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GCNN.py
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GCNN.py
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#
from lib import models, dataLoading
import tensorflow as tf
import matplotlib.pyplot as plt
# # 1 Load Data.
# train_test_dataset(None)
# training, validation, test data.
# [vertices_geometry, vertices_fourier, adjacencies, labels]
# train_dataset,val_dataset,test_dataset=load_data('./data/test_data_batch_mini.json')
# train_dataset, val_dataset, test_dataset = input_data.load_data('./data/testExtra0725r.json', dataSeparation=[0.6, 0.2, 0.2]) #
train_dataset, val_dataset, test_dataset = dataLoading.load_data('./lib/data/test20ri.json', dataSeparation=[0.8, 0.18, 0.02]) # test228r
# Number of samples.
n_train = train_dataset[4]
# Number of classes.
n_class = len(set(train_dataset[2]))
# GeometryDescriptors or Fourier
i_type = 0
# print the Graph structure.
if True:
print('Graph structure: {0}x{0} Graph with {1} dimensionaal vecter.'.format(train_dataset[1].shape[1], train_dataset[0].shape[2]))
print('labels set: {}'.format(set(train_dataset[2])))
print(' train dataset:')
print(' vertices : {0}'.format(train_dataset[0].shape))
print(' adjacencies: {0}'.format(train_dataset[1].shape))
print(' labels : {0}'.format(train_dataset[2].shape))
print(' validation dataset:')
print(' vertices : {0}'.format(val_dataset[0].shape))
print(' adjacencies: {0}'.format(val_dataset[1].shape))
print(' labels : {0}'.format(val_dataset[2].shape))
print(' test dataset:')
print(' vertices : {0}'.format(test_dataset[0].shape))
print(' adjacencies: {0}'.format(test_dataset[1].shape))
print(' labels : {0}'.format(test_dataset[2].shape))
# # 2 Graph Convolution.
params = dict()
# The dimensions of vectives.
params['Vs'] = train_dataset[0].shape[2]
# The size of adjacencies (Number of vectices).
params['As'] = train_dataset[1].shape[1]
# The parameters of convolation and pooling layer. Two layers
params['Fs'] = [16, 16, 16, 16, 16]
params['Ks'] = [3, 3, 3, 3, 3]
params['Ps'] = [1, 1, 1, 1, 1]
# The parameters of full connection layer.
params['Cs'] = [16, n_class]
params['filter'] = 'monomial'
params['brelu'] = 'b1relu'
params['pool'] = 'm1pool'
params['num_epochs'] = 150
params['batch_size'] = 100
params['decay_steps'] = n_train / params['batch_size']
params['eval_frequency'] = 50
# Hyper-parameters.
params['regularization'] = 5e-4
params['dropout'] = 0.5
params['learning_rate'] = 0.001
params['decay_rate'] = 0.95
params['momentum'] = 0
params['dir_name'] = 'buildings'
# momentum,learning_rate = [0.9, 0.01]
# momentum,learning_rate = [0, 0.002, 0.003, 0.001]
# # 3 TRAINING AND EVALUTING NETWORK
# We often want to monitor:
# 1) The convergence, i.e. the training loss and the classification accuracy on the validation set.
# 2) The performance, i.e. the classification accuracy on the testing set (to be compared with the training set accuracy to spot overfitting).
# DIFINE THE MODEL COLLECTION
# multi train networks.
# The `model_perf` class in [models.py](models.py) can be used to compactly evaluate multiple models.
model_perf = models.model_perf()
if True:
# Test depth of network structures.
for i in [1,2,3,4,5,6,7,8]:
params['Fs'] = [16]*i
params['Ks'] = [3] *i
params['Ps'] = [1] *i
name = 'depth={}'.format(i)
params['dir_name'] = name
model_perf.test(models.gcnn(**params), name, params, train_dataset, val_dataset, test_dataset)
model_perf.show()
# Test input variable important
if False:
# DEFINE THE CNN ARCHITECTURE
params['Fs'] = [16, 16, 16, 16, 16]
params['Ks'] = [3, 3, 3, 3, 3]
params['Ps'] = [1, 1, 1, 1, 1]
name = 'newfs_24'
import copy
# GET THE DIMENSION OF INPUT VARIABLES
dim_variables = train_dataset[0].shape[2]
for i in range(0, dim_variables):
# Only variable i
params['Vs'] = 1
filtered_list = [i]
train_copy, val_copy, test_copy = copy.deepcopy(train_dataset), copy.deepcopy(val_dataset), copy.deepcopy(test_dataset)
train_copy[0] = train_dataset[0][:,:,filtered_list]
val_copy[0] = val_dataset[0][:,:,filtered_list]
test_copy[0] = test_dataset[0][:,:,filtered_list]
params['dir_name'] = name + '_one_' + str(i).zfill(2)
model_perf.test(models.gcnn(**params), name, params, i_type, train_copy, val_copy, test_copy)
# Except variable i
params['Vs'] = dim_variables - 1
filtered_list = [j for j in range(dim_variables)]
del filtered_list[i]
train_copy[0] = train_dataset[0][:,:,filtered_list]
val_copy[0] = val_dataset[0][:,:,filtered_list]
test_copy[0] = test_dataset[0][:,:,filtered_list]
params['dir_name'] = name + '_exc_' + str(i).zfill(2)
model_perf.test(models.gcnn(**params), name, params, i_type, train_copy, val_copy, test_copy)
del train_copy, val_copy, test_copy
model_perf.show()
# Test input variable important
if False:
# DEFINE THE CNN ARCHITECTURE
params['Fs'] = [16, 16, 16, 16, 16]
params['Ks'] = [3, 3, 3, 3, 3]
params['Ps'] = [1, 1, 1, 1, 1]
name = 'newfs_21'
params['Vs'] = 21
filtered_list = [i for i in range(0,21)]
import copy
train_copy, val_copy, test_copy = copy.deepcopy(train_dataset), copy.deepcopy(val_dataset), copy.deepcopy(test_dataset)
train_copy[0] = train_dataset[0][:,:,filtered_list]
val_copy[0] = val_dataset[0][:,:,filtered_list]
test_copy[0] = test_dataset[0][:,:,filtered_list]
for i in [1,2,3,4,5,6]:
params['Ks'] = [i, i, i, i, i]
name = 'newfs_24_MST3_k={}'.format(i)
params['dir_name'] = name
model_perf.test(models.gcnn(**params), name, params, i_type, train_copy, val_copy, test_copy)
del train_copy, val_copy, test_copy
model_perf.show()
if False:
# DEFINE THE CNN ARCHITECTURE
name = 'oldfs_25_3'
params['dir_name'] = name
model_perf.test(models.gcnn(**params), name, params, i_type, train_dataset, val_dataset, test_dataset)
model_perf.show()
# Test graph structure and neighbors.
if False:
# For DT or MST model
for i in [1,2,3,4,5,6]:
params['Fs'] = [16, 16, 16, 16, 16]
params['Ks'] = [i, i, i, i, i]
params['Ps'] = [1, 1, 1, 1, 1]
name = 'newfs_24_MST2_k={}'.format(i)
params['dir_name'] = name
model_perf.test(models.gcnn(**params), name, params, i_type, train_dataset, val_dataset, test_dataset)
model_perf.show()
if False:
if True:
params['Fs'] = [16, 16, 16, 16, 16]
params['Ks'] = [3, 3, 3, 3, 3]
params['Ps'] = [1, 1, 1, 1, 1]
name = 'newfeatures_24'
params['dir_name'] = name
model_perf.test(models.gcnn(**params), name, params, i_type, train_dataset, val_dataset, test_dataset)
if False:
params['Fs'] = [16, 16, 16, 16, 16]
params['Ks'] = [3, 3, 3, 3, 3]
params['Ps'] = [1, 1, 1, 1, 1]
name = 'Convolutions_5_16_normalizing'
params['dir_name'] = name
model_perf.test(models.gcnn(**params), name, params, i_type, train_dataset, val_dataset, test_dataset)
if False:
# test the order K.
for i in [1,2,3,4,5,6]:
params['Fs'] = [16, 16, 16, 16, 16]
params['Ks'] = [i, i, i, i, i]
params['Ps'] = [1, 1, 1, 1, 1]
name = 'MST_k_2={}'.format(i)
params['dir_name'] = name
model_perf.test(models.gcnn(**params), name, params, i_type, train_dataset, val_dataset, test_dataset)
if False:
# test the order K.
for i in [1,2,3,4,5,6]:
params['Fs'] = [16, 16, 16, 16, 16]
params['Ks'] = [i, i, i, i, i]
params['Ps'] = [1, 1, 1, 1, 1]
name = 'DT_k={}'.format(i)
params['dir_name'] = name
model_perf.test(models.gcnn(**params), name, params, i_type, train_dataset, val_dataset, test_dataset)
if False:
# test input features.
for i in range(0,12):
if i==1:continue
i_type = i
params['Vs'] = train_dataset[i_type].shape[2]
params['As'] = train_dataset[2].shape[1]
params['Fs'] = [24, 24, 24, 24]
params['Ks'] = [3, 3, 3, 3]
params['Ps'] = [1, 1, 1, 1]
name = 'Convolutions_features2={}'.format(i)
params['dir_name'] = name
model_perf.test(models.gcnn(**params), name, params, i_type, train_dataset, val_dataset, test_dataset)
if False:
# test the order K.
for i in [1,2,3,4,5,6]:
params['Fs'] = [24, 24, 24, 24]
params['Ks'] = [i, i, i, i]
params['Ps'] = [1, 1, 1, 1]
name = 'Convolutions_k={}'.format(i)
params['dir_name'] = name
model_perf.test(models.gcnn(**params), name, params, i_type, train_dataset, val_dataset, test_dataset)
# The following models will be tested at night.
if False:
params['Fs'] = [16, 16]
params['Ks'] = [3, 3]
params['Ps'] = [1, 1]
for rate in [1]:
name = 'test_{}'.format(rate)
params['dir_name'] = name
model_perf.test(models.gcnn(**params), name, params, i_type, train_dataset, val_dataset, test_dataset)
if False:
params['Fs'] = [12, 12, 12]
params['Ks'] = [3, 3, 3]
params['Ps'] = [1, 1, 1]
for rate in [1, 2]:
name = 'Convolutions_3_12_{}_{}'.format(rate, 0.001)
params['dir_name'] = name
model_perf.test(models.gcnn(**params), name, params, i_type, train_dataset, val_dataset, test_dataset)
if False:
params['Fs'] = [16, 16, 16]
params['Ks'] = [3, 3, 3]
params['Ps'] = [1, 1, 1]
for rate in [3, 4]:
name = 'Convolutions_3_16_{}_{}'.format(rate, 0.001)
params['dir_name'] = name
model_perf.test(models.gcnn(**params), name, params, i_type, train_dataset, val_dataset, test_dataset)
if False:
params['Fs'] = [24, 24, 24, 24, 24, 24, 24, 24, 24, 24]
params['Ks'] = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
params['Ps'] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
for rate in [3]:
name = 'Convolutions_10_24_test{}'.format(rate)
params['dir_name'] = name
model_perf.test(models.gcnn(**params), name, params, i_type, train_dataset, val_dataset, test_dataset)
if False:
params['Fs'] = [36, 36, 36, 36, 36, 36, 36, 36, 36, 36]
params['Ks'] = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
params['Ps'] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
for i_type in [0, 1]:
params['Vs'] = train_dataset[i_type].shape[2]
name = 'Fourier_Geometry{}'.format(i_type)
params['dir_name'] = name
model_perf.test(models.gcnn(**params), name, params, i_type, train_dataset, val_dataset, test_dataset)
if False:
params['Fs'] = [16, 16, 16, 16]
params['Ks'] = [3, 3, 3, 3]
params['Ps'] = [1, 1, 1, 1]
for rate in [1, 2]:
name = 'Convolutions_4_16_{}_{}'.format(rate, 0.001)
params['dir_name'] = name
model_perf.test(models.gcnn(**params), name, params, i_type, train_dataset, val_dataset, test_dataset)
if False:
params['Fs'] = [36, 36, 36, 36, 36]
params['Ks'] = [3, 3, 3, 3, 3]
params['Ps'] = [1, 1, 1, 1, 1]
for rate in [6]:
name = 'Convolutions_5_36_test{}'.format(rate)
params['dir_name'] = name
model_perf.test(models.gcnn(**params), name, params, i_type, train_dataset, val_dataset, test_dataset)
model_perf.show()