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change the deprecated torch.Varialbe api into torch.tensor with requires_grad ==true #69

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change the deprecated torch.Varialbe api into torch.tensor with requires_grad ==true #69

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32 changes: 15 additions & 17 deletions VAE/vanilla_vae/vae_pytorch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,6 @@
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import os
from torch.autograd import Variable
from tensorflow.examples.tutorials.mnist import input_data


Expand All @@ -23,19 +22,20 @@
def xavier_init(size):
in_dim = size[0]
xavier_stddev = 1. / np.sqrt(in_dim / 2.)
return Variable(torch.randn(*size) * xavier_stddev, requires_grad=True)
n=np.random.randn(*size)*xavier_stddev
return torch.tensor(n, requires_grad=True,dtype=torch.float32)


# =============================== Q(z|X) ======================================

Wxh = xavier_init(size=[X_dim, h_dim])
bxh = Variable(torch.zeros(h_dim), requires_grad=True)
bxh = torch.zeros(h_dim, requires_grad=True)

Whz_mu = xavier_init(size=[h_dim, Z_dim])
bhz_mu = Variable(torch.zeros(Z_dim), requires_grad=True)
bhz_mu = torch.zeros(Z_dim, requires_grad=True)

Whz_var = xavier_init(size=[h_dim, Z_dim])
bhz_var = Variable(torch.zeros(Z_dim), requires_grad=True)
bhz_var = torch.zeros(Z_dim, requires_grad=True)


def Q(X):
Expand All @@ -46,17 +46,17 @@ def Q(X):


def sample_z(mu, log_var):
eps = Variable(torch.randn(mb_size, Z_dim))
eps = torch.randn(mb_size, Z_dim)
return mu + torch.exp(log_var / 2) * eps


# =============================== P(X|z) ======================================

Wzh = xavier_init(size=[Z_dim, h_dim])
bzh = Variable(torch.zeros(h_dim), requires_grad=True)
bzh = torch.zeros(h_dim, requires_grad=True)

Whx = xavier_init(size=[h_dim, X_dim])
bhx = Variable(torch.zeros(X_dim), requires_grad=True)
bhx = torch.zeros(X_dim, requires_grad=True)


def P(z):
Expand All @@ -74,7 +74,7 @@ def P(z):

for it in range(100000):
X, _ = mnist.train.next_batch(mb_size)
X = Variable(torch.from_numpy(X))
X = torch.from_numpy(X)

# Forward
z_mu, z_var = Q(X)
Expand All @@ -85,23 +85,21 @@ def P(z):
recon_loss = nn.binary_cross_entropy(X_sample, X, size_average=False) / mb_size
kl_loss = torch.mean(0.5 * torch.sum(torch.exp(z_var) + z_mu**2 - 1. - z_var, 1))
loss = recon_loss + kl_loss

# Backward
loss.backward()

# Update
solver.step()

solver.zero_grad()
# Housekeeping
for p in params:
if p.grad is not None:
data = p.grad.data
p.grad = Variable(data.new().resize_as_(data).zero_())
# for p in params:
# if p.grad is not None:
# data = p.grad.data
# p.grad = data.new().resize_as_(data).zero_()

# Print and plot every now and then
if it % 1000 == 0:
print('Iter-{}; Loss: {:.4}'.format(it, loss.data[0]))

print('Iter-{}; Loss: {:.4}'.format(it, loss.data.item()))
samples = P(z).data.numpy()[:16]

fig = plt.figure(figsize=(4, 4))
Expand Down