Я тренируюсь в Капсульной сети с кодером и декодером. Он прекрасно работает со всеми классами (10 классов) набора данных MNIST. Но когда я извлекаю одно слово класса (класс 0 или класс 5), а затем тренирую капсульную сеть, восстановление изображения очень плохое.
Где мне нужно изменить настройки сети или у меня ошибка при подготовке данных?
Я пытался:
- Я изменил общий класс с 10 (для десяти цифр на 1 для 1 цифры и даже для 2 для 2 цифр).
- Когда я использую набор данных MNIST по умолчанию, я не получаю ошибку или размер тензора, но когда я извлекаю определенный класс и затем передаю его в сеть, я сталкиваюсь с такими проблемами, как: а) проблемы с размерами; б) тензор с плавающей точкой. предупреждение.
Я исправил эти вещи, но вручную добавил измерение и преобразовал данные в тензор data.float (). Cuda (). Я сделал это как для случая, то есть когда я использую 10-значные капсулы, так и когда я использую 1-значные капсулы для обучения одного класса.
Но после этого сеть работает нормально, но я получаю очень размытые и плохие реконструкции. Хотя, когда я тренирую весь набор данных MNIST, не извлекая какой-либо класс и не передавая его в сеть, он не выдает никакой ошибки, и реконструкция работает очень хорошо.
Я хотел бы поделиться более подробной информацией и другими частями кода -
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.optim import Adam
from torchvision import datasets, transforms
USE_CUDA = True
### **Here we prepare the data for the complete 10 class digit training**###
class Mnist:
def __init__(self, batch_size):
dataset_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
train_dataset = datasets.MNIST('../data', train=True, download=True, transform=dataset_transform)
test_dataset = datasets.MNIST('../data', train=False, download=True, transform=dataset_transform)
self.train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
self.test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
## **Here is my code for extracting a single class digit extraction**##
class Mnist:
def __init__(self,batch_size):
dataset_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
train_mnist = datasets.MNIST("../data", train=True)
test_mnist = datasets.MNIST("../data", train= False)
train_image, train_label = train_mnist.train_data, train_mnist.train_labels
test_image, test_label = test_mnist.test_data, test_mnist.test_labels
train_0, test_0 = [train_image[key] for (key, label) in enumerate(train_label) if int(label) == 5],[test_image[key] for (key, label) in enumerate(test_label) if int(label) == 5]
train_label_0, test_label_0 = zero__train = [train_label[key] for (key, label) in enumerate(train_label) if int(label) == 5],[test_label[key] for (key, label) in enumerate(test_label) if int(label) == 5]
train_dataset = tuple(zip(train_0, train_label_0))
test_dataset = tuple(zip(test_0, test_label_0))
self.train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
self.test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
# Here is the main code for the capsule training.
''' The below code is used for training the 1 class but using the 10 Digit capsules
'''
class ConvLayer(nn.Module):
def __init__(self, in_channels=1, out_channels=256, kernel_size=9):
super(ConvLayer, self).__init__()
self.conv = nn.Conv2d(in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=1
)
def forward(self, x):
return F.relu(self.conv(x))
class PrimaryCaps(nn.Module):
def __init__(self, num_capsules=8, in_channels=256, out_channels=32, kernel_size=9):
super(PrimaryCaps, self).__init__()
self.capsules = nn.ModuleList([
nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=2, padding=0)
for _ in range(num_capsules)])
def forward(self, x):
u = [capsule(x) for capsule in self.capsules]
u = torch.stack(u, dim=1)
u = u.view(x.size(0), 32 * 6 * 6, -1)
return self.squash(u)
def squash(self, input_tensor):
squared_norm = (input_tensor ** 2).sum(-1, keepdim=True)
output_tensor = squared_norm * input_tensor / ((1. + squared_norm) * torch.sqrt(squared_norm))
return output_tensor
class DigitCaps(nn.Module):
def __init__(self, num_capsules=10, num_routes=32 * 6 * 6, in_channels=8, out_channels=16):
super(DigitCaps, self).__init__()
self.in_channels = in_channels
self.num_routes = num_routes
self.num_capsules = num_capsules
self.W = nn.Parameter(torch.randn(1, num_routes, num_capsules, out_channels, in_channels))
def forward(self, x):
batch_size = x.size(0)
x = torch.stack([x] * self.num_capsules, dim=2).unsqueeze(4)
# print(f"x at epoch {epoch} is equal to : {x}")
W = torch.cat([self.W] * batch_size, dim=0)
# print(f"W at epoch {epoch} is equal to : {W}")
u_hat = torch.matmul(W, x)
# print(f"u_hatat epoch {epoch} is equal to : {u_hat}")
b_ij = Variable(torch.zeros(1, self.num_routes, self.num_capsules, 1))
if USE_CUDA:
b_ij = b_ij.cuda()
# print(f"b_ij at epoch {epoch} is equal to : {b_ij}")
num_iterations = 3
for iteration in range(num_iterations):
c_ij = F.softmax(b_ij, dim =1)
c_ij = torch.cat([c_ij] * batch_size, dim=0).unsqueeze(4)
s_j = (c_ij * u_hat).sum(dim=1, keepdim=True)
v_j = self.squash(s_j)
# print(f"b_ij at iteration {iteration} is equal to : {b_ij}")
if iteration < num_iterations - 1:
a_ij = torch.matmul(u_hat.transpose(3, 4), torch.cat([v_j] * self.num_routes, dim=1))
b_ij = b_ij + a_ij.squeeze(4).mean(dim=0, keepdim=True)
return v_j.squeeze(1)
def squash(self, input_tensor):
squared_norm = (input_tensor ** 2).sum(-1, keepdim=True)
output_tensor = squared_norm * input_tensor / ((1. + squared_norm) * torch.sqrt(squared_norm))
return output_tensor
class Decoder(nn.Module):
def __init__(self):
super(Decoder, self).__init__()
self.reconstraction_layers = nn.Sequential(
nn.Linear(16 * 10, 512),
nn.ReLU(inplace=True),
nn.Linear(512, 1024),
nn.ReLU(inplace=True),
nn.Linear(1024, 784),
nn.Sigmoid()
)
def forward(self, x, data):
classes = torch.sqrt((x ** 2).sum(2))
classes = F.softmax(classes, dim =1)
_, max_length_indices = classes.max(dim=1)
masked = Variable(torch.sparse.torch.eye(10))
if USE_CUDA:
masked = masked.cuda()
masked = masked.index_select(dim=0, index=max_length_indices.squeeze(1).data)
reconstructions = self.reconstraction_layers((x * masked[:, :, None, None]).view(x.size(0), -1))
reconstructions = reconstructions.view(-1, 1, 28, 28)
return reconstructions, masked
class CapsNet(nn.Module):
def __init__(self):
super(CapsNet, self).__init__()
self.conv_layer = ConvLayer()
self.primary_capsules = PrimaryCaps()
self.digit_capsules = DigitCaps()
self.decoder = Decoder()
self.mse_loss = nn.MSELoss()
def forward(self, data):
output = self.digit_capsules(self.primary_capsules(self.conv_layer(data)))
reconstructions, masked = self.decoder(output, data)
return output, reconstructions, masked
def loss(self, data, x, target, reconstructions):
return self.margin_loss(x, target) + self.reconstruction_loss(data, reconstructions)
# return self.reconstruction_loss(data, reconstructions)
def margin_loss(self, x, labels, size_average=True):
batch_size = x.size(0)
v_c = torch.sqrt((x**2).sum(dim=2, keepdim=True))
left = F.relu(0.9 - v_c).view(batch_size, -1)
right = F.relu(v_c - 0.1).view(batch_size, -1)
# print(f"shape of labels, left and right respectively - {labels.size(), left.size(), right.size()}")
loss = labels * left + 0.5 * (1.0 - labels) * right
loss = loss.sum(dim=1).mean()
return loss
def reconstruction_loss(self, data, reconstructions):
loss = self.mse_loss(reconstructions.view(reconstructions.size(0), -1), data.view(reconstructions.size(0), -1))
return loss*0.0005
capsule_net = CapsNet()
if USE_CUDA:
capsule_net = capsule_net.cuda()
optimizer = Adam(capsule_net.parameters())
capsule_net
##### Here is the problem while training####
batch_size = 100
mnist = Mnist(batch_size)
n_epochs = 5
for epoch in range(n_epochs):
capsule_net.train()
train_loss = 0
for batch_id, (data, target) in enumerate(mnist.train_loader):
target = torch.eye(10).index_select(dim=0, index=target)
data, target = Variable(data), Variable(target)
if USE_CUDA:
data, target = data.cuda(), target.cuda()
data, target = data.float().cuda(), target.float().cuda() # Here I changed the data to float and it's required only when I am using my extracted dataset for a single class
data = data[:,:,:] # Use this when 1st MNist data is used
# data = data[:,None,:,:] # Use this when I am using my extracted single class digits
optimizer.zero_grad()
output, reconstructions, masked = capsule_net(data)
loss = capsule_net.loss(data, output, target, reconstructions)
loss.backward()
optimizer.step()
train_loss += loss.item()
# if batch_id % 100 == 0:
# print ("train accuracy:", sum(np.argmax(masked.data.cpu().numpy(), 1) ==
# np.argmax(target.data.cpu().numpy(), 1)) / float(batch_size))
print (train_loss / len(mnist.train_loader))
I used this to see the main data as image and the reconstructed image
import matplotlib
import matplotlib.pyplot as plt
def plot_images_separately(images):
"Plot the six MNIST images separately."
fig = plt.figure()
for j in range(1, 10):
ax = fig.add_subplot(1, 10, j)
ax.matshow(images[j-1], cmap = matplotlib.cm.binary)
plt.xticks(np.array([]))
plt.yticks(np.array([]))
plt.show()
plot_images_separately(data[:10,0].data.cpu().numpy())
plot_images_separately(reconstructions[:10,0].data.cpu().numpy())
