Why does my Neural Network (from scratch) regression learn only first sample of the dataset?
Implementing a NN for regression task with 16(features)+1(bias) inputs and 1 output, I’m only using numpy and vectorization, when I train it on the training set, the first sample of the input is the only one learned perfectly, the others are kinda learned but not well at all.
Is there something I did wrong in backpropagation’s operations?
The bias is implemented in the first layer by adding a feature with value 1 to the training samples.
Why does my Neural Network (from scratch) regression learns only first sample of the dataset?
Implementing a NN for regression task with 16(features)+1(bias) inputs and 1 output, I’m only using numpy and vectorization, when I train it on the training set, the first sample of the input is the only one learned perfectly, the others are kinda learned but not well at all.
Is there something I did wrong in backpropagation’s operations?
The bias is implemented in the first layer by adding a feature with value 1 to the training samples.