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Question

我最近参加CS231n在线课程。尝试使用CIFAR-10数据集在http://cs231n.github.io/classification/

中实现简单的图像分类代码

在运行此代码时，在预测功能上，时间过长，甚至没有完成。我基本上复制并粘贴了所有代码。我的桌面拥有最先进的机器。

代码如下所示。

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class NearestNeighbor:
    def __init__(self):
        pass

    def train(self, X, y):
        """X is N x D where each row is an example. Y is 1-dimension of size N"""
        # the nearest neighbor classifier simply remembers all the training data
        self.Xtr = X
        self.ytr = y

    def predict(self, X):
        """X is N x D where each row is an example we wish to predict label for"""
        num_test = X.shape[0]
        # lets make sure that the output type matches the input type
        Ypred = np.zeros(num_test, dtype = self.ytr.dtype)

        # loop over all test rows
        for i in xrange(num_test):
            # find the nearest training image to the i'th test image
            # using the L1 distance (sum of absolute value differences)
            distances = np.sum(np.abs(self.Xtr - X[i,:]), axis = 1)
            min_index = np.argmin(distances) # get the index with smallest distance
            Ypred[i] = self.ytr[min_index] # predict the label of the nearest example

        return Ypred

Xtr, Ytr, Xte, Yte = load_CIFAR10('/home/tegg/Downloads/cifar-10-batches-py/')
# flatten out all images to be one-dimensional
Xtr_rows = Xtr.reshape(Xtr.shape[0], 32*32*3) # Xtr_rows become 50000 x 3072
Xte_rows = Xte.reshape(Xte.shape[0], 32*32*3) # Xte_rows become 10000 x 3072

nn = NearestNeighbor() # create a Nearest Neighbor classifier class
nn.train(Xtr_rows, Ytr) # train the classifier on the training images and labels
Yte_predict = nn.predict(Xte_rows) # predict labels on the test images
# and now print the classification accuracy, which is the average number
# of examples that are correctly predicted (i.e. label matches)

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Yte_predict = nn.predict(Xte_rows)

我认为最后一行遇到了麻烦。

import {test} from "tape";
import {default as nightmare} from "nightmare";

const page = nightmare().goto("http://localhost:4000/index.html");

page.evaluate(() => document.getElementsByTagName("body").length).end()
  .then((result) => {
    test("detect page body", (assert) => {
      assert.equal(1, result);
      assert.end();
    });
  });

使用ipython notebook运行此代码时，它会继续运行代码，而不显示结果（完成编译）。我的代码有什么问题吗？

关于cs231n图像分类实现的困惑

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最近邻函数

0 个答案: