Deep learning using Caffe - Python
There is a fundamental difference between weights and input data: the training data is used to learn the weights (aka "trainable parameters") during training. Once the net is trained, the training data is no longer needed while the weights are kept as part of the model to be used for testing/deployment.
Make sure this difference is clear to you before you precede.
Layers with trainable parameters has a filler to set the weights initially.
On the other hand, an input data layer does not have trainable parameters, but it should supply the net with input data. Thus, input layers has no filler.
Based on the type of input layer you use, you will need to prepare your training data.
I am studying deep learning and trying to implement it using CAFFE- Python. can anybody tell that how we can assign the weights to each node in input layer instead of using weight filler in caffe?