def run_attack(
net: Network, x: torch.Tensor, y: torch.Tensor, epsilons: List[float],
batch_id: int
) -> Tuple[pandas.DataFrame, List[torch.Tensor], List[torch.Tensor]]:
x = x.to(net.device)
y = y.to(net.device)
x.requires_grad = True
pred = net(x.view(-1, 28 * 28))
loss = F.nll_loss(pred, y)
net.zero_grad()
loss.backward()
data_grad = x.grad.data
tmp_dict = {"id": [], "epsilon": [], "y": [], "y_": []}
pertubed_images = []
pertubation = []
for epsilon in epsilons:
pertubed_image, pert = fgsm_attack(x, epsilon, data_grad)
pertubed_images.append(pertubed_image)
pertubation.append(pert)
pred = net(pertubed_image.view(-1, 28 * 28))
y_ = pred.data.max(1).indices.item()
tmp_dict["id"].append(batch_id)
tmp_dict["epsilon"].append(epsilon)
tmp_dict["y"].append(y.item())
tmp_dict["y_"].append(y_)
return pandas.DataFrame.from_dict(tmp_dict), pertubed_images, pertubation
def main():
training_data, validation_data, test_data = mnist_loader.load_data_wrapper(
)
net = Network([784, 30, 10])
net.SGD(training_data=training_data,
epochs=30,
mini_batch_size=10,
eta=3.0,
test_data=test_data)
def __init__(self, env, trainable=True, learning_rate=0.001, hidden=30):
nn.Module.__init__(self)
Network.__init__(self)
self.env = env
self.hidden = hidden
self.actions = env.action_space.n
self.build_model_()
if trainable:
self.optimizer = optim.Adam(self.parameters(), lr=learning_rate)
if USE_CUDA:
self.cuda()
def train(net: Network, optimizer: torch.optim.Adam,
train_loader: torch.utils.data.DataLoader, epoch: int):
net.train()
for batch_idx, (x, y) in enumerate(train_loader):
optimizer.zero_grad()
output = net(x.view(-1, 28 * 28).to(net.device))
loss = F.nll_loss(output, y.to(net.device))
loss.backward()
optimizer.step()
if batch_idx % 100 == 0:
print(
f"Train Epoch: {epoch}, Step: {batch_idx*len(x)}/{len(train_loader.dataset)}, Loss: {loss.item()}"
)
def training():
config = Configuration()
net = Network()
optimizer = optim.Adam(net.parameters(), lr=0.01)
train_loader = get_train_loader()
test_loader = get_test_loader()
for epoch in range(config.nn_training_epochs):
train(net, optimizer, train_loader, epoch)
test(net, test_loader)
net.save_model()
def __init__(self):
'''
'''
self.BATCH_SIZE = 32
self.EPOCHS = 100000
# self.image_dim = [-1, 32, 32, 3] # CIFAR-10
self.image_dim = [-1, 28, 28, 1] # MNIST
# self.discriminator_input_dim = (None, 32, 32, 3) # CIFAR-10
self.discriminator_input_dim = (None, 784) # MNIST
self.net = Network()
# self.data = utils.get_cifar10() # CIFAR-10
self.data = utils.get_mnist() # MNIST
# self.logdir = "train_logs/cifar10/" # CIFAR-10
self.logdir = "train_logs/mnist/" # MNIST
def test(net: Network, test_loader: torch.utils.data.DataLoader):
net.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for x, y in test_loader:
x = x.to(net.device)
y = y.to(net.device)
pred = net(x.view(-1, 28 * 28))
test_loss += F.nll_loss(pred, y, size_average=False).item()
correct += torch.eq(pred.max(1).indices, y).sum().item()
test_loss /= len(test_loader.dataset)
print(
f"\nTest set, Average loss: {test_loss}, Accuracy: {float(correct) / float(len(test_loader.dataset))}\n"
)
def main():
config = Configuration()
image_id = 1362
bnn = BNNWrapper()
bnn.load_model()
loss_fn = pyro.infer.Trace_ELBO(
num_particles=config.bnn_adversary_samples
).differentiable_loss
nn = Network()
nn.load_model()
test_loader = get_test_loader(1, shuffle=False)
x, y = test_loader.dataset[image_id]
y = torch.tensor([y])
bnn_d, bnn_imgs, bnn_pertubation_imgs = bnn_adversary.run_attack(
bnn, loss_fn, x, y, config.epsilons, image_id
)
nn_d, nn_imgs, nn_pertubation_imgs = nn_adversary.run_attack(
nn, x, y, config.epsilons, 3
)
ids = [2, 5]
utils.img_show(
x,
f"Image, BNN Prediction: {bnn_d.iloc[0]['y_']}, NN Prediction: {nn_d.iloc[0]['y_']}",
)
for id in ids:
utils.img_two_show(
bnn_pertubation_imgs[id].cpu(),
f"BNN Noise (epsilon: {bnn_d.iloc[id]['epsilon']})",
nn_pertubation_imgs[id].cpu(),
f"NN Noise (epsilon: {bnn_d.iloc[id]['epsilon']})",
)
utils.img_two_show(
bnn_imgs[id].cpu(),
f"Noise added to image, BNN Prediction: {bnn_d.iloc[id]['y_']}",
nn_imgs[id].cpu(),
f"Noise added to image, NN Prediction: {nn_d.iloc[id]['y_']}",
)
def main():
D_train = DatasetCIFAR10('Train', 'datasets/cifar-10-batches-py/')
(D_train, D_val) = split(D_train)
network = Network(mini_inception_architecture,
input_shape=(32, 32, 3),
depth=32,
stride=1,
n_hidden=64,
n_classes=10)
train(network, D_train, D_val)
def __init__(self, sess, batch_size, num_episodes, actor_target,
actor_trainer, critic_target, critic_trainer,
trading_state_model, replay_buffer, datacontainer, gamma,
tau):
self.sess = sess
self.batch_size = batch_size
self.num_episodes = num_episodes
self.actor_target = actor_target
self.actor_trainer = actor_trainer
self.critic_target = critic_target
self.critic_trainer = critic_trainer
self.tsm = trading_state_model
self.rpb = replay_buffer
self.datacontainer = datacontainer
self.gamma = gamma
self.tau = tau
self.sess.run(tf.global_variables_initializer())
self.sess.run(
Network.assign_target_graph("actor-trainer", "actor-target"))
self.sess.run(
Network.assign_target_graph("critic-trainer", "critic-target"))
def main():
config = Configuration()
image_id = 1362
nn = Network()
nn.load_model()
test_loader = get_test_loader(1, shuffle=False)
x, y = test_loader.dataset[image_id]
y = torch.tensor([y])
nn_d, nn_imgs, nn_pertubation_imgs = nn_adversary.run_attack(
nn, x, y, config.epsilons, 3)
id = 2
utils.img_show(x, f"Image, NN Prediction: {nn_d.iloc[0]['y_']}")
utils.img_show(nn_pertubation_imgs[id].cpu(),
f"Noise (epsilon: {nn_d.iloc[id]['epsilon']})")
utils.img_show(nn_imgs[id].cpu(),
f"Noise added to image, Prediction: {nn_d.iloc[id]['y_']}")
def __init__(self, sess, batch_size, num_episodes, episode_length,
actor_target, actor_trainer, critic_target, critic_trainer,
env, replay_buffer, gamma, tau, actor_noise):
self.sess = sess
self.batch_size = batch_size
self.num_episodes = num_episodes
self.episode_length = episode_length
self.actor_target = actor_target
self.actor_trainer = actor_trainer
self.critic_target = critic_target
self.critic_trainer = critic_trainer
self.env = env
self.rpb = replay_buffer
self.gamma = gamma
self.tau = tau
self.actor_noise = actor_noise
self.sess.run(tf.global_variables_initializer())
self.sess.run(
Network.assign_target_graph("actor-trainer", "actor-target"))
self.sess.run(
Network.assign_target_graph("critic-trainer", "critic-target"))
self.writer = tf.summary.FileWriter("./tensorboard", sess.graph)
self.build_summaries()
def __init__(self, *args, shared_network=None,
value_network_path=None, policy_network_path=None, **kwargs):
super().__init__(*args, **kwargs)
if shared_network is None:
self.shared_network = Network.get_shared_network(
net=self.net, num_steps=self.num_steps,
input_dim=self.num_features)
else:
self.shared_network = shared_network
self.value_network_path = value_network_path
self.policy_network_path = policy_network_path
if self.value_network is None:
self.init_value_network(shared_network=shared_network)
if self.policy_network is None:
self.init_policy_network(shared_network=shared_network)
def main():
D_train = DatasetCIFAR10('Train', 'datasets/cifar-10-batches-py/')
enlarge_plot_area()
display = Displayer(
load_labels('datasets/cifar-10-batches-py/batches.meta'))
display(D_train)
(D_train, D_val) = split(D_train)
network = Network(mini_inception_architecture,
input_shape=(32, 32, 3),
depth=32,
stride=1,
n_hidden=64,
n_classes=10)
train(network, D_train, D_val, report=plot_report)
def __init__(self,
*args,
list_stock_code=None,
list_chart_data=None,
list_training_data=None,
list_min_trading_unit=None,
list_max_trading_unit=None,
value_network_path=None,
policy_network_path=None,
**kwargs):
assert len(list_training_data) > 0
super().__init__(*args, **kwargs)
self.num_features += list_training_data[0].shape[1]
# 공유 신경망 생성
self.shared_network = Network.get_shared_network(
net=self.net,
num_steps=self.num_steps,
input_dim=self.num_features)
self.value_network_path = value_network_path
self.policy_network_path = policy_network_path
if self.value_network is None:
self.init_value_network(shared_network=self.shared_network)
if self.policy_network is None:
self.init_policy_network(shared_network=self.shared_network)
# A2CLearner 생성
self.learners = []
for (stock_code, chart_data, training_data, min_trading_unit,
max_trading_unit) in zip(list_stock_code, list_chart_data,
list_training_data,
list_min_trading_unit,
list_max_trading_unit):
learner = A2CLearner(*args,
stock_code=stock_code,
chart_data=chart_data,
training_data=training_data,
min_trading_unit=min_trading_unit,
max_trading_unit=max_trading_unit,
shared_network=self.shared_network,
value_network=self.value_network,
policy_network=self.policy_network,
**kwargs)
self.learners.append(learner)
def main(_):
# load bottleneck data
(D_train, D_val) = load_bottleneck_data(FLAGS.training_file, FLAGS.validation_file, FLAGS.breadth)
print(D_train.X.shape, D_train.y.shape)
print(D_val.X.shape, D_val.y.shape)
# TODO: define your model and hyperparams here
# make sure to adjust the number of classes based on
# the dataset
# 10 for cifar10
# 43 for traffic
network = Network(
bottleneck_architecture,
input_shape=D_train.X.shape[1:],
n_hidden=64,
n_classes=FLAGS.breadth
)
# TODO: train your model here
train(network, D_train, D_val, learning_rate=0.01, epochs=100)
class NetworkTraining:
def __init__(self, train_config, image_config):
self.train_config = train_config
self.image_config = image_config
self.network = Network(train_config)
def load_to_arrays(self, iteration=0, images_num_to_process=1):
pure_input_train = self.image_config.image_to_array(
iteration, images_num_to_process,
self.train_config.get_image_to_array_train_input("pure"))
noisy_input_train = self.image_config.image_to_array(
iteration, images_num_to_process,
self.train_config.get_image_to_array_train_input("noisy"))
return pure_input_train, noisy_input_train
def train(self):
# Get the file paths
kb.clear_session()
gpus = tf.config.experimental.list_physical_devices('GPU')
print("Num GPUs Available: ",
len(tf.config.experimental.list_physical_devices('GPU')))
if gpus:
try:
# Currently, memory growth needs to be the same across GPUs
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices(
'GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus),
"Logical GPUs")
except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized
print(e)
print('Starting a training process ..')
print('Preparing training data for CNN ..')
# save output to logs
old_stdout = sys.stdout
timestr = time.strftime("%Y%m%d-%H%M%S")
model_name = 'DEPTH_' + timestr + '.model'
name = self.train_config.logs_path + r'/loss_output_' + model_name + '.log'
log_file = open(name, "w")
sys.stdout = log_file
print('Loss function output of model :', model_name, '..')
# Create a basic model instance
model = self.network.get()
compiled_model = model.compile()
save_model_name = self.train_config.models_path + '/' + model_name
images_num_to_process = 1000
all_cropped_num = len(
os.listdir(self.train_config.cropped_train_images_pure))
iterations = all_cropped_num // images_num_to_process
if all_cropped_num % images_num_to_process > 0:
iterations += 1
for i in range(iterations):
print('*************** Iteration : ', i, '****************')
first_image = i * images_num_to_process
if i == iterations - 1:
images_num_to_process = all_cropped_num - i * images_num_to_process
if self.train_config.LOAD_TRAINED_MODEL:
# create a dir where we want to copy and rename
save_model_name = self.train_config.load_model_name + '_new'
if not os.path.isdir(save_model_name):
shutil.copytree(self.train_config.load_model_name,
save_model_name)
compiled_model = keras.models.load_model(
save_model_name) # used to continue training old models
pure_input_train, noisy_input_train = self.load_to_arrays(
first_image, images_num_to_process)
if self.train_config.OUTPUT_EQUALS_INPUT:
pure_input_train = noisy_input_train
model.train(compiled_model, noisy_input_train, pure_input_train,
self.train_config.models_path)
# save the model
compiled_model.save(
save_model_name) # check if using same name is ok
compiled_model = keras.models.load_model(save_model_name)
#model = keras.models.load_model(save_model_name, custom_objects={'keras_custom_loss_function':keras_custom_loss_function})
sys.stdout = old_stdout
log_file.close()
pred = net(pertubed_image.view(-1, 28 * 28))
y_ = pred.data.max(1).indices.item()
tmp_dict["id"].append(batch_id)
tmp_dict["epsilon"].append(epsilon)
tmp_dict["y"].append(y.item())
tmp_dict["y_"].append(y_)
return pandas.DataFrame.from_dict(tmp_dict), pertubed_images, pertubation
if __name__ == "__main__":
config = Configuration()
net = Network()
net.load_model()
net.eval()
test_loader = get_test_loader(batch_size=1, shuffle=False)
result = []
for batch_id, (x, y) in enumerate(test_loader):
df, _ = run_attack(net, x, y, config.epsilons, batch_id)
result.append(df)
if batch_id % 100 == 0:
print(f"Step {batch_id}/{len(test_loader.dataset)}")
result_df = pandas.concat(result) # type: pandas.DataFrame
from networks import Network
import numpy as np
import os
import datetime
import input_data
BATCH_SIZE = 128
NOISE_SIZE = 100
EPOCHS = 500000
img_width, img_height, channel=32,32,1
is_training = True
LOG_FILE = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
helper.log_file(is_training, LOG_FILE)
with tf.device('/device:GPU:0'):
net = Network()
noise_vector = tf.placeholder(tf.float32,shape=(BATCH_SIZE, NOISE_SIZE))
image = tf.placeholder(tf.float32,shape=(BATCH_SIZE, img_width, img_height,channel))
initializer = tf.truncated_normal_initializer(stddev=0.02)
generated_image = net.generator(noise_vector = noise_vector, initializer = initializer)
Dx = net.discriminator(image = image, initializer = initializer)
Dg = net.discriminator(image = generated_image, initializer = initializer, reuse = True)
generator_loss = -tf.reduce_mean(tf.log(Dg))
discriminator_loss = -tf.reduce_mean(tf.add(tf.log(Dx), tf.log(1.-Dg)))
tvars = tf.trainable_variables()
optimizerG = tf.train.AdamOptimizer(learning_rate=0.0002, beta1=0.5)
optimizerD = tf.train.AdamOptimizer(learning_rate=0.0002, beta1=0.5)
def __init__(self, networks, *args, **kwargs):
Network.__init__(self)
self.networks = [
Network(*args, **kwargs)
for Network in networks
]
#x_train_ex_batch, _ = dataset.get_train_minibatch(100)
#print(x_train_ex_batch.shape)
num_samples = 30
batch_size = 20
#layers
latent_units = [2]
#latent_units = [50]
hidden_units_q = [[200, 200]]
hidden_units_p = [[200, 200]]
# Create the model
x = tf.placeholder(tf.float32, [batch_size, dataset.dim],
name='placeholder_x')
iwae = Network.build_network(x, num_samples, latent_units, hidden_units_q,
hidden_units_p, dataset.train_bias)
params = iwae.params
with tf.Session() as sess:
# for debugging
#sess = tf.InteractiveSession()
#sess = tf_debug.LocalCLIDebugWrapperSession(sess)
#sess.add_tensor_filter("has_nan", debug_has_nan)
writer = tf.summary.FileWriter('./graphs', sess.graph)
# Checkpoints
dataset_name = 'MNIST'
traintype = 'iwae'
#path = ckpt_path_name(dataset_name, num_samples, traintype)
from PIL import Image
parser = argparse.ArgumentParser(
description='Uses mnist nework to classify a png image')
parser.add_argument('--imagePath',
default=(str(Path.home())) +
'/Downloads/pixil-frame-0.png')
parser.add_argument('--networkPath', default='outputs/network.pkl')
args = parser.parse_args()
imagePath = args.imagePath
networkPath = args.networkPath
# load network
parameters = get_params_from_file(networkPath)
my_network = Network(*parameters, 'classification')
# load image
img = Image.open(imagePath)
img.thumbnail((28, 28), Image.ANTIALIAS)
img = np.asarray(img)
img_array = img / 255 # rescale to between 0 and 1
img_array = 1 - img_array
img_array = img_array[:, :,
0] # image should be greyscale, pick only one channel
network_input = img_array.reshape(
[-1, 1]) # reshapes image to 1d numpy array to feed into network
network_output = my_network.activate(network_input)
network_output = np.ravel(network_output)
x = np.arange(10) # x labels
import time
import json
from pyb import Timer
import os
from adc import Adc
from lcd import Lcd
from scan import Scan
from outctl import Outctl
from networks import Network
adc = Adc()
lcd = Lcd()
scan = Scan()
outctl = Outctl()
network = Network()
up_state_cnt = 0 #上位机状态计数器
file_flag = 0 #是否写入文件
adc_freq = 1000 #adc采样频率
voltage_b = 0
current_b = 0
warnvalue = 10
warntime = 20
stopvalue = 20
stoptime = 20
time_now = ''
def up_state(tt):
global up_state_cnt, file_flag
n.add_person_to_picture(per1, pic4)
n.add_person_to_picture(per5, pic4)
return n
#n = dummy_network()
#print("Dummy network:", n)
#Network.save(n, 'test.p')
#n_load = Network.load('test.p')
n_load = Network.dummy(150, 500)
print(n_load.people)
source = list(n_load.people)[0]
#n_load.show()
print('source', source)
for neighbor in n_load.neighbors(source):
print(neighbor)
print('Now traversing all')
for neighbor in n_load.traverse_all(source):
def __init__(self, train_config, image_config):
self.train_config = train_config
self.image_config = image_config
self.network = Network(train_config)
def dummy_network():
n = Network()
pic1 = Picture('famille.png')
pic2 = Picture('annif.png')
pic3 = Picture('fete.png')
pic4 = Picture('soiree')
per1 = Person('Jesus')
per2 = Person('Juda')
per3 = Person('Moise')
per4 = Person('Charlie')
per5 = Person('Antoine')
n.add_person_to_picture(per1, pic1)
n.add_person_to_picture(per2, pic1)
n.add_person_to_picture(per3, pic1)
n.add_person_to_picture(per3, pic2)
n.add_person_to_picture(per4, pic2)
n.add_person_to_picture(per5, pic2)
n.add_person_to_picture(per1, pic3)
n.add_person_to_picture(per5, pic3)
n.add_person_to_picture(per1, pic4)
n.add_person_to_picture(per5, pic4)
return n
class GAN(object):
def __init__(self):
'''
'''
self.BATCH_SIZE = 32
self.EPOCHS = 100000
# self.image_dim = [-1, 32, 32, 3] # CIFAR-10
self.image_dim = [-1, 28, 28, 1] # MNIST
# self.discriminator_input_dim = (None, 32, 32, 3) # CIFAR-10
self.discriminator_input_dim = (None, 784) # MNIST
self.net = Network()
# self.data = utils.get_cifar10() # CIFAR-10
self.data = utils.get_mnist() # MNIST
# self.logdir = "train_logs/cifar10/" # CIFAR-10
self.logdir = "train_logs/mnist/" # MNIST
def train_ops(self):
'''
:return:
'''
self.input_placeholder = tf.placeholder(
tf.float32, shape=self.discriminator_input_dim, name='input')
random_z = tf.random_normal([self.BATCH_SIZE, 100],
mean=0.0,
stddev=1.0,
name='random_z')
self.generator = self.net.mnist_generator(random_z,
is_training=True,
name='generator')
self.real_discriminator = self.net.mnist_discriminator(
self.input_placeholder, is_training=True, name='discriminator')
self.fake_discriminator = self.net.mnist_discriminator(
self.generator, is_training=True, reuse=True, name='discriminator')
self.real_discriminator_loss = tf.losses.sigmoid_cross_entropy(
tf.constant(1, shape=[self.BATCH_SIZE]),
self.real_discriminator,
scope='real_discriminator_loss')
self.fake_discriminator_loss = tf.losses.sigmoid_cross_entropy(
tf.constant(0, shape=[self.BATCH_SIZE]),
self.fake_discriminator,
scope='fake_discriminator_loss')
self.discriminator_loss = self.real_discriminator_loss + self.fake_discriminator_loss
self.generator_loss = tf.losses.sigmoid_cross_entropy(
tf.constant(1, shape=[self.BATCH_SIZE]),
self.fake_discriminator,
scope='generator_loss')
## training variables
self.discriminator_variables = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, scope='discriminator')
self.generator_variables = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, scope='generator')
## update ops
discriminator_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS,
scope='discriminator')
generator_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS,
scope='generator')
global_step = tf.Variable(0, name='global_step', trainable=False)
with tf.control_dependencies(discriminator_update_ops):
self.train_discriminator = tf.train.AdamOptimizer(learning_rate=0.0002, beta1=0.5). \
minimize(self.discriminator_loss, var_list=self.discriminator_variables)
with tf.control_dependencies(generator_update_ops):
self.train_generator = tf.train.AdamOptimizer(learning_rate=0.0002, beta1=0.5). \
minimize(self.generator_loss, var_list=self.generator_variables, global_step=global_step)
def summary_ops(self):
'''
:return:
'''
self.summary_discriminator = tf.summary.merge([
tf.summary.scalar('summary/real_discriminator_loss',
self.real_discriminator_loss),
tf.summary.scalar('summary/fake_discriminator_loss',
self.fake_discriminator_loss),
tf.summary.scalar('summary/discriminator_loss',
self.discriminator_loss)
])
input_img = tf.reshape(self.input_placeholder, self.image_dim)
gen_img = tf.reshape(self.generator, self.image_dim)
# input_visualisation = tf.cast(((self.input_placeholder / 2.0) + 0.5) * 255.0, tf.uint8)
# generator_visualisation = tf.cast(((self.generator / 2.0) + 0.5) * 255.0, tf.uint8)
input_visualisation = tf.cast(((input_img / 2.0) + 0.5) * 255.0,
tf.uint8)
generator_visualisation = tf.cast(((gen_img / 2.0) + 0.5) * 255.0,
tf.uint8)
self.summary_input = tf.summary.image('summary/input',
input_visualisation,
max_outputs=3)
self.summary_generator = tf.summary.merge([
tf.summary.image('summary/generator',
generator_visualisation,
max_outputs=3),
tf.summary.scalar('summary/generator_loss', self.generator_loss)
])
def train(self):
'''
:return:
'''
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
logwriter = tf.summary.FileWriter(self.logdir, sess.graph)
for i in range(self.EPOCHS):
inp = self.data[np.random.choice(self.data.shape[0],
self.BATCH_SIZE)]
(_, sum_dis) = sess.run(
(self.train_discriminator, self.summary_discriminator),
feed_dict={self.input_placeholder: inp})
(_, sum_gen) = sess.run(
(self.train_generator, self.summary_generator))
s = sess.run(self.summary_input,
feed_dict={self.input_placeholder: inp})
if i % 100 == 0:
print(i)
logwriter.add_summary(sum_dis, i)
logwriter.add_summary(sum_gen, i)
logwriter.add_summary(s, i)
def train_without_pruning():
tf.compat.v1.reset_default_graph()
# Inference
network = Network(NUM_CLASSES)
inputs = tf.compat.v1.placeholder(tf.float32, [None, INPUT_SIZE, INPUT_SIZE, INPUT_CHANNEL], 'inputs')
logits = network.inference(inputs)
# loss & accuracy
labels = tf.compat.v1.placeholder(tf.int64, [None, ], 'labels')
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels))
prediction = tf.argmax(tf.nn.softmax(logits), axis=1)
acc = tf.reduce_mean(tf.cast(tf.equal(prediction, labels), dtype=tf.float32))
# optimizer
global_step = tf.train.get_or_create_global_step()
optimizer = tf.compat.v1.train.MomentumOptimizer(learning_rate=LEARNING_RATE, momentum=0.9)
train_op = optimizer.minimize(loss, global_step)
# loading data
train_next = load_tfrecords('train')
test_next = load_tfrecords('test')
with tf.compat.v1.Session() as sess:
sess.run(tf.compat.v1.global_variables_initializer())
# summaries
logs_dir = './logs/without_pruning'
if not os.path.exists(logs_dir):
os.makedirs(logs_dir)
tf.compat.v1.summary.scalar('monitor/loss', loss)
tf.compat.v1.summary.scalar('monitor/acc', acc)
merged_summary_op = tf.compat.v1.summary.merge_all()
train_summary_writer = tf.compat.v1.summary.FileWriter(os.path.join(logs_dir, 'train'), graph=sess.graph)
test_summary_writer = tf.compat.v1.summary.FileWriter(os.path.join(logs_dir, 'test'), graph=sess.graph)
best_acc = 0
saver = tf.compat.v1.train.Saver()
for epoch in range(NUM_EPOCHS):
# training
num_steps = TRAIN_SIZE // BATCH_SIZE
train_acc = 0
train_loss = 0
for step in range(num_steps):
x, y = sess.run(train_next)
_, summary, train_acc_batch, train_loss_batch = sess.run([train_op, merged_summary_op, acc, loss],
feed_dict={inputs: x, labels: y})
train_acc += train_acc_batch
train_loss += train_loss_batch
sys.stdout.write("\r epoch %d, step %d, training accuracy %g, training loss %g" %
(epoch + 1, step + 1, train_acc_batch, train_loss_batch))
sys.stdout.flush()
train_summary_writer.add_summary(summary, global_step=epoch * num_steps + step)
train_summary_writer.flush()
print("\n epoch %d, training accuracy %g, training loss %g" %
(epoch + 1, train_acc / num_steps, train_loss / num_steps))
# testing
num_steps = TEST_SIZE // BATCH_SIZE
test_acc = 0
test_loss = 0
for step in range(num_steps):
x, y = sess.run(test_next)
summary, test_acc_batch, test_loss_batch = sess.run([merged_summary_op, acc, loss],
feed_dict={inputs: x, labels: y})
test_acc += test_acc_batch
test_loss += test_loss_batch
test_summary_writer.add_summary(summary, global_step=(epoch * num_steps + step) * (TRAIN_SIZE // TEST_SIZE))
test_summary_writer.flush()
print(" epoch %d, testing accuracy %g, testing loss %g" %
(epoch + 1, test_acc / num_steps, test_loss / num_steps))
if test_acc / num_steps > best_acc:
best_acc = test_acc / num_steps
saver.save(sess, './ckpt_without_pruning/model')
print(" Best Testing Accuracy %g" % best_acc)
testloaders.append(DataLoader(BIO_tests[j], shuffle=False,batch_size=BATCH_SIZE))
j +=1
device = "cuda" if torch.cuda.is_available() else "cpu" # Configure device
print('GPU USED?',torch.cuda.is_available())
################# MODELS#####################
models =[]
optimizers =[]
criterions =[]
j = 0
for i in range(len_class):
for event in events:
model = Network(output_dim=numb_class[i])
model = model.to(device)
model.eval()
models.append(model)
weights_list[j] = weights_list[j].to(device)
criterions.append(nn.CrossEntropyLoss(weight=weights_list[j]))
optimizers.append(optim.Adam(model.parameters(), lr=LEARNING_RATE, weight_decay=WEIGHT_DECAY))
del model
j +=1
#### MODE-SPECIFIC CLASSIFIER SCHEME
# 1 : LW, 2:RA, 3:RD, 4:SA, 5:SD, 6:Stand
# MODEL1: LW(1)-> LW(1), RA(2), RD(3), SA(4), SD(5), Stand(6)
# MODEL2: RA(2)-> LW(1), RA(2)
# MODEL3: RD(3)-> LW(1), RD(3)
def create_model(args, atomrefs, means, stddevs, properties, avg_n_atoms):
ssp = rescaled_act.ShiftedSoftplus(beta=args.beta)
kernel_conv = create_kernel_conv(
cutoff=args.rad_maxr,
n_bases=args.rad_nb,
n_neurons=args.rad_h,
n_layers=args.rad_L,
act=ssp,
radial_model=args.radial_model
)
sp = rescaled_act.Softplus(beta=args.beta)
if args.res:
net = ResNetwork(
kernel_conv=kernel_conv,
embed=args.embed,
l0=args.l0,
l1=args.l1,
l2=args.l2,
l3=args.l3,
L=args.L,
scalar_act=sp,
gate_act=rescaled_act.sigmoid,
avg_n_atoms=avg_n_atoms
)
else:
net = Network(
kernel_conv=kernel_conv,
embed=args.embed,
l0=args.l0,
l1=args.l1,
l2=args.l2,
l3=args.l3,
L=args.L,
scalar_act=sp,
gate_act=rescaled_act.sigmoid,
avg_n_atoms=avg_n_atoms
)
ident = torch.nn.Identity()
if args.mlp_out:
outnet = OutputMLPNetwork(
kernel_conv=kernel_conv,
previous_Rs=net.Rs[-1],
l0=args.outnet_l0,
l1=args.outnet_l1,
l2=args.outnet_l2,
l3=args.outnet_l3,
L=args.outnet_L,
scalar_act=sp,
gate_act=rescaled_act.sigmoid,
mlp_h=args.outnet_neurons,
mlp_L=args.outnet_layers,
avg_n_atoms=avg_n_atoms
)
else:
outnet = OutputScalarNetwork(
kernel_conv=kernel_conv,
previous_Rs=net.Rs[-1],
scalar_act=ident,
avg_n_atoms=avg_n_atoms
)
output_modules = [
spk.atomistic.Atomwise(
property=prop,
mean=means[prop],
stddev=stddevs[prop],
atomref=atomrefs[prop],
outnet=outnet,
# aggregation_mode='sum'
) for prop in properties
]
model = spk.AtomisticModel(net, output_modules)
return model
