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Showing posts with the label Loss Function
Bcewithlogitsloss In Keras

Bcewithlogitsloss In Keras

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How to implement BCEWithLogitsLoss in keras and use it as custom loss function while using Tensorfl… Read more Bcewithlogitsloss In Keras
Which Loss Function To Use In Keras Sequential Model

Which Loss Function To Use In Keras Sequential Model

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I am using a Keras sequential model, a prediction output is of the shape (1, 5) (5 features). I hav… Read more Which Loss Function To Use In Keras Sequential Model
Keras Custom Loss Function Per Tensor Group

Keras Custom Loss Function Per Tensor Group

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I am writing a custom loss function that requires calculating ratios of predicted values per group.… Read more Keras Custom Loss Function Per Tensor Group
How Can I Predict The Expected Value And The Variance Simultaneously With A Neural Network?

How Can I Predict The Expected Value And The Variance Simultaneously With A Neural Network?

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I'd like to use a neural network to predict a scalar value which is the sum of a function of th… Read more How Can I Predict The Expected Value And The Variance Simultaneously With A Neural Network?
Custom Loss Function Implementation Issue In Keras

Custom Loss Function Implementation Issue In Keras

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I am implementing a custom loss function in keras. The output of the model is 10 dimensional softma… Read more Custom Loss Function Implementation Issue In Keras
Using Sgd Without Using Sklearn (logloss Increasing With Every Epoch)

Using Sgd Without Using Sklearn (logloss Increasing With Every Epoch)

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def train(X_train,y_train,X_test,y_test,epochs,alpha,eta0): w,b = initialize_weights(X_train[0]… Read more Using Sgd Without Using Sklearn (logloss Increasing With Every Epoch)