CNN.py 2.83 KB
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# -*- coding: utf-8 -*-
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data

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from CNN.split_cnn import *
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class Net(nn.Module):
    def __init__(self,cluster_split):
        super(Net, self).__init__()
        self.cluster_split=cluster_split
        self.conv1 = nn.Conv1d(in_channels=129, out_channels=258, kernel_size=5, stride=1, padding=2)
        self.pool = nn.MaxPool1d(5)
        self.conv2 = nn.Conv1d(in_channels=258, out_channels=64, kernel_size=5, stride=1, padding=2)
        self.fc1 = nn.Linear(8*9*20, 120)
        self.fc2 = nn.Linear(120, 60)
        self.fc3 = nn.Linear(60, 2)
        self.split_conv1=SplitCNN(self.cluster_split,out_channels=2*self.cluster_split,kernel_size=5,stride=1,padding=2)
        self.split_conv2=SplitCNN(self.cluster_split,out_channels=8*np.ones_like(self.cluster_index),kernel_size=5,stride=1,padding=2)

    def forward(self, x):
        x = self.pool(F.relu(self.split_conv1.forward(x)))
        self.cluster_split=self.split_conv1.channels
        x = self.pool(F.relu(self.split_conv2.forward(x)))
        self.cluster_split=self.split_conv2.channels
        x = x.view(-1, np.sum(self.cluster_split)*20)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

def run(trainX, trainY):
    #load the data
    dataset = torch.utils.data.TensorDataset(trainX, trainY)
    trainloader = torch.utils.data.DataLoader(dataset, batch_size=2)
    cluster_index,cluster_split=cluster()

    # define the network
    net = Net(cluster_split)

    # define the optimizer
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

    # train
    train(trainloader=trainloader, net=net, optimizer=optimizer, criterion=criterion,cluster_index=cluster_index)

    # save our trained model
    PATH = './cifar_net.pth'
    torch.save(net.state_dict(), PATH)

def train(trainloader, net, optimizer, criterion,cluster_index, epoch=50):

    for epoch in range(2):  # loop over the dataset multiple times
        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            # get the inputs; data is a list of [inputs, labels]
            inputs, labels = data
            inputs=inputs[cluster_index,...]

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            outputs = net(inputs)
            loss = criterion(outputs, labels.squeeze(1))
            loss.backward()
            optimizer.step()

            # print statistics
            running_loss += loss.item()
            if i % 200 == 0:  # print every 2000 mini-batches
                print('[%d, %5d] loss: %.3f' %
                    (epoch + 1, i + 1, running_loss / 200))
                running_loss = 0.0

    print('Finished Training')