【深さ学習画像認識コース】tenssorflow移行学習シリーズ:VGG 16花分類

一、VGGNet
1、VGGNetの紹介
通常、私達は自分で大型の神経ネットワークを訓練しません.ImageNet(http://www.image-net.org/)にはすでに数週間の訓練モデルがたくさんあります.ここではプリトレーニングモデルVGGNtを使って花画像(画像ライブラリはTensorflow Inception tutorialから来ています)の分類を行います.

VGG論文:
http://www.robots.ox.ac.uk/~vgg/reearch/very_deep/
https://arxiv.org/pdf/1409.1556.pdf
 
2、パッケージのインストール
コードを書く前に、まずAnanconda Promptを開けて、2つのプログラムパッケージtqdmとscikit-mageをインストールします.

pip install tqdm

conda install scikit-image

 
3、フラワーデータベースの紹介
種類5種類:daisy雛菊、dandelion蒲公英、roseバラ、sunflowerひまわり、tulipsチューリップ
数:    633,           898,                  641,         699,               799            
総数量:3670
 
二、実戦:VGGNetは花の分類を実現する.
1、VG 16モデルを読み込みます.

from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm

vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
    raise Exception("VGG directory doesn't exist!")

class DLProgress(tqdm):
    last_block = 0

    def hook(self, block_num=1, block_size=1, total_size=None):
        self.total = total_size
        self.update((block_num - self.last_block) * block_size)
        self.last_block = block_num
if not isfile(vgg_dir + "vgg16.npy"):
    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='VGG16 Parameters') as pbar:
        urlretrieve(
            'https://s3.amazonaws.com/content.udacity-data.com/nd101/vgg16.npy',
            vgg_dir + 'vgg16.npy',
            pbar.hook)
else:
    print("Parameter file already exists!")

下記の明るいファイルをダウンロードしました.

 
2、ギャラリー読み込み

import tarfile

dataset_folder_path = 'flower_photos'

class DLProgress(tqdm):
    last_block = 0

    def hook(self, block_num=1, block_size=1, total_size=None):
        self.total = total_size
        self.update((block_num - self.last_block) * block_size)
        self.last_block = block_num

if not isfile('flower_photos.tar.gz'):
    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='Flowers Dataset') as pbar:
        urlretrieve(
            'http://download.tensorflow.org/example_images/flower_photos.tgz',
            'flower_photos.tar.gz',
            pbar.hook)

if not isdir(dataset_folder_path):
    with tarfile.open('flower_photos.tar.gz') as tar:
        tar.extractall()
        tar.close()

 

 
下記の高亮ファイルをダウンロードします.flower_.photos.tar.gz

 
3、コードを畳み込む
参考ソース:

self.conv1_1 = self.conv_layer(bgr, "conv1_1")
self.conv1_2 = self.conv_layer(self.conv1_1, "conv1_2")
self.pool1 = self.max_pool(self.conv1_2, 'pool1')

self.conv2_1 = self.conv_layer(self.pool1, "conv2_1")
self.conv2_2 = self.conv_layer(self.conv2_1, "conv2_2")
self.pool2 = self.max_pool(self.conv2_2, 'pool2')

self.conv3_1 = self.conv_layer(self.pool2, "conv3_1")
self.conv3_2 = self.conv_layer(self.conv3_1, "conv3_2")
self.conv3_3 = self.conv_layer(self.conv3_2, "conv3_3")
self.pool3 = self.max_pool(self.conv3_3, 'pool3')

self.conv4_1 = self.conv_layer(self.pool3, "conv4_1")
self.conv4_2 = self.conv_layer(self.conv4_1, "conv4_2")
self.conv4_3 = self.conv_layer(self.conv4_2, "conv4_3")
self.pool4 = self.max_pool(self.conv4_3, 'pool4')

self.conv5_1 = self.conv_layer(self.pool4, "conv5_1")
self.conv5_2 = self.conv_layer(self.conv5_1, "conv5_2")
self.conv5_3 = self.conv_layer(self.conv5_2, "conv5_3")
self.pool5 = self.max_pool(self.conv5_3, 'pool5')

self.fc6 = self.fc_layer(self.pool5, "fc6")
self.relu6 = tf.nn.relu(self.fc6)

with tf.Session() as sess:
    vgg = vgg16.Vgg16()
    input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])
    with tf.name_scope("content_vgg"):
        vgg.build(input_)

feed_dict = {input_: images}
codes = sess.run(vgg.relu6, feed_dict=feed_dict)

tenssor flow中vgg_16が採用した上記構造.本プロジェクトのコードは以下の通りです.

import os

import numpy as np
import tensorflow as tf

from tensorflow_vgg import vgg16
from tensorflow_vgg import utils

data_dir = 'flower_photos/'
contents = os.listdir(data_dir)
classes = [each for each in contents if os.path.isdir(data_dir + each)]

画像一括batchをVGGモデルに通して、新しい入力として出力します.

# Set the batch size higher if you can fit in in your GPU memory
batch_size = 10
codes_list = []
labels = []
batch = []

codes = None

with tf.Session() as sess:
    vgg = vgg16.Vgg16()
    input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])
    with tf.name_scope("content_vgg"):
        vgg.build(input_)

    for each in classes:
        print("Starting {} images".format(each))
        class_path = data_dir + each
        files = os.listdir(class_path)
        for ii, file in enumerate(files, 1):
            # Add images to the current batch
            # utils.load_image crops the input images for us, from the center
            img = utils.load_image(os.path.join(class_path, file))
            batch.append(img.reshape((1, 224, 224, 3)))
            labels.append(each)
            
            # Running the batch through the network to get the codes
            if ii % batch_size == 0 or ii == len(files):
                images = np.concatenate(batch)

                feed_dict = {input_: images}
                codes_batch = sess.run(vgg.relu6, feed_dict=feed_dict)
                
                # Here I'm building an array of the codes
                if codes is None:
                    codes = codes_batch
                else:
                    codes = np.concatenate((codes, codes_batch))
                
                # Reset to start building the next batch
                batch = []
                print('{} images processed'.format(ii))

 
4、モデルの構築とテスト
画像処理コードとラベル:

# read codes and labels from file
import csv

with open('labels') as f:
    reader = csv.reader(f, delimiter='
')
    labels = np.array([each for each in reader if len(each) > 0]).squeeze()
with open('codes') as f:
    codes = np.fromfile(f, dtype=np.float32)
    codes = codes.reshape((len(labels), -1))

4.1画像前処理

from sklearn.preprocessing import LabelBinarizer

lb = LabelBinarizer()
lb.fit(labels)

labels_vecs = lb.transform(labels)

ラベルに対してone-hotコードを行います.daisy雛菊です. dandelionタンポポ ローズ sunflowerひまわりtulipsチューリップ
                    デイリーデージー        1               0                       0                0                    0
         dandelionタンポポ        0               1                       0                0                    0
                     ローズ        0               0                       1                0                    0
          sunflowerひまわり        0               0                       0                1                    0
                 tulipsチューリップ        0               0                       0                0                    1
ランダムにデータセットを分割します.(前のように集中している部分の画像を直接取り出して検証します.テストは役に立たないです.ここのデータセットは各種類が集中しています.その中の一部を直接取り出したら、検証セットまたはテストセットは同じ花です.).スカイティ-learnの関数Stratified ShuffleSplitができます.ここでは、20%の画像をランダムに取り出して検証とテストを行い、セットとテストセットをそれぞれ半分ずつ検証します.

from sklearn.model_selection import StratifiedShuffleSplit

ss = StratifiedShuffleSplit(n_splits=1, test_size=0.2)

train_idx, val_idx = next(ss.split(codes, labels))

half_val_len = int(len(val_idx)/2)
val_idx, test_idx = val_idx[:half_val_len], val_idx[half_val_len:]

train_x, train_y = codes[train_idx], labels_vecs[train_idx]
val_x, val_y = codes[val_idx], labels_vecs[val_idx]
test_x, test_y = codes[test_idx], labels_vecs[test_idx]

print("Train shapes (x, y):", train_x.shape, train_y.shape)
print("Validation shapes (x, y):", val_x.shape, val_y.shape)
print("Test shapes (x, y):", test_x.shape, test_y.shape)

総数量:3670なら、訓練画像:3670*0.8=2936、検証画像:3670*0.2*0.5=367、テスト画像:3670*0.2*0.5=367.
 
4.2層
上記のvggに基づいて、256個の要素の全接続層を追加し、最後にソフトマックス層を加えて、交差エントロピーを計算して最後の分類を行います.

inputs_ = tf.placeholder(tf.float32, shape=[None, codes.shape[1]])
labels_ = tf.placeholder(tf.int64, shape=[None, labels_vecs.shape[1]])

fc = tf.contrib.layers.fully_connected(inputs_, 256)
    
logits = tf.contrib.layers.fully_connected(fc, labels_vecs.shape[1], activation_fn=None)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=labels_, logits=logits)
cost = tf.reduce_mean(cross_entropy)

optimizer = tf.train.AdamOptimizer().minimize(cost)

predicted = tf.nn.softmax(logits)
correct_pred = tf.equal(tf.argmax(predicted, 1), tf.argmax(labels_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

 
4.3トレーニング:batchとepoches

def get_batches(x, y, n_batches=10):
    """ Return a generator that yields batches from arrays x and y. """
    batch_size = len(x)//n_batches
    
    for ii in range(0, n_batches*batch_size, batch_size):
        # If we're not on the last batch, grab data with size batch_size
        if ii != (n_batches-1)*batch_size:
            X, Y = x[ii: ii+batch_size], y[ii: ii+batch_size] 
        # On the last batch, grab the rest of the data
        else:
            X, Y = x[ii:], y[ii:]
        # I love generators
        yield X, Y

epochs = 10
iteration = 0
saver = tf.train.Saver()
with tf.Session() as sess:
    
    sess.run(tf.global_variables_initializer())
    for e in range(epochs):
        for x, y in get_batches(train_x, train_y):
            feed = {inputs_: x,
                    labels_: y}
            loss, _ = sess.run([cost, optimizer], feed_dict=feed)
            print("Epoch: {}/{}".format(e+1, epochs),
                  "Iteration: {}".format(iteration),
                  "Training loss: {:.5f}".format(loss))
            iteration += 1
            
            if iteration % 5 == 0:
                feed = {inputs_: val_x,
                        labels_: val_y}
                val_acc = sess.run(accuracy, feed_dict=feed)
                print("Epoch: {}/{}".format(e, epochs),
                      "Iteration: {}".format(iteration),
                      "Validation Acc: {:.4f}".format(val_acc))
    saver.save(sess, "checkpoints/flowers.ckpt")

検証集の正答率は90%に達し、すでに高くなりました.
 
4.4テスト

with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))
    
    feed = {inputs_: test_x,
            labels_: test_y}
    test_acc = sess.run(accuracy, feed_dict=feed)
    print("Test accuracy: {:.4f}".format(test_acc))

%matplotlib inline

import matplotlib.pyplot as plt
from scipy.ndimage import imread

test_img_path = 'flower_photos/roses/10894627425_ec76bbc757_n.jpg'
test_img = imread(test_img_path)
plt.imshow(test_img)

 

with tf.Session() as sess:
    input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])
    vgg = vgg16.Vgg16()
    vgg.build(input_)

with tf.Session() as sess:
    img = utils.load_image(test_img_path)
    img = img.reshape((1, 224, 224, 3))

    feed_dict = {input_: img}
    code = sess.run(vgg.relu6, feed_dict=feed_dict)
        
saver = tf.train.Saver()
with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))
    
    feed = {inputs_: code}
    prediction = sess.run(predicted, feed_dict=feed).squeeze()

plt.imshow(test_img)

 

plt.barh(np.arange(5), prediction)
_ = plt.yticks(np.arange(5), lb.classes_)

上の花はRoseが一番可能で、小さな確率はTulipsです.