def test(model, test_data, noise, normalize):
"""
test function
"""
count = 0
for img, _ in test_data:
img = img.view(-1, 784)
noise_img = add_noise(img, noise=noise, normalize=normalize)
out = model(noise_img.to(device))
out = out / out.max().item()
out = out.detach().cpu()
img = torch.chunk(img, args.batch_size_test)
noise_img = torch.chunk(noise_img, args.batch_size_test)
out = torch.chunk(out, args.batch_size_test)
# store training progress in tensorboard if requested
if args.tensorboard:
writer.add_image(
'test_model',
image_to_tensor(
plot_to_image(
create_plot_grid(
img[0].view(28, 28),
noise_img[0].view(28, 28),
np.clip(out[0].view(28, 28), 0., 1.),
names=['raw',
'noise %s' % noise, 'denoised']))), count)
count = count + 1
def process_graphic_error_hopfield(vowel,hopfield_net,title,its = 100):
x = [0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80]
y = [0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
for i in range(0,its):
for j in range(0,len(y)):
new_p = add_noise(vowel,x[j],1,-1)
retrive_p = hopfield_net.retrieve(new_p)
y[j] += get_accurracy(vowel,retrive_p)
y = [i/100 for i in y]
plt.plot(x,y)
plt.title(title)
plt.xlabel("Noise")
plt.ylabel("Acurracy %")
plt.show()
def test(model, test_data, noise, normalize):
""" test function """
count = 0
for img, _ in test_data:
noise_img = add_noise(img, noise=noise, normalize=normalize)
out = model(noise_img.to(device))
out = out / out.max().item()
out = out.detach().cpu()
img = torch.chunk(img, args.batch_size_test)
noise_img = torch.chunk(noise_img, args.batch_size_test)
out = torch.chunk(out, args.batch_size_test)
# store training progress in tensorboard if requested
if args.tensorboard:
writer.add_image(
'test_model',
image_to_tensor(
plot_to_image(
create_plot_grid(
img[0].view(28, 28),
noise_img[0].view(28, 28),
np.clip(out[0].view(28, 28), 0., 1.),
names=['raw',
'noise %s' % noise, 'denoised']))), count)
# display summary
if count == 0:
dims = (1400, 600)
summary = Image.new('L', (dims[0], 5 * dims[1]), 255)
i = 0
while i < 5:
tmp_img = plot_to_image(
create_plot_grid(img[i].view(28, 28),
noise_img[i].view(28, 28),
out[i].view(28, 28),
names=None))
summary.paste(tmp_img, (0, i * dims[1]))
i += 1
plt.imshow(summary, cmap=plt.cm.gray)
plt.show()
count = count + 1
def train(model, data_train, optim, loss_fn, epochs, noise, norm):
"""
train function
"""
loss_list_result = []
for epoch in range(epochs):
loss = 0
for inputs, _ in data_train:
# reshape inputs
inputs = inputs.view(-1, 784)
# add noise
noise_img = add_noise(inputs, noise=noise, normalize=norm)
# reset gradients
optim.zero_grad()
# move model (ie. net) to appropiate device
model.to(device)
# compute reconstructions
outputs = model(noise_img.to(device))
# compute a training reconstruction loss
train_loss = loss_fn(outputs.to(device), inputs.to(device))
# compute accumulated gradients
train_loss.backward()
# update parameters based on current gradients
optim.step()
#add the batch training loss to epoch loss
loss += train_loss.item()
# compute the epoch training loss
loss = loss / len(data_train)
# and add it to results list
loss_list_result.append(loss)
# print info every epoch
if args.verbose and not args.suppress:
print('Epoch {} of {}, loss={:.3}'.format(epoch + 1, epochs, loss))
else:
# print info every 10th epoch
if epoch % 10 == 0 and not args.suppress:
print('Epoch {} of {}, loss={:.3}'.format(
epoch + 1, epochs, loss))
# store training progress in tensorboard if requested
if args.tensorboard:
writer.add_scalar('train/Epoch loss', loss, epoch)
writer.flush()
return loss_list_result
def process_graphic_error_steinbuch(vowel,steinbuch_net,title,target,its = 100):
x = [0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80]
y = [0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
t = np.array([0,0,0,0,0])
t[target] = 1
t = t.reshape(-1,1)
for i in range(0,its):
for j in range(0,len(y)):
new_p = add_noise(vowel,x[j],1,0)
retrieve_p = steinbuch_net.retrieve(new_p)
y[j] += get_accurracy(t,retrieve_p)
y = [i/100 for i in y]
plt.plot(x,y)
plt.title(title)
plt.xlabel("Noise")
plt.ylabel("Acurracy %")
plt.show()