创建一个与cifar-10数据集格式相同的数据集

Question

我想创建一个与cifar-10数据集具有相同格式的数据集，以便与Tensorflow一起使用。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，而且对机器学习来说也是全新的。

Answer 1

CIFAR-10是一个更大的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，保存并运行现有的代码。例如，您可能想要选择车辆1和车辆2超级类别。这将为您提供6000张标签图像，内容包括：自行车，公共汽车，摩托车，皮卡车，火车，割草机，火箭，路面电车，坦克和拖拉机班。然后，您可以构建车辆类型的预测值 - 这对于更熟悉机器学习非常酷。 ： - ）

在cifar10.py文​​件中，您可以看到从'http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz'下载的用于训练文件的目录。无需更改任何代码，只需使用数据更新这些虚增的培训文件即可。看看/ tmp/cifar10_data/cifar-10-batches-bin目录。例如。 batches.meta.txt文件包含如下“二进制版本”部分中所述的标签：https://www.cs.toronto.edu/~kriz/cifar.html

Answer 2

我也必须这样做，并且制作了一组函数来将图像和文本文件格式化为可读格式为tensorflow。这里是我做了用一组图像中一个文件夹，名为图像（我用的水珠通过他们迭代），并与有关编码的图像信息的文本文件的修改（我有一系列的数字为每个图像，其中，数字描述了在每次拍摄图像时用户指导机器人的位置）。我做了一个函数来生成minibatches，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一个热点矢量以适应（如果需要，您可以使用它，但可能无用）。

#!/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.}) 

这不会是一个很好的工作，NN（该数据不被标准化，学习率是两头高，并且训练精度没有设置），但图像处理代码工作。

希望这会有所帮助！