inception_resnet_v2
def iception_resnet_v1(inputs,is_training=True,
dropout_keep_prob=0.8,
bottleneck_layer_size=128,
reuse=None,
scope='InceptionResNetV1'):
bottleneck_layer_size is the full connect output size,and is the embedding size.
构造计算图，prelogits为最后一层的输出
prelogits, _ = network.inference(image_batch, args.keep_probability,
phase_train=phase_train_placeholder, bottleneck_layer_size=args.embedding_size,
weight_decay=args.weight_decay)
# 对最后的输出进行标准化，即为该图像的embedding
embeddings = tf.nn.l2_normalize(prelogits, 1, 1e-10, name='embeddings')
# Split embeddings into anchor, positive and negative and calculate triplet loss
# 将输出的embeddings分为anchor，正样本， 负样本三个部分
anchor, positive, negative = tf.unstack(tf.reshape(embeddings, [-1,3,args.embedding_size]), 3, 1)
#根据上面三个部分计算triplet-loss
triplet_loss = facenet.triplet_loss(anchor, positive, negative, args.alpha)
#定义优化方法
learning_rate = tf.train.exponential_decay(learning_rate_placeholder, global_step,
args.learning_rate_decay_epochs*args.epoch_size, args.learning_rate_decay_factor,
staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
# Calculate the total losses
#加入正则化损失
regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
# 整体的损失即为triplet-loss+正则损失
total_loss = tf.add_n([triplet_loss] + regularization_losses, name='total_loss')
# Build a Graph that trains the model with one batch of examples and updates the model
# parameters
# 用上述定义的优化方法和loss进行优化
train_op = facenet.train(total_loss, global_step, args.optimizer,
learning_rate, args.moving_average_decay, tf.global_variables())
facenet hase two network architecture
inception_resnet_v2 def iception_resnet_v1(inputs,is_training=True, dropout_keep_prob=0.8, bottleneck_layer_size=128, reuse=None, scope='InceptionResNetV1'): bottleneck_layer_size is the full connect output size,and is the embedding size.
构造计算图，prelogits为最后一层的输出