Question

为训练输入数据到tensorflow的文件格式是什么？

是否必须

图片/路径/标签

Answer 1

有几种方法可以在张量流中处理图像。例如，我在这里整理了使用numpy准备Cifar10数据的代码片段来处理张量流。

但首先从here下载Cifar10数据，然后将代码（.py文件）放在tar.gz文件旁边

"""Base code is here:
https://github.com/tflearn/tflearn/blob/master/tflearn/datasets/cifar10.py
"""

import urllib
import os
import tarfile
import tarfile

import numpy as np
import pickle


def Untar(fname):
  if (fname.endswith("tar.gz")):
    tar = tarfile.open(fname)
    tar.extractall(path = '/'.join(fname.split('/')[:-1]))
    tar.close()
    print("File Extracted in Current Directory")
  else:
    print("Not a tar.gz file: '%s '" % sys.argv[0])


def LoadBatch(fpath):
    with open(fpath, 'rb') as f:
      d = pickle.load(f)
    data = d["data"]
    labels = d["labels"]
    return data, labels


def LoadCifarData(filepath='cifar-10-python.tar.gz',
                  extract_dir='cifar-10-batches-py/', 
                  one_hot=False):
  Untar(filepath)
  X_train = []
  Y_train = []
  for i in range(1, 6):
    fpath = os.path.join(extract_dir, 'data_batch_' + str(i))
    data, labels = LoadBatch(fpath)
    if i == 1:
      X_train = data
      Y_train = labels
    else:
      X_train = np.concatenate([X_train, data], axis=0)
      Y_train = np.concatenate([Y_train, labels], axis=0)

  fpath = os.path.join(extract_dir, 'test_batch')
  X_test, Y_test = LoadBatch(fpath)

  X_train = np.dstack((X_train[:, :1024], X_train[:, 1024:2048],
                       X_train[:, 2048:])) / 255.
  X_train = np.reshape(X_train, [-1, 32, 32, 3])
  X_test = np.dstack((X_test[:, :1024], X_test[:, 1024:2048],
                      X_test[:, 2048:])) / 255.
  X_test = np.reshape(X_test, [-1, 32, 32, 3])

  if one_hot:
    Y_train = to_categorical(Y_train, 10)
    Y_test = to_categorical(Y_test, 10)

  print X_train
  return (X_train, Y_train), (X_test, Y_test)


def main():
  LoadCifarData()


if __name__ == '__main__':
  main()

加载的数据集应该是这样的： [[0.5372549 0.51764706 0.49411765] [0.50980392 0.49803922 0.47058824] [0.49019608 0.4745098 0.45098039] ... [0.70980392 0.70588235 0.69803922] [0.79215686 0.78823529 0.77647059] [0.83137255 0.82745098 0.81176471]]

  [[ 0.47843137  0.46666667  0.44705882]
   [ 0.4627451   0.45490196  0.43137255]
   [ 0.47058824  0.45490196  0.43529412]
   ...,
   [ 0.70196078  0.69411765  0.67843137]
   [ 0.64313725  0.64313725  0.63529412]
   [ 0.63921569  0.63921569  0.63137255]]]]

加载此数据后，您可以通过创建卷积神经网络来对图像进行分类，这样就可以使用

https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_cifar10.py

准备图像数据集以输入Tensorflow深度学习

1 个答案: