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replace views.py and urls.py in api folder

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The codes are based on: https://www.datacamp.com/community/tutorials/machine-learning-models-api-python
First, in command line, run(to get model.pkl and model_columns.pkl):
python trainmodel.py 

Then, run:
python api.py

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Sometimes you need to keep two upstreams in sync with eachother. For example, you might need to both push to your testing environment and your GitHub repo at the same time. In order to do this simultaneously in one git command, here's a little trick to add multiple push URLs to a single remote.

Once you have a remote set up for one of your upstreams, run these commands with:

git remote set-url --add --push [remote] [original repo URL]
git remote set-url --add --push [remote] [second repo URL]

Once set up, git remote -v should show two (push) URLs and one (fetch) URL. Something like this:

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import argparse
import os
import shutil
import time

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim

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"""
Create train, valid, test iterators for CIFAR-10 [1].
Easily extended to MNIST, CIFAR-100 and Imagenet.

[1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4
"""

import torch
import numpy as np

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This post provides an overview of performing diagnostic and performance evaluation on logistic regression models in R. After training a statistical model, it’s important to understand how well that model did in regards to it’s accuracy and predictive power. The following content will provide the background and theory to ensure that the right technique are being utilized for evaluating logistic regression models in R.

Logistic Regression Example
We will use the GermanCredit dataset in the caret package for this example. It contains 62 characteristics and 1000 observations, with a target variable (Class) that is allready defined. The response variable is coded 0 for bad consumer and 1 for good. It’s always recommended that one looks at the coding of the response variable to ensure that it’s a factor variable that’s coded accurately with a 0/1 scheme or two factor levels in the right order. The first step is to partition the data into training and testing sets.

```
library(caret)
data(GermanCredit)
Train <- cr