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How do Channels differ from Samples/Dimension?

By : informatikadomicile
Date : October 14 2020, 08:10 PM
this one helps. Each pixel is a feature which has 3 channels, but there are 2D of these features. in a sense you are right there are more than 2D features but spatially it is 2D. This is important because convolution operation takes is sliding filters over spatial dimensions.
Similarly, characters can be considered as features of sentences with 300D embeddings features. When applying convolutions, sentences are considered as 1D because the sliding window is moving along time-dimension only. The embeddings can be considered as channels in this case.
code :

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Portable library to play samples on individual 5.1 channels with C/C++?

By : ScythianTrigon
Date : March 29 2020, 07:55 AM
like below fixes the issue
I had a look at OpenAL. However, I can only specify the position from which the sound should come, but it seems to me that I cannot say something like "use only the front left channel to play this sound".

Batch normalization: fixed samples or different samples by dimension?

By : G.S.Priya Jewellers
Date : March 29 2020, 07:55 AM
it should still fix some issue I haven't found this question on cross-validated and data-science, so I can only answer it here. Feel free to migrate if necessary.
The mean and variance are computed for all dimensions in each mini-batch at once, using moving averages. Here's how it looks like in code in TF:
code :
mean, variance = tf.nn.moments(incoming, axis)
update_moving_mean = moving_averages.assign_moving_average(moving_mean, mean, decay)
update_moving_variance = moving_averages.assign_moving_average(moving_variance, variance, decay)
with tf.control_dependencies([update_moving_mean, update_moving_variance]):
  return tf.identity(mean), tf.identity(variance)

How to fix dimension problem in Python shape of x and y differ

By : user2664481
Date : March 29 2020, 07:55 AM
help you fix your problem My code runs and returns the value of K as expected but the graph does not display w, due to an issue with dimensions. I'd be thankful for any help. , This will do:
code :
import numpy as np
import pylab as pl

k = np.linspace(0,0.1,1000)
h = 50
g = 9.81
w = 0.5*(np.ones(len(k)))

w = np.sqrt((g*k)*np.tanh(h*k))

kmax = max(k[w<=0.5])
print("The wave number, k = %.4f" % kmax)

pl.plot(k, w)
import numpy as np
import pylab as pl

k = np.linspace(0,0.1,1000)
h = 50
g = 9.81
w = 0.5*(np.ones(len(k)))

w = np.sqrt((g*k)*np.tanh(h*k))

kmax = max(k[w<=0.5])
print("The wave number, k = %.4f" % kmax)

pl.plot(k, w)

scikit learn PCA dimension reduction - data lot of features and few samples

By : Andrwfn
Date : March 29 2020, 07:55 AM
I hope this helps . The maximum number of principal components that can be extracted from and M x N dataset is min(M, N). Its not an algorithm issue. Fundamentally, that is the maximum number that there are.

Pytorch maxpooling over channels dimension

By : Amit P
Date : March 29 2020, 07:55 AM
I hope this helps you . I was trying to build a cnn to with Pytorch, and had difficulty in maxpooling. I have taken the cs231n held by Stanford. As I recalled, maxpooling can be used as a dimensional deduction step, for example, I have this (1, 20, height, width) input ot max_pool2d (assuming my batch_size is 1). And if I use (1, 1) kernel, I want to get output like this: (1, 1, height, width), which means the kernel should be slide over the channel dimension. However, after checking the pytorch docs, it says the kernel slides over height and width. And thanks to @ImgPrcSng on Pytorch forum who told me to use max_pool3d, and it turned out worked well. But there is still a reshape operation between the output of the conv2d layer and the input of the max_pool3d layer. So it is hard to be aggregated into a nn.Sequential, so I wonder is there another way to do this? , Would something like this work?
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