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