kandi X-RAY | DeepLearning Summary
kandi X-RAY | DeepLearning Summary
Top functions reviewed by kandi - BETA
- Compute the forward layer .
- Initialize layer1 .
- Softmax loss function .
- Initialize hidden layers .
- Returns the length of the array .
- get item at index
DeepLearning Key Features
DeepLearning Examples and Code Snippets
def enable_mixed_precision_graph_rewrite_v1(opt, loss_scale='dynamic'):
"""Enable mixed precision via a graph rewrite.
Mixed precision is the use of both float32 and float16 data types when
training a model to improve performance. This is achi
Trending Discussions on DeepLearning
When I run the program below, it gives me an error. The problem seems to be in the loss function but I can't find it. I have read the Pytorch Documentation for nn.CrossEntropyLoss but still can't find the problem.
Image size is (1 x 256 x 256), Batch size is 1
I am new to PyTorch, thanks....
ANSWERAnswered 2021-Jun-05 at 03:06
loss = compute_loss(y_hat, torch.tensor())
I am trying to tune the hyperparameters in
mlr using the
tuneParams function. However, I can't make sense of the results it is giving me (or else Im using it incorrectly).
For example, if I create some data with a binary response and then create an
h2o classification model and then check the accuracy and AUC I will get some values.
Then, if I use
tuneParams on some parameters and find a better accuracy and AUC and then plug them into my model. The resulting accuracy and AUC (for the model) does not match that found by using
Hopefully the code below will illustrate my issue:...
ANSWERAnswered 2021-May-27 at 15:33
You're getting different results because you're evaluating the learner using different train and test data. If I use the same 3-fold CV, I get the same results:
I'm having an issue building a docker image from a dockerfile that used to work:
(My dockerfile has more steps, but this is enough to reproduce)...
ANSWERAnswered 2021-May-20 at 14:13
This is a known issue. Read this for more info.
You can first add the correct repository GPG key using the following command.
The following error(s) and solution go for deploying a stack through YAML in portainer but they can surely be applied to docker otherwise.
ANSWERAnswered 2021-Apr-13 at 05:55
Hence, the first idea that comes to mind would be simply defining something like
shm_size: 9gb in the YAML file of the stack. However, this might not work as shown for e.g in this issue.
Therefore, in the end, I had to use the following workaround (also described here, but poorly documented):
I'm using MATLAB to predict a trend with a machine learning approach.
My data file is an .xlsx file containing a timeline in one column (various sampling timestamps, i.e. numbers that represents seconds), and in the other columns I have some integers representing my trend.
My .xlsx file is pretty much like this:...
ANSWERAnswered 2021-Apr-03 at 20:46
I would distinguish the forecasting problem from the data sampling time problem. You are dealing substantially with missing data.
Forecasting problem: You may use any machine learning technique just ignoring missing data. If you are not familiar with machine learning, I would suggest you to use LASSO (least absolute shrinkage and selection operator), which has been demonstrated to have predicting power (see "Sparse Signals in the Cross-Section of Returns" by ALEX CHINCO, ADAM D. CLARK-JOSEPH, and MAO YE).
Missing imputation problem: In the first place you should consider the reason why you have missing data. Sometime it makes no sense to impute values because the information that the value is missing is itself important and should not be overridden. Otherwise you have multiple options, other than linear interpolation, to estimate the missing values. For example check the MATLAB function
I just begin to learn Pytorch and create my first CNN. The dataset contains 3360 RGB images and I converted them to a
[3360, 3, 224, 224] tensor. The data and label are in the
dataset(torch.utils.data.TensorDataset). Below is the training code.
ANSWERAnswered 2021-Apr-03 at 14:34
that error is actually refering to the weights of the conv layer which are in
float32 by default when the matrix multiplication is called. Since your input is
float64 in pytorch) while the weights in conv are
So the solution in your case is :
My docker run is failing because git complains that I didnt set a user config which I never needed for my older images....
ANSWERAnswered 2021-Mar-11 at 11:15
I didn't find why the error occured but I found a solution to remove it. Instead of cloning master then pulling the branch, I directly clone the branch I want to use.
The cloning line is now :
Can GCP VM's run while I am offline? I am using a GCP Deeplearning notebook VM with GPU to train a neural network. When I close the Jupyter notebook tab, the code stops executing while the instance is still alive and I get billed. Is there a way to run the code while I am offline? I think this must be possible....
ANSWERAnswered 2021-Mar-07 at 00:24
Thanks to everybody who commented on this question.
You can run python scripts in GCP Deeplearning notebook VM in the background through
I am using the files from a video tutorial. At the beginning, it starts to spread the files of input image data by copying them in various folders. The code works in the tutorial but I wonder why I get the following error:
[Errno 22] Invalid argument: 'D:\Machine Learning\Deep Learning\SRU-deeplearning-workshop-master\catdogKaggle\train\cat.1.jpg'
Here is the code. At first it creates the directories.(The catdogKaggle\train contains the input images):...
ANSWERAnswered 2021-Mar-03 at 16:29
You are on Windows which is why you need to escape the backslashes or use raw strings to store file paths, i.e.:
I am using the H2O R package.
My understanding is, that this package requires you to have an internet connection as well as connect to the the h2o servers? If you use the h2o package run machine learning models on your data, does h2o "see" your data? I turned off my wifi and tried running some machine learning models using h2o :...
ANSWERAnswered 2021-Feb-21 at 09:35
From the documentation of
h2o.init() (emphasis mine):
This method first checks if H2O is connectible. If it cannot connect and
startH2O = TRUEwith IP of localhost, it will attempt to start an instance of H2O with IP = localhost, port = 54321. Otherwise, it stops immediately with an error. When initializing H2O locally, this method searches for
h2o.jarin the R library resources [...], and if the file does not exist, it will automatically attempt to download the correct version from Amazon S3. The user must have Internet access for this process to be successful. Once connected, the method checks to see if the local H2O R package version matches the version of H2O running on the server. If there is a mismatch and the user indicates she wishes to upgrade, it will remove the local H2O R package and download/install the H2O R package from the server.
h2o.init() with the default setting
ip = "127.0.0.1", as here, connects the R session with the H2O instance (sometimes referred to as "server") in your local machine. If all the necessary package files are in place and up to date, no internet connection is necessary; the package will attempt to connect to the internet only to download stuff in case something is not present or up to date. No data is uploaded anywhere.
No vulnerabilities reported
You can use DeepLearning 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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