TEST=False
#The following parameters must have the same value in 'training' and 'test' modes.
num_layer=1
cell='lstm'
hidden_unit = 1000
time_step = 80
seed_timestep = 20 # 0.6 second motion seed / 2 second motion prediction
batch_Frame = 1
frame_time = 30
save_period = 0
'''Execution'''
if TEST :
MotionNet(TEST=TEST , save_period=1 , num_layer=num_layer , cell=cell, hidden_unit=hidden_unit , time_step = time_step ,
seed_timestep = seed_timestep , batch_Frame= batch_Frame , frame_time=frame_time , graphviz=True)
else:
#batch learning
completed = MotionNet(epoch=700000 , batch_size=75 , save_period=save_period, cost_limit=0.1 ,
optimizer='adam', learning_rate=0.0003 , lr_step=5000, lr_factor=0.99, stop_factor_lr=1e-08 , use_gpu=True ,
TEST=TEST , num_layer=num_layer , cell=cell , hidden_unit=hidden_unit , time_step = time_step ,
seed_timestep = seed_timestep , batch_Frame = batch_Frame , frame_time=frame_time , graphviz=True)
print(completed)
from network import MotionNet
import glob
test=False
time_step=140
seed_timestep=10
batch_Frame=1
epoch=300000
save_period=None
file_directory= glob.glob("Data/ACCAD/Transform_Male1_bvh/Short_data/*.bvh")
print(file_directory)
'''implement'''
if test==False:
start_value=1
geometric_progression=2
#Sequential learningg
for i in range(1,len(file_directory)+1,1):
completed,save_period=MotionNet(order = i, epoch=epoch*start_value , batch_size=i, save_period=save_period, optimizer='Adam', learning_rate=0.001 , lr_step=5000, lr_factor=0.99, stop_factor_lr=1e-08 , use_gpu=True , use_cudnn=True , test=test , predict_size=i ,time_step = time_step , seed_timestep = seed_timestep , batch_Frame= batch_Frame , frame_time=30)
print("{}-th data learning".format(i)+completed)
#geometric series
start_value*=geometric_progression
else : #test
MotionNet(order = len(file_directory) , epoch=None , batch_size=None , save_period=1360 , optimizer='sgd', learning_rate=0.5 , lr_step=1000, lr_factor=0.99, stop_factor_lr=1e-08 , use_gpu=True , use_cudnn=True , test=test , predict_size=None , time_step = time_step , seed_timestep = seed_timestep , batch_Frame= batch_Frame , frame_time=30)
hidden_unit = 1000
time_step = 90
seed_timestep = 30 #0.9 second motion seed / 2 second motion prediction
batch_Frame = 1
frame_time = 24
save_period = 100000
parameter_shared = True # Parameters that determine whether or not the encoder decoder will share parameters
'''Execution'''
if TEST:
MotionNet(TEST=TEST,
save_period=save_period,
num_layer=num_layer,
cell=cell,
hidden_unit=hidden_unit,
time_step=time_step,
seed_timestep=seed_timestep,
batch_Frame=batch_Frame,
frame_time=frame_time,
graphviz=True,
parameter_shared=parameter_shared,
Model=Model)
else:
#batch learning
MotionNet(epoch=100000,
batch_size=68,
save_period=save_period,
cost_limit=0.01,
optimizer='adam',
learning_rate=0.0001,
import mxnet as mx
from network import MotionNet
'''implement'''
MotionNet(epoch=1000,
batch_size=10,
save_period=1000,
optimizer='sgd',
learning_rate=0.001,
use_cudnn=True)
from network import MotionNet
TEST=True
#The following parameters must have the same value in 'training' and 'test' modes.
num_layer=1
hidden_unit = 1000
time_step = 90
batch_Frame = 1
save_period = 1000
use_gpu=True
use_cudnn=True
'''Execution'''
if TEST:
MotionNet(TEST=TEST , save_period=save_period, num_layer=num_layer , hidden_unit=hidden_unit , time_step = time_step , batch_Frame= batch_Frame , use_gpu=use_gpu , use_cudnn=use_cudnn , graphviz=False)
else:
#batch learning
MotionNet(epoch=1000 , batch_size=68 , save_period=save_period, optimizer='adam', learning_rate=0.01 ,Dropout=0.2 , use_gpu=use_gpu , use_cudnn=use_cudnn ,
TEST=TEST , num_layer=num_layer , hidden_unit=hidden_unit , time_step = time_step , batch_Frame = batch_Frame , graphviz=False )