""" This file contains third party support code that I have used from elsewhere. I have
cited them appropriately where ever needed """
import tensorflow as tf
from math import sqrt
[docs]def put_kernels_on_grid (kernel, pad = 1, name = 'visualizer'):
"""Visualize convolutional filters as an image (mostly for the 1st layer).
Arranges filters into a grid, with some paddings between adjacent filters.
Args:
kernel: tensor of shape [Y, X, NumChannels, NumKernels] (HWCN)
pad: number of black pixels around each filter (between them)
name: name for tensorflow scope
Return:
Tensor of shape [1, (Y+2*pad)*grid_Y, (X+2*pad)*grid_X, NumChannels].
Notes:
This is not my method. This was written by kukurza and was hosted at:
https://gist.github.com/kukuruza/03731dc494603ceab0c5
"""
with tf.variable_scope(name) as scope:
def factorization(n):
for i in range(int(sqrt(float(n))), 0, -1):
if n % i == 0:
if i == 1: print('Who would enter a prime number of filters')
return (i, int(n / i))
(grid_Y, grid_X) = factorization (kernel.get_shape()[3].value)
x_min = tf.reduce_min(kernel)
x_max = tf.reduce_max(kernel)
kernel1 = (kernel - x_min) / (x_max - x_min)
# pad X and Y
x1 = tf.pad(kernel1, tf.constant( [[pad,pad],[pad, pad],[0,0],[0,0]] ), mode = 'CONSTANT')
# X and Y dimensions, w.r.t. padding
Y = kernel1.get_shape()[0] + 2 * pad
X = kernel1.get_shape()[1] + 2 * pad
channels = kernel1.get_shape()[2]
# put NumKernels to the 1st dimension
x2 = tf.transpose(x1, (3, 0, 1, 2))
# organize grid on Y axis
x3 = tf.reshape(x2, tf.stack([grid_X, Y * grid_Y, X, channels])) #3
# switch X and Y axes
x4 = tf.transpose(x3, (0, 2, 1, 3))
# organize grid on X axis
x5 = tf.reshape(x4, tf.stack([1, X * grid_X, Y * grid_Y, channels])) #3
# back to normal order (not combining with the next step for clarity)
x6 = tf.transpose(x5, (2, 1, 3, 0))
# to tf.image_summary order [batch_size, height, width, channels],
# where in this case batch_size == 1
x7 = tf.transpose(x6, (3, 0, 1, 2))
# scale to [0, 255] and convert to uint8
x = tf.image.convert_image_dtype(x7, dtype = tf.uint8)
return x