optimizer = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) lr_sched = rate_scheduler(lr=0.01, decay=0.99) file_name = os.path.join(direc_data, dataset + ".npz") training_data = np.load(file_name) class_weights = training_data["class_weights"] for iterate in range(1): model = the_model(batch_shape=(1, 512, 512, 1), n_features=3, reg=1e-5, softmax=True, permute=True) train_model(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, class_weight=class_weights, rotation_range=180, flip=True, shear=False)
optimizer = SGD(lr=1e-2, decay=1e-6, momentum=0.9, nesterov=True) lr_sched = rate_scheduler(lr=1e-2, decay=0.99) file_name = os.path.join(direc_data, dataset + ".npz") training_data = np.load(file_name) for iterate in range(1): model = the_model(batch_shape=(1, 1, 5, 256, 256), n_features=3, reg=1e-5, location=False, permute=True, softmax=False) trained_model = train_model(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, number_of_frames=5, lr_sched=lr_sched, rotation_range=180, flip=True, shear=False)