本文整理匯總了Python中tensorflow.contrib.slim.xavier_initializer_conv2d方法的典型用法代碼示例。如果您正苦於以下問題：Python slim.xavier_initializer_conv2d方法的具體用法？Python slim.xavier_initializer_conv2d怎麽用？Python slim.xavier_initializer_conv2d使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tensorflow.contrib.slim的用法示例。
在下文中一共展示了slim.xavier_initializer_conv2d方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: _arg_scope
# 需要導入模塊: from tensorflow.contrib import slim [as 別名]
# 或者: from tensorflow.contrib.slim import xavier_initializer_conv2d [as 別名]
def _arg_scope(self, is_training, reuse=None):
weight_decay = 0.0
keep_probability = 1.0
batch_norm_params = {
'is_training': is_training,
# Decay for the moving averages.
'decay': 0.995,
# epsilon to prevent 0s in variance.
'epsilon': 0.001
}
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_initializer=slim.xavier_initializer_conv2d(uniform=True),
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params):
with tf.variable_scope(self._scope, self._scope, reuse=reuse):
with slim.arg_scope([slim.batch_norm, slim.dropout],
is_training=is_training) as sc:
return sc 示例2: inference
# 需要導入模塊: from tensorflow.contrib import slim [as 別名]
# 或者: from tensorflow.contrib.slim import xavier_initializer_conv2d [as 別名]
def inference(images, keep_probability, phase_train=True, bottleneck_layer_size=128, weight_decay=0.0, reuse=None):
batch_norm_params = {
# Decay for the moving averages.
'decay': 0.995,
# epsilon to prevent 0s in variance.
'epsilon': 0.001,
# force in-place updates of mean and variance estimates
'updates_collections': None,
# Moving averages ends up in the trainable variables collection
'variables_collections': [ tf.GraphKeys.TRAINABLE_VARIABLES ],
}
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_initializer=slim.xavier_initializer_conv2d(uniform=True),
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params):
with tf.variable_scope('squeezenet', [images], reuse=reuse):
with slim.arg_scope([slim.batch_norm, slim.dropout],
is_training=phase_train):
net = slim.conv2d(images, 96, [7, 7], stride=2, scope='conv1')
net = slim.max_pool2d(net, [3, 3], stride=2, scope='maxpool1')
net = fire_module(net, 16, 64, scope='fire2')
net = fire_module(net, 16, 64, scope='fire3')
net = fire_module(net, 32, 128, scope='fire4')
net = slim.max_pool2d(net, [2, 2], stride=2, scope='maxpool4')
net = fire_module(net, 32, 128, scope='fire5')
net = fire_module(net, 48, 192, scope='fire6')
net = fire_module(net, 48, 192, scope='fire7')
net = fire_module(net, 64, 256, scope='fire8')
net = slim.max_pool2d(net, [3, 3], stride=2, scope='maxpool8')
net = fire_module(net, 64, 256, scope='fire9')
net = slim.dropout(net, keep_probability)
net = slim.conv2d(net, 1000, [1, 1], activation_fn=None, normalizer_fn=None, scope='conv10')
net = slim.avg_pool2d(net, net.get_shape()[1:3], scope='avgpool10')
net = tf.squeeze(net, [1, 2], name='logits')
net = slim.fully_connected(net, bottleneck_layer_size, activation_fn=None,
scope='Bottleneck', reuse=False)
return net, None