import os import datetime import numpy as np batch_size = 256 n_classes = 3 n_epoch = 25 model = the_model(n_channels=2, n_features=3, reg=1e-4, drop=0.5) dataset = "nuclei_all_61x61" direc_save = "/home/nquach/DeepCell2/trained_networks/" direc_data = "/home/nquach/DeepCell2/training_data_npz/" optimizer = RMSprop(lr=0.001, rho=0.95, epsilon=1e-8) lr_sched = rate_scheduler(lr=0.001, decay=0.95) expt = "feature_net_61x61_drop_reg4" iterate = 4 train_model_sample(model=model, dataset=dataset, optimizer=optimizer, expt=expt, it=iterate, batch_size=batch_size, n_epoch=n_epoch, direc_save=direc_save, direc_data=direc_data, lr_sched=lr_sched, rotate=True, flip=True, shear=0)
import os import datetime import numpy as np batch_size = 128 n_classes = 3 n_epoch = 50 model = the_model(n_channels=2, n_features=3, reg=1e-4) dataset = "HeLa_set1_81x81" direc_save = "/home/nquach/DeepCell2/trained_networks/" direc_data = "/home/nquach/DeepCell2/training_data_npz/" optimizer = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) lr_sched = rate_scheduler(lr=0.01, decay=0.95) expt = "bn81x81_higher_reg" iterate = 0 train_model_sample(model=model, dataset=dataset, optimizer=optimizer, expt=expt, it=iterate, batch_size=batch_size, n_epoch=n_epoch, direc_save="/home/nquach/DeepCell2/trained_networks/", direc_data="/home/nquach/DeepCell2/training_data_npz/", lr_sched=lr_sched, rotate=True, flip=True, shear=0)