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03.08.2018 · I built a custom architecture with keras (a convnet). The network has 4 heads, each outputting a tensor of different size. I am trying to write a custom loss function as a function of this 4 outputs. I have been implementing cusutom losses before, but it was either a different loss for each head or the same loss for each head.

Hi Saad, If I understand you correctly, you want to train a model with a loss function that works on two outputs — and ask if this will work with one loss function because the outputs have strong correlation. Now, in TensorFlow/Keras, you can use the Functional API to define multiple output branches. You give a different name to each output layer, and then add multiple loss functions …

I have a model with multiple outputs from different layers: O: output from ... `m = keras.models. ... My custom loss function will be like:

17.10.2016 · multiple output in keras custom loss function #4093. shwetgarg opened this issue on Oct 17, 2016 · 1 comment. Labels. stale. Comments. stale bot added the stale label on May 23, 2017. stale bot closed this on Jun 22, 2017. jjallaire mentioned this issue on Feb 8, 2018.

Note that if the model has multiple outputs, you can use a different loss on each output by passing a dictionary or a list of losses. The loss ...

As you are mixing y_true[0] with y_pred[0] and y_true[1] with y_pred[1] , you could consider having different losses for each, ...

A machine learning model may need custom loss function. different loss on each output by passing a dictionary or a list of losses. be minimized by the model ...

Jun 04, 2018 · 96.24% accuracy on the testing set. And for the color output we reached: 99.60% accuracy on the training set. 98.61% accuracy on the testing set. Below you can find the plots for each of our multiple losses: Figure 7: Our Keras deep learning multi-output classification training losses are plotted with matplotlib.

Mar 26, 2019 · I have a model with multiple outputs from different layers: O: output from softmax layer; y1,y2: from intermediate hidden layer. `m = keras.models.Model (inputs=x, outputs=[O,y1,y2])` I want to compute cross-entropy loss between O and tr...

26.03.2019 · I have a model with multiple outputs from different layers: O: output from softmax layer; y1,y2: from intermediate hidden layer. `m = keras.models.Model (inputs=x, outputs=[O,y1,y2])` I want to compute cross-entropy loss between O and tr...

Aug 04, 2018 · I built a custom architecture with keras (a convnet). The network has 4 heads, each outputting a tensor of different size. I am trying to write a custom loss function as a function of this 4 outputs. I have been implementing cusutom losses before, but it was either a different loss for each head or the same loss for each head.

But after an extensive search, when implementing my custom loss function, I can only pass as parameters y_true and y_pred even though I have two "y_true's" and two "y_pred's". I have tried using indexing to get those values but I'm pretty sure it is not working. The modeling of the network and the custom loss function is in the code below:

04.06.2018 · In today’s blog post, we learned how to utilize multiple outputs and multiple loss functions in the Keras deep learning library. To accomplish this task, we defined a Keras architecture that is used for fashion/clothing classification called FashionNet. The FashionNet architecture contains two forks:

Learn how to use multiple fully-connected heads and multiple loss functions to create a multi-output deep neural network using Python, Keras ...

But after an extensive search, when implementing my custom loss function, I can only pass as parameters y_true and y_pred even though I have two "y_true's" and two "y_pred's". I have tried using indexing to get those values but I'm pretty sure it is not working. The modeling of the network and the custom loss function is in the code below:

You could try the model.add_loss() function. The idea is to construct your custom loss as a tensor instead of a function, ...

In this tutorial, I show how to share neural network layer weights and define custom loss functions. The example code assumes beginner ...

Loss functions applied to the output of a model aren't the only way to create losses. When writing the call method of a custom layer or a subclassed model, you ...

Oct 17, 2016 · multiple output in keras custom loss function #4093. shwetgarg opened this issue on Oct 17, 2016 · 1 comment. Labels. stale. Comments. stale bot added the stale label on May 23, 2017. stale bot closed this on Jun 22, 2017. jjallaire mentioned this issue on Feb 8, 2018.

Do not report issues about multi-backend Keras (Keras 2.3.1 and lower), only report issues about the TensorFlow implementation of Keras (tf.