class VAERNN(torch.nn.Module):
def __init__(self):
super(VAERNN, self).__init__()
self.z_size = 32
self.kl_tolerance = 0.5
self.vae = VAE()
self.rnn = RNN()
self.vae.train()
self.rnn.train()
self.init_()
self.is_cuda = False
def load(self):
self.vae.load_state_dict(torch.load(vae_model_path, map_location=lambda storage, loc: storage))
self.rnn.load_state_dict(torch.load(rnn_model_path, map_location=lambda storage, loc: storage))
def init_(self):
self.h = self.rnn.init_()
def forward(self, inputs):
z = self.vae(inputs)
return z
def when_train(self, inputs, one, outputs):
if self.is_cuda:
self.vae.is_cuda = True
self.vae.cuda()
self.rnn.is_cuda = True
self.rnn.cuda()
# self.rnn.init_()
z = self.vae(inputs)
# z = self.vae(inputs)
# self.next_kl_loss = self.vae.kl_loss
# self.next_r_loss = self.vae.r_loss
z = z.unsqueeze(0)
z_a = torch.cat((z, one), dim=2)
self.rnn(z_a)
z_next = self.vae(outputs)
self.next_kl_loss = self.vae.kl_loss
self.next_r_loss = self.vae.r_loss
z_next = z_next.unsqueeze(0)
# z_next = z
self.pred_loss = self.rnn.prediction_loss_f(z_next)
self.mdn_loss = self.rnn.mdn_loss_f(z_next)
class VAERNN(torch.nn.Module):
def __init__(self):
super(VAERNN, self).__init__()
self.z_size = 32
self.kl_tolerance = 0.5
self.vae = VAE()
self.rnn = RNN()
self.vae.train()
self.rnn.train()
self.init_()
self.is_cuda = False
def load(self):
self.vae.load_state_dict(
torch.load(vae_model_path,
map_location=lambda storage, loc: storage))
self.rnn.load_state_dict(
torch.load(rnn_model_path,
map_location=lambda storage, loc: storage))
def init_(self):
self.h = self.rnn.init_()
def forward(self, inputs):
z = self.vae(inputs)
return z
def when_train(self, inputs, one, outputs):
if self.is_cuda:
self.vae.is_cuda = True
self.vae.cuda()
self.rnn.is_cuda = True
self.rnn.cuda()
# with torch.no_grad():
z = self.vae(inputs)
self.next_kl_loss = self.vae.kl_loss
self.next_r_loss = self.vae.r_loss
class VAERNN(torch.nn.Module):
def __init__(self):
super(VAERNN, self).__init__()
self.z_size = 32
self.kl_tolerance = 0.5
self.vae = VAE()
self.rnn = RNN()
self.vae.load_state_dict(
torch.load(vae_model_path,
map_location=lambda storage, loc: storage))
self.rnn.load_state_dict(
torch.load(rnn_model_path,
map_location=lambda storage, loc: storage))
self.vae.train()
self.rnn.train()
self.init_()
self.is_cuda = False
def init_(self):
self.h = self.rnn.init_()
def forward(self, inputs):
z = self.vae(inputs)
# z = z.unsqueeze(0)
# z = self.rnn(z)
print('z', z.shape)
print('h', self.h.shape)
return z, self.h
def when_train(self, inputs, one, outputs):
self.vae.is_cuda = True
self.vae.cuda()
self.rnn.is_cuda = True
self.rnn.cuda()
# print('inputs outputs')
# print(inputs.shape)
# print(outputs.shape)
with torch.no_grad():
z = self.vae(inputs)
# print(z.shape)
z = z.unsqueeze(0)
# print(z.shape)
z_a = torch.cat((z, one), dim=2)
self.rnn(z_a)
z_next = self.vae(outputs)
self.next_kl_loss = self.vae.kl_loss
self.next_r_loss = self.vae.r_loss
# print('z_next', z_next.shape)
# print(next_kl_loss.shape)
# print(next_r_loss.shape)
# print('rnn now')
# print(self.rnn.z_prediction.shape)
z_next = z_next.unsqueeze(0)
# print(z_next.shape)
# input('hi')
self.pred_loss = self.rnn.prediction_loss_f(z_next)
self.mdn_loss = self.rnn.mdn_loss_f(z_next)
# print(pred_loss.shape)
# print(mdn_loss.shape)
z_next_hat = self.rnn.z_prediction
# print('making v m error')
# print(z_next_hat.shape)
# print(outputs.shape)
z_next_hat = z_next_hat.squeeze(0)
self.pred_recon_loss = self.vae.reconstruction_error_f(
z_next_hat, inputs)
# print(pred_recon_loss.shape)
'''
w = self.rnn.logweight_mdn
m = self.rnn.mean_mdn
s = self.rnn.logstd_mdn
print('w', w.shape)
print(w[0, 0, 0])
a = w[0, 0, 0]
b = torch.exp(a)
print(b)
n = b.multinomial(num_samples=1).data
print(n)
weight = torch.exp(w)
ns = weight.multinomial(num_samples=1).data
print(ns.shape)
c = weight[0, 0]
d = c.multinomial(num_samples=1).data
print(c.shape)
print(d.shape)
weight = weight.squeeze(0)
print('ww', weight.shape)
a = torch.reshape(weight, (-1, 5))
print(a.shape)
d = a.multinomial(num_samples=5).data
print('d is ', d.shape)
b = torch.reshape(d, (-1, 32, 5))
print(b.shape)
#c = (weight==b)
#print(c.shape)
#print(c[200,30,4])
c = b[:,:,0:1]
c = c.unsqueeze(0)
print(c[0,250,20,0])
print(c[0,c[0,250,20,0],20,0])
print(c.shape)
samples = c
# z_a = z_a.unsqueeze(0)
'''
# print(z_a.shape)
def make_prediction(self, action):
one = one_hot(action)
one = torch.from_numpy(one)
one = one.unsqueeze(0)
one = one.type(torch.float)
z_a = torch.cat((z, one), dim=1)
z_a = z_a.unsqueeze(0)
idx = indices[0:block]
data_z = data_z[idx]
data_action = data_action[idx]
N = len(data_action)
batch_size = 50
num_batchs = int(N / batch_size)
EPOCH = 100
rnn_model = RNN()
if is_load:
rnn_model.load_state_dict(torch.load(rnn_model_path))
rnn_model.train()
if is_cuda:
rnn_model.cuda()
rnn_model.is_cuda = True
optimizer = optim.Adam(rnn_model.parameters())
# one_hot(data_action[0])
for epoch in range(EPOCH):
mdn_loss_s = []
pred_loss_s = []
indices = np.random.permutation(N)
for idx in indices:
# from_to = indices[idx * batch_size : (idx + 1) * batch_size]
z = data_z[idx]
a = data_action[idx]
one = one_hot(a)