crfs | CRFS : Container Registry Filesystem | Continuous Deployment library

Ideally you should store the tokens in the backend and perhaps use a simple session cookies against between the client and backend.

Some services (like ASP.NET Core) will by default store the tokens as a cookie. To protect the cookie they will encrypt it before sending it to the browser. So this means that even if the cookie is stolen, it can't be decrypted and hence the tokens are safe.

Yes, the cookie size will be pretty big but ASP.NET Core solves this by breaking up large cookies into chunks of 4 Kb. If you use HTTP/2 with its header compression, large cookies will not impact the transfer time.

I will answer all question sequentially

how do I get this working so that the model would forecast the next N sequences for both groups?

I would suggest two modifications to your model.
The first is using sigmoid activation for the last layer.

Why?? Consider binary cross entropy loss function (I borrowed the equation from here)

Where L is calculated loss, p is network prediction and y is target values.

The Loss is defined for . If p is outside of this open interval range then the loss is undefined. The default activation of lstm layer in keras is tanh and it's output range is (-1, 1). This implies that the output of the model is not suitable for binary cross-entropy loss. If you try to train the model you might end up getting nan for loss.

The second modification (is part of the first modification) either add sigmoid activation before the last layer. For this, you have three options.

1. Add dense layer with sigmoid activation between your output and last lstm layer.
2. Or change the activation of the lstm layer to sigmoid.
3. Or add Activation layer with sigmoid activation after the output layer.

Even though all cases would work, I would suggest using dense layer with sigmoid activation because it almost always works better. Now the model with suggested changes would be

I hope the following answer will be of help.

With two subqueries for the two teams (A and B), the max date for every Ticket is brought. A left join between these two tables is performed to have these information in the same row in order to perform DATEDIFF. The last WHERE clause keeps the row with the dates greater for A team than team B.

Please change [YourDB] and [MytableName] in the following code with your names.

I found the problem. You need to encode only the token (and the UTF8 character), not the full post data.

This works for me (for some reason I need to run the code two times)

CRF Postprocessing Approch

You don't need to set thresholds to use a CRF. I'm not familiar with any python libraries for CRFs, but in principle, what you need to define is:

1. A probability distribution of the 10 classes for each of the nodes (pixels), which is simply the output of your network.
2. Pairwise potentials: 10*10 matrix, where element Aij denotes the "strength" of the configuration that one pixel is of class i and the other of class j. If you set the potentials to have a value alpha (alpha >> 1) in the diagonal and 1 elsewhere, then alpha is the regularization force that gives you consistency of the predictions (if pixel X is of class Y, then the neighboring pixels of X are more likely to be of the same class).

This is just one example of how you can define your CRF.

End to End NN Approach

Add a loss to your network that will penalize pixels that have neighbors of a different class. Please note that you will still end up with a tune-able parameter for the weight of the new regularization loss.

We have accepted your PR exempting cross-origin calls, so beginning with the next release (likely tomorrow), this issue will be resolved and should not face the next developer.

One of my friend told me the reason. Serialized object is a python2 object, so if you load with Python2, it's opening directly without any problem.

But If you would like to load with Python3, you need to add encoding parameters to pickle not into open function. Here is sample code:

You need to define custom IEqualityComparer to compare the arrays by value instead of by reference. An implementation of this would be :