classFullyConnectedNet(object):""" A fully-connected neural network with an arbitrary number of hidden layers, ReLU nonlinearities, and a softmax loss function. This will also implement dropout and batch/layer normalization as options. For a network with L layers, the architecture will be {affin...
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...
class TwoLayerNet(object): """ A two-layer fully-connected neural network with ReLU nonlinearity and softmax loss that uses a modular layer design. We assume an input dimension of D, a hidden dimension of H, and perform classification over C classes. The architecure should be affine - relu...
之前在作业1的时候也完成过一次两层全连接网络的编写two_layer_net.ipynb,但这次得implement modular versions。在fc_net.py完成TwoLayerNet类的编写,主要是一个参数的初始化还有loss和grad的计算,这两层网络的结构就是affine - relu - affine - softmax,可以考虑调用前面定义好的affine_relu_forward和affine_forwar...
摘要:assignment2 Q1: Fully-connected Neural Network (25 points) “独立完成” Fully-connected Neural Network 作业,还是挺开心的 5 层 Fully-connected Neural Network 加上 dropo阅读全文 posted @2018-03-24 21:14tmortred阅读(242)评论(0)推荐(0)编辑 ...
课程作业2 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的全连接网络。为了对模型进行最优化,还需要实现几个常用的更新方法。 Q2:批量归一化(30分) 在IPython Notebook文件BatchNormalization.ipynb中,你需要实现批量归一化,然后...
cs231n assignment(一) SVM线性分类器 目录 序 线性分类器 梯度验证 模型建立与SGD 验证集验证与超参数调优(交叉验证) 测试集测试与权重可视化 序 原来都是用的c++学习的传统图像分割算法。主要学习聚类分割、水平集、图割,欢迎一起讨论学习。 刚刚开始学习cs231n的课程,正好学习python,也做些实战加深对模型的理解...
weight_scale=1e-2, dtype=np.float32, seed=None):""" Initialize a new FullyConnectedNet. Inputs: - hidden_dims: A list of integers giving the size of each hidden layer. - input_dim: An integer giving the size of the input.