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neural_c1.py
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neural_c1.py
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import numpy as np
import pandas as pd
import math
import sys
from matplotlib import pyplot as plt
import time
def loss(X,Y):
ga = math.pow(10,-15)
r = X.shape[0]
X = np.log(np.clip(X,ga,1-ga))
Y = np.multiply(Y,X)
ans = -np.sum(Y)/r
return ans
def sigmoid(X):
X = 1/(1+np.exp(-X))
return X
def softmax(X):
Y = np.exp(X-np.max(X,axis=1).reshape(-1,1))
d = np.sum(Y,axis=1).reshape(-1,1)
X = Y/d
return X
def tanh(X):
ans = (np.exp(X)-np.exp(-X))/(np.exp(X)+np.exp(-X))
return ans
def relu(X):
ans = np.where(X > 0,X,0)
return ans
def forward(X,w,af):
z = []
z0 = X
z0 = np.c_[np.ones(X.shape[0]),z0]
z.append(z0)
l = len(w)
i = 0
while(i < l-1):
z0 = np.matmul(z0,w[i])
if(af == 0):
z0 = sigmoid(z0)
elif(af == 1):
z0 = tanh(z0)
elif(af == 2):
z0 = relu(z0)
z0 = np.c_[np.ones(X.shape[0]),z0]
z.append(z0)
i = i+1
z0 = np.matmul(z0,w[l-1])
z0 = softmax(z0)
z.append(z0)
return z
def backward(z,w,ytrain,si,af,lis,iter,vt,mt):
n0 = 0.003
l = len(z)
p = z[l-1]-ytrain
r = z[l-2].T
g = np.matmul(r,p)/si
q = np.multiply(z[l-2],1-z[l-2])
if(af == 1):
q = 1-np.square(z[l-2])
elif(af == 2):
q = np.where(z[l-2] == 0,0,1)
ans = []
wg = np.asarray(w[l-2])
wt = wg.T
p = np.matmul(p,wt)
p = np.multiply(p,q)
p = p[:,1:]
if(lis == 0):
wg = wg-n0*g
if(lis == 1 or lis == 2):
vt[l-2] = 0.9*vt[l-2]+n0*g
wg = wg-vt[l-2]
elif(lis == 3):
sd = np.square(g)
vt[l-2] = 0.9*vt[l-2]+0.1*sd
ep = math.pow(10,-8)
sd = np.sqrt(vt[l-2]+ep)
num = n0/sd
wg = wg-np.multiply(num,g)
elif(lis == 4):
sd = np.square(g)
mt[l-2] = 0.9*mt[l-2]+0.1*g
vt[l-2] = 0.999*vt[l-2]+0.001*sd
mthat = mt[l-2]/(1-math.pow(0.9,iter))
vthat = vt[l-2]/(1-math.pow(0.999,iter))
epsi = math.pow(10,-8)
vthat = np.sqrt(vthat)+epsi
num = n0/vthat
wg = wg-np.multiply(num,mthat)
elif(lis == 5):
sd = np.square(g)
mt[l-2] = 0.9*mt[l-2]+0.1*g
vt[l-2] = 0.999*vt[l-2]+0.001*sd
mthat = mt[l-2]/(1-math.pow(0.9,iter))
vthat = vt[l-2]/(1-math.pow(0.999,iter))
epsi = math.pow(10,-8)
vthat = np.sqrt(vthat)+epsi
num = n0/vthat
mthat = 0.9*mthat+(0.1*g)/(1-math.pow(0.9,iter))
wg = wg-np.multiply(num, mthat)
ans.append(wg)
i = l-2
while(i > 0):
r = z[i-1].T
g = np.matmul(r,p)/si
q = np.multiply(z[i-1],1-z[i-1])
if(af == 1):
q = 1-np.square(z[i-1])
elif(af == 2):
q = np.where(z[i-1] == 0,0,1)
wg = np.asarray(w[i-1])
wt = wg.T
p = np.matmul(p,wt)
p = np.multiply(p,q)
p = p[:,1:]
if(lis == 0):
wg = wg-n0*g
if(lis == 1 or lis == 2):
vt[i-1] = 0.9*vt[i-1]+n0*g
wg = wg-vt[i-1]
elif(lis == 3):
sd = np.square(g)
vt[i-1] = 0.9*vt[i-1]+0.1*sd
ep = math.pow(10,-8)
sd = np.sqrt(vt[i-1]+ep)
num = n0/sd
wg = wg-np.multiply(num,g)
elif(lis == 4):
sd = np.square(g)
mt[i-1] = 0.9*mt[i-1]+0.1*g
vt[i-1] = 0.999*vt[i-1]+0.001*sd
mthat = mt[i-1]/(1-math.pow(0.9,iter))
vthat = vt[i-1]/(1-math.pow(0.999,iter))
epsi = math.pow(10,-8)
vthat = np.sqrt(vthat)+epsi
num = n0/vthat
wg = wg-np.multiply(num,mthat)
elif(lis == 5):
sd = np.square(g)
mt[i-1] = 0.9*mt[i-1]+0.1*g
vt[i-1] = 0.999*vt[i-1]+0.001*sd
mthat = mt[i-1]/(1-math.pow(0.9,iter))
vthat = vt[i-1]/(1-math.pow(0.999,iter))
epsi = math.pow(10,-8)
vthat = np.sqrt(vthat)+epsi
num = n0/vthat
mthat = 0.9*mthat+(0.1*g)/(1-math.pow(0.9,iter))
wg = wg-np.multiply(num,mthat)
ans.append(wg)
i = i-1
ans.reverse()
return ans
input_path = sys.argv[1]
output_path = sys.argv[2]
s = input_path+"train_data_shuffled.csv"
dftrain = pd.read_csv(s,header = None)
ytrain = dftrain.iloc[:,-1]
Xtrain = np.asarray(dftrain.iloc[:,:-1])
Xtrain = Xtrain/255
ytrain = np.asarray(pd.get_dummies(ytrain))
n = Xtrain.shape[0]
np.random.seed(1)
epochs = 100
layer = [256,46]
arr = [2048]
jk = 0
af = 2
while(jk < 1):
lis = 3
while(lis == 3):
w = []
m = Xtrain.shape[1]
l = len(layer)
i = 0
while(i < l):
x = int(layer[i])
h = math.sqrt(2/(m+1+x))
w1 = np.float32(np.random.normal(size=(m+1,x))*h)
w.append(w1)
m = x
i = i+1
losses = []
xa = []
vt = []
mt = []
l = len(w)
i = 0
while(i < l):
r,c = w[i].shape
kl = np.zeros((r,c))
vt.append(kl)
mt.append(kl)
i = i+1
i = 1
curr = time.time()
while(i <= epochs and time.time()-curr < 280):
k = arr[jk]
j = 0
while((j+1)*k <= n):
X = Xtrain[j*k:(j+1)*k]
y = ytrain[j*k:(j+1)*k]
z = forward(X,w,af)
if(lis == 1):
wy = []
yr = 0
l = len(w)
while(yr < l):
w1 = w[yr]-0.9*vt[yr]
wy.append(w1)
yr = yr+1
z = forward(X,wy,af)
w = backward(z,w,y,k,af,lis,i,vt,mt)
j = j+1
z = forward(Xtrain,w,af)
l = len(z)
d = np.asarray(z[l-1])
num = loss(d,ytrain)
losses.append(num)
xa.append(i)
i = i+1
plt.plot(xa,losses,label = str(lis))
stri = "Architecture_22_Batchsize="+str(arr[jk])+"_Optimizer="+str(lis)
dfk = pd.DataFrame(list(zip(xa,losses)),columns=['epochs','Loss'])
op = output_path+stri+".csv"
dfk.to_csv(op,index=False)
lis = lis+1
plt.xlabel("epochs")
plt.ylabel("Loss")
stri = "Architecture_22_Batchsize="+str(arr[jk])
plt.title(stri)
plt.legend()
op = output_path+stri+".png"
plt.savefig(op)
plt.clf()
jk = jk+1
epochs = 100
layer = [256,46]
arr = [100,200,300,500]
jk = 0
af = 2
while(jk < 4):
lis = 0
while(lis <= 5):
w = []
m = Xtrain.shape[1]
l = len(layer)
i = 0
while(i < l):
x = int(layer[i])
h = math.sqrt(2/(m+1+x))
w1 = np.float32(np.random.normal(size=(m+1,x))*h)
w.append(w1)
m = x
i = i+1
losses = []
xa = []
vt = []
mt = []
l = len(w)
i = 0
while(i < l):
r,c = w[i].shape
kl = np.zeros((r,c))
vt.append(kl)
mt.append(kl)
i = i+1
i = 1
curr = time.time()
while(i <= epochs and time.time()-curr < 280):
k = arr[jk]
j = 0
while((j+1)*k <= n):
X = Xtrain[j*k:(j+1)*k]
y = ytrain[j*k:(j+1)*k]
z = forward(X,w,af)
if(lis == 1):
wy = []
yr = 0
l = len(w)
while(yr < l):
w1 = w[yr]-0.9*vt[yr]
wy.append(w1)
yr = yr+1
z = forward(X,wy,af)
w = backward(z,w,y,k,af,lis,i,vt,mt)
j = j+1
z = forward(Xtrain,w,af)
l = len(z)
d = np.asarray(z[l-1])
num = loss(d,ytrain)
losses.append(num)
xa.append(i)
i = i+1
plt.plot(xa,losses,label = str(lis))
stri = "Architecture_1_Batchsize="+str(arr[jk])+"_Optimizer="+str(lis)
dfk = pd.DataFrame(list(zip(xa,losses)),columns=['epochs','Loss'])
op = output_path+stri+".csv"
dfk.to_csv(op,index=False)
lis = lis+1
plt.xlabel("epochs")
plt.ylabel("Loss")
stri = "Architecture_1_Batchsize="+str(arr[jk])
plt.title(stri)
op = output_path+stri+".png"
plt.savefig(op)
plt.clf()
jk = jk+1