All Questions
13 questions
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What would be the advantage of making channel dimension first in TensorFlow Keras implementation?
I was reproducing the findings of a research article in which I discovered that they had switched the Channel dimension from last to first. To clarify this concept, I went through A Gentle ...
- 41
1
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0
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111
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Does the order of data augmentation and normalization matter?
What is the preferred order of data augmentation and normalization? Is it the former followed by the latter?
- 11
1
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1
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172
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Keras 1D CNN always predicts the same result even if accuracy is high on training set
The validation accuracy of my 1D CNN is stuck on 0.5 and that's because I'm always getting the same prediction out of a balanced data set. At the same time my training accuracy keeps increasing and ...
1
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1
answer
1k
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What happens if there is no activation function in some layers of a neural network?
What if I don't apply an activation function on some layers in a neural network. How will it affect the model?
Take for instance the following code snippet:
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1
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0
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41
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Is using a filter of size (1, x, y) on a 3D convolutional layer the same as using a filter of size (x,y) on a 2D convolutional layer?
I'm trying to predict some properties of videos with Keras using the following rough architecture:
Feed each frame through the same 2-D convolutional layer.
Take the outputs of this 2-D ...
- 111
1
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1
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3k
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How can I merge outputs of two separate layers so that the overall performance improves?
I am training a combined model (fine-tuned VGG16 for images and shallow FCN for numerical data) to do a binary classification. However, the overall AUC score is not what I expected it to be.
Image-...
- 241
1
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0
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49
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Wouldn't training the model with this data lead to inaccuracies since the testing data would not be normalized in a similar way?
I was trying to normalize my input data images for feeding to my convolutional neural network and wanted to use standardize my input data.
I referred to this post, which says that ...
- 11
1
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1
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111
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Hand-Signs Recognition using Deep Learning Convolutional Neural Networks
I am developing a CNN model to recognize 24 hand-signs of American Sign Language. I have 2500 Images/hand-sign. The data split is:
Training = 1250 Images/hand-sign
Validation = 625 Images/hand-sign
...
- 51
1
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1
answer
41
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Semantic issues with predictions made by my trained model
I'm new to Deep Learning. I used Keras and trained a inception_resnet_v2 model for my binary classification application (fire ...
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4
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1
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2k
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Dice loss gives binary output whereas binary crossentropy produces probability output map
On recommendation of Kanak on stackoverflow I am posting this question here:
Currently I am experimenting with various loss functions and optimizers for my binary image segmentation problem. The loss ...
5
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0
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365
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What are the ways to calculate the error rate of a deep Convolutional Neural Network, when the network produces different results using the same data?
I am new to the object recognition community. Here I am asking about the broadly accepted ways to calculate the error rate of a deep CNN when the network produces different results using the same data....
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1
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2
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94
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CNN Pooling layers unhelpful when location important?
I'm trying to use a CNN to analyse statistical images. These images are not 'natural' images (cats, dogs, etc) but images generated by visualising a dataset. The idea is that these datasets hopefully ...
- 343
3
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0
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521
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Deep NN architecture for predicting a matrix from two matrices
Recently my friend asked me a question: having two input matrices X and Y (each size NxD) where D >> N, and ground truth matrix Z of size DxD, what deep architecture shall I use to learn a deep model ...
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