Cédric Bovar

It would look something like: ```csharp var cns = new ConvNetSharp(); var input = cns.PlaceHolder("input"); var flatten = cns.Flatten(input); var fc1 = cns.Dense(flatten, 512); var fc21 = cns.Dense(fc1, 11); var...

1. It seems that `size ` can be computed outside the for loop 2. I understand your training accuracy doesn't get better when you provide more training data. Have you...

1. You should present a _different_ input every time you call `Train` method. It seems you call **NumberOfTrainingSteps** times `Train` with the same data. This will make the network forget...

The **learning rate** determines the size of the steps we take to reach a (local) minimum. Basically the gradients are multiplied by the learning rate before being used to update...

Does the loss decrease? It should output the same proba when **yh** is low but should not when **yh** start to grow.

The input shape you use for testing seems odd: new Shape (999 * 705, 1, 1) instead of new shape(999, 705, 1). I'm not sure that's the source of the...

Also, could you try decreasing the learning rate and post a new plot the loss? Maybe divide it by 10.

What is the value of LR? Any chance to have the full code so I can run it ? I think I just need **FalseSamp**, **TrueSamp**, **FalseSampTest**, **TrueSampTest**

Hi, I haven't done any comparison with other deep learning frameworks. That would be interesting to do (especially to detect possible errors in ConvNetSharp) but I currently haven't got much...

Hi, That's interesting. Can you show your keras code as well? Could you try to set L2Decay to 0 in Keras and in ConvNetSharp? It's not currently used in ConvNetSharp.