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A Python implementation of PCANet https://arxiv.org/abs/1404.3606

  • 源代码名称:PCANet
  • 源代码网址:http://www.github.com/IshitaTakeshi/PCANet
  • PCANet源代码文档
  • PCANet源代码下载
  • Git URL:
    git://www.github.com/IshitaTakeshi/PCANet.git
  • Git Clone代码到本地:
    git clone http://www.github.com/IshitaTakeshi/PCANet
  • Subversion代码到本地:
    $ svn co --depth empty http://www.github.com/IshitaTakeshi/PCANet
    Checked out revision 1.
    $ cd repo
    $ svn up trunk
  • PCANet

    基于PCANet的图像分类学习网络。

    顾名思义,网络中的权重是用主值来计算的。 由于这种特性,PCANet的训练非常迅速。 此外,PCANet本身训练时不需要类标签。
    详细描述在中的原始纸张。

    安装

    正在运行 python3 setup.py install
    如果你喜欢 pip,那么 PCANet root 目录中的pip3 install.

    用法

    from pcanet import PCANet# Arguments are basically passed as tuple in the form (height, width) but int is also allowed. # If int is given, the parameter will be converted into (size, size) implicitly.pcanet = PCANet(
     image_shape=28, # the size of an input image# kernel size, kernel step size, and the number of filters in the first layer, respectivelyfilter_shape_l1=2, step_shape_l1=1, n_l1_output=4,
     # kernel size, kernel step size, and the number of filters in the second layer, respectivelyfilter_shape_l2=2, step_shape_l2=1, n_l2_output=4,
     block_shape=2# the size of area to calculate histogram)# Check whether all pixels can be considered. Raise ValueError if the structure is not valid.# Calling this function is optional. PCANet works without this line.pcanet.validate_structure()
    pcanet.fit(images_train) # Train PCANet# Trained PCANet behaves as a transformer from images into features.# `images` is a 3d array in the form (n_images, height, width), who are transformed into feature vectors.X_train = pcanet.transform(images_train)
    X_test = pcanet.transform(images_test)# Fit any models you likefrom sklearn.ensemble import RandomForestClassifier
    model = RandomForestClassifier()
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)

    有关详细信息,请参阅 run_mnist.py

    示例

    如果为GPU标识指定了负值,则使用 CPU

    火车

    python3 run_mnist.py --gpu <GPU ID> train --out <output directory (default='result')>

    测试

    python3 run_mnist.py --gpu <GPU ID> test --pretrained-model <path to dir (default='result')>
    文档

    可以在 docs 目录中运行 make html 来生成文档。

    引用

    :,:,et,et,et,et 。" 一种简单的图像分类深度学习基线"图像处理 24.12 ( 2015 ) ):? 5017 -5032.




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