获取数据模板

import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable
from torch.nn import Linear

import scipy
import numpy as np
import matplotlib.pyplot as plt

# x1 = np.loadtxt('2-negtive-mixed-15features.txt')
# x2 = np.loadtxt('2-positive-mixed-15features.txt')
def get_data():
    train_X = np.asarray([2.0, 3.0, 4.0, 6.0])
    train_Y = np.asarray([5.0, 3.0, 9.0, 7.0])
    print(train_X)
    dtype = torch.FloatTensor
    X = Variable(torch.from_numpy(train_X).type(dtype), requires_grad=True).view(4,1)
    Y = Variable(torch.from_numpy(train_Y).type(dtype), requires_grad=False)
    return X,Y

def get_weight():
    w = Variable(torch.randn(1),requires_grad = True)
    b = Variable(torch.randn(1),requires_grad = True)
    return w, b

def simple_network():
    y_pred = torch.matmul(x,w)+b
    return y_pred


inp = Variable(torch.ones(1,10))
myLayer = Linear(in_features=10,out_features=5,bias=True)
myLayer(inp)


x,y = get_data()
w,b = get_weight()
yhat = simple_network()
print(x)
print(y)
print(w)
print(b)
print(yhat)

print(myLayer(inp))
print(myLayer.weight)
print(myLayer.bias)