本文整理汇总了Python中tensorflow.contrib.slim.python.slim.nets.resnet_v2.resnet_v2_block方法的典型用法代码示例。如果您正苦于以下问题：Python resnet_v2.resnet_v2_block方法的具体用法？Python resnet_v2.resnet_v2_block怎么用？Python resnet_v2.resnet_v2_block使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.contrib.slim.python.slim.nets.resnet_v2的用法示例。
在下文中一共展示了resnet_v2.resnet_v2_block方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: construct_embedding
# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_v2 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_v2 import resnet_v2_block [as 别名]
def construct_embedding(self):
"""Builds an embedding function on top of images.
Method to be overridden by implementations.
Returns:
embeddings: A 2-d float32 `Tensor` of shape [batch_size, embedding_size]
holding the embedded images.
"""
with tf.variable_scope('tcn_net', reuse=self._reuse) as vs:
self._adaptation_scope = vs.name
net = self._pretrained_output
# Define some adaptation blocks on top of the pre-trained resnet output.
adaptation_blocks = []
adaptation_block_params = [map(
int, i.split('_')) for i in self._config.adaptation_blocks.split('-')]
for i, (depth, num_units) in enumerate(adaptation_block_params):
block = resnet_v2.resnet_v2_block(
'adaptation_block_%d' % i, base_depth=depth, num_units=num_units,
stride=1)
adaptation_blocks.append(block)
# Stack them on top of the resent output.
net = resnet_utils.stack_blocks_dense(
net, adaptation_blocks, output_stride=None)
# Average pool the output.
net = tf.reduce_mean(net, [1, 2], name='adaptation_pool', keep_dims=True)
if self._config.emb_connection == 'fc':
# Use fully connected layer to project to embedding layer.
fc_hidden_sizes = self._config.fc_hidden_sizes
if fc_hidden_sizes == 'None':
fc_hidden_sizes = []
else:
fc_hidden_sizes = map(int, fc_hidden_sizes.split('_'))
fc_hidden_keep_prob = self._config.dropout.keep_fc
net = tf.squeeze(net)
for fc_hidden_size in fc_hidden_sizes:
net = slim.layers.fully_connected(net, fc_hidden_size)
if fc_hidden_keep_prob < 1.0:
net = slim.dropout(net, keep_prob=fc_hidden_keep_prob,
is_training=self._is_training)
# Connect last FC layer to embedding.
embedding = slim.layers.fully_connected(net, self._embedding_size,
activation_fn=None)
else:
# Use 1x1 conv layer to project to embedding layer.
embedding = slim.conv2d(
net, self._embedding_size, [1, 1], activation_fn=None,
normalizer_fn=None, scope='embedding')
embedding = tf.squeeze(embedding)
# Optionally L2 normalize the embedding.
if self._embedding_l2:
embedding = tf.nn.l2_normalize(embedding, dim=1)
return embedding