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How to use CNN for non-image data?
For example, in the image, the connection between pixels in some area gives you another feature (e.g. edge) instead of a feature from one pixel (e.g. color). So, as long as you can shaping your data, and your data have spatial features, you can use CNN.
How to predict my own image using CNN in keras?
I have made a convolutional neural network to predict handwritten digits using MNIST dataset but now I am stuck at predicting my own image as input to cnn,I have saved weights after training cnn and want to use that to predict my own image (NOTE : care is taken that my input image is 28×28)
Can you use CNN on any spatial data?
You can use CNN on any data, but it’s recommended to use CNN only on data that have spatial features (It might still work on data that doesn’t have spatial features, see DuttaA’s comment below).
What can you use CNN for in speech recognition?
For Text classification, there are connections between characters (that form words) so you can use CNN for text classification in character level. For Speech recognition, there is also a connection between frequencies from one frame with some previous and next frames, so you can also use CNN for speech recognition.
How does a CNN model work in Kaggle?
Pass an input image to the first convolutional layer. The convoluted output is obtained as an activation map. The filters applied in the convolution layer extract relevant features from the input image to pass further. Each filter shall give a different feature to aid the correct class prediction.
How to create a simple CNN model beginner guide?
Three basic components to define a basic convolutional neural network. In this layer if we have an image of size 6 6. We define a weight matrix which extracts certain features from the images We have initialized the weight as a 3 3 matrix.
How can neural networks be used for non-image data?
That means that when you have a lot of features (like an image does), using a CNN, you can get comparable learning potential with far fewer parameters. As a result, you can train faster and use less data as well (usually). If CNNs can do more for less, why don’t we use them for everything?
How to train a CNN with numerical data?
I am trying to train a CNN with a numerical data set. Once the input data is not an image format. Assuming my data has a size of 1850*11 matrix. With a 1850*1 label (for output). In libsvm I can use it directly and in the Neural Network Toolbox (nnstart) as well.