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What if all weights in neural network are same?
E.g. if all weights are initialized to 1, each unit gets signal equal to sum of inputs (and outputs sigmoid(sum(inputs)) ). If all weights are zeros, which is even worse, every hidden unit will get zero signal. No matter what was the input – if all weights are the same, all units in hidden layer will be the same too.
Do input layers have weights?
The input layer has its own weights that multiply the incoming data. The input layer then passes the data through the activation function before passing it on. The data is then multiplied by the first hidden layer’s weights.
Why do nodes have different weights in a neural network?
Specifically, nodes that are side-by-side in a hidden layer connected to the same inputs must have different weights for the learning algorithm to update the weights. This is often referred to as the need to break symmetry during training.
Can you use the same weights every time you train a neural network?
We can use the same set of weights each time we train the network; for example, you could use the values of 0.0 for all weights. In this case, the equations of the learning algorithm would fail to make any changes to the network weights, and the model will be stuck.
Why are weights of neural networks initialized in machine learning?
If the weights are zero, complexity of the whole deep net would be the same as that of a single neuron and the predictions would be nothing better than random. Nodes that are side-by-side in a hidden layer connected to the same inputs must have different weights for the learning algorithm to update the weights.
Why do not all neural networks learn the same way?
Just like people, not all neural network layers learn at the same speed. So when the backprop algorithm propagates the error gradient from the output layer to the first layers, the gradients get smaller and smaller until they’re almost negligible when they reach the first layers.