BatchNormLayer(net_out1,
act=tf.identity,
is_train=is_train,
gamma_init=gamma_init,
name='en/out1/batch_norm')
# net_out1 = DenseLayer(net_h4, n_units=z_dim, act=tf.nn.relu,
# W_init = w_init, name='en/h4/lin_sigmoid')
z_mean = net_out1.outputs # (b_size,512)
# log of variance of z(covariance matrix is diagonal)
net_h5 = l.FlattenLayer(net_h3, name='en/h5/flatten')
net_out2 = l.DenseLayer(net_h5,
n_units=z_dim,
act=tf.identity,
W_init=w_init,
name='en/h4/lin_sigmoid')
net_out2 = l.BatchNormLayer(net_out2,
act=tf.nn.softplus,
is_train=is_train,
gamma_init=gamma_init,
name='en/out2/batch_norm')
# net_out2 = DenseLayer(net_h5, n_units=z_dim, act=tf.nn.relu,
# W_init = w_init, name='en/h5/lin_sigmoid')
z_log_sigma_sq = net_out2.outputs + 1e-6 # (b_size,512)
return net_out1, net_out2, z_mean, z_log_sigma_sq
def generator(self, inputs, is_train=True, reuse=False):
'''
generator of GAN, which can also be seen as a decoder of VAE
inputs: latent representation from encoder. [b_size,z_dim]
'''
image_size = self.output_size # 64 the output size of generator
s2, s4, s8, _ = int(image_size / 2), int(image_size / 4), int(
image_size / 8), int(image_size / 16) # 32,16,8,4
gf_dim = 64
c_dim = self.c_dim # n_color 3
batch_size = self.batch_size # 64
w_init = tf.random_normal_initializer(stddev=0.02)
gamma_init = tf.random_normal_initializer(1., 0.02)
with tf.variable_scope("generator", reuse=reuse):
tl.layers.set_name_reuse(reuse)
net_in = l.InputLayer(inputs, name='g/in')
net_h0 = l.DenseLayer(net_in,
n_units=gf_dim * 4 * s8 * s8,
W_init=w_init,
act=tf.identity,
name='g/h0/lin')
# net_h0.outputs._shape = (b_size,256*8*8)
net_h0 = l.ReshapeLayer(net_h0,
shape=[-1, s8, s8, gf_dim * 4],
name='g/h0/reshape')
# net_h0.outputs._shape = (b_size,8,8,256)
net_h0 = l.BatchNormLayer(net_h0,
act=tf.nn.relu,
is_train=is_train,
gamma_init=gamma_init,
name='g/h0/batch_norm')
# upsampling
net_h1 = l.DeConv2d(net_h0,
gf_dim * 4, (5, 5),
out_size=(s4, s4),
strides=(2, 2),
padding='SAME',
batch_size=batch_size,
act=None,
W_init=w_init,
name='g/h1/decon2d')
net_h1 = l.BatchNormLayer(net_h1,
act=tf.nn.relu,
is_train=is_train,
gamma_init=gamma_init,
name='g/h1/batch_norm')
# net_h1.outputs._shape = (b_size,16,16,256)
net_h2 = l.DeConv2d(net_h1,
gf_dim * 2, (5, 5),
out_size=(s2, s2),
strides=(2, 2),
padding='SAME',
batch_size=batch_size,
act=None,
W_init=w_init,
name='g/h2/decon2d')
net_h2 = l.BatchNormLayer(net_h2,
act=tf.nn.relu,
is_train=is_train,
gamma_init=gamma_init,
name='g/h2/batch_norm')
# net_h2.outputs._shape = (b_size,32,32,128)
net_h3 = l.DeConv2d(net_h2,
gf_dim // 2, (5, 5),
out_size=(image_size, image_size),
strides=(2, 2),
padding='SAME',
batch_size=batch_size,
act=None,
W_init=w_init,
name='g/h3/decon2d')
net_h3 = l.BatchNormLayer(net_h3,
act=tf.nn.relu,
is_train=is_train,
gamma_init=gamma_init,
name='g/h3/batch_norm')
# net_h3.outputs._shape = (b_size,64,64,32)
# no BN on last deconv
net_h4 = l.DeConv2d(net_h3,
c_dim, (5, 5),
out_size=(image_size, image_size),
strides=(1, 1),
padding='SAME',
batch_size=batch_size,
act=None,
W_init=w_init,
name='g/h4/decon2d')
# net_h4.outputs._shape = (b_size,64,64,3)
# net_h4 = Conv2d(net_h3, c_dim, (5,5),(1,1), padding='SAME', W_init=w_init, name='g/h4/conv2d')
logits = net_h4.outputs
net_h4.outputs = tf.nn.tanh(net_h4.outputs)
return net_h4, logits