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Utils.py
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Wed, Aug 14, 15:49

Utils.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Feb 8 22:10:18 2020
@author: srpv
"""
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import torch
from prettytable import PrettyTable
from sklearn.metrics import confusion_matrix
import seaborn as sns
import pandas as pd
#%%
def plot_confusion_matrix(y_true, y_pred,classes,plotname):
# Build confusion matrix
cm = confusion_matrix(y_true, y_pred)
# Normalise
cmn = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
cmn=cmn*100
fig, ax = plt.subplots(figsize=(12,9))
sns.set(font_scale=3)
b=sns.heatmap(cmn, annot=True, fmt='.1f', xticklabels=classes, yticklabels=classes,cmap="coolwarm",linewidths=0.1,annot_kws={"size": 25},cbar_kws={'label': 'Classification Accuracy %'})
for b in ax.texts: b.set_text(b.get_text() + " %")
plt.ylabel('Actual',fontsize=25)
plt.xlabel('Predicted',fontsize=25)
plt.margins(0.2)
ax.set_yticklabels(ax.get_yticklabels(), rotation=90, va="center", fontsize= 20)
ax.set_xticklabels(ax.get_xticklabels(), va="center",fontsize= 20)
# plt.setp(ax.get_yticklabels(), rotation='vertical')
plotname=str(plotname)
plt.savefig(plotname,bbox_inches='tight',dpi=100)
plt.show()
plt.clf()
#%%
# def plots(iteration,Loss_value,Total_Epoch,Accuracy,Learning_rate,Training_loss_mean,Training_loss_std):
def plots(iteration,Loss_value,Total_Epoch,Accuracy,Learning_rate,model_name):
Accuracyfile = str(model_name)+'_Accuracy'+'.npy'
Lossfile = str(model_name)+'_Loss_value'+'.npy'
np.save(Accuracyfile,Accuracy,allow_pickle=True)
np.save(Lossfile,Loss_value, allow_pickle=True)
fig, ax = plt.subplots()
plt.plot(Loss_value,'r',linewidth =2.0)
# ax.fill_between(Loss_value, Training_loss_mean - Training_loss_std, Training_loss_mean + Training_loss_std, alpha=0.9)
plt.title('Iteration vs Loss_Value')
plt.xlabel('Iteration')
plt.ylabel('Loss_Value')
plot_1= str(model_name)+'_Loss_value_'+ '.png'
plt.savefig(plot_1, dpi=600,bbox_inches='tight')
plt.show()
plt.clf()
plt.figure(2)
plt.plot(Total_Epoch,Accuracy,'g',linewidth =2.0)
plt.title('Total_Epoch vs Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plot_2= str(model_name)+'_Accuracy_'+'.png'
plt.savefig(plot_2, dpi=600,bbox_inches='tight')
plt.show()
plt.figure(3)
plt.plot(Total_Epoch,Learning_rate,'b',linewidth =2.0)
plt.title('Total_Epoch vs Learning_Rate')
plt.xlabel('Epochs')
plt.ylabel('Learning_Rate')
plot_3= str(model_name)+'_Learning_rate_'+ '.png'
plt.savefig(plot_3, dpi=600,bbox_inches='tight')
plt.show()
#%%
def count_parameters(model):
table = PrettyTable(["Modules", "Parameters"])
total_params = 0
for name, parameter in model.named_parameters():
if not parameter.requires_grad: continue
param = parameter.numel()
table.add_row([name, param])
total_params+=param
print(table)
print(f"Total Trainable Params: {total_params}")
return total_params

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