The second option is the good one. You can select the last output that correspond to a not padded input and using it for your loss. Or you can directly express that explicitly : in keras set the flag return_sequence to False and your RNN layer will only give you the last output that correspond to a not padded input.
I'm using an RNN consisting of GRU cells to compare two bounding box trajectories and determine whether they belong to the same agent or not. In other words, I am only interested in a single final probability score at the final time step. What I'm unsure about is how to formulate the loss function in this case. I see two options:
29.01.2019 · Now that we have the basis of a problem and model, we can take a look evaluating three common loss functions that are appropriate for a regression predictive modeling problem. Although an MLP is used in these examples, the same loss functions can be used when training CNN and RNN models for regression. Want Better Results with Deep Learning?
Loss function In the case of a recurrent neural network, the loss function $\mathcal {L}$ of all time steps is defined based on the loss at every time step as follows: \ [\boxed {\mathcal {L} (\widehat {y},y)=\sum_ {t=1}^ {T_y}\mathcal {L} (\widehat {y}^ {< t >},y^ {< t >})}\]
It has better memory than RNN since RNN has vanishing gradient problem(Important knowledge at specific time can remove). · It has cell state which keeps all ...
05.01.2020 · The loss function is equal to the summation of the true probability and log of the predicted probability. For ‘m’ training samples, the total loss would be equal to the average of overall loss (Where c indicates the correct class or true class).
Architecture of a traditional RNN · Applications of RNNs · Loss function · Backpropagation through time · Commonly used activation functions · Vanishing/exploding ...
Each ANN is a copy of the original RNN, sharing the same weights and activation functions. BPTT works backwards through the chain, calculating loss and ...
Nov 16, 2019 · Loss Function. The purpose of the loss function is to tell the model that some correction needs to be done in the learning process. In the context of sequence classification problem, to compare two probability distributions (true distribution and predicted distribution) we will use the cross-entropy loss function. The loss function is equal to the summation of the true probability and log of the predicted probability.
Loss function In the case of a recurrent neural network, the loss function $\mathcal {L}$ of all time steps is defined based on the loss at every time step as follows: \ [\boxed {\mathcal {L} (\widehat {y},y)=\sum_ {t=1}^ {T_y}\mathcal {L} (\widehat {y}^ {< t >},y^ {< t >})}\]
Aug 25, 2020 · Although an MLP is used in these examples, the same loss functions can be used when training CNN and RNN models for binary classification. Binary Cross-Entropy Loss. Cross-entropy is the default loss function to use for binary classification problems. It is intended for use with binary classification where the target values are in the set {0, 1}.