tensorflow, I want to change input image size - machine-learning

Question

I check tutorial of Tnesorflow. And now, I want to change input IMAGE_SIZE from 28x28 to 56x56 for improving accuracy. So, I change IMAGE_SIZE variable but that program throw error. Below is an original code and I want to change input image size. Where should I change?
# -*- coding: utf-8 -*-
import sys
import cv2
import numpy as np
import tensorflow as tf
import tensorflow.python.platform
NUM_CLASSES = 6
IMAGE_SIZE = 28
IMAGE_PIXELS = IMAGE_SIZE*IMAGE_SIZE*3
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_string('train', 'train.txt', 'File name of train data')
flags.DEFINE_string('test', 'test.txt', 'File name of train data')
flags.DEFINE_string('train_dir', '/tmp/data', 'Directory to put the training data.')
flags.DEFINE_integer('max_steps', 200, 'Number of steps to run trainer.')
flags.DEFINE_integer('batch_size', 10, '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):
""" 予測モデルを作成する関数
引数:
images_placeholder: 画像のplaceholder
keep_prob: dropout率のplace_holder
返り値:
y_conv: 各クラスの確率(のようなもの)
"""
# 重みを標準偏差0.1の正規分布で初期化
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
# バイアスを標準偏差0.1の正規分布で初期化
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
# 畳み込み層の作成
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
# プーリング層の作成
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
# 入力を28x28x3に変形
x_image = tf.reshape(images_placeholder, [-1, 28, 28, 3])
# 畳み込み層1の作成
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_image, W_conv1) + b_conv1)
# プーリング層1の作成
with tf.name_scope('pool1') as scope:
h_pool1 = max_pool_2x2(h_conv1)
# 畳み込み層2の作成
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)
# プーリング層2の作成
with tf.name_scope('pool2') as scope:
h_pool2 = max_pool_2x2(h_conv2)
# 全結合層1の作成
with tf.name_scope('fc1') as scope:
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
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)
# 全結合層2の作成
with tf.name_scope('fc2') as scope:
W_fc2 = weight_variable([1024, NUM_CLASSES])
b_fc2 = bias_variable([NUM_CLASSES])
# ソフトマックス関数による正規化
with tf.name_scope('softmax') as scope:
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
# 各ラベルの確率のようなものを返す
return y_conv
def loss(logits, labels):
""" lossを計算する関数
引数:
logits: ロジットのtensor, float - [batch_size, NUM_CLASSES]
labels: ラベルのtensor, int32 - [batch_size, NUM_CLASSES]
返り値:
cross_entropy: 交差エントロピーのtensor, float
"""
# 交差エントロピーの計算
cross_entropy = -tf.reduce_sum(labels*tf.log(logits))
# TensorBoardで表示するよう指定
tf.scalar_summary("cross_entropy", cross_entropy)
return cross_entropy
def training(loss, learning_rate):
""" 訓練のOpを定義する関数
引数:
loss: 損失のtensor, loss()の結果
learning_rate: 学習係数
返り値:
train_step: 訓練のOp
"""
train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)
return train_step
def accuracy(logits, labels):
""" 正解率(accuracy)を計算する関数
引数:
logits: inference()の結果
labels: ラベルのtensor, int32 - [batch_size, NUM_CLASSES]
返り値:
accuracy: 正解率(float)
"""
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__':
# ファイルを開く
f = open(FLAGS.train, 'r')
# データを入れる配列
train_image = []
train_label = []
for line in f:
# 改行を除いてスペース区切りにする
line = line.rstrip()
l = line.split()
# データを読み込んで28x28に縮小
img = cv2.imread('tmp/data/' + l[0])
img = cv2.resize(img, (28, 28))
# 一列にした後、0-1のfloat値にする
train_image.append(img.flatten().astype(np.float32)/255.0)
# ラベルを1-of-k方式で用意する
tmp = np.zeros(NUM_CLASSES)
tmp[int(l[1])] = 1
train_label.append(tmp)
# numpy形式に変換
train_image = np.asarray(train_image)
train_label = np.asarray(train_label)
f.close()
f = open(FLAGS.test, 'r')
test_image = []
test_label = []
for line in f:
line = line.rstrip()
l = line.split()
img = cv2.imread('tmp/data/' + l[0])
img = cv2.resize(img, (28, 28))
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)
f.close()
with tf.Graph().as_default():
# 画像を入れる仮のTensor
images_placeholder = tf.placeholder("float", shape=(None, IMAGE_PIXELS))
# ラベルを入れる仮のTensor
labels_placeholder = tf.placeholder("float", shape=(None, NUM_CLASSES))
# dropout率を入れる仮のTensor
keep_prob = tf.placeholder("float")
# inference()を呼び出してモデルを作る
logits = inference(images_placeholder, keep_prob)
# loss()を呼び出して損失を計算
loss_value = loss(logits, labels_placeholder)
# training()を呼び出して訓練
train_op = training(loss_value, FLAGS.learning_rate)
# 精度の計算
acc = accuracy(logits, labels_placeholder)
# 保存の準備
saver = tf.train.Saver()
# Sessionの作成
sess = tf.Session()
# 変数の初期化
sess.run(tf.initialize_all_variables())
# TensorBoardで表示する値の設定
summary_op = tf.merge_all_summaries()
summary_writer = tf.train.SummaryWriter("/tmp/log/loglog1", sess.graph)
# 訓練の実行
for step in range(FLAGS.max_steps):
for i in range(len(train_image)/FLAGS.batch_size):
# batch_size分の画像に対して訓練の実行
batch = FLAGS.batch_size*i
# 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+FLAGS.batch_size],
labels_placeholder: train_label[batch:batch+FLAGS.batch_size],
keep_prob: 0.5})
# 1 step終わるたびに精度を計算する
train_accuracy = sess.run(acc, feed_dict={
images_placeholder: train_image,
labels_placeholder: train_label,
keep_prob: 1.0})
print "step %d, training accuracy %g"%(step, train_accuracy)
# 1 step終わるたびにTensorBoardに表示する値を追加する
summary_str = sess.run(summary_op, feed_dict={
images_placeholder: test_image,
labels_placeholder: test_label,
keep_prob: 1.0})
summary_writer.add_summary(summary_str, step)
print "test accuracy %g"%sess.run(acc, feed_dict={
images_placeholder: test_image,
labels_placeholder: test_label,
keep_prob: 1.0})
# 訓練が終了したらテストデータに対する精度を表示
print "test accuracy %g"%sess.run(acc, feed_dict={
images_placeholder: test_image,
labels_placeholder: test_label,
keep_prob: 1.0})
# 最終的なモデルを保存
save_path = saver.save(sess, "model.ckpt")

Answer 1

There are at least two other places in the code that depend on the image size:
The definition of x_image hard-codes the image size:
x_image = tf.reshape(images_placeholder, [-1, 28, 28, 3])
Assuming you set IMAGE_SIZE to be 56, you should replace it with:
x_image = tf.reshape(images_placeholder, [-1, IMAGE_SIZE, IMAGE_SIZE, 3])
The number of neurons in the output fully connected layer depends on the image size (downsampled by the pooling layers), and will increase by 4x when you increase the number of pixels in the input by 4x. The following lines:
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
...should be replaced with:
W_fc1 = weight_variable([14 * 14 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 14 * 14 *64])

Answer 2

You use some hardcoded numbers while building the model. Please change them to use IMAGE_SIZE instead as follows :
# 入力を28x28x3に変形
x_image = tf.reshape(images_placeholder, [-1, IMAGE_SIZE, IMAGE_SIZE, 3])