尚义住房和城乡规划建设局网站,中国建设布网站,黄页荔枝抖音,关于建设网站的通知文章目录 嫌啰嗦直接看源码Q5 :PyTorch on CIFAR-10three_layer_convnet题面解析代码输出 Training a ConvNet题面解析代码输出 ThreeLayerConvNet题面解析代码输出 Train a Three-Layer ConvNet题面解析代码输出 Sequential API: Three-Layer ConvNet题面解析代码输出 CIFAR-1… 文章目录 嫌啰嗦直接看源码Q5 :PyTorch on CIFAR-10three_layer_convnet题面解析代码输出 Training a ConvNet题面解析代码输出 ThreeLayerConvNet题面解析代码输出 Train a Three-Layer ConvNet题面解析代码输出 Sequential API: Three-Layer ConvNet题面解析代码输出 CIFAR-10 open-ended challenge题面解析代码输出 嫌啰嗦直接看源码 Q5 :PyTorch on CIFAR-10 three_layer_convnet 题面 让我们使用Pytorch来实现一个三层神经网络 解析 看下pytorch是怎么用的原理我们其实都清楚了自己去查下文档就好了 具体的可以看上一个cell上面给出的文档地址 For convolutions: http://pytorch.org/docs/stable/nn.html#torch.nn.functional.conv2d; pay attention to the shapes of convolutional filters!代码 def three_layer_convnet(x, params):Performs the forward pass of a three-layer convolutional network with thearchitecture defined above.Inputs:- x: A PyTorch Tensor of shape (N, 3, H, W) giving a minibatch of images- params: A list of PyTorch Tensors giving the weights and biases for thenetwork; should contain the following:- conv_w1: PyTorch Tensor of shape (channel_1, 3, KH1, KW1) giving weightsfor the first convolutional layer- conv_b1: PyTorch Tensor of shape (channel_1,) giving biases for the firstconvolutional layer- conv_w2: PyTorch Tensor of shape (channel_2, channel_1, KH2, KW2) givingweights for the second convolutional layer- conv_b2: PyTorch Tensor of shape (channel_2,) giving biases for the secondconvolutional layer- fc_w: PyTorch Tensor giving weights for the fully-connected layer. Can youfigure out what the shape should be?- fc_b: PyTorch Tensor giving biases for the fully-connected layer. Can youfigure out what the shape should be?Returns:- scores: PyTorch Tensor of shape (N, C) giving classification scores for xconv_w1, conv_b1, conv_w2, conv_b2, fc_w, fc_b paramsscores None################################################################################# TODO: Implement the forward pass for the three-layer ConvNet. ################################################################################## *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****x F.conv2d(x, conv_w1, biasconv_b1, padding2)x F.relu(x)x F.conv2d(x, conv_w2, biasconv_b2, padding1)x F.relu(x)x flatten(x)scores x.mm(fc_w) fc_b# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****################################################################################# END OF YOUR CODE #################################################################################return scores输出 注意这里需要注意有没有使用Gpu版本的pytorch,我就是在这里发现我的pytorch没有cuda Training a ConvNet 题面 解析 按照题面意思来就好了 代码 learning_rate 3e-3channel_1 32 channel_2 16conv_w1 None conv_b1 None conv_w2 None conv_b2 None fc_w None fc_b None################################################################################ # TODO: Initialize the parameters of a three-layer ConvNet. # ################################################################################ # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****conv_w1 random_weight((channel_1, 3, 5, 5)) conv_b1 zero_weight(channel_1) conv_w2 random_weight((channel_2, channel_1, 3, 3)) conv_b2 zero_weight(channel_2) fc_w random_weight((channel_2 * 32 * 32, 10)) fc_b zero_weight(10)# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** ################################################################################ # END OF YOUR CODE # ################################################################################params [conv_w1, conv_b1, conv_w2, conv_b2, fc_w, fc_b] train_part2(three_layer_convnet, params, learning_rate)输出 ThreeLayerConvNet 题面 解析 就是让我们熟悉一下几个api 代码 class ThreeLayerConvNet(nn.Module):def __init__(self, in_channel, channel_1, channel_2, num_classes):super().__init__()######################################################################### TODO: Set up the layers you need for a three-layer ConvNet with the ## architecture defined above. ########################################################################## *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****self.conv1 nn.Conv2d(in_channel, channel_1, kernel_size5, padding2)self.conv2 nn.Conv2d(channel_1, channel_2, kernel_size3, padding1)self.fc3 nn.Linear(channel_2 * 32 * 32, num_classes)nn.init.kaiming_normal_(self.conv1.weight)nn.init.kaiming_normal_(self.conv2.weight)nn.init.kaiming_normal_(self.fc3.weight)# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****######################################################################### END OF YOUR CODE #########################################################################def forward(self, x):scores None######################################################################### TODO: Implement the forward function for a 3-layer ConvNet. you ## should use the layers you defined in __init__ and specify the ## connectivity of those layers in forward() ########################################################################## *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****x F.relu(self.conv1(x))x F.relu(self.conv2(x))scores self.fc3(flatten(x))# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****######################################################################### END OF YOUR CODE #########################################################################return scores输出 Train a Three-Layer ConvNet 题面 解析 就仿照上面的两层全连接改写就好了 关于optim 我试过sgd 和 adam,但是我发现还是sgd效果对于这个样本好一点。。。。 代码 learning_rate 3e-3 channel_1 32 channel_2 16model None optimizer None ################################################################################ # TODO: Instantiate your ThreeLayerConvNet model and a corresponding optimizer # ################################################################################ # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****model ThreeLayerConvNet(in_channel3, channel_1channel_1, channel_2channel_2, num_classes10) optimizer optim.SGD(model.parameters(), lrlearning_rate)# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** ################################################################################ # END OF YOUR CODE # ################################################################################train_part34(model, optimizer)输出 Sequential API: Three-Layer ConvNet 题面 解析 也是仿照上面写就好了 代码 channel_1 32 channel_2 16 learning_rate 1e-2model None optimizer None################################################################################ # TODO: Rewrite the 2-layer ConvNet with bias from Part III with the # # Sequential API. # ################################################################################ # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****model nn.Sequential(nn.Conv2d(in_channels3, out_channelschannel_1, kernel_size5, padding2),nn.ReLU(),nn.Conv2d(in_channelschannel_1, out_channelschannel_2, kernel_size3, padding1),nn.ReLU(),Flatten(),nn.Linear(channel_2 * 32 * 32, 10) ) optimizer optim.SGD(model.parameters(), lrlearning_rate, momentum0.9, nesterovTrue)# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** ################################################################################ # END OF YOUR CODE # ################################################################################train_part34(model, optimizer)输出 CIFAR-10 open-ended challenge 题面 就是让我们自己尝试搭建一种网络结构使其准确率大于70% 解析 自己试吧 代码 ################################################################################ # TODO: # # Experiment with any architectures, optimizers, and hyperparameters. # # Achieve AT LEAST 70% accuracy on the *validation set* within 10 epochs. # # # # Note that you can use the check_accuracy function to evaluate on either # # the test set or the validation set, by passing either loader_test or # # loader_val as the second argument to check_accuracy. You should not touch # # the test set until you have finished your architecture and hyperparameter # # tuning, and only run the test set once at the end to report a final value. # ################################################################################ model None optimizer None# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****model nn.Sequential(nn.Conv2d(3, 32, kernel_size3, stride1, padding1),nn.ReLU(),nn.MaxPool2d(kernel_size2, stride2),nn.Conv2d(32, 64, kernel_size3, stride1, padding1),nn.ReLU(),nn.MaxPool2d(kernel_size2, stride2),nn.Conv2d(64, 128, kernel_size3, stride1, padding1),nn.ReLU(),nn.MaxPool2d(kernel_size2, stride2),Flatten(),nn.Linear(128 * 4 * 4, 1024), ) optimizer optim.Adam(model.parameters(), lr1e-3)# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** ################################################################################ # END OF YOUR CODE # ################################################################################# You should get at least 70% accuracy. # You may modify the number of epochs to any number below 15. train_part34(model, optimizer, epochs10)输出