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Music Producer , Cellist
Christophe Anjolras. Sam Baron.
Craft in detail - III. Click 2x to zoom. Product description This Swiss Made stationery brand reset stationery standards by developing innovative products, fun and environmentally friendly. Their first product: an infinitely reusable notepad that has been awarded many times internationally and has been sold more thanpcs!
Reusable, the WhyNote Book says stop to wasting paper, every page that can be erased more than times, 1 notebook is equals to more than 6, sheets of paper. Login Create an account. Brand Description. Show phone number Add. Add Add. More Stationery store New Add Add.
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Product description This Swiss Made stationery brand reset stationery standards by developing innovative products, fun and environmentally friendly.
Their first product: an infinitely reusable notepad that has been awarded many times internationally and has been sold more thanpcs! Reusable, the WhyNote Book says stop to wasting paper, every page that can be erased more than times, 1 notebook is equals to more than 6, sheets of paper.
Login Create an account. Brand Description. Show phone number Add. Add Add. More Stationery store New Add Add. Bookmarks, French made. Access your account. Do you already have an account? Join MOM!Whynot S. MacKeigan, C. The accused was charged with first degree murder, contrary to s. The accused was tried before a judge and jury and was acquitted.
The Crown appealed, submitting, inter alia, that the trial judge misdirected the jury with respect to the defence of self-defence under s. The Nova Scotia Court of Appeal allowed the appeal and ordered a new trial, holding that the trial judge erred in leaving the issue of self-defence before the jury. Murder - Defences - Self-defence - A woman shot and killed her common law spouse while he was drunk and passed out, later submitting that she acted in self-defence under s.
Murder - Defences - Self-defence - The Nova Scotia Court of Appeal reviewed the law relating to self-defence and the scope of its application as a defence to a charge of murder - See paragraphs 24 to 27; 36 to Procedure - Charge or directions to jury - Direction on evidence generally - A trial judge, while charging a jury in a murder trial, spent five hours reiterating the evidence given at trial - The Nova Scotia Court of Appeal held that a repetition of the evidence was unnecessary - The court stated that the trial judge's duty was to refer only to those parts of evidence which were important in relation to the theories of the Crown and the defence and the principles of law about which the judge had charged the jury on - See paragraph Relevant facts - Relevance and materiality - Criminal cases - Victim's character - Both the Crown and the defence in a murder trial adduced evidence about the character of the deceased - The Nova Scotia Court of Appeal held that the character evidence that was not relevant to a defence that could properly be put to a jury and served only to create sympathy for the accused was inadmissible and should have been excluded at trial - See paragraph Sunbeam Corporation Canada Ltd.
Lampard,  3 C. Wu,  S. Gee43 N. Brisson,  2 S. Squire,  2 S. Baxter33 C. Criminal Code of Canada, R. C, sect. Kenneth W.Hi all, I'm an enthusiastic producer and I'm looking for some projects to work on.
I am pretty versatile as to music genre, I like new challenges and I'm open to new ideas. My own taste span quite a large spectrum of genre. I have been a musician for over 20 years, started learning classical music at the National Music Conservatory of Marseille, earning a 1st price in Cello and Music Theory. I have since diversified, played various instruments cello, bass, double-bass, keys in various bands jazz-blues, pop-rock, hip-hopand now I specialize in Music Production.
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Music Producer , Cellist
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I'm trying to train a CNN to categorize text by topic. I don't understand why this is. It's a multiclass problem, doesn't that mean that I have to use categorical cross-entropy and that the results with binary cross-entropy are meaningless? Intuitively it makes sense why I'd want to use categorical cross-entropy, I don't understand why I get good results with binary, and poor results with categorical. I would like to elaborate more on this, demonstrate the actual underlying issue, explain it, and offer a remedy.
In other words, while your first compilation option. Why is that? What happens under the hood is that, since you have selected binary cross entropy as your loss function and have not specified a particular accuracy metric, Keras wrongly To remedy this, i.
In the MNIST example, after training, scoring, and predicting the test set as I show above, the two metrics now are the same, as they should be:. It all depends on the type of classification problem you are dealing with. There are three main categories. In the first case, binary cross-entropy should be used and targets should be encoded as one-hot vectors. In the second case, categorical cross-entropy should be used and targets should be encoded as one-hot vectors.WHYNOTE - Carnet de jeux - Gamebook
In the last case, binary cross-entropy should be used and targets should be encoded as one-hot vectors. Each output neuron or unit is considered as a separate random binary variable, and the loss for the entire vector of outputs is the product of the loss of single binary variables. Therefore it is the product of binary cross-entropy for each single output unit.
It seems that problem was with wrong activation function. The correct settings were:. This means that up to a constant multiplication factor your losses are equivalent. The weird behaviour that you are observing during a training phase might be an example of a following phenomenon:. This makes loss from a less frequent classes balancing the influence of a dominant class loss at the beginning of a training and in a further part of an optimization process.
This is the actual reason behind this weird behaviour as in case of multiclassification such normalization harms a training. After commenting Marcin answer, I have more carefully checked one of my students code where I found the same weird behavior, even after only 2 epochs! So Marcin's explanation was not very likely in my case. You can check that by recomputing the accuracy yourself first call the Keras method "predict" and then compute the number of correct answers returned by predict : you get the true accuracy, which is much lower than the Keras "evaluate" one.
That's why we make assumption that there is only ONE label positive. If your targets are integer classes, you can convert them to the expected format via:. And, in the documentationit says that:.