resnet

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 Let's say I have a ResNet50 model and I wish to connect the output layer of this model to the input layer of a VGG model. This is the ResNet model and the output tensor of ResNet50: img_shape = (164, 164, 3) resnet50_model = ResNet50(include_top=False, input_shape=img_shape, weights = None) print(resnet50_model.output.shape) I get the output: TensorShape([Dimension(None), Dimension(6), Dimension(6), Dimension(2048)]) Now I want a new layer where I reshape this output tensor to (64,64,18) Then

问题 Let's say I have a ResNet50 model and I wish to connect the output layer of this model to the input layer of a VGG model. This is the ResNet model and the output tensor of ResNet50: img_shape = (164, 164, 3) resnet50_model = ResNet50(include_top=False, input_shape=img_shape, weights = None) print(resnet50_model.output.shape) I get the output: TensorShape([Dimension(None), Dimension(6), Dimension(6), Dimension(2048)]) Now I want a new layer where I reshape this output tensor to (64,64,18) Then

问题 I used python 3 and when i insert transform random crop size 224 it gives miss match error. here my code what did i wrong ? 回答1: Your code makes variations on resnet: you changed the number of channels, the number of bottlenecks at each "level", and you removed a "level" entirely. As a result, the dimension of the feature map you have at the end of layer3 is not 64: you have a larger spatial dimension than you anticipated by the nn.AvgPool2d(8). The error message you got actually tells you

问题 I used python 3 and when i insert transform random crop size 224 it gives miss match error. here my code what did i wrong ? 回答1: Your code makes variations on resnet: you changed the number of channels, the number of bottlenecks at each "level", and you removed a "level" entirely. As a result, the dimension of the feature map you have at the end of layer3 is not 64: you have a larger spatial dimension than you anticipated by the nn.AvgPool2d(8). The error message you got actually tells you

问题 I used python 3 and when i insert transform random crop size 224 it gives miss match error. here my code what did i wrong ? 回答1: Your code makes variations on resnet: you changed the number of channels, the number of bottlenecks at each "level", and you removed a "level" entirely. As a result, the dimension of the feature map you have at the end of layer3 is not 64: you have a larger spatial dimension than you anticipated by the nn.AvgPool2d(8). The error message you got actually tells you

问题 I am using keras applications for transfer learning with resnet 50 and inception v3 but when predicting always get [[ 0.]] The below code is for a binary classification problem. I have also tried vgg19 and vgg16 but they work fine, its just resnet and inception. The dataset is a 50/50 split. And I am only changing the model = applications.resnet50.ResNet50 line of code for each model. below is the code: from keras.callbacks import EarlyStopping early_stopping = EarlyStopping(monitor='val_loss

问题 I am using keras applications for transfer learning with resnet 50 and inception v3 but when predicting always get [[ 0.]] The below code is for a binary classification problem. I have also tried vgg19 and vgg16 but they work fine, its just resnet and inception. The dataset is a 50/50 split. And I am only changing the model = applications.resnet50.ResNet50 line of code for each model. below is the code: from keras.callbacks import EarlyStopping early_stopping = EarlyStopping(monitor='val_loss

问题 I am trying to do image recognition with ResNet50 in Python ( keras ). I tried to do the same task with VGG16 , and I got some results like these (which seem okay to me): resultsVGG16 . The training and validation accuracy/loss functions are getting better with each step, so the network must learn. However, with ResNet50 the training functions are betting better, while the validation functions are not changing: resultsResNet I've used the same code and data in both of the times, only the