So, I have this usual error message where the number of rows and columns of my images don't match (in cross ref).
I have generalised one of my images and then use expand to make the resolutions match again.
However, in the process I lost a few columns (which doesn't bother me), however, I don't know what to do in order to make my both images the same size again.
Can someone help me ?
Thank you very much
L.
To make similar column and rows,
Convert layer from raster to vector (rastervector conversion tool)
Convert the vector converted layer in to raster (resample the layer that has required column and row)
Related
I have a large quantity of missing values that appear at random in my data. Unfortunately, I cannot simply drop observations with missing data as I am grouping observations by a feature and cannot drop NaNs without affecting the entire group.
I was hoping to simply mask features that were missing. So a single group might have 8 items in it, and each item may have 0 to N features, depending on how many got masked due to being missing.
I have been experimenting a lot with RaggedTensors, but have encountered a lot of issues ranging from not being able to flatten the RaggedTensor, not being able to concatenate it with regular tensors of uniform shape, and Dense layers requiring the last dimension of their input to be known, aka the number of features.
Does anybody know if there is a way to do this?
I have a multi-dimensional, hyper-spectral image (channels, width, height = 15, 2500, 2500). I want to compress its 15 channel dimensions into 5 channels.So, the output would be (channels, width, height = 5, 2500, 2500). One simple way to do is to apply PCA. However, performance is not so good. Thus, I want to use Variational AutoEncoder(VAE).
When I saw the available solution in Tensorflow or keras library, it shows an example of clustering the whole images using Convolutional Variational AutoEncoder(CVAE).
https://www.tensorflow.org/tutorials/generative/cvae
https://keras.io/examples/generative/vae/
However, I have a single image. What is the best practice to implement CVAE? Is it by generating sample images by moving window approach?
One way of doing it would be to have a CVAE that takes as input (and output) values of all the spectral features for each of the spatial coordinates (the stacks circled in red in the picture). So, in the case of your image, you would have 2500*2500 = 6250000 input data samples, which are all vectors of length 15. And then the dimension of the middle layer would be a vector of length 5. And, instead of 2D convolutions that are normally used along the spatial domain of images, in this case it would make sense to use 1D convolution over the spectral domain (since the values of neighbouring wavelengths are also correlated). But I think using only fully-connected layers would also make sense.
As a disclaimer, I haven’t seen CVAEs used in this way before, but like this, you would also get many data samples, which is needed in order for the learning generalise well.
Another option would be indeed what you suggested -- to just generate the samples (patches) using a moving window (maybe with a stride that is the half size of the patch). Even though you wouldn't necessarily get enough data samples for the CVAE to generalise really well on all HSI images, I guess it doesn't matter (if it overfits), since you want to use it on that same image.
I had some confusion about how bucketized feature columns represent input to the model. According to the blog post on feature columns, when we bucketize a feature like year this puts each value in buckets based on the defined boundaries, and creates a binary vector, turning on each bucket based on the input value, but the example in the documentation shows the output as a single integer. I'm confused as to how the input is to the model when using a bucketized column. Can anyone clarify this for me please?
From the dimensions of the first hidden layer of the estimator, it seems like for each feature column that is a tf.feature_column.bucketized_column, a one hot encoded vector is created based on the boundaries.
I have a set of 2D input arrays m x n namely A,B,C and I have to predict two 2D output arrays namely d,e for which I do have the expected values. You can think of the inputs/outputs as grey images if you like.
Because of the spatial information is relevant (these are actually 2D physical domains) I want to use a Convolutional Neural Network to predict d and e. My design (not tested yet) looks as follows:
Because I have multiple inputs, I guess I should use multiple columns (or branches) to find different features for each of the inputs (they look fairly different). Each of these columns follows a encoding-decoding architecture used in segmentation (see SegNet): Conv2D block involves a convolution+batch normalisation+ReLU layer. Deconv2D involves a deconvolution+batch normalisation+ReLU.
Then, I can merge the output of each column by either concatenating, averaging or taking the maximum for example. To obtain the original m x n shape for each of the outputs I have seen I could do this with a 1 x 1 kernel convolution.
I want to predict the two outputs from that single layer. Is that okay from the network structure point of view? Finally my loss function depends on the outputs themselves compared to the target plus another relation I want to impose.
A would like to have some expert opinion on this since this is my first design of a CNN and I am not sure if I it makes sense as it is now and/or if there are better approaches (or network architectures) to this problem.
I posted this originally in datascience but I did not get much feedback. I am now posting it here since there is a bigger community on these topics plus I would be very grateful to receive implementation tips beside network architectural ones. Thanks.
I think your design makes sense in general:
since A, B, and C are fairly different, you make each input a transform sub-network, and then fuse them together, which is your intermediate representation.
from the intermediate representation, you apply additional CNN to decode D and E, respectively.
Several things:
A, B, and C looking different does not necessarily mean you can't stack them together as a 3-channel input. The decision should be made upon the fact that whether the values in A, B, and C mean differently or not. For example, if A is a gray scale image, B is a depth map, C is a also a gray image captured by a different camera. Then A and B are better processed in your suggested way, but A and C can be concatenated as one single input before feeding it to your network.
D and E are two outputs of the network and will be trained in the multi-task manner. Of course, they should share some latent feature, and one should split at this feature to apply a down-stream non-shared weight branch for each output. However, where to split is usually tricky.
It is really a broad question, asking for answers relying mostly on opinions. Here are my two cents though, which you might find interesting as it does not go along the previous answers here and on datascience.
First, I wouldn't go with separate columns for each input. AFAIK, when different inputs are processed by different columns, it is almost always the case that the network is some sort of Siemese network and the columns share the same weights; or at least the columns all need to produce a similar code. It is not your case here, so I would simply not bother.
Second, you are blessed with a problem with a dense output and no need to learn a code. This should direct you straight to U-nets, which outperforms any bottleneck-designed network without much effort. U-nets were introduced for dense segmentation but they shine at any dense-output problem really.
In short, just stack your inputs together and use a U-net.
I am attempting to reproduce a Convolution Neural Network from a research paper using Tensorflow.
There are many times in the diagram where the results of convolutions are concatenated. Currently I am using tf.concat(https://www.tensorflow.org/api_docs/python/tf/concat) along the last axis (representing channels) to concatenate these feature maps. I originally believed that I would want to concatenate along all axes, but this does not seem to be an option in tensorflow. Now I am facing the problem where the paper indicates that tensors(feature maps) of different sizes should be concatenated. tf.concat does not support concatenations of different sizes, so I am wondering if this was the correct command to use in the first place. In summary, what is the correct way to concatenate feature maps(sometimes of different sizes) in tensorflow?
Thank you.
It's impossible and meaningless to concatenate features maps with different sizes.
If you want to concatenate 2 tensors, every dimension except the concatenation one must be equal.
From the image you posted, in fact, you can see that every feature map that gets concatenated, has the same spatial extent (but different depth) of the other one.
If you can't concatenate in that way, probabily that's something wrong in your code, and probably the problem is the lack of padding = valid in the convolution operation.
The problem that you encounter for inception network may be resolved by using padding in convolutional layers to keep the size same. For inception blocks, instead of using "VALID" padding, change it to "SAME" one. So, without requiring any resizing, you can concatenate the outputs.
Alternatively, you can append padding to the feature maps that are going to be concatenated. You can do that by using tf.pad().
If you don't prefer to do this one, you can use tf.image.resize_images function to resize them to same values. However, this is a dirty and computationally expensive approach.
Tensors can only be concatenated along one axis. If you need to concatenate feature maps of different sizes, you must somehow manipulate the sizes of the original tensors.