How to use the deeplabcut.train_network function in deeplabcut

print("CREATING TRAININGSET")
deeplabcut.create_training_dataset(path_config_file)

posefile=os.path.join(cfg['project_path'],'dlc-models/iteration-'+str(cfg['iteration'])+'/'+ cfg['Task'] + cfg['date'] + '-trainset' + str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(1),'train/pose_cfg.yaml')

DLC_config=deeplabcut.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['save_iters']=10
DLC_config['display_iters']=1
DLC_config['multi_step']=[[0.001,10]]

print("CHANGING training parameters to end quickly!")
deeplabcut.auxiliaryfunctions.write_plainconfig(posefile,DLC_config)

print("TRAIN")
deeplabcut.train_network(path_config_file)

print("TRAIN again... different loss?")
deeplabcut.train_network(path_config_file)


DLC_config=deeplabcut.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['dataset_type']='deterministic'
DLC_config['deterministic']=True
deeplabcut.auxiliaryfunctions.write_plainconfig(posefile,DLC_config)

print("TRAIN")
deeplabcut.train_network(path_config_file)

print("TRAIN again... the same losses!")
deeplabcut.train_network(path_config_file)

print("CREATING TRAININGSET")
deeplabcut.create_training_dataset(path_config_file)

cfg=read_config(path_config_file)
posefile=os.path.join(cfg['project_path'],'dlc-models/iteration-'+str(cfg['iteration'])+'/'+ cfg['Task'] + cfg['date'] + '-trainset' + str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(1),'train/pose_cfg.yaml')
DLC_config=read_config(posefile)
DLC_config['save_iters']=5
DLC_config['display_iters']=1
DLC_config['multi_step']=[[0.05,5]]

print("CHANGING training parameters to end quickly!")
write_config(posefile,DLC_config)

print("TRAIN")
deeplabcut.train_network(path_config_file)


print("ALL DONE!!! - default cases are functional.")

print("CREATING TRAININGSET")
deeplabcut.create_training_dataset(path_config_file,net_type=net_type,augmenter_type=augmenter_type)

posefile=os.path.join(cfg['project_path'],'dlc-models/iteration-'+str(cfg['iteration'])+'/'+ cfg['Task'] + cfg['date'] + '-trainset' + str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(1),'train/pose_cfg.yaml')

DLC_config=deeplabcut.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['save_iters']=numiter
DLC_config['display_iters']=2
DLC_config['multi_step']=[[0.001,numiter]]

print("CHANGING training parameters to end quickly!")
deeplabcut.auxiliaryfunctions.write_plainconfig(posefile,DLC_config)

print("TRAIN")
deeplabcut.train_network(path_config_file)

print("EVALUATE")
deeplabcut.evaluate_network(path_config_file,plotting=True)
#deeplabcut.evaluate_network(path_config_file,plotting=True,trainingsetindex=33)
print("CUT SHORT VIDEO AND ANALYZE (with dynamic cropping!)")

# Make super short video (so the analysis is quick!)

try: #you need ffmpeg command line interface
    #subprocess.call(['ffmpeg','-i',video[0],'-ss','00:00:00','-to','00:00:00.4','-c','copy',newvideo])
    newvideo=deeplabcut.ShortenVideo(video[0],start='00:00:00',stop='00:00:00.4',outsuffix='short',outpath=os.path.join(cfg['project_path'],'videos'))
    vname=Path(newvideo).stem
except: # if ffmpeg is broken
    vname='brief'
    newvideo=os.path.join(cfg['project_path'],'videos',vname+'.mp4')
    from moviepy.editor import VideoFileClip,VideoClip

deeplabcut.create_training_dataset(path_config_file,Shuffles=[3],trainIndexes=trainIndexes,testIndexes=testIndexes)
for shuffle in [2,3]:
	if shuffle==3:
		posefile=os.path.join(cfg['project_path'],'dlc-models/iteration-'+str(cfg['iteration'])+'/'+ cfg['Task'] + cfg['date'] + '-trainset' + str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(shuffle),'train/pose_cfg.yaml')

		DLC_config=deeplabcut.auxiliaryfunctions.read_plainconfig(posefile)
		DLC_config['dataset_type']='tensorpack'
		deeplabcut.auxiliaryfunctions.write_plainconfig(posefile,DLC_config)
'''

###Note that the new function in DLC 2.1 does that much easier...
deeplabcut.create_training_model_comparison(path_config_file,num_shuffles=1,net_types=['resnet_50'],augmenter_types=['imgaug','default','tensorpack'])

for shuffle in [2,3]:
	print("TRAIN NETWORK", shuffle)
	deeplabcut.train_network(path_config_file, shuffle=shuffle,saveiters=10000,displayiters=200,maxiters=5,max_snapshots_to_keep=11)

	print("EVALUATE")
	deeplabcut.evaluate_network(path_config_file, Shuffles=[shuffle],plotting=True)

	print("Analyze Video")

	videofile_path = os.path.join(os.getcwd(),'openfield-Pranav-2018-10-30','videos','m3v1mp4.mp4')

	deeplabcut.analyze_videos(path_config_file,[videofile_path], shuffle=shuffle)

	print("Create Labeled Video and plot")
	deeplabcut.create_labeled_video(path_config_file,[videofile_path], shuffle=shuffle)
	deeplabcut.plot_trajectories(path_config_file,[videofile_path], shuffle=shuffle)

posefile=os.path.join(cfg['project_path'],'dlc-models/iteration-'+str(cfg['iteration'])+'/'+ cfg['Task'] + cfg['date'] + '-trainset' + str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(1),'train/pose_cfg.yaml')

DLC_config=deeplabcut.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['save_iters']=10
DLC_config['display_iters']=1
DLC_config['multi_step']=[[0.001,10]]

print("CHANGING training parameters to end quickly!")
deeplabcut.auxiliaryfunctions.write_plainconfig(posefile,DLC_config)

print("TRAIN")
deeplabcut.train_network(path_config_file)

print("TRAIN again... different loss?")
deeplabcut.train_network(path_config_file)


DLC_config=deeplabcut.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['dataset_type']='deterministic'
DLC_config['deterministic']=True
deeplabcut.auxiliaryfunctions.write_plainconfig(posefile,DLC_config)

print("TRAIN")
deeplabcut.train_network(path_config_file)

print("TRAIN again... the same losses!")
deeplabcut.train_network(path_config_file)

print("ALL DONE!!! - default cases are functional.")

print("CREATING TRAININGSET for shuffle 2")
print("will be used for 3D testscript...")
deeplabcut.create_training_dataset(path_config_file,Shuffles=[2],net_type=net_type,augmenter_type=augmenter_type3)

posefile=os.path.join(cfg['project_path'],'dlc-models/iteration-'+str(cfg['iteration'])+'/'+ cfg['Task'] + cfg['date'] + '-trainset' + str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(2),'train/pose_cfg.yaml')

DLC_config=deeplabcut.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['save_iters']=10
DLC_config['display_iters']=2
DLC_config['multi_step']=[[0.001,10]]

print("CHANGING training parameters to end quickly!")
deeplabcut.auxiliaryfunctions.write_plainconfig(posefile,DLC_config)

print("TRAINING shuffle 2, with smaller allocated memory")
deeplabcut.train_network(path_config_file,shuffle=2,allow_growth=True)

print("ANALYZING some individual frames")
deeplabcut.analyze_time_lapse_frames(path_config_file,os.path.join(cfg['project_path'],'labeled-data/reachingvideo1/'))
print("Attempting to ANALYZING some individual frames..")
deeplabcut.analyze_time_lapse_frames(path_config_file,os.path.join(cfg['project_path'],'labeled-data/reachingvideo1/'))


print("ALL DONE!!! - default cases are functional.")
print("Re-import DLC with env. variable set to test DLC light mode.")
os.environ['DLClight']='True'
subprocess.call(['python3',"-c","import deeplabcut"])

#shuffle=11 #>> imageio functions!
shuffle=12

deeplabcut.create_training_dataset(path_config_file,Shuffles=[shuffle])
cfg=deeplabcut.auxiliaryfunctions.read_config(path_config_file)
posefile=os.path.join(cfg['project_path'],'dlc-models/iteration-'+str(cfg['iteration'])+'/'+ cfg['Task'] + cfg['date'] + '-trainset' + str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(shuffle),'train/pose_cfg.yaml')

DLC_config=deeplabcut.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['save_iters']=10
DLC_config['display_iters']=2
DLC_config['multi_step']=[[0.005,15001]]
deeplabcut.auxiliaryfunctions.write_plainconfig(posefile,DLC_config)


print("TRAIN NETWORK")
deeplabcut.train_network(path_config_file, shuffle=shuffle,saveiters=15000,displayiters=1000,max_snapshots_to_keep=15)

print("EVALUATE")
deeplabcut.evaluate_network(path_config_file, Shuffles=[shuffle],plotting=True)

dataFrame.to_hdf(os.path.join(cfg['project_path'],'labeled-data',videoname,"CollectedData_" + scorer + '.h5'),'df_with_missing',format='table', mode='w')

print("CREATING TRAININGSET")
deeplabcut.create_training_dataset(path_config_file)

posefile=os.path.join(cfg['project_path'],'dlc-models/iteration-'+str(cfg['iteration'])+'/'+ cfg['Task'] + cfg['date'] + '-trainset' + str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(1),'train/pose_cfg.yaml')
DLC_config=read_config(posefile)
DLC_config['save_iters']=10
DLC_config['display_iters']=2
DLC_config['multi_step']=[[0.001,10]]

print("CHANGING training parameters to end quickly!")
write_config(posefile,DLC_config)

print("TRAIN")
deeplabcut.train_network(path_config_file)

#this is much easier now: deeplabcut.train_network(path_config_file,gputouse=0,max_snapshots_to_keep=None,saveiters=1)

print("EVALUATE")
deeplabcut.evaluate_network(path_config_file,plotting=True)

print("CUT SHORT VIDEO AND ANALYZE")

# Make super short video (so the analysis is quick!)
vname='brief'
newvideo=os.path.join(cfg['project_path'],'videos',vname+'.avi')

subprocess.call(['ffmpeg','-i',video[0],'-ss','00:00:00','-to','00:00:00.4','-c','copy',newvideo])
deeplabcut.analyze_videos(path_config_file,[newvideo])

print("CREATE VIDEO")