[Caffe] mnist 인식 프로세스

#!/usr/bin/env sh
# This scripts downloads the mnist data and unzips it.

DIR="$( cd "$(dirname "$0")" ; pwd -P )"
cd "$DIR"

echo "Downloading..."

for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte
do
    if [ ! -e $fname ]; then
        wget --no-check-certificate http://yann.lecun.com/exdb/mnist/${fname}.gz
        gunzip ${fname}.gz
    fi
done

#!/usr/bin/env sh
# This script converts the mnist data into lmdb/leveldb format,
# depending on the value assigned to $BACKEND.
set -e

EXAMPLE=examples/mnist
DATA=data/mnist
BUILD=build/examples/mnist

BACKEND="lmdb"

echo "Creating ${BACKEND}..."

rm -rf $EXAMPLE/mnist_train_${BACKEND}
rm -rf $EXAMPLE/mnist_test_${BACKEND}

$BUILD/convert_mnist_data.bin $DATA/train-images-idx3-ubyte \
  $DATA/train-labels-idx1-ubyte $EXAMPLE/mnist_train_${BACKEND} --backend=${BACKEND}
$BUILD/convert_mnist_data.bin $DATA/t10k-images-idx3-ubyte \
  $DATA/t10k-labels-idx1-ubyte $EXAMPLE/mnist_test_${BACKEND} --backend=${BACKEND}

echo "Done."

# The train/test net protocol buffer definition
net: "examples/mnist/lenet_train_test.prototxt"
# test_iter specifies how many forward passes the test should carry out.
# In the case of MNIST, we have test batch size 100 and 100 test iterations,
# covering the full 10,000 testing images.
test_iter: 100
# Carry out testing every 500 training iterations.
test_interval: 500
# The base learning rate, momentum and the weight decay of the network.
base_lr: 0.01
momentum: 0.9
weight_decay: 0.0005
# The learning rate policy
lr_policy: "inv"
gamma: 0.0001
power: 0.75
# Display every 100 iterations
display: 100
# The maximum number of iterations
max_iter: 10000
# snapshot intermediate results
snapshot: 5000
snapshot_prefix: "examples/mnist/lenet"
# solver mode: CPU or GPU
solver_mode: CPU

#!/usr/bin/python
# -*- coding: utf-8 -*-

from PIL import Image

im = Image.open('4.png')
im.thumbnail((28, 28))
tt =  im.convert('1')
tt.save('test4.bmp')

import os
import sys
import numpy as np
import matplotlib.pyplot as plt

caffe_root = '/usr/local/Cellar/caffe/'

sys.path.insert(0, caffe_root + 'python')
import caffe
MODEL_FILE = caffe_root+'examples/mnist/lenet.prototxt'
PRETRAINED = caffe_root+'examples/mnist/lenet_iter_5000.caffemodel'
IMAGE_FILE = caffe_root+'examples/images/test4.bmp'

input_image = caffe.io.load_image(IMAGE_FILE, color=False)
net = caffe.Classifier(MODEL_FILE, PRETRAINED) 
prediction = net.predict([input_image], oversample = False)
caffe.set_mode_cpu()
print 'predicted class:', prediction[0].argmax()