def __init__(self, input_shape, num_nodes=1, rand_state=np.random):
        super(DenseLayer, self).__init__(input_shape)
        self.output_shape = np.array([num_nodes])
        self.weights_ = rand_state.randn(np.prod(self.input_shape), num_nodes).astype('float32')
        self.weights_ /= np.sqrt(np.prod(self.weights_.shape))
Dense/fully connected layer.
dataset/ETHPy150Open bbabenko/simple_convnet/simple_convnet/convnet.py/DenseLayer.__init__