jainxy

"""
A collection of models we'll use to attempt to classify videos.
"""
from keras.layers import Dense, Flatten, Dropout, ZeroPadding3D
from keras.layers.recurrent import LSTM
from keras.models import Sequential, load_model
from keras.optimizers import Adam, RMSprop
from keras.layers.wrappers import TimeDistributed
from keras.layers.convolutional import (Conv2D, MaxPooling3D, Conv3D,
    MaxPooling2D)

jainxy

"""
    Training
    Validation on a holdout set generated from the original training data
    Evaluation on the test data
 - correct and test batch generation
 - Normalize input by 255?
 - add batchnorm layers? use model(x, training=False) then
 - tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(batch_size) ? dataset = dataset.cache()?
 - get_compiled_model()
 - test last batch having non-dividing batch-size aka residual batch issue

jainxy

# Which attributes/features to choose?
# Which model to use?
# Tune/optimize the chosen model for the best performance
# Ensuring the trained model will generalize to unseen data
# Estimate performance of the trained model on unseen data

# imports
import sklearn
import IPython.display
import matplotlib.pyplot as plt

jainxy

Let machine M be the Main machine with the repo, and A the Auxiliary machine which wants to help out.

First time setup

1. Machine M creates bundle with complete repo:

git bundle create repo.bundle HEAD master

2. M sends repo.bundle to A.
3. A clones repo from bundle:

jainxy

from concurrent.futures import ProcessPoolExecutor, as_completed
import cv2
import multiprocessing
import os
import sys


def print_progress(iteration, total, prefix='', suffix='', decimals=3, bar_length=100):
    """
    Call in a loop to create standard out progress bar

jainxy

import cv2  # still used to save images out
import os
import numpy as np
from decord import VideoReader
from decord import cpu, gpu


def extract_frames(video_path, frames_dir, overwrite=False, start=-1, end=-1, every=1):
    """
    Extract frames from a video using decord's VideoReader

jainxy

jainxy

$ sudo mkdir -p /var/www/test.com/public_html
$ sudo mkdir -p /var/www/example.com/public_html

$ sudo chown -R $USER:$USER /var/www/example.com/public_html
$ sudo chown -R $USER:$USER /var/www/test.com/public_html

$ sudo chmod -R 755 /var/www

Create the virtual hosts configuration files for our two sites
$ sudo cp /etc/apache2/sites-available/000-default.conf /etc/apache2/sites-available/test.com.conf

jainxy

## 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

jainxy

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