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# Create dataset of labels and predictions for the model from https://github.com/idealo/image-quality-assessment/
# Use tensorflow >= 1.10.0 to load this model. I had weird errors.

from os import path
import numpy as np
import pandas as pd
import cv2

from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array

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## Author: Victor Dibia
## Load hand tracking model, spin up web socket and web application.


from utils import detector_utils as detector_utils
from utils import object_id_utils as id_utils
import cv2
import tensorflow as tf
import multiprocessing
from multiprocessing import Queue, Pool

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## Author: Victor Dibia
## Load hand tracking model, spin up web socket and web application.


from utils import detector_utils as detector_utils
from utils import object_id_utils as id_utils
import cv2
import tensorflow as tf
import multiprocessing
from multiprocessing import Queue, Pool

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# My answer here: https://stackoverflow.com/questions/52793096/reload-and-zoom-image/52818151#52818151

from PIL import ImageTk, Image

from scipy.ndimage import rotate
from scipy.misc import imresize
import numpy as np
import time

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# create flask app to be run by tornado process

# process-specific globals
import global_vars 

from flask import Flask, request, Response, json, abort, jsonify

# oridnary (non-flask) json if needed
import json as json2
 

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# Simple example of Q-learning inability to go in loops
# Though it is strictly forbibben by the code (line 101), 
# but you can comment out that logic and see that algorithm just becomes less stable
# The reason is that loop is impossible in this setup, 
# as only a single Q-value exists for each position on the map


import numpy as np
np.random.seed(0)

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Which color is normally a cat?;Black
How tall was the longest man on earth?;272 cm
Is the earth round?;Yes 
Which color is normally a cat?;Black
How tall was the longest man on earth?;272 cm
Is the earth round?;Yes 
Which color is normally a cat?;Black
How tall was the longest man on earth?;272 cm
Is the earth round?;Yes 
Which color is normally a cat?;Black

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# Layer-wise training neural network with second order (Newton). 
# New layer is added on each iteration and optimized with Newton Method.
# And example of Tensorflow eager execution
# Combines gradiets, hessian, and call to Optimizer

# Might contain logical errors though, so think yourself when adapting this code



# Newton's method in Tensorflow

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# Newton's method in Tensorflow

# WARNING! This code is memory and computationally intensive, better run it on GPU
# having bigger dimensionality increases computing time significantly
# Original dataset is passable on GTX 1050 GPU, but if you have time/memory problems, uncomment PCA compression
# Also, you can probably remove line 159 (hessian fixing) if you use PCA

# 'Vanilla' N.m. intended to work when loss function to be optimized is convex. 
# One-layer linear network without activation is convex.
# If activation function is monotonic, the error surface associated with a single-layer model is convex.

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# Newton's method in Tensorflow

# 'Vanilla' N.m. intended to work when loss function to be optimized is convex. 
# One-layer linear network without activation is convex.
# If activation function is monotonic, the error surface associated with a single-layer model is convex.

# In other cases, Hessian will have negative eigenvalues in saddle points and other non-convex places of the surface
# To fix that, you can try different methods. One of those approaches is to do eigendecomposition of H and invert negative eigenvalues,
# making H "pushing out" in those directions, as described in this paper: Identifying and attacking the saddle point problem in high-dimensional non-convex optimization (https://papers.nips.cc/paper/5486-identifying-and-attacking-the-saddle-point-problem-in-high-dimensional-non-convex-optimization.pdf)