# print ('opt iwae',np.mean(iwaes))
# print()
# print ('opt vae flex',np.mean(vaes_flex))
# print ('opt iwae flex',np.mean(iwaes_flex))
# print()
# VAE_train = test_vae(model=model, data_x=train_x[:n_data], batch_size=n_data, display=10, k=50)
# IW_train = test(model=model, data_x=train_x[:n_data], batch_size=n_data, display=10, k=50)
# print ('amortized VAE',VAE_train)
# print ('amortized IW',IW_train)
print()
AIS_train = test_ais(model=model, data_x=train_x[:n_data], batch_size=n_data, display=2, k=100, n_intermediate_dists=2000)
print ('AIS_train',AIS_train)
print()
print()
print ('AIS_train',AIS_train)
# print()
print ('opt vae flex',np.mean(vaes_flex))
# print()
print ('opt vae',np.mean(vaes))
# print()
print ('amortized VAE',VAE_train)
print()
data_x=train_x[:n_data],
batch_size=n_data,
display=10,
k=50)
IW_train = test(model=model,
data_x=train_x[:n_data],
batch_size=n_data,
display=10,
k=50)
print('amortized VAE', VAE_train)
print('amortized IW', IW_train)
print()
AIS_train = test_ais(model=model,
data_x=train_x[:n_data],
batch_size=n_data,
display=2,
k=50,
n_intermediate_dists=500)
print('AIS_train', AIS_train)
print()
print()
print('AIS_train', AIS_train)
# print()
print('opt vae flex', np.mean(vaes_flex))
# print()
print('opt vae', np.mean(vaes))
# print()
print('amortized VAE', VAE_train)
print()
IW_test = test(model=model,
data_x=test_x,
batch_size=batch_size_IW,
display=10,
k=k_IW)
print('IW_test', IW_test)
with open(experiment_log, "a") as myfile:
myfile.write('IW_test ' + str(IW_test) + '\n')
myfile.write('time' + str(time.time() - start_time) + '\n\n')
iw_test_list.append(IW_test)
print('\nTesting with AIS, Train set, B' + str(batch_size_AIS) + ' k' +
str(k_AIS) + ' intermediates' + str(n_intermediate_dists))
AIS_train = test_ais(model=model,
data_x=train_x,
batch_size=batch_size_AIS,
display=2,
k=k_AIS,
n_intermediate_dists=n_intermediate_dists)
print('AIS_train', AIS_train)
with open(experiment_log, "a") as myfile:
myfile.write('AIS_train ' + str(AIS_train) + '\n')
myfile.write('time' + str(time.time() - start_time) + '\n\n')
ais_train_list.append(AIS_train)
print('\nTesting with AIS, Test set, B' + str(batch_size_AIS) + ' k' +
str(k_AIS) + ' intermediates' + str(n_intermediate_dists))
AIS_test = test_ais(model=model,
data_x=test_x,
batch_size=batch_size_AIS,
display=2,
k=k_AIS,
print ('IW_train', IW_train)
with open(experiment_log, "a") as myfile:
myfile.write('IW_train '+ str(IW_train) +'\n')
myfile.write('time'+str(time.time()-start_time)+'\n\n')
print('\nTesting with IW, Test set, B'+str(batch_size_IW)+' k'+str(k_IW))
IW_test = test(model=model, data_x=test_x, batch_size=batch_size_IW, display=10, k=k_IW)
print ('IW_test', IW_test)
with open(experiment_log, "a") as myfile:
myfile.write('IW_test '+ str(IW_test) +'\n')
myfile.write('time'+str(time.time()-start_time)+'\n\n')
print('\nTesting with AIS, Train set, B'+str(batch_size_AIS)+' k'+str(k_AIS)+' intermediates'+str(n_intermediate_dists))
AIS_train = test_ais(model=model, data_x=train_x, batch_size=batch_size_AIS, display=2, k=k_AIS, n_intermediate_dists=n_intermediate_dists)
print ('AIS_train', AIS_train)
with open(experiment_log, "a") as myfile:
myfile.write('AIS_train '+ str(AIS_train) +'\n')
myfile.write('time'+str(time.time()-start_time)+'\n\n')
print('\nTesting with AIS, Test set, B'+str(batch_size_AIS)+' k'+str(k_AIS)+' intermediates'+str(n_intermediate_dists))
AIS_test = test_ais(model=model, data_x=test_x, batch_size=batch_size_AIS, display=2, k=k_AIS, n_intermediate_dists=n_intermediate_dists)
print ('AIS_test', AIS_test)
with open(experiment_log, "a") as myfile:
myfile.write('AIS_test '+ str(AIS_test) +'\n\n')
myfile.write('time'+str(time.time()-start_time)+'\n\n')
# # log results
batch_size=batch_size,
display=100,
k=k_IW)
print('\nTesting with IW, Test set, B' + str(batch_size) + ' k' +
str(k_IW))
model.test(data_x=test_x, batch_size=batch_size, display=100, k=k_IW)
if eval_AIS:
k_AIS = 10
batch_size = 1000
n_intermediate_dists = 500
print('\nTesting with AIS, Train set[:10000], B' + str(batch_size) +
' k' + str(k_AIS) + ' intermediates' + str(n_intermediate_dists))
LL_ais_train = test_ais(model,
data_x=train_x[:10000],
batch_size=batch_size,
display=1,
k=k_AIS,
n_intermediate_dists=n_intermediate_dists)
print('\nTesting with AIS, Test set, B' + str(batch_size) + ' k' +
str(k_AIS) + ' intermediates' + str(n_intermediate_dists))
LL_ais_test = test_ais(model,
data_x=test_x,
batch_size=batch_size,
display=1,
k=k_AIS,
n_intermediate_dists=n_intermediate_dists)
k_IW = 5000
batch_size = 20
print('\nTesting with IW, Train set[:10000], B'+str(batch_size)+' k'+str(k_IW))
model.test(data_x=train_x[:10000], batch_size=batch_size, display=100, k=k_IW)
print('\nTesting with IW, Test set, B'+str(batch_size)+' k'+str(k_IW))
model.test(data_x=test_x, batch_size=batch_size, display=100, k=k_IW)
if eval_AIS:
k_AIS = 10
batch_size = 1000
n_intermediate_dists = 500
print('\nTesting with AIS, Train set[:10000], B'+str(batch_size)+' k'+str(k_AIS)+' intermediates'+str(n_intermediate_dists))
LL_ais_train = test_ais(model, data_x=train_x[:10000], batch_size=batch_size, display=1, k=k_AIS, n_intermediate_dists=n_intermediate_dists)
print('\nTesting with AIS, Test set, B'+str(batch_size)+' k'+str(k_AIS)+' intermediates'+str(n_intermediate_dists))
LL_ais_test = test_ais(model, data_x=test_x, batch_size=batch_size, display=1, k=k_AIS, n_intermediate_dists=n_intermediate_dists)