r/keras. Is it like this: or like this or something else? This decay policy follows a time-based decay that we’ll get into in the next section, but for now, let’s familiarize ourselves with the basic formula, Suppose our initial learning rate = 0.01 and decay = 0.001, we would expect the learning rate to become, 0.1 * (1/ (1+0.01*1)) = 0.099 after the 1st epoch. The model training should occur on an optimal number of epochs to increase its generalization capacity. Is there a way to fix this? Epoch: 6 Training Loss: 0.296088 Accuracy 0.917120 Validation Loss: 0.845122 Epoch: 7 Training Loss: 0.298336 Accuracy 0.908692 Validation Loss: 0.848735 Epoch 7: … Print the validation loss in each epoch in PyTorch - STACKOOM Validation Choose the learning rate one order lower than the learning rate where loss is minimum ( if loss is low at 0.1, good value to start is 0.01). But validation loss and validation acc decrease straight after the 2nd epoch itself. The overall testing after training gives an accuracy around 60s. I've already cleaned, shuffled, down-sampled (all classes have 42427 number of data samples) and split the data properly to training (70%) / validation (10%) / testing (20%). maybe i could try that, even then I expect at least a general upward or downward trend which i do not observe it at all. Thank you, Jason, I have tried to get more samples from training data to validation data to increase the validation data sample size, still the learning curve shows that although both validation data loss and training data loss reduces along with epochs, but the reduction of validation data loss is much smaller than training data, finally, the loss (mse, standardised … Posted by By american legion baseball registration January 13, 2022 nob hill, san francisco … Read more: ‍. validation loss

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