How to change the activation layer of a Pytorch pretrained network? Here is my code : Here is my output: Answer ._modules solves the problem for me.
Tag: neural-network
Keras image generator keep giving different number of labels
I am trying to make a simple fine turned Resnet50 model using the Market1501 dataset and keras. So the data set contains images (12000 or so) and 751 labels that I want to use (0-750). I can fit the data into a single go so I have to use a image generator for this. So my base model is like
keras apply threshold for loss function
I am developing a Keras model. My dataset is badly unbalanced, so I want to set a threshold for training and testing. If I’m not mistaken, when doing a backward propagation, neural network checks the predicted values with the original ones and calculate the error and based on the error, set new weights for neurons. As I know, Keras uses
How to train keras models consecutively
I’m trying to train different models consecutively without needing to re-run my program or change my code all the time, so this way I can let my PC training different models. I use a for loop while feeding different information from a dictionary for building different models each time, and so I can train a new model each time de
Split autoencoder on encoder and decoder keras
I am trying to create an autoencoder for: Train the model Split encoder and decoder Visualise compressed data (encoder) Use arbitrary compressed data to get the output (decoder) How to split train it and split with the trained weights? Answer Make encoder: Make decoder: Make autoencoder: Now you can use any of them any way you want to. train the
Keras: Adding MDN Layer to LSTM Network
My question in brief: Is the Long Short Term Memory Network detailed below appropriately designed to generate new dance sequences, given dance sequence training data? Context: I am working with a dancer who wishes to use a neural network to generate new dance sequences. She sent me the 2016 chor-rnn paper that accomplished this task using an LSTM network with
Calculate the accuracy every epoch in PyTorch
I am working on a Neural Network problem, to classify data as 1 or 0. I am using Binary cross entropy loss to do this. The loss is fine, however, the accuracy is very low and isn’t improving. I am assuming I did a mistake in the accuracy calculation. After every epoch, I am calculating the correct predictions after thresholding
How do you use Keras LeakyReLU in Python?
I am trying to produce a CNN using Keras, and wrote the following code: I want to use Keras’s LeakyReLU activation layer instead of using Activation(‘relu’). However, I tried using LeakyReLU(alpha=0.1) in place, but this is an activation layer in Keras, and I get an error about using an activation layer and not an activation function. How can I use
Why do we need to call zero_grad() in PyTorch?
Why does zero_grad() need to be called during training? Answer In PyTorch, for every mini-batch during the training phase, we typically want to explicitly set the gradients to zero before starting to do backpropragation (i.e., updating the Weights and biases) because PyTorch accumulates the gradients on subsequent backward passes. This accumulating behaviour is convenient while training RNNs or when we
Keras LSTM – why different results with “same” model & same weights?
(NOTE: Properly fixing the RNG state before each model creating as described in comment in comment practically fixed my problem, as within 3 decimals results are consistent, but they aren’t exactly so, so there’s somewhere a hidden source of randomness not fixed by seeding the RNG… probably some lib uses time milisecs or smth…if anyone has an idea on that,