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I have executed a program with image classification and it was running good .I was running the code with conv1D and conv2D . I am getting accuracy of 0.854 for both conv1D and conv2D.
Can i know the exact differences between these two things in detail?
Conv1d is a convolution filter of 1 dimension (imagine it like a one dimension array). Conv2d is a filter with 2 dimensions (like a 2d array) and it is more suitable for data like images where it can retain more spatial information in a data point because it is applied to all the neighbors. You can see what is a kernel to understand why this is better for data like images. For non image data I guess it will not have significant impact whether you use 1d or 2d convolution arrays.
Note: Also this site is for programming problems, maybe you should ask your question in Data Science
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Do you have any code example or paper that refers to something like the following diagram?
I want to know why we want to stack multiple resnet blocks as opposed to multiple convolutional block as in more traditional architectures? Any code sample or referring to one will be really helpful.
Also, how can I transfer that to something like the following that can contain self-attention module for each resnet block?
Applying self-attention to the outputs of Resnet blocks at the very high resolution of the input image may lead to memory issues: The memory requirements of self-attention blocks grow quadratically with the input size (=resolution). This is why in, e.g., Xiaolong Wang, Ross Girshick, Abhinav Gupta, Kaiming He Non-Local Neural Networks (CVPR 2018) they introduced self-attention only at a very deep layer of the architecture, once the feature map was substantially sub-sampled.
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my question is:
for example: i have a trained FCC and i want to implement it on Hardware(ASIC). i want to ask how to utilize weights and biases from trained model in verilog ?
Should i make RAM and then store the values in it, or is any other way to be used?
I need this values(weights and biases) to propagate them to MAC units.
The weights and biases need to be converted into specific number format (say Fixed Point) and then stored in RAM.
Then the values should be fetched and given to the MAC units.
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I already trained a CNN for steering angle prediction with regression output. Also I trained CNN for classification (Two classes: road, no road). But I want to combine it. How to do it? Train a new model is not a problem.
You will need to update your y values to have both the category and the angle, and write a custom loss function that adds the regression loss and the category loss.
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I'm currently learning machine learning.i get confused what is Model weights term. please explain to me what is model weight really means
Weights are the numbers you use to turn your samples into a prediction. In many (most?) cases this is what you are learning with your system. For example, suppose you want to predict house price using only the house size (x). You might use a simple linear regression model that tries to fit a line to the data. The formula you will use is the formula for a line:
y = w * x + b
Here x is given (the house size) and you use w and b to predict y the price. In this case w and b are your weights. The goal is to determine which w and b give the best fit to the data.
In more complex models like neural networks (or even more complicated linear regression) you may have dramatically more weights in you model, but the basic idea of finding those weights that best fit the data is the same.
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What do people do when handling long audio clip(2min-5min, 44.1khz) in machine learning tasks such as music classification?
Is there any methods except downsampling that would help to reduce the dimensionality of audio data?
Usually you are extracting frequency features like spectrogram or MFCC and then you classify them. They have less values than raw audio, so they are easier to analyze.
You can find some visualizations of spectrograms and MFCC here (related to speech, but scales):
https://www.kaggle.com/davids1992/speech-visualization-and-exploration
Note that pooling somehow reduces dimensionality of data in CNN.
So find about spectral analysis. You are rarely working with raw waves, although they are starting to work also, like WaveNet:
https://deepmind.com/blog/wavenet-generative-model-raw-audio/