'siam_k': 2,
'siam_ne': 20,
'spec_ne': 100,
'siam_lr': 1e-3,
'spec_lr': 1e-6,
'siam_patience': 10,
'spec_patience': 20,
'siam_drop': 0.1,
'spec_drop': 0.001,
'batch_size': 64,
'siam_reg': None,
'spec_reg': None ,
'siam_n': None,
'siamese_tot_pairs': 3200,
'arch': [
{'type': 'relu', 'size': 512},
{'type': 'relu', 'size': 512},
{'type': 'relu', 'size': 4},
],
'use_approx': False,
'use_all_data': True,
}
# preprocess dataset
data = get_data(params)
# run spectral net
x_spectralnet, y_spectralnet = run_net(data, params)
for x in train_motoric_indices
]),
axis=0)
eval_x_motoric = np.concatenate(tuple([
preprocessed_data[1][x - delay:x + 750 - delay]
for x in eval_motoric_indices
]),
axis=0)
eval_y_motoric = np.concatenate(tuple([
preprocessed_data[3][x - delay:x + 750 - delay]
for x in eval_motoric_indices
]),
axis=0)
else:
train_x_motoric = preprocessed_data[0]
train_y_motoric = preprocessed_data[2]
eval_x_motoric = preprocessed_data[1]
eval_y_motoric = preprocessed_data[3]
statistics.distances(
(train_x_motoric, eval_x_motoric, train_y_motoric, eval_y_motoric),
'takens_MI_only')
# Exploring data statistics
# Process into spectral-net format, create pairs for siamese network
data = get_data(
params,
(train_x_motoric, eval_x_motoric, train_y_motoric, eval_y_motoric))
# run spectral net
x_spectralnet, y_spectralnet = run_net(data, params)
'siam_n':
...,
'siamese_tot_pairs':
...,
'arch': [
{
'type': 'relu',
'size':...
},
{
'type': 'relu',
'size':...
},
{
'type': 'relu',
'size':...
},
],
'use_approx':
...,
}
# load dataset
x_train, x_test, y_train, y_test = load_new_dataset_data()
new_dataset_data = (x_train, x_test, y_train, y_test)
# preprocess dataset
data = get_data(params, new_dataset_data)
# run spectral net
x_spectralnet, y_spectralnet = run_net(data, params)
'''
Experiments on Caltech101-20 and NoisyMNIST.
'''
import os
import sys
# add directories in src/ to path
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__),'..')))
# set cuda
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
from applications.MvSCN import run_net
from core.Config import load_config
from core.data import get_data
# load config for NoisyMNIST
config = load_config('./config/noisymnist.yaml')
# load config for Caltech101-20
# config = load_config('./config/Caltech101-20.yaml')
# use pretrained SiameseNet.
config['siam_pre_train'] = True
# LOAD DATA
data_list = get_data(config)
# RUN EXPERIMENT
x_final_list, scores = run_net(data_list, config)
