谁能解释一下以下python代码的输出:

from theano import tensor as T
from theano import function, shared

a, b = T.dmatrices('a', 'b')
diff = a - b
abs_diff = abs(diff)
diff_squared = diff ** 2

f = function([a, b], [diff, abs_diff, diff_squared])

print f([[1, 1], [1, 1]], [[0, 1], [2, 3]])

测试功能

print f( [ [1,1],[1,1] ], 
         [ [0,1],[2,3] ])  

Output:  [ [[ 1.,  0.], [-1., -2.]], 
           [[ 1.,  0.], [ 1.,  2.]], 
           [[ 1.,  0.], [ 1.,  4.]]]

我正在尝试为Numpy编写的神经网络实现softmax函数.设h是给定信号i的softmax值.

我一直在努力实现softmax激活函数的偏导数.

我目前陷入困境,随着训练的进行,所有偏导数都接近0.我用这个优秀的答案交叉引用了我的数学,但我的数学似乎没有成功.

import numpy as np
def softmax_function( signal, derivative=False ):
    # Calculate activation signal
    e_x = np.exp( signal )
    signal = e_x / np.sum( e_x, axis = 1, keepdims = True )

    if derivative:
        # Return the partial derivation of the activation function
        return np.multiply( signal, 1 - signal ) + sum(
            # handle the off-diagonal values
            - signal * np.roll( signal, i, axis = 1 )
            for i in xrange(1, signal.shape[1] …

关于LSTM网络的结构

如果我想创建LSTM网络来解决时间序列预测,我应该如何构建神经网络的隐藏层？

• A LSTM memory block代表一个隐藏层,层中的所有节点都用cells？表示？
• 每个隐藏层应该由众多组成,LSTM memory blocks这些块的集合将形成一个层？

图示:

以这种方式:

或者像这样？