christopher-beckham

import theano
from theano import OpFromGraph
from theano import tensor as T
import numpy as np
import lasagne
from lasagne.layers import *

# suppose we have the network architecture:
# input -> conv1 -> conv2 -> dense
# and we want to make (conv1 -> conv2) a block

christopher-beckham

import theano
from theano import tensor as T
from theano import OpFromGraph
import lasagne
from lasagne.layers import *
from lasagne.init import *
from lasagne.nonlinearities import *
from lasagne.objectives import *
from lasagne.updates import *
from lasagne.regularization import *

christopher-beckham

<lasagne.layers.input.InputLayer object at 0x7fa6e38a95d0> (None, 3, 32, 32)
<lasagne.layers.conv.Conv2DLayer object at 0x7fa6e38a9950> (None, 64, 15, 15)
<lasagne.layers.normalization.BatchNormLayer object at 0x7fa6e38a9c50> (None, 64, 15, 15)
<lasagne.layers.special.NonlinearityLayer object at 0x7fa6e38b59d0> (None, 64, 15, 15)
<lasagne.layers.conv.Conv2DLayer object at 0x7fa6e38b5bd0> (None, 128, 7, 7)
<lasagne.layers.normalization.BatchNormLayer object at 0x7fa6e38b5ed0> (None, 128, 7, 7)
<lasagne.layers.special.NonlinearityLayer object at 0x7fa6e38404d0> (None, 128, 7, 7)
<lasagne.layers.conv.Conv2DLayer object at 0x7fa6e38406d0> (None, 192, 3, 3)
<lasagne.layers.normalization.BatchNormLayer object at 0x7fa6e38409d0> (None, 192, 3, 3)
<lasagne.layers.special.NonlinearityLayer object at 0x7fa6e3840f90> (None, 192, 3, 3)

christopher-beckham

;; init.el --- Emacs configuration

;; INSTALL PACKAGES
;; --------------------------------------

(require 'package)

(add-to-list 'package-archives
       '("melpa" . "http://melpa.org/packages/") t)

christopher-beckham

import glob
import random
import os
import numpy as np
import torch

from torch.utils.data import Dataset
from PIL import Image
import torchvision.transforms as transforms

christopher-beckham

# Original source: https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/stargan/datasets.py
# Modified by Christopher Beckham

import glob
import random
import os
import numpy as np
import torch

from torch.utils.data import Dataset

christopher-beckham

import numpy as np
import matplotlib.pyplot as plt

###########################################################
# dates.txt is created by `git log | grep Date > dates.txt`
###########################################################
hours = []
with open("dates.txt") as f:
    for line in f:
        hours.append( int(line.rstrip().split()[4].split(":")[0]) )

christopher-beckham

christopher-beckham

launch_any.sh:

#!/bin/bash
#SBATCH --ntasks=1
#SBATCH -c 4
#SBATCH --gres=gpu
#SBATCH --mem=12G

# ^ Flags here only are used if sbatch is invoked

christopher-beckham

test