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"))