Removed plot since this is not used

doc/plot/convolve.py

-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
-
-
-def norm_image(image):
-    return (255 * ((image - numpy.min(image)) / (numpy.max(image) - numpy.min(image)))).astype("uint8")
-
-
-# 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][0:1]
-images = numpy.reshape(images, (1, 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, end_point="conv1")
-
-# loading my model and projecting
-filename = os.path.join(pkg_resources.resource_filename("bob.ip.tensorflow_extractor", 'data'), 'model.ckp.meta')
-extractor = bob.ip.tensorflow_extractor.Extractor(filename, inputs, graph)
-
-# Getting the convolved images
-convs = extractor(images)
-
-from matplotlib import pyplot
-
-pyplot.subplot(2, 3, 1)
-pyplot.imshow(norm_image(images[0, :, :, 0]), cmap='Greys_r')
-
-pyplot.subplot(2, 3, 2)
-pyplot.imshow(norm_image(convs[0, :, :, 0]), cmap='Greys_r')
-
-pyplot.subplot(2, 3, 3)
-pyplot.imshow(norm_image(convs[0, :, :, 5]), cmap='Greys_r')
-
-pyplot.subplot(2, 3, 4)
-pyplot.imshow(norm_image(convs[0, :, :, 2]), cmap='Greys_r')
-
-pyplot.subplot(2, 3, 5)
-pyplot.imshow(norm_image(convs[0, :, :, 3]), cmap='Greys_r')
-
-pyplot.subplot(2, 3, 6)
-pyplot.imshow(norm_image(convs[0, :, :, 4]), cmap='Greys_r')
-