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inference.py
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inference.py
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import tensorflow as tf
import tensorlayer as tl
class siamese:
# Create model
def __init__(self):
self.x1 = tf.placeholder(tf.float32, [None, 784])
self.x2 = tf.placeholder(tf.float32, [None, 784])
with tf.variable_scope("siamese") as scope:
self.o1 = self.network(self.x1)
scope.reuse_variables()
self.o2 = self.network(self.x2)
# Create loss
self.y_ = tf.placeholder(tf.float32, [None])
self.loss = self.loss_with_spring()
def network(self, x):
weights = []
fc1 = self.fc_layer(x, 1024, "fc1")
ac1 = tf.nn.relu(fc1)
fc2 = self.fc_layer(ac1, 1024, "fc2")
ac2 = tf.nn.relu(fc2)
fc3 = self.fc_layer(ac2, 2, "fc3")
return fc3
def fc_layer(self, bottom, n_weight, name):
assert len(bottom.get_shape()) == 2
n_prev_weight = bottom.get_shape()[1]
initer = tf.truncated_normal_initializer(stddev=0.01)
W = tf.get_variable(name+'W', dtype=tf.float32, shape=[n_prev_weight, n_weight], initializer=initer)
b = tf.get_variable(name+'b', dtype=tf.float32, initializer=tf.constant(0.01, shape=[n_weight], dtype=tf.float32))
fc = tf.nn.bias_add(tf.matmul(bottom, W), b)
return fc
def loss_with_spring(self):
margin = 5.0
labels_t = self.y_
labels_f = tf.sub(1.0, self.y_, name="1-yi") # labels_ = !labels;
eucd2 = tf.pow(tf.sub(self.o1, self.o2), 2)
eucd2 = tf.reduce_sum(eucd2, 1)
eucd = tf.sqrt(eucd2+1e-6, name="eucd")
C = tf.constant(margin, name="C")
# yi*||CNN(p1i)-CNN(p2i)||^2 + (1-yi)*max(0, C-||CNN(p1i)-CNN(p2i)||^2)
pos = tf.mul(labels_t, eucd2, name="yi_x_eucd2")
# neg = tf.mul(labels_f, tf.sub(0.0,eucd2), name="yi_x_eucd2")
# neg = tf.mul(labels_f, tf.maximum(0.0, tf.sub(C,eucd2)), name="Nyi_x_C-eucd_xx_2")
neg = tf.mul(labels_f, tf.pow(tf.maximum(tf.sub(C, eucd), 0), 2), name="Nyi_x_C-eucd_xx_2")
losses = tf.add(pos, neg, name="losses")
loss = tf.reduce_mean(losses, name="loss")
return loss
def loss_with_step(self):
margin = 5.0
labels_t = self.y_
labels_f = tf.sub(1.0, self.y_, name="1-yi") # labels_ = !labels;
eucd2 = tf.pow(tf.sub(self.o1, self.o2), 2)
eucd2 = tf.reduce_sum(eucd2, 1)
eucd = tf.sqrt(eucd2+1e-6, name="eucd")
C = tf.constant(margin, name="C")
pos = tf.mul(labels_t, eucd, name="y_x_eucd")
neg = tf.mul(labels_f, tf.maximum(0.0, tf.sub(C, eucd)), name="Ny_C-eucd")
losses = tf.add(pos, neg, name="losses")
loss = tf.reduce_mean(losses, name="loss")
return loss
class siamese_tl:
# Create model
def __init__(self):
self.x1 = tf.placeholder(tf.float32, [None, 784])
self.x2 = tf.placeholder(tf.float32, [None, 784])
with tf.variable_scope("siamese") as scope:
self.o1 = self.network(self.x1)
scope.reuse_variables()
self.o2 = self.network(self.x2)
# Create loss
self.y_ = tf.placeholder(tf.float32, [None])
self.loss = self.loss_with_spring()
def network(self, x):
# Define the neural network structure
network = tl.layers.InputLayer(x, name='input_layer')
network = tl.layers.DenseLayer(network, n_units=1024, act = tf.nn.relu, name='relu1')
network = tl.layers.DenseLayer(network, n_units=1024, act = tf.nn.relu, name='relu2')
network = tl.layers.DenseLayer(network, n_units=2, act = tf.identity, name='output_layer')
return network.outputs
def fc_layer(self, bottom, n_weight, name):
assert len(bottom.get_shape()) == 2
n_prev_weight = bottom.get_shape()[1]
initer = tf.truncated_normal_initializer(stddev=0.01)
W = tf.get_variable(name+'W', dtype=tf.float32, shape=[n_prev_weight, n_weight], initializer=initer)
b = tf.get_variable(name+'b', dtype=tf.float32, initializer=tf.constant(0.01, shape=[n_weight], dtype=tf.float32))
fc = tf.nn.bias_add(tf.matmul(bottom, W), b)
return fc
def loss_with_spring(self):
margin = 5.0
labels_t = self.y_
labels_f = tf.sub(1.0, self.y_, name="1-yi") # labels_ = !labels;
eucd2 = tf.pow(tf.sub(self.o1, self.o2), 2)
eucd2 = tf.reduce_sum(eucd2, 1)
eucd = tf.sqrt(eucd2+1e-6, name="eucd")
C = tf.constant(margin, name="C")
# yi*||CNN(p1i)-CNN(p2i)||^2 + (1-yi)*max(0, C-||CNN(p1i)-CNN(p2i)||^2)
pos = tf.mul(labels_t, eucd2, name="yi_x_eucd2")
# neg = tf.mul(labels_f, tf.sub(0.0,eucd2), name="yi_x_eucd2")
# neg = tf.mul(labels_f, tf.maximum(0.0, tf.sub(C,eucd2)), name="Nyi_x_C-eucd_xx_2")
neg = tf.mul(labels_f, tf.pow(tf.maximum(tf.sub(C, eucd), 0), 2), name="Nyi_x_C-eucd_xx_2")
losses = tf.add(pos, neg, name="losses")
loss = tf.reduce_mean(losses, name="loss")
return loss
def loss_with_step(self):
margin = 5.0
labels_t = self.y_
labels_f = tf.sub(1.0, self.y_, name="1-yi") # labels_ = !labels;
eucd2 = tf.pow(tf.sub(self.o1, self.o2), 2)
eucd2 = tf.reduce_sum(eucd2, 1)
eucd = tf.sqrt(eucd2+1e-6, name="eucd")
C = tf.constant(margin, name="C")
pos = tf.mul(labels_t, eucd, name="y_x_eucd")
neg = tf.mul(labels_f, tf.maximum(0.0, tf.sub(C, eucd)), name="Ny_C-eucd")
losses = tf.add(pos, neg, name="losses")
loss = tf.reduce_mean(losses, name="loss")
return loss
class siamese_tl2:
# Create model
def __init__(self):
self.x1 = tf.placeholder(tf.float32, [None, 784])
self.x2 = tf.placeholder(tf.float32, [None, 784])
#with tf.variable_scope("siamese") as scope:
self.o1 = self.network(self.x1, reuse=False) #
#scope.reuse_variables()
self.o2 = self.network(self.x2, reuse=True) #
# Create loss
self.y_ = tf.placeholder(tf.float32, [None])
self.loss = self.loss_with_spring()
def network(self, x, reuse):
# Define the neural network structure
with tf.variable_scope("siamese", reuse=reuse):
tl.layers.set_name_reuse(reuse)
network = tl.layers.InputLayer(x, name='input_layer')
network = tl.layers.DenseLayer(network, n_units=1024, act = tf.nn.relu, name='relu1')
network = tl.layers.DenseLayer(network, n_units=1024, act = tf.nn.relu, name='relu2')
network = tl.layers.DenseLayer(network, n_units=2, act = tf.identity, name='output_layer')
return network.outputs
def fc_layer(self, bottom, n_weight, name):
assert len(bottom.get_shape()) == 2
n_prev_weight = bottom.get_shape()[1]
initer = tf.truncated_normal_initializer(stddev=0.01)
W = tf.get_variable(name+'W', dtype=tf.float32, shape=[n_prev_weight, n_weight], initializer=initer)
b = tf.get_variable(name+'b', dtype=tf.float32, initializer=tf.constant(0.01, shape=[n_weight], dtype=tf.float32))
fc = tf.nn.bias_add(tf.matmul(bottom, W), b)
return fc
def loss_with_spring(self):
margin = 5.0
labels_t = self.y_
labels_f = tf.sub(1.0, self.y_, name="1-yi") # labels_ = !labels;
eucd2 = tf.pow(tf.sub(self.o1, self.o2), 2)
eucd2 = tf.reduce_sum(eucd2, 1)
eucd = tf.sqrt(eucd2+1e-6, name="eucd")
C = tf.constant(margin, name="C")
# yi*||CNN(p1i)-CNN(p2i)||^2 + (1-yi)*max(0, C-||CNN(p1i)-CNN(p2i)||^2)
pos = tf.mul(labels_t, eucd2, name="yi_x_eucd2")
# neg = tf.mul(labels_f, tf.sub(0.0,eucd2), name="yi_x_eucd2")
# neg = tf.mul(labels_f, tf.maximum(0.0, tf.sub(C,eucd2)), name="Nyi_x_C-eucd_xx_2")
neg = tf.mul(labels_f, tf.pow(tf.maximum(tf.sub(C, eucd), 0), 2), name="Nyi_x_C-eucd_xx_2")
losses = tf.add(pos, neg, name="losses")
loss = tf.reduce_mean(losses, name="loss")
return loss
def loss_with_step(self):
margin = 5.0
labels_t = self.y_
labels_f = tf.sub(1.0, self.y_, name="1-yi") # labels_ = !labels;
eucd2 = tf.pow(tf.sub(self.o1, self.o2), 2)
eucd2 = tf.reduce_sum(eucd2, 1)
eucd = tf.sqrt(eucd2+1e-6, name="eucd")
C = tf.constant(margin, name="C")
pos = tf.mul(labels_t, eucd, name="y_x_eucd")
neg = tf.mul(labels_f, tf.maximum(0.0, tf.sub(C, eucd)), name="Ny_C-eucd")
losses = tf.add(pos, neg, name="losses")
loss = tf.reduce_mean(losses, name="loss")
return loss
class siamese_tl3:
# Create model
def __init__(self,batch_size):
#batch_size = 20 # actually it is 10, 0~10 are x1, 11~20 are x2
self.x = tf.placeholder(tf.float32, [2*batch_size, 784])
self.o = self.network(self.x, reuse=False)
self.x_ = tf.placeholder(tf.float32, [None, 784])
self.o_ = self.network(self.x_, reuse=True)
# Create loss
self.y_ = tf.placeholder(tf.float32, [batch_size])
self.loss = self.loss_with_spring(batch_size)
def predict(self, image, sess):
#return self.o_.eval({self.x_: image})
return sess.run(self.o_, feed_dict={self.x_: image})
def network(self, x, reuse):
# Define the neural network structure
with tf.variable_scope("siamese", reuse=reuse):
tl.layers.set_name_reuse(reuse)
network = tl.layers.InputLayer(x, name='input_layer')
network = tl.layers.DenseLayer(network, n_units=1024, act = tf.nn.relu, name='relu1')
network = tl.layers.DenseLayer(network, n_units=1024, act = tf.nn.relu, name='relu2')
network = tl.layers.DenseLayer(network, n_units=2, act = tf.identity, name='output_layer')
#network = tl.layers.DenseLayer(network, n_units=1024, act = tf.identity, name='relu3')
return network.outputs
def loss_with_spring(self,batch_size): #contrastive loss
margin = 5.0
labels_t = self.y_
labels_f = tf.sub(1.0, self.y_, name="1-yi") # labels_ = !labels;
eucd2 = tf.pow(tf.sub(self.o[:batch_size], self.o[batch_size:]), 2)
eucd2 = tf.reduce_sum(eucd2, 1)
eucd = tf.sqrt(eucd2+1e-6, name="eucd")
C = tf.constant(margin, name="C")
# yi*||CNN(p1i)-CNN(p2i)||^2 + (1-yi)*max(0, C-||CNN(p1i)-CNN(p2i)||^2)
pos = tf.mul(labels_t, eucd2, name="yi_x_eucd2")
# neg = tf.mul(labels_f, tf.sub(0.0,eucd2), name="yi_x_eucd2")
# neg = tf.mul(labels_f, tf.maximum(0.0, tf.sub(C,eucd2)), name="Nyi_x_C-eucd_xx_2")
neg = tf.mul(labels_f, tf.pow(tf.maximum(tf.sub(C, eucd), 0), 2), name="Nyi_x_C-eucd_xx_2")
losses = tf.add(pos, neg, name="losses")
loss = tf.reduce_mean(losses, name="loss")
return 0.5*loss
def loss_with_step(self):
margin = 5.0
labels_t = self.y_
labels_f = tf.sub(1.0, self.y_, name="1-yi") # labels_ = !labels;
eucd2 = tf.pow(tf.sub(self.o1, self.o2), 2)
eucd2 = tf.reduce_sum(eucd2, 1)
eucd = tf.sqrt(eucd2+1e-6, name="eucd")
C = tf.constant(margin, name="C")
pos = tf.mul(labels_t, eucd, name="y_x_eucd")
neg = tf.mul(labels_f, tf.maximum(0.0, tf.sub(C, eucd)), name="Ny_C-eucd")
losses = tf.add(pos, neg, name="losses")
loss = tf.reduce_mean(losses, name="loss")
return loss
# def train(self, X_train, y_train):
# err, _ = sess.run([self.loss, self.train_op], feed_dict={self.x: X_train, self.y_:y_train})