再配以系数1/N之后,就得到了损失函数关于输入矩阵z的“梯度矩阵” dz#在例子中,对probs矩阵确切的切片含义是 probs[np.array([0, 1 ,2]), np.array([2, 0, 1])]#这就像是定义了经纬度一样,指定了确切的行列数,要求切片出相应的数值。
affine_ln_relu_backward四个函数位于layer_utils.py文件,笔者自行加入 classFullyConnectedNet(object):"""A fully-connected neural network with an arbitrary number of hidden layers,ReLUnonlinearities, and a softmax loss function. This will also implementdropout and batch/layer normalization as options. F...
labels, of shape (N,) where y[i] is the label for x[i] and 0 <= y[i] < C Returns a tuple of: - loss: Scalar giving the loss - dx: Gradient of the loss with respect to x """ shifted_logits = x - np.max(x, axis=1, keepdims=True) #将每行中的数值进行平移,使得最大...
无论你是在云终端还是在本地完成作业,一旦完成作业,就运行collectSubmission.sh脚本;这样将会产生一个assignment2.zip的文件,然后将这个文件上传到你的dropbox中这门课的作业页面。 Q1:全连接神经网络(30分) IPython Notebook文件FullyConnectedNets.ipynb将会向你介绍我们的模块化设计,然后使用不同的层来构建任意深度...
也给大家透露下Assignment2 Q1-Q5的主要内容: Q1 - Q3: 利用简单的cifar10数据集,并以图片分类识别为目的教大家一步一步构建神经网络。 详细的解释了神经网络中前向传播和反向传播的原理 很细节的Python代码解析 + 神经网络中矩阵运算的图像化解释 + 模块化Python代码的流程图解析。
CS231N Assignment1 softmax 笔记 2023-10-053.CS231N Assigenment1 two_layer_net笔记2024-01-224.CS231N Assignment2 笔记2024-01-30 5.CS231N Assignment3 入门笔记(Q1&Q2)2024-01-296.CS231N Assignment3 入门笔记(Q3 Transformer)2024-01-297.CS231N Assignment3 入门笔记(Q4 GANs)2024-01-30 ...
Whether you work on the assignment locally or using Terminal, once you are done working run the collectSubmission.sh script; this will produce a file called assignment2.zip. Upload this file to your dropbox on the coursework page for the course. Q1: Two-layer Neural Network (30 points) Th...
Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see theassignments pageon the course website. The kNN classifier consists of two stages: ...
你可以通过在assignment2 中运行start_ipython_osx.sh来解决问assignment3.zip的文件,然后将这个文件上传到你的dropbox中Q1RNN进行图像标注(40分Q2LSTM进行图像标注(35分IPythonNotebookLSTM_Captioning.ipynb将会带你实现LSTM,并应用于在微软COCO数据集上进行图像标注。Q3:图像梯度:显著图和高效图像(10分IPythonNote...
时间:2019/3/24 内容: 1.数据驱动方法 2.KNN 任务: 完成Assignment1 Q1 https://cs231n.github.io/assignments2018/assignment1/ 课时4:数据驱动方法 numpy教程: http://cs231n.github.io/python-numpy-tutorial/ Google Cloud教程: http://cs2...CS...