我有两个输入:qi_pos & qi_neg
具有相同形状的输入。它们应该由两个mlp层处理,最后得到两个结果作为分数。这是我的代码:
self.mlp1_pos = nn_layers.full_connect_(qi_pos, 256, activation='relu', use_bn = None, keep_prob=self.keep_prob, name = 'deep_mlp_1')
self.mlp2_pos = nn_layers.full_connect_(self.mlp1_pos, 128, activation='relu', use_bn = True, keep_prob=self.keep_prob, name = 'deep_mlp_2')
self.pos_pair_sim = nn_layers.full_connect_(self.mlp2_pos, 1, activation=None, use_bn = True, keep_prob=self.keep_prob, name = 'deep_mlp_3')
tf.get_variable_scope().reuse_variables()
self.mlp1_neg = nn_layers.full_connect_(qi_neg, 256, activation='relu', use_bn = None, keep_prob=self.keep_prob, name = 'deep_mlp_1')
self.mlp2_neg = nn_layers.full_connect_(self.mlp1_neg, 128, activation='relu', use_bn = True, keep_prob=self.keep_prob, name = 'deep_mlp_2')
self.neg_pair_sim = nn_layers.full_connect_(self.mlp2_neg, 1, activation=None, use_bn = True, keep_prob=self.keep_prob, name = 'deep_mlp_3')
我使用 BN 层来规范化隐藏层中的节点:
def full_connect_(inputs, num_units, activation=None, use_bn = None, keep_prob = 1.0, name='full_connect_'):
with tf.variable_scope(name):
shape = [inputs.get_shape()[-1], num_units]
weight = weight_variable(shape)
bias = bias_variable(shape[-1])
outputs_ = tf.matmul(inputs, weight) + bias
if use_bn:
outputs_ = tf.contrib.layers.batch_norm(outputs_, center=True, scale=True, is_training=True,decay=0.9,epsilon=1e-5, scope='bn')
if activation=="relu":
outputs = tf.nn.relu(outputs_)
elif activation == "tanh":
outputs = tf.tanh(outputs_)
elif activation == "sigmoid":
outputs = tf.nn.sigmoid(outputs_)
else:
outputs = outputs_
return outputs
with tf.name_scope('predictions'):
self.sim_diff = self.pos_pair_sim - self.neg_pair_sim # shape = (batch_size, 1)
self.preds = tf.sigmoid(self.sim_diff) # shape = (batch_size, 1)
self.infers = self.pos_pair_sim
以下是损失定义。似乎没事。
with tf.name_scope('predictions'):
sim_diff = pos_pair_sim - neg_pair_sim
predictions = tf.sigmoid(sim_diff)
self.infers = pos_pair_sim
## loss and optim
with tf.name_scope('loss'):
self.loss = nn_layers.cross_entropy_loss_with_reg(self.labels, self.preds)
tf.summary.scalar('loss', self.loss)
我不确定我是否以正确的方式使用了 BN 图层。我的意思是,BN 参数是从两个独立部分的隐藏单元派生的,这两个隐藏单元基于 qi_pos
张量和qi_neg
张量作为输入。无论如何,任何人都可以帮忙检查一下吗?
你的代码对我来说似乎很好,在网络的不同分支中应用 BN 没有问题。但我想在这里提几点:
-
BN超参数非常标准,所以我通常不会手动设置
decay
,epsilon
和renorm_decay
。这并不意味着您不能更改它们,在大多数情况下根本不必要。 -
您在激活函数之前应用 BN,但是,有证据表明,如果在激活后应用它会更好。例如,请参阅此讨论。再一次,这并不意味着它是一个错误,只是要考虑的另一种架构。