utils.py

#Mnist utils taken from: https://github.com/tensorflow/models/blob/master/tutorials/image/mnist/convolutional.py 
import argparse
import gzip
import os
import sys
import time

import numpy
from six.moves import urllib
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf

SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
WORK_DIRECTORY = 'data'
IMAGE_SIZE = 28
NUM_CHANNELS = 1
PIXEL_DEPTH = 255
NUM_LABELS = 10

def variable_summaries(prefix, var, name):
    """Attach a lot of summaries to a Tensor.
        This is also quite expensive.
    """
    with tf.name_scope(None):        
        amean = tf.reduce_mean(tf.abs(var))
        tf.summary.scalar(prefix + '/amean/' + name, amean)
        mean = tf.reduce_mean(var)
        tf.summary.scalar(prefix + '/mean/' + name, mean)
        stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
        tf.summary.scalar(prefix + '/sttdev/' + name, stddev)
        tf.summary.scalar(prefix + '/max/' + name, tf.reduce_max(var))
        tf.summary.scalar(prefix + '/min/' + name, tf.reduce_min(var))
    tf.summary.histogram(name, var)


def maybe_download(filename):
  """Download the data from Yann's website, unless it's already here."""
  if not tf.gfile.Exists(WORK_DIRECTORY):
    tf.gfile.MakeDirs(WORK_DIRECTORY)
  filepath = os.path.join(WORK_DIRECTORY, filename)
  if not tf.gfile.Exists(filepath):
    filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
    with tf.gfile.GFile(filepath) as f:
      size = f.size()
    print('Successfully downloaded', filename, size, 'bytes.')
  return filepath


def extract_data(filename, num_images):
  """Extract the images into a 4D tensor [image index, y, x, channels].
  Values are rescaled from [0, 255] down to [-0.5, 0.5].
  """
  print('Extracting', filename)
  with gzip.open(filename) as bytestream:
    bytestream.read(16)
    buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images * NUM_CHANNELS)
    data = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.float32)
    #data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH
    data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH + 0.5
    data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS)
    return data


def extract_labels(filename, num_images):
  """Extract the labels into a vector of int64 label IDs."""
  print('Extracting', filename)
  with gzip.open(filename) as bytestream:
    bytestream.read(8)
    buf = bytestream.read(1 * num_images)
    labels = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.int64)
  return labels
#end of mnist utils