dataset = libs_dataset.CellsDataset(training_files, training_labels, testing_files, testing_labels, window_size=128, classes_count=2, augmentations_count=100) #train 100 epochs epoch_count = 100 #cyclic learning rate cheduler learning_rates = [0.001, 0.001, 0.0001, 0.0001, 0.0001, 0.00001, 0.00001] train = libs.Train(dataset, Modelconv_4d05, batch_size=256, learning_rates=learning_rates) train.step_epochs(epoch_count, log_path="../models/model_conv_4d05") ''' training result saved into ../models/model_conv_4d05/result training progress saved into file training.log columns description epoch [int] training_accuracy [%] testing_accuracy [%] training_loss_mean [float] testing_loss_mean [float] training_loss_std [float] testing_loss_std [float] best model is saved into ../models/model_conv_4d05/trained
dataset = libs_dataset.CellsDataset(training_files, training_labels, testing_files, testing_labels, window_size=64, classes_count=2, augmentations_count=20) #train 100 epochs epoch_count = 100 #cyclic learning rate cheduler learning_rates = [0.001, 0.001, 0.0001, 0.0001, 0.0001, 0.00001, 0.00001] train = libs.Train(dataset, Modellstm_64_ws64, batch_size=256, learning_rates=learning_rates) train.step_epochs(epoch_count, log_path="../models/model_lstm_64_ws64") ''' training result saved into ../models/model_lstm_64_ws64/result training progress saved into file training.log columns description epoch [int] training_accuracy [%] testing_accuracy [%] training_loss_mean [float] testing_loss_mean [float] training_loss_std [float] testing_loss_std [float] best model is saved into ../models/model_lstm_64_ws64/trained
''' create dataset with pairs training testing labels corresponds to class IDs for details see libs_dataset/cells_dataset.py ''' dataset = libs_dataset.CellsDataset(training_files, training_labels, testing_files, testing_labels, window_size = 128, classes_count = 2, augmentations_count=100) #train 100 epochs epoch_count = 100 #cyclic learning rate cheduler learning_rates = [0.001, 0.001, 0.0001, 0.0001, 0.0001, 0.00001, 0.00001] train = libs.Train(dataset, Modelresnet_1_d05, batch_size = 256, learning_rates = learning_rates) train.step_epochs(epoch_count, log_path = "../models/model_resnet_1_d05") ''' training result saved into ../models/model_resnet_1_d05/result training progress saved into file training.log columns description epoch [int] training_accuracy [%] testing_accuracy [%] training_loss_mean [float] testing_loss_mean [float] training_loss_std [float] testing_loss_std [float] best model is saved into ../models/model_resnet_1_d05/trained
dataset = libs_dataset.CellsDataset(training_files, training_labels, testing_files, testing_labels, window_size=128, classes_count=2, augmentations_count=100) #train 100 epochs epoch_count = 100 #cyclic learning rate cheduler learning_rates = [0.001, 0.001, 0.0001, 0.0001, 0.0001, 0.00001, 0.00001] train = libs.Train(dataset, Modelresnet_1_4d001_beforeResBlock, batch_size=256, learning_rates=learning_rates) train.step_epochs(epoch_count, log_path="../models/model_resnet_1_4d001_beforeResBlock") ''' training result saved into ../models/model_resnet_1_4d001_beforeResBlock/result training progress saved into file training.log columns description epoch [int] training_accuracy [%] testing_accuracy [%] training_loss_mean [float] testing_loss_mean [float] training_loss_std [float] testing_loss_std [float]
dataset = libs_dataset.CellsDataset(training_files, training_labels, testing_files, testing_labels, window_size=128, classes_count=2, augmentations_count=20) #train 100 epochs epoch_count = 100 #cyclic learning rate cheduler learning_rates = [0.001, 0.001, 0.0001, 0.0001, 0.0001, 0.00001, 0.00001] train = libs.Train(dataset, Modelgru, batch_size=256, learning_rates=learning_rates) train.step_epochs(epoch_count, log_path="../models/model_gru") ''' training result saved into ../models/model_gru/result training progress saved into file training.log columns description epoch [int] training_accuracy [%] testing_accuracy [%] training_loss_mean [float] testing_loss_mean [float] training_loss_std [float] testing_loss_std [float] best model is saved into ../models/model_gru/trained
import sys sys.path.insert(0, '../..') import libs import libs_dataset import models.net_0.model as Model0 import models.net_1.model as Model1 epoch_count = 20 learning_rates = [0.001, 0.001, 0.0001] #dataset = libs_dataset.DatasetLineFollower(width = 8, height = 8, classes_count = 5, training_count = 50000, testing_count = 5000) #train = libs.Train(dataset, Model0, libs.MetricsClassification, batch_size = 64, learning_rates = learning_rates) #train.step_epochs(epoch_count, log_path = "./models/net_0") dataset = libs_dataset.DatasetLineFollowerStream() train = libs.Train(dataset, Model1, libs.MetricsRegression, batch_size=64, learning_rates=learning_rates) train.step_epochs(epoch_count, log_path="./models/net_1")