def init_conv_w_kaiming(shape, gain=2.):
    input_n = np.prod(shape[1:])
    std = np.sqrt(gain/float(input_n))
    return np.random.normal(0., std, shape)
Initialize convolution layer's weights like torch's nninit kaiming,     called by _init_conv_weights
dataset/ETHPy150Open dnlcrl/PyFunt/pyfunt/layers/init.py/init_conv_w_kaiming