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#!/usr/bin/env bash | |
set -e | |
set -x | |
GPU=0 | |
CWD=$(pwd) | |
CAFFE_DIR=/raid/jdonahue/philkr-caffe | |
MAGIC_DIR=~/magic_init | |
CLASS_DIR=/raid/jdonahue/voc-classification |
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#!/bin/bash | |
set -x | |
set -e | |
export LD_LIBRARY_PATH='/usr/local/cuda/lib64/' | |
export PYTHONUNBUFFERED="True" | |
NAME="scratch" | |
NAME="/tmp/magic_caffenet.caffenet" | |
if [ "$#" -ge 1 ]; then | |
NAME=${2} |
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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 |
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#!/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() |
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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)] |
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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') |
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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 |
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#!/usr/local/bin/python3 | |
import itertools | |
import string | |
class Node: | |
"""A trie node.""" | |
def __init__(self, char, parent=None): | |
self._char = char |
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