Chebnet understanding

Convolution

convolution in spectral domain is just a frequency filter.

let the graph Laplacian is $n * n$ .

\begin{matrix} y = U H U^{'} x = U g_{θ} (λ) U^{'} x = g_{θ} (L) x \end{matrix}

but to learn a filter's complicity is $O (n)$ . (there are $n$ free parameters)

the problem is how to make it faster.

we need parametrization of the filter.

we use Chebyshev polynomial to approximate it.

\begin{matrix} g_{θ} (λ) = d i a g (θ) \to \sum_{k = 0}^{K - 1} θ_{k} Λ^{k} \to \sum_{k = 0}^{K - 1} θ_{k} T_{k} (\tilde{Λ}) \\ Λ = d i a g (λ) \\ g_{θ} (L) \to U g_{θ} (λ) U^{'} = \sum_{k = 0}^{K - 1} θ_{k} T_{k} (\tilde{L}) \\ t h e n y = g_{θ} (L) x \\ l e t \bar{x_{k}} = T_{k} (\tilde{L}) x \\ y = \bar{X} θ \end{matrix}

due to the property of Chebyshev:

\begin{matrix} \bar{x_{k}} = 2 \tilde{L} \bar{x_{k - 1}} - \bar{x_{k - 2}} \\ \tilde{L} = 2 L / λ_{m a x} - I \end{matrix}

so we avoided computing eigenvalues and Fourier basis $U$ , and just use $L$ and $λ_{m a x}$ to recursively calculate $y$ from $x$ .

time complexity is from $O (n^{2})$ to $O (K | E |)$

Pooling

pooling is clustering or cutting for a graph.

graph clustering is NP-hard.

we need multi-level clustering: Graclus, which also need not calculating eigenvalues.

Graclus
- coarsening
- base-clustering: kernel k-means or spectral clustering
- refinement: kernel k-means

Then we pool it by adding fake nodes to build a balanced binary tree.

Finally, we feed it to a Fully connected Layer for Output.

Codes

graph

grid(m, dtype=np.float32)
'''
return a meshgrid of [m*m, 2] in [0,1] (uniformly divided by m)
'''

distance_*(z, k=4, metirc = "euclidean")
'''
input
  z is a N*M matrix of N examples in dimension M.
  k is the kNN parameter.
output
  d: N*k matrix, each row is the closest k distance.
  idx: N*k matrix, each row is the closest k index.
'''

adjacency(dist, idx)
'''
input
  output of dist_*()
output
  adjacency matrix of N*N.
  in fact it is a **Mutral** K-NN graph.
'''

replace_random_edges(A, noise_level)
'''
just add random noise.
'''

laplacian(W, normalized=True)
'''
input
  Adjacency matrix N*N
output
  graph laplacian
'''

lmax(L, normalized=True)
'''
compute Lmax for Chebyshev approximation.
if not normalized, only calc the largest Eigenvalue.
'''

fourier(L, algo='eigh', k=1)
'''
return sorted lamb,U
in fact, not used in standard cnn_graph.
'''

plot_spectrum(L, algo="eig")
'''
input: L is a list of Laplacians.
output
  plot of lamb values.
'''

rescale_L(L, lmax=2)
'''
scale L into [-1, 1], for chebyshev approx.
\tilde L = 2*L/lmax - I
'''

chebyshev(L, X, K)
'''
input
  L: N*N graph laplacian, need to be normalized and rescaled ?
  X: N*M matrix
  K: int, max order
output
  Xt: K*N*M matrix. Xt[k] is the k order chebyshev 
'''

lanczos(L, X, K)
'''

'''

coarsening

metis(W, levels, rid=None)
'''
input
  W: N*N adjacency matrix.
  levels: to coarsen.
  rid: a permutation list of N, if None, set to random.
output
  graphs: #levels*1 vector of multi-level Adjacency matrix. graphs[0] is W. 
  Supernode' weight is sum of links between its child nodes.
  parents: #(levels-1)*1 vector, cluster_id for each graph.
'''

metis_one_level(rr,cc,vv,rid,weights)
'''
input
  rr,cc,vv: ordered non-zero entries of W(N*N).
  rid: as in metis.
  weights: N*1 vector, degree of each node.
output
  cluster_id: N*1 vector. each node's label of supernode.
'''

compute_perm(parents)
'''
input: output of metis.
output: reordered permutation to satisfy a binary tree.
  by finding parents and adding fake nodes, we build the unique tree from a $parents list.

assert (compute_perm([np.array([4,1,1,2,2,3,0,0,3]),np.array([2,1,0,1,0])])
        == [[3,4,0,9,1,2,5,8,6,7,10,11],[2,4,1,3,0,5],[0,1,2]])
'''

perm_adjacency(A, indices)
'''
input
  A: adjacency matrix
  indices: eg.compute_perms(parents)[0]
output
  A: adjacency after adding fake nodes, and sorted by indices.
'''

coarsen(A, levels, self_connections=False)
'''
input
  A, levels : as in metis
  self_connections: whether to use self_connection(diag of Adjacency), unimplemented ?
output
  graphs: as in metis, but sorted and fake nodes added.
  perms[0]: nodes' order of graph[0]
'''


perm_data(X, indices)
'''
input
  X: dataset, N*M
  indices: perm[0], returned by coarsen()
output
  Xnew: dataset sorted and fake nodes added.
'''

models

# tensorflow models
class base_model:
    predict(data)
    evaluate(data, labels)
    fit(train_Data, train_labels, val_data, val_labels)
    get_var(name)
    build_graph(M_0)
    inference(data, dropout)
    probabilities(logits)
    predition(logits)
    loss(logits, labels, regularization)
    training(loss, lr, ds, ...)


class cgcnn(base_model):
    # chebyshev approx
    __init__(self, **params)
    '''
    L: normalized graph Laplacian
    F: number of graph conv filters, eg.[32, 64]
    K: polynomial orders, eg.[20, 20]
    p: pooling size eg.[4, 2]
    M: output dim of FC layers, eg.[512, y.max()+1]

    dir_name='' : output directory

    filter='chebyshev5' :
    brelu='b1relu' :
    pool='mpool1' :
    num_epochs=20 :
    learning_rate=0.1 :
    decay_rate=0.95 :
    decay_steps=None :  eg. n_train/batch_size
    momentum=0.9 :
    regularization=0 :
    dropout=0 :
    batch_size=100 :
    eval_frequenct=200 : eg.30*epoch

    ###
    the structure of the NN is defined as:
    for i in F,P,K
      * conv of F[i] out_channels
          the chebyshev poly approx order is K[i]
      * pool of P[i] pool_size
    for i in M
      * fc of M[i] out_dims
          (usually the last of M is n_Class)

    '''


# for some examples of parameters:
common = {}
common['dir_name']       = 'mnist/'
common['num_epochs']     = 20
common['batch_size']     = 100
common['decay_steps']    = mnist.train.num_examples / common['batch_size']
common['eval_frequency'] = 30 * common['num_epochs']
common['brelu']          = 'b1relu'
common['pool']           = 'mpool1'
C = max(mnist.train.labels) + 1  # number of classes
model_perf = utils.model_perf()

if True:
    name = 'softmax'
    params = common.copy()
    params['dir_name'] += name
    params['regularization'] = 5e-4
    params['dropout']        = 1
    params['learning_rate']  = 0.02
    params['decay_rate']     = 0.95
    params['momentum']       = 0.9
    params['F']              = []
    params['K']              = []
    params['p']              = []
    params['M']              = [C]
    model_perf.test(models.cgcnn(L, **params), name, params,
                    train_data, train_labels, val_data, val_labels, test_data, test_labels)
    '''
    NN architecture
    input: M_0 = 1040
    layer 1: logits (softmax)
      representation: M_1 = 10
      weights: M_0 * M_1 = 1040 * 10 = 10400
          biases: M_1 = 10
    '''

# Common hyper-parameters for networks with one convolutional layer.
common['regularization'] = 0
common['dropout']        = 1
common['learning_rate']  = 0.02
common['decay_rate']     = 0.95
common['momentum']       = 0.9
common['F']              = [10]
common['K']              = [20]
common['p']              = [1]
common['M']              = [C]
if True:
    name = 'cgconv_softmax'
    params = common.copy()
    params['dir_name'] += name
    params['filter'] = 'chebyshev5'
    model_perf.test(models.cgcnn(L, **params), name, params,
                    train_data, train_labels, val_data, val_labels, test_data, test_labels)

    '''
    NN architecture
    input: M_0 = 1040
    layer 1: cgconv1
      representation: M_0 * F_1 / p_1 = 1040 * 10 / 1 = 10400
     weights: F_0 * F_1 * K_1 = 1 * 10 * 20 = 200
     biases: F_1 = 10
    layer 2: logits (softmax)
     representation: M_2 = 10
     weights: M_1 * M_2 = 10400 * 10 = 104000
      biases: M_2 = 10
    '''

# Common hyper-parameters for LeNet5-like networks.
common['regularization'] = 5e-4
common['dropout']        = 0.5
common['learning_rate']  = 0.02  # 0.03 in the paper but sgconv_sgconv_fc_softmax has difficulty to converge
common['decay_rate']     = 0.95
common['momentum']       = 0.9
common['F']              = [32, 64]
common['K']              = [25, 25]
common['p']              = [4, 4]
common['M']              = [512, C]
if True:
    name = 'cgconv_cgconv_fc_softmax'  # 'Chebyshev'
    params = common.copy()
    params['dir_name'] += name
    params['filter'] = 'chebyshev5'
    model_perf.test(models.cgcnn(L, **params), name, params,
                    train_data, train_labels, val_data, val_labels, test_data, test_labels)
'''
NN architecture
  input: M_0 = 1008
  layer 1: cgconv1
    representation: M_0 * F_1 / p_1 = 1008 * 32 / 4 = 8064
    weights: F_0 * F_1 * K_1 = 1 * 32 * 25 = 800
    biases: F_1 = 32
  layer 2: cgconv2
    representation: M_1 * F_2 / p_2 = 252 * 64 / 4 = 4032
    weights: F_1 * F_2 * K_2 = 32 * 64 * 25 = 51200
    biases: F_2 = 64
  layer 3: fc1
    representation: M_3 = 512
    weights: M_2 * M_3 = 4032 * 512 = 2064384
    biases: M_3 = 512
  layer 4: logits (softmax)
    representation: M_4 = 10
    weights: M_3 * M_4 = 512 * 10 = 5120
    biases: M_4 = 10
'''

utils

grid_search(params, grid_params, train_data, train_labels, val_data, val_labels, test_data, test_labels, model)
'''

'''

class model_perf:
    test()
    show()
    '''
    used for testing many structures and show their performance together.
    '''

Chebnet understanding

Convolution

Pooling

Codes

Example