def clear_monitor_files(training_dir):
files = detect_monitor_files(training_dir)
if len(files) == 0:
return
for file in files:
print(file)
os.unlink(file)
if __name__ == '__main__':
#REMEMBER!: turtlebot_nn_setup.bash must be executed.
env = gym.make('Gazebo_ENPH_Ai_Adeept_Awr_Empty_NN-v0')
outdir = '/tmp/gazebo_gym_experiments/'
path = '/tmp/turtle_c2_dqn_ep'
plotter = liveplot.LivePlot(outdir)
continue_execution = False
#fill this if continue_execution=True
resume_epoch = '200' # change to epoch to continue from
resume_path = path + resume_epoch
weights_path = resume_path + '.h5'
monitor_path = resume_path
params_json = resume_path + '.json'
if not continue_execution:
#Each time we take a sample and update our weights it is called a mini-batch.
#Each time we run through the entire dataset, it's called an epoch.
#PARAMETER LIST
epochs = 10000
steps = 100
optimizer_G = torch.optim.Adam(generator.parameters(),
lr=opt.lr,
betas=(opt.b1, opt.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(),
lr=opt.lr,
betas=(opt.b1, opt.b2))
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
D_hist = []
G_hist = []
bound_min = (1, -1)
bound_max = (-1, -1)
plot = lp.LivePlot((800, 600))
def lerp(v0, v1, t):
return v0 + t * (v1 - v0)
etqdm = tqdm.trange(opt.n_epochs)
for epoch in etqdm:
btqdm = tqdm.tqdm(enumerate(dataloader), total=len(dataloader))
for i, (imgs, _) in btqdm:
valid = Variable(Tensor(imgs.size(0), 1).fill_(1.),
requires_grad=False)
fake = Variable(Tensor(imgs.size(0), 1).fill_(0.), requires_grad=False)
real_imgs = Variable(imgs.type(Tensor))
