User guide

Using as a feature extractor

In this example we’ll take pretrained network using MNIST and

In this example we take the output of the layer fc7 of the VGG face model as features.

>>> import numpy
>>> import bob.ip.tensorflow_extractor
>>> import bob.db.mnist
>>> from bob.ip.tensorflow_extractor import scratch_network
>>> import os
>>> import pkg_resources
>>> import tensorflow as tf

>>> # Loading some samples from mnist
>>> db = bob.db.mnist.Database()
>>> images = db.data(groups='train', labels=[0,1,2,3,4,5,6,7,8,9])[0][0:3]
>>> images = numpy.reshape(images, (3, 28, 28, 1)) * 0.00390625 # Normalizing the data

>>> # preparing my inputs
>>> inputs = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
>>> graph = scratch_network(inputs)

>>> # loading my model and projecting
>>> filename = os.path.join(pkg_resources.resource_filename("bob.ip.tensorflow_extractor", 'data'), 'model.ckp')
>>> extractor = bob.ip.tensorflow_extractor.Extractor(filename, inputs, graph)
>>> extractor(images).shape
(3, 10)

Note

The models will automatically download to the data folder of this package as soon as you start using them.

Using as a convolutional filter

In this example we plot some outputs of the convolutional layer conv1.

Facenet Model

bob.bio.base wrapper Facenet model. Check here for more info

Note

The models will automatically download to the data folder of this package and save it in [env-path]./bob/ip/tensorflow_extractor/data/FaceNet. If you want want set another path for this model do:

$ bob config set bob.ip.tensorflow_extractor.facenet_modelpath /path/to/mydatabase

DRGan from L.Tran @ MSU:

bob.bio.base wrapper to the DRGan model trained by L.Tran @ MSU. Check here for more info

Note

The models will automatically download to the data folder of this package and save it in [env-path]./bob/ip/tensorflow_extractor/data/DR_GAN_model. If you want want set another path for this model do:

$ bob config set bob.ip.tensorflow_extractor.drgan_modelpath /path/to/mydatabase