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Batch normalization in pretrained vgg in tensorflow

0

I have a naive question about how to implement Batch Normalization in Tensorflow. I appreciate your explanation, sample codes, and links.

To use dropout we can determine the amount of dropping as an input when calling the model as follows:

with slim.arg_scope(vgg.vgg_arg_scope(weight_decay=args.weight_decay)):
 model_outputs, _ = vgg.vgg_16(x_inputs, num_classes=TOT_CLASSES, is_training=True, **dropout_keep_prob=args.DROPOUT_PROB**)

1- Do we have something like this to use Batch Normalization?

2- if I want to implement the instructions of this link https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization, do I need to change network codes i.e. https://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py?

OR

I can simply use my_inputs_norm = tf.layers.batch_normalization(x, training=training) before calling the model, like this:

**my_inputs_norm** = tf.layers.batch_normalization(x, training=training)
with slim.arg_scope(vgg.vgg_arg_scope(weight_decay=args.weight_decay)):
 model_outputs, _ = vgg.vgg_16(**my_inputs_norm**, num_classes=TOT_CLASSES, is_training=True, **dropout_keep_prob=args.DROPOUT_PROB**)

tensorflow batch-normalization

share|improve this question

asked Mar 6 at 17:39

JacobJacob

207

add a comment | 

0

I have a naive question about how to implement Batch Normalization in Tensorflow. I appreciate your explanation, sample codes, and links.

To use dropout we can determine the amount of dropping as an input when calling the model as follows:

with slim.arg_scope(vgg.vgg_arg_scope(weight_decay=args.weight_decay)):
 model_outputs, _ = vgg.vgg_16(x_inputs, num_classes=TOT_CLASSES, is_training=True, **dropout_keep_prob=args.DROPOUT_PROB**)

1- Do we have something like this to use Batch Normalization?

2- if I want to implement the instructions of this link https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization, do I need to change network codes i.e. https://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py?

OR

I can simply use my_inputs_norm = tf.layers.batch_normalization(x, training=training) before calling the model, like this:

**my_inputs_norm** = tf.layers.batch_normalization(x, training=training)
with slim.arg_scope(vgg.vgg_arg_scope(weight_decay=args.weight_decay)):
 model_outputs, _ = vgg.vgg_16(**my_inputs_norm**, num_classes=TOT_CLASSES, is_training=True, **dropout_keep_prob=args.DROPOUT_PROB**)

tensorflow batch-normalization

share|improve this question

asked Mar 6 at 17:39

JacobJacob

207

add a comment | 

I have a naive question about how to implement Batch Normalization in Tensorflow. I appreciate your explanation, sample codes, and links.

To use dropout we can determine the amount of dropping as an input when calling the model as follows:

with slim.arg_scope(vgg.vgg_arg_scope(weight_decay=args.weight_decay)):
 model_outputs, _ = vgg.vgg_16(x_inputs, num_classes=TOT_CLASSES, is_training=True, **dropout_keep_prob=args.DROPOUT_PROB**)

1- Do we have something like this to use Batch Normalization?

2- if I want to implement the instructions of this link https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization, do I need to change network codes i.e. https://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py?

OR

I can simply use my_inputs_norm = tf.layers.batch_normalization(x, training=training) before calling the model, like this:

**my_inputs_norm** = tf.layers.batch_normalization(x, training=training)
with slim.arg_scope(vgg.vgg_arg_scope(weight_decay=args.weight_decay)):
 model_outputs, _ = vgg.vgg_16(**my_inputs_norm**, num_classes=TOT_CLASSES, is_training=True, **dropout_keep_prob=args.DROPOUT_PROB**)

tensorflow batch-normalization

share|improve this question

asked Mar 6 at 17:39

JacobJacob

207

with slim.arg_scope(vgg.vgg_arg_scope(weight_decay=args.weight_decay)):
 model_outputs, _ = vgg.vgg_16(x_inputs, num_classes=TOT_CLASSES, is_training=True, **dropout_keep_prob=args.DROPOUT_PROB**)

**my_inputs_norm** = tf.layers.batch_normalization(x, training=training)
with slim.arg_scope(vgg.vgg_arg_scope(weight_decay=args.weight_decay)):
 model_outputs, _ = vgg.vgg_16(**my_inputs_norm**, num_classes=TOT_CLASSES, is_training=True, **dropout_keep_prob=args.DROPOUT_PROB**)

share|improve this question

asked Mar 6 at 17:39

JacobJacob

207

share|improve this question

asked Mar 6 at 17:39

JacobJacob

207

asked Mar 6 at 17:39

JacobJacob

207

asked Mar 6 at 17:39

JacobJacob

207