我之前已经问过这个问题，但是我之前没有回答过一个特定的问题。
我试图在Tensorflow中定义一个Siamese网络，如下所示：
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)
如您所见，我在build_model1()函数中定义了Siamese网络。包含函数get_features()，conv()，resblock()和resconv()已包含在内，以便正确理解。
我想问一下，我的实施是否正确？我一直看到人们使用tf.get_variable()初始化权重和偏差来定义一个连体网络（例如，在this SO问题的答案中）。我假设我不必使用tf.get_variable('weights', shape=(x, y), ..)，因为slim.conv2d()可能在内部执行此操作。请帮我疑问。