Python tensorflow.contrib.slim.xavier_initializer_conv2d() Examples

The following are 7 code examples of tensorflow.contrib.slim.xavier_initializer_conv2d(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.contrib.slim , or try the search function .
Example #1
Source File: squeezenet.py    From tf_ctpn with MIT License 6 votes vote down vote up 
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
Example #2
Source File: squeezenet.py    From TNT with GNU General Public License v3.0 5 votes vote down vote up 
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
Example #3
Source File: squeezenet.py    From tindetheus with MIT License 5 votes vote down vote up 
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
Example #4
Source File: squeezenet.py    From facenet_mtcnn_to_mobile with MIT License 5 votes vote down vote up 
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
Example #5
Source File: squeezenet.py    From uai-sdk with Apache License 2.0 5 votes vote down vote up 
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
Example #6
Source File: squeezenet.py    From facenet-demo with MIT License 5 votes vote down vote up 
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
Example #7
Source File: squeezenet.py    From facenet with MIT License 5 votes vote down vote up 
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