joeyism

import pickle


def __extract_file__(fname):
    with open(fname, 'rb') as fo:
        d = pickle.load(fo, encoding='bytes')
    return d

joeyism

import pickle


def __extract_file__(fname):
    with open(fname, 'rb') as fo:
        d = pickle.load(fo, encoding='bytes')
    return d


def __extract_reshape_file__(fname):

joeyism

import numpy as np
import pickle


def __extract_file__(fname):
    with open(fname, 'rb') as fo:
        d = pickle.load(fo, encoding='bytes')
    return d

joeyism

import numpy as np
import pickle
import os


def __extract_file__(fname):
    with open(fname, 'rb') as fo:
        d = pickle.load(fo, encoding='bytes')
    return d

joeyism

import numpy as np
import pickle
import os
import math

def __extract_file__(fname):
    with open(fname, 'rb') as fo:
        d = pickle.load(fo, encoding='bytes')
    return d

joeyism

import tensorflow as tf

image_size = 32
input_images = tf.placeholder(tf.float32,
                              shape=[None, image_size, image_size, 3], 
                              name="input_images")

joeyism

import tensorflow as tf

image_size = 32
input_images = tf.placeholder(tf.float32,
                              shape=[None, image_size, image_size, 3], 
                              name="input_images")

# First CONV layer
kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 96],
                                         dtype=tf.float32,

joeyism

import tensorflow as tf

image_size = 32
input_images = tf.placeholder(tf.float32,
                              shape=[None, image_size, image_size, 3], 
                              name="input_images")

# First CONV layer
kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 96],
                                         dtype=tf.float32,

joeyism

import tensorflow as tf

image_size = 32
input_images = tf.placeholder(tf.float32,
                              shape=[None, image_size, image_size, 3], 
                              name="input_images")

# First CONV layer
kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 96],
                                         dtype=tf.float32,

joeyism

import tensorflow as tf

image_size = 32
input_images = tf.placeholder(tf.float32,
                              shape=[None, image_size, image_size, 3], 
                              name="input_images")

# First CONV layer
kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 96],
                                         dtype=tf.float32,