Q&A
###前提・実現したいこと
現在Tensorflowで顔認識のプログラムをサンプルやネットの情報を元に試しています。
実現したいことは、再学習で認識できる顔の数を増やしていきたいのです。
###発生している問題・エラーメッセージ
具体的に言いますと、１回目の学習でAさん、Bさん、Cさんの顔を学習させることはできたのですが、
２回目の学習でDさんが新たに加わった場合の学習のやり方が分からないです。
そもそもtensorflowのサンプルなどを見るに予め分類する数を指定するところがあるので、始めから３人なら３人の分類しかできないのでしょうか？
###１回目の学習時のソースコード
import cv2
import numpy as np
import tensorflow as tf
NUM_CLASSES = 3
IMAGE_SIZE = 48
IMAGE_NN_SIZE = int(IMAGE_SIZE / 2 / 2)
IMAGE_PIXELS = IMAGE_SIZEIMAGE_SIZE3
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_string('save_model', 'model.ckpt', 'File name of model data')
flags.DEFINE_string('train', 'train.txt', 'File name of train data')
flags.DEFINE_string('test', 'test.txt', 'File name of test data')
flags.DEFINE_string('train_dir', 'summary', 'Directory to put the training data.')
flags.DEFINE_integer('max_steps', 100, 'Number of steps to run trainer.')
flags.DEFINE_integer('batch_size', 256, 'Batch size'
'Must divide evenly into the dataset sizes.')
flags.DEFINE_float('learning_rate', 1e-4, 'Initial learning rate.')
def inference(images_placeholder, keep_prob):
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
x_images = tf.reshape(images_placeholder, [-1, IMAGE_SIZE, IMAGE_SIZE, 3])
with tf.name_scope('conv1') as scope:
W_conv1 = weight_variable([5, 5, 3, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_images, W_conv1) + b_conv1)
with tf.name_scope('pool1') as scope:
h_pool1 = max_pool_2x2(h_conv1)
with tf.name_scope('conv2') as scope:
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
with tf.name_scope('pool2') as scope:
h_pool2 = max_pool_2x2(h_conv2)
with tf.name_scope('fc1') as scope:
W_fc1 = weight_variable([IMAGE_NN_SIZEIMAGE_NN_SIZE64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, IMAGE_NN_SIZEIMAGE_NN_SIZE64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
# dropoutの設定
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
with tf.name_scope('fc2') as scope:
W_fc2 = weight_variable([1024, NUM_CLASSES])
b_fc2 = bias_variable([NUM_CLASSES])
with tf.name_scope('softmax') as scope:
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
return y_conv
def loss(logits, labels):
cross_entropy = -tf.reduce_sum(labels*tf.log(tf.clip_by_value(logits,1e-10,1.0)))
tf.scalar_summary("cross_entropy", cross_entropy)
return cross_entropy
def training(loss, learning_rate):
train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)
return train_step
def accuracy(logits, labels):
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
tf.scalar_summary("accuracy", accuracy)
return accuracy
if name == 'main':
with open(FLAGS.train, 'r') as f: # train.txt
train_image = []
train_label = []
for line in f:
line = line.rstrip()
l = line.split()
img = cv2.imread(l[0])
# img = cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE))
train_image.append(img.flatten().astype(np.float32)/255.0)
tmp = np.zeros(NUM_CLASSES)
tmp[int(l[1])] = 1
train_label.append(tmp)
train_image = np.asarray(train_image)
train_label = np.asarray(train_label)
train_len = len(train_image)
with open(FLAGS.test, 'r') as f: # test.txt test_image = [] test_label = [] for line in f: line = line.rstrip() l = line.split() print(l[0]) img = cv2.imread(l[0]) # img = cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE)) test_image.append(img.flatten().astype(np.float32)/255.0) tmp = np.zeros(NUM_CLASSES) tmp[int(l[1])] = 1 test_label.append(tmp) test_image = np.asarray(test_image) test_label = np.asarray(test_label) test_len = len(test_image) with tf.Graph().as_default(): images_placeholder = tf.placeholder("float", shape=(None, IMAGE_PIXELS)) labels_placeholder = tf.placeholder("float", shape=(None, NUM_CLASSES)) keep_prob = tf.placeholder("float") logits = inference(images_placeholder, keep_prob) # logits = inference(images_placeholder) loss_value = loss(logits, labels_placeholder) train_op = training(loss_value, FLAGS.learning_rate) acc = accuracy(logits, labels_placeholder) saver = tf.train.Saver() sess = tf.Session() sess.run(tf.initialize_all_variables()) summary_op = tf.merge_all_summaries() summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph_def) # 訓練の実行 if train_len % FLAGS.batch_size is 0: train_batch = int(train_len/FLAGS.batch_size) else: train_batch = int((train_len/FLAGS.batch_size)+1) print("train_batch = "+str(train_batch)) for step in range(FLAGS.max_steps): for i in range(train_batch): batch = FLAGS.batch_size*i batch_plus = FLAGS.batch_size*(i+1) if batch_plus > train_len: batch_plus = train_len # feed_dictでplaceholderに入れるデータを指定する sess.run(train_op, feed_dict={ images_placeholder: train_image[batch:batch_plus], labels_placeholder: train_label[batch:batch_plus], keep_prob: 0.5}) if step % 10 == 0: train_accuracy = 0.0 for i in range(train_batch): batch = FLAGS.batch_size*i batch_plus = FLAGS.batch_size*(i+1) if batch_plus > train_len: batch_plus = train_len train_accuracy += sess.run(acc, feed_dict={ images_placeholder: train_image[batch:batch_plus], labels_placeholder: train_label[batch:batch_plus], keep_prob: 1.0}) if i is not 0: train_accuracy /= 2.0 print("step %d, training accuracy %g"%(step, train_accuracy)) if test_len % FLAGS.batch_size is 0: test_batch = int(test_len/FLAGS.batch_size) else: test_batch = int((test_len/FLAGS.batch_size)+1) print("test_batch = "+str(test_batch)) test_accuracy = 0.0 for i in range(test_batch): batch = FLAGS.batch_size*i batch_plus = FLAGS.batch_size*(i+1) if batch_plus > train_len: batch_plus = train_len test_accuracy += sess.run(acc, feed_dict={ images_placeholder: test_image[batch:batch_plus], labels_placeholder: test_label[batch:batch_plus], keep_prob: 1.0}) if i is not 0: test_accuracy /= 2.0 print("test accuracy %g"%(test_accuracy)) save_path = saver.save(sess, FLAGS.save_model)
###補足情報(言語/FW/ツール等のバージョンなど)
言語：pytho3.5
tensorflow：0.8.0
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