我查看了Tnesorflow的教程。现在，我想将输入 IMAGE_SIZE 从 28x28 更改为 56x56 以提高准确性。因此，我更改了 IMAGE_SIZE 变量，但该程序抛出错误。下面是原始代码，我想更改输入图像大小。我应该在哪里改变？
# -*- 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")
代码中至少还有两个地方取决于图像大小：
x_imagex_image = tf.reshape(images_placeholder, [-1, 28, 28, 3])
假设您将
IMAGE_SIZEx_image = tf.reshape(images_placeholder, [-1, IMAGE_SIZE, IMAGE_SIZE, 3])
输出全连接层中的神经元数量取决于图像大小（由池化层下采样），并且当输入中的像素数量增加 4 倍时，神经元数量将增加 4 倍。以下几行：
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
...应替换为：
W_fc1 = weight_variable([14 * 14 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 14 * 14 *64])
您在构建模型时使用一些硬编码的数字。请更改它们以使用
IMAGE_SIZE# 入力を28x28x3に変形
x_image = tf.reshape(images_placeholder, [-1, IMAGE_SIZE, IMAGE_SIZE, 3])
在将图像输入模型之前调整图像大小的一种方法是使用 ImageDataGenerator：
from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator()
train_generator = train_datagen.flow_from_directory(train_dir,
target_size = (150, 150))
train_dir 是存储输入的位置，您可以为输入定义恒定的目标大小。
请确保对测试图像也执行此操作。
相同的代码甚至可以用于增强图像，或者仅通过添加更多参数来重新缩放图像。