train_neg_datapath = '/data/Kaggle/neg-filt-png' # parameters im_dims = (1024, 1024) n_channels = 1 batch_size = 4 learnRate = 1e-4 filt_nums = 16 num_blocks = 5 val_split = .15 train_weights_filepath = 'Best_Kaggle_Classification_Weights_{}_v4.h5' cur_weights_path = train_weights_filepath.format('1024train') # datagen params full_train_params = get_train_params(batch_size, (1024, 1024), 1) full_val_params = get_val_params(batch_size, (1024, 1024), 1) # Create model full_model = Inception_model(input_shape=(1024, 1024)+(n_channels,)) full_model.load_weights(cur_weights_path) # Get datagen _, full_val_gen, _ = get_class_datagen( train_pos_datapath, train_neg_datapath, full_train_params, full_val_params, val_split) # Calculate confusion matrix print('Calculating classification confusion matrix...') full_val_gen.shuffle = False preds = full_model.predict_generator(full_val_gen, verbose=1) labels = [full_val_gen.labels[f] for f in full_val_gen.list_IDs] y_pred = np.rint(preds)
batch_size = 4 batch_size_1024 = 1 learnRate = 1e-5 val_split = .15 epochs_unfreeze = [5, 10] # epochs before and after unfreezing weights full_epochs = 30 # epochs trained on 1024 data with only large masks #full_epochs_all = 10 # epochs trained on all positive masks # model parameters filt_nums = 16 num_blocks = 4 # datagen params pre_train_params = get_train_params(pre_batch_size, pre_im_dims, pre_n_channels) pre_val_params = get_val_params(pre_batch_size, pre_im_dims, pre_n_channels) train_params = get_train_params(batch_size, im_dims, n_channels) val_params = get_val_params(batch_size, im_dims, n_channels) full_train_params = get_train_params(batch_size_1024, (1024, 1024), 1) full_val_params = get_val_params(batch_size_1024, (1024, 1024), 1) #I've saved the preprocessed data with clahe, so remove it #pre_train_params["preprocessing_function"] = 'None' #pre_val_params["preprocessing_function"] = 'None' train_params["preprocessing_function"] = 'None' val_params["preprocessing_function"] = 'None' full_train_params["preprocessing_function"] = 'None' full_val_params["preprocessing_function"] = 'None' # %% ~~~~~~~~~~~~~~~~~~~~~~~ # ~~~~Pre-training~~~~~~