def main(_):
pp.pprint(flags.FLAGS.__flags)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
#memory allocation
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
#run_config.gpu_options.per_process_gpu_memory_fraction = 0.4
#tf.Session: A class for running TensorFlow operations.
#config=~ : tells to use specific configuration(setting)
with tf.Session(config=run_config) as sess:
#initialization
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
test_batch_size=FLAGS.test_batch_size,
sample_num=FLAGS.batch_size,
y_dim=10,
z_dim=FLAGS.generate_test_images,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
test_dir=FLAGS.test_dir)
else:
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
test_batch_size=FLAGS.test_batch_size,
sample_num=FLAGS.batch_size,
z_dim=FLAGS.generate_test_images,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
test_dir=FLAGS.test_dir)
show_all_variables()
if FLAGS.train:
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
if FLAGS.anomaly_test:
dcgan.anomaly_detector()
assert len(dcgan.test_data_names) > 0
fp = open('anomaly_score_record.txt', 'a+')
#for idx in range(2):
for idx in range(len(dcgan.test_data_names)):
test_input = np.expand_dims(dcgan.test_data[idx],
axis=0) ###################
test_name = dcgan.test_data_names[idx]
dcgan.train_anomaly_detector(FLAGS, test_input, test_name) ##
#assert: Python evealuates the statement, if false, exception error will raise
#image = np.expand_dims(image, <your desired dimension>)
# Below is codes for visualization
OPTION = 1
visualize(sess, dcgan, FLAGS, OPTION)
def main(_):
pp.pprint(flags.FLAGS.__flags)
if FLAGS.output_height is None:
FLAGS.output_height = FLAGS.input_height
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
gpu_options = {
'allow_growth': True,
'per_process_gpu_memory_fraction': 0.9
}
run_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=True,
gpu_options=gpu_options)
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
test_batch_size=FLAGS.test_batch_size,
sample_num=FLAGS.batch_size,
y_dim=10,
data_dir=FLAGS.data_dir,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
max_to_keep=FLAGS.max_to_keep,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
test_dir=FLAGS.test_dir)
else:
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
test_batch_size=FLAGS.test_batch_size,
sample_num=FLAGS.batch_size,
data_dir=FLAGS.data_dir,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
max_to_keep=FLAGS.max_to_keep,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
test_dir=FLAGS.test_dir)
show_all_variables()
if FLAGS.train:
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
if FLAGS.anomaly_test:
dcgan.anomaly_detector()
assert len(dcgan.test_data_names) > 0
for idx in range(len(dcgan.test_data_names)):
test_input = np.expand_dims(dcgan.test_data[idx], axis=0)
test_name = dcgan.test_data_names[idx]
dcgan.train_anomaly_detector(FLAGS, test_input, test_name)
img_path = "temp"
if not os.path.isdir("temp"):
os.mkdir("temp")
with tf.Session(config=sess_config) as sess:
saver = tf.train.Saver(tf.global_variables(), max_to_keep=1000)
#summary_op = tf.summary.merge_all()
init = tf.global_variables_initializer()
sess.run(init)
model_checkpoint_name = config.PATH_CHECKPOINT + "/model.ckpt"
print('Loaded latest model checkpoint')
saver.restore(sess, model_checkpoint_name)
model.anomaly_detector()
#lr = tf.train.exponential_decay(0.01, model.global_step, decay_steps=8000, decay_rate=0.9)
#with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='anomaly_detector')):
#train_Z = tf.train.AdamOptimizer(learning_rate=lr).minimize(model.anomaly_score, global_step=model.global_step, var_list=model.z_vars)
train_Z = tf.train.AdamOptimizer(0.0002, beta1=0.5).minimize(model.anomaly_score, var_list=model.z_vars)
global_vars = tf.global_variables()
is_not_initialized = sess.run([tf.is_variable_initialized(var) for var in global_vars])
not_initialized_vars = [v for (v,f) in zip(global_vars, is_not_initialized) if not f]
print([str(i.name) for i in not_initialized_vars])
sess.run(tf.variables_initializer(not_initialized_vars))
for epoch in range(config.EPOCH):
for idx in range(num_iters):
