Tag Archives: Deep learning

MobaXterm error cuda:out of memory

MobaXterm error cuda:out of memory

When using mobaxterm training model, the cuda:out of In addition to the fact that the conventional video memory is too small and the value of subdivisions needs to be adjusted, there may also be a literal meaning that the storage space is insufficient and the data set is too large. At this time, you only need to reduce the capacity of the data set and then reduce it.

Keras-nightly Import package Error: cannot import name ‘Adam‘ from ‘keras.optimizers‘

Version keras nightly = 2.5.0.dev2021032900

Error information

    from keras.optimizers import Adam
ImportError: cannot import name 'Adam' from 'keras.optimizers'

Solution

error code

from keras.optimizers import Adam
opt = Adam(lr=lr, decay=lr/epochs)

modify

from keras.optimizers import adam_v2
opt = adam_v2.Adam(learning_rate=lr, decay=lr/epochs)

reason

After the keras library is updated, the package cannot be imported in the original way. Open the optimizers.py source code and find the following two key codes. You can see that Adam import has changed, so it is modified as above.

from keras.optimizer_v2 import adam as adam_v2
'adam': adam_v2.Adam,

Keras import a custom metric model error: unknown metric function: Please ensure this object is passed to`custom_object‘

Keras’s model defines metric or loss,
there is no problem when saving to H5, but when using load_ When importing the model, an error will be reported:

unknown metric function: HammingScore. Please ensure this object is passed to the custom_ objects argument.

This is because the custom parameters are not passed in. There are two solutions:

          1. if you only need to predict and no longer train, you can add

        compile = false directly

model = keras.models.load_model('model.h5', compile = False)

If you need further training or modification, add your own metrics code and compile it again

model.compile(loss='binary_crossentropy',
              optimizer=Ada,
              metrics=[HammingScore]) # 这里HammingScore是我自定义的metric
      1. when importing, the user-defined metric/loss is passed into

Custom as a key value_ objects

      1. :
model = keras.models.load_model('model.h5', custom_objects={'HammingScore': HammingScore} )

Note that the key value should be consistent

Successfully solved the problem of “runtimee” in RESNET dataset classification rror:expected scalar type Long but found Float”

Recently, I encountered some mistakes as shown in the title when doing deep learning classification, but I don’t know how to modify them. Finally, after exploration, I successfully solved them
the problems and solutions are reported directly below.

Error

Solution

In practice, the label of classification should be long, and the image should be float32
therefore, modifying the data type will succeed, but it doesn’t matter. I’ll share it with you after I solve it successfully!

Condahttperror:HTTP000 Connection Failed for URL https://repo.anaconda.com/pkgs/main/win-6 

CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://repo.anaconda.com/pkgs/main/win-64/repodata.json>
Elapsed: -

An HTTP error occurred when trying to retrieve this URL.
HTTP errors are often intermittent, and a simple retry will get you on your way.

Solution:
Win + R

then find

and replace it with

channels:
  - http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
  - http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
  - http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/
  - http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda/
  - http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
show_channel_urls: true
ssl_verify: false

mmdetection Error when running voc.py: KeyError: ‘NumClassCheckHook is already registered in hook‘

This error is usually caused by the version of mmcv
just click in one by one according to the following error prompt to find utils.py file:
change the code in the circle below:

add in brackets:

finally, it can run successfully.

Similar questions:
keyerror: ‘XXX is already registered in xxx’

Results: it runs perfectly

Cuda Runtime error (38) : no CUDA-capable device is detected

When training the model in vscode, the following errors appear:
CUDA runtime error (38): no CUDA capable device is detected at/Torch/aten/SRC/THC/thcgeneral cpp:51

The reason is that CUDA is not selected correctly, so you need to check how many graphics cards you have first:
Enter Python in terminal; Enter the following code

import torch
print(torch.cuda.device_count()) #Number of available GPUs

View CUDA version

nvcc --version # Check your own CUDA version

I have two graphics cards in my library. Choose one from 0 or 1 and write it before
model = torch.nn.dataparallel (net). Cuda()

os.environ['CUDA_VISIBLE_DEVICES'] = '0'
model = torch.nn.DataParallel(net).cuda()

Problem solving.

To solve the problem of increasing video memory when training network (torch)

Method 1

torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True

Principle:

Cundnn follows the following criteria:

    if the dimension or type of network input data changes little, set    torch.backends.cudnn.benchmark  =  true   It can increase the operation efficiency; If the input data of the network changes every iteration, cndnn will find the optimal configuration every time, which will improve the operation efficiency

     
    Method 2

    Tensor calculation should be written as follows:

    train_loss += loss.item()

Viewing events.out.tfevents file visually in tensorboard

Command line

Tensorboard — logdir = event dir (event save path)

 
After meeting the problem of “no dashboards are active for the current data set”, it is not solved after searching on the Internet for several hours; I tried several times according to my own ideas and finally solved the problem. Now I share the solutions as follows:

 
1> Find the path of tensorboard, and then CD to the path;

2> Copy and paste the directory of the file to be displayed to the path of the tensorboard;

3> CD to the directory of tensorboard, enter:

tensorboard –logdir=   
4> Copy address http://localhost : 6006 /, input to browser, finish.