conv-neural-network

问题 My team is training a CNN in Tensorflow for binary classification of damaged/acceptable parts. We created our code by modifying the cifar10 example code. In my prior experience with Neural Networks, I always trained until the loss was very close to 0 (well below 1). However, we are now evaluating our model with a validation set during training (on a separate GPU), and it seems like the precision stopped increasing after about 6.7k steps, while the loss is still dropping steadily after over

问题 What would be the most straightforward way to implement a hypernetwork in Keras? That is, where one leg of the network creates the weights for another? In particular, I would like to do template matching where I feed the template in to a CNN leg that generates a convolutional kernel for a leg that operates on the main image. The part I'm unsure of is where I have a CNN layer that is fed weights externally, yet the gradients still flow through properly for training. 回答1: The weights leg: For

问题 What would be the most straightforward way to implement a hypernetwork in Keras? That is, where one leg of the network creates the weights for another? In particular, I would like to do template matching where I feed the template in to a CNN leg that generates a convolutional kernel for a leg that operates on the main image. The part I'm unsure of is where I have a CNN layer that is fed weights externally, yet the gradients still flow through properly for training. 回答1: The weights leg: For

问题 I am trying to configure a network for character recognition of sequential data like license plates. Now I would like to use the architecture which is noted in Table 3 in Deep Automatic Licence Plate Recognition system (link: http://www.ee.iisc.ac.in/people/faculty/soma.biswas/Papers/jain_icgvip2016_alpr.pdf). The architecture the authors presented is this one: The first layers are very common, but where I was stumbling was the top (the part in the red frame) of the architecture. They mention

问题 I am trying to configure a network for character recognition of sequential data like license plates. Now I would like to use the architecture which is noted in Table 3 in Deep Automatic Licence Plate Recognition system (link: http://www.ee.iisc.ac.in/people/faculty/soma.biswas/Papers/jain_icgvip2016_alpr.pdf). The architecture the authors presented is this one: The first layers are very common, but where I was stumbling was the top (the part in the red frame) of the architecture. They mention

问题 I am trying to configure a network for character recognition of sequential data like license plates. Now I would like to use the architecture which is noted in Table 3 in Deep Automatic Licence Plate Recognition system (link: http://www.ee.iisc.ac.in/people/faculty/soma.biswas/Papers/jain_icgvip2016_alpr.pdf). The architecture the authors presented is this one: The first layers are very common, but where I was stumbling was the top (the part in the red frame) of the architecture. They mention

问题 I am using PyTorch. I am going to use the already trained model on multiple GPUs with CPU. how to do this task? I tried on Anaconda 3 and pytorch with cpu only i dont have gpu model = models.get_pose_net(config, is_train=False) gpus = [int(i) for i in config.GPUS.split(',')] model = torch.nn.DataParallel(model, device_ids=gpus).cuda() print('Created model...') print(model) checkpoint = torch.load(config.MODEL.RESUME) model.load_state_dict(checkpoint) model.eval() print('Loaded pretrained

问题 I am using PyTorch. I am going to use the already trained model on multiple GPUs with CPU. how to do this task? I tried on Anaconda 3 and pytorch with cpu only i dont have gpu model = models.get_pose_net(config, is_train=False) gpus = [int(i) for i in config.GPUS.split(',')] model = torch.nn.DataParallel(model, device_ids=gpus).cuda() print('Created model...') print(model) checkpoint = torch.load(config.MODEL.RESUME) model.load_state_dict(checkpoint) model.eval() print('Loaded pretrained

问题 I'm trying to achieve the Block Toeplitz's matrix for a 2D convolution with padding=same (similar to keras). I saw, read and search a lot info, but I don't get an implementation of it. Some references I have taken (also I'm reading papers, but anyone talks about convd with padding, only full or valid): McLawrence's answer: answer. He says literally: "his is for padding = 0 but can easily be adjusted by changing h_blocks and w_blocks and W_conv[i+j, :, j, :]." But i dont know how implement

问题 I'm using TensorFlow to train a Convolutional Neural Network (CNN) for a sign language application. The CNN has to classify 27 different labels, so unsurprisingly, a major problem has been addressing overfitting. I've taken several steps to accomplish this: I've collected a large amount of high-quality training data (over 5000 samples per label). I've built a reasonably sophisticated pre-processing stage to help maximize invariance to things like lighting conditions. I'm using dropout on the