def train_and_test(nb_epochs, weight, method, degree, random_seed, nc):
""" Runs the Bigan on the KDD dataset
Note:
Saves summaries on tensorboard. To display them, please use cmd line
tensorboard --logdir=model.training_logdir() --port=number
Args:
nb_epochs (int): number of epochs
weight (float, optional): weight for the anomaly score composition
method (str, optional): 'fm' for ``Feature Matching`` or "cross-e"
for ``cross entropy``, "efm" etc.
anomalous_label (int): int in range 0 to 10, is the class/digit
which is considered outlier
"""
logger = logging.getLogger("BiGAN.train.kdd.{}".format(method))
data.set_nc(nc)
print("get_shape", data.get_shape_input()[1])
# Placeholders
input_pl = tf.placeholder(tf.float32,
shape=data.get_shape_input(),
name="input")
is_training_pl = tf.placeholder(tf.bool, [], name='is_training_pl')
learning_rate = tf.placeholder(tf.float32, shape=(), name="lr_pl")
# Data
trainx, trainy = data.get_train()
trainx_copy = trainx.copy()
testx, testy = data.get_test()
# Parameters
starting_lr = network.learning_rate
batch_size = network.batch_size
latent_dim = network.latent_dim
ema_decay = 0.9999
rng = np.random.RandomState(RANDOM_SEED)
nr_batches_train = int(trainx.shape[0] / batch_size)
nr_batches_test = int(testx.shape[0] / batch_size)
logger.info('Building training graph...')
logger.warn("The BiGAN is training with the following parameters:")
display_parameters(batch_size, starting_lr, ema_decay, weight, method,
degree)
gen = network.decoder
enc = network.encoder
dis = network.discriminator
with tf.variable_scope('encoder_model'):
z_gen = enc(input_pl, is_training=is_training_pl)
with tf.variable_scope('generator_model'):
z = tf.random_normal([batch_size, latent_dim])
x_gen = gen(z, is_training=is_training_pl)
with tf.variable_scope('discriminator_model'):
l_encoder, inter_layer_inp = dis(z_gen,
input_pl,
is_training=is_training_pl)
l_generator, inter_layer_rct = dis(z,
x_gen,
is_training=is_training_pl,
reuse=True)
with tf.name_scope('loss_functions'):
# discriminator
loss_dis_enc = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.ones_like(l_encoder), logits=l_encoder))
loss_dis_gen = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.zeros_like(l_generator), logits=l_generator))
loss_discriminator = loss_dis_gen + loss_dis_enc
# generator
loss_generator = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.ones_like(l_generator), logits=l_generator))
# encoder
loss_encoder = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.zeros_like(l_encoder), logits=l_encoder))
with tf.name_scope('optimizers'):
# control op dependencies for batch norm and trainable variables
tvars = tf.trainable_variables()
dvars = [var for var in tvars if 'discriminator_model' in var.name]
gvars = [var for var in tvars if 'generator_model' in var.name]
evars = [var for var in tvars if 'encoder_model' in var.name]
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
update_ops_gen = [
x for x in update_ops if ('generator_model' in x.name)
]
update_ops_enc = [x for x in update_ops if ('encoder_model' in x.name)]
update_ops_dis = [
x for x in update_ops if ('discriminator_model' in x.name)
]
optimizer_dis = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=0.5,
name='dis_optimizer')
optimizer_gen = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=0.5,
name='gen_optimizer')
optimizer_enc = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=0.5,
name='enc_optimizer')
with tf.control_dependencies(update_ops_gen):
gen_op = optimizer_gen.minimize(loss_generator, var_list=gvars)
with tf.control_dependencies(update_ops_enc):
enc_op = optimizer_enc.minimize(loss_encoder, var_list=evars)
with tf.control_dependencies(update_ops_dis):
dis_op = optimizer_dis.minimize(loss_discriminator, var_list=dvars)
# Exponential Moving Average for estimation
dis_ema = tf.train.ExponentialMovingAverage(decay=ema_decay)
maintain_averages_op_dis = dis_ema.apply(dvars)
with tf.control_dependencies([dis_op]):
train_dis_op = tf.group(maintain_averages_op_dis)
gen_ema = tf.train.ExponentialMovingAverage(decay=ema_decay)
maintain_averages_op_gen = gen_ema.apply(gvars)
with tf.control_dependencies([gen_op]):
train_gen_op = tf.group(maintain_averages_op_gen)
enc_ema = tf.train.ExponentialMovingAverage(decay=ema_decay)
maintain_averages_op_enc = enc_ema.apply(evars)
with tf.control_dependencies([enc_op]):
train_enc_op = tf.group(maintain_averages_op_enc)
with tf.name_scope('summary'):
with tf.name_scope('dis_summary'):
tf.summary.scalar('loss_discriminator', loss_discriminator,
['dis'])
tf.summary.scalar('loss_dis_encoder', loss_dis_enc, ['dis'])
tf.summary.scalar('loss_dis_gen', loss_dis_gen, ['dis'])
with tf.name_scope('gen_summary'):
tf.summary.scalar('loss_generator', loss_generator, ['gen'])
tf.summary.scalar('loss_encoder', loss_encoder, ['gen'])
sum_op_dis = tf.summary.merge_all('dis')
sum_op_gen = tf.summary.merge_all('gen')
logger.info('Building testing graph...')
with tf.variable_scope('encoder_model'):
z_gen_ema = enc(input_pl,
is_training=is_training_pl,
getter=get_getter(enc_ema),
reuse=True)
with tf.variable_scope('generator_model'):
reconstruct_ema = gen(z_gen_ema,
is_training=is_training_pl,
getter=get_getter(gen_ema),
reuse=True)
with tf.variable_scope('discriminator_model'):
l_encoder_ema, inter_layer_inp_ema = dis(z_gen_ema,
input_pl,
is_training=is_training_pl,
getter=get_getter(dis_ema),
reuse=True)
l_generator_ema, inter_layer_rct_ema = dis(z_gen_ema,
reconstruct_ema,
is_training=is_training_pl,
getter=get_getter(dis_ema),
reuse=True)
with tf.name_scope('Testing'):
with tf.variable_scope('Reconstruction_loss'):
delta = input_pl - reconstruct_ema
delta_flat = tf.contrib.layers.flatten(delta)
gen_score = tf.norm(delta_flat,
ord=degree,
axis=1,
keep_dims=False,
name='epsilon')
with tf.variable_scope('Discriminator_loss'):
if method == "cross-e":
dis_score = tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.ones_like(l_generator_ema),
logits=l_generator_ema)
elif method == "fm":
fm = inter_layer_inp_ema - inter_layer_rct_ema
fm = tf.contrib.layers.flatten(fm)
dis_score = tf.norm(fm,
ord=degree,
axis=1,
keep_dims=False,
name='d_loss')
dis_score = tf.squeeze(dis_score)
with tf.variable_scope('Score'):
list_scores = (1 - weight) * gen_score + weight * dis_score
logdir = create_logdir(weight, method, random_seed)
sv = tf.train.Supervisor(logdir=logdir,
save_summaries_secs=None,
save_model_secs=120)
logger.info('Start training...')
with sv.managed_session() as sess:
logger.info('Initialization done')
writer = tf.summary.FileWriter(logdir, sess.graph)
train_batch = 0
epoch = 0
while not sv.should_stop() and epoch < nb_epochs:
lr = starting_lr
begin = time.time()
# construct randomly permuted minibatches
trainx = trainx[rng.permutation(
trainx.shape[0])] # shuffling dataset
trainx_copy = trainx_copy[rng.permutation(trainx.shape[0])]
train_loss_dis, train_loss_gen, train_loss_enc = [0, 0, 0]
# training
for t in range(nr_batches_train):
display_progression_epoch(t, nr_batches_train)
ran_from = t * batch_size
ran_to = (t + 1) * batch_size
# train discriminator
feed_dict = {
input_pl: trainx[ran_from:ran_to],
is_training_pl: True,
learning_rate: lr
}
_, ld, sm = sess.run(
[train_dis_op, loss_discriminator, sum_op_dis],
feed_dict=feed_dict)
train_loss_dis += ld
writer.add_summary(sm, train_batch)
# train generator and encoder
feed_dict = {
input_pl: trainx_copy[ran_from:ran_to],
is_training_pl: True,
learning_rate: lr
}
_, _, le, lg, sm = sess.run([
train_gen_op, train_enc_op, loss_encoder, loss_generator,
sum_op_gen
],
feed_dict=feed_dict)
train_loss_gen += lg
train_loss_enc += le
writer.add_summary(sm, train_batch)
train_batch += 1
train_loss_gen /= nr_batches_train
train_loss_enc /= nr_batches_train
train_loss_dis /= nr_batches_train
logger.info('Epoch terminated')
print(
"Epoch %d | time = %ds | loss gen = %.4f | loss enc = %.4f | loss dis = %.4f "
% (epoch, time.time() - begin, train_loss_gen, train_loss_enc,
train_loss_dis))
epoch += 1
logger.warn('Testing evaluation...')
inds = rng.permutation(testx.shape[0])
testx = testx[inds] # shuffling dataset
testy = testy[inds] # shuffling dataset
scores = []
inference_time = []
# Create scores
for t in range(nr_batches_test):
# construct randomly permuted minibatches
ran_from = t * batch_size
ran_to = (t + 1) * batch_size
begin_val_batch = time.time()
feed_dict = {
input_pl: testx[ran_from:ran_to],
is_training_pl: False
}
scores += sess.run(list_scores, feed_dict=feed_dict).tolist()
inference_time.append(time.time() - begin_val_batch)
logger.info('Testing : mean inference time is %.4f' %
(np.mean(inference_time)))
ran_from = nr_batches_test * batch_size
ran_to = (nr_batches_test + 1) * batch_size
size = testx[ran_from:ran_to].shape[0]
fill = np.ones([batch_size - size, data.get_shape_input()[1]])
batch = np.concatenate([testx[ran_from:ran_to], fill], axis=0)
feed_dict = {input_pl: batch, is_training_pl: False}
batch_score = sess.run(list_scores, feed_dict=feed_dict).tolist()
scores += batch_score[:size]
# Highest 80% are anomalous
per = np.percentile(scores, 80)
y_pred = scores.copy()
y_pred = np.array(y_pred)
inds = (y_pred < per)
inds_comp = (y_pred >= per)
y_pred[inds] = 0
y_pred[inds_comp] = 1
precision, recall, f1, _ = precision_recall_fscore_support(
testy, y_pred, average='binary')
print("Testing(%d) : Prec = %.4f | Rec = %.4f | F1 = %.4f " %
(nc, precision, recall, f1))
content = "Testing(%d) : Prec = %.4f | Rec = %.4f | F1 = %.4f " % (
nc, precision, recall, f1)
f1 = open('./output.txt', 'a')
f1.write(content + "\r\n")
def train_and_test(nb_epochs, weight, method, degree, random_seed):
""" Runs the Bigan on the KDD dataset
Note:
Saves summaries on tensorboard. To display them, please use cmd line
tensorboard --logdir=model.training_logdir() --port=number
Args:
nb_epochs (int): number of epochs
weight (float, optional): weight for the anomaly score composition
method (str, optional): 'fm' for ``Feature Matching`` or "cross-e"
for ``cross entropy``, "efm" etc.
anomalous_label (int): int in range 0 to 10, is the class/digit
which is considered outlier
"""
logger = logging.getLogger("GAN.train.kdd.{}".format(method))
# Placeholders
input_pl = tf.placeholder(tf.float32,
shape=data.get_shape_input(),
name="input")
is_training_pl = tf.placeholder(tf.bool, [], name='is_training_pl')
learning_rate = tf.placeholder(tf.float32, shape=(), name="lr_pl")
# Data
trainx, trainy = data.get_train()
trainx_copy = trainx.copy()
testx, testy = data.get_test()
# Parameters
starting_lr = network.learning_rate
batch_size = network.batch_size
latent_dim = network.latent_dim
ema_decay = 0.9999
rng = np.random.RandomState(RANDOM_SEED)
nr_batches_train = int(trainx.shape[0] / batch_size)
nr_batches_test = int(testx.shape[0] / batch_size)
logger.info('Building training graph...')
logger.warn("The GAN is training with the following parameters:")
display_parameters(batch_size, starting_lr, ema_decay, weight, method,
degree)
gen = network.generator
dis = network.discriminator
# Sample noise from random normal distribution
random_z = tf.random_normal([batch_size, latent_dim],
mean=0.0,
stddev=1.0,
name='random_z')
# Generate images with generator
generator = gen(random_z, is_training=is_training_pl)
# Pass real and fake images into discriminator separately
real_d, inter_layer_real = dis(input_pl, is_training=is_training_pl)
fake_d, inter_layer_fake = dis(generator,
is_training=is_training_pl,
reuse=True)
with tf.name_scope('loss_functions'):
# Calculate seperate losses for discriminator with real and fake images
real_discriminator_loss = tf.losses.sigmoid_cross_entropy(
tf.constant(1, shape=[batch_size]),
real_d,
scope='real_discriminator_loss')
fake_discriminator_loss = tf.losses.sigmoid_cross_entropy(
tf.constant(0, shape=[batch_size]),
fake_d,
scope='fake_discriminator_loss')
# Add discriminator losses
discriminator_loss = real_discriminator_loss + fake_discriminator_loss
# Calculate loss for generator by flipping label on discriminator output
generator_loss = tf.losses.sigmoid_cross_entropy(
tf.constant(1, shape=[batch_size]), fake_d, scope='generator_loss')
with tf.name_scope('optimizers'):
# control op dependencies for batch norm and trainable variables
dvars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='discriminator')
gvars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='generator')
update_ops_gen = tf.get_collection(tf.GraphKeys.UPDATE_OPS,
scope='generator')
update_ops_dis = tf.get_collection(tf.GraphKeys.UPDATE_OPS,
scope='discriminator')
optimizer_dis = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=0.5,
name='dis_optimizer')
optimizer_gen = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=0.5,
name='gen_optimizer')
with tf.control_dependencies(
update_ops_gen): # attached op for moving average batch norm
gen_op = optimizer_gen.minimize(generator_loss, var_list=gvars)
with tf.control_dependencies(update_ops_dis):
dis_op = optimizer_dis.minimize(discriminator_loss, var_list=dvars)
dis_ema = tf.train.ExponentialMovingAverage(decay=ema_decay)
maintain_averages_op_dis = dis_ema.apply(dvars)
with tf.control_dependencies([dis_op]):
train_dis_op = tf.group(maintain_averages_op_dis)
gen_ema = tf.train.ExponentialMovingAverage(decay=ema_decay)
maintain_averages_op_gen = gen_ema.apply(gvars)
with tf.control_dependencies([gen_op]):
train_gen_op = tf.group(maintain_averages_op_gen)
with tf.name_scope('training_summary'):
with tf.name_scope('dis_summary'):
tf.summary.scalar('real_discriminator_loss',
real_discriminator_loss, ['dis'])
tf.summary.scalar('fake_discriminator_loss',
fake_discriminator_loss, ['dis'])
tf.summary.scalar('discriminator_loss', discriminator_loss,
['dis'])
with tf.name_scope('gen_summary'):
tf.summary.scalar('loss_generator', generator_loss, ['gen'])
sum_op_dis = tf.summary.merge_all('dis')
sum_op_gen = tf.summary.merge_all('gen')
logger.info('Building testing graph...')
with tf.variable_scope("latent_variable"):
z_optim = tf.get_variable(
name='z_optim',
shape=[batch_size, latent_dim],
initializer=tf.truncated_normal_initializer())
reinit_z = z_optim.initializer
# EMA
generator_ema = gen(z_optim,
is_training=is_training_pl,
getter=get_getter(gen_ema),
reuse=True)
# Pass real and fake images into discriminator separately
real_d_ema, inter_layer_real_ema = dis(input_pl,
is_training=is_training_pl,
getter=get_getter(gen_ema),
reuse=True)
fake_d_ema, inter_layer_fake_ema = dis(generator_ema,
is_training=is_training_pl,
getter=get_getter(gen_ema),
reuse=True)
with tf.name_scope('error_loss'):
delta = input_pl - generator_ema
delta_flat = tf.contrib.layers.flatten(delta)
gen_score = tf.norm(delta_flat,
ord=degree,
axis=1,
keep_dims=False,
name='epsilon')
with tf.variable_scope('Discriminator_loss'):
if method == "cross-e":
dis_score = tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.ones_like(fake_d_ema), logits=fake_d_ema)
elif method == "fm":
fm = inter_layer_real_ema - inter_layer_fake_ema
fm = tf.contrib.layers.flatten(fm)
dis_score = tf.norm(fm,
ord=degree,
axis=1,
keep_dims=False,
name='d_loss')
dis_score = tf.squeeze(dis_score)
with tf.variable_scope('Total_loss'):
loss = (1 - weight) * gen_score + weight * dis_score
with tf.variable_scope("Test_learning_rate"):
step = tf.Variable(0, trainable=False)
boundaries = [300, 400]
values = [0.01, 0.001, 0.0005]
learning_rate_invert = tf.train.piecewise_constant(
step, boundaries, values)
reinit_lr = tf.variables_initializer(
tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope="Test_learning_rate"))
with tf.name_scope('Test_optimizer'):
optimizer = tf.train.AdamOptimizer(learning_rate_invert).minimize(
loss, global_step=step, var_list=[z_optim], name='optimizer')
reinit_optim = tf.variables_initializer(
tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='Test_optimizer'))
reinit_test_graph_op = [reinit_z, reinit_lr, reinit_optim]
with tf.name_scope("Scores"):
list_scores = loss
logdir = create_logdir(method, weight, random_seed)
sv = tf.train.Supervisor(logdir=logdir,
save_summaries_secs=None,
save_model_secs=120)
logger.info('Start training...')
with sv.managed_session() as sess:
logger.info('Initialization done')
writer = tf.summary.FileWriter(logdir, sess.graph)
train_batch = 0
epoch = 0
while not sv.should_stop() and epoch < nb_epochs:
lr = starting_lr
begin = time.time()
trainx = trainx[rng.permutation(
trainx.shape[0])] # shuffling unl dataset
trainx_copy = trainx_copy[rng.permutation(trainx.shape[0])]
train_loss_dis, train_loss_gen = [0, 0]
# training
for t in range(nr_batches_train):
display_progression_epoch(t, nr_batches_train)
# construct randomly permuted minibatches
ran_from = t * batch_size
ran_to = (t + 1) * batch_size
# train discriminator
feed_dict = {
input_pl: trainx[ran_from:ran_to],
is_training_pl: True,
learning_rate: lr
}
_, ld, sm = sess.run(
[train_dis_op, discriminator_loss, sum_op_dis],
feed_dict=feed_dict)
train_loss_dis += ld
writer.add_summary(sm, train_batch)
# train generator
feed_dict = {
input_pl: trainx_copy[ran_from:ran_to],
is_training_pl: True,
learning_rate: lr
}
_, lg, sm = sess.run(
[train_gen_op, generator_loss, sum_op_gen],
feed_dict=feed_dict)
train_loss_gen += lg
writer.add_summary(sm, train_batch)
train_batch += 1
train_loss_gen /= nr_batches_train
train_loss_dis /= nr_batches_train
logger.info('Epoch terminated')
print(
"Epoch %d | time = %ds | loss gen = %.4f | loss dis = %.4f " %
(epoch, time.time() - begin, train_loss_gen, train_loss_dis))
epoch += 1
logger.warn('Testing evaluation...')
inds = rng.permutation(testx.shape[0])
testx = testx[inds] # shuffling unl dataset
testy = testy[inds]
scores = []
inference_time = []
# Testing
for t in range(nr_batches_test):
# construct randomly permuted minibatches
ran_from = t * batch_size
ran_to = (t + 1) * batch_size
begin_val_batch = time.time()
# invert the gan
feed_dict = {
input_pl: testx[ran_from:ran_to],
is_training_pl: False
}
for step in range(STEPS_NUMBER):
_ = sess.run(optimizer, feed_dict=feed_dict)
scores += sess.run(list_scores, feed_dict=feed_dict).tolist()
inference_time.append(time.time() - begin_val_batch)
sess.run(reinit_test_graph_op)
logger.info('Testing : mean inference time is %.4f' %
(np.mean(inference_time)))
ran_from = nr_batches_test * batch_size
ran_to = (nr_batches_test + 1) * batch_size
size = testx[ran_from:ran_to].shape[0]
fill = np.ones([batch_size - size, 121])
batch = np.concatenate([testx[ran_from:ran_to], fill], axis=0)
feed_dict = {input_pl: batch, is_training_pl: False}
for step in range(STEPS_NUMBER):
_ = sess.run(optimizer, feed_dict=feed_dict)
batch_score = sess.run(list_scores, feed_dict=feed_dict).tolist()
scores += batch_score[:size]
per = np.percentile(scores, 80)
y_pred = scores.copy()
y_pred = np.array(y_pred)
inds = (y_pred < per)
inds_comp = (y_pred >= per)
y_pred[inds] = 0
y_pred[inds_comp] = 1
precision, recall, f1, _ = precision_recall_fscore_support(
testy, y_pred, average='binary')
print("Testing : Prec = %.4f | Rec = %.4f | F1 = %.4f " %
(precision, recall, f1))
def train_and_test(nb_epochs, weight, method, degree, random_seed):
""" Runs the Bigan on the KDD dataset
Note:
Saves summaries on tensorboard. To display them, please use cmd line
tensorboard --logdir=model.training_logdir() --port=number
Args:
nb_epochs (int): number of epochs
weight (float, optional): weight for the anomaly score composition
method (str, optional): 'fm' for ``Feature Matching`` or "cross-e"
for ``cross entropy``, "efm" etc.
anomalous_label (int): int in range 0 to 10, is the class/digit
which is considered outlier
"""
logger = logging.getLogger("BiGAN.train.kdd.{}".format(method))
# Placeholders
input_pl = tf.placeholder(tf.float32,
shape=data.get_shape_input(),
name="input")
is_training_pl = tf.placeholder(tf.bool, [], name='is_training_pl')
learning_rate = tf.placeholder(tf.float32, shape=(), name="lr_pl")
# Data
trainx, trainy = data.get_train()
trainx_copy = trainx.copy()
testx, testy = data.get_test()
# Parameters
starting_lr_gen = network.learning_rate_gen
starting_lr_gen_mine = network.learning_rate_gen_mine
starting_lr = network.learning_rate
batch_size = network.batch_size
latent_dim = network.latent_dim
ema_decay = 0.9999
rng = np.random.RandomState(RANDOM_SEED)
nr_batches_train = int(trainx.shape[0] / batch_size)
nr_batches_test = int(testx.shape[0] / batch_size)
logger.info('Building training graph...')
logger.warn("The BiGAN is training with the following parameters:")
display_parameters(batch_size, starting_lr, ema_decay, weight, method,
degree)
gen = network.decoder
enc = network.encoder
dis = network.discriminator
with tf.variable_scope('encoder_model'):
z_gen = enc(input_pl, is_training=is_training_pl)
with tf.variable_scope('generator_model'):
z = tf.random_normal([batch_size, latent_dim])
x_gen = gen(z, is_training=is_training_pl)
with tf.variable_scope('discriminator_model'):
l_encoder, inter_layer_inp = dis(z_gen,
input_pl,
is_training=is_training_pl)
l_generator, inter_layer_rct = dis(z,
x_gen,
is_training=is_training_pl,
reuse=True)
with tf.name_scope('loss_functions'):
# discriminator
loss_dis_enc = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.ones_like(l_encoder), logits=l_encoder))
loss_dis_gen = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.zeros_like(l_generator), logits=l_generator))
loss_discriminator = loss_dis_gen + loss_dis_enc
# generator
# loss_generator1 = tf.reduce_mean(tf.keras.losses.MSLE(input_pl,x_gen))
loss_generator1 = tf.reduce_mean(tf.keras.losses.MSE(input_pl, x_gen))
# loss_generator1 = tf.reduce_mean(tf.keras.losses.KLD(input_pl,x_gen))
# loss_generator1 = tf.reduce_mean(-(x_gen.log_prob(input_pl))) #layerlari probability yapip bunu yazsam olacak mi acaba!!!!!??????
loss_generator = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.ones_like(l_generator), logits=l_generator))
#loss_generator = loss_generator = loss_generator1
# encoder
loss_encoder = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.zeros_like(l_encoder), logits=l_encoder))
with tf.name_scope('optimizers'):
# control op dependencies for batch norm and trainable variables
tvars = tf.trainable_variables()
dvars = [var for var in tvars if 'discriminator_model' in var.name]
gvars = [var for var in tvars if 'generator_model' in var.name]
evars = [var for var in tvars if 'encoder_model' in var.name]
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
update_ops_gen = [
x for x in update_ops if ('generator_model' in x.name)
]
update_ops_enc = [x for x in update_ops if ('encoder_model' in x.name)]
update_ops_dis = [
x for x in update_ops if ('discriminator_model' in x.name)
]
optimizer_dis = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=0.5,
name='dis_optimizer')
optimizer_gen = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=0.5,
name='gen_optimizer')
optimizer_enc = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=0.5,
name='enc_optimizer')
optimizer_gen1 = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=0.5,
name='gen_optimizer1')
with tf.control_dependencies(update_ops_gen):
gen_op = optimizer_gen.minimize(loss_generator, var_list=gvars)
with tf.control_dependencies(update_ops_gen):
gen_op1 = optimizer_gen1.minimize(loss_generator1, var_list=gvars)
with tf.control_dependencies(update_ops_enc):
enc_op = optimizer_enc.minimize(loss_encoder, var_list=evars)
with tf.control_dependencies(update_ops_dis):
dis_op = optimizer_dis.minimize(loss_discriminator, var_list=dvars)
# Exponential Moving Average for estimation
dis_ema = tf.train.ExponentialMovingAverage(decay=ema_decay)
maintain_averages_op_dis = dis_ema.apply(dvars)
with tf.control_dependencies([dis_op]):
train_dis_op = tf.group(maintain_averages_op_dis)
gen_ema = tf.train.ExponentialMovingAverage(decay=ema_decay)
maintain_averages_op_gen = gen_ema.apply(gvars)
with tf.control_dependencies([gen_op]):
train_gen_op = tf.group(maintain_averages_op_gen)
with tf.control_dependencies([gen_op1]):
train_gen_op1 = tf.group(maintain_averages_op_gen)
enc_ema = tf.train.ExponentialMovingAverage(decay=ema_decay)
maintain_averages_op_enc = enc_ema.apply(evars)
with tf.control_dependencies([enc_op]):
train_enc_op = tf.group(maintain_averages_op_enc)
with tf.name_scope('summary'):
with tf.name_scope('dis_summary'):
tf.summary.scalar('loss_discriminator', loss_discriminator,
['dis'])
tf.summary.scalar('loss_dis_encoder', loss_dis_enc, ['dis'])
tf.summary.scalar('loss_dis_gen', loss_dis_gen, ['dis'])
with tf.name_scope('gen_summary'):
tf.summary.scalar('loss_generator', loss_generator, ['gen'])
tf.summary.scalar('loss_generator1', loss_generator1, ['gen1'])
tf.summary.scalar('loss_encoder', loss_encoder, ['gen'])
sum_op_dis = tf.summary.merge_all('dis')
sum_op_gen = tf.summary.merge_all('gen')
sum_op_gen1 = tf.summary.merge_all('gen1')
logger.info('Building testing graph...')
with tf.variable_scope('encoder_model'):
z_gen_ema = enc(input_pl,
is_training=is_training_pl,
getter=get_getter(enc_ema),
reuse=True)
with tf.variable_scope('generator_model'):
reconstruct_ema = gen(z_gen_ema,
is_training=is_training_pl,
getter=get_getter(gen_ema),
reuse=True)
with tf.variable_scope('discriminator_model'):
l_encoder_ema, inter_layer_inp_ema = dis(z_gen_ema,
input_pl,
is_training=is_training_pl,
getter=get_getter(dis_ema),
reuse=True)
l_generator_ema, inter_layer_rct_ema = dis(z_gen_ema,
reconstruct_ema,
is_training=is_training_pl,
getter=get_getter(dis_ema),
reuse=True)
with tf.name_scope('Testing'):
with tf.variable_scope('Reconstruction_loss'):
delta = input_pl - reconstruct_ema
delta_flat = tf.contrib.layers.flatten(delta)
gen_score = tf.norm(delta_flat,
ord=degree,
axis=1,
keep_dims=False,
name='epsilon')
with tf.variable_scope('Discriminator_loss'):
if method == "cross-e":
dis_score = tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.ones_like(l_generator_ema),
logits=l_generator_ema)
elif method == "fm":
fm = inter_layer_inp_ema - inter_layer_rct_ema
fm = tf.contrib.layers.flatten(fm)
dis_score = tf.norm(fm,
ord=degree,
axis=1,
keep_dims=False,
name='d_loss')
dis_score = tf.squeeze(dis_score)
with tf.variable_scope('Score'):
list_scores = (
1 - weight
) * gen_score + weight * dis_score # bastaki 0 carpanini kaldir ve dis scoredaki 1 yerine weight yaz
logdir = create_logdir(weight, method, random_seed)
sv = tf.train.Supervisor(logdir=logdir,
save_summaries_secs=None,
save_model_secs=120)
logger.info('Start training...')
with sv.managed_session() as sess:
logger.info('Initialization done')
writer = tf.summary.FileWriter(logdir, sess.graph)
train_batch = 0
epoch = 0
lr_gen_mine = starting_lr_gen_mine
tt = []
xx = time.time()
while not sv.should_stop() and epoch < nb_epochs:
lr = starting_lr
lr_gen = starting_lr_gen
lr_gen_mine = starting_lr_gen_mine
begin = time.time()
# construct randomly permuted minibatches
trainx = trainx[rng.permutation(
trainx.shape[0])] # shuffling dataset
trainx_copy = trainx_copy[rng.permutation(trainx.shape[0])]
train_loss_dis, train_loss_gen, train_loss_enc, train_loss_gen1 = [
0, 0, 0, 0
]
print('newloss running')
# training
zz = []
if epoch == 0:
xy = time.time()
trainx = trainx[rng.permutation(
trainx.shape[0])] # shuffling dataset
trainx_copy = trainx_copy[rng.permutation(trainx.shape[0])]
train_loss_gen1 = 0
for k in range(100):
kk = time.time()
for t in range(nr_batches_train):
#display_progression_epoch(t, nr_batches_train)
ran_from = t * batch_size
ran_to = (t + 1) * batch_size
#
# pretrain generator
feed_dict = {
input_pl: trainx_copy[ran_from:ran_to],
is_training_pl: True,
learning_rate: lr_gen_mine
}
_, lg1, sm1 = sess.run(
[train_gen_op1, loss_generator1, sum_op_gen1],
feed_dict=feed_dict)
zz.extend([time.time() - kk])
print(k)
print('mean :', np.mean(zz))
print('std :', np.std(zz))
print(time.time() - xy)
if epoch > 0:
for t in range(nr_batches_train):
display_progression_epoch(t, nr_batches_train)
ran_from = t * batch_size
ran_to = (t + 1) * batch_size
# train discriminator
feed_dict = {
input_pl: trainx[ran_from:ran_to],
is_training_pl: True,
learning_rate: lr
}
_, ld, sm = sess.run(
[train_dis_op, loss_discriminator, sum_op_dis],
feed_dict=feed_dict)
train_loss_dis += ld
writer.add_summary(sm, train_batch)
# train generator and encoder
feed_dict = {
input_pl: trainx_copy[ran_from:ran_to],
is_training_pl: True,
learning_rate: lr_gen
}
_, _, le, lg, sm = sess.run([
train_gen_op, train_enc_op, loss_encoder,
loss_generator, sum_op_gen
],
feed_dict=feed_dict)
# mine
feed_dict = {
input_pl: trainx_copy[ran_from:ran_to],
is_training_pl: True,
learning_rate: lr_gen_mine
}
_, lg1, sm1 = sess.run(
[train_gen_op1, loss_generator1, sum_op_gen1],
feed_dict=feed_dict)
#lg1 = 0
train_loss_gen1 += lg1
train_loss_gen += lg
train_loss_enc += le
writer.add_summary(sm, train_batch)
train_batch += 1
train_loss_gen /= nr_batches_train
train_loss_gen1 /= nr_batches_train
train_loss_enc /= nr_batches_train
train_loss_dis /= nr_batches_train
print(
"Epoch %d | time = %.4fs | loss gen = %.4f | loss gen1 = %.4f| loss enc = %.4f | loss dis = %.4f "
% (epoch, time.time() - begin, train_loss_gen, train_loss_gen1,
train_loss_enc, train_loss_dis))
tt.extend([time.time() - begin])
epoch += 1
print(time.time() - xx)
print('mean :', np.mean(tt))
print('std :', np.std(tt))
logger.warn('Testing evaluation...')
inds = rng.permutation(testx.shape[0])
testx = testx[inds] # shuffling dataset
testy = testy[inds] # shuffling dataset
scores = []
inference_time = []
# Create scores
for t in range(nr_batches_test):
# construct randomly permuted minibatches
ran_from = t * batch_size
ran_to = (t + 1) * batch_size
begin_val_batch = time.time()
feed_dict = {
input_pl: testx[ran_from:ran_to],
is_training_pl: False
}
scores += sess.run(list_scores, feed_dict=feed_dict).tolist()
inference_time.append(time.time() - begin_val_batch)
logger.info('Testing : mean inference time is %.4f' %
(np.mean(inference_time)))
ran_from = nr_batches_test * batch_size
ran_to = (nr_batches_test + 1) * batch_size
size = testx[ran_from:ran_to].shape[0]
fill = np.ones([batch_size - size, 39])
batch = np.concatenate([testx[ran_from:ran_to], fill], axis=0)
feed_dict = {input_pl: batch, is_training_pl: False}
batch_score = sess.run(list_scores, feed_dict=feed_dict).tolist()
scores += batch_score[:size]
# Highest 80% are anomalous
per = np.percentile(scores, 50)
res = pd.concat([pd.Series(scores), pd.Series(testy)], axis=1)
#res.to_csv('/Users/oguzkaplan/Documents/repo/thesis/notebooks/bigan_kdd_scores_newloss_method1_corr_32_egbad_like.csv')
y_pred = scores.copy()
y_pred = np.array(y_pred)
inds = (y_pred < per)
inds_comp = (y_pred >= per)
y_pred[inds] = 1
y_pred[inds_comp] = 0
precision, recall, f1, _ = precision_recall_fscore_support(
testy, y_pred, average='binary')
from sklearn.metrics import accuracy_score
acc = accuracy_score(testy, y_pred)
print("Testing : Prec = %.4f | Rec = %.4f | F1 = %.4f | Acc = %.4f" %
(precision, recall, f1, acc))
return args
args = parse_args()
#################################################
# Create Directories
#################################################
root = Path(args.save_path)
if root.exists():
os.system("rm -rf %s" % str(root))
root.mkdir()
(root / "models").mkdir()
writer = SummaryWriter(str(root))
#################################################
train_set = get_train()[0]
test_set = get_test()
netG = Generator(args.z_dim).cuda()
netE = EnergyModel().cuda()
netH = StatisticsNetwork(args.z_dim).cuda()
params = {"lr": 1e-4, "betas": (0.5, 0.9)}
optimizerE = torch.optim.Adam(netE.parameters(), **params)
optimizerG = torch.optim.Adam(netG.parameters(), **params)
optimizerH = torch.optim.Adam(netH.parameters(), **params)
##################################################
start_time = time.time()
e_costs = []