cifar-10 | Simple C++ reader for CIFAR-10 dataset | Dataset library
kandi X-RAY | cifar-10 Summary
kandi X-RAY | cifar-10 Summary
Simple C++ reader for CIFAR-10 dataset
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QUESTION
I'm trying to use the Cifar-10 dataset to practice my CNN skills.
If I do this it's ok:
...ANSWER
Answered 2021-May-21 at 11:44You can also extract them like this:
QUESTION
I'm trying to work with the Cifar-10 dataset using Tensorflow as this,
...ANSWER
Answered 2021-May-21 at 10:00You can use the str2int method of the ClassLabel object:
QUESTION
I need to create a Neural Network where I use binary gates to zero-out certain tensors, which are the output of disabled circuits.
To improve runtime speed, I was looking forward to use torch.bool binary gates to stop backpropagation along disabled circuits in the network. However, I created a small experiment using the official PyTorch example for the CIFAR-10 dataset, and the runtime speed is exactly the same for any values for gate_A and gate_B: (this means that the idea is not working)
ANSWER
Answered 2021-May-13 at 18:28You could use torch.no_grad (the code below can probably be made more concise):
QUESTION
Here is my code. The packages imported are not shown. I am trying to feed the CIFAR-10 test data into alexnet. The dictionary at the end needs to be sorted so I can find the most common classification. Please help, I have tried everything!
............................................................................................................................................................................................................................................................................................................
...ANSWER
Answered 2021-Apr-09 at 06:50This line is the problem
QUESTION
I'm learning image classification using PyTorch (using CIFAR-10 dataset) following this link.
I'm trying to understand the input & output parameters for the given Conv2d code:
ANSWER
Answered 2021-Mar-29 at 07:36These are the dimensions of the image size itself (i.e. Height x Width).
Unpadded convolutionsUnless you pad your image with zeros, a convolutional filter will shrink the size of your output image by filter_size - 1 across the height and width:
You can add padding in Pytorch by setting Conv2d(padding=...).
Since it has gone through:
Layer Shape Transformation one conv layer (without padding)(h, w) -> (h-4, w-4)
a MaxPool
-> ((h-4)//2, (w-4)//2)
another conv layer (without padding)
-> ((h-8)//2, (w-8)//2)
another MaxPool
-> ((h-8)//4, (w-8)//4)
a Flatten
-> ((h-8)//4 * (w-8)//4)
We go from the original image size of (32,32) to (28,28) to (14,14) to (10,10) to (5,5) to (5x5).
To visualise this you can use the torchsummary package:
QUESTION
I am trying to code a ResNet CNN architecture based on the paper by using Python3, TensorFlow2 and CIFAR-10 dataset. You can access the Jupyter notebook here.
During training the model using "model.fit()", after just one epoch of training, I get the following error:
ValueError: Input 0 is incompatible with layer model: expected shape=(None, 32, 32, 3), found shape=(32, 32, 3)
The training images are batched using batch_size = 128, hence the training loop gives the following 4-d tensor which TF Conv2D expects- (128, 32, 32, 3).
What's the source of this error?
...ANSWER
Answered 2021-Mar-05 at 19:48Ok, I found a small issue in your code. The problem occurs in the test data set. You forget to transform it properly. So currently you have like this
QUESTION
- I am trying to make cifar-10 object detection. However, all I can get is
%20accuracy.. - I don't use any preprocessing though but still isn't
%20is too low? Also I useCanny Edge Detectorand saved those edges to some folder. - When I use that edge images of Canny output instead of Gray Scale normal images, accuracy gets drop down to
%15... - I believe while using the output of the Canny I should get a higher accuracy, not lower. Here is how I create data generator:
ANSWER
Answered 2021-Jan-21 at 08:38CIFAR-10 is a difficult dataset to generalize with 90%+ accuracy. You can try:
- More complex network
- Flattening 1 time ( I see you did it two times, that might affect.)
- Using more augmentation options like zooming etc.
- Using callbacks such as ReduceLROnPlateau
- While making the network complex, you should use regularization to avoid overfitting.
After flattening you can use Dense layers like this, you didn't pass any activations to your dense layers at the end:
QUESTION
I am trying to implement ANN on a Cifar-10 dataset using keras but for some reason I dont know I am getting only 10% accuracy ?
I have used 5 hidden layers iwth 8,16,32,64,128 neurons respectively.
...ANSWER
Answered 2021-Jan-18 at 15:49That's very normal accuracy for a such network like this. You only have Dense layers which is not sufficient for this dataset. Cifar-10 is an image dataset, so:
Consider using CNNs
Use 'relu' activation instead of sigmoid.
Try to use image augmentation
To avoid overfitting do not forget to regularize your model.
Also batch size of 500 is high. Consider using 32 - 64 - 128.
QUESTION
I have 50.000 in a folder that have names "1.png" to "50000.png". And I want to save them to some array in spesicfic order, because I have labels for them in order to classify them. So when order of the images get messed up, labels become useless. (i.e: 1.png will be saved to first index and 2.png will be saved to second index etc.). I try this code for this purpose:
...ANSWER
Answered 2021-Jan-15 at 13:24It is in order though.
Since image_folder is a list of strings, image_folder.sort() will perform lexicographical sorting, where "1" < "10" < "100" < "1000" < "1001" < ... < "2" < "20" and so on.
If you want to treat "100.png" like "integer 100 + file extension", do this:
QUESTION
I am using PyTorch 1.7 and Python 3.8 with CIFAR-10 dataset. I am trying to create a block with: conv -> conv -> pool -> fc. Fully connected layer (fc) has 256 neurons. The code for this is as follows:
...ANSWER
Answered 2021-Jan-08 at 07:38you need to flat the output of nn.MaxPool2d layer for giving input in nn.Linear layer.
try to use x = x.view(x.size(0), -1) before giving input to fc layer for flatten tensor.
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