gCn | globalCALCnet hub , bridges , and service-provider bridges
kandi X-RAY | gCn Summary
kandi X-RAY | gCn Summary
gCn is a method of connecting local-area CALCnet networks over the internet, as well as connecting calculators to internet services.
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Top functions reviewed by kandi - BETA
- Update the repo index
- Write string to stdout
- Write string to log file
- Search the hierarchy for the given parent folder
- Search the files in the index
- Search the names index in the name index
- Obtain the number of elements that match the given search string
- Returns the results of a given search string
- Formats the output
- Gets the FileInfo object for the given file url
- Tokenize a string
- Nodes reply handler
- Called when a CTCP packet is received
- Store a file in the folder
- Close the connection
- Fetch a file from the cache
- Partition a channel
- Remove user from channel
- Nick
- Join channel
- Checks if the connection is established
- Quit channel
- Return the current limit
- Return the key for the key
- Setup the log file
- Start the client
gCn Key Features
gCn Examples and Code Snippets
Community Discussions
Trending Discussions on gCn
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:
QUESTION
class GCN:
def __init__(self,alpha,adj,feature,hiddenlayer_neurons,output_layer_neurons):
self.alpha=alpha
self.adj=adj
self.feature=feature
self.hiddenlayer_neurons=hiddenlayer_neurons
self.output_layer_neurons=output_layer_neurons
def weightlayers(self):
self.weights1= np.random.normal(loc=0,scale=0.5,size=(features.shape[1],self.hiddenlayer_neurons))
print(features.shape)
print(adj.shape)
self.weights2= np.random.normal(loc=0,scale=0.5,size=(self.hiddenlayer_neurons,self.output_layer_neurons))
self.bias1= np.random.normal(loc=0, scale=0.05, size=self.hiddenlayer_neurons)
self.bias2=np.random.normal(loc=0, scale=0.05, size= self.output_layer_neurons)
return self.weights1,self.weights2,self.bias1,self.bias2
def sigmoid(self,x):
sigma=1/(1+np.exp(-x))
return sigma
def softmax(self,inputs):
inputs=inputs.astype(np.float)
inputs=np.vectorize(inputs)
f=np.exp(inputs) / float(sum(np.exp(inputs)))
#f2 = np.vectorize(f)
return f
def forwardpropagation(self):
self.weights1,self.weights2,self.bias1,self.bias2=self.weightlayers()
self.bias1=(np.reshape(self.bias1,(-1,1))).T
self.bias2=(np.reshape(self.bias2,(-1,1))).T
print(self.bias1.ndim)
#self.sigmoid=self.sigmoid()
self.adj=self.adj.T
self.input= self.adj.dot(self.feature).dot(self.weights1) + (self.bias1)
print(self.input.shape)
self.sigmaactivation= self.sigmoid(self.input)
self.hiddeninput=(self.sigmaactivation @ self.weights2 ) + (self.bias2)
self.output=self.softmax(self.hiddeninput)
return self.output
ANSWER
Answered 2021-May-25 at 21:05For inputs as 2d numeric array, you don't need all that vectorize or float conversion.
Consider a small 2d array (integer dtype, but doesn't matter):
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've been banging my head for a few hours over this with no luck. Can't seem to find any other stackoverflow questions that help either. Basically, I have a component that takes two children nodes like so:
...ANSWER
Answered 2021-May-03 at 23:57Not sure this answers your question, but you could create an interface extending the ReactNode class. e.g.
QUESTION
So... I have checked a few posts on this issue (there should be many that I haven't checked but I think it's reasonable to seek help with a question now), but I haven't found any solution that might suit my situation.
This OOM error message always emerge (with no single exception) in the second round of a whatever-fold training loop, and when re-running the training code again after a first run. So this might be an issue related to this post: A previous stackoverflow question for OOM linked with tf.nn.embedding_lookup(), but I am not sure which function my issue lies in.
My NN is a GCN with two graph convolutional layers, and I am running the code on a server with several 10 GB Nvidia P102-100 GPUs. Have set batch_size to 1 but nothing has changed. Also am using Jupyter Notebook rather than running python scripts with command because in command line I cannot even run one round... Btw does anyone know why some code can run without problem on Jupyter while popping OOM in command line? It seems a bit strange to me.
UPDATE: After replacing Flatten() with GlobalMaxPool(), the error disappeared and I can run the code smoothly. However, if I further add one GC layer, the error would come in the first round. Thus, I guess the core issue is still there...
UPDATE2: Tried to replace tf.Tensor with tf.SparseTensor. Successful but of no use. Also tried to set up the mirrored strategy as mentioned in ML_Engine's answer, but it looks like one of the GPU is occupied most highly and OOM still came out. Perhaps it's kind of "data parallel" and cannot solve my problem since I have set batch_size to 1?
Code (adapted from GCNG):
...ANSWER
Answered 2021-Apr-20 at 13:42You can make use of distributed strategies in tensorflow to make sure that your multi-GPU set up is being used appropriately:
QUESTION
I am trying to implement a fixed size multi-dimensional array whose size is determined at runtime. with the (2) overload of make_shared (template shared_ptr make_shared(std::size_t N) // T is U[]). However, I am facing compilation errors (logs below). The error is not present if I change the shareds to their unique counterparts. My question is,
- What is this error about?
- Why
uniqueworks? - Any better way to implement such runtime-fixed multi-dimentional array container?
Minimal working example:
...ANSWER
Answered 2021-Apr-22 at 09:39For your first question "What is this error about?":
GCC libstdc++ and Clang libc++ has no support for "Extending std::make_shared() to support arrays " which introduced in c++20 yet. So these compilers will try to use template< class T, class... Args > shared_ptr make_shared( Args&&... args );, which trying to forward your arguments (in this case, a cell_t = std::size_t) to construct a std::shared_ptr[]. It cannot be done, so they complain about it.
You can check compiler compatibility here: Compiler support for C++20
QUESTION
I am trying to train and test a pytorch GCN model that is supposed to identify person. But the test accuracy is quite jumpy like it gives 49% at 23 epoch then goes below near 45% at 41 epoch. So it's not increasing all the time though loss seems to decrease at every epoch.
My question is not about implementation errors rather I want to know why this happens. I don't think there is something wrong in my coding as I saw SOTA architecture has this type of behavior as well. The author just picked the best result and published saying that their models gives that result.
Is it normal for the accuracy to be jumpy (up-down) and am I just to take the best ever weights that produce that?
...ANSWER
Answered 2021-Apr-22 at 02:24Accuracy is naturally more "jumpy", as you put it. In terms of accuracy, you have a discrete outcome for each sample - you either get it right or wrong. This makes it so that the result fluctuate, especially if you have a relatively low number of samples (as you have a higher sampling variance).
On the other hand, the loss function should vary more smoothly. It is based on the probabilities for each class calculated at your softmax layer, which means that they vary continuously. With a small enough learning rate, the loss function should vary monotonically. Any bumps you see are due to the optimization algorithm taking discrete steps, with the assumption that the loss function is roughly linear in the vicinity of the current point.
QUESTION
newbie in Gatsby and React. I am trying to import this responsive navbar React component into this Gatsby starter:
Instead of the Menu component in the starter, I created a MenuBar, which I call from another component called Layout.
The code on top works (slightly modified from starter), not using external component.
...ANSWER
Answered 2021-Apr-10 at 14:40Error: Element type is invalid: expected a string (for built-in components) or a class/function (for composite components) but got: undefined.
In 99% of the cases, this issue is related to the import/export method, if some component is exported as default but imported as named (or vice versa) it will cause the prompted issue.
In your case, you are returning a class-based component but your issue doesn't come from that. You are missing the importation of React and Component since you are extending it. Following the dependency example:
QUESTION
I am looking at this:
...ANSWER
Answered 2021-Jan-14 at 07:47Very brief overview for GCC:
GCC's .md machine definition files tell it what instructions are available and what they do, using similar constraint syntax to GNU C inline asm. (GCC doesn't know about machine code, only asm text, that's why it can only output a .s for as to assemble separately.) There are also some C functions that know about generic rules for that architecture, and I guess stuff like register names.
The GCC-internals manual has a section 6.3.9 Anatomy of a Target Back End that documents where the relevant files are in the GCC source tree.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
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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