循环神经网络的简单实现: import tensorflow as tf x=[1,2] state=[0.0,0.0] w_cell_state=np.array([[0.1,0.2],[0.3,0.4]]) w_cell_input=np.array([0.5,0.6]) b_cell=np.array([0.1,-0.1]) w_output=np.array([1.0,2.0]) b_output=0.1 for i in range(len(x)): before_a=np.dot(s
一:vanilla RNN 使用机器学习技术处理输入为基于时间的序列或者可以转化为基于时间的序列的问题时,我们可以对每个时间步采用递归公式,如下,We can process a sequence of vector x by applying a recurrence formula at every time step: ht = fW( ht-1,xt ) 其中xt 是在第t个时间步的输入(input vector at time step t):ht 是新状态量(new state),
http://cs231n.github.io/neural-networks-1 https://arxiv.org/pdf/1603.07285.pdf https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/ Applied Deep Learning - Part 1: Artificial Neural Ne