创建与 cifar-10 数据集格式相同的数据集
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【中文标题】创建与 cifar-10 数据集格式相同的数据集【英文标题】:create a dataset that has the same format as the cifar-10 data set 【发布时间】:2017-05-15 16:51:27 【问题描述】:我想创建一个与 cifar-10 数据集格式相同的数据集,以用于 Tensorflow。它应该有图像和标签。基本上,我希望能够采用 cifar-10 代码但不同的图像和标签,并运行该代码。我还没有找到任何关于如何在线执行此操作的信息,并且对机器学习完全陌生。
【问题讨论】:
您基本上已经描述了您需要什么:您需要找到一组带有标签的新图像。您可以 a) 查找现有的一组图像和标签,或者 b) 下载您自己的图像并自己标记它们。现有的 60,000 张图像(50,000 次训练/10,000 次测试)不适合您的用例?这是开始执行计算机视觉的一个非常棒的数据集。 向我们提供有关 cifar-10 数据集的更多信息,以便我们能够帮助您创建相同的结构。不知道 cifar-10 数据集是如何构建的人没有时间研究它来帮助你。 【参考方案1】:CIFAR-10 是更大的dataset 的子集。您需要的图像是具有三个颜色通道的高度和宽度为 32 像素的缩放彩色图像。实现目标的一种方法是从 CIFAR-100 数据集中选择 10 个不同的标签开始,保存并运行现有代码。例如,您可能想要选择车辆 1 和车辆 2 超类。这将为您提供 6000 个标记图像,涵盖:自行车、公共汽车、摩托车、皮卡车、火车、割草机、火箭、有轨电车、坦克和拖拉机类。然后,您可以构建车辆类型的预测器——这是一种更熟悉机器学习的非常酷的方法。 :-)
在 cifar10.py 文件中,您可以看到用于从“http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz”下载的训练文件的目录。无需更改任何代码,您只需使用您的数据更新这些膨胀的训练文件。查看 /tmp/cifar10_data/cifar-10-batches-bin 目录。例如。 batches.meta.txt 文件包含此处“二进制版本”部分所述的标签:https://www.cs.toronto.edu/~kriz/cifar.html
【讨论】:
【参考方案2】:我也必须这样做,并制作了一堆函数来将图像和文本文件格式化为 tensorflow 的可读格式。这是我在一个名为 images 的文件夹中使用一组图像(我使用 glob 遍历它们)和一个包含编码图像信息的文本文件所做的修改(我为每个图像设置了一系列数字,其中数字描述了用户在拍摄每张图像时引导机器人的位置)。我做了一个函数来生成小批量,并创建一个训练和测试数据集。我还将与每个图像关联的数字转换为单热向量以适应(如果需要,可以使用它,但可能没有用)。
#!/usr/bin/python
import cv2
import numpy as np
import tensorflow as tf
import glob
import re
import random
# Parameters
learning_rate = 0.001
training_iters = 20000
batch_size = 120
display_step = 10
# Network Parameters
n_input = 784 # MNIST data input (img shape: 28*28)
n_classes = 1 # MNIST total classes (0-9 digits)
dropout = 0.75 # Dropout, probability to keep units
image = np.reshape(np.asarray(mnist.train.images[0]), (28,28))
#Process Images
cv_img = []
for img in glob.glob("./images/*.jpeg"):
n = cv2.cvtColor(cv2.resize(cv2.imread(img), (28,28)), cv2.COLOR_BGR2GRAY)
n = np.asarray(n)
n = np.reshape(n, n_input)
cv_img.append(n)
#Process File for angle, here we read the text line by line and make a list
with open("./images/allinfo.txt") as f:
content = f.readlines()
#Initialize arrays to unpack data file
angle = []
image_number = []
#Iterate through the text list and split each one by the comma separating the values.
#Turn the text into floats for use in the network
for i in range(len(content)):
content[i] = content[i][:-1].split(',')
image_number.append(float(content[i][1]))
angle.append(float(content[i][7]))
#Divide both angle and image number into test and train data sets
angle = np.atleast_2d(angle).T
##Encode angle into 10 classes (it ranges -1 to 1)
for i in range(len(angle)):
angle[i] = random.uniform(-1,1)
angle[i] = int((angle[i]+1.0)*n_classes/2.)
#Create a one-hot version of angle
angle_one_hot = np.zeros((len(angle),n_classes))
for c in range(len(angle)):
one_hot = np.zeros(n_classes)
one_hot[int(angle[c])] = 1
angle_one_hot[c] = one_hot
image_number = np.atleast_2d(image_number).T
test_data = np.hstack((image_number, angle))
#print test_data
train_percent = .8
train_number = int(len(test_data)*train_percent)
train_data = np.zeros((train_number, 2))
for i in range(train_number):
rand = random.randrange(0,len(test_data))
train_data[i] = test_data[rand]
test_data = np.delete(test_data, rand, 0)
test_data_images = test_data[:,0]
test_data_angles = test_data[:,1]
train_data_images, train_data_angles = train_data[:,0], train_data[:,1]
def gen_batch(angles, images, batch_size, image_array=cv_img):
indices = random.sample(xrange(0,len(images)), batch_size)
batch_images = []
batch_angles = []
# print angles
for i in range(batch_size):
batch_images.append(image_array[int(images[indices[i]])][:])
batch_angles.append(angles[indices[i]])
batch_images = np.asarray(batch_images)
batch_angles = np.asarray(batch_angles)
return batch_images, batch_angles
# tf Graph input
x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32)
keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)
# Create some wrappers for simplicity
def conv2d(x, W, b, strides=1):
# Conv2D wrapper, with bias and relu activation
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x)
def maxpool2d(x, k=2):
# MaxPool2D wrapper
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],
padding='SAME')
# Create model
def conv_net(x, weights, biases, dropout):
# Reshape input picture
x = tf.reshape(x, shape=[-1, 28, 28, 1])
# Convolution Layer
conv1 = conv2d(x, weights['wc1'], biases['bc1'])
# Max Pooling (down-sampling)
conv1 = maxpool2d(conv1, k=2)
# Convolution Layer
conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
# Max Pooling (down-sampling)
conv2 = maxpool2d(conv2, k=2)
# Fully connected layer
# Reshape conv2 output to fit fully connected layer input
fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1)
# Apply Dropout
fc1 = tf.nn.dropout(fc1, dropout)
# Output, class prediction
out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
return out
# Store layers weight & bias
weights =
# 5x5 conv, 1 input, 32 outputs
'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
# 5x5 conv, 32 inputs, 64 outputs
'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
# fully connected, 7*7*64 inputs, 1024 outputs
'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),
# 1024 inputs, 10 outputs (class prediction)
'out': tf.Variable(tf.random_normal([1024, n_classes]))
biases =
'bc1': tf.Variable(tf.random_normal([32])),
'bc2': tf.Variable(tf.random_normal([64])),
'bd1': tf.Variable(tf.random_normal([1024])),
'out': tf.Variable(tf.random_normal([n_classes]))
# Construct model
pred = conv_net(x, weights, biases, keep_prob)
# Define loss and optimizer
#cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
cost = tf.reduce_mean(pred)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize((pred-y)**2)
# Evaluate model
correct_pred = y
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Initializing the variables
init = tf.initialize_all_variables()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
step = 1
print(y)
# Keep training until reach max iterations
while step * batch_size < training_iters:
batch_x, batch_y = gen_batch(train_data_angles, train_data_images, batch_size)
#cv2.imshow('trash', batch_x[0,:].reshape((28,28)))
#cv2.waitKey(0)
#print(batch_y)
# Run optimization op (backprop)
sess.run(optimizer, feed_dict=x: batch_x, y: batch_y,
keep_prob: dropout)
if step % display_step == 0:
# Calculate batch loss and accuracy
loss, acc = sess.run([cost, accuracy], feed_dict=x: batch_x,
y: batch_y,
keep_prob: 1.)
print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
":.6f".format(loss) + ", Training Accuracy= " + \
":.5f".format(acc)
step += 1
print "Optimization Finished!"
# Calculate accuracy for all test images
img, lbls = gen_batch(test_data_angles, test_data_images, len(test_data_angles))
print "Testing Accuracy:", \
sess.run(accuracy, feed_dict=x: img,
y: lbls,
keep_prob: 1.)
这不是一个好的nn(数据没有归一化,学习率是2,训练精度还没有编程)但是图像处理代码可以工作。
希望这会有所帮助!
【讨论】:
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