conv-neural-network

来源： https://stackoverflow.com/questions/60253082/weighting-samples-in-multiclass-image-segmentation-using-keras

来源： https://stackoverflow.com/questions/51210836/keras-feed-images-into-cnn-and-get-image-output

来源： https://stackoverflow.com/questions/51210836/keras-feed-images-into-cnn-and-get-image-output

来源： https://stackoverflow.com/questions/63074499/vehicle-detection-using-cnn

来源： https://stackoverflow.com/questions/56430566/transfer-learning-val-loss-strange-behaviour

来源： https://stackoverflow.com/questions/56430566/transfer-learning-val-loss-strange-behaviour

来源： https://stackoverflow.com/questions/63073760/using-dilated-convolution-in-keras

来源： https://stackoverflow.com/questions/63073760/using-dilated-convolution-in-keras

问题 According to the convolution theorem, convolution changes to pointwise multiplication in the fourier domain, and the overheads of taking the fourier transform have been shown to be overshadowed by the gain due to conversion of convolution operation to pointwise multiplication operation in many previous works like the following - https://arxiv.org/abs/1312.5851. To replicate this, I was trying to replace the keras.layers.Conv2D() layer by a custom layer that accepts the rfft of input data (I

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