def Gan3DTrain(discriminator,
generator,
datapath,
EventsperFile,
nEvents,
WeightsDir,
pklfile,
resultfile,
mod=0,
nb_epochs=30,
batch_size=128,
latent_size=200,
gen_weight=6,
aux_weight=0.2,
ecal_weight=0.1,
lr=0.001,
rho=0.9,
decay=0.0,
g_weights='params_generator_epoch_',
d_weights='params_discriminator_epoch_',
xscale=1,
analysis=False,
energies=[]):
start_init = time.time()
verbose = False
particle = 'Ele'
f = [0.9, 0.1]
print('[INFO] Building discriminator')
#discriminator.summary()
discriminator.compile(optimizer=RMSprop(),
loss=[
'binary_crossentropy',
'mean_absolute_percentage_error',
'mean_absolute_percentage_error'
],
loss_weights=[gen_weight, aux_weight, ecal_weight])
# build the generator
print('[INFO] Building generator')
#generator.summary()
generator.compile(optimizer=RMSprop(), loss='binary_crossentropy')
# build combined Model
latent = Input(shape=(latent_size, ), name='combined_z')
fake_image = generator(latent)
discriminator.trainable = False
fake, aux, ecal = discriminator(fake_image)
combined = Model(input=[latent],
output=[fake, aux, ecal],
name='combined_model')
combined.compile(
#optimizer=Adam(lr=adam_lr, beta_1=adam_beta_1),
optimizer=RMSprop(),
loss=[
'binary_crossentropy', 'mean_absolute_percentage_error',
'mean_absolute_percentage_error'
],
loss_weights=[gen_weight, aux_weight, ecal_weight])
# Getting Data
Trainfiles, Testfiles = gan.DivideFiles(datapath,
nEvents=nEvents,
EventsperFile=EventsperFile,
datasetnames=["ECAL"],
Particles=[particle])
print('The total data was divided in {} Train files and {} Test files'.
format(len(Trainfiles), len(Testfiles)))
nb_test = int(nEvents * f[1])
#Read test data into a single array
for index, dtest in enumerate(Testfiles):
if index == 0:
X_test, Y_test, ecal_test = GetprocData(dtest, xscale=xscale)
else:
if X_test.shape[0] < nb_test:
X_temp, Y_temp, ecal_temp = GetprocData(dtest, xscale=xscale)
X_test = np.concatenate((X_test, X_temp))
Y_test = np.concatenate((Y_test, Y_temp))
ecal_test = np.concatenate((ecal_test, ecal_temp))
X_test, Y_test, ecal_test = X_test[:
nb_test], Y_test[:
nb_test], ecal_test[:
nb_test]
nb_train = int(nEvents * f[0]) #
total_batches = int(nb_train / batch_size)
print(
'In this experiment {} events will be used for training as {}batches'.
format(nb_train, total_batches))
print('{} events will be used for Testing'.format(nb_test))
train_history = defaultdict(list)
test_history = defaultdict(list)
analysis_history = defaultdict(list)
init_time = time.time() - start_init
print('Initialization time is {} seconds'.format(init_time))
for epoch in range(nb_epochs):
epoch_start = time.time()
print('Epoch {} of {}'.format(epoch + 1, nb_epochs))
X_train, Y_train, ecal_train = GetprocData(Trainfiles[0],
xscale=xscale)
nb_file = 1
nb_batches = int(X_train.shape[0] / batch_size)
if verbose:
progress_bar = Progbar(target=total_batches)
epoch_gen_loss = []
epoch_disc_loss = []
file_index = 0
for index in np.arange(total_batches):
if verbose:
progress_bar.update(index)
else:
if index % 100 == 0:
print('processed {}/{} batches'.format(
index + 1, total_batches))
loaded_data = X_train.shape[0]
used_data = file_index * batch_size
if (loaded_data - used_data) < batch_size + 1 and (
nb_file < len(Trainfiles)):
X_temp, Y_temp, ecal_temp = GetprocData(Trainfiles[nb_file],
xscale=xscale)
print("\nData file loaded..........", Trainfiles[nb_file])
nb_file += 1
X_left = X_train[(file_index * batch_size):]
Y_left = Y_train[(file_index * batch_size):]
ecal_left = ecal_train[(file_index * batch_size):]
X_train = np.concatenate((X_left, X_temp))
Y_train = np.concatenate((Y_left, Y_temp))
ecal_train = np.concatenate((ecal_left, ecal_temp))
nb_batches = int(X_train.shape[0] / batch_size)
print("{} batches loaded..........".format(nb_batches))
file_index = 0
image_batch = X_train[(file_index * batch_size):(file_index + 1) *
batch_size]
energy_batch = Y_train[(file_index * batch_size):(file_index + 1) *
batch_size]
ecal_batch = ecal_train[(file_index *
batch_size):(file_index + 1) * batch_size]
file_index += 1
noise = np.random.normal(0, 1, (batch_size, latent_size))
sampled_energies = np.random.uniform(0.1, 5, (batch_size, 1))
generator_ip = np.multiply(sampled_energies, noise)
#ecal sum from fit
ecal_ip = gan.GetEcalFit(sampled_energies, particle, mod, xscale)
generated_images = generator.predict(generator_ip, verbose=0)
real_batch_loss = discriminator.train_on_batch(
image_batch,
[gan.BitFlip(np.ones(batch_size)), energy_batch, ecal_batch])
fake_batch_loss = discriminator.train_on_batch(
generated_images,
[gan.BitFlip(np.zeros(batch_size)), sampled_energies, ecal_ip])
epoch_disc_loss.append([
(a + b) / 2 for a, b in zip(real_batch_loss, fake_batch_loss)
])
trick = np.ones(batch_size)
gen_losses = []
for _ in np.arange(2):
noise = np.random.normal(0, 1, (batch_size, latent_size))
sampled_energies = np.random.uniform(0.1, 5, (batch_size, 1))
generator_ip = np.multiply(sampled_energies, noise)
ecal_ip = gan.GetEcalFit(sampled_energies, particle, mod,
xscale)
gen_losses.append(
combined.train_on_batch(
[generator_ip],
[trick,
sampled_energies.reshape((-1, 1)), ecal_ip]))
epoch_gen_loss.append([(a + b) / 2 for a, b in zip(*gen_losses)])
print('The training took {} seconds.'.format(time.time() -
epoch_start))
print('\nTesting for epoch {}:'.format(epoch + 1))
test_start = time.time()
noise = np.random.normal(0.1, 1, (nb_test, latent_size))
sampled_energies = np.random.uniform(0.1, 5, (nb_test, 1))
generator_ip = np.multiply(sampled_energies, noise)
generated_images = generator.predict(generator_ip,
verbose=False,
batch_size=batch_size)
ecal_ip = gan.GetEcalFit(sampled_energies, particle, mod, xscale)
sampled_energies = np.squeeze(sampled_energies, axis=(1, ))
X = np.concatenate((X_test, generated_images))
y = np.array([1] * nb_test + [0] * nb_test)
ecal = np.concatenate((ecal_test, ecal_ip))
aux_y = np.concatenate((Y_test, sampled_energies), axis=0)
discriminator_test_loss = discriminator.evaluate(X, [y, aux_y, ecal],
verbose=False,
batch_size=batch_size)
discriminator_train_loss = np.mean(np.array(epoch_disc_loss), axis=0)
noise = np.random.normal(0.1, 1, (2 * nb_test, latent_size))
sampled_energies = np.random.uniform(0.1, 5, (2 * nb_test, 1))
generator_ip = np.multiply(sampled_energies, noise)
ecal_ip = gan.GetEcalFit(sampled_energies, particle, mod, xscale)
trick = np.ones(2 * nb_test)
generator_test_loss = combined.evaluate(
generator_ip,
[trick, sampled_energies.reshape((-1, 1)), ecal_ip],
verbose=False,
batch_size=batch_size)
generator_train_loss = np.mean(np.array(epoch_gen_loss), axis=0)
train_history['generator'].append(generator_train_loss)
train_history['discriminator'].append(discriminator_train_loss)
test_history['generator'].append(generator_test_loss)
test_history['discriminator'].append(discriminator_test_loss)
print('{0:<22s} | {1:4s} | {2:15s} | {3:5s}| {4:5s}'.format(
'component', *discriminator.metrics_names))
print('-' * 65)
ROW_FMT = '{0:<22s} | {1:<4.2f} | {2:<15.2f} | {3:<5.2f}| {4:<5.2f}'
print(
ROW_FMT.format('generator (train)',
*train_history['generator'][-1]))
print(
ROW_FMT.format('generator (test)', *test_history['generator'][-1]))
print(
ROW_FMT.format('discriminator (train)',
*train_history['discriminator'][-1]))
print(
ROW_FMT.format('discriminator (test)',
*test_history['discriminator'][-1]))
# save weights every epoch
generator.save_weights(WeightsDir +
'/{0}{1:03d}.hdf5'.format(g_weights, epoch),
overwrite=True)
discriminator.save_weights(WeightsDir +
'/{0}{1:03d}.hdf5'.format(d_weights, epoch),
overwrite=True)
print(
"The Testing for {} epoch took {} seconds. Weights are saved in {}"
.format(epoch,
time.time() - test_start, WeightsDir))
pickle.dump({
'train': train_history,
'test': test_history
}, open(pklfile, 'wb'))
if analysis:
var = gan.sortEnergy([X_test, Y_test], ecal_test, energies, ang=0)
result = gan.OptAnalysisShort(var,
generated_images,
energies,
ang=0)
print('Analysing............')
# All of the results correspond to mean relative errors on different quantities
analysis_history['total'].append(result[0])
analysis_history['energy'].append(result[1])
analysis_history['moment'].append(result[2])
print('Result = ', result)
pickle.dump({'results': analysis_history}, open(resultfile, 'wb'))
generator = generator(latent_size)
discriminator = discriminator()
print('[INFO] Building discriminator')
discriminator.summary()
#discriminator.load_weights('veganweights/params_discriminator_epoch_019.hdf5')
# Add Horovod Distributed Optimizer.
opt = hvd.DistributedOptimizer(RMSprop())
discriminator.compile(
#optimizer=Adam(lr=adam_lr, beta_1=adam_beta_1),
optimizer=opt,
loss=[
'binary_crossentropy', 'mean_absolute_percentage_error',
'mean_absolute_percentage_error'
],
loss_weights=[6, 2, 1]
#loss=['binary_crossentropy', 'kullback_leibler_divergence']
)
# build the generator
print('[INFO] Building generator')
generator.summary()
#generator.load_weights('veganweights/params_generator_epoch_019.hdf5')
generator.compile(
#optimizer=Adam(lr=adam_lr, beta_1=adam_beta_1),
optimizer=opt,
loss='binary_crossentropy')
latent = Input(shape=(latent_size, ), name='combined_z')