# -*- coding: utf-8 -*-
#Libraries
import torch
import torch.nn.functional as F
from torch import autograd, nn
from torch.autograd import Variable
import numpy as np
import matplotlib.pyplot as plt
from torchvision import transforms, datasets
from torch.utils import data
"""
Olivetti face dataset
"""
from sklearn.datasets import fetch_olivetti_faces
# Olivetti dataset download
olivetti = fetch_olivetti_faces()
train = olivetti.images
label = olivetti.target
X = train
Y = label
print("Format for X:", X.shape)
print("Format for Y: ", Y.shape)
print("\nDownload Ok")
"""
Set for train
"""
train_rate = 0.8
X_train = np.zeros([int(train_rate * X.shape[0]),64,64], dtype=float)
Y_train = np.zeros([int(train_rate * X.shape[0])], dtype=int)
X_val = np.zeros([int((1-train_rate) * X.shape[0]+1),64,64], dtype=float)
Y_val = np.zeros([int((1-train_rate) * X.shape[0]+1)], dtype=int)
#Split data for train and validation
for i in range(X.shape[0]):
ie=0
iv=0
if (i%10)/10 >= train_rate:
X_train[ie] = X[i]
Y_train[ie] = Y[i]
ie += 1
else:
X_val[iv] = X[i]
Y_val[iv] = Y[i]
iv += 1
X_train = X_train.reshape(320,-1,64,64)
X_val = X_val.reshape(80,-1,64,64)
print(Y_train.shape)
X_train = torch.Tensor(X_train)
Y_train = torch.Tensor(Y_train)
X_val = torch.Tensor(X_val)
Y_val = torch.Tensor(Y_val)
batch_size = 20
train_loader = torch.utils.data.DataLoader(X_train,
batch_size=batch_size,
)
val_loader = torch.utils.data.DataLoader(X_val,
batch_size=batch_size,
)
class CNNModule(nn.Module):
def __init__(self):
super(CNNModule, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 13 * 13, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 40)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 13 * 13)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def make_train(model,dataset,n_iters,gpu):
# Organize data
X_train,Y_train,X_val,Y_val = dataset
kriter = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(),lr=0.05)
#Arrays to save loss and accuracy
tl=np.zeros(n_iters) #For train loss
ta=np.zeros(n_iters) #For train accuracy
vl=np.zeros(n_iters) #For validation loss
va=np.zeros(n_iters) #For validation accuracy
# Convert labels to long
Y_train = Y_train.long()
Y_val = Y_val.long()
# GPU control
if gpu:
X_train,Y_train = X_train.cuda(),Y_train.cuda()
X_val,Y_val = X_val.cuda(),Y_val.cuda()
model = model.cuda() # Parameters to GPU!
print("Using GPU")
else:
print("Using CPU")
# print(X_train.shape)
# print(Y_train.shape)
for i in range(n_iters):
# train forward
train_out = model.forward(X_train)
train_loss = kriter(train_out,Y_train)
# Backward and optimization
train_loss.backward()
optimizer.step()
optimizer.zero_grad()
# Compute train accuracy
train_predict = train_out.cpu().detach().argmax(dim=1)
train_accuracy = (train_predict.cpu().numpy()==Y_train.cpu().numpy()).mean()
# For validation
val_out = model.forward(X_val)
val_loss = kriter(val_out,Y_val)
# Compute validation accuracy
val_predict = val_out.cpu().detach().argmax(dim=1)
val_accuracy = (val_predict.cpu().numpy()==Y_val.cpu().numpy()).mean()
tl[i] = train_loss.cpu().detach().numpy()
ta[i] = train_accuracy
vl[i] = val_loss.cpu().detach().numpy()
va[i] = val_accuracy
# Show result each 5 loop
if i%5==0:
print("Loop --> ",i)
print("Train Loss :",train_loss.cpu().detach().numpy())
print("Train Accuracy :",train_accuracy)
print("Validation Loss :",val_loss.cpu().detach().numpy())
print("Validation Accuracy :",val_accuracy)
model = model.cpu()
#Print result
plt.subplot(2,2,1)
plt.plot(np.arange(n_iters), tl, 'r-')
plt.subplot(2,2,2)
plt.plot(np.arange(n_iters), ta, 'b--')
plt.subplot(2,2,3)
plt.plot(np.arange(n_iters), vl, 'r-')
plt.subplot(2,2,4)
plt.plot(np.arange(n_iters), va, 'b--')
dataset = X_train,Y_train,X_val,Y_val
gpu = True
gpu = gpu and torch.cuda.is_available()
model = CNNModule()
make_train(model,dataset,100,gpu)
When I start to train my model, Loss values decreasing but Accuracy values never change.I don’t know why?
OUTPUT:
Using CPU Loop --> 0 Train Loss : 3.6302185 Train Accuracy : 0.0 Validation Loss : 3.6171098 Validation Accuracy : 0.0 Loop --> 5 Train Loss : 3.557933 Train Accuracy : 0.996875 Validation Loss : 3.545982 Validation Accuracy : 0.9875 . . . Loop --> 95 Train Loss : 0.04211783 Train Accuracy : 0.996875 Validation Loss : 0.13397054 Validation Accuracy : 0.9875
