lucienne999

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

lucienne999

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

lucienne999

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd
from scipy import stats

TotalMSELoss = [0.2830, 0.4853, 0.6963, 1.0990, 1.2796, 1.7281, 1.8727, 2.0605, 2.1643, 2.5878, 2.7190, 3.0497, 3.1828, 3.3756, 3.4804,  3.9982, 4.6670]
NewLoss =      [1.3913, 1.4027, 1.4132, 1.4496, 1.4591, 1.4848, 1.4941, 1.5148, 1.5144, 1.5435, 1.5481, 1.5704, 1.5802, 1.5903, 1.5958,  1.6268, 1.6650]
Acc =          [75.52 , 75.00 , 74.66 , 74.76 , 74.46 , 74.36 , 74.43 , 74.57 , 74.52 , 74.55 , 74.34 , 73.80 , 74.33 , 74.14 , 73.86 ,  73.85 , 73.58]
MnasNet =      [49.74,     49.22,   48.13,  48.02,  47.76,  46.91,  46.68,  47.74,  47.77,  48.23,  47.61,  46.83,  47.90,  46.80,  47.11,  46.61,  46.11] # lr=0.001

lucienne999

import numpy as np
from matplotlib import pyplot as plt

supernet = [0.524, 0.569, 0.588, 0.633, 0.438] 
retrain = [0.734, 0.562, 0.556, 0.581, 0.373]

def make_patch_spines_invisible(ax):
    ax.set_frame_on(True)
    ax.patch.set_visible(False)
    for sp in ax.spines.values():

lucienne999

Step 1: Checkout a new working branch from updated master

 git checkout -b <branchname>

Step 2: Make Changes

 git add
 git commit -m "description of changes"

Step 3: Sync with remote

lucienne999

import os
import pickle
import torch
import torch.nn as nn
class EVALscore(nn.Module):
    """
    Classifier is trained to predict the score between two black/white rope images.
    The score is high if they are within a few steps apart, and low other wise.
    """
    def __init__(self):

lucienne999

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lucienne999

lucienne999

from datetime import date
from functools import wraps
import time
import logging
import sys
from termcolor import colored
import os
import pprint
from tabulate import tabulate

lucienne999

04379243 table 
03593526 jar
04225987 skateboard
02958343 car 
02876657 bottle
04460130 tower
03001627 chair
02871439 bookshelf
02942699 camera
02691156 airplane