def __init__(self, opts):
for key, value in opts.items():
setattr(self, key, value)
try:
makedirs(self.training_results_path)
except:
pass
# datasets and loaders
self.train_loader = get_loader(self, 'train', drop_last=True)
self.test_loader = get_loader(self, 'test', drop_last=False)
# model
self.model = Network().construct(self.net, self)
self.model.to(self.device)
# loss
func = getattr(nn, self.crit)
self.criterion = func()
# optimizer and learning rate schedualer
func = getattr(optim, self.optim)
self.optimizer = func(self.model.parameters(),
lr=self.lr,
**self.optim_kwargs)
self.lr_scheduler = MultiStepLR(self.optimizer,
milestones=self.milestones,
gamma=self.gamma)
def __init__(self):
# register signal handler
signal.signal(signal.SIGINT, self._signal_handler)
signal.signal(signal.SIGTERM, self._signal_handler)
# flag for activity before quit
self.is_running = True
self.is_sig = False
# user's attributes
self.nick = None
self.ip = None
self.port = None
# create Hnefatafl
self.hnef = Hnefatafl()
# create client network
self.net = Network(self)
# create gui
self.gui = Gui(self)
import random
FTRAIN = '/home/mihael/Documents/9. semestar/VIROKR/Projekt/Detecting-Facial-Features-CNN/dataset/kaggle/training.csv'
# dataset specs
X_train, y_train = load_dataset_spplited(fname=FTRAIN, test=False)
print("splitted=", X_train.shape, y_train.shape)
pic_width = 96
pic_height = 96
pic_channels = 1 # grayscale
num_classes = 15 * 2
# other
input = PicturePlaceholder(
sample_input_shape=[pic_height, pic_width, pic_channels])
output = LabelsPlaceholder(num_classes=num_classes)
cnn = Network()
# First CNN layer
cnn.add_layer(BatchNormLayer(name="batch_norm1"))\
.add_layer(ConvolutionalLayer(name="conv1", filter_size=5, num_filters=24, strides=[1, 1, 1, 1])) \
.add_layer(ActivationLayer(name="relu1", activation_fn=tf.nn.relu))\
.add_layer(MaxPoolLayer(name="pool1", padding="VALID"))
# Second CNN layer
cnn.add_layer(ConvolutionalLayer(name="conv2", filter_size=5, num_filters=36, strides=[1, 1, 1, 1], padding="VALID")) \
.add_layer(ActivationLayer(name="relu2", activation_fn=tf.nn.relu))\
.add_layer(MaxPoolLayer(name="pool2", padding="VALID"))
# Third CNN layer
cnn.add_layer(ConvolutionalLayer(name="conv3", filter_size=5, num_filters=48, strides=[1, 1, 1, 1], padding="VALID")) \
.add_layer(ActivationLayer(name="relu3", activation_fn=tf.nn.relu))\
.add_layer(MaxPoolLayer(name="pool3", padding="VALID"))
import random
FTRAIN = '/home/mihael/Documents/9. semestar/VIROKR/Projekt/Detecting-Facial-Features-CNN/dataset/kaggle/training.csv'
# dataset specs
X_train, y_train = load_dataset(fname=FTRAIN, reshaped=True)
pic_width = 96
pic_height = 96
pic_channels = 1 # grayscale
num_classes = 15 * 2
# other
input = PicturePlaceholder(
sample_input_shape=[pic_height, pic_width, pic_channels], is_reshaped=True)
output = LabelsPlaceholder(num_classes=num_classes)
diter = FlipDatasetIteratorWrapper(StandardDatasetIterator())
cnn = Network(dataset_iter=diter)
# First CNN layer
cnn.add_layer(BatchNormLayer(name="batch_norm1"))\
.add_layer(ConvolutionalLayer(name="conv1", filter_size=5, num_filters=24, strides=[1, 1, 1, 1])) \
.add_layer(ActivationLayer(name="relu1", activation_fn=tf.nn.relu))\
.add_layer(MaxPoolLayer(name="pool1", padding="VALID"))
# Second CNN layer
cnn.add_layer(ConvolutionalLayer(name="conv2", filter_size=5, num_filters=36, strides=[1, 1, 1, 1], padding="VALID")) \
.add_layer(ActivationLayer(name="relu2", activation_fn=tf.nn.relu))\
.add_layer(MaxPoolLayer(name="pool2", padding="VALID"))
# Third CNN layer
cnn.add_layer(ConvolutionalLayer(name="conv3", filter_size=5, num_filters=48, strides=[1, 1, 1, 1], padding="VALID")) \
.add_layer(ActivationLayer(name="relu3", activation_fn=tf.nn.relu))\
.add_layer(MaxPoolLayer(name="pool3", padding="VALID"))