burrussmp

burrussmp

What would this look like

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Looks like we will find out

I Think it looks pretty good

really??

burrussmp

## imports
import flask
from flask import Flask
from flask_cors import CORS
from flask_ngrok import run_with_ngrok
import threading
import trace 
import sys
import time

burrussmp

response = {
  while(1){
    let response = await $.ajax({
      url: `${server_url}/data`,
      contentType: 'application/json',
      type: 'GET'
    });
    yield Promises.delay(6000, response);
  }
}

burrussmp

import cv2
import numpy as np
from sklearn.mixture import GaussianMixture

def preprocess(x): 
   return (x - x.mean(axis=(0,1,2), keepdims=True)) / x.std(axis=(0,1,2), keepdims=True)

# EM hyper parameters
epsilon = 1e-4 # stopping criterion
R = 10 # number of re-runs

burrussmp

def dice_loss(input, target):
    smooth = 1.

    iflat = input.view(-1)
    tflat = target.view(-1)
    intersection = (iflat * tflat).sum()
    
    return 1 - ((2. * intersection + smooth) /
              (iflat.sum() + tflat.sum() + smooth))
    

burrussmp

class DoubleConv2D(nn.Module):
    """(convolution => [BN] => ReLU) * 2"""

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.double_conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),

burrussmp

# hyper-parameters
batch_size = 32
learning_rate = 0.001
scheduler_step = 30
epochs = 190
gamma = 0.5
lr_scheduler_step_size = 12
adam_betas = (0.9,0.999)
use_cuda = torch.cuda.is_available()
torch.manual_seed(123456)

burrussmp

# used to penalize the model less when it predicts a 0 to account for
# slight frequency issues in the training space i.e. imbalances of label 0 and other labels
weights = np.ones(27)
weights[0] = 0.25
class_weights = torch.FloatTensor(weights).cuda()

"""
Function to train model for a single epoch
@params
model: PyTorch.nn.Module

burrussmp

"""
Helper function for the Pytorch data loader
@params
type: string
  Specifies if training (train), validation (valid), or testing (test) list
  should be generated
@return
mlist: A nested Python list
  A list of number of input-output pairs where each element is a list of size 2
  The first element is the path to the .npy input file and the second element is