theory

CTNet 提出了一种新型神经网络架构，把计算过程重新定义为持久状态的演化，而非逐层重写，融合了可重入记忆、多尺度一致性和投影式输出。

# 深度线性网络中的非线性计算 来源：[https://openai.com/index/nonlinear-computation-in-deep-linear-networks/](https://openai.com/index/nonlinear-computation-in-deep-linear-networks/) `` ``` 1x = tf.placeholder(dtype=tf.float32, shape=[batch_size,784]) 2y = tf.placeholder(dtype=tf.float32, shape=[batch_size,10]) 34w1 = tf.Variable(np.random.normal(scale=np.sqrt(2./784),size=[784,512]).astype(np.float32)) 5b1 = tf.Variable(np.zeros(512,dtype=np.float32)) 6w2 = tf.Variable(np.random