Let's say an image has 28*28 pixels, which leads to 784 input nodes, with various greyscale values, and 10 output nodes, if the image can be classified into 1 of 10 numbers.

When the training data is used for a set of known pictures of numbers, the weights and hidden layers uses forward- and backpropagation to get get the proper hidden and weight layers for the known layer.

However, doesn't a new training picture destroy the trained and balanced weights and nodes' values? Because the weights and hidden nodes have been calibrated to recognize the former training picture?

Let's say an image has 28*28 pixels, which leads to 784 input nodes in a feed-forward neural network. If an image can be classified into 1 of 10 numbers (e.g. MNIST), there are 10 output nodes.

We train (with gradient descent and back-propagation) the FFNN with a set of known pictures until we get a good accuracy.

Successively, we get a new training picture, which we want to use to train the FFNN even further. However, wouldn't this new training picture destroy the previously learned weights, which have been calibrated to recognize the former training pictures?

I have a question about the training sequence regarding Neural Network recognition. Let's say an image has 28*28 pixels, which leads to 784 Input Nodes with various greyscale values and 10 output nodes, if the image shows a number 0-9. Then when the training data is used for a set of known pictures of numbers, the weights and hidden layers uses forward- and backpropagation to get get the proper hidden and weight layers for the known layer. However, doesn't a new training picture destroy the trained and balanced weights and nodes values? Because the weights and hidden nodes have been calibrated to recognize the former training picture? Thank you for assistance.

Kind regards David

Let's say an image has 28*28 pixels, which leads to 784 input nodes, with various greyscale values, and 10 output nodes, if the image can be classified into 1 of 10 numbers.

When the training data is used for a set of known pictures of numbers, the weights and hidden layers uses forward- and backpropagation to get get the proper hidden and weight layers for the known layer. 

However, doesn't a new training picture destroy the trained and balanced weights and nodes' values? Because the weights and hidden nodes have been calibrated to recognize the former training picture?