Category "deep-learning"

ValueError: The first argument to `Layer.call` must always be passed

I was trying to build a model with the Sequential API (it has already worked for me with the Functional API). Here is the model that I'm trying to built in Sequ

How to mount onedrive to google colaboratory?

I use google colab with google drive in deep-learning training, but although I've 100gb premium account, sometimes it accouring error that find: ‘/content

how to modify resnet 50 with 4 channels as input using pre-trained weights in Pytorch?

I would like to change the resnet50 so that I can switch to 4 channel input, use the same weights for the rgb channels and initialize the last channel with a no

How to extract relation between entities for stock prediction

I am trying to extract relation between two entities (entity1- relation- entity2) from news articles for stock prediction. I have used NER for entity extraction

Extracting labels after applying softmax

I have a multi class classification neural network. I apply softmax at the end to get probabilities for my classes. However, now I want to pick the maximum prob

Why the initialization of weights in darknet?

there! I am studying Mr. Redmon's darknet code from https://github.com/pjreddie/darknet I found the initialization of weights of a connected layer is like below

Why the initialization of weights in darknet?

there! I am studying Mr. Redmon's darknet code from https://github.com/pjreddie/darknet I found the initialization of weights of a connected layer is like below

UnimplementedError: Fused conv implementation does not support grouped convolutions for now

I am trying to build a CNN model to recognise human sketch using the TU-Berlin dataset. I downloaded the png zip file, imported the data to Google Colab and the

what is the number of layers in EfficientNetB2?

Knowing that the total number of layers in EfficientNet-B0 is 237 and in EfficientNet-B7 the total comes out to 813, what is the total number of layers in Effic

Pretraining a language model on a small custom corpus

I was curious if it is possible to use transfer learning in text generation, and re-train/pre-train it on a specific kind of text. For example, having a pre

Variational AutoEncoder - TypeError

I am trying to implement a VAE for MNIST using convolutional layers using TensorFlow-2.6 and Python-3.9. The code I have is: # Specify latent space dimensions-

Derivates from a class instance in TF1

I am using the Physics Informed Neural Networks (PINNs) methodology to solve non-linear PDEs in high dimension. Specifically, I am using this class https://git

logits and labels must be broadcastable error in Tensorflow RNN

I am new to Tensorflow and deep leaning. I am trying to see how the loss decreases over 10 epochs in my RNN model that I created to read a dataset from kaggle w

How to clean garbage from CUDA in Pytorch?

I teached my neural nets and realized that even after torch.cuda.empty_cache() and gc.collect() my cuda-device memory is filled. In Colab Notebooks we can see t

Correct Implementation of Dice Loss in Tensorflow / Keras

I've been trying to experiment with Region Based: Dice Loss but there have been a lot of variations on the internet to a varying degree that I could not find tw

How to get the location of all text present in an image using OpenCV?

I have this image that contains text (numbers and alphabets) in it. I want to get the location of all the text and numbers present in this image. Also I want to

LSTM is Showing very low accuracy and large loss

I am applying LSTM on a dataset that has 53699 entries for the training set and 23014 entries for the test set. The shape of the input training set is (53699,4)

why does the VQ-VAE require 2 Stage training?

According the the paper, VQ-VAE goes through two stage training. First to train the encoder and the vector quantization and then train an auto-regressive model

Random cropping data augmentation convolutional neural networks

I am training a convolutional neural network, but have a relatively small dataset. So I am implementing techniques to augment it. Now this is the first time i a

How to understand masked multi-head attention in transformer

I'm currently studying code of transformer, but I can not understand the masked multi-head of decoder. The paper said that it is to prevent you from seeing the