def decoder(inputs, decode_layer):
net = tflearn.fully_connected(inputs, (side // 2**2)**2 * 32,
name='DecFC1')
d = tf.transpose(net.W)
print "Decoder Weights shape", d.get_shape()
net = tflearn.batch_normalization(net, name='DecBN1')
net = tflearn.elu(net)
print "========================"
print "dec-L1", net.get_shape()
print "========================"
net = tflearn.reshape(net, (-1, side // 2**2, side // 2**2, 32))
net = tflearn.conv_2d(net, 32, 3, name='DecConv1')
net = tflearn.batch_normalization(net, name='DecBN2')
net = tflearn.elu(net)
print "========================"
print "dec-L2", net.get_shape()
print "========================"
net = tflearn.conv_2d_transpose(net,
16,
3, [side // 2, side // 2],
strides=2,
padding='same',
name='DecConvT1')
net = tflearn.batch_normalization(net, name='DecBN3')
net = tflearn.elu(net)
print "========================"
print "dec-L3", net.get_shape()
print "========================"
net = tflearn.conv_2d(net, 16, 3, name='DecConv2')
net = tflearn.batch_normalization(net, name='DecBN4')
net = tflearn.elu(net)
print "========================"
print "dec-L4", net.get_shape()
print "========================"
net = tflearn.conv_2d_transpose(net,
channel,
3, [side, side],
strides=2,
padding='same',
activation='sigmoid',
name='DecConvT2')
print "========================"
print "output layer", net.get_shape()
print "========================"
return [net, d]
def encoder(inputs, hidden_layer):
nb_feature = 64
net = tflearn.conv_2d(inputs, 16, 3, strides=2)
net = tflearn.batch_normalization(net)
net = tflearn.elu(net)
print "========================"
print "enc-L1", net.get_shape()
print "========================"
net = tflearn.conv_2d(net, 16, 3, strides=1)
net = tflearn.batch_normalization(net)
net = tflearn.elu(net)
print "========================"
print "enc-L2", net.get_shape()
print "========================"
net = tflearn.conv_2d(net, 32, 3, strides=2)
net = tflearn.batch_normalization(net)
net = tflearn.elu(net)
print "========================"
print "enc-L3", net.get_shape()
print "========================"
net = tflearn.conv_2d(net, 32, 3, strides=1)
net = tflearn.batch_normalization(net)
net = tflearn.elu(net)
print "========================"
print "enc-L4", net.get_shape()
print "========================"
net = tflearn.flatten(net)
#net = tflearn.fully_connected(net, nb_feature,activation="sigmoid")
net = tflearn.fully_connected(net, nb_feature)
h = net.W
print "Encoder Weights shape", h.get_shape()
net = tflearn.batch_normalization(net)
net = tflearn.sigmoid(net)
print "========================"
print "hidden", net.get_shape()
print "========================"
return [net, h]
def decoder(inputs):
net = tflearn.fully_connected(inputs, 1200 * 32, name='DecFC1')
net = tflearn.batch_normalization(net, name='DecBN1')
net = tflearn.elu(net)
print "========================"
print "dec-L1",net.get_shape()
print "========================"
net = tflearn.reshape(net, (-1, side1 // 2**2, side2 // 2**2, 32))
net = tflearn.conv_2d(net, 32, 3, name='DecConv1')
net = tflearn.batch_normalization(net, name='DecBN2')
net = tflearn.elu(net)
print "========================"
print "dec-L2",net.get_shape()
print "========================"
net = tflearn.conv_2d_transpose(net, 16, 3, [side1 // 2, side2 // 2],
strides=2, padding='same', name='DecConvT1')
net = tflearn.batch_normalization(net, name='DecBN3')
net = tflearn.elu(net)
print "========================"
print "dec-L3",net.get_shape()
print "========================"
net = tflearn.conv_2d(net, 16, 3, name='DecConv2')
net = tflearn.batch_normalization(net, name='DecBN4')
net = tflearn.elu(net)
print "========================"
print "dec-L4",net.get_shape()
print "========================"
net = tflearn.conv_2d_transpose(net, channel, 3, [side1, side2],
strides=2, padding='same', activation='sigmoid',
name='DecConvT2')
decode_layer = net
print "========================"
print "output layer",net.get_shape()
print "========================"
return [net,decode_layer]
def encoder(inputs,hidden_layer):
net = tflearn.conv_2d(inputs, 16, 3, strides=2)
net = tflearn.batch_normalization(net)
net = tflearn.elu(net)
print "========================"
print "enc-L1",net.get_shape()
print "========================"
net = tflearn.conv_2d(net, 16, 3, strides=1)
net = tflearn.batch_normalization(net)
net = tflearn.elu(net)
print "========================"
print "enc-L2",net.get_shape()
print "========================"
net = tflearn.conv_2d(net, 32, 3, strides=2)
net = tflearn.batch_normalization(net)
net = tflearn.elu(net)
print "========================"
print "enc-L3",net.get_shape()
print "========================"
net = tflearn.conv_2d(net, 32, 3, strides=1)
net = tflearn.batch_normalization(net)
net = tflearn.elu(net)
print "========================"
print "enc-L4",net.get_shape()
print "========================"
net = tflearn.flatten(net)
#net = tflearn.fully_connected(net, nb_feature,activation="sigmoid")
net = tflearn.fully_connected(net, nb_feature)
hidden_layer = net
net = tflearn.batch_normalization(net)
net = tflearn.sigmoid(net)
print "========================"
print "hidden",net.get_shape()
print "========================"
return [net,hidden_layer]
def encoder(self,inputs,hidden_layer): net = tflearn.conv_2d(inputs, 16, 3, strides=2) net = tflearn.batch_normalization(net) net = tflearn.elu(net) print "========================" print "enc-L1",net.get_shape() print "========================" net = tflearn.conv_2d(net, 16, 3, strides=1) net = tflearn.batch_normalization(net) net = tflearn.elu(net) print "========================" print "enc-L2",net.get_shape() print "========================" net = tflearn.conv_2d(net, 32, 3, strides=2) net = tflearn.batch_normalization(net) net = tflearn.elu(net) print "========================" print "enc-L3",net.get_shape() print "========================" net = tflearn.conv_2d(net, 32, 3, strides=1) net = tflearn.batch_normalization(net) net = tflearn.elu(net) print "========================" print "enc-L4",net.get_shape() print "========================" net = tflearn.flatten(net) #net = tflearn.fully_connected(net, nb_feature,activation="sigmoid") net = tflearn.fully_connected(net, self.instance_dim) hidden_layer = net net = tflearn.batch_normalization(net) net = tflearn.sigmoid(net) print "========================" print "hidden",net.get_shape() print "========================" return [net,hidden_layer]
def encoder(inputs):
net = tflearn.conv_2d(inputs, 16, 3, strides=2)
net = tflearn.batch_normalization(net)
net = tflearn.elu(net)
print "========================"
print "enc-L1",net.get_shape()
print "========================"
net = tflearn.conv_2d(net, 16, 3, strides=1)
net = tflearn.batch_normalization(net)
net = tflearn.elu(net)
print "========================"
print "enc-L2",net.get_shape()
print "========================"
net = tflearn.conv_2d(net, 32, 3, strides=2)
net = tflearn.batch_normalization(net)
net = tflearn.elu(net)
print "enc-L3",net.get_shape()
net = tflearn.conv_2d(net, 32, 3, strides=1)
net = tflearn.batch_normalization(net)
net = tflearn.elu(net)
print "========================"
print "enc-L4",net.get_shape()
print "========================"
net = tflearn.flatten(net)
net = tflearn.fully_connected(net, nb_feature)
hidden_layer = net
net = tflearn.batch_normalization(net)
net = tflearn.sigmoid(net)
print "========================"
print "enc-hidden_L",net.get_shape()
print "========================"
return [net,hidden_layer]
def decoder(self,inputs,decode_layer): net = tflearn.fully_connected(inputs, self.hidden_dim, name='DecFC1') net = tflearn.batch_normalization(net, name='DecBN1') net = tflearn.elu(net) print "========================" print "dec-L1",net.get_shape() print "========================" net = tflearn.reshape(net, (-1, 1, 1, self.hidden_dim)) net = tflearn.conv_2d(net, 32, 3, name='DecConv1') net = tflearn.batch_normalization(net, name='DecBN2') net = tflearn.elu(net) print "========================" print "dec-L2",net.get_shape() print "========================" net = tflearn.conv_2d_transpose(net, 16, 3, [1, self.hidden_dim], strides=2, padding='same', name='DecConvT1') net = tflearn.batch_normalization(net, name='DecBN3') net = tflearn.elu(net) print "========================" print "dec-L3",net.get_shape() print "========================" net = tflearn.conv_2d(net, 16, 3, name='DecConv2') net = tflearn.batch_normalization(net, name='DecBN4') net = tflearn.elu(net) print "========================" print "dec-L4",net.get_shape() print "========================" net = tflearn.conv_2d_transpose(net, 1, 3, [1, self.hidden_dim], strides=2, padding='same', activation='sigmoid', name='DecConvT2') decode_layer = net print "========================" print "output layer",net.get_shape() print "========================" return [net,decode_layer]
