Howtosignalprocessing
- How many inputs can a neural network handle?
- What is the input size of a neural network?
- How can neural networks deal with varying input sizes?
- What should be the size of the input layer?
How many inputs can a neural network handle?
In popular nets the length and height of input images are usually less than three hundred which makes the number of input features 90000 . Also you can employ max-pooling after some convolution layers, if you are using convolutional nets, to reduce the number of parameters.
What is the input size of a neural network?
In Keras, the input dimension needs to be given excluding the batch-size (number of samples). In this neural network, the input shape is given as (32, ). 32 refers to the number of features in each input sample. Instead of not mentioning the batch-size, even a placeholder can be given.
How can neural networks deal with varying input sizes?
For variable sized inputs where there is no particular ordering among the inputs, one can design networks which: use a repetition of the same subnetwork for each of the groups of inputs (i.e. with shared weights). This repeated subnetwork learns a representation of the (groups of) inputs.
What should be the size of the input layer?
You choose the size of the input layer based on the size of your data. If you data contains 100 pieces of information per example, then your input layer will have 100 nodes. If you data contains 56,123 pieces of data per example, then your input layer will have 56,123 nodes.
