gnn | Growing Neural Networks Library
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Growing Neural Networks Library
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QUESTION
I have two classes of networks of neurons one of GNN type and the other simple of linear type, the latter is instantiated in the first !!! how can I train both at the same time? here is an example:
...ANSWER
Answered 2021-Sep-10 at 16:44You must declare it in the __init__(...)
:
QUESTION
Following the documentation in the source code of my interest from github, I run py dataset.py -path adversarial-training --train
from the folder I have cloned repository to.
However, I receive an error
...ANSWER
Answered 2021-Jul-23 at 11:05I think you need to add it to the environment variables (path). For example: https://datatofish.com/add-python-to-windows-path/
It is not finding in the environment varaiables
QUESTION
I am new to Pytorch and am trying to transfer my previous code from Tensorflow to Pytorch due to memory issues. However, when trying to reproduce Flatten
layer, some issues kept coming out.
In my DataLoader
object, batch_size
is mixed with the first dimension of input (in my GNN, the input unpacked from DataLoader
object is of size [batch_size*node_num, attribute_num], e.g. [4*896, 32] after the GCNConv layers). Basically, if I implement torch.flatten()
after GCNConv
, samples are mixed together (to [4*896*32]) and there would be only 1 output from this network, while I expect #batch_size outputs. And if I use nn.Flatten()
instead, nothing seems to happen (still [4*896, 32]). Should I set batch_size as the first dim of the input at the very beginning, or should I directly use view()
function? I tried directly using view()
and it (seemed to have) worked, although I am not sure if this is the same as Flatten. Please refer to my code below. I am currently using global_max_pool because it works (it can separate batch_size
directly).
By the way, I am not sure why training is so slow in Pytorch... When node_num
is raised to 13000, I need an hour to go through an epoch, and I have 100 epoch per test fold and 10 test folds. In tensorflow the whole training process only takes several hours. Same network architecture and raw input data, as shown here in another post of mine, which also described the memory issues I met when using TF.
Have been quite frustrated for a while. I checked this and this post, but it seems their problems somewhat differ from mine. Would greatly appreciate any help!
Code:
...ANSWER
Answered 2021-May-11 at 14:39The way you want the shape to be batch_size*node_num, attribute_num
is kinda weird.
Usually it should be batch_size, node_num*attribute_num
as you need to match the input to the output. And Flatten
in Pytorch does exactly that.
If what you want is really batch_size*node_num, attribute_num
then you left with only reshaping the tensor using view
or reshape
. And actually Flatten itself just calls .reshape
.
tensor.view
: This will reshape the existing tensor to a new shape, if you edit this new tensor the old one will change too.
tensor.reshape
: This will create a new tensor using the data from old tensor but with new shape.
QUESTION
I have the following data frame
...ANSWER
Answered 2021-Apr-08 at 10:09The marker size s
of scatter is set in units of points. So, if your markers are too small, scale the argument you are passing to s
.
Here is an example:
QUESTION
I have the following code for the stack bar chart
...ANSWER
Answered 2021-Mar-12 at 18:50The specific problem is that b_AE
is calculated wrong. (Also, there is a list called count_AM
for which there is no label).
The more general problem, is that calculating all these values "by hand" is very prone to errors and difficult to adapt when there are changes. It helps to write things in a loop.
The magic of numpy's broadcasting and vectorization lets you initialize bottom
as a single zero, and then use numpy's adding to add the counts.
To have a bit neater x-axis, you can put the individual words on separate lines. Also, plt.tight_layout()
tries to make sure all text fits nicely into the plot.
QUESTION
Is there any difference between the following two codes related to initializing a class in Python?
...ANSWER
Answered 2021-Feb-02 at 18:45No. there is no difference between these two approaches in your case with this level of information. but could they? Yes. they could. if they have some modifications in their setters or getters. later in my answer I'll show you how.
First of all, I prefer using this one:
QUESTION
I am looking to fine tune a GNN and my supervisor suggested exploring different learning rates. I came across this tutorial video where he mentions that a randomised log space search of hyper parameters is typically done in practice. For sake of the introductory tutorial this was not covered.
Any help or pointers on how to achieve this in PyTorch is greatly appreciated. Thank you!
...ANSWER
Answered 2021-Feb-05 at 05:27Setting the scale in logarithm terms let you take into account more desirable values of the learning rate, usually values lower than 0.1
Imagine you want to take learning rate values between 0.1 (1e-1) and 0.001 (1e-4). Then you can set this lower and upper bound on a logarithm scale by applying a logarithm base 10 on it, log10(0.1) = -1 and log10(0.001) = -4. Andrew Ng provides a clearer explanation in this video.
In Python you can use np.random.uniform()
for this
QUESTION
I have been using the FUNSD dataset to predict sequence labeling in unstructured documents per this paper: LayoutLM: Pre-training of Text and Layout for Document Image Understanding . The data after cleaning and moving from a dict to a dataframe, looks like this: The dataset is laid out as follows:
- The column
id
is the unique identifier for each word group inside a document, shown in columntext
(like Nodes) - The column
label
identifies whether the word group are classified as a 'question' or an 'answer' - The column
linking
denoting the WordGroups which are 'linked' (like Edges), linking corresponding 'questions' to 'answers' - The column
'box'
denoting the location coordinates (x,y top left, x,ybottom right) of the word group relative to the top left corner (0.0). - The Column
'words'
holds each individual word inside the wordgroup, and its location (box).
I aim to train a classifier to identify words inside the column 'words'
that are linked together by using a Graph Neural Net, and the first step is to be able to transform my current dataset into a Network. My questions are as follows:
Is there a way to break each row in the column
'words'
into a two columns[box_word, text_word]
, each only for one word, while replicating the other columns which remain the same:[id, label, text, box]
, resulting in a final dataframe with these columns:[box,text,label,box_word, text_word]
I can Tokenize the columns
'text'
andtext_word
, one hot encode columnlabel
, split columns with more than one numericbox
andbox_word
into individual columns , but How do I split up/rearrange the colum'linking'
to define the edges of my Network Graph?Am I taking the correct route in Using the dataframe to generate a Network, and use it to train a GNN?
Any and all help/tips is appreciated.
...ANSWER
Answered 2020-Oct-07 at 02:55Edit: process multiple entries in the column words
.
Your questions 1 and 2 are answered in the code. Actually quite simple (assuming the data format is correctly represented by what shown in the screenshot). Digest:
Q1: apply
the splitting function on the column and unpack by .tolist()
such that separate columns can be created. See this post also.
Q2: Use list comprehension to unpack the extra list layer and retain only non-empty edges.
Q3: Yes and no. Yes because pandas
is good at organizing data with heterogeneous types. For example, lists, dict, int and float can be present at different columns. Several I/O functions, such as pd.read_csv()
or pd.read_json()
, are also very handy.
However, there is overhead in data access, and that is especially costly for iterating over rows (records). Therefore, the transformed data that feeds directly into your model is usually converted into numpy.array
or more efficient formats. Such a format conversion task is the data scientist's sole responsibility.
I make up my own sample dataset. Irrelevant columns were ignored (as I am not obliged to and shouldn't do).
QUESTION
I want to display a String of names in the text view of recycler view. the .xml of this step is below
...ANSWER
Answered 2020-Aug-26 at 15:06You are applying differente adapters to the same recyclerview, which means the last one that will be visible will be the last one.
You can see it here:
QUESTION
I have a json file where I need to read it in a structured way to insert in a database each value in its respective column, but in the tag "customFields"
the fields change index, example: "Tribe / Customer"
can be index 0 (row['customFields'][0])
in a json block, and in the other one be index 3 (row['customFields'][3])
, so I tried to read the data using the name of the row field ['customFields'] ['Tribe / Customer']
, but I got the error below:
TypeError: list indices must be integers or slices, not str
Script:
...ANSWER
Answered 2020-Jul-10 at 17:05You'll have to parse the list of custom fields into something you can access by name. Since you're accessing multiple entries from the same list, a dictionary is the most appropriate choice.
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