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|
- import tensorflow as tf
-
- slim = tf.contrib.slim
-
-
- def vgg_arg_scope(weight_decay=0.0005):
- with slim.arg_scope([slim.conv2d, slim.fully_connected],
- activation_fn=tf.nn.relu,
- weights_regularizer=slim.l2_regularizer(weight_decay),
- biases_initializer=tf.zeros_initializer()):
- with slim.arg_scope([slim.conv2d], padding='SAME') as arg_sc:
- return arg_sc
-
-
- def vgg_16(inputs, scope='vgg_16'):
- with tf.variable_scope(scope, 'vgg_16', [inputs]) as sc:
- with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d]):
- net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
- net = slim.max_pool2d(net, [2, 2], scope='pool1')
- net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
- net = slim.max_pool2d(net, [2, 2], scope='pool2')
- net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
- net = slim.max_pool2d(net, [2, 2], scope='pool3')
- net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
- net = slim.max_pool2d(net, [2, 2], scope='pool4')
- net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
-
- return net
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