For each layer in neural networks (pytorch), how many biases should be there?
I have a simple model in pytorch. 

model = Network()


It's details are:

Network(
  (hidden): Linear(in_features=784, out_features=256, bias=True)
  (output): Linear(in_features=256, out_features=10, bias=True)
  (sigmoid): Sigmoid()
  (softmax): Softmax(dim=1)
)


There are 3 neurons' layers in total. 1 input(786 neurons), 1 hidden(256 neurons) and 1 output (10 neurons). Therefore there'll be two weight layers. So there must be two biases (simply two floating point numbers) for both weight layers right? (correct me if i am wrong).  

Now after initializing my network i was curious about the two bias values. So i wanted to check the bias value of my hidden layer so i wrote:

model.hidden.bias


And what i got as the result was not i expected! I actually expected one value! And this is what i actually got: 

tensor([-1.6868e-02, -3.5661e-02,  1.2489e-02, -2.7880e-02,  1.4025e-02,
        -2.6085e-02,  1.2625e-02, -3.1748e-02,  5.0335e-03,  3.8031e-03,
        -3.1648e-02, -3.4881e-02, -2.0026e-02,  1.9728e-02,  6.2461e-03,
         9.3936e-04, -5.9270e-03, -2.7183e-02, -1.9850e-02, -3.5693e-02,
        -1.9393e-02,  2.6555e-02,  2.3482e-02,  2.1230e-02, -2.2175e-02,
        -2.4386e-02,  3.4848e-02, -2.6044e-02,  1.3575e-02,  9.4125e-03,
         3.0012e-02, -2.6078e-02,  7.1615e-05, -1.7061e-02,  6.6355e-03,
        -3.4966e-02,  2.9311e-02,  1.4060e-02, -2.5763e-02, -1.4020e-02,
         2.9852e-02, -7.9176e-03, -1.8396e-02,  1.6927e-02, -1.1001e-03,
         1.5595e-02,  1.2169e-02, -1.2275e-02, -2.9270e-03, -6.5685e-04,
        -2.4297e-02,  3.0048e-02,  2.9692e-03, -2.5398e-02,  2.9955e-03,
        -9.3653e-04, -1.2932e-02,  2.4232e-02, -3.5182e-02, -1.6163e-02,
         3.0025e-02,  3.1227e-02, -8.2498e-04,  2.7102e-02, -2.3830e-02,
        -3.4958e-02, -1.1886e-02,  1.6097e-02,  1.4579e-02, -2.6744e-02,
         1.1900e-02, -3.4855e-02, -4.2208e-03, -5.2035e-03,  1.7055e-02,
        -4.8580e-03,  3.4088e-03,  1.6923e-02,  3.5570e-04, -3.0478e-02,
         8.4647e-03,  2.5704e-02, -2.3255e-02,  6.9396e-03, -1.2521e-03,
        -9.4101e-03, -2.5798e-02, -1.4438e-03, -7.2684e-03,  3.5417e-02,
        -3.4388e-02,  1.3706e-02, -5.1430e-03,  1.6174e-02,  1.8135e-03,
        -2.9018e-02, -2.9083e-02,  7.4100e-03, -2.7758e-02,  2.4367e-02,
        -3.8350e-03,  9.4390e-03, -1.0844e-02,  1.6381e-02, -2.5268e-02,
         1.3553e-02, -1.0545e-02, -1.3782e-02,  2.8519e-02,  2.3630e-02,
        -1.9703e-02, -2.0147e-02, -1.0485e-02,  2.4637e-02,  1.9989e-02,
         5.6601e-03,  1.9121e-02, -1.5286e-02,  2.5996e-02, -2.9833e-02,
        -2.9458e-02,  2.3944e-02, -3.0107e-02, -1.2307e-02, -1.8419e-02,
         3.3551e-02,  1.2396e-02,  2.9356e-02,  3.3274e-02,  5.4677e-03,
         3.1715e-02,  1.3361e-02,  3.3042e-02,  2.7843e-03,  2.2837e-02,
        -3.4981e-02,  3.2355e-02, -2.7658e-03,  2.2184e-02, -2.0203e-02,
        -3.3264e-02, -3.4858e-02,  1.0820e-03, -1.4279e-02, -2.8041e-02,
         4.1962e-03,  2.4266e-02, -3.5704e-02, -2.6172e-02,  2.3335e-02,
         2.0657e-02, -3.0387e-03, -5.7096e-03, -1.1062e-02,  1.3450e-02,
        -3.3965e-02,  1.9623e-03, -2.0067e-02, -3.3858e-02, -2.1931e-02,
        -1.5414e-02,  2.4454e-02,  2.5668e-02, -1.1932e-02,  5.7540e-04,
         1.5130e-02,  1.3916e-02, -2.1521e-02, -3.0575e-02,  1.8841e-02,
        -2.3240e-02, -2.7297e-02, -3.2668e-02, -1.5544e-02, -5.9408e-03,
         3.0241e-02,  2.2039e-02, -2.4389e-02,  3.1703e-02,  3.5305e-02,
        -2.7501e-03,  2.0154e-02, -5.3489e-03,  1.4177e-02,  1.6829e-02,
         3.3066e-02, -1.3425e-02, -3.2565e-02,  6.5624e-03, -1.5681e-02,
         2.3047e-02,  6.5880e-03, -3.3803e-02,  2.3790e-02, -5.5061e-03,
         2.9413e-02,  1.2290e-02, -1.0958e-02,  1.2680e-03,  1.3343e-02,
         6.6689e-03, -2.2975e-03, -1.2068e-02,  1.6523e-02, -3.1612e-02,
        -1.7529e-02, -2.2220e-02, -1.4723e-02, -1.3495e-02, -5.1805e-03,
        -2.9620e-02,  3.0571e-02, -3.0999e-02,  3.3681e-03,  1.3579e-02,
         1.4837e-02,  1.5694e-02, -1.1178e-02,  4.6233e-03, -2.2583e-02,
        -3.5281e-03,  3.0918e-02,  2.6407e-02,  1.5822e-04, -3.0181e-03,
         8.6989e-03,  2.8998e-02, -1.5975e-02, -3.1574e-02, -1.5609e-02,
         1.0472e-02,  5.8976e-03,  7.0131e-03, -3.2047e-02,  2.6045e-02,
        -2.8882e-02, -2.2121e-02, -3.2960e-02,  1.8268e-02,  3.0984e-02,
         1.4824e-02,  3.0010e-02, -5.7523e-03, -2.0017e-02,  4.8700e-03,
         1.4997e-02, -1.4898e-02,  6.8572e-03,  9.7713e-03,  1.3410e-02,
         4.9619e-03,  3.1016e-02,  3.1240e-02, -3.0203e-02,  2.1435e-02,
         2.7331e-02], requires_grad=True)


Can someone explain to me this behaviour? Why did i get 256 values instead of one?

Edit1:

Here is my understanding of the layers: 
For a whole layer of neurons, a bias is just a single value. Am i right? But what i am seeing as the output about are 256 values ? why ? did pytorch assume that i have a bias with each neuron ? is that okay?


So first it's important to realize what's going on inside of one of these layers. When you write:

Linear(in_features=784, out_features=256, bias=True)


You are modeling a linear relationship between the input and the output. You're probably familiar with this from basic math:

Y = MX + B


However instead of a "slope" and a "y-intercept", you have a weights matrix and a bias term. This is still a linear relationship, but with matrices as our input and output. 

Y is our output, M is our weights matrix, X is our input, and B is our Bias. You define that the input is a (N x 784) matrix, and our output is a (N x 256) matrix (N is the number of samples). 

If you're familiar with matrix multiplication this means that our weights matrix is (784 X 256). The output of MX will be a (N x 256) matrix, so our bias term must also be (N x 256) for the MX + B to work out.

In general the number of values in the bias term will be the same as the number of out_features.
