import matplotlib.pyplot as plt

import numpy as np

plt.plot(range(5), linestyle='--', linewidth=3, color='purple')

plt.ylabel('Numbers from 1-5')

plt.xlabel('Love yohanna')

plt.show()

#plt.clf()

#plt.close()

N = 1000

x = np.random.randn(N)

y = np.random.randn(N)

z = np.random.randn(N)

plt.scatter(z, y, color='yellow', linewidths=0.05)

plt.scatter(x, y, color='green', linewidths=0.2)

plt.axis([-4, 4, -4, 4])

plt.show()

# This file will train minist dataset , using pytorch

from __future__ import print_function

import argparse

import torch

import torch.nn as nn

import torch.nn.functional as F

import torch.optim as optim

from torchvision import datasets, transforms

import pdb

# Training settings

parser = argparse.ArgumentParser(description='PyTorch MNIST Example')

parser.add_argument('--batch-size', type=int, default=64, metavar='N',

                    help='input batch size for training(default: 64)')

parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',

                    help='input batch size for testing (default: 1000)')

parser.add_argument('--epochs', type=int, default=10, metavar='N',

                    help='number of epochs to train (default: 10)')

parser.add_argument('--lr', type=float, default=0.01, metavar='LR',

                    help='learning rate(default: 0.01)')

parser.add_argument('--momentum', type=float, default=0.5, metavar='M',

                    help='SGD momentum (default: 0.5)')

parser.add_argument('--no-cuda', action='store_true', default=False,

                    help='disables CUDA training')

parser.add_argument('--seed', type=int, default=1, metavar='S',

                    help='random seed (default: 1)')

parser.add_argument('--log-interval', type=int, default=10, metavar='N',

                    help='how many batches to wait before logging training status')

args = parser.parse_args()

use_cuda = not args.no_cuda and torch.cuda.is_available()

torch.manual_seed(args.seed)

device = torch.device('cuda' if use_cuda else 'cpu')

kwargs = {'num_workers':1, 'pin_memory': True} if use_cuda else {}

train_loader = torch.utils.data.DataLoader(

    datasets.MNIST('../data', train=True, download=True,

                   transform=transforms.Compose([transforms.ToTensor(),

                                                  transforms.Normalize((0.1307,),(0.3081,))])),

    batch_size=args.batch_size, shuffle=True, **kwargs)

test_loader = torch.utils.data.DataLoader(

    datasets.MNIST('../data', train=False, transform=transforms.Compose([transforms.ToTensor(),

                                            transforms.Normalize((0.1307,),(0.3081,))])),

    batch_size=args.batch_size, shuffle=True, **kwargs)

class Net(nn.Module):

    def __init__(self):

        super(Net, self).__init__()

        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)

        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)

        self.conv2_drop = nn.Dropout2d()

        self.fc1 = nn.Linear(320, 50)

        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):

        x = F.relu(F.max_pool2d(self.conv1(x), 2))

        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))

        x = x.view(-1, 320)

        x = F.relu(self.fc1(x))

        x = F.dropout(x, training=self.training)

        x = self.fc2(x)

        return F.log_softmax(x, dim=1)

model = Net().to(device)

optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)

def train(epoch):

    #pdb.set_trace()

    model.train()

    for batch_idx, (data, target) in enumerate(train_loader):

        data, target = data.to(device), target.to(device)

        optimizer.zero_grad()

        output = model(data)

        loss = F.nll_loss(output, target)

        loss.backward()

        optimizer.step()

        if batch_idx % args.log_interval == 0:

            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(

                epoch, batch_idx * len(data), len(train_loader.dataset),

                100. * batch_idx / len(train_loader), loss.item()

            ))

def test():

    model.eval()

    test_loss = 0

    correct = 0

    with torch.no_grad():

        for data, target in test_loader:

            data, target = data.to(device), target.to(device)

            output = model(data)

            test_loss += F.nll_loss(output, target, size_average=False).item() # sum up batch loss

            pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability

            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(

        test_loss, correct, len(test_loader.dataset),

        100. * correct / len(test_loader.dataset)

    ))

for epoch in range(1, args.epochs+1):

    train(epoch)

    test()