喀拉斯邦神经网络的权重倾销混乱

Question

I created a simple 2-layer network, one hidden layer. I am dumping the weights from the middle layer to visualize what the hidden neurons are learning. I am using

weights = model.layers[0].get_weights() 

When I look at the weights structure I get:

So len(weights) = 2 , len(weights[0]) = 500 , len(weights[1]) = 100 .

I want to create an array m of size (500,100) , so that m.shape = (500,100) . I tried numpy.reshape(weights, 500, 100) , zip(weights[0], weights[1]) , then, by chance, I wrote numpy.array(weights[0]) and this came back with shape (500,100) .

Can someone explain why?

Answer 1

The Keras tensors work differently, they are n-dimensional lists. To illustrate the concept consider the list:

>>> list=[[[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3]],[1,2,3]]

Here, the first element in list contains n-length elements and second list can also be an n-length elements. When you do:

>>> len(list)

Output is:

2( which is 2 in your case)

Also,

>>> len(list[0])

5(which is 500 in your case)

>>> len(list[1])

3(which is 100 in your case)

But when you try to convert to array:

>>> np.array(list[0]).shape

The answer is:

(5, 3) (which is 500,100 in your case)

This is because you are having an n-length list element inside your list[0] (which is weights[0] in your case). So when I asked you to return

len(weights[0][0]) 

it returned:

100

because it contains 100 length elements in that list and 500 such elements in it. Now, if you are wondering what does each 100 values mean, so they are corressponding weights of the connections ie

weights[0][0] = weights between first input to all 100 hidden neurons