I know this question has been asked before, however I've a specific question which has not been answered before.
I am trying to define a Siamese network in Tensorflow as follows:
def conv(self, x, num_out_maps, ksize, stride, activation_fn=tf.nn.relu):
padding_length = np.floor((ksize-1)/2).astype(np.int32)
padded_input = tf.pad(x, [[0, 0], [padding_length, padding_length], [padding_length, padding_length], [0, 0]])
return slim.conv2d(padded_input, num_out_maps, ksize, stride, padding='VALID', activation_fn=activation_fn)
def resconv(self, x, num_out_maps, ksize, stride):
# Our residual block is: conv-relu-conv, then element-wise sum
shortcut = None
flag = tf.shape(x)[3] != num_out_maps or stride != 1
conv1 = self.conv(x, num_out_maps, ksize, stride)
conv2 = self.conv(conv1, num_out_maps, ksize, stride, activation_fn=None)
if flag==1:
shortcut = self.conv(x, num_out_maps, ksize, stride)
else:
shortcut = x
return shortcut + conv2
def resblock(self, x, num_blocks, num_out_maps, ksize, stride):
out = x
for i in range(num_blocks):
out = self.resconv(out, num_out_maps, ksize, stride)
return out
def get_features(self, input_image):
conv1 = self.conv(input_image, 32, 5, 2, activation_fn=None)
# 5 residual blocks
out = self.resblock(conv1, 5, 32, 3, 1)
return out
def build_model1(self): # Siamese Code
with tf.variable_scope('siamese', reuse=False):
self.left_features = self.get_features(self.left)
with tf.variable_scope('siamese', reuse=True):
self.right_features = self.get_features(self.right)
As you can see, I am defining Siamese network in build_model1() function. The wrapper functions get_features(), conv(), resblock() and resconv() have been included for proper understanding.
I want to ask, is my implementation correct? I have always seen people using tf.get_variable() for initializing weights and biases to define a Siamese network (such as, in the answer to this SO question). I am assuming I don't have to use tf.get_variable('weights', shape=(x, y), ..) because slim.conv2d() might be doing it internally. Please help me with my doubt.
