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## Save tf model under graph-model

saver = tf.train.Saver() # general saver object
#saves a model every 2 hours and maximum 4 latest models are saved.
saver = tf.train.Saver(max_to_keep=4, keep_checkpoint_every_n_hours=2)
saver = tf.train.Saver([w1,w2]) # save a list of variables

saver.save(sess, 'my-test-model') / saver.save(sess, 'my_test_model',global_step=1000)
saver.save(sess, 'my-model', global_step=step,write_meta_graph=False) # dont save the model graph

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=======================
FLASK module ->
from flask import Flask, g, request, redirect, url_for
=======================

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 === Use conda environment to run a python script ===
SHELL=/bin/bash
CONDA_PREFIX=/home/code/packages/miniconda3
CONDA_INIT="/home/code/packages/miniconda3/etc/conda/activate.d/proj4-activate.sh"
PYTHON=/home/code/packages/miniconda3/bin/python
12 13 * * * . $CONDA_INIT ; $PYTHON <python file>

 === Use date utility for naming a log file ===
DATEVAR="date +%Y%m%d_%H%M"
17 * * * * echo "logs_$($DATEVAR).txt"

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References

• Bash Scripting: Everything you need to know about Bash-shell programming

Notes

Introduction

• Bash (AKA Bourne Again Shell) is a type of interpreter that processes shell commands.
• A shell interpreter takes commands in plain text format and calls Operating System services to do something.
• Bash is the improved version of Sh (Bourne Shell).
• A shell scripting is writing a program for the shell to execute and a shell script is a file or program that shell will execute.
• Terminal is the interface to the shell interpreter.
• A shell script is a fully-fledged programming language in itself. It can define variables, functions and we can do conditional execution of shell commands as well.

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"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

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References - 
1. https://www.howtoforge.com/tutorial/how-to-create-docker-images-with-dockerfile/
2. https://takacsmark.com/dockerfile-tutorial-by-example-dockerfile-best-practices-2018/
3. https://docs.docker.com/engine/reference/builder/
Notes - 
 - A script or receipe having collection of commands and instructions, which will be executed in sequence, to build a new docker image.
 - Below are some Dockerfile commands and their usage.

1. FROM
 - Base docker image to build new image on top. This is 1st command in the Dockerfile. E.g. dockerized ubuntu image.

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------------------------------
VSCode
https://realpython.com/python-development-visual-studio-code/
https://flaviocopes.com/vscode/
https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.vscode-remote-extensionpack - DONE
------------------------------
RPN notes

https://medium.com/@jonathan_hui/understanding-region-based-fully-convolutional-networks-r-fcn-for-object-detection-828316f07c99 - DONE
https://medium.com/@jonathan_hui/what-do-we-learn-from-region-based-object-detectors-faster-r-cnn-r-fcn-fpn-7e354377a7c9 - DONE

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# load the iris dataset 
from sklearn.datasets import load_iris 
iris = load_iris() 
  
# store the feature matrix (X) and response vector (y) 
X = iris.data 
y = iris.target 
  
# splitting X and y into training and testing sets 

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# Ref -> https://colab.research.google.com/notebooks/io.ipynb#scrollTo=BaCkyg5CV5jF
# ---------- UPLOAD FROM LOCAL FILE SYSTEM --------------------
# files.upload returns a dictionary of the files which were uploaded. The dictionary is keyed by the file name, the value is the data which was uploaded.

# files.download will invoke a browser download of the file to the user's local computer.