for neural networks with the ReLU activation function: the initial value of each weight between the layer $i$ and the layer $i+1$ is set according to either of the normal distributions $$\label{He-initialization}
N \left( 0, \frac{2}{ n_i } \right) \ \ \mbox{or} \ \ N \left( 0, \frac{2}{ n_{i+1} } \right)$$ independently where $n_i$ is the number of neurons in the layer $i$. Eq. (\[He-initialization\]) was derived by keeping the variance of the net input vector in each layer equal and keeping the variance of the back-propagated gradients equal, respectively, for the purpose of avoiding their saturation.

The normalized initialization is inappropriate for a comparison object of the even initialization because it is invalid for neural networks with the ReLU activation function as described above. Then we compare below the even initialization method with the standard initialization and He initialization. A schematic relationship among the standard initialization, He initialization and the even initialization is shown in Fig. \[fig8\]. The interval of the initial values of weights initialized by the even initialization is contained in both of those of the standard initialization and He initialization because $1/n \leq 1/\sqrt{n}$ holds true for any $n=1,2, \cdots$ where $n$ is the number of 