deep-residual-networks

问题 This line works fine self.conv = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False) I introduced ResNet18 self.conv = ResNet18() **ResNet Class** '''ResNet in PyTorch. For Pre-activation ResNet, see 'preact_resnet.py'. Reference: [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun Deep Residual Learning for Image Recognition. arXiv:1512.03385 ''' import torch import torch.nn as nn import torch.nn.functional as F class BasicBlock(nn.Module): expansion = 1 def __init__(self, in

问题 This line works fine self.conv = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False) I introduced ResNet18 self.conv = ResNet18() **ResNet Class** '''ResNet in PyTorch. For Pre-activation ResNet, see 'preact_resnet.py'. Reference: [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun Deep Residual Learning for Image Recognition. arXiv:1512.03385 ''' import torch import torch.nn as nn import torch.nn.functional as F class BasicBlock(nn.Module): expansion = 1 def __init__(self, in

问题 # import the necessary packages import keras from keras.initializers import glorot_uniform from keras.layers import AveragePooling2D, Input, Add from keras.models import Model from keras.layers.normalization import BatchNormalization from keras.layers.convolutional import Conv2D from keras.layers.convolutional import MaxPooling2D from keras.layers.core import Activation from keras.layers.core import Flatten from keras.layers.core import Dropout from keras.layers.core import Dense class

问题 I am attempting to replicate this image from a research paper. In the image, the orange arrow indicates a shortcut using residual learning and the layer outlined in red indicates a dilated convolution. In the code below, r5 is the result of the relu seen in the image. I have excluded the code between the relu and the dilation layer for simplicity. In tensorflow, how would I properly combine the result of the relu and dilated convolution to execute the residual shortcut? #relu layer r5 = tf.nn

问题 I am attempting to replicate this image from a research paper. In the image, the orange arrow indicates a shortcut using residual learning and the layer outlined in red indicates a dilated convolution. In the code below, r5 is the result of the relu seen in the image. I have excluded the code between the relu and the dilation layer for simplicity. In tensorflow, how would I properly combine the result of the relu and dilated convolution to execute the residual shortcut? #relu layer r5 = tf.nn

问题 With the residual block in residual neural networks, is the addition at the end of the block true element addition or is it concatenation? For example, would addition([1, 2], [3, 4]) produce [1, 2, 3, 4] or [4, 6] ? 回答1: It would result in [4, 6], and you can find out more in this paper 来源： https://stackoverflow.com/questions/46902386/residual-neural-network-concatenation-or-element-addition

问题 I'm currently using a resnet built in keras to do two class classification. I am using model checkpoint to save the best models based off of validation accuracy. Better and better models are saved until I go through all my datapoints a few times. Keras keeps saving new models showing they have higher accuracy but when I test the models they perform worse than previous models. Here is an output of testing each model with validation data. The first number in the model name is the epoch, the

问题 I am reading through Residual learning, and I have a question. What is "linear projection" mentioned in 3.2? Looks pretty simple once got this but could not get the idea... I am basically not a computer science person, so I would very appreciate if someone provide me a simple example. 回答1: First up, it's important to understand what x , y and F are and why they need any projection at all. I'll try explain in simple terms, but basic understanding of ConvNets is required. x is an input data

问题 I am training a deep residual network with 10 hidden layers with game data. Does anyone have an idea why I don't get any overfitting here? Training and test loss still decreasing after 100 epochs of training. https://imgur.com/Tf3DIZL 回答1: Just a couple of advice: for deep learning is recommended to do even 90/10 or 95/5 splitting (Andrew Ng) this small difference between curves means that your learning_rate is not tuned; try to increase it (and, probably, number of epochs if you will

问题 I would like to calculate constant convolution like blurring or resampling and want it never change during training. Can I initialize convolution kernel to constant and exclude it from training in Keras? More specifically, I don't want to use this for purposes declared in the doc. I want to implement residual network this way: one branch does normal trainable convolution, while parallel branch does something constant, like averaging. 回答1: You should be able to pass a trainable = False