http://scs.ryerson.ca/~aharley/vis/fc/
Adam Harley has created a 3d visualization of a Multi Layer Perceptron which has already been trained (using Backpropagation) on the MNIST Database of handwritten digits.
The network takes 784 numeric pixel values as inputs from a 28 x 28 image of a handwritten digit (it has 784 nodes in the Input Layer corresponding to pixels). The network has 300 nodes in the first hidden layer, 100 nodes in the second hidden layer, and 10 nodes in the output layer (corresponding to the 10 digits)
Although the network described here is much larger (uses more hidden layers and nodes) compared to the one we discussed in the previous section, all computations in the forward propagation step and backpropagation step are done in the same way (at each node) as discussed before.
A node which has a higher output value than others is represented by a brighter color. In the Input layer, the bright nodes are those which receive higher numerical pixel values as input. Notice how in the output layer, the only bright node corresponds to the digit 5 (it has an output probability of 1, which is higher than the other nine nodes which have an output probability of 0). This indicates that the MLP has correctly classified the input digit. I highly recommend playing around with this visualization and observing connections between nodes of different layers.
Check the following example
Draw a label of 5 (Numeric Digit)
Numeric Digit 5
Numeric Digit 5 with Hidden layer weight
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