Spaces:
Sleeping
Sleeping
| # Written by Dr Daniel Buscombe, Marda Science LLC | |
| # for the SandSnap Program | |
| # | |
| # MIT License | |
| # | |
| # Copyright (c) 2020-2021, Marda Science LLC | |
| # | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy | |
| # of this software and associated documentation files (the "Software"), to deal | |
| # in the Software without restriction, including without limitation the rights | |
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| # copies of the Software, and to permit persons to whom the Software is | |
| # furnished to do so, subject to the following conditions: | |
| # | |
| # The above copyright notice and this permission notice shall be included in all | |
| # copies or substantial portions of the Software. | |
| # | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| # SOFTWARE. | |
| ##> Release v1.4 (Aug 2021) | |
| from sedinet_models import * | |
| ###=================================================== | |
| def run_training_siso_simo(vars, train_csvfile, test_csvfile, val_csvfile, name, res_folder, | |
| mode, greyscale, dropout, numclass): #scale | |
| """ | |
| This function generates, trains and evaluates a sedinet model for | |
| continuous prediction | |
| """ | |
| if numclass>0: | |
| ID_MAP = dict(zip(np.arange(numclass), [str(k) for k in range(numclass)])) | |
| # ##====================================== | |
| # ## this randomly selects imagery for training and testing imagery sets | |
| # ## while also making sure that both training and tetsing sets have | |
| # ## at least 3 examples of each category | |
| # train_idx, train_df, _ = get_df(train_csvfile,fortrain=True) | |
| # test_idx, test_df, _ = get_df(test_csvfile,fortrain=True) | |
| ##============================================== | |
| ## create a sedinet model to estimate category | |
| if numclass>0: | |
| SM = make_cat_sedinet(ID_MAP, dropout) | |
| else: | |
| SM = make_sedinet_siso_simo(vars, greyscale, dropout) | |
| # if scale==True: | |
| # CS = [] | |
| # for var in vars: | |
| # cs = RobustScaler() ##alternative = MinMaxScaler() | |
| # cs.fit_transform( | |
| # np.r_[train_df[var].values, test_df[var].values].reshape(-1,1) | |
| # ) | |
| # CS.append(cs) | |
| # del cs | |
| # else: | |
| # CS = [] | |
| ##============================================== | |
| ## train model | |
| if numclass==0: | |
| if type(BATCH_SIZE)==list: | |
| SMs = []; weights_path = [] | |
| for batch_size, valid_batch_size in zip(BATCH_SIZE, VALID_BATCH_SIZE): | |
| sm, wp,train_df, test_df, val_df, train_idx, test_idx, val_idx = train_sedinet_siso_simo(SM, name, | |
| train_csvfile, test_csvfile, val_csvfile, vars, mode, greyscale, #CS, | |
| dropout, batch_size, valid_batch_size, | |
| res_folder)#, scale) | |
| SMs.append(sm) | |
| weights_path.append(wp) | |
| gc.collect() | |
| else: | |
| SM, weights_path,train_df, test_df, val_df, train_idx, test_idx, val_idx = train_sedinet_siso_simo(SM, name, | |
| train_csvfile, test_csvfile, val_csvfile, vars, mode, greyscale, #CS, | |
| dropout, BATCH_SIZE, VALID_BATCH_SIZE, | |
| res_folder)#, scale) | |
| else: | |
| if type(BATCH_SIZE)==list: | |
| SMs = []; weights_path = [] | |
| for batch_size, valid_batch_size in zip(BATCH_SIZE, VALID_BATCH_SIZE): | |
| sm, wp = train_sedinet_cat(SM, train_df, test_df, train_idx, | |
| test_idx, ID_MAP, vars, greyscale, name, mode, | |
| batch_size, valid_batch_size, res_folder) | |
| SMs.append(sm) | |
| weights_path.append(wp) | |
| gc.collect() | |
| else: | |
| SM, weights_path = train_sedinet_cat(SM, train_df, test_df, train_idx, | |
| test_idx, ID_MAP, vars, greyscale, name, mode, | |
| BATCH_SIZE, VALID_BATCH_SIZE, res_folder) | |
| classes = np.arange(len(ID_MAP)) | |
| K.clear_session() | |
| ##============================================== | |
| # test model | |
| do_aug = False | |
| for_training = False | |
| if type(test_df)==list: | |
| print('Reading in all train files and memory mapping in batches ... takes a while') | |
| test_gen = [] | |
| for df,id in zip(test_df,test_idx): | |
| test_gen.append(get_data_generator_Nvars_siso_simo(df, id, for_training, | |
| vars, len(id), greyscale, do_aug, DO_STANDARDIZE, IM_HEIGHT)) #CS, | |
| x_test = []; test_vals = []; files = [] | |
| for gen in test_gen: | |
| a, b = next(gen) | |
| outfile = TemporaryFile() | |
| files.append(outfile) | |
| dt = a.dtype; sh = a.shape | |
| fp = np.memmap(outfile, dtype=dt, mode='w+', shape=sh) | |
| fp[:] = a[:] | |
| fp.flush() | |
| del a | |
| del fp | |
| a = np.memmap(outfile, dtype=dt, mode='r', shape=sh) | |
| x_test.append(a) | |
| test_vals.append(b) | |
| else: | |
| # train_gen = get_data_generator_Nvars_siso_simo(train_df, train_idx, for_training, | |
| # vars, len(train_idx), greyscale, do_aug, DO_STANDARDIZE, IM_HEIGHT)#CS, | |
| # x_train, train_vals = next(train_gen) | |
| test_gen = get_data_generator_Nvars_siso_simo(test_df, test_idx, for_training, | |
| vars, len(test_idx), greyscale, do_aug, DO_STANDARDIZE, IM_HEIGHT) | |
| x_test, test_vals = next(test_gen) | |
| # if numclass==0: | |
| # # suffix = 'train' | |
| # if type(BATCH_SIZE)==list: | |
| # count_in = 0 | |
| # predict_train_siso_simo(x_train, train_vals, vars, #train_df, test_df, train_idx, test_idx, vars, x_test, test_vals, | |
| # SMs, weights_path, name, mode, greyscale,# CS, | |
| # dropout, DO_AUG,DO_STANDARDIZE, count_in)#scale, | |
| # else: | |
| # if type(x_train)==list: | |
| # for count_in, (a, b) in enumerate(zip(x_train, train_vals)): #x_test, test_vals | |
| # predict_train_siso_simo(a, b, vars, #train_df, test_df, train_idx, test_idx, vars, c, d, | |
| # SM, weights_path, name, mode, greyscale,# CS, | |
| # dropout, DO_AUG,DO_STANDARDIZE, count_in)#scale, | |
| # plot_all_save_all(weights_path, vars) | |
| # else: | |
| # count_in = 0; consolidate = False | |
| # predict_train_siso_simo(x_train, train_vals, vars, #train_df, test_df, train_idx, test_idx, vars, x_test, test_vals, | |
| # SM, weights_path, name, mode, greyscale,# CS, | |
| # dropout, DO_AUG,DO_STANDARDIZE, count_in)#scale, | |
| if numclass==0: | |
| if type(BATCH_SIZE)==list: | |
| count_in = 0 | |
| predict_train_siso_simo(x_test, test_vals, vars, | |
| SMs, weights_path, name, mode, greyscale, | |
| dropout, DO_AUG,DO_STANDARDIZE, count_in) | |
| else: | |
| if type(x_test)==list: | |
| for count_in, (a, b) in enumerate(zip(x_test, test_vals)): | |
| predict_train_siso_simo(a, b, vars, | |
| SM, weights_path, name, mode, greyscale, | |
| dropout, DO_AUG,DO_STANDARDIZE, count_in) | |
| plot_all_save_all(weights_path, vars) | |
| else: | |
| count_in = 0; #consolidate = False | |
| predict_train_siso_simo(x_test, test_vals, vars, | |
| SM, weights_path, name, mode, greyscale, | |
| dropout, DO_AUG,DO_STANDARDIZE, count_in) | |
| else: | |
| if type(BATCH_SIZE)==list: | |
| predict_test_train_cat(train_df, test_df, train_idx, test_idx, vars[0], | |
| SMs, [i for i in ID_MAP.keys()], weights_path, greyscale, | |
| name, DO_AUG,DO_STANDARDIZE) | |
| else: | |
| predict_test_train_cat(train_df, test_df, train_idx, test_idx, vars[0], | |
| SM, [i for i in ID_MAP.keys()], weights_path, greyscale, | |
| name, DO_AUG,DO_STANDARDIZE) | |
| K.clear_session() | |
| # | |
| ##=================================== | |
| ## move model files and plots to the results folder | |
| tidy(name, res_folder) | |
| ###================================== | |
| def train_sedinet_cat(SM, train_csvfile, test_csvfile, #train_df, test_df, train_idx, test_idx, | |
| ID_MAP, vars, greyscale, name, mode, batch_size, valid_batch_size, | |
| res_folder): | |
| """ | |
| This function trains an implementation of SediNet | |
| """ | |
| ##================================ | |
| ## create training and testing file generators, set the weights path, | |
| ## plot the model, and create a callback list for model training | |
| for_training=True | |
| train_gen = get_data_generator_1image(train_df, train_idx, for_training, ID_MAP, | |
| vars[0], batch_size, greyscale, DO_AUG, DO_STANDARDIZE, IM_HEIGHT) ##BATCH_SIZE | |
| do_aug = False | |
| valid_gen = get_data_generator_1image(test_df, test_idx, for_training, ID_MAP, | |
| vars[0], valid_batch_size, greyscale, do_aug, DO_STANDARDIZE, IM_HEIGHT) ##VALID_BATCH_SIZE | |
| if SHALLOW is True: | |
| if DO_AUG is True: | |
| weights_path = name+"_"+mode+"_batch"+str(batch_size)+"_im"+str(IM_HEIGHT)+\ | |
| "_"+str(IM_WIDTH)+"_shallow_"+vars[0]+"_"+CAT_LOSS+"_aug.hdf5" | |
| else: | |
| weights_path = name+"_"+mode+"_batch"+str(batch_size)+"_im"+str(IM_HEIGHT)+\ | |
| "_"+str(IM_WIDTH)+"_shallow_"+vars[0]+"_"+CAT_LOSS+"_noaug.hdf5" | |
| else: | |
| if DO_AUG is True: | |
| weights_path = name+"_"+mode+"_batch"+str(batch_size)+"_im"+str(IM_HEIGHT)+\ | |
| "_"+str(IM_WIDTH)+"_"+vars[0]+"_"+CAT_LOSS+"_aug.hdf5" | |
| else: | |
| weights_path = name+"_"+mode+"_batch"+str(batch_size)+"_im"+str(IM_HEIGHT)+\ | |
| "_"+str(IM_WIDTH)+"_"+vars[0]+"_"+CAT_LOSS+"_noaug.hdf5" | |
| if os.path.exists(weights_path): | |
| SM.load_weights(weights_path) | |
| print("==========================================") | |
| print("Loading weights that already exist: %s" % (weights_path) ) | |
| print("Skipping model training") | |
| elif os.path.exists(res_folder+os.sep+weights_path): | |
| weights_path = res_folder+os.sep+weights_path | |
| SM.load_weights(weights_path) | |
| print("==========================================") | |
| print("Loading weights that already exist: %s" % (weights_path) ) | |
| print("Skipping model training") | |
| else: | |
| try: | |
| plot_model(SM, weights_path.replace('.hdf5', '_model.png'), | |
| show_shapes=True, show_layer_names=True) | |
| except: | |
| pass | |
| callbacks_list = [ | |
| ModelCheckpoint(weights_path, monitor='val_loss', verbose=1, | |
| save_best_only=True, mode='min', | |
| save_weights_only = True) | |
| ] | |
| print("=========================================") | |
| print("[INFORMATION] schematic of the model has been written out to: "+\ | |
| weights_path.replace('.hdf5', '_model.png')) | |
| print("[INFORMATION] weights will be written out to: "+weights_path) | |
| ##============================================== | |
| ## set checkpoint file and parameters that control early stopping, | |
| ## and reduction of learning rate if and when validation | |
| ## scores plateau upon successive epochs | |
| # reduceloss_plat = ReduceLROnPlateau(monitor='val_loss', factor=FACTOR, | |
| # patience=STOP_PATIENCE, verbose=1, mode='auto', min_delta=MIN_DELTA, | |
| # cooldown=STOP_PATIENCE, min_lr=MIN_LR) | |
| # | |
| earlystop = EarlyStopping(monitor="val_loss", mode="min", patience=10) | |
| model_checkpoint = ModelCheckpoint(weights_path, monitor='val_loss', | |
| verbose=1, save_best_only=True, mode='min', | |
| save_weights_only = True) | |
| ##============================================== | |
| ## train the model | |
| ## with non-adaptive exponentially decreasing learning rate | |
| #exponential_decay_fn = exponential_decay(MAX_LR, NUM_EPOCHS) | |
| #lr_scheduler = LearningRateScheduler(exponential_decay_fn) | |
| callbacks_list = [model_checkpoint, earlystop] #lr_scheduler | |
| ## train the model | |
| history = SM.fit(train_gen, | |
| steps_per_epoch=len(train_idx)//batch_size, ##BATCH_SIZE | |
| epochs=NUM_EPOCHS, | |
| callbacks=callbacks_list, | |
| validation_data=valid_gen, #use_multiprocessing=True, | |
| validation_steps=len(test_idx)//valid_batch_size) #max_queue_size=10 ##VALID_BATCH_SIZE | |
| ###=================================================== | |
| ## Plot the loss and accuracy as a function of epoch | |
| plot_train_history_1var(history) | |
| # plt.savefig(vars+'_'+str(IM_HEIGHT)+'_batch'+str(batch_size)+'_history.png', ##BATCH_SIZE | |
| # dpi=300, bbox_inches='tight') | |
| plt.savefig(weights_path.replace('.hdf5','_history.png'),dpi=300, bbox_inches='tight') | |
| plt.close('all') | |
| # serialize model to JSON to use later to predict | |
| model_json = SM.to_json() | |
| with open(weights_path.replace('.hdf5','.json'), "w") as json_file: | |
| json_file.write(model_json) | |
| return SM, weights_path | |
| ###=================================================== | |
| def train_sedinet_siso_simo(SM, name, train_csvfile, test_csvfile, val_csvfile, #train_df, test_df, train_idx, test_idx, | |
| vars, mode, greyscale, dropout, batch_size, valid_batch_size,#CS, | |
| res_folder):#, scale): | |
| """ | |
| This function trains an implementation of sedinet | |
| """ | |
| ##============================================== | |
| ## create training and testing file generators, set the weights path, | |
| ## plot the model, and create a callback list for model training | |
| # get a string saying how many variables, fr the output files | |
| varstring = str(len(vars))+'vars' #''.join([str(k)+'_' for k in vars]) | |
| # mae the appropriate weights file | |
| if SHALLOW is True: | |
| if DO_AUG is True: | |
| # if len(CS)>0:#scale is True: | |
| # weights_path = name+"_"+mode+"_batch"+str(batch_size)+"_im"+str(IM_HEIGHT)+\ | |
| # "_"+str(IM_WIDTH)+"_shallow_"+varstring+"_"+CONT_LOSS+"_aug_scale.hdf5" | |
| # else: | |
| weights_path = name+"_"+mode+"_batch"+str(batch_size)+"_im"+str(IM_HEIGHT)+\ | |
| "_"+str(IM_WIDTH)+"_shallow_"+varstring+"_"+CONT_LOSS+"_aug.hdf5" | |
| else: | |
| # if len(CS)>0:#scale is True: | |
| # weights_path = name+"_"+mode+"_batch"+str(batch_size)+"_im"+str(IM_HEIGHT)+\ | |
| # "_"+str(IM_WIDTH)+"_shallow_"+varstring+"_"+CONT_LOSS+"_noaug_scale.hdf5" | |
| # else: | |
| weights_path = name+"_"+mode+"_batch"+str(batch_size)+"_im"+str(IM_HEIGHT)+\ | |
| "_"+str(IM_WIDTH)+"_shallow_"+varstring+"_"+CONT_LOSS+"_noaug.hdf5" | |
| else: | |
| if DO_AUG is True: | |
| # if len(CS)>0:#scale is True: | |
| # weights_path = name+"_"+mode+"_batch"+str(batch_size)+"_im"+str(IM_HEIGHT)+\ | |
| # "_"+str(IM_WIDTH)+"_"+varstring+"_"+CONT_LOSS+"_aug_scale.hdf5" | |
| # else: | |
| weights_path = name+"_"+mode+"_batch"+str(batch_size)+"_im"+str(IM_HEIGHT)+\ | |
| "_"+str(IM_WIDTH)+"_"+varstring+"_"+CONT_LOSS+"_aug.hdf5" | |
| else: | |
| # if len(CS)>0:#scale is True: | |
| # weights_path = name+"_"+mode+"_batch"+str(batch_size)+"_im"+str(IM_HEIGHT)+\ | |
| # "_"+str(IM_WIDTH)+"_"+varstring+"_"+CONT_LOSS+"_noaug_scale.hdf5" | |
| # else: | |
| weights_path = name+"_"+mode+"_batch"+str(batch_size)+"_im"+str(IM_HEIGHT)+\ | |
| "_"+varstring+"_"+CONT_LOSS+"_noaug.hdf5" | |
| # if it already exists, skip training | |
| if os.path.exists(weights_path): | |
| SM.load_weights(weights_path) | |
| print("==========================================") | |
| print("Loading weights that already exist: %s" % (weights_path) ) | |
| print("Skipping model training") | |
| ##====================================== | |
| ## this randomly selects imagery for training and testing imagery sets | |
| ## while also making sure that both training and tetsing sets have | |
| ## at least 3 examples of each category | |
| train_idx, train_df, _ = get_df(train_csvfile,fortrain=False) | |
| test_idx, test_df, _ = get_df(test_csvfile,fortrain=False) | |
| val_idx, test_df, _ = get_df(val_csvfile,fortrain=False) | |
| for_training = False | |
| train_gen = get_data_generator_Nvars_siso_simo(train_df, train_idx, for_training, | |
| vars, batch_size, greyscale, | |
| DO_AUG, DO_STANDARDIZE, IM_HEIGHT) # CS, | |
| do_aug = False | |
| valid_gen = get_data_generator_Nvars_siso_simo(val_df, val_idx, for_training, | |
| vars, valid_batch_size, greyscale, | |
| do_aug, DO_STANDARDIZE, IM_HEIGHT) ##only augment training # CS, | |
| # do_aug = False | |
| # test_gen = get_data_generator_Nvars_siso_simo(test_df, test_idx, for_training, | |
| # vars, valid_batch_size, greyscale, | |
| # do_aug, DO_STANDARDIZE, IM_HEIGHT) ##only augment training # CS, | |
| # if it already exists in res_folder, skip training | |
| elif os.path.exists(res_folder+os.sep+weights_path): | |
| weights_path = res_folder+os.sep+weights_path | |
| SM.load_weights(weights_path) | |
| print("==========================================") | |
| print("Loading weights that already exist: %s" % (weights_path) ) | |
| print("Skipping model training") | |
| ##====================================== | |
| ## this randomly selects imagery for training and testing imagery sets | |
| ## while also making sure that both training and tetsing sets have | |
| ## at least 3 examples of each category | |
| train_idx, train_df, _ = get_df(train_csvfile,fortrain=False) | |
| test_idx, test_df, _ = get_df(test_csvfile,fortrain=False) | |
| val_idx, val_df, _ = get_df(val_csvfile,fortrain=False) | |
| for_training = False | |
| train_gen = get_data_generator_Nvars_siso_simo(train_df, train_idx, for_training, | |
| vars, batch_size, greyscale, | |
| DO_AUG, DO_STANDARDIZE, IM_HEIGHT) # CS, | |
| do_aug = False | |
| valid_gen = get_data_generator_Nvars_siso_simo(val_df, val_idx, for_training, | |
| vars, valid_batch_size, greyscale, | |
| do_aug, DO_STANDARDIZE, IM_HEIGHT) ##only augment training # CS, | |
| # do_aug = False | |
| # test_gen = get_data_generator_Nvars_siso_simo(test_df, test_idx, for_training, | |
| # vars, valid_batch_size, greyscale, | |
| # do_aug, DO_STANDARDIZE, IM_HEIGHT) ##only augment training # CS, | |
| else: #train | |
| ##====================================== | |
| ## this randomly selects imagery for training and testing imagery sets | |
| ## while also making sure that both training and tetsing sets have | |
| ## at least 3 examples of each category | |
| train_idx, train_df, _ = get_df(train_csvfile,fortrain=True) | |
| test_idx, test_df, _ = get_df(test_csvfile,fortrain=True) | |
| val_idx, val_df, _ = get_df(val_csvfile,fortrain=True) | |
| for_training = True | |
| train_gen = get_data_generator_Nvars_siso_simo(train_df, train_idx, for_training, | |
| vars, batch_size, greyscale, | |
| DO_AUG, DO_STANDARDIZE, IM_HEIGHT) # CS, | |
| # do_aug = False | |
| # test_gen = get_data_generator_Nvars_siso_simo(test_df, test_idx, for_training, | |
| # vars, valid_batch_size, greyscale, | |
| # do_aug, DO_STANDARDIZE, IM_HEIGHT) ##only augment training # CS, | |
| do_aug = False | |
| valid_gen = get_data_generator_Nvars_siso_simo(val_df, val_idx, for_training, | |
| vars, valid_batch_size, greyscale, | |
| do_aug, DO_STANDARDIZE, IM_HEIGHT) ##only augment training # CS, | |
| # if scaler=true (CS=[]), dump out scalers to pickle file | |
| # if len(CS)==0: | |
| # pass | |
| # else: | |
| # joblib.dump(CS, weights_path.replace('.hdf5','_scaler.pkl')) | |
| # print('Wrote scaler to pkl file') | |
| try: # plot the model if pydot/graphviz installed | |
| plot_model(SM, weights_path.replace('.hdf5', '_model.png'), | |
| show_shapes=True, show_layer_names=True) | |
| print("model schematic written to: "+\ | |
| weights_path.replace('.hdf5', '_model.png')) | |
| except: | |
| pass | |
| print("==========================================") | |
| print("weights will be written out to: "+weights_path) | |
| ##============================================== | |
| ## set checkpoint file and parameters that control early stopping, | |
| ## and reduction of learning rate if and when validation scores plateau upon successive epochs | |
| # reduceloss_plat = ReduceLROnPlateau(monitor='val_loss', factor=FACTOR, | |
| # patience=STOP_PATIENCE, verbose=1, mode='auto', | |
| # min_delta=MIN_DELTA, cooldown=5, | |
| # min_lr=MIN_LR) | |
| earlystop = EarlyStopping(monitor="val_loss", mode="min", | |
| patience=10) | |
| # set model checkpoint. only save best weights, based on min validation loss | |
| model_checkpoint = ModelCheckpoint(weights_path, monitor='val_loss', verbose=1, | |
| save_best_only=True, mode='min', | |
| save_weights_only = True) | |
| #tqdm_callback = tfa.callbacks.TQDMProgressBar() | |
| # callbacks_list = [model_checkpoint, reduceloss_plat, earlystop] #, tqdm_callback] | |
| try: #write summary of the model to txt file | |
| with open(weights_path.replace('.hdf5','') + '_report.txt','w') as fh: | |
| # Pass the file handle in as a lambda function to make it callable | |
| SM.summary(print_fn=lambda x: fh.write(x + '\n')) | |
| fh.close() | |
| print("model summary written to: "+ \ | |
| weights_path.replace('.hdf5','') + '_report.txt') | |
| with open(weights_path.replace('.hdf5','') + '_report.txt','r') as fh: | |
| tmp = fh.readlines() | |
| print("===============================================") | |
| print("Total parameters: %s" %\ | |
| (''.join(tmp).split('Total params:')[-1].split('\n')[0])) | |
| fh.close() | |
| print("===============================================") | |
| except: | |
| pass | |
| ##============================================== | |
| ## train the model | |
| ## non-adaptive exponentially decreasing learning rate | |
| # exponential_decay_fn = exponential_decay(MAX_LR, NUM_EPOCHS) | |
| #lr_scheduler = LearningRateScheduler(exponential_decay_fn) | |
| callbacks_list = [model_checkpoint, earlystop] #lr_scheduler | |
| ## train the model | |
| history = SM.fit(train_gen, | |
| steps_per_epoch=len(train_idx)//batch_size, ##BATCH_SIZE | |
| epochs=NUM_EPOCHS, | |
| callbacks=callbacks_list, | |
| validation_data=valid_gen, #use_multiprocessing=True, | |
| validation_steps=len(val_idx)//valid_batch_size) #max_queue_size=10 ##VALID_BATCH_SIZE | |
| ###=================================================== | |
| ## Plot the loss and accuracy as a function of epoch | |
| if len(vars)==1: | |
| plot_train_history_1var_mae(history) | |
| else: | |
| plot_train_history_Nvar(history, vars, len(vars)) | |
| varstring = ''.join([str(k)+'_' for k in vars]) | |
| plt.savefig(weights_path.replace('.hdf5', '_history.png'), dpi=300, | |
| bbox_inches='tight') | |
| plt.close('all') | |
| # serialize model to JSON to use later to predict | |
| model_json = SM.to_json() | |
| with open(weights_path.replace('.hdf5','.json'), "w") as json_file: | |
| json_file.write(model_json) | |
| return SM, weights_path,train_df, test_df, val_df, train_idx, test_idx, val_idx | |
| # | |