An amateur tried Deep Learning using Caffe (Practice)

Introduction

This article

1. What is Deep Learning? Overview
2. Install Caffe, one of the Deep Learning libraries Introduction
3. Let's do simple learning using Caffe Practical edition (this post) It is composed of three

Although it is also in the title, it is a post about a record left by an amateur who is not a researcher of Deep Learning, so please forgive me for any mistakes and read it. (If there is something wrong, I would appreciate it if you could point it out in the comments) Also, since there are many things that I do not understand unexpectedly, the practical version will be updated little by little ...

Recognize the MNIST database (handwritten characters)

As a first step

1. Learn MNIST based on official information (so far described in the tutorial)
2. Recognize MNIST 28 * 28pixel JPEG images using the learned network (for some reason not written in the tutorial) I will do

Let MNIST learn

Trace what is written in Official

Create a MNIST database

In the original state cloned from git, there is no handwritten character data to be learned, so I will drop it below Assuming that CAFFE_ROOT is set as an environment variable (if it is not set, set the root of the caffe repository to be CAFFE_ROOT).

python

cd $CAFFE_ROOT
./data/mnist/get_mnist.sh
./examples/mnist/create_mnist.sh

This should hopefully create two folders under examples / mnist, mnist_test_lmdb and mnist_train_lmdb, with a database inside.

Learn using a database

Once you have created the database, you can use the included network and solver to learn handwriting without doing anything special. Specifically, it ends with the following one line. The time depends on the environment, but I think it will be over in 10 minutes.

python

cd $CAFFE_ROOT
./examples/mnist/train_lenet.sh

Now, if the learning is done properly, lenet_iter_10000.caffemodel and lenet_iter_5000.caffemodel will be created under examples / mnist. The only difference between 10000 and 5000 is whether the learning iteration is the 10000th network or the 5000th network.

See how it is actually recognized by the learned MNIST network

If you learn from the database according to the tutorial, you can see that the network has learned something, but if you do not actually use the network, you can not tell whether it was successful or not. So, let's convert the above database that stores 10000 characters to JPEG one character each, give it to the network, and see what kind of output it will be. The scripts used in this chapter are on GitHub, so please use them if you like.

Make the database JPEG to make it easier to understand the recognition target

Even if you look at the database, it is difficult to understand what number is what character, so make the data stored in the database JPEG. This time, I made my own script and made it JPEG Like this (python)

import scipy
import numpy as np
import lmdb
import sys
from caffe.io import caffe_pb2

def convert_to_jpeg(db_dir):
    env = lmdb.open(db_dir)
    datum = caffe_pb2.Datum()
    with env.begin() as txn:
        cursor = txn.cursor()
        for key_val,ser_str in cursor:
            datum.ParseFromString(ser_str)
            print "\nKey val: ", key_val
            print "\nLabel: ", datum.label
            rows = datum.height;
            cols = datum.width;
            img_pre = np.fromstring(datum.data,dtype=np.uint8)
            img = img_pre.reshape(rows, cols)
            file_name = str(key_val) + "_" + str(datum.label) + ".jpg "
            scipy.misc.toimage(img, cmin=0.0, cmax=255.0).save("data/mnist/jpg/" + file_name)

If it is a database in mnist_test_lmdb, 10000 jpg images will be generated by doing the following

python

cd $CAFFE_ROOT
python mnist_jpg_converter.py examples/mnist/mnist_test_lmdb/

Give the image to the network and see the result

First rewrite python / classify.py that comes with caffe to load the network Like this.

def main(argv):
    # --Abbreviation--

    # Make classifier.
    classifier = caffe.Classifier(args.model_def, args.pretrained_model)

    # Load numpy array (.npy), directory glob (*.jpg), or image file.
    args.input_file = os.path.expanduser(args.input_file)
    print("Loading file: %s" % args.input_file)
    grayimg = caffe.io.load_image(args.input_file, color=False)[:,:,0]
    inputs = [np.reshape(grayimg, (28, 28, 1))]

    print("Classifying %d inputs." % len(inputs))

    # Classify.
    start = time.time()
    predictions = classifier.predict(inputs)
    print("Done in %.2f s." % (time.time() - start))

    # --Abbreviation--

After that, use this script

python

cd $CAFFE_ROOT
python lenet_classify.py data/mnist/jpg/00000007_9.jpg result.npy

If you do, the classification result will be output to result.npy.

python

python my/show_mnist_result.py result.npy 
[[  6.68664742e-03   2.82594631e-03   8.81279539e-03   1.06628540e-05
    4.27712619e-01   1.90626510e-04   1.27627791e-04   9.20879841e-03
    4.14795056e-02   5.02944708e-01]]

Since the items in the column are arranged in the order of 0,1,2, ..., 9, the probability of 9 is about 50%, which is higher than any other classification result, so that the network is learning properly. You can check (The next highest probability is 4, but 9 and 4 look similar, so I think it's a convincing result.)

Thank you for your hard work

References

Deep Learning with Caffe, focusing on places where you can easily trip Easy image classification with Caffe