jeffdonahue

#!/usr/bin/env bash

set -e
set -x

GPU=0
CWD=$(pwd)
CAFFE_DIR=/raid/jdonahue/philkr-caffe
MAGIC_DIR=~/magic_init
CLASS_DIR=/raid/jdonahue/voc-classification

jeffdonahue

#!/bin/bash

set -x
set -e
export LD_LIBRARY_PATH='/usr/local/cuda/lib64/'
export PYTHONUNBUFFERED="True"
NAME="scratch"
NAME="/tmp/magic_caffenet.caffenet"
if [ "$#" -ge 1 ]; then
  NAME=${2}

jeffdonahue

net: "train_val.prototxt"
test_iter: 736
test_interval: 1000000 # py solving tests
display: 1
average_loss: 100
# lr_policy: "fixed"
lr_policy: "step"
stepsize: 50000
gamma: 0.1
# base_lr: 1e-4

jeffdonahue

#!/usr/bin/env python

import numpy as np

def convert(image):
    image = np.asarray(image)
    if len(image.shape) == 2:
        image = image[..., np.newaxis]
    assert len(image.shape) == 3
    return image.transpose(2, 0, 1).copy()

jeffdonahue

import numpy as np
import theano
import theano.tensor as T

pred, target = T.vectors('pt') # pred in (-inf, +inf), target in [0, 1]

L1 = T.nnet.binary_crossentropy(T.nnet.sigmoid(pred), target).sum()
L2 = T.nnet.sigmoid_binary_crossentropy(pred, target).sum()
fl1, fl2 = [theano.function([pred, target], L) for L in (L1, L2)]
g1, g2 = [theano.grad(L, [pred, target]) for L in (L1, L2)]

jeffdonahue

import numpy as np
import theano
import theano.tensor as T

low = 'float32'
high = 'float64'

dtype_low, dtype_high = [T.TensorType(f, (False,)) for f in [low, high]]
pred32, target32 = dtype_low('p'), dtype_low('t')
pred64, target64 = dtype_high('p'), dtype_high('t')

jeffdonahue

config defaultToCurrentScreen true
config nudgePercentOf screenSize
config resizePercentOf screenSize

# Resize Bindings
# bind right:alt       resize +10% +0
# bind left:alt        resize -10% +0
# bind up:alt          resize +0   -10%
# bind down:alt        resize +0   +10%
# bind right:ctrl;alt  resize -10% +0 bottom-right

jeffdonahue

#!/usr/local/bin/python3

import itertools
import string

class Node:
  """A trie node."""

  def __init__(self, char, parent=None):
    self._char = char