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February 23, 2020 17:55
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import torch | |
import torch.optim as optim | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from umap.umap_ import fuzzy_simplicial_set, find_ab_params | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from sklearn.manifold import SpectralEmbedding | |
from scipy.sparse import save_npz, load_npz | |
import random | |
from functools import partial | |
MIN_DIST=0.1 | |
SPREAD=1.0 | |
EPS = 1e-12 | |
N_EPOCHS = 50 | |
NEG_RATE = 5 | |
BATCH_SIZE = 4096 * NEG_RATE | |
D_GRAD_CLIP = 19006880743424 | |
DATA_NPZ_PATH = 'mnist_70000.npz' | |
def get_activation(act): | |
if act == 'lrelu': | |
return nn.LeakyReLU(0.2, inplace=True) | |
elif act == 'relu': | |
return nn.ReLU(inplace=True) | |
raise Exception('unsupported activation function') | |
class FCEncoder(nn.Module): | |
def __init__(self, dim, num_layers=3, act='lrelu'): | |
super(FCEncoder, self).__init__() | |
self.dim = dim | |
self.num_layers = num_layers | |
self.act = partial(get_activation, act=act) | |
hidden_dim = 256 | |
layers = [ | |
(nn.Linear(dim, hidden_dim*2)), | |
self.act(), | |
(nn.Linear(hidden_dim*2, hidden_dim)), | |
self.act(), | |
] | |
layers += [ | |
(nn.Linear(hidden_dim, hidden_dim)), | |
self.act(), | |
] * num_layers | |
layers += [ | |
(nn.Linear(hidden_dim, 2)), | |
] | |
self.net = nn.Sequential(*layers) | |
def forward(self, X): | |
return self.net(X) | |
def make_graph(P, n_epochs=-1): | |
graph = P.tocoo() | |
graph.sum_duplicates() | |
n_vertices = graph.shape[1] | |
if n_epochs <= 0: | |
# For smaller datasets we can use more epochs | |
if graph.shape[0] <= 10000: | |
n_epochs = 500 | |
else: | |
n_epochs = 200 | |
graph.data[graph.data < (graph.data.max() / float(n_epochs))] = 0.0 | |
graph.eliminate_zeros() | |
return graph | |
def make_epochs_per_sample(weights, n_epochs): | |
result = -1.0 * np.ones(weights.shape[0], dtype=np.float64) | |
n_samples = n_epochs * (weights / weights.max()) | |
result[n_samples > 0] = float(n_epochs) / n_samples[n_samples > 0] | |
return result | |
def neg_squared_euc_dists(X): | |
sum_X = X.pow(2).sum(dim=1) | |
D = (-2 * X @ X.transpose(1, 0) + sum_X).transpose(1, 0) + sum_X | |
return -D | |
def w_tsne(Y, a, b): | |
distances = neg_squared_euc_dists(Y) | |
inv_distances = 1. / (1. - a * (distances)) #1 / (1+ad^2b) | |
return inv_distances | |
def KLD(P, Q): | |
return P * torch.log((P+EPS) / Q) | |
def CE(V, W): | |
return - V * torch.log(W + EPS) - (1 - V) * torch.log(1 - W + EPS) | |
def MXLK(P, w, gamma=7.0): | |
return P * torch.log(w + EPS) + gamma * (1 - P) * torch.log(1 - w + EPS) | |
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
print('Device:', device) | |
print('load data') | |
mnist = np.load(DATA_NPZ_PATH) | |
data = mnist['data'] | |
print('estimate a, b') | |
ua, ub = find_ab_params(SPREAD, MIN_DIST) | |
# ua, ub = 1.0, 1.0 | |
# ub = 1.0 | |
print('a:', ua, 'b:', ub) | |
print('calc V') | |
try: | |
V_csc = load_npz('V_csc.npz') | |
print('Use V cache') | |
except: | |
print('Use new V') | |
V_csc = fuzzy_simplicial_set(data, n_neighbors=15, | |
random_state=np.random.RandomState(42), metric='euclidean') | |
save_npz('V_csc', V_csc) | |
# V = torch.Tensor(V_csc.toarray()) | |
print('make_graph') | |
graph = make_graph(V_csc, N_EPOCHS) | |
print('make_epochs_per_sample') | |
epochs_per_sample = make_epochs_per_sample(graph.data, N_EPOCHS) | |
print('Trying to put X into GPU') | |
X = torch.from_numpy(data).float() | |
X = X.to(device) | |
# X = X.float() | |
print('Constructing NN') | |
encoder = FCEncoder(784, num_layers=5) | |
encoder = encoder.to(device) | |
encoder = encoder.float() | |
init_lr = 1e-3 | |
optimizer = optim.SGD(encoder.parameters(), lr=init_lr, weight_decay=0) | |
epochs_per_negative_sample = epochs_per_sample / NEG_RATE | |
epoch_of_next_negative_sample = epochs_per_negative_sample.copy() | |
epoch_of_next_sample = epochs_per_sample.copy() | |
head = graph.row | |
tail = graph.col | |
rnd_max_idx = X.shape[0] | |
print('optimizing...') | |
for epoch in range(1, N_EPOCHS): | |
batch_i = [] | |
batch_j = [] | |
batch_neg_i = [] | |
for i in range(epochs_per_sample.shape[0]): | |
if epoch_of_next_sample[i] <= epoch: | |
i_idx, j_idx = head[i], tail[i] | |
batch_i.append(i_idx) | |
batch_j.append(j_idx) | |
epoch_of_next_sample[i] += epochs_per_sample[i] | |
n_neg_samples = int( | |
(epoch - epoch_of_next_negative_sample[i]) | |
/ epochs_per_negative_sample[i] | |
) | |
for _ in range(n_neg_samples): | |
batch_neg_i.append(i_idx) | |
epoch_of_next_negative_sample[i] += ( | |
n_neg_samples * epochs_per_negative_sample[i] | |
) | |
batch_neg_j = torch.randint(0, rnd_max_idx, (len(batch_neg_i),)).tolist() | |
batch_r = torch.zeros(len(batch_i), dtype=torch.long).tolist() + torch.ones(len(batch_neg_i), dtype=torch.long).tolist() | |
batch_i += batch_neg_i | |
batch_j += batch_neg_j | |
rnd_perm = torch.randperm(len(batch_i)) | |
batch_i = torch.Tensor(batch_i).long()[rnd_perm] | |
batch_j = torch.Tensor(batch_j).long()[rnd_perm] | |
batch_r = torch.Tensor(batch_r).long()[rnd_perm] | |
for i in range(0, len(batch_i), BATCH_SIZE): | |
bi = batch_i[i:i+BATCH_SIZE] | |
bj = batch_j[i:i+BATCH_SIZE] | |
br = batch_r[i:i+BATCH_SIZE] | |
optimizer.zero_grad() | |
Y_bi = encoder(X[bi]) | |
Y_bj = encoder(X[bj]) | |
Y_bj[br==1] = Y_bj[br==1].detach() | |
d = (Y_bi - Y_bj).pow(2).sum(dim=1) | |
d.register_hook(lambda grad: grad.clamp(min=-D_GRAD_CLIP, max=D_GRAD_CLIP)) | |
dp = d.pow(ub) | |
w = (1/(1+ua*(dp))).clamp(min=0, max=1) | |
pw = w[br==0] | |
rw = w[br==1] | |
loss = - (torch.log(pw + EPS)).sum() | |
loss += - (torch.log(1 - rw + EPS)).sum() | |
loss.backward() | |
torch.nn.utils.clip_grad_value_(encoder.parameters(), 4) | |
optimizer.step() | |
with torch.no_grad(): | |
Y = encoder(X) | |
# w = w_tsne(Y, ua, ub).clamp(min=0, max=1) | |
# loss = CE(V, w).sum() | |
new_lr = (1 - epoch / N_EPOCHS) * init_lr | |
for param_group in optimizer.param_groups: | |
param_group['lr'] = new_lr | |
np.savez_compressed('umap_fast_nn_Y', Y=Y.detach().cpu().numpy()) | |
np.savez_compressed('umap_nn/{:04d}'.format(epoch), Y=Y.detach().cpu().numpy()) | |
print("{:04d}".format(epoch), "{:.7f}".format(new_lr), "{:.2f}".format(loss.mean().item())) | |
print('Done.') |
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UMAP version: '0.3.10'