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import torch 
import numpy as np
import torch.nn as nn
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

bc =  datasets.load_breast_cancer()
x, y = bc.data, bc.target

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# 1) Design model (input, output size, forward pass)
# 2) Construct loss and optimizer 
# 3) Training loop
# - forward pass: compute prediction
# - backward pass: gradients
# - update weights

import torch
import torch.nn as nn
import numpy as np

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import torch
import numpy as np

# f = w * x
# f = 2 * x

X = np.array([1,2,3,4,5],dtype=np.float32)
Y = np.array([5,10,15,20,25],dtype=np.float32)

w = 0.0

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import torch
'''
Backpropgation

eg. x = [1.,1.,1]  -> "This is the fixed feature value:"
    y = x + 2  -> a(x) -> "This is the respective y value:"
    z = y*y*3  -> b(y) -> "This is the example for loss function"
Backpropgation    x -> a(x) -> b(y) -> z

dz/dx = dz/dy * dy/dx (Chain Rule)

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


class WineDataset(Dataset):
    def __init__(self):
        # Data Loading

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# MNIST 
# DataLoader Neural Net, activation function
# Loss and Optimizer
# Training Loop (batch training)
# Model evaluation
# GPU Support
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms

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

# HyperParameter
device = torch.device('cuda' if torch.cuda.is_available else 'cpu')

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import os
import time

import tensorflow as tf
import numpy as np

from functools import reduce

from sklearn.metrics import accuracy_score

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