import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets
from torchvision import transforms

from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F


import torch.optim as optim

class DiabetesDataset(Dataset):
    def __init__(self, filepath):
        xy = np.loadtxt(filepath, delimiter=',', dtype=np.float32)
        self.len = xy.shape[0]
        self.x_data = torch.from_numpy(xy[:, :-1])
        self.y_data = torch.from_numpy(xy[:, [-1]])
    def __getitem__(self, index):
        x_d=self.x_data[index]

        x_p=self.y_data[index]
        return x_d, x_p
    def __len__(self):
        return self.len

transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307, ), (0.3081, )) ])
train_dataset = datasets.MNIST(root='../dataset/mnist', train=True,transform= transform,download=True)
test_dataset = datasets.MNIST(root='../dataset/mnist',train=False,transform= transform, download=True)

train_loader = DataLoader(dataset=train_dataset, batch_size=32,shuffle=True)
test_loader = DataLoader(dataset=test_dataset,
                         batch_size=32,
                         shuffle=False)
batch_size=32

class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        super(Model, self).__init__()

        self.conv1 = torch.nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5)
        self.pooling = torch.nn.MaxPool2d(2)
        self.fc = torch.nn.Linear(320, 10)

    def forward(self, x):

         # Flatten data from (n, 1, 28, 28) to (n, 784) batch_size = x.size(0)
        batch_size = x.size(0)
        x = F.relu(self.pooling(self.conv1(x)))
        x = F.relu(self.pooling(self.conv2(x)))
        x = x.view(batch_size, -1)  # flatten
        x = self.fc(x)
        return x



model = Model()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)


criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)



def train(epoch):
    running_loss = 0.0
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, target = data
        inputs, target = inputs.to(device), target.to(device)
        optimizer.zero_grad()
        # forward + backward + update
        outputs = model(inputs)
        loss = criterion(outputs, target)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if batch_idx % 300 == 299:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))
            running_loss = 0.0

def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            inputs, target = data
            inputs, target = inputs.to(device), target.to(device)
            outputs = model(inputs)
            _, predicted = torch.max(outputs.data, dim=1)
            total += target.size(0)
            correct += (predicted == target).sum().item()

    print('Accuracy on test set: %d %% [%d/%d]' % (100 * correct / total, correct, total))


if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        test()

import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets
from torchvision import transforms

from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F


import torch.optim as optim

class DiabetesDataset(Dataset):
    def __init__(self, filepath):
        xy = np.loadtxt(filepath, delimiter=',', dtype=np.float32)
        self.len = xy.shape[0]
        self.x_data = torch.from_numpy(xy[:, :-1])
        self.y_data = torch.from_numpy(xy[:, [-1]])
    def __getitem__(self, index):
        x_d=self.x_data[index]

        x_p=self.y_data[index]
        return x_d, x_p
    def __len__(self):
        return self.len

transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307, ), (0.3081, )) ])
train_dataset = datasets.MNIST(root='../dataset/mnist', train=True,transform= transform,download=True)
test_dataset = datasets.MNIST(root='../dataset/mnist',train=False,transform= transform, download=True)

train_loader = DataLoader(dataset=train_dataset, batch_size=32,shuffle=True)
test_loader = DataLoader(dataset=test_dataset,
                         batch_size=32,
                         shuffle=False)
batch_size=32

class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        super(Model, self).__init__()

        self.conv1 = torch.nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5)
        self.pooling = torch.nn.MaxPool2d(2)
        self.fc = torch.nn.Linear(320, 10)

    def forward(self, x):

         # Flatten data from (n, 1, 28, 28) to (n, 784) batch_size = x.size(0)
        batch_size = x.size(0)
        x = F.relu(self.pooling(self.conv1(x)))
        x = F.relu(self.pooling(self.conv2(x)))
        x = x.view(batch_size, -1)  # flatten
        x = self.fc(x)
        return x



model = Model()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)


criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)



def train(epoch):
    running_loss = 0.0
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, target = data
        inputs, target = inputs.to(device), target.to(device)
        optimizer.zero_grad()
        # forward + backward + update
        outputs = model(inputs)
        loss = criterion(outputs, target)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if batch_idx % 300 == 299:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))
            running_loss = 0.0

def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            inputs, target = data
            inputs, target = inputs.to(device), target.to(device)
            outputs = model(inputs)
            _, predicted = torch.max(outputs.data, dim=1)
            total += target.size(0)
            correct += (predicted == target).sum().item()

    print('Accuracy on test set: %d %% [%d/%d]' % (100 * correct / total, correct, total))


if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        test()

# Flatten data from (n, 1, 28, 28) to (n, 784) batch_size = x.size(0) # flatten # forward + backward + update