gcn | Implementation of Graph Convolutional Networks in TensorFlow
kandi X-RAY | gcn Summary
kandi X-RAY | gcn Summary
Implementation of Graph Convolutional Networks in TensorFlow
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- Load pandas data
- Generate a numpy array of values from l
- Parse an index file
- Call the convolve function
- Compute the dot product of x and y
- Apply sparse dropout
- Computes the Chebyshev polynomials from adjacency matrix
- Convert a sparse matrix to a tuple
- R Normalize the adjacency matrix
- Calculate loss
- Calculate softmax cross entropy
- Evaluate the model
- Construct a feed dictionary
- Build the model
- Preprocess features
- Construct the feed dictionary
- Apply the function to input tensor
- Preprocess the adjacency matrix
gcn Key Features
gcn Examples and Code Snippets
Lines of Code : 30
License : No License
Copy@article{Qian2019PUGCNPC,
title={PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks},
author={Guocheng Qian and Abdulellah Abualshour and G. Li and A. Thabet and Bernard Ghanem},
journal={ArXiv},
year={2019},
volume={abs/1912 #!/usr/bin/env python
import gcn
# Define your custom handler here.
def handler(payload, root):
# Get the IVORN, or unique VOEvent ID, and print it.
print(root.attrib['ivorn'])
# Print all of the event attributes.
for param in root. @article{li2019pgcn,
title={PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks},
author={Li, Yu and Kuwahara, Hiroyuki and Yang, Peng and Song, Le and Gao, Xin},
journal={bioRxiv},
page import argparse
from time import time
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from data_loader import Data
from models import CompGCN_ConvE
from utils import in_out_norm #!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
import math
import time
import numpy as np
import torch as th
import torch.nn.functional as F
import torch.optim as optim
from matplotlib import pyplot as plt
from matplotlib.ticker impo import argparse
import time
import torch
import torch.nn.functional as F
from dataset import EllipticDataset
from model import EvolveGCNH, EvolveGCNO
from utils import Measure
import dgl
def train(args, device):
elliptic_dataset = EllipticDat Community Discussions
Trending Discussions on gcn
QUESTION
I am in the process of learning MPI in fortran, and so I've been writing a bunch of small functions that do simple tasks in MPI to get a good handle on it. However, halfway through, i began getting this error when trying to compile
...ANSWER
Answered 2022-Apr-15 at 07:23implicit none must appear before include 'mpif.h'
That being said, this is very error prone, and you should at least use
QUESTION
I'm trying to create a new historical table which saves any of the updates that has been made to a table TempLimitBook.
When executing the following code, the table TempLimitBookHistory (hsitorical table) gets two records even if any of the values has been updated.
Does anyone have an idea of what could be wrong in the following code?
...ANSWER
Answered 2022-Mar-29 at 13:50In the first trans part
QUESTION
I want to use the new parallel facilities of C++17 but my computer don't.
This works :
...ANSWER
Answered 2022-Feb-12 at 16:45std::accumulate is meant to apply the binary operation in-order, so it doesn't make any sense to use an execution policy. It doesn't have any overload accepting one (see https://en.cppreference.com/w/cpp/algorithm/accumulate).
If you want to allow out-of-order evaluation with some execution policy, replace std::accumulate by std::reduce. std::reduce may assume commutativity and associativity of the operation to reorder it into any permutation and apply it in any grouping of the elements.
QUESTION
I have a file (PatchFinder.py) with two classes:
...ANSWER
Answered 2021-Dec-07 at 08:11Your import PatchFinder imports the module, so when you call PatchFinder() you are trying to "initialise" the module, not the class. There's guidance here: https://docs.python.org/3/tutorial/modules.html
All you need to do is specify that you want to initialise the object defined within the module. Change tf = PatchFinder() to tf = PatchFinder.PatchFinder() and it should work.
QUESTION
I learn how to explore Python package. Sometime I cannot find the entry-point of this package.
For example:
https://github.com/thunlp/OpenNE/tree/pytorch/src/openne
The online materials tell me that I can find the entry point in the setup.py. However, for this project, the setup.py does not have the entry point.
Given this package and assume the ** README.md is missing**, how can I explore the package? like how I can know that I can use
python -m openne --model gcn --dataset cora --sparse --no-save.
Can someone give me step-by-step to how to explore an python package?
Many Thanks
...ANSWER
Answered 2021-Nov-30 at 08:13i think "if __name__ == "__main__": is entry point
QUESTION
I am trying to implement a GCN layer using tensorflow, but it is not learning. Can someone check what potential issue could be?
I have tried normalizing the adjacency matrix and even replaced it with identity so that the GCN layer becomes a simple MLP. But there is no change. I think, I have made some fundamental/silly mistake in my implementation which I am not able to find. Can someone let me know what the issue could be?
...ANSWER
Answered 2021-Nov-26 at 03:48Your model is learning but doesn't converge. Consider checking/adding data ,use simpler model, or tuning parameters while training (e.g: learning rate, batches size).
QUESTION
I am assessing the impact of hotspot single nucleotide polymorphism (SNPs) from a next generation sequencing (NGS) experiment on the protein sequence of a virus. I have the reference DNA sequence and a list of hotspots. I need to first figure out the reading frame of where these hotspots are seen. To do this, I generated a DNAStringSetList with all human codons and want to use a vmatchpattern or matchpattern from the Biostrings package to figure out where the hotspots land in the codon reading frame.
I often struggle with lapply and other apply functions, so I tend to utilize for loops instead. I am trying to improve in this area, so welcome a apply solution should one be available.
Here is the code for the list of codons:
...ANSWER
Answered 2021-Oct-12 at 22:59For the current code, change the way the codons is formed. Currently the output of codons looks like this:
QUESTION
I want to use the R stats::optimize function in Rcpp because I haven't been able to find an Rcpp equivalent. The code below is my attempt at a simple example based on the Example in the optimize help, but fails. Here's the R function and results
...ANSWER
Answered 2021-Aug-06 at 00:49One of your problems here is that you assume that becomes a function you submit to compilation under Rcpp::sourceCpp() is callable under its exported name.
It is not. Try Rcpp::sourceCpp(..., verbose=TRUE), i.e. add that arguments, to see what is really called. Those you could pass around (using SEXP argunments and results, but they are unwieldy).
To prove, here is a 'working but useless' version of your code. If we pass f() from R too, everything is callable.
Morale: The interface still is SEXP .Call("name", SEXP a, SEXP b, ...) even if Rcpp hides that. No Free Lunch (TM). But as my comment hinted, there are optimization packages you can use with Rcpp.
QUESTION
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class Graphconvlayer(nn.Module):
def __init__(self,adj,input_feature_neurons,output_neurons):
super(Graphconvlayer, self).__init__()
self.adj=adj
self.input_feature_neurons=input_feature_neurons
self.output_neurons=output_neurons
self.weights=Parameter(torch.normal(mean=0.0,std=torch.ones(input_feature_neurons,output_neurons)))
self.bias=Parameter(torch.normal(mean=0.0,std=torch.ones(input_feature_neurons)))
def forward(self,inputfeaturedata):
output1= torch.mm(self.adj,inputfeaturedata)
print(output1.shape)
print(self.weights.shape)
print(self.bias.shape)
output2= torch.matmul(output1,self.weights.t())+ self.bias
return output2
class GCN(nn.Module):
def __init__(self,lr,dropoutvalue,adjmatrix,inputneurons,hidden,outputneurons):
super(GCN, self).__init__()
self.lr=lr
self.dropoutvalue=dropoutvalue
self.adjmatrix=adjmatrix
self.inputneurons=inputneurons
self.hidden=hidden
self.outputneurons=outputneurons
self.gcn1 = Graphconvlayer(adjmatrix,inputneurons,hidden)
self.gcn2 = Graphconvlayer(adjmatrix,hidden,outputneurons)
def forward(self,x,adj):
x= F.relu(self.gcn1(adj,x,64))
x= F.dropout(x,self.dropoutvalue)
x= self.gcn2(adj,x,7)
return F.log_softmax(x,dim=1)
a=GCN(lr=0.001,dropoutvalue=0.5,adjmatrix=adj,inputneurons=features.shape[1],hidden=64,outputneurons=7)
a.forward(adj,features)
ANSWER
Answered 2021-Jun-02 at 07:01Your GCN is composed of two Graphconvlayer.
As defined in the code you posted, Graphconvlayer's forward method expects only one input argument: inputfeaturedata. However, when GCN calls self.gcn1 or self.gcn2 (in its forward method) it passes 3 arguments: self.gcn1(adj,x,64) and self.gcn2(adj,x,7).
Hence, instead of a single input argument, self.gcn1 and self.gcn2 are receiving 3 -- this is the error you are getting.
QUESTION
I tried running an example from stellargraph's examples, but I encountered a weird error:
tensorflow/core/framework/op_kernel.cc:1744] OP_REQUIRES failed at cast_op.cc:121 : Unimplemented: Cast string to float is not supported
The example code I used is this:
...ANSWER
Answered 2021-Jun-01 at 10:49Apparently, adding the line:
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Install gcn
You can use gcn like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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