Neural networks are a series of stacked layers. Deeper the network, higher the number of layers. Layer 1 is called the input layer and Layer 3 is called the Output layer. The intermediate layer are called the hidden layer. The number of hidden layer might vary depending on the network complexity. In this case, Layer 2 is the hidden layer.
g is the activation function . x is the input vector . a is the activation output.
S be the units in Layer 1 .
S = 3 ( Discarding Bias Unit )
T be the units in Layer 2.
T = 3 ( Discarding the Bias activation Unit )
The Dimension is given by ( S * T+1 ).
The Dimension of weight matrix is 3 * 4