def resNet_v1(self):
model_params = {
"conv1": [5, 5, 64],
"rb1_1": [3, 3, 64],
"rb1_2": [3, 3, 64],
"rb2_1": [3, 3, 128],
"rb2_2": [3, 3, 128],
"fc3": 10,
}
with tf.name_scope("resNet_v1"):
net = nf.convolution_layer(self.inputs,
model_params["conv1"], [1, 2, 2, 1],
name="conv1")
id_rb1 = tf.nn.max_pool(net,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME')
net = nf.convolution_layer(id_rb1,
model_params["rb1_1"], [1, 1, 1, 1],
name="rb1_1")
id_rb2 = nf.convolution_layer(net,
model_params["rb1_2"], [1, 1, 1, 1],
name="rb1_2")
id_rb2 = nf.shortcut(id_rb2, id_rb1, name="rb1")
net = nf.convolution_layer(id_rb2,
model_params["rb2_1"], [1, 2, 2, 1],
padding="SAME",
name="rb2_1")
id_rb3 = nf.convolution_layer(net,
model_params["rb2_2"], [1, 1, 1, 1],
name="rb2_2")
id_rb3 = nf.shortcut(id_rb3, id_rb2, name="rb2")
net = nf.global_avg_pooling(id_rb3, flatten=True)
net = tf.layers.dropout(net,
rate=self.dropout,
training=self.is_training,
name='dropout2')
logits = nf.fc_layer(net,
model_params["fc3"],
name="logits",
activat_fn=None)
return logits
def baseline(self, kwargs={}):
init = tf.random_normal_initializer(stddev=0.01)
feature_size = 64
model_params = {
'conv1': [11, 11, feature_size * 2],
'conv2': [5, 5, feature_size * 4],
'resblock': [3, 3, feature_size * 4],
'conv3': [3, 3, feature_size * 4],
'fc4': 64 * 64 * 4,
'fc5': 64 * 64 * 2,
'fc_code': 1024,
}
### Generator
num_resblock = 16
g_input = self.inputs
with tf.name_scope("Detector"):
# 256x256x1
x = nf.convolution_layer(g_input,
model_params["conv1"], [1, 2, 2, 1],
name="conv1",
activat_fn=tf.nn.relu,
initializer=init)
conv_1 = tf.nn.max_pool(x,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='VALID')
print("conv_1: %s" % conv_1.get_shape())
x = nf.convolution_layer(conv_1,
model_params["conv2"], [1, 2, 2, 1],
name="conv2",
activat_fn=tf.nn.relu,
initializer=init)
conv_2 = tf.nn.max_pool(x,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='VALID')
print("conv_2: %s" % conv_2.get_shape())
x = conv_2
# 128x128xfeature_size
with tf.variable_scope("detector_resblock", reuse=False):
#Add the residual blocks to the model
for i in range(num_resblock):
x = nf.resBlock(x,
feature_size * 4,
scale=1,
reuse=False,
idx=i,
initializer=init)
x = nf.convolution_layer(x,
model_params["conv3"], [1, 1, 1, 1],
name="conv3",
activat_fn=tf.nn.relu,
initializer=init)
x += conv_2
print("conv_3: %s" % x.get_shape())
x = nf.convolution_layer(x,
model_params["conv3"], [1, 2, 2, 1],
name="conv4",
activat_fn=tf.nn.relu,
initializer=init,
flatten=True)
print("conv_4: %s" % x.get_shape())
fc_4 = nf.fc_layer(x,
model_params["fc4"],
name="fc_4",
activat_fn=tf.nn.relu)
print("fc_4: %s" % fc_4.get_shape())
fc_5 = nf.fc_layer(fc_4,
model_params["fc5"],
name="fc_5",
activat_fn=tf.nn.relu)
print("fc_5: %s" % fc_5.get_shape())
fc_code = nf.fc_layer(fc_5,
model_params["fc_code"],
name="fc_code",
activat_fn=None)
print("fc_code: %s" % fc_code.get_shape())
return fc_code
def alex_net(self, kwargs={}):
init = tf.random_normal_initializer(stddev=0.01)
l2_reg = tf.contrib.layers.l2_regularizer(1e-5)
model_params = {
'conv1': [11, 11, 96],
'conv2': [5, 5, 256],
'conv3': [3, 3, 384],
'conv4': [3, 3, 384],
'conv5': [3, 3, 256],
'fc6': 8192,
'fc7': 8192,
'fc_code': 4096,
# 'fc7': 4096,
# 'fc_code': 1024,
}
with tf.name_scope("Detector"):
conv_1 = nf.convolution_layer(self.inputs,
model_params["conv1"], [1, 4, 4, 1],
name="conv1",
activat_fn=tf.nn.relu,
initializer=init,
reg=l2_reg,
padding='VALID')
conv_1 = tf.nn.max_pool(conv_1,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='VALID')
conv_2 = nf.convolution_layer(conv_1,
model_params["conv2"], [1, 1, 1, 1],
name="conv2",
activat_fn=tf.nn.relu,
initializer=init,
reg=l2_reg,
padding='SAME')
conv_2 = tf.nn.max_pool(conv_2,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='VALID')
conv_3 = nf.convolution_layer(conv_2,
model_params["conv3"], [1, 1, 1, 1],
name="conv3",
activat_fn=tf.nn.relu,
initializer=init,
reg=l2_reg,
padding='SAME')
conv_4 = nf.convolution_layer(conv_3,
model_params["conv4"], [1, 1, 1, 1],
name="conv4",
activat_fn=tf.nn.relu,
initializer=init,
reg=l2_reg,
padding='SAME')
conv_5 = nf.convolution_layer(conv_4,
model_params["conv5"], [1, 1, 1, 1],
name="conv5",
activat_fn=tf.nn.relu,
initializer=init,
reg=l2_reg,
padding='SAME')
conv_5 = tf.nn.max_pool(conv_5,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='VALID')
conv_5 = tf.reshape(conv_5,
[-1, int(np.prod(conv_5.get_shape()[1:]))],
name="conv5_flatout")
fc6 = nf.fc_layer(conv_5,
model_params["fc6"],
name="fc6",
activat_fn=tf.nn.relu,
reg=l2_reg)
fc7 = nf.fc_layer(fc6,
model_params["fc7"],
name="fc7",
activat_fn=tf.nn.relu,
reg=l2_reg)
dropout = tf.layers.dropout(fc7,
rate=self.dropout,
training=self.is_training,
name='dropout2')
fc_code = nf.fc_layer(dropout,
model_params["fc_code"],
name="fc_code",
activat_fn=None,
reg=l2_reg)
return fc_code
def googleLeNet_v1(self):
model_params = {
"conv1": [5, 5, 64],
"conv2": [3, 3, 128],
"inception_1": {
"1x1": 64,
"3x3": {
"1x1": 96,
"3x3": 128
},
"5x5": {
"1x1": 16,
"5x5": 32
},
"s1x1": 32
},
"inception_2": {
"1x1": 128,
"3x3": {
"1x1": 128,
"3x3": 192
},
"5x5": {
"1x1": 32,
"5x5": 96
},
"s1x1": 64
},
"fc3": 10,
}
with tf.name_scope("googleLeNet_v1"):
net = nf.convolution_layer(self.inputs,
model_params["conv1"], [1, 2, 2, 1],
name="conv1")
net = tf.nn.max_pool(net,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME')
net = tf.nn.local_response_normalization(
net,
depth_radius=5,
bias=1.0,
alpha=0.0001,
beta=0.75,
name='LocalResponseNormalization')
net = nf.convolution_layer(net,
model_params["conv2"], [1, 1, 1, 1],
name="conv2",
flatten=False)
net = tf.nn.local_response_normalization(
net,
depth_radius=5,
bias=1.0,
alpha=0.0001,
beta=0.75,
name='LocalResponseNormalization')
net = nf.inception_v1(net,
model_params,
name="inception_1",
flatten=False)
net = nf.inception_v1(net,
model_params,
name="inception_2",
flatten=False)
net = tf.nn.avg_pool(net,
ksize=[1, 3, 3, 1],
strides=[1, 1, 1, 1],
padding='VALID')
net = tf.reshape(net, [-1, int(np.prod(net.get_shape()[1:]))])
net = tf.layers.dropout(net,
rate=self.dropout,
training=self.is_training,
name='dropout2')
logits = nf.fc_layer(net,
model_params["fc3"],
name="logits",
activat_fn=None)
return logits
def attention_network(image_input, layers, channels, is_training):
with tf.variable_scope("attention"):
att_net = nf.convolution_layer(image_input, [3, 3, 64],
[1, 2, 2, 1],
name="conv1-1")
att_net = nf.convolution_layer(att_net, [3, 3, 64],
[1, 1, 1, 1],
name="conv1-2")
att_net = tf.nn.max_pool(att_net,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME')
att_net = nf.convolution_layer(att_net, [3, 3, 128],
[1, 1, 1, 1],
name="conv2-1")
att_net = nf.convolution_layer(att_net, [3, 3, 128],
[1, 1, 1, 1],
name="conv2-2")
att_net = tf.nn.max_pool(att_net,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME')
att_net = nf.convolution_layer(att_net, [3, 3, 256],
[1, 1, 1, 1],
name="conv3-1")
att_net = nf.convolution_layer(att_net, [3, 3, 256],
[1, 1, 1, 1],
name="conv3-2")
att_net = tf.nn.max_pool(att_net,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME')
att_net = nf.convolution_layer(att_net, [3, 3, 512],
[1, 1, 1, 1],
name="conv4-1")
#att_net = nf.convolution_layer(att_net, [3,3,512], [1,1,1,1],name="conv4-2")
att_net = tf.nn.max_pool(att_net,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME')
bsize, a, b, c = att_net.get_shape().as_list()
if bsize == None:
bsize = -1
att_net = tf.reshape(
att_net,
[bsize, int(np.prod(att_net.get_shape()[1:]))])
att_net = nf.fc_layer(att_net, 2048, name="fc1")
att_net = nf.fc_layer(att_net, 2048, name="fc2")
#att_net = tf.layers.dropout(att_net, rate=dropout, training=is_training, name='dropout1')
logits = nf.fc_layer(att_net,
channels * layers,
name="logits",
activat_fn=None)
bsize = tf.shape(logits)[0]
#logits = tf.reshape(logits, (bsize,1,1,channels*layers))
logits = tf.reshape(logits, (bsize, 1, 1, channels, layers))
weighting = tf.nn.softmax(logits)
"""
max_index = tf.argmax(tf.nn.softmax(logits),4)
weighting = tf.one_hot(max_index,
depth=layers,
on_value=1.0,
axis = -1)
"""
return weighting
def EXAMPLE_CNN(self, kwargs):
model_params = {
"conv_1": [3, 3, 128],
"conv_2": [3, 3, 256],
"fc_1": 1024,
"fc_2": 512,
"fc_out": 10,
}
reuse = kwargs["reuse"]
l2_reg = tf.contrib.layers.l2_regularizer(1e-5)
print(
"==================================================================="
)
with tf.variable_scope("CNN", reuse=reuse):
input = kwargs["input"]
print("[EXAMPLE_CNN] input: %s" % input.get_shape())
conv_1_1 = nf.convolution_layer(input,
model_params["conv_1"],
[1, 1, 1, 1],
name="conv_1_1",
padding='SAME',
activat_fn=nf.lrelu,
is_bn=True,
is_training=self.is_training,
reg=l2_reg)
conv_1_2 = nf.convolution_layer(conv_1_1,
model_params["conv_1"],
[1, 1, 1, 1],
name="conv_1_2",
padding='SAME',
activat_fn=nf.lrelu,
is_bn=True,
is_training=self.is_training,
reg=l2_reg)
conv_1 = conv_1_1 + conv_1_2
conv_1 = tf.nn.max_pool(conv_1, [1, 2, 2, 1], [1, 2, 2, 1],
padding='VALID')
conv_1 = tf.layers.dropout(conv_1,
rate=self.dropout,
training=self.is_training,
name='conv_1_dropout')
print("conv_1: %s" % conv_1.get_shape())
conv_2_1 = nf.convolution_layer(conv_1,
model_params["conv_2"],
[1, 1, 1, 1],
name="conv_2_1",
padding='SAME',
activat_fn=nf.lrelu,
is_bn=True,
is_training=self.is_training,
reg=l2_reg)
conv_2_2 = nf.convolution_layer(conv_2_1,
model_params["conv_2"],
[1, 1, 1, 1],
name="conv_2_2",
padding='SAME',
activat_fn=nf.lrelu,
is_bn=True,
is_training=self.is_training,
reg=l2_reg)
conv_2 = conv_2_1 + conv_2_2
conv_2 = tf.nn.max_pool(conv_2, [1, 2, 2, 1], [1, 2, 2, 1],
padding='VALID')
conv_2 = tf.layers.dropout(conv_2,
rate=self.dropout,
training=self.is_training,
name='conv_2_dropout')
print("conv_2: %s" % conv_2.get_shape())
conv_code = tf.reshape(conv_2,
[tf.shape(self.inputs)[0], 7 * 7 * 256])
fc_1 = nf.fc_layer(conv_code,
model_params["fc_1"],
name="fc_1",
activat_fn=nf.lrelu,
is_bn=True,
is_training=self.is_training,
reg=l2_reg)
fc_1 = tf.layers.dropout(fc_1,
rate=self.dropout,
training=self.is_training,
name='fc_1_dropout')
print("fc_1: %s" % fc_1.get_shape())
fc_2 = nf.fc_layer(fc_1,
model_params["fc_2"],
name="fc_2",
activat_fn=nf.lrelu,
is_bn=True,
is_training=self.is_training,
reg=l2_reg)
fc_2 = tf.layers.dropout(fc_2,
rate=self.dropout,
training=self.is_training,
name='fc_2_dropout')
print("fc_2: %s" % fc_2.get_shape())
fc_out = nf.fc_layer(fc_2,
model_params["fc_out"],
name="fc_out",
activat_fn=None)
print("fc_out: %s" % fc_out.get_shape())
return fc_out
def ResNet10(self, kwargs):
model_params = {
"conv1": [7, 7, 64],
"conv2_1": [3, 3, 64],
"conv2_2": [3, 3, 64],
"conv3_1": [3, 3, 128],
"conv3_2": [3, 3, 128],
"conv3_sc": [1, 1, 128],
"conv4_1": [3, 3, 256],
"conv4_2": [3, 3, 256],
"conv4_sc": [1, 1, 256],
"conv5_1": [3, 3, 512],
"conv5_2": [3, 3, 512],
"conv5_sc": [1, 1, 512],
"convM_1": [3, 3, 512],
"convM_2": [3, 3, 512],
"fcM_3": 8,
"fcM_4": 2,
}
reuse = kwargs["reuse"]
l2_reg = tf.contrib.layers.l2_regularizer(1e-5)
print(
"==================================================================="
)
with tf.variable_scope("ResNet10", reuse=reuse):
input = kwargs["input"]
print("[ResNet-10] input: %s" % input.get_shape())
# conv 1
x = nf.convolution_layer(input,
model_params["conv1"], [1, 2, 2, 1],
name="conv1",
padding='SAME',
activat_fn=tf.nn.relu,
is_bn=True,
is_training=self.is_training)
x = tf.nn.max_pool(x, [1, 3, 3, 1], [1, 2, 2, 1],
padding='SAME',
name='max_1')
print("conv1: %s" % x.get_shape())
conv1 = x
# conv 2
sc_2 = x
x = nf.convolution_layer(x,
model_params["conv2_1"], [1, 1, 1, 1],
name="conv2_1",
padding='SAME',
activat_fn=tf.nn.relu,
is_bn=True,
is_training=self.is_training)
x = nf.convolution_layer(x,
model_params["conv2_2"], [1, 1, 1, 1],
name="conv2_2",
padding='SAME',
activat_fn=None,
is_bn=True,
is_training=self.is_training)
x = tf.add(x, sc_2)
x = tf.nn.relu(x, name="conv2_2" + "_out")
print("[residual_simple_block] conv2: %s" % x.get_shape())
# conv 3
sc_3 = nf.convolution_layer(x,
model_params["conv3_sc"], [1, 2, 2, 1],
name="conv3_sc",
padding='SAME',
activat_fn=None,
is_bn=True,
is_training=self.is_training)
x = nf.convolution_layer(x,
model_params["conv3_1"], [1, 2, 2, 1],
name="conv3_1",
padding='SAME',
activat_fn=tf.nn.relu,
is_bn=True,
is_training=self.is_training)
x = nf.convolution_layer(x,
model_params["conv3_2"], [1, 1, 1, 1],
name="conv3_2",
padding='SAME',
activat_fn=None,
is_bn=True,
is_training=self.is_training)
x = tf.add(x, sc_3)
x = tf.nn.relu(x, name="conv3_2" + "_out")
print("[residual_simple_block] conv3: %s" % x.get_shape())
# conv 4
sc_4 = nf.convolution_layer(x,
model_params["conv4_sc"], [1, 2, 2, 1],
name="conv4_sc",
padding='SAME',
activat_fn=None,
is_bn=True,
is_training=self.is_training)
x = nf.convolution_layer(x,
model_params["conv4_1"], [1, 2, 2, 1],
name="conv4_1",
padding='SAME',
activat_fn=tf.nn.relu,
is_bn=True,
is_training=self.is_training)
x = nf.convolution_layer(x,
model_params["conv4_2"], [1, 1, 1, 1],
name="conv4_2",
padding='SAME',
activat_fn=None,
is_bn=True,
is_training=self.is_training)
x = tf.add(x, sc_4)
x = tf.nn.relu(x, name="conv4_2" + "_out")
print("[residual_simple_block] conv4: %s" % x.get_shape())
# conv 5
sc_5 = nf.convolution_layer(x,
model_params["conv5_sc"], [1, 2, 2, 1],
name="conv5_sc",
padding='SAME',
activat_fn=None,
is_bn=True,
is_training=self.is_training)
x = nf.convolution_layer(x,
model_params["conv5_1"], [1, 2, 2, 1],
name="conv5_1",
padding='SAME',
activat_fn=tf.nn.relu,
is_bn=True,
is_training=self.is_training)
x = nf.convolution_layer(x,
model_params["conv5_2"], [1, 1, 1, 1],
name="conv5_2",
padding='SAME',
activat_fn=None,
is_bn=True,
is_training=self.is_training)
x = tf.add(x, sc_5)
x = tf.nn.relu(x, name="conv5_2" + "_out")
print("[residual_simple_block] conv5: %s" % x.get_shape())
# module conv 1
x = nf.convolution_layer(x,
model_params["convM_1"], [1, 1, 1, 1],
name="convM_1",
padding='SAME',
activat_fn=tf.nn.relu,
is_bn=True,
is_training=self.is_training,
reg=l2_reg)
x = tf.nn.max_pool(x, [1, 2, 2, 1], [1, 2, 2, 1],
padding='VALID',
name='max_1')
print("[relation_module] convM_1: %s" % x.get_shape())
# module conv 2
x = nf.convolution_layer(x,
model_params["convM_2"], [1, 1, 1, 1],
name="convM_2",
padding='SAME',
activat_fn=tf.nn.relu,
is_bn=True,
is_training=self.is_training,
reg=l2_reg)
x = tf.nn.max_pool(x, [1, 2, 2, 1], [1, 2, 2, 1],
padding='VALID',
name='max_2')
print("[relation_module] convM_2: %s" % x.get_shape())
# module fc 3
x = tf.reshape(x, [tf.shape(input)[0], 3 * 1 * 512])
x = nf.fc_layer(x,
model_params["fcM_3"],
name="fcM_3",
activat_fn=tf.nn.relu,
is_bn=False,
is_training=self.is_training,
reg=l2_reg)
print("[relation_module] fcM_3: %s" % x.get_shape())
# module fc 4
x = nf.fc_layer(x,
model_params["fcM_4"],
name="fcM_4",
activat_fn=None,
is_bn=False,
is_training=self.is_training,
reg=l2_reg)
print("[relation_module] fcM_4: %s" % x.get_shape())
return x, conv1
def CNN_1st_v1(self, kwargs):
model_params = {
"conv_1": [11, 11, 128],
"conv_2": [5, 5, 256],
"conv_3": [3, 3, 512],
"conv_4": [3, 3, 1024],
"conv_5": [3, 3, 1024],
"fc_1": 1024,
"fc_2": 512,
"fc_out": 2,
}
reuse = kwargs["reuse"]
l2_reg = tf.contrib.layers.l2_regularizer(1e-5)
print(
"==================================================================="
)
with tf.variable_scope("CNN", reuse=reuse):
input = kwargs["input"]
print("[CNN_1st_v1] input: %s" % input.get_shape())
conv_1_1 = nf.convolution_layer(input,
model_params["conv_1"],
[1, 4, 4, 1],
name="conv_1_1",
padding='SAME',
activat_fn=nf.lrelu,
is_bn=True,
is_training=self.is_training,
reg=l2_reg)
#conv_1_2 = nf.convolution_layer(conv_1_1, model_params["conv_1"], [1,1,1,1], name="conv_1_2", padding='SAME', activat_fn=nf.lrelu, is_bn=True, is_training=self.is_training, reg=l2_reg)
#conv_1 = conv_1_1 + conv_1_2
conv_1 = tf.nn.max_pool(conv_1_1, [1, 3, 3, 1], [1, 2, 2, 1],
padding='VALID')
conv_1 = tf.layers.dropout(conv_1,
rate=self.dropout,
training=self.is_training,
name='conv_1_dropout')
print("conv_1: %s" % conv_1.get_shape())
conv_2_1 = nf.convolution_layer(conv_1,
model_params["conv_2"],
[1, 2, 2, 1],
name="conv_2_1",
padding='SAME',
activat_fn=nf.lrelu,
is_bn=True,
is_training=self.is_training,
reg=l2_reg)
#conv_2_2 = nf.convolution_layer(conv_2_1, model_params["conv_2"], [1,1,1,1], name="conv_2_2", padding='SAME', activat_fn=nf.lrelu, is_bn=True, is_training=self.is_training, reg=l2_reg)
#conv_2 = conv_2_1 + conv_2_2
conv_2 = tf.nn.max_pool(conv_2_1, [1, 3, 3, 1], [1, 2, 2, 1],
padding='VALID')
conv_2 = tf.layers.dropout(conv_2,
rate=self.dropout,
training=self.is_training,
name='conv_2_dropout')
print("conv_2: %s" % conv_2.get_shape())
conv_3_1 = nf.convolution_layer(conv_2,
model_params["conv_3"],
[1, 1, 1, 1],
name="conv_3_1",
padding='SAME',
activat_fn=nf.lrelu,
is_bn=True,
is_training=self.is_training,
reg=l2_reg)
#conv_3_2 = nf.convolution_layer(conv_3_1, model_params["conv_3"], [1,1,1,1], name="conv_3_2", padding='SAME', activat_fn=nf.lrelu, is_bn=True, is_training=self.is_training, reg=l2_reg)
#conv_3 = conv_3_1 + conv_3_2
conv_3 = tf.layers.dropout(conv_3_1,
rate=self.dropout,
training=self.is_training,
name='conv_3_dropout')
print("conv_3: %s" % conv_3.get_shape())
#conv_4_1 = nf.convolution_layer(conv_3, model_params["conv_4"], [1,1,1,1], name="conv_4_1", padding='SAME', activat_fn=nf.lrelu, is_bn=True, is_training=self.is_training, reg=l2_reg)
#conv_4_2 = nf.convolution_layer(conv_4_1, model_params["conv_4"], [1,1,1,1], name="conv_4_2", padding='SAME', activat_fn=nf.lrelu, is_bn=True, is_training=self.is_training, reg=l2_reg)
#conv_4 = conv_4_1 + conv_4_2
#conv_4 = tf.layers.dropout(conv_4, rate=self.dropout, training=self.is_training, name='conv_4_dropout')
#print("conv_4: %s" % conv_4.get_shape())
conv_5_1 = nf.convolution_layer(conv_3,
model_params["conv_5"],
[1, 1, 1, 1],
name="conv_5_1",
padding='SAME',
activat_fn=nf.lrelu,
is_bn=True,
is_training=self.is_training,
reg=l2_reg)
#conv_5_2 = nf.convolution_layer(conv_5_1, model_params["conv_5"], [1,1,1,1], name="conv_5_2", padding='SAME', activat_fn=nf.lrelu, is_bn=True, is_training=self.is_training, reg=l2_reg)
#conv_5 = conv_5_1 + conv_5_2
conv_5 = tf.nn.max_pool(conv_5_1, [1, 3, 3, 1], [1, 2, 2, 1],
padding='VALID')
conv_5 = tf.layers.dropout(conv_5,
rate=self.dropout,
training=self.is_training,
name='conv_5_dropout')
print("conv_5: %s" % conv_5.get_shape())
conv_code = tf.reshape(conv_5,
[tf.shape(self.inputs)[0], 4 * 2 * 1024])
fc_1 = nf.fc_layer(conv_code,
model_params["fc_1"],
name="fc_1",
activat_fn=nf.lrelu,
is_bn=True,
is_training=self.is_training,
reg=l2_reg)
fc_1 = tf.layers.dropout(fc_1,
rate=self.dropout,
training=self.is_training,
name='fc_1_dropout')
print("fc_1: %s" % fc_1.get_shape())
fc_2 = nf.fc_layer(fc_1,
model_params["fc_2"],
name="fc_2",
activat_fn=nf.lrelu,
is_bn=True,
is_training=self.is_training,
reg=l2_reg)
fc_2 = tf.layers.dropout(fc_2,
rate=self.dropout,
training=self.is_training,
name='fc_2_dropout')
print("fc_2: %s" % fc_2.get_shape())
fc_out = nf.fc_layer(fc_2,
model_params["fc_out"],
name="fc_out",
activat_fn=None)
print("fc_out: %s" % fc_out.get_shape())
return fc_out, conv_1