Hi, very fresh to ML in general, I’m overwhelmed by choice for loss functions and accuracy measurements. Doing my masters project and my supervisor wants me to teach myself tf and throw the data through a model.
I’m feeding in an array of particle data from a decay, and asking for it to predict a 1 or -1 at the end representing parity of the original particle.
What loss fn and accuracy metric is best to use for something like this.
Other parameters, about 2 million events, 6particles in each decay/event. Their 4 momenta in each. That makes 24 in put strings.
I am trying to have a layer with only positive weights generate a partially negative output. What would the least hacky way to achieve this be? My initial idea was using a modified activation function that is shifted by 1 along the x axis, however, this feels a bit hacky to me and I was wondering if there was a better way to achieve this
Is there a way to store a tensor in multiple GPUs? I have a tensor that is so large that requires >16Gb of GPU RAM to store. I was wonder how that can be achieved. Thanks!
I am working on a project to replace layers by new layers to see if the changes affected positively or negatively. I want to then get the output feature map and input feature map after replacement. The issue I am having is that after a couple of changes, I get that I have multiple connections and a new column called ‘connnected to’ appears. Here are the summaries and the code I am using for replacing layers. I sometimes get this warning after replacing a convolutional layer with the code provided.
I have tried to create an Input layer and then use the same functional approach. My first layer being the input layer and the second the conv2d_0 layer. However, I get ValueError Disconnected from Graph for the input layer after two layer changes.
Code:
inputs = self.model.layers[0].input x = self.model.layers[0](inputs) for layer in self.model.layers[1:]: if layer.name == layer_name: new_layer = #creation of custom layer that generates output of same shape as replaced layer. x = new_layer(x) else: layer.trainable = False x = layer(x) self.model = tf.keras.Model(inputs, x)
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I’m trying to implement fastspeech_quat.tflite into a flutter app. I’m using tflite_flutter package. I’ve loaded up the model like this Interpreter _interpreter = await Interpreter.fromAsset(‘fastspeech_quant.tflite’);
Next I wanted to run an inference on some text so I would use _interpreter.runForMultipleInputs(input, output)
I just don’t understand how to format the input and output for the model. So I ran _interpreter.getInputTensors() and I get
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I basically have a simple CNN that outputs a single integer value at the end. This number is corresponding to a certain angle, so i know that the bounds have to be between 0 and 359. My intuition tells me that if i were to somehow limit the final value to this range instead of being unbounded like in most activation functions, i would reach any form of convergence sooner.
To try this, I changed the last layer and applied the sigmoid activation function, then added an additional lambda layer where i just multiplied the value by 359. However, this model still has a very high loss (using MSE, at times it’s actually greater than 3592 which is leading me to believe i’m not actually bounding the output between 0 and 359).
Is it a good idea to bound my output like this, and what would be the best way to implement it?
I have 15 classes, each one has around 90 training images and 7 validation images. Am I doing something wrong or are my images just really bad? It’s supposed to identify between 15 different fish species, and some of them do look pretty similar. Any help is appreciated
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