I just googled:
A Convolutional Encoder Model for Neural Machine Translation, by Gehring et al., link
Convolutional Sequence to Sequence Learning, by Gehring et al. link
Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction, by Elbayad et al. link
All the implementations I found on GitHub are in PyTorch. I'm not ...
I had the same problem and I solved it adding a BatchNormalization layer.
# create model ConvLSTM
input_convlstm = Input(name='convlstm', shape=(n_steps, 1, n_length, n_metadata))
branch_convlstm = BatchNormalization()(input_convlstm)
branch_convlstm = ConvLSTM2D(filters=64, kernel_size=(1,3), activation='tanh', input_shape=(n_steps, 1, ...
The main reason is that you want the Generator and the Discriminator to be equally powerful. The base intuition of GANs is that the two Networks can improve themselves through competition. If one is way better than the other, they will get stuck into some unwanted equilibrium in which one beats the other all, or almost all the times.
For this reason, they ...
Yes, at least you can identify what pixels' are contributing most in the prediction.
Tool like Layerwise Relevance Propagation, used for Explainable AI, serves the similar purpose and evaluate the values(weights) during back propagation and evaluate what pixels are contributing most.
Many opensource implementation are available and on similar track, ...
1) Will the error subsurface be flat?
One cannot find the hessian or any such error subsurface of the pooling layer because common pooling layers like max and avg pooling do not have any parameters to learn (as you mentioned)!
2) But, we can speak for the effect of the pooling layers on the error surface of the previous layers. The effect is different for ...
I dont think you are rescaling the image after reading it. Because you have rescaled it during training.
img = image.load_img(path_to_file, target_size=(150,150))
img = image.img_to_array(img)
img = img/255 # this must be done.
Indeed, convolution and cross-correlation are closely related. The former is a bit more natural in some areas of mathematics; most notably, in the convolution theorem for the Fourier transform, which states that the Fourier transform of the convolution of two functions is equal, under certain conditions, to the product of their Fourier transforms:
This answer might not make a lot of sense without a little background on quantum computing
A QCNN (https://arxiv.org/abs/1810.03787) is a type of quantum model that the authors in this paper use to model quantum data. At the core it is just a quantum circuit acting on a set of qubits in order to model quantum data that is on those qubits. The authors use it ...
I have built many data sets. The latest was a data set of species of birds. I has 100 species of birds so I had 100 classes. For each species (class) I had 100 training images, 5 test images and 5 validation images. Thats as total of 11,000 images.. The classifier I built had a final accuracy of 98% on 500 test images (5 test images per specie)
Here are ...
Given that the environment is somewhat consistent throughout all samples, is there a ballpark number / rule of thumb for a decent number of samples to use per class? (100 train / 100 test)? This is a proof of concept project, so I'm just looking for something with reasonable accuracy (80%+)
This really differs from problem to problem. The number of ...