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cnn.py
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# %matplotlib inline
# %config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import torch
from torchvision import datasets, transforms
import helper
from torch.nn import Conv2d, functional as F, Linear, MaxPool2d, Module
from torch import nn as nn
import torchvision
import pandas as pd
import numpy as np
from torch.utils.data import random_split
transform = transforms.Compose([transforms.Resize(128),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_dataset = datasets.ImageFolder('./melspectro_data/train', transform=transform)
test_dataset = datasets.ImageFolder('./melspectro_data/test', transform=transform)
# split train into valid and train
train_size = int(0.8 * len(train_dataset))
valid_size = len(train_dataset) - train_size
partial_train_ds, valid_ds = random_split(train_dataset, [train_size, valid_size])
train_dataloader = torch.utils.data.DataLoader(partial_train_ds, batch_size=4, shuffle=True)
valid_dataloader = torch.utils.data.DataLoader(partial_train_ds, batch_size=4, shuffle=True)
test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=4, shuffle=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# classes = train_dataloader.dataset.classes
class Net(Module):
def __init__(self):
super(Net, self).__init__()
# COnv1
# input shape = [32, 3, 128, 320]
self.conv1 = Conv2d(3, 18, kernel_size=3, stride=1, padding=1)
# outout shape [32, 18, 64, 160]
# bathc normalization
self.bn1 = nn.BatchNorm2d(num_features=18)
self.relu1 = nn.ReLU()
self.pool1 = MaxPool2d(kernel_size=2, stride=2, padding=0)
# Conv2
# output [32, 18, 64, 160] [4, 18, 64, 160]
self.conv2 = Conv2d(18, 4, kernel_size=3, stride=1, padding=1)
self.pool2 = MaxPool2d(kernel_size=2, stride=2, padding=0)
# Conv3 [4, 4, 32, 80]
self.conv3 = Conv2d(4, 4, kernel_size=3, stride=1, padding=1)
self.pool3 = MaxPool2d(kernel_size=2, stride=2, padding=0)
# output size [4, 4, 16, 40]
# [4, 4, 32, 80]
self.fc1 = Linear(4*16*40, 512)
self.fc2 = Linear(512, 256)
self.fc3 = Linear(256, 128)
self.fc4 = Linear(128, 64)
self.fc5 = Linear(64, 32)
self.fc6 = Linear(32, 16)
self.fc7 = Linear(16, 4)
def forward(self, x):
# conv1 layer
x = F.relu(self.conv1(x))
x= self.bn1(x)
x = self.pool1(x)
# conv2 layer
x = F.relu(self.conv2(x))
# x= self.bn2(x)
x = self.pool2(x)
x = F.relu(self.conv3(x))
# x= self.bn2(x)
x = self.pool3(x)
# print(x.size())
x = x.view(-1, 4*16*40)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
x = F.relu(self.fc4(x))
x = F.relu(self.fc5(x))
x = F.relu(self.fc6(x))
x = self.fc7(x)
# print(x.size())
return x
net = Net().to(device)
import torch.optim as optim
import torch.nn as nn
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
train_loss_arr = []
valid_loss_arr = []
epochs = []
for epoch in range(150): # loop over the dataset multiple times
train_loss = 0.0
net.train()
for i, data in enumerate(train_dataloader):
# print(len(data))
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
# print(labels)
inputs = inputs.to(device)
labels = labels.to(device)
# # zero the parameter gradients
optimizer.zero_grad()
# # forward + backward + optimize
outputs = net(inputs)
# print(torch.isnan(outputs))
# print(outputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# # print statistics
train_loss += loss.item()
if i % 100 == 99: # print every 100 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, train_loss / 100))
train_loss = 0.0
net.eval()
valid_loss = 0
# turn off gradients for validation
with torch.no_grad():
for data, target in valid_dataloader:
# forward pass
data = data.to(device)
target = target.to(device)
output = net(data)
# validation batch loss
loss = criterion(output, target)
# accumulate the valid_loss
valid_loss += loss.item()
#########################
## PRINT EPOCH RESULTS ##
#########################
train_loss /= len(train_dataloader)
valid_loss /= len(valid_dataloader)
train_loss_arr.append(train_loss)
valid_loss_arr.append(valid_loss)
epochs.append(epoch+1)
print(f'Epoch: {epoch+1}.. Training loss: {train_loss}.. Validation Loss: {valid_loss}')
print('Finished Training')
torch.save(net.state_dict(),'./cnn_models/model2.pth')
# train_loss_arr.append(train_loss)
# valid_loss_arr.append(valid_loss)
# epochs.append(epoch+1)
loss_csv = pd.DataFrame(np.array([epochs,train_loss_arr,valid_loss_arr]).T, columns=['epoch','train_loss','valid_loss'])
loss_csv.to_csv('./cnn_models/model_train_valid_loss1.csv',index=False)