ConvNN/withoutPool.py at master · MDProject/ConvNN · GitHub

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import torch
import torch.nn as nn
from torch.autograd import Variable
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torch.nn.functional as F
import torch.optim as optim
import os.path as IO
import numpy as np

conv1 = []
conv2 = []
conv3 = []
FC = []
class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        # 1 input image channel, 6 output channels, 5x5 square convolution
        # kernel
        self.conv1 = nn.Conv2d(1, 5, 5) # 28*28 -- 24*24
        self.conv2 = nn.Conv2d(5, 10, 5) # 24*24 -- 20*20
        self.conv3 = nn.Conv2d(10, 20, 5) # 20*20 -- 16*16
        # an affine operation: y = Wx + b
        self.fc1 = nn.Linear(20 * 16 * 16, 64)
        self.fc2 = nn.Linear(64, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        #conv1.append(x)
        x = F.relu(self.conv2(x))
        #conv2.append(x)
        x = F.relu(self.conv3(x))
        #conv3.append(x)
        x = x.view(-1, self.num_flat_features(x))
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

    def num_flat_features(self, x):
        size = x.size()[1:]  # all dimensions except the batch dimension
        num_features = 1
        for s in size:
            num_features *= s
        return num_features

net = Net()
for layer in list(net._modules.items()):
    print(layer[1])

"""
def for_hook(module, input, output):
    print(module)
    for val in input:
        print("input val:",val.size())
    for out_val in output:
        print("output val:", out_val.size())
"""
# net.conv2.register_forward_hook(for_hook)
# net.conv3.register_forward_hook(for_hook)
batchSize = 50
trans = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
# print(IO.exists('../MNIST_DATA/train-labels-idx1-ubyte'))
train_set = dset.MNIST('../MNIST_DATA/', train=True, transform=trans, download=True)
test_set = dset.MNIST('../MNIST_DATA/', train=False, transform=trans, download=True)
# img, label = train_set[0]

train_loader = torch.utils.data.DataLoader(dataset=train_set,
                                           batch_size=batchSize,
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_set,
                                          batch_size=batchSize,
                                          shuffle=False)

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(),lr=1e-3)
savePath = './Project/WithoutPool/param'
appendix = '.t7'
#   print(IO.exists(savePath))
loss_bound = 0.01
def train(epoch): # epoch -- ensemble number
    for i in range(epoch):
        for batch_idx, (data, target) in enumerate(train_loader):
        #   target = target.float()
            data, target = Variable(data), Variable(target)
            optimizer.zero_grad()
            output = net(data)
            loss = criterion(output, target)
            loss.backward()
            optimizer.step()
            if loss.item() < loss_bound:
                print("Epoch {0}:  Batch idx: {1}  Loss: {2} -- IO".format(i,batch_idx,loss.item()))
                break
            if batch_idx % 500 == 0:
                print("Epoch {0}:  Batch idx: {1}  Loss: {2}".format(i,batch_idx,loss.item()))
        path = savePath + str(i) + appendix
        torch.save(net.state_dict(),path)
Ensemble_num = 10
train(1)
net.eval()  # eval mode (batchnorm uses moving mean/variance instead of mini-batch mean/variance)
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))