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Backpropagation : Derivation (Matrix form)

Based on: https://sudeepraja.github.io/Neural/

Notations

1. Input matrix is $x_0$
2. Layer 1 weight matrix is $W_1$
3. Layer 1 output is $x_1 = f_1(W_1x_0)$, where $f$ is the activation function for layer 1
4. There are 4 layers (including input layer)
5. Hence, network output is $x_3 = f_3(W_3x_2)$
6. Assuming MSE loss function, with $t$ as target variable, $E = \frac{1}{2}|x_3 - t|_2^2$

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import os
import sys
import argparse

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('infile')
    parser.add_argument('padfile')
    parser.add_argument('outfile')
    chunksize = 8192

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function snip() {
    PS1="\n\[\033[35m\]\$(/bin/date)\n\[\033[32m\]\w\n\[\033[1;31m\]\u@\h: \[\033[1;34m\]\$(/usr/bin/tty | /bin/sed -e 's:/dev/::'): \[\033[1;36m\]\$(/bin/ls -1 | /usr/bin/wc -l | /bin/sed 's: ::g') files \[\033[1;33m\]\$(/bin/ls -lah | /bin/grep -m 1 total | /bin/sed 's/total //')b\[\033[0m\] -> \[\033[0m\] "
}

function swog() {
    PS1="\[\e[36;45m\]▓\[\e[m\]\[\e[36;45m\]▒\[\e[m\]\[\e[36;45m\]░\[\e[m\]\[\e[36;45m\]\u\[\e[m\]\[\e[36;45m\]@\[\e[m\]\[\e[36;45m\]\h\[\e[m\]\[\e[36;45m\]:\[\e[m\]\[\e[35;46m\]\w\[\e[m\]\[\e[35;46m\]░\[\e[m\]\[\e[35;46m\]▒\[\e[m\]\[\e[35;46m\]▓\[\e[m\]>>> "
}

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"""
Replace following four appropriately first:
  yourname yourcompany http://yourcompany.com http://yourcompany.com/logo.png
"""
import os
import sys
import urllib
import argparse

parser = argparse.ArgumentParser()

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#include <stdio.h>
#include <stdlib.h>
#include <stdint.h>
#ifdef _MSC_VER
#include <intrin.h> /* for rdtscp and clflush */
#pragma optimize("gt",on)
#else
#include <x86intrin.h> /* for rdtscp and clflush */
#endif

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import os
import sys
import torch
import librosa
import numpy as np
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from sklearn.model_selection import train_test_split

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import cv2
import sys
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt

def get_wld_feats(gray_img):
    f00 =np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]])
    f10 = np.array([[0, -1, 0], [0, 0, 0], [0, 1, 0]])
    f11 = np.array([[0, 0, 0], [1, 0, -1], [0, 0, 0]])

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from keras.layers import Recurrent
import keras.backend as K
from keras import activations
from keras import initializers
from keras import regularizers
from keras import constraints
from keras.engine import Layer
from keras.engine import InputSpec

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"""
:|
- Email provider doesn't allow me to send certain file types.
- I have python installed on all my machines.
- Inflates the file size by ~4.5X, still remains under 25M for me.
"""

from __future__ import print_function
import sys