def model(x, y_, reuse, is_train=False):
W_init = tf.truncated_normal_initializer(stddev=5e-2)
W_init2 = tf.truncated_normal_initializer(stddev=0.04)
b_init2 = tf.constant_initializer(value=0.1)
with tf.variable_scope("model", reuse=reuse):
tl.layers.set_name_reuse(reuse)
net = InputLayer(x, name='input')
net = Conv2d(net,
32, (3, 3), (1, 1),
act=tf.nn.relu,
padding='SAME',
W_init=W_init,
name='cnn1')
net = Conv2d(net,
32, (3, 3), (1, 1),
act=tf.nn.relu,
W_init=W_init,
name='cnn2',
padding="VALID")
net = MaxPool2d(net, name='pool1', padding="VALID")
net = DropoutLayer(net, keep=0.75, is_train=is_train, name='drop1')
net = Conv2d(net,
64, (3, 3), (1, 1),
act=tf.nn.relu,
padding='SAME',
W_init=W_init,
name='cnn3')
net = Conv2d(net,
64, (3, 3), (1, 1),
act=tf.nn.relu,
W_init=W_init,
name='cnn4',
padding="VALID")
net = MaxPool2d(net, name='pool2', padding="VALID")
net = DropoutLayer(net, keep=0.75, is_train=is_train, name='drop2')
net = FlattenLayer(net, name='flatten')
net = DenseLayer(net,
n_units=512,
act=tf.nn.relu,
W_init=W_init2,
b_init=b_init2,
name='d1relu')
net = DenseLayer(net,
n_units=10,
act=tf.identity,
W_init=tf.truncated_normal_initializer(stddev=1 /
192.0),
name='output') # output: (batch_size, 10)
y = net.outputs
loss = tl.cost.cross_entropy(y, y_, name='cost')
correct_prediction = tf.equal(tf.argmax(y, 1), y_)
acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
return net, loss, acc
def model(x, is_train, reuse):
with tf.variable_scope("STN", reuse=reuse):
nin = InputLayer(x, name='in')
## 1. Localisation network
# use MLP as the localisation net
nt = FlattenLayer(nin, name='flatten')
nt = DenseLayer(nt, n_units=20, act=tf.nn.tanh, name='dense1')
nt = DropoutLayer(nt, 0.8, True, is_train, name='drop1')
# you can also use CNN instead for MLP as the localisation net
# nt = Conv2d(nin, 16, (3, 3), (2, 2), act=tf.nn.relu, padding='SAME', name='tc1')
# nt = Conv2d(nt, 8, (3, 3), (2, 2), act=tf.nn.relu, padding='SAME', name='tc2')
## 2. Spatial transformer module (sampler)
n = SpatialTransformer2dAffineLayer(nin,
nt,
out_size=[40, 40],
name='spatial')
s = n
## 3. Classifier
n = Conv2d(n,
16, (3, 3), (2, 2),
act=tf.nn.relu,
padding='SAME',
name='conv1')
n = Conv2d(n,
16, (3, 3), (2, 2),
act=tf.nn.relu,
padding='SAME',
name='conv2')
n = FlattenLayer(n, name='flatten2')
n = DenseLayer(n, n_units=1024, act=tf.nn.relu, name='out1')
n = DenseLayer(n, n_units=10, act=tf.identity, name='out2')
return n, s
def _build_net(self, is_train=True, reuse=None):
with tf.variable_scope(self.name, reuse=reuse):
n = InputLayer(self.x / 255, name='in')
n = Conv2d(n, 32, (3, 3), (1, 1), tf.nn.relu, "VALID", name='c1/1')
n = Conv2d(n, 32, (3, 3), (1, 1), tf.nn.relu, "VALID", name='c1/2')
n = MaxPool2d(n, (2, 2), (2, 2), 'VALID', name='max1')
n = DropoutLayer(n,
0.75,
is_fix=True,
is_train=is_train,
name='drop1')
n = Conv2d(n, 64, (3, 3), (1, 1), tf.nn.relu, "VALID", name='c2/1')
n = Conv2d(n, 64, (3, 3), (1, 1), tf.nn.relu, "VALID", name='c2/2')
n = MaxPool2d(n, (2, 2), (2, 2), 'VALID', name='max2')
n = DropoutLayer(n,
0.75,
is_fix=True,
is_train=is_train,
name='drop2')
n = FlattenLayer(n, name='f')
n = DenseLayer(n, 512, tf.nn.relu, name='dense1')
n = DropoutLayer(n,
0.5,
is_fix=True,
is_train=is_train,
name='drop3')
n = DenseLayer(n, n_action, tf.nn.tanh, name='o')
if is_train:
self.n_train = n
else:
self.n_test = n
def squeezenetv1(cls, x, end_with='output', is_train=False, reuse=None):
with tf.compat.v1.variable_scope("squeezenetv1", reuse=reuse):
with tf.compat.v1.variable_scope("input"):
n = InputLayer(x)
# n = Conv2d(n, 96, (7,7),(2,2),tf.nn.relu,'SAME',name='conv1')
n = Conv2d(n,
64, (3, 3), (2, 2),
tf.nn.relu,
'SAME',
name='conv1')
n = MaxPool2d(n, (3, 3), (2, 2), 'VALID', name='max')
if end_with in n.outputs.name:
return n
with tf.compat.v1.variable_scope("fire2"):
n = Conv2d(n,
16, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='squeeze1x1')
n1 = Conv2d(n,
64, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='expand1x1')
n2 = Conv2d(n,
64, (3, 3), (1, 1),
tf.nn.relu,
'SAME',
name='expand3x3')
n = ConcatLayer([n1, n2], -1, name='concat')
if end_with in n.outputs.name:
return n
with tf.compat.v1.variable_scope("fire3"):
n = Conv2d(n,
16, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='squeeze1x1')
n1 = Conv2d(n,
64, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='expand1x1')
n2 = Conv2d(n,
64, (3, 3), (1, 1),
tf.nn.relu,
'SAME',
name='expand3x3')
n = ConcatLayer([n1, n2], -1, name='concat')
n = MaxPool2d(n, (3, 3), (2, 2), 'VALID', name='max')
if end_with in n.outputs.name:
return n
with tf.compat.v1.variable_scope("fire4"):
n = Conv2d(n,
32, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='squeeze1x1')
n1 = Conv2d(n,
128, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='expand1x1')
n2 = Conv2d(n,
128, (3, 3), (1, 1),
tf.nn.relu,
'SAME',
name='expand3x3')
n = ConcatLayer([n1, n2], -1, name='concat')
if end_with in n.outputs.name:
return n
with tf.compat.v1.variable_scope("fire5"):
n = Conv2d(n,
32, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='squeeze1x1')
n1 = Conv2d(n,
128, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='expand1x1')
n2 = Conv2d(n,
128, (3, 3), (1, 1),
tf.nn.relu,
'SAME',
name='expand3x3')
n = ConcatLayer([n1, n2], -1, name='concat')
n = MaxPool2d(n, (3, 3), (2, 2), 'VALID', name='max')
if end_with in n.outputs.name:
return n
with tf.compat.v1.variable_scope("fire6"):
n = Conv2d(n,
48, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='squeeze1x1')
n1 = Conv2d(n,
192, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='expand1x1')
n2 = Conv2d(n,
192, (3, 3), (1, 1),
tf.nn.relu,
'SAME',
name='expand3x3')
n = ConcatLayer([n1, n2], -1, name='concat')
if end_with in n.outputs.name:
return n
with tf.compat.v1.variable_scope("fire7"):
n = Conv2d(n,
48, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='squeeze1x1')
n1 = Conv2d(n,
192, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='expand1x1')
n2 = Conv2d(n,
192, (3, 3), (1, 1),
tf.nn.relu,
'SAME',
name='expand3x3')
n = ConcatLayer([n1, n2], -1, name='concat')
if end_with in n.outputs.name:
return n
with tf.compat.v1.variable_scope("fire8"):
n = Conv2d(n,
64, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='squeeze1x1')
n1 = Conv2d(n,
256, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='expand1x1')
n2 = Conv2d(n,
256, (3, 3), (1, 1),
tf.nn.relu,
'SAME',
name='expand3x3')
n = ConcatLayer([n1, n2], -1, name='concat')
if end_with in n.outputs.name:
return n
with tf.compat.v1.variable_scope("fire9"):
n = Conv2d(n,
64, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='squeeze1x1')
n1 = Conv2d(n,
256, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='expand1x1')
n2 = Conv2d(n,
256, (3, 3), (1, 1),
tf.nn.relu,
'SAME',
name='expand3x3')
n = ConcatLayer([n1, n2], -1, name='concat')
if end_with in n.outputs.name:
return n
with tf.compat.v1.variable_scope("output"):
n = DropoutLayer(n,
keep=0.5,
is_fix=True,
is_train=is_train,
name='drop1')
n = Conv2d(n,
1000, (1, 1), (1, 1),
padding='VALID',
name='conv10') # 13, 13, 1000
n = GlobalMeanPool2d(n)
if end_with in n.outputs.name:
return n
raise Exception("end_with : input, fire2, fire3 ... fire9, output")
def __get_network__(self,
model_name,
encode_seqs,
reuse=False,
is_train=True):
with tf.variable_scope(model_name, reuse=reuse):
tl.layers.set_name_reuse(reuse)
net_in = InputLayer(inputs=encode_seqs, name="in_word_embed")
'''
net_in = ReshapeLayer(
net_in,
(-1, self.max_length, self.word_embedding_dim, 1),
name="reshape"
)
'''
filter_length = [2, 4, 8]
n_filter = 600
net_cnn_list = list()
for fsz in filter_length:
net_cnn = Conv1d(net_in,
n_filter=n_filter,
filter_size=fsz,
stride=1,
act=tf.nn.relu,
name="cnn%d" % fsz)
net_cnn.outputs = tf.reduce_max(net_cnn.outputs,
axis=1,
name="global_maxpool%d" % fsz)
net_cnn_list.append(net_cnn)
net_cnn = ConcatLayer(net_cnn_list, concat_dim=-1)
'''
net_cnn = Conv1d(net_in, 400, 8, act=tf.nn.relu, name="cnn_1")
net_cnn = MaxPool1d(net_cnn, 2, 2, padding="valid", name="maxpool_1")
net_cnn = Conv1d(net_cnn, 600, 4, act=tf.nn.relu, name="cnn_2")
net_cnn = MaxPool1d(net_cnn, 2, 2, padding="valid", name="maxpool_2")
net_cnn = Conv1d(net_cnn, 600, 2, act=tf.nn.relu, name="cnn_3")
net_cnn = MaxPool1d(net_cnn, 2, 2, padding="valid", name="maxpool_3")
net_cnn = FlattenLayer(net_cnn, name="flatten")
'''
'''
net_cnn = Conv2d(net_in, 64, (8, 8), act=tf.nn.relu, name="cnn_1")
net_cnn = MaxPool2d(net_cnn, (2, 2), padding="valid", name="maxpool_1")
net_cnn = Conv2d(net_cnn, 32, (4, 4), act=tf.nn.relu, name="cnn_2")
net_cnn = MaxPool2d(net_cnn, (2, 4), padding="valid", name="maxpool_2")
net_cnn = Conv2d(net_cnn, 8, (2, 2), act=tf.nn.relu, name="cnn_3")
net_cnn = MaxPool2d(net_cnn, (2, 2), padding="valid", name="maxpool_3")
net_cnn = FlattenLayer(net_cnn, name="flatten")
'''
net_cnn = DropoutLayer(net_cnn,
keep=0.5,
is_fix=True,
is_train=is_train,
name='drop1')
net_fc = DenseLayer(net_cnn,
n_units=400,
act=tf.nn.relu,
name="fc_1")
net_fc = DropoutLayer(net_fc,
keep=0.5,
is_fix=True,
is_train=is_train,
name='drop2')
net_fc = DenseLayer(net_fc,
n_units=100,
act=tf.nn.relu,
name="fc_2")
net_fc = DropoutLayer(net_fc,
keep=0.5,
is_fix=True,
is_train=is_train,
name='drop3')
net_fc = DenseLayer(net_fc,
n_units=self.number_of_seen_classes,
act=tf.nn.relu,
name="fc_3")
return net_fc
def __get_network__(self, model_name, encode_seqs, class_label_seqs, kg_vector, reuse=False, is_train=True):
with tf.variable_scope(model_name, reuse=reuse):
tl.layers.set_name_reuse(reuse)
net_word_embed = InputLayer(
inputs=encode_seqs,
name="in_word_embed"
)
net_class_label_embed = InputLayer(
inputs=class_label_seqs,
name="in_class_label_embed"
)
net_kg = InputLayer(
inputs=kg_vector,
name='in_kg'
)
net_kg = ReshapeLayer(
net_kg,
shape=(-1, self.kg_embedding_dim),
name="reshape_kg_1"
)
net_kg = ReshapeLayer(
net_kg,
shape=(-1, self.max_length, self.kg_embedding_dim),
name="reshape_kg_2"
)
if config.model == "vwvcvkg":
# dbpedia and 20news
net_in = ConcatLayer(
[net_word_embed, net_class_label_embed, net_kg],
concat_dim=-1,
name='concat_vw_vwc_vc'
)
elif config.model == "vwvc":
net_in = ConcatLayer(
[net_word_embed, net_class_label_embed],
concat_dim=-1,
name='concat_vw_vc'
)
elif config.model == "vwvkg":
net_in = ConcatLayer(
[net_word_embed, net_kg],
concat_dim=-1,
name='concat_vw_vwc'
)
elif config.model == "vcvkg":
net_in = ConcatLayer(
[net_class_label_embed, net_kg],
concat_dim=-1,
name='concat_vc_vwc'
)
elif config.model == "kgonly":
net_in = ConcatLayer(
[net_kg],
concat_dim=-1,
name='concat_vwc'
)
else:
raise Exception("config.model value error")
filter_length = [2, 4, 8]
# dbpedia
n_filter = 600
# n_filter = 200
net_cnn_list = list()
for fsz in filter_length:
net_cnn = Conv1d(
net_in,
n_filter=n_filter,
filter_size=fsz,
stride=1,
act=tf.nn.relu,
name="cnn%d" % fsz
)
net_cnn.outputs = tf.reduce_max(net_cnn.outputs, axis=1, name="global_maxpool%d" % fsz)
net_cnn_list.append(net_cnn)
'''
if config.model == "vwvcvkg":
net_class_label_embed.outputs = tf.slice(
net_class_label_embed.outputs,
[0, 0, 0],
[config.batch_size, 1, self.word_embedding_dim],
name="slice_word"
)
net_class_label_embed.outputs = tf.squeeze(
net_class_label_embed.outputs,
name="squeeze_word"
)
net_cnn = ConcatLayer(net_cnn_list + [net_class_label_embed], concat_dim=-1)
else:
net_cnn = ConcatLayer(net_cnn_list, concat_dim=-1)
'''
net_cnn = ConcatLayer(net_cnn_list, concat_dim=-1)
net_fc = DropoutLayer(net_cnn, keep=0.5, is_fix=True, is_train=is_train, name='drop1')
net_fc = DenseLayer(
net_fc,
n_units=400,
act=tf.nn.relu,
name="fc_1"
)
net_fc = DropoutLayer(net_fc, keep=0.5, is_fix=True, is_train=is_train, name='drop2')
# dbpedia
net_fc = DenseLayer(
net_fc,
n_units=100,
act=tf.nn.relu,
name="fc_2"
)
net_fc = DropoutLayer(net_fc, keep=0.5, is_fix=True, is_train=is_train, name='drop3')
net_fc = DenseLayer(
net_fc,
n_units=1,
act=tf.nn.sigmoid,
name="fc_3"
)
return net_fc, net_cnn
def __get_network_rnnfc__(self, model_name, encode_seqs, class_label_seqs, reuse=False, is_train=True):
with tf.variable_scope(model_name, reuse=reuse):
tl.layers.set_name_reuse(reuse)
net_word_embed = InputLayer(
inputs=encode_seqs,
name="in_word_embed"
)
net_class_label_embed = InputLayer(
inputs=class_label_seqs,
name="in_class_label_embed"
)
net_class_label_embed.outputs = tf.slice(
net_class_label_embed.outputs,
[0, 0, 0],
[config.batch_size, 1, self.word_embedding_dim],
name="slice_word"
)
net_class_label_embed.outputs = tf.squeeze(
net_class_label_embed.outputs,
name="squeeze_word"
)
net_in = ConcatLayer(
[net_word_embed],
concat_dim=-1,
name='concat_vw'
)
net_rnn = RNNLayer(
net_in,
cell_fn=tf.contrib.rnn.BasicLSTMCell,
n_hidden = 512,
n_steps = self.max_length,
return_last = True,
name = 'lstm'
)
net_fc = ConcatLayer([net_rnn, net_class_label_embed], concat_dim=-1)
net_fc = DropoutLayer(net_fc, keep=0.5, is_fix=True, is_train=is_train, name='drop1')
net_fc = DenseLayer(
net_fc,
n_units=400,
act=tf.nn.relu,
name="fc_1"
)
net_fc = DropoutLayer(net_fc, keep=0.5, is_fix=True, is_train=is_train, name='drop2')
# dbpedia
net_fc = DenseLayer(
net_fc,
n_units=100,
act=tf.nn.relu,
name="fc_2"
)
net_fc = DropoutLayer(net_fc, keep=0.5, is_fix=True, is_train=is_train, name='drop3')
net_fc = DenseLayer(
net_fc,
n_units=1,
act=tf.nn.sigmoid,
name="fc_3"
)
return net_fc
def __get_network_cnnfc__(self, model_name, encode_seqs, class_label_seqs, reuse=False, is_train=True):
with tf.variable_scope(model_name, reuse=reuse):
tl.layers.set_name_reuse(reuse)
net_word_embed = InputLayer(
inputs=encode_seqs,
name="in_word_embed"
)
net_class_label_embed = InputLayer(
inputs=class_label_seqs,
name="in_class_label_embed"
)
net_class_label_embed.outputs = tf.slice(
net_class_label_embed.outputs,
[0, 0, 0],
[config.batch_size, 1, self.word_embedding_dim],
name="slice_word"
)
net_class_label_embed.outputs = tf.squeeze(
net_class_label_embed.outputs,
name="squeeze_word"
)
net_in = ConcatLayer(
[net_word_embed],
concat_dim=-1,
name='concat_vw'
)
filter_length = [2, 4, 8]
# dbpedia
n_filter = 600
# n_filter = 200
net_cnn_list = list()
for fsz in filter_length:
net_cnn = Conv1d(
net_in,
n_filter=n_filter,
filter_size=fsz,
stride=1,
act=tf.nn.relu,
name="cnn%d" % fsz
)
net_cnn.outputs = tf.reduce_max(net_cnn.outputs, axis=1, name="global_maxpool%d" % fsz)
net_cnn_list.append(net_cnn)
net_cnn = ConcatLayer(net_cnn_list + [net_class_label_embed], concat_dim=-1)
net_fc = DropoutLayer(net_cnn, keep=0.5, is_fix=True, is_train=is_train, name='drop1')
net_fc = DenseLayer(
net_fc,
n_units=400,
act=tf.nn.relu,
name="fc_1"
)
net_fc = DropoutLayer(net_fc, keep=0.5, is_fix=True, is_train=is_train, name='drop2')
# dbpedia
net_fc = DenseLayer(
net_fc,
n_units=100,
act=tf.nn.relu,
name="fc_2"
)
net_fc = DropoutLayer(net_fc, keep=0.5, is_fix=True, is_train=is_train, name='drop3')
net_fc = DenseLayer(
net_fc,
n_units=1,
act=tf.nn.sigmoid,
name="fc_3"
)
return net_fc
def squeezenet(x, is_train=True, reuse=False):
# model from: https://github.com/wohlert/keras-squeezenet
# https://github.com/DT42/squeezenet_demo/blob/master/model.py
with tf.variable_scope("squeezenet", reuse=reuse):
with tf.variable_scope("input"):
n = InputLayer(x)
# n = Conv2d(n, 96, (7,7),(2,2),tf.nn.relu,'SAME',name='conv1')
n = Conv2d(n, 64, (3, 3), (2, 2), tf.nn.relu, 'SAME', name='conv1')
n = MaxPool2d(n, (3, 3), (2, 2), 'VALID', name='max')
with tf.variable_scope("fire2"):
n = Conv2d(n,
16, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='squeeze1x1')
n1 = Conv2d(n,
64, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='expand1x1')
n2 = Conv2d(n,
64, (3, 3), (1, 1),
tf.nn.relu,
'SAME',
name='expand3x3')
n = ConcatLayer([n1, n2], -1, name='concat')
with tf.variable_scope("fire3"):
n = Conv2d(n,
16, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='squeeze1x1')
n1 = Conv2d(n,
64, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='expand1x1')
n2 = Conv2d(n,
64, (3, 3), (1, 1),
tf.nn.relu,
'SAME',
name='expand3x3')
n = ConcatLayer([n1, n2], -1, name='concat')
n = MaxPool2d(n, (3, 3), (2, 2), 'VALID', name='max')
with tf.variable_scope("fire4"):
n = Conv2d(n,
32, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='squeeze1x1')
n1 = Conv2d(n,
128, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='expand1x1')
n2 = Conv2d(n,
128, (3, 3), (1, 1),
tf.nn.relu,
'SAME',
name='expand3x3')
n = ConcatLayer([n1, n2], -1, name='concat')
with tf.variable_scope("fire5"):
n = Conv2d(n,
32, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='squeeze1x1')
n1 = Conv2d(n,
128, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='expand1x1')
n2 = Conv2d(n,
128, (3, 3), (1, 1),
tf.nn.relu,
'SAME',
name='expand3x3')
n = ConcatLayer([n1, n2], -1, name='concat')
n = MaxPool2d(n, (3, 3), (2, 2), 'VALID', name='max')
with tf.variable_scope("fire6"):
n = Conv2d(n,
48, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='squeeze1x1')
n1 = Conv2d(n,
192, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='expand1x1')
n2 = Conv2d(n,
192, (3, 3), (1, 1),
tf.nn.relu,
'SAME',
name='expand3x3')
n = ConcatLayer([n1, n2], -1, name='concat')
with tf.variable_scope("fire7"):
n = Conv2d(n,
48, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='squeeze1x1')
n1 = Conv2d(n,
192, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='expand1x1')
n2 = Conv2d(n,
192, (3, 3), (1, 1),
tf.nn.relu,
'SAME',
name='expand3x3')
n = ConcatLayer([n1, n2], -1, name='concat')
with tf.variable_scope("fire8"):
n = Conv2d(n,
64, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='squeeze1x1')
n1 = Conv2d(n,
256, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='expand1x1')
n2 = Conv2d(n,
256, (3, 3), (1, 1),
tf.nn.relu,
'SAME',
name='expand3x3')
n = ConcatLayer([n1, n2], -1, name='concat')
with tf.variable_scope("fire9"):
n = Conv2d(n,
64, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='squeeze1x1')
n1 = Conv2d(n,
256, (1, 1), (1, 1),
tf.nn.relu,
'SAME',
name='expand1x1')
n2 = Conv2d(n,
256, (3, 3), (1, 1),
tf.nn.relu,
'SAME',
name='expand3x3')
n = ConcatLayer([n1, n2], -1, name='concat')
with tf.variable_scope("output"):
n = DropoutLayer(n,
keep=0.5,
is_fix=True,
is_train=is_train,
name='drop1')
n = Conv2d(n, 1000, (1, 1), (1, 1), padding='VALID',
name='conv10') # 13, 13, 1000
n = GlobalMeanPool2d(n)
return n
def create_model(self):
if self.name == 'mnist-mnistm':
drop_prob = {'Ft': 0.2, 'F1': 0.5, 'F2': 0.5}
self.x = tf.placeholder(tf.float32, shape=[None, 28, 28, 3])
self.y_ = tf.placeholder(tf.float32, shape=[None, 10])
self.istrain = tf.placeholder(tf.bool, shape=[])
_input = InputLayer(self.x, name='input_layer')
_shared_net = _input
_shared_net = Conv2d(_shared_net,
n_filter=32,
filter_size=(5, 5),
strides=(1, 1),
act=tf.nn.relu,
padding='SAME',
name='cnn1')
_shared_net = MaxPool2d(_shared_net,
filter_size=(2, 2),
strides=(2, 2),
padding='SAME',
name='pool_layer1')
_shared_net = Conv2d(_shared_net,
n_filter=48,
filter_size=(5, 5),
strides=(1, 1),
act=tf.identity,
padding='SAME',
name='cnn2')
_shared_net = BatchNormLayer(_shared_net,
is_train=True,
act=tf.nn.relu)
_shared_net = MaxPool2d(_shared_net,
filter_size=(2, 2),
strides=(2, 2),
padding='SAME',
name='pool_layer2')
_shared_net = FlattenLayer(_shared_net)
feature = _shared_net.outputs
_F1_net = _shared_net
_F2_net = _shared_net
_Ft_net = _shared_net
with tf.variable_scope("F1") as scope:
_F1_net = DropoutLayer(_F1_net,
keep=drop_prob['F1'],
name='drop1',
is_fix=True,
is_train=self.istrain)
_F1_net = DenseLayer(_F1_net,
n_units=100,
act=tf.identity,
name='relu1')
_F1_net = BatchNormLayer(_F1_net,
is_train=True,
act=tf.nn.relu,
name='bn1')
_F1_net = DropoutLayer(_F1_net,
keep=drop_prob['F1'],
name='drop2',
is_fix=True,
is_train=self.istrain)
_F1_net = DenseLayer(_F1_net,
n_units=100,
act=tf.identity,
name='relu2')
_F1_net = BatchNormLayer(_F1_net,
is_train=True,
act=tf.nn.relu,
name='bn2')
_F1_net = DenseLayer(_F1_net,
n_units=10,
act=tf.nn.softmax,
name='output')
self.F1_out = _F1_net.outputs
with tf.variable_scope("F2") as scope:
_F2_net = DropoutLayer(_F2_net,
keep=drop_prob['F2'],
name='drop1',
is_fix=True,
is_train=self.istrain)
_F2_net = DenseLayer(_F2_net,
n_units=100,
act=tf.identity,
name='relu1')
_F2_net = BatchNormLayer(_F2_net,
is_train=True,
act=tf.nn.relu,
name='bn1')
_F2_net = DropoutLayer(_F2_net,
keep=drop_prob['F2'],
name='drop2',
is_fix=True,
is_train=self.istrain)
_F2_net = DenseLayer(_F2_net,
n_units=100,
act=tf.identity,
name='relu2')
_F2_net = BatchNormLayer(_F2_net,
is_train=True,
act=tf.nn.relu,
name='bn2')
_F2_net = DenseLayer(_F2_net,
n_units=10,
act=tf.nn.softmax,
name='output')
self.F2_out = _F2_net.outputs
with tf.variable_scope("Ft") as scope:
_Ft_net = DropoutLayer(_Ft_net,
keep=drop_prob['Ft'],
name='drop1',
is_fix=True,
is_train=self.istrain)
_Ft_net = DenseLayer(_Ft_net,
n_units=100,
act=tf.identity,
name='relu1')
_Ft_net = BatchNormLayer(_Ft_net,
is_train=True,
act=tf.nn.relu,
name='bn1')
_Ft_net = DropoutLayer(_Ft_net,
keep=drop_prob['Ft'],
name='drop2',
is_fix=True,
is_train=self.istrain)
_Ft_net = DenseLayer(_Ft_net,
n_units=100,
act=tf.identity,
name='relu2')
_Ft_net = BatchNormLayer(_Ft_net,
is_train=True,
act=tf.nn.relu,
name='bn2')
_Ft_net = DenseLayer(_Ft_net,
n_units=10,
act=tf.nn.softmax,
name='output')
self.Ft_out = _Ft_net.outputs
#self.cost = cross_entropy(F1_out,self.y_,name='F1_loss')#+cross_entropy(F2_out,self.y_,name='F2_loss')+cross_entropy(Ft_out,self.y_,name='Ft_loss')
self.F1_loss = -tf.reduce_mean(self.y_ * tf.log(self.F1_out))
self.F2_loss = -tf.reduce_mean(self.y_ * tf.log(self.F2_out))
self.Ft_loss = -tf.reduce_mean(self.y_ * tf.log(self.Ft_out))
self.F1_acc = tf.reduce_mean(
tf.cast(
tf.equal(tf.argmax(self.F1_out, 1), tf.argmax(self.y_, 1)),
tf.float32))
self.F2_acc = tf.reduce_mean(
tf.cast(
tf.equal(tf.argmax(self.F2_out, 1), tf.argmax(self.y_, 1)),
tf.float32))
self.Ft_acc = tf.reduce_mean(
tf.cast(
tf.equal(tf.argmax(self.Ft_out, 1), tf.argmax(self.y_, 1)),
tf.float32))
self.cost = self.F1_loss + self.F2_loss + self.Ft_loss + tf.reduce_sum(
tf.abs(
tf.multiply(tf.transpose(_F1_net.all_params[14]),
_F2_net.all_params[14])))
self.labeling_cost = self.F1_loss + self.F2_loss + tf.reduce_sum(
tf.abs(
tf.multiply(tf.transpose(_F1_net.all_params[14]),
_F2_net.all_params[14])))
self.targetspecific_cost = self.Ft_loss
self.F1F2Ft_op = tf.train.AdamOptimizer(
learning_rate=0.01).minimize(self.cost)
self.F1F2_op = tf.train.AdamOptimizer(learning_rate=0.01).minimize(
self.labeling_cost)
self.Ft_op = tf.train.AdamOptimizer(learning_rate=0.01).minimize(
self.targetspecific_cost)
tl.layers.initialize_global_variables(self.sess)
print '********************************************************************************************************************************************************'
_shared_net.print_params()
_shared_net.print_layers()
print '********************************************************************************************************************************************************'
_F1_net.print_params()
_F1_net.print_layers()
print '********************************************************************************************************************************************************'
_F2_net.print_params()
_F2_net.print_layers()
print '********************************************************************************************************************************************************'
_Ft_net.print_params()
_Ft_net.print_layers()