[Introduction to Pytorch] I tried categorizing Cifar10 with VGG16 ♬
Since Pytorch is the best for a while, I tried to categorize MNIST and Cifar 10 while looking at the following reference.
What i did
・ Pytorch installation ・ Try moving MNIST ・ Try moving Cifar10 ・ Try to move with VGG16
・ Pytorch installation
If you enter the following reference page according to your environment, the command will be specified automatically.
【reference】
⓪https://pytorch.org/
So, in the Uwan environment, I was able to install it with the following command.
(keras-gpu) C:\Users\user\pytorch>conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
Actually, I had a little problem here. I installed it in the (keras-gpu) environment because various tools are also installed. Then, although the installation was successful, the following three events occurred.
-
- keras environment removed
- Some Libs have been downgraded
-
- Some have been updated In other words, the Keras-gpu environment seems to have been destroyed. Therefore, we strongly recommend that you install it in a normal conda environment. The crispness is that when the installation is complete, the standard output is erased and the display changes to done.
・ Try moving MNIST
I think that this will work if you follow the reference (1) below, so I will omit it. 【reference】 ① MNIST with PyTorch However, an error may occur when reading data. It is according to the following reference ② ** "2. Error related to DataLoader settings. BrokenPipeError: [Errno 32] Broken pipe -> This case could be avoided by setting num_workers = 0 based on the reference URL②. "** So, changing the code to num_workers = 0 eliminated the error.
② [I checked the operation of PyTorch (15)](https://start0x00url.net/2018/11/08/pytorch-%E3%81%AE%E5%8B%95%E4%BD%9C% E7% A2% BA% E8% AA% 8D% E3% 82% 92% E3% 81% 97% E3% 81% A6% E3% 81% BF% E3% 81% 9F% EF% BC% 88% EF% BC% 91% EF% BC% 95% EF% BC% 89 /)
・ Try moving Cifar10
A code example of Cifar10 is shown in detail in the reference below. However, for unknown reasons, this code didn't work much. 【reference】 ③TRAINING A CLASSIFIER So, I extended the above MNIST code to Cifar 10 while looking at the code in ③. The result is as follows. First, the Lib etc. to be used are as follows
'''
PyTorch Cifar10 sample
'''
import argparse
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torchvision
import torchvision.transforms as transforms
from torchvision.datasets import CIFAR10 #MNIST
import torch.optim as optim
from torchsummary import summary
#from Net_cifar10 import Net_cifar10
from Net_vgg16 import VGG16
import matplotlib.pyplot as plt
import numpy as np
The following is an image drawing function.
# functions to show an image
def imshow(img):
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.pause(1)
The following are argument-related functions that give initial values.
def parser():
'''
argument
'''
parser = argparse.ArgumentParser(description='PyTorch Cifar10')
parser.add_argument('--epochs', '-e', type=int, default=20,
help='number of epochs to train (default: 2)')
parser.add_argument('--lr', '-l', type=float, default=0.01,
help='learning rate (default: 0.01)')
args = parser.parse_args()
return args
Below is the main () function. First is the data reading part. You can see that Classes are different between MNIST and Cifar 10. I also learned with bach_size = 32.
def main():
'''
main
'''
args = parser()
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, #batch_size=4
shuffle=True, num_workers=0) #num_workers=2
testset = CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=32, #batch_size=4
shuffle=False, num_workers=0) #num_workers=2
#classes = tuple(np.linspace(0, 9, 10, dtype=np.uint8))
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
Next, the images and labels of the training data are displayed and printed.
# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()
# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
The following defines device in preparation for calculations using the GPU.
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") #for gpu
# Assuming that we are on a CUDA machine, this should print a CUDA device:
print(device)
Define the model. Here, I made some definitions and looked at the changes. summary(model,(3,32,32)) As shown in Reference ④, you can get the same information as Keras model.summary (). 【reference】 ④ Visdom and torch summary to assist Pytorch model construction and evaluation Visdom seems to be a tool that can display graphs like tensorboard, but I have not used it this time.
# model
#net = Net_cifar10()
#net = VGG13()
net = VGG16()
model = net.to(device) #for gpu
summary(model,(3,32,32))
The criterion and optimizer are defined below. In addition, it seems that the parameters are different between MNIST and Cifar10.
- When I actually tried it, it did not converge at all if it was the MNIST value.
# define loss function and optimier
criterion = nn.CrossEntropyLoss()
#optimizer = optim.SGD(net.parameters(),lr=args.lr, momentum=0.99, nesterov=True)
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
You will learn below. I just commented out the code for the cpu and left it. At the time of MNIST, the accuracy was evaluated for the Test data at the end, but like Keras etc., it is evaluated every time or once every 200 times at the same timing as the learning loss.
# train
for epoch in range(args.epochs):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs; data is a list of [inputs, labels]
#inputs, labels = data #for cpu
inputs, labels = data[0].to(device), data[1].to(device) #for gpu
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 200 == 199: # print every 2000 mini-batches
# test
correct = 0
total = 0
with torch.no_grad():
for (images, labels) in testloader:
outputs = net(images.to(device)) #for gpu
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels.to(device)).sum().item()
#print('Accuracy: {:.2f} %'.format(100 * float(correct/total)))
print('[%d, %5d] loss: %.3f '% (epoch + 1, i + 1, running_loss / 200), 'Accuracy: {:.2f} %'.format(100 * float(correct/total)))
running_loss = 0.0
When you finish learning, save the resulting net.state_dict ().
print('Finished Training')
PATH = './cifar_net.pth'
torch.save(net.state_dict(), PATH)
Below, the accuracy of Test is calculated and output again.
# test
correct = 0
total = 0
with torch.no_grad():
for (images, labels) in testloader:
outputs = net(images.to(device)) #for gpu
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels.to(device)).sum().item()
print('Accuracy: {:.2f} %'.format(100 * float(correct/total)))
Below you will see the Test data, predict and display the results.
dataiter = iter(testloader)
images, labels = dataiter.next()
# print images
imshow(torchvision.utils.make_grid(images))
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
outputs = net(images.to(device))
_, predicted = torch.max(outputs, 1)
print('Predicted: ', ' '.join('%5s' % classes[predicted[j]] for j in range(4)))
Finally, calculate the accuracy of the forecast for each class.
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
for data in testloader:
images, labels = data #for cpu
#inputs, labels = data[0].to(device), data[1].to(device) #for gpu
outputs = net(images.to(device))
_, predicted = torch.max(outputs, 1)
c = (predicted == labels.to(device)).squeeze()
for i in range(4):
label = labels[i]
class_correct[label] += c[i].item()
class_total[label] += 1
for i in range(10):
print('Accuracy of %5s : %2d %%' % (
classes[i], 100 * class_correct[i] / class_total[i]))
When main () finishes, the time required for the calculation is displayed.
if __name__ == '__main__':
start_time = time.time()
main()
print('elapsed time: {:.3f} [sec]'.format(time.time() - start_time))
The model used on the Pytorch page is as follows, and the simple one is used.
Net_cifar10.py
import torch.nn as nn
import torch.nn.functional as F
class Net_cifar10(nn.Module):
def __init__(self):
super(Net_cifar10, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
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 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
・ Try to move with VGG16
There are various models of Pytorch's VGG family when you google, but the following reference is easy to understand. 【reference】 ⑤ PyTorch 0.4.1 examples (code explanation): Image classification – Oxford flower 17 species (VGG) However, only VGG13 is illustrated here. So, referring to Previous Uwan article, I extended it to VGG16 as follows.
Net_vgg16.py
import torch.nn as nn
import torch.nn.functional as F
class VGG16(nn.Module):
def __init__(self): # , num_classes):
super(VGG16, self).__init__()
num_classes=10
self.block1_output = nn.Sequential (
nn.Conv2d(3, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.block2_output = nn.Sequential (
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, kernel_size=3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.block3_output = nn.Sequential (
nn.Conv2d(128, 256, kernel_size=3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.block4_output = nn.Sequential (
nn.Conv2d(256, 512, kernel_size=3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.block5_output = nn.Sequential (
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.classifier = nn.Sequential(
nn.Linear(512, 512), #512 * 7 * 7, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(512, 32 ), #4096, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(32, num_classes), #4096
)
def forward(self, x):
x = self.block1_output(x)
x = self.block2_output(x)
x = self.block3_output(x)
x = self.block4_output(x)
x = self.block5_output(x)
#print(x.size())
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
In addition, the calculation examples of Cifar10 for various models are posted.
Summary
・ I tried to categorize Cifar10 with Pytorch ・ Initially, there were some errors, but somehow it became possible to calculate stably.
・ I would like to move an example unique to Pytorch.
bonus
(keras-gpu) C:\Users\user\pytorch\cifar10>python pytorch_cifar10_.py
Files already downloaded and verified
Files already downloaded and verified
cuda:0
[1, 200] loss: 2.303 Accuracy: 13.34 %
[1, 400] loss: 2.299 Accuracy: 14.55 %
[1, 600] loss: 2.296 Accuracy: 14.71 %
[1, 800] loss: 2.284 Accuracy: 16.72 %
[1, 1000] loss: 2.248 Accuracy: 17.70 %
[1, 1200] loss: 2.144 Accuracy: 24.59 %
[1, 1400] loss: 2.039 Accuracy: 27.71 %
[2, 200] loss: 1.943 Accuracy: 30.32 %
[2, 400] loss: 1.900 Accuracy: 31.92 %
[2, 600] loss: 1.883 Accuracy: 32.70 %
[2, 800] loss: 1.831 Accuracy: 34.42 %
[2, 1000] loss: 1.802 Accuracy: 34.84 %
[2, 1200] loss: 1.776 Accuracy: 35.06 %
[2, 1400] loss: 1.733 Accuracy: 37.69 %
[3, 200] loss: 1.688 Accuracy: 37.61 %
[3, 400] loss: 1.657 Accuracy: 38.20 %
[3, 600] loss: 1.627 Accuracy: 41.01 %
[3, 800] loss: 1.636 Accuracy: 41.60 %
[3, 1000] loss: 1.596 Accuracy: 41.73 %
[3, 1200] loss: 1.582 Accuracy: 41.52 %
[3, 1400] loss: 1.543 Accuracy: 43.17 %
[4, 200] loss: 1.517 Accuracy: 44.28 %
[4, 400] loss: 1.508 Accuracy: 45.50 %
[4, 600] loss: 1.503 Accuracy: 45.83 %
[4, 800] loss: 1.493 Accuracy: 46.98 %
[4, 1000] loss: 1.480 Accuracy: 45.65 %
[4, 1200] loss: 1.472 Accuracy: 47.23 %
[4, 1400] loss: 1.465 Accuracy: 47.72 %
[5, 200] loss: 1.440 Accuracy: 47.90 %
[5, 400] loss: 1.406 Accuracy: 50.01 %
[5, 600] loss: 1.419 Accuracy: 49.09 %
[5, 800] loss: 1.393 Accuracy: 50.10 %
[5, 1000] loss: 1.362 Accuracy: 49.50 %
[5, 1200] loss: 1.367 Accuracy: 49.13 %
[5, 1400] loss: 1.392 Accuracy: 51.04 %
[6, 200] loss: 1.336 Accuracy: 52.19 %
[6, 400] loss: 1.329 Accuracy: 52.20 %
[6, 600] loss: 1.312 Accuracy: 51.44 %
[6, 800] loss: 1.315 Accuracy: 51.34 %
[6, 1000] loss: 1.323 Accuracy: 52.54 %
[6, 1200] loss: 1.323 Accuracy: 53.76 %
[6, 1400] loss: 1.302 Accuracy: 53.15 %
[7, 200] loss: 1.257 Accuracy: 53.11 %
[7, 400] loss: 1.258 Accuracy: 53.91 %
[7, 600] loss: 1.262 Accuracy: 54.56 %
[7, 800] loss: 1.280 Accuracy: 55.07 %
[7, 1000] loss: 1.249 Accuracy: 54.81 %
[7, 1200] loss: 1.255 Accuracy: 54.41 %
[7, 1400] loss: 1.234 Accuracy: 55.69 %
[8, 200] loss: 1.213 Accuracy: 56.52 %
[8, 400] loss: 1.214 Accuracy: 56.52 %
[8, 600] loss: 1.213 Accuracy: 56.60 %
[8, 800] loss: 1.202 Accuracy: 55.38 %
[8, 1000] loss: 1.200 Accuracy: 57.14 %
[8, 1200] loss: 1.190 Accuracy: 56.84 %
[8, 1400] loss: 1.173 Accuracy: 57.08 %
[9, 200] loss: 1.144 Accuracy: 57.51 %
[9, 400] loss: 1.170 Accuracy: 57.25 %
[9, 600] loss: 1.136 Accuracy: 56.35 %
[9, 800] loss: 1.169 Accuracy: 58.69 %
[9, 1000] loss: 1.141 Accuracy: 57.84 %
[9, 1200] loss: 1.146 Accuracy: 56.51 %
[9, 1400] loss: 1.150 Accuracy: 57.88 %
[10, 200] loss: 1.128 Accuracy: 58.77 %
[10, 400] loss: 1.123 Accuracy: 58.69 %
[10, 600] loss: 1.120 Accuracy: 59.92 %
[10, 800] loss: 1.102 Accuracy: 58.37 %
[10, 1000] loss: 1.104 Accuracy: 59.26 %
[10, 1200] loss: 1.101 Accuracy: 59.45 %
[10, 1400] loss: 1.106 Accuracy: 59.75 %
[11, 200] loss: 1.081 Accuracy: 58.35 %
[11, 400] loss: 1.098 Accuracy: 59.52 %
[11, 600] loss: 1.040 Accuracy: 60.00 %
[11, 800] loss: 1.083 Accuracy: 60.39 %
[11, 1000] loss: 1.073 Accuracy: 60.55 %
[11, 1200] loss: 1.074 Accuracy: 61.02 %
[11, 1400] loss: 1.075 Accuracy: 60.78 %
[12, 200] loss: 1.027 Accuracy: 59.02 %
[12, 400] loss: 1.052 Accuracy: 60.14 %
[12, 600] loss: 1.025 Accuracy: 61.39 %
[12, 800] loss: 1.047 Accuracy: 59.45 %
[12, 1000] loss: 1.047 Accuracy: 61.99 %
[12, 1200] loss: 1.055 Accuracy: 60.82 %
[12, 1400] loss: 1.023 Accuracy: 62.17 %
[13, 200] loss: 0.994 Accuracy: 61.23 %
[13, 400] loss: 1.008 Accuracy: 61.94 %
[13, 600] loss: 1.014 Accuracy: 61.18 %
[13, 800] loss: 1.013 Accuracy: 62.04 %
[13, 1000] loss: 1.018 Accuracy: 61.59 %
[13, 1200] loss: 1.010 Accuracy: 61.81 %
[13, 1400] loss: 0.998 Accuracy: 61.81 %
[14, 200] loss: 0.961 Accuracy: 61.17 %
[14, 400] loss: 0.985 Accuracy: 61.63 %
[14, 600] loss: 0.977 Accuracy: 62.18 %
[14, 800] loss: 0.996 Accuracy: 61.84 %
[14, 1000] loss: 0.978 Accuracy: 61.70 %
[14, 1200] loss: 0.974 Accuracy: 61.63 %
[14, 1400] loss: 0.980 Accuracy: 62.09 %
[15, 200] loss: 0.935 Accuracy: 61.29 %
[15, 400] loss: 0.944 Accuracy: 63.11 %
[15, 600] loss: 0.936 Accuracy: 62.98 %
[15, 800] loss: 0.961 Accuracy: 62.76 %
[15, 1000] loss: 0.961 Accuracy: 62.42 %
[15, 1200] loss: 0.956 Accuracy: 61.82 %
[15, 1400] loss: 0.975 Accuracy: 62.35 %
[16, 200] loss: 0.901 Accuracy: 63.24 %
[16, 400] loss: 0.906 Accuracy: 62.88 %
[16, 600] loss: 0.924 Accuracy: 63.13 %
[16, 800] loss: 0.905 Accuracy: 62.71 %
[16, 1000] loss: 0.930 Accuracy: 62.22 %
[16, 1200] loss: 0.950 Accuracy: 62.95 %
[16, 1400] loss: 0.953 Accuracy: 63.11 %
[17, 200] loss: 0.894 Accuracy: 63.93 %
[17, 400] loss: 0.896 Accuracy: 63.65 %
[17, 600] loss: 0.880 Accuracy: 62.02 %
[17, 800] loss: 0.889 Accuracy: 63.14 %
[17, 1000] loss: 0.897 Accuracy: 63.36 %
[17, 1200] loss: 0.918 Accuracy: 63.98 %
[17, 1400] loss: 0.925 Accuracy: 63.66 %
[18, 200] loss: 0.853 Accuracy: 63.52 %
[18, 400] loss: 0.852 Accuracy: 62.60 %
[18, 600] loss: 0.877 Accuracy: 64.43 %
[18, 800] loss: 0.872 Accuracy: 63.48 %
[18, 1000] loss: 0.879 Accuracy: 63.45 %
[18, 1200] loss: 0.905 Accuracy: 63.76 %
[18, 1400] loss: 0.897 Accuracy: 63.30 %
[19, 200] loss: 0.823 Accuracy: 63.08 %
[19, 400] loss: 0.833 Accuracy: 63.93 %
[19, 600] loss: 0.855 Accuracy: 62.89 %
[19, 800] loss: 0.845 Accuracy: 63.44 %
[19, 1000] loss: 0.872 Accuracy: 63.94 %
[19, 1200] loss: 0.861 Accuracy: 64.28 %
[19, 1400] loss: 0.853 Accuracy: 64.58 %
[20, 200] loss: 0.817 Accuracy: 63.54 %
[20, 400] loss: 0.809 Accuracy: 63.82 %
[20, 600] loss: 0.813 Accuracy: 63.07 %
[20, 800] loss: 0.815 Accuracy: 64.33 %
[20, 1000] loss: 0.852 Accuracy: 64.66 %
[20, 1200] loss: 0.850 Accuracy: 63.97 %
[20, 1400] loss: 0.844 Accuracy: 64.47 %
Finished Training
Accuracy: 64.12 %
GroundTruth: cat ship ship plane
Predicted: cat ship ship ship
Accuracy of plane : 61 %
Accuracy of car : 80 %
Accuracy of bird : 50 %
Accuracy of cat : 53 %
Accuracy of deer : 50 %
Accuracy of dog : 52 %
Accuracy of frog : 66 %
Accuracy of horse : 67 %
Accuracy of ship : 82 %
Accuracy of truck : 75 %
elapsed time: 602.200 [sec]
import torch.nn as nn
import torch.nn.functional as F
class VGG13(nn.Module):
def __init__(self): # , num_classes):
super(VGG13, self).__init__()
num_classes=10
self.block1_output = nn.Sequential (
nn.Conv2d(3, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.block2_output = nn.Sequential (
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, kernel_size=3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.block3_output = nn.Sequential (
nn.Conv2d(128, 256, kernel_size=3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.block4_output = nn.Sequential (
nn.Conv2d(256, 512, kernel_size=3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.block5_output = nn.Sequential (
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.classifier = nn.Sequential(
nn.Linear(512, 512), #512 * 7 * 7, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(512, 32 ), #4096, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(32, num_classes), #4096
)
def forward(self, x):
x = self.block1_output(x)
x = self.block2_output(x)
x = self.block3_output(x)
x = self.block4_output(x)
x = self.block5_output(x)
#print(x.size())
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
(keras-gpu) C:\Users\user\pytorch\cifar10>python pytorch_cifar10_.py
Files already downloaded and verified
Files already downloaded and verified
cuda:0
[1, 200] loss: 2.156 Accuracy: 24.39 %
[1, 400] loss: 1.869 Accuracy: 33.88 %
[1, 600] loss: 1.728 Accuracy: 39.04 %
[1, 800] loss: 1.578 Accuracy: 43.44 %
[1, 1000] loss: 1.496 Accuracy: 47.75 %
[1, 1200] loss: 1.436 Accuracy: 52.35 %
[1, 1400] loss: 1.363 Accuracy: 54.04 %
[2, 200] loss: 1.231 Accuracy: 57.80 %
[2, 400] loss: 1.209 Accuracy: 58.82 %
[2, 600] loss: 1.163 Accuracy: 61.31 %
[2, 800] loss: 1.131 Accuracy: 61.99 %
[2, 1000] loss: 1.115 Accuracy: 62.97 %
[2, 1200] loss: 1.084 Accuracy: 63.12 %
[2, 1400] loss: 1.028 Accuracy: 65.87 %
[3, 200] loss: 0.925 Accuracy: 65.56 %
[3, 400] loss: 0.928 Accuracy: 66.94 %
[3, 600] loss: 0.910 Accuracy: 68.22 %
[3, 800] loss: 0.916 Accuracy: 67.86 %
[3, 1000] loss: 0.902 Accuracy: 69.14 %
[3, 1200] loss: 0.848 Accuracy: 69.07 %
[3, 1400] loss: 0.883 Accuracy: 70.32 %
[4, 200] loss: 0.752 Accuracy: 71.35 %
[4, 400] loss: 0.782 Accuracy: 71.42 %
[4, 600] loss: 0.757 Accuracy: 71.67 %
[4, 800] loss: 0.767 Accuracy: 72.89 %
[4, 1000] loss: 0.767 Accuracy: 73.36 %
[4, 1200] loss: 0.746 Accuracy: 73.61 %
[4, 1400] loss: 0.764 Accuracy: 73.88 %
[5, 200] loss: 0.647 Accuracy: 74.12 %
[5, 400] loss: 0.627 Accuracy: 74.62 %
[5, 600] loss: 0.618 Accuracy: 74.07 %
[5, 800] loss: 0.663 Accuracy: 75.19 %
[5, 1000] loss: 0.661 Accuracy: 74.28 %
[5, 1200] loss: 0.649 Accuracy: 76.79 %
[5, 1400] loss: 0.650 Accuracy: 74.59 %
[6, 200] loss: 0.556 Accuracy: 77.10 %
[6, 400] loss: 0.543 Accuracy: 75.73 %
[6, 600] loss: 0.528 Accuracy: 76.50 %
[6, 800] loss: 0.552 Accuracy: 76.03 %
[6, 1000] loss: 0.568 Accuracy: 77.13 %
[6, 1200] loss: 0.580 Accuracy: 76.73 %
[6, 1400] loss: 0.563 Accuracy: 76.20 %
[7, 200] loss: 0.475 Accuracy: 77.29 %
[7, 400] loss: 0.470 Accuracy: 77.17 %
[7, 600] loss: 0.503 Accuracy: 77.16 %
[7, 800] loss: 0.484 Accuracy: 77.60 %
[7, 1000] loss: 0.485 Accuracy: 78.23 %
[7, 1200] loss: 0.491 Accuracy: 78.32 %
[7, 1400] loss: 0.480 Accuracy: 78.08 %
[8, 200] loss: 0.386 Accuracy: 78.60 %
[8, 400] loss: 0.413 Accuracy: 78.82 %
[8, 600] loss: 0.401 Accuracy: 78.03 %
[8, 800] loss: 0.421 Accuracy: 78.75 %
[8, 1000] loss: 0.450 Accuracy: 77.68 %
[8, 1200] loss: 0.439 Accuracy: 78.55 %
[8, 1400] loss: 0.420 Accuracy: 79.05 %
[9, 200] loss: 0.315 Accuracy: 79.21 %
[9, 400] loss: 0.366 Accuracy: 78.72 %
[9, 600] loss: 0.374 Accuracy: 79.63 %
[9, 800] loss: 0.378 Accuracy: 79.75 %
[9, 1000] loss: 0.371 Accuracy: 78.52 %
[9, 1200] loss: 0.377 Accuracy: 79.65 %
[9, 1400] loss: 0.396 Accuracy: 79.51 %
[10, 200] loss: 0.306 Accuracy: 79.25 %
[10, 400] loss: 0.320 Accuracy: 79.06 %
[10, 600] loss: 0.341 Accuracy: 79.20 %
[10, 800] loss: 0.340 Accuracy: 79.21 %
[10, 1000] loss: 0.327 Accuracy: 78.73 %
[10, 1200] loss: 0.334 Accuracy: 79.49 %
[10, 1400] loss: 0.335 Accuracy: 79.33 %
[11, 200] loss: 0.253 Accuracy: 78.67 %
[11, 400] loss: 0.267 Accuracy: 79.47 %
[11, 600] loss: 0.278 Accuracy: 79.17 %
[11, 800] loss: 0.294 Accuracy: 80.12 %
[11, 1000] loss: 0.311 Accuracy: 79.86 %
[11, 1200] loss: 0.299 Accuracy: 80.65 %
[11, 1400] loss: 0.297 Accuracy: 80.39 %
[12, 200] loss: 0.226 Accuracy: 80.51 %
[12, 400] loss: 0.237 Accuracy: 80.22 %
[12, 600] loss: 0.253 Accuracy: 79.49 %
[12, 800] loss: 0.261 Accuracy: 79.71 %
[12, 1000] loss: 0.252 Accuracy: 80.68 %
[12, 1200] loss: 0.272 Accuracy: 80.75 %
[12, 1400] loss: 0.281 Accuracy: 80.64 %
[13, 200] loss: 0.201 Accuracy: 80.44 %
[13, 400] loss: 0.234 Accuracy: 80.49 %
[13, 600] loss: 0.220 Accuracy: 79.90 %
[13, 800] loss: 0.221 Accuracy: 80.00 %
[13, 1000] loss: 0.236 Accuracy: 80.46 %
[13, 1200] loss: 0.216 Accuracy: 80.66 %
[13, 1400] loss: 0.239 Accuracy: 80.45 %
[14, 200] loss: 0.168 Accuracy: 80.75 %
[14, 400] loss: 0.203 Accuracy: 77.86 %
[14, 600] loss: 0.231 Accuracy: 80.50 %
[14, 800] loss: 0.192 Accuracy: 80.81 %
[14, 1000] loss: 0.195 Accuracy: 80.73 %
[14, 1200] loss: 0.209 Accuracy: 81.04 %
[14, 1400] loss: 0.207 Accuracy: 80.03 %
[15, 200] loss: 0.142 Accuracy: 81.15 %
[15, 400] loss: 0.169 Accuracy: 80.88 %
[15, 600] loss: 0.174 Accuracy: 80.52 %
[15, 800] loss: 0.167 Accuracy: 80.88 %
[15, 1000] loss: 0.208 Accuracy: 80.02 %
[15, 1200] loss: 0.181 Accuracy: 81.65 %
[15, 1400] loss: 0.198 Accuracy: 81.14 %
[16, 200] loss: 0.125 Accuracy: 81.02 %
[16, 400] loss: 0.142 Accuracy: 81.41 %
[16, 600] loss: 0.172 Accuracy: 80.92 %
[16, 800] loss: 0.157 Accuracy: 82.58 %
[16, 1000] loss: 0.140 Accuracy: 81.21 %
[16, 1200] loss: 0.179 Accuracy: 80.29 %
[16, 1400] loss: 0.185 Accuracy: 81.94 %
[17, 200] loss: 0.125 Accuracy: 80.94 %
[17, 400] loss: 0.155 Accuracy: 80.92 %
[17, 600] loss: 0.140 Accuracy: 81.45 %
[17, 800] loss: 0.169 Accuracy: 81.80 %
[17, 1000] loss: 0.162 Accuracy: 81.31 %
[17, 1200] loss: 0.141 Accuracy: 81.42 %
[17, 1400] loss: 0.185 Accuracy: 80.21 %
[18, 200] loss: 0.140 Accuracy: 81.76 %
[18, 400] loss: 0.129 Accuracy: 80.78 %
[18, 600] loss: 0.135 Accuracy: 81.52 %
[18, 800] loss: 0.139 Accuracy: 82.01 %
[18, 1000] loss: 0.149 Accuracy: 81.43 %
[18, 1200] loss: 0.134 Accuracy: 81.39 %
[18, 1400] loss: 0.162 Accuracy: 80.56 %
[19, 200] loss: 0.102 Accuracy: 82.01 %
[19, 400] loss: 0.100 Accuracy: 80.91 %
[19, 600] loss: 0.148 Accuracy: 80.74 %
[19, 800] loss: 0.115 Accuracy: 82.43 %
[19, 1000] loss: 0.110 Accuracy: 81.74 %
[19, 1200] loss: 0.115 Accuracy: 80.78 %
[19, 1400] loss: 0.142 Accuracy: 81.88 %
[20, 200] loss: 0.109 Accuracy: 82.20 %
[20, 400] loss: 0.112 Accuracy: 81.65 %
[20, 600] loss: 0.139 Accuracy: 81.70 %
[20, 800] loss: 0.109 Accuracy: 82.88 %
[20, 1000] loss: 0.116 Accuracy: 82.73 %
[20, 1200] loss: 0.112 Accuracy: 82.07 %
[20, 1400] loss: 0.123 Accuracy: 82.28 %
Finished Training
Accuracy: 82.00 %
GroundTruth: cat ship ship plane
Predicted: cat ship ship plane
Accuracy of plane : 88 %
Accuracy of car : 91 %
Accuracy of bird : 75 %
Accuracy of cat : 55 %
Accuracy of deer : 84 %
Accuracy of dog : 70 %
Accuracy of frog : 84 %
Accuracy of horse : 81 %
Accuracy of ship : 92 %
Accuracy of truck : 87 %
elapsed time: 6227.035 [sec]
(keras-gpu) C:\Users\user\pytorch\cifar10>pip install torchsummary
Collecting torchsummary
Downloading https://files.pythonhosted.org/packages/7d/18/1474d06f721b86e6a9b9d7392ad68bed711a02f3b61ac43f13c719db50a6/torchsummary-1.5.1-py3-none-any.whl
Installing collected packages: torchsummary
Successfully installed torchsummary-1.5.1
(keras-gpu) C:\Users\user\pytorch\cifar10>python pytorch_cifar10_.py
Files already downloaded and verified
Files already downloaded and verified
cuda:0
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 32, 32] 1,792
BatchNorm2d-2 [-1, 64, 32, 32] 128
ReLU-3 [-1, 64, 32, 32] 0
Conv2d-4 [-1, 64, 32, 32] 36,928
BatchNorm2d-5 [-1, 64, 32, 32] 128
ReLU-6 [-1, 64, 32, 32] 0
MaxPool2d-7 [-1, 64, 16, 16] 0
Conv2d-8 [-1, 128, 16, 16] 73,856
BatchNorm2d-9 [-1, 128, 16, 16] 256
ReLU-10 [-1, 128, 16, 16] 0
Conv2d-11 [-1, 128, 16, 16] 147,584
BatchNorm2d-12 [-1, 128, 16, 16] 256
ReLU-13 [-1, 128, 16, 16] 0
MaxPool2d-14 [-1, 128, 8, 8] 0
Conv2d-15 [-1, 256, 8, 8] 295,168
BatchNorm2d-16 [-1, 256, 8, 8] 512
ReLU-17 [-1, 256, 8, 8] 0
Conv2d-18 [-1, 256, 8, 8] 590,080
BatchNorm2d-19 [-1, 256, 8, 8] 512
ReLU-20 [-1, 256, 8, 8] 0
MaxPool2d-21 [-1, 256, 4, 4] 0
Conv2d-22 [-1, 512, 4, 4] 1,180,160
BatchNorm2d-23 [-1, 512, 4, 4] 1,024
ReLU-24 [-1, 512, 4, 4] 0
Conv2d-25 [-1, 512, 4, 4] 2,359,808
BatchNorm2d-26 [-1, 512, 4, 4] 1,024
ReLU-27 [-1, 512, 4, 4] 0
MaxPool2d-28 [-1, 512, 2, 2] 0
Conv2d-29 [-1, 512, 2, 2] 2,359,808
BatchNorm2d-30 [-1, 512, 2, 2] 1,024
ReLU-31 [-1, 512, 2, 2] 0
Conv2d-32 [-1, 512, 2, 2] 2,359,808
BatchNorm2d-33 [-1, 512, 2, 2] 1,024
ReLU-34 [-1, 512, 2, 2] 0
MaxPool2d-35 [-1, 512, 1, 1] 0
Linear-36 [-1, 512] 262,656
ReLU-37 [-1, 512] 0
Dropout-38 [-1, 512] 0
Linear-39 [-1, 32] 16,416
ReLU-40 [-1, 32] 0
Dropout-41 [-1, 32] 0
Linear-42 [-1, 10] 330
================================================================
Total params: 9,690,282
Trainable params: 9,690,282
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 5.97
Params size (MB): 36.97
Estimated Total Size (MB): 42.95
----------------------------------------------------------------
(keras-gpu) C:\Users\user\pytorch\cifar10>python pytorch_cifar10_.py
Files already downloaded and verified
Files already downloaded and verified
cuda:0
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 32, 32] 1,792
BatchNorm2d-2 [-1, 64, 32, 32] 128
ReLU-3 [-1, 64, 32, 32] 0
Conv2d-4 [-1, 64, 32, 32] 36,928
BatchNorm2d-5 [-1, 64, 32, 32] 128
ReLU-6 [-1, 64, 32, 32] 0
MaxPool2d-7 [-1, 64, 16, 16] 0
Conv2d-8 [-1, 128, 16, 16] 73,856
BatchNorm2d-9 [-1, 128, 16, 16] 256
ReLU-10 [-1, 128, 16, 16] 0
Conv2d-11 [-1, 128, 16, 16] 147,584
BatchNorm2d-12 [-1, 128, 16, 16] 256
ReLU-13 [-1, 128, 16, 16] 0
MaxPool2d-14 [-1, 128, 8, 8] 0
Conv2d-15 [-1, 256, 8, 8] 295,168
BatchNorm2d-16 [-1, 256, 8, 8] 512
ReLU-17 [-1, 256, 8, 8] 0
Conv2d-18 [-1, 256, 8, 8] 590,080
BatchNorm2d-19 [-1, 256, 8, 8] 512
ReLU-20 [-1, 256, 8, 8] 0
MaxPool2d-21 [-1, 256, 4, 4] 0
Conv2d-22 [-1, 512, 4, 4] 1,180,160
BatchNorm2d-23 [-1, 512, 4, 4] 1,024
ReLU-24 [-1, 512, 4, 4] 0
Conv2d-25 [-1, 512, 4, 4] 2,359,808
BatchNorm2d-26 [-1, 512, 4, 4] 1,024
ReLU-27 [-1, 512, 4, 4] 0
MaxPool2d-28 [-1, 512, 2, 2] 0
Conv2d-29 [-1, 512, 2, 2] 2,359,808
BatchNorm2d-30 [-1, 512, 2, 2] 1,024
ReLU-31 [-1, 512, 2, 2] 0
Conv2d-32 [-1, 512, 2, 2] 2,359,808
BatchNorm2d-33 [-1, 512, 2, 2] 1,024
ReLU-34 [-1, 512, 2, 2] 0
MaxPool2d-35 [-1, 512, 1, 1] 0
Linear-36 [-1, 4096] 2,101,248
ReLU-37 [-1, 4096] 0
Dropout-38 [-1, 4096] 0
Linear-39 [-1, 4096] 16,781,312
ReLU-40 [-1, 4096] 0
Dropout-41 [-1, 4096] 0
Linear-42 [-1, 10] 40,970
================================================================
Total params: 28,334,410
Trainable params: 28,334,410
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 6.14
Params size (MB): 108.09
Estimated Total Size (MB): 114.24
----------------------------------------------------------------
[1, 200] loss: 1.935 Accuracy: 37.73 %
[1, 400] loss: 1.564 Accuracy: 46.54 %
[1, 600] loss: 1.355 Accuracy: 51.21 %
[1, 800] loss: 1.243 Accuracy: 57.66 %
[1, 1000] loss: 1.149 Accuracy: 61.24 %
[1, 1200] loss: 1.081 Accuracy: 64.30 %
[1, 1400] loss: 1.037 Accuracy: 65.43 %
[2, 200] loss: 0.876 Accuracy: 68.62 %
[2, 400] loss: 0.840 Accuracy: 68.47 %
[2, 600] loss: 0.819 Accuracy: 70.76 %
[2, 800] loss: 0.812 Accuracy: 70.56 %
[2, 1000] loss: 0.776 Accuracy: 72.58 %
[2, 1200] loss: 0.772 Accuracy: 72.98 %
[2, 1400] loss: 0.737 Accuracy: 73.90 %
[3, 200] loss: 0.590 Accuracy: 74.99 %
[3, 400] loss: 0.589 Accuracy: 74.98 %
[3, 600] loss: 0.575 Accuracy: 76.83 %
[3, 800] loss: 0.603 Accuracy: 76.16 %
[3, 1000] loss: 0.586 Accuracy: 75.61 %
[3, 1200] loss: 0.594 Accuracy: 77.48 %
[3, 1400] loss: 0.575 Accuracy: 77.80 %
[4, 200] loss: 0.421 Accuracy: 76.95 %
[4, 400] loss: 0.474 Accuracy: 79.14 %
[4, 600] loss: 0.450 Accuracy: 78.46 %
[4, 800] loss: 0.458 Accuracy: 78.70 %
[4, 1000] loss: 0.436 Accuracy: 78.99 %
[4, 1200] loss: 0.460 Accuracy: 78.49 %
[4, 1400] loss: 0.439 Accuracy: 79.29 %
[5, 200] loss: 0.324 Accuracy: 80.00 %
[5, 400] loss: 0.326 Accuracy: 79.82 %
[5, 600] loss: 0.340 Accuracy: 79.58 %
[5, 800] loss: 0.355 Accuracy: 79.85 %
[5, 1000] loss: 0.353 Accuracy: 78.64 %
[5, 1200] loss: 0.358 Accuracy: 79.53 %
[5, 1400] loss: 0.375 Accuracy: 80.18 %
[6, 200] loss: 0.197 Accuracy: 80.41 %
[6, 400] loss: 0.240 Accuracy: 79.51 %
[6, 600] loss: 0.253 Accuracy: 80.12 %
[6, 800] loss: 0.257 Accuracy: 79.99 %
[6, 1000] loss: 0.280 Accuracy: 80.19 %
[6, 1200] loss: 0.290 Accuracy: 80.65 %
[6, 1400] loss: 0.279 Accuracy: 80.54 %
[7, 200] loss: 0.163 Accuracy: 80.61 %
[7, 400] loss: 0.159 Accuracy: 80.54 %
[7, 600] loss: 0.214 Accuracy: 80.71 %
[7, 800] loss: 0.207 Accuracy: 80.06 %
[7, 1000] loss: 0.230 Accuracy: 80.94 %
[7, 1200] loss: 0.202 Accuracy: 80.87 %
[7, 1400] loss: 0.229 Accuracy: 80.88 %
[8, 200] loss: 0.111 Accuracy: 81.43 %
[8, 400] loss: 0.117 Accuracy: 80.23 %
[8, 600] loss: 0.141 Accuracy: 81.27 %
[8, 800] loss: 0.144 Accuracy: 80.94 %
[8, 1000] loss: 0.162 Accuracy: 81.23 %
[8, 1200] loss: 0.186 Accuracy: 80.36 %
[8, 1400] loss: 0.172 Accuracy: 81.31 %
[9, 200] loss: 0.115 Accuracy: 82.08 %
[9, 400] loss: 0.093 Accuracy: 81.80 %
[9, 600] loss: 0.110 Accuracy: 80.76 %
[9, 800] loss: 0.124 Accuracy: 80.36 %
[9, 1000] loss: 0.121 Accuracy: 81.47 %
[9, 1200] loss: 0.127 Accuracy: 82.10 %
[9, 1400] loss: 0.126 Accuracy: 82.00 %
[10, 200] loss: 0.069 Accuracy: 81.54 %
[10, 400] loss: 0.076 Accuracy: 81.65 %
[10, 600] loss: 0.086 Accuracy: 81.65 %
[10, 800] loss: 0.096 Accuracy: 81.21 %
[10, 1000] loss: 0.097 Accuracy: 81.36 %
[10, 1200] loss: 0.125 Accuracy: 81.14 %
[10, 1400] loss: 0.115 Accuracy: 81.67 %
[11, 200] loss: 0.065 Accuracy: 82.97 %
[11, 400] loss: 0.072 Accuracy: 82.64 %
[11, 600] loss: 0.068 Accuracy: 81.99 %
[11, 800] loss: 0.078 Accuracy: 82.35 %
[11, 1000] loss: 0.092 Accuracy: 80.93 %
[11, 1200] loss: 0.097 Accuracy: 82.51 %
[11, 1400] loss: 0.089 Accuracy: 82.36 %
[12, 200] loss: 0.052 Accuracy: 82.49 %
[12, 400] loss: 0.044 Accuracy: 82.01 %
[12, 600] loss: 0.059 Accuracy: 82.71 %
[12, 800] loss: 0.060 Accuracy: 82.39 %
[12, 1000] loss: 0.073 Accuracy: 82.73 %
[12, 1200] loss: 0.057 Accuracy: 82.53 %
[12, 1400] loss: 0.067 Accuracy: 82.27 %
[13, 200] loss: 0.050 Accuracy: 82.59 %
[13, 400] loss: 0.051 Accuracy: 82.51 %
[13, 600] loss: 0.046 Accuracy: 83.08 %
[13, 800] loss: 0.041 Accuracy: 82.59 %
[13, 1000] loss: 0.057 Accuracy: 82.74 %
[13, 1200] loss: 0.072 Accuracy: 82.47 %
[13, 1400] loss: 0.055 Accuracy: 82.31 %
[14, 200] loss: 0.046 Accuracy: 82.98 %
[14, 400] loss: 0.048 Accuracy: 82.69 %
[14, 600] loss: 0.036 Accuracy: 82.45 %
[14, 800] loss: 0.066 Accuracy: 82.31 %
[14, 1000] loss: 0.047 Accuracy: 82.56 %
[14, 1200] loss: 0.057 Accuracy: 82.21 %
[14, 1400] loss: 0.052 Accuracy: 81.95 %
[15, 200] loss: 0.045 Accuracy: 82.63 %
[15, 400] loss: 0.042 Accuracy: 82.32 %
[15, 600] loss: 0.033 Accuracy: 82.95 %
[15, 800] loss: 0.045 Accuracy: 82.65 %
[15, 1000] loss: 0.050 Accuracy: 82.56 %
[15, 1200] loss: 0.051 Accuracy: 81.83 %
[15, 1400] loss: 0.056 Accuracy: 82.11 %
[16, 200] loss: 0.029 Accuracy: 82.95 %
[16, 400] loss: 0.024 Accuracy: 82.57 %
[16, 600] loss: 0.036 Accuracy: 81.98 %
[16, 800] loss: 0.036 Accuracy: 82.66 %
[16, 1000] loss: 0.042 Accuracy: 82.54 %
[16, 1200] loss: 0.032 Accuracy: 82.41 %
[16, 1400] loss: 0.041 Accuracy: 82.57 %
[17, 200] loss: 0.028 Accuracy: 82.20 %
[17, 400] loss: 0.027 Accuracy: 83.26 %
[17, 600] loss: 0.025 Accuracy: 83.30 %
[17, 800] loss: 0.027 Accuracy: 82.94 %
[17, 1000] loss: 0.037 Accuracy: 81.51 %
[17, 1200] loss: 0.031 Accuracy: 82.83 %
[17, 1400] loss: 0.034 Accuracy: 82.57 %
[18, 200] loss: 0.030 Accuracy: 82.78 %
[18, 400] loss: 0.024 Accuracy: 83.46 %
[18, 600] loss: 0.020 Accuracy: 83.02 %
[18, 800] loss: 0.016 Accuracy: 83.47 %
[18, 1000] loss: 0.030 Accuracy: 82.85 %
[18, 1200] loss: 0.031 Accuracy: 82.56 %
[18, 1400] loss: 0.040 Accuracy: 82.16 %
[19, 200] loss: 0.023 Accuracy: 82.91 %
[19, 400] loss: 0.015 Accuracy: 82.99 %
[19, 600] loss: 0.017 Accuracy: 83.53 %
[19, 800] loss: 0.025 Accuracy: 82.35 %
[19, 1000] loss: 0.033 Accuracy: 82.55 %
[19, 1200] loss: 0.040 Accuracy: 82.92 %
[19, 1400] loss: 0.029 Accuracy: 82.75 %
[20, 200] loss: 0.020 Accuracy: 82.80 %
[20, 400] loss: 0.016 Accuracy: 83.21 %
[20, 600] loss: 0.017 Accuracy: 82.76 %
[20, 800] loss: 0.017 Accuracy: 82.93 %
[20, 1000] loss: 0.018 Accuracy: 83.16 %
[20, 1200] loss: 0.024 Accuracy: 83.23 %
[20, 1400] loss: 0.023 Accuracy: 82.91 %
Finished Training
Accuracy: 82.15 %
GroundTruth: cat ship ship plane
Predicted: cat ship ship plane
Accuracy of plane : 84 %
Accuracy of car : 91 %
Accuracy of bird : 69 %
Accuracy of cat : 59 %
Accuracy of deer : 81 %
Accuracy of dog : 76 %
Accuracy of frog : 90 %
Accuracy of horse : 86 %
Accuracy of ship : 94 %
Accuracy of truck : 88 %
elapsed time: 2177.621 [sec]
(keras-gpu) C:\Users\user\pytorch\cifar10>python pytorch_cifar10_.py
Files already downloaded and verified
Files already downloaded and verified
cuda:0
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 32, 32] 1,792
BatchNorm2d-2 [-1, 64, 32, 32] 128
ReLU-3 [-1, 64, 32, 32] 0
Conv2d-4 [-1, 64, 32, 32] 36,928
BatchNorm2d-5 [-1, 64, 32, 32] 128
ReLU-6 [-1, 64, 32, 32] 0
MaxPool2d-7 [-1, 64, 16, 16] 0
Conv2d-8 [-1, 128, 16, 16] 73,856
BatchNorm2d-9 [-1, 128, 16, 16] 256
ReLU-10 [-1, 128, 16, 16] 0
Conv2d-11 [-1, 128, 16, 16] 147,584
BatchNorm2d-12 [-1, 128, 16, 16] 256
ReLU-13 [-1, 128, 16, 16] 0
MaxPool2d-14 [-1, 128, 8, 8] 0
Conv2d-15 [-1, 256, 8, 8] 295,168
BatchNorm2d-16 [-1, 256, 8, 8] 512
ReLU-17 [-1, 256, 8, 8] 0
Conv2d-18 [-1, 256, 8, 8] 590,080
BatchNorm2d-19 [-1, 256, 8, 8] 512
ReLU-20 [-1, 256, 8, 8] 0
MaxPool2d-21 [-1, 256, 4, 4] 0
Conv2d-22 [-1, 512, 4, 4] 1,180,160
BatchNorm2d-23 [-1, 512, 4, 4] 1,024
ReLU-24 [-1, 512, 4, 4] 0
Conv2d-25 [-1, 512, 4, 4] 2,359,808
BatchNorm2d-26 [-1, 512, 4, 4] 1,024
ReLU-27 [-1, 512, 4, 4] 0
MaxPool2d-28 [-1, 512, 2, 2] 0
Conv2d-29 [-1, 512, 2, 2] 2,359,808
BatchNorm2d-30 [-1, 512, 2, 2] 1,024
ReLU-31 [-1, 512, 2, 2] 0
Conv2d-32 [-1, 512, 2, 2] 2,359,808
BatchNorm2d-33 [-1, 512, 2, 2] 1,024
ReLU-34 [-1, 512, 2, 2] 0
MaxPool2d-35 [-1, 512, 1, 1] 0
Linear-36 [-1, 10] 5,130
================================================================
Total params: 9,416,010
Trainable params: 9,416,010
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 5.96
Params size (MB): 35.92
Estimated Total Size (MB): 41.89
----------------------------------------------------------------
[1, 200] loss: 1.694 Accuracy: 45.04 %
[1, 400] loss: 1.393 Accuracy: 52.05 %
[1, 600] loss: 1.245 Accuracy: 59.09 %
[1, 800] loss: 1.119 Accuracy: 63.34 %
[1, 1000] loss: 1.034 Accuracy: 67.15 %
[1, 1200] loss: 0.987 Accuracy: 64.93 %
[1, 1400] loss: 0.922 Accuracy: 69.80 %
[2, 200] loss: 0.732 Accuracy: 71.40 %
[2, 400] loss: 0.765 Accuracy: 70.54 %
[2, 600] loss: 0.730 Accuracy: 72.81 %
[2, 800] loss: 0.703 Accuracy: 74.63 %
[2, 1000] loss: 0.726 Accuracy: 74.41 %
[2, 1200] loss: 0.695 Accuracy: 75.12 %
[2, 1400] loss: 0.676 Accuracy: 76.17 %
[3, 200] loss: 0.484 Accuracy: 76.41 %
[3, 400] loss: 0.496 Accuracy: 76.92 %
[3, 600] loss: 0.519 Accuracy: 76.57 %
[3, 800] loss: 0.521 Accuracy: 76.75 %
[3, 1000] loss: 0.523 Accuracy: 77.10 %
[3, 1200] loss: 0.499 Accuracy: 77.52 %
[3, 1400] loss: 0.506 Accuracy: 78.88 %
[4, 200] loss: 0.320 Accuracy: 79.10 %
[4, 400] loss: 0.348 Accuracy: 78.58 %
[4, 600] loss: 0.368 Accuracy: 78.86 %
[4, 800] loss: 0.398 Accuracy: 79.05 %
[4, 1000] loss: 0.387 Accuracy: 79.22 %
[4, 1200] loss: 0.409 Accuracy: 79.54 %
[4, 1400] loss: 0.416 Accuracy: 78.79 %
[5, 200] loss: 0.212 Accuracy: 79.96 %
[5, 400] loss: 0.243 Accuracy: 80.23 %
[5, 600] loss: 0.257 Accuracy: 79.61 %
[5, 800] loss: 0.270 Accuracy: 79.62 %
[5, 1000] loss: 0.297 Accuracy: 79.50 %
[5, 1200] loss: 0.282 Accuracy: 79.86 %
[5, 1400] loss: 0.307 Accuracy: 79.68 %
[6, 200] loss: 0.159 Accuracy: 80.35 %
[6, 400] loss: 0.168 Accuracy: 78.92 %
[6, 600] loss: 0.176 Accuracy: 80.20 %
[6, 800] loss: 0.198 Accuracy: 79.92 %
[6, 1000] loss: 0.203 Accuracy: 79.62 %
[6, 1200] loss: 0.196 Accuracy: 80.84 %
[6, 1400] loss: 0.223 Accuracy: 80.23 %
[7, 200] loss: 0.117 Accuracy: 80.72 %
[7, 400] loss: 0.112 Accuracy: 80.82 %
[7, 600] loss: 0.111 Accuracy: 80.64 %
[7, 800] loss: 0.134 Accuracy: 80.78 %
[7, 1000] loss: 0.137 Accuracy: 79.52 %
[7, 1200] loss: 0.160 Accuracy: 80.54 %
[7, 1400] loss: 0.149 Accuracy: 80.22 %
[8, 200] loss: 0.080 Accuracy: 80.49 %
[8, 400] loss: 0.080 Accuracy: 79.94 %
[8, 600] loss: 0.081 Accuracy: 81.20 %
[8, 800] loss: 0.087 Accuracy: 79.86 %
[8, 1000] loss: 0.107 Accuracy: 79.85 %
[8, 1200] loss: 0.128 Accuracy: 81.13 %
[8, 1400] loss: 0.124 Accuracy: 80.82 %
[9, 200] loss: 0.064 Accuracy: 81.60 %
[9, 400] loss: 0.070 Accuracy: 81.56 %
[9, 600] loss: 0.076 Accuracy: 80.87 %
[9, 800] loss: 0.079 Accuracy: 81.40 %
[9, 1000] loss: 0.109 Accuracy: 79.99 %
[9, 1200] loss: 0.112 Accuracy: 80.14 %
[9, 1400] loss: 0.092 Accuracy: 80.49 %
[10, 200] loss: 0.075 Accuracy: 81.39 %
[10, 400] loss: 0.052 Accuracy: 80.67 %
[10, 600] loss: 0.055 Accuracy: 80.81 %
[10, 800] loss: 0.048 Accuracy: 81.62 %
[10, 1000] loss: 0.050 Accuracy: 81.03 %
[10, 1200] loss: 0.072 Accuracy: 80.54 %
[10, 1400] loss: 0.092 Accuracy: 80.93 %
[11, 200] loss: 0.051 Accuracy: 81.15 %
[11, 400] loss: 0.042 Accuracy: 81.66 %
[11, 600] loss: 0.052 Accuracy: 81.73 %
[11, 800] loss: 0.044 Accuracy: 81.80 %
[11, 1000] loss: 0.045 Accuracy: 81.38 %
[11, 1200] loss: 0.041 Accuracy: 81.75 %
[11, 1400] loss: 0.051 Accuracy: 81.69 %
[12, 200] loss: 0.043 Accuracy: 82.13 %
[12, 400] loss: 0.026 Accuracy: 82.22 %
[12, 600] loss: 0.038 Accuracy: 81.66 %
[12, 800] loss: 0.030 Accuracy: 82.17 %
[12, 1000] loss: 0.040 Accuracy: 81.41 %
[12, 1200] loss: 0.036 Accuracy: 82.57 %
[12, 1400] loss: 0.040 Accuracy: 81.92 %
[13, 200] loss: 0.028 Accuracy: 82.66 %
[13, 400] loss: 0.028 Accuracy: 83.11 %
[13, 600] loss: 0.028 Accuracy: 81.71 %
[13, 800] loss: 0.023 Accuracy: 83.15 %
[13, 1000] loss: 0.018 Accuracy: 82.23 %
[13, 1200] loss: 0.025 Accuracy: 82.45 %
[13, 1400] loss: 0.030 Accuracy: 82.09 %
[14, 200] loss: 0.019 Accuracy: 82.08 %
[14, 400] loss: 0.029 Accuracy: 81.89 %
[14, 600] loss: 0.029 Accuracy: 82.36 %
[14, 800] loss: 0.019 Accuracy: 82.19 %
[14, 1000] loss: 0.020 Accuracy: 81.79 %
[14, 1200] loss: 0.028 Accuracy: 81.67 %
[14, 1400] loss: 0.037 Accuracy: 81.56 %
[15, 200] loss: 0.029 Accuracy: 82.03 %
[15, 400] loss: 0.024 Accuracy: 82.66 %
[15, 600] loss: 0.024 Accuracy: 82.21 %
[15, 800] loss: 0.022 Accuracy: 81.62 %
[15, 1000] loss: 0.024 Accuracy: 82.61 %
[15, 1200] loss: 0.028 Accuracy: 82.36 %
[15, 1400] loss: 0.032 Accuracy: 82.21 %
[16, 200] loss: 0.018 Accuracy: 82.14 %
[16, 400] loss: 0.013 Accuracy: 82.07 %
[16, 600] loss: 0.016 Accuracy: 82.62 %
[16, 800] loss: 0.014 Accuracy: 82.77 %
[16, 1000] loss: 0.017 Accuracy: 82.30 %
[16, 1200] loss: 0.031 Accuracy: 82.07 %
[16, 1400] loss: 0.021 Accuracy: 82.14 %
[17, 200] loss: 0.021 Accuracy: 82.37 %
[17, 400] loss: 0.019 Accuracy: 81.47 %
[17, 600] loss: 0.016 Accuracy: 82.76 %
[17, 800] loss: 0.014 Accuracy: 82.85 %
[17, 1000] loss: 0.012 Accuracy: 82.11 %
[17, 1200] loss: 0.021 Accuracy: 82.27 %
[17, 1400] loss: 0.025 Accuracy: 81.77 %
[18, 200] loss: 0.017 Accuracy: 82.24 %
[18, 400] loss: 0.015 Accuracy: 82.22 %
[18, 600] loss: 0.010 Accuracy: 82.42 %
[18, 800] loss: 0.011 Accuracy: 83.26 %
[18, 1000] loss: 0.014 Accuracy: 82.56 %
[18, 1200] loss: 0.020 Accuracy: 82.53 %
[18, 1400] loss: 0.025 Accuracy: 82.08 %
[19, 200] loss: 0.017 Accuracy: 82.10 %
[19, 400] loss: 0.014 Accuracy: 82.57 %
[19, 600] loss: 0.012 Accuracy: 82.03 %
[19, 800] loss: 0.014 Accuracy: 82.27 %
[19, 1000] loss: 0.010 Accuracy: 82.89 %
[19, 1200] loss: 0.006 Accuracy: 82.79 %
[19, 1400] loss: 0.010 Accuracy: 82.54 %
[20, 200] loss: 0.006 Accuracy: 83.22 %
[20, 400] loss: 0.005 Accuracy: 83.32 %
[20, 600] loss: 0.010 Accuracy: 82.79 %
[20, 800] loss: 0.008 Accuracy: 82.95 %
[20, 1000] loss: 0.007 Accuracy: 83.04 %
[20, 1200] loss: 0.017 Accuracy: 82.34 %
[20, 1400] loss: 0.022 Accuracy: 81.85 %
Finished Training
Accuracy: 82.37 %
GroundTruth: cat ship ship plane
Predicted: cat ship ship plane
Accuracy of plane : 79 %
Accuracy of car : 88 %
Accuracy of bird : 75 %
Accuracy of cat : 65 %
Accuracy of deer : 79 %
Accuracy of dog : 79 %
Accuracy of frog : 81 %
Accuracy of horse : 84 %
Accuracy of ship : 88 %
Accuracy of truck : 91 %
elapsed time: ．．．
(keras-gpu) C:\Users\user\pytorch\cifar10>python pytorch_cifar10_.py
Files already downloaded and verified
Files already downloaded and verified
cuda:0
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 32, 32] 1,792
BatchNorm2d-2 [-1, 64, 32, 32] 128
ReLU-3 [-1, 64, 32, 32] 0
Conv2d-4 [-1, 64, 32, 32] 36,928
BatchNorm2d-5 [-1, 64, 32, 32] 128
ReLU-6 [-1, 64, 32, 32] 0
MaxPool2d-7 [-1, 64, 16, 16] 0
Conv2d-8 [-1, 128, 16, 16] 73,856
BatchNorm2d-9 [-1, 128, 16, 16] 256
ReLU-10 [-1, 128, 16, 16] 0
Conv2d-11 [-1, 128, 16, 16] 147,584
BatchNorm2d-12 [-1, 128, 16, 16] 256
ReLU-13 [-1, 128, 16, 16] 0
MaxPool2d-14 [-1, 128, 8, 8] 0
Conv2d-15 [-1, 256, 8, 8] 295,168
BatchNorm2d-16 [-1, 256, 8, 8] 512
ReLU-17 [-1, 256, 8, 8] 0
Conv2d-18 [-1, 256, 8, 8] 590,080
BatchNorm2d-19 [-1, 256, 8, 8] 512
ReLU-20 [-1, 256, 8, 8] 0
Conv2d-21 [-1, 256, 8, 8] 590,080
BatchNorm2d-22 [-1, 256, 8, 8] 512
ReLU-23 [-1, 256, 8, 8] 0
MaxPool2d-24 [-1, 256, 4, 4] 0
Conv2d-25 [-1, 512, 4, 4] 1,180,160
BatchNorm2d-26 [-1, 512, 4, 4] 1,024
ReLU-27 [-1, 512, 4, 4] 0
Conv2d-28 [-1, 512, 4, 4] 2,359,808
BatchNorm2d-29 [-1, 512, 4, 4] 1,024
ReLU-30 [-1, 512, 4, 4] 0
Conv2d-31 [-1, 512, 4, 4] 2,359,808
BatchNorm2d-32 [-1, 512, 4, 4] 1,024
ReLU-33 [-1, 512, 4, 4] 0
MaxPool2d-34 [-1, 512, 2, 2] 0
Conv2d-35 [-1, 512, 2, 2] 2,359,808
BatchNorm2d-36 [-1, 512, 2, 2] 1,024
ReLU-37 [-1, 512, 2, 2] 0
Conv2d-38 [-1, 512, 2, 2] 2,359,808
BatchNorm2d-39 [-1, 512, 2, 2] 1,024
ReLU-40 [-1, 512, 2, 2] 0
Conv2d-41 [-1, 512, 2, 2] 2,359,808
BatchNorm2d-42 [-1, 512, 2, 2] 1,024
ReLU-43 [-1, 512, 2, 2] 0
MaxPool2d-44 [-1, 512, 1, 1] 0
Linear-45 [-1, 10] 5,130
================================================================
Total params: 14,728,266
Trainable params: 14,728,266
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 6.57
Params size (MB): 56.18
Estimated Total Size (MB): 62.76
----------------------------------------------------------------
[1, 200] loss: 1.799 Accuracy: 40.17 %
[1, 400] loss: 1.469 Accuracy: 48.53 %
[1, 600] loss: 1.295 Accuracy: 58.68 %
[1, 800] loss: 1.183 Accuracy: 59.18 %
[1, 1000] loss: 1.091 Accuracy: 63.12 %
[1, 1200] loss: 1.016 Accuracy: 67.31 %
[1, 1400] loss: 0.943 Accuracy: 67.08 %
[2, 200] loss: 0.774 Accuracy: 69.65 %
[2, 400] loss: 0.773 Accuracy: 72.26 %
[2, 600] loss: 0.739 Accuracy: 72.27 %
[2, 800] loss: 0.742 Accuracy: 73.00 %
[2, 1000] loss: 0.716 Accuracy: 73.47 %
[2, 1200] loss: 0.730 Accuracy: 75.37 %
[2, 1400] loss: 0.686 Accuracy: 75.08 %
[3, 200] loss: 0.530 Accuracy: 75.96 %
[3, 400] loss: 0.532 Accuracy: 76.04 %
[3, 600] loss: 0.557 Accuracy: 76.72 %
[3, 800] loss: 0.540 Accuracy: 77.04 %
[3, 1000] loss: 0.560 Accuracy: 76.86 %
[3, 1200] loss: 0.541 Accuracy: 78.71 %
[3, 1400] loss: 0.534 Accuracy: 77.87 %
[4, 200] loss: 0.367 Accuracy: 78.03 %
[4, 400] loss: 0.385 Accuracy: 78.14 %
[4, 600] loss: 0.399 Accuracy: 77.48 %
[4, 800] loss: 0.421 Accuracy: 80.07 %
[4, 1000] loss: 0.423 Accuracy: 79.78 %
[4, 1200] loss: 0.419 Accuracy: 77.99 %
[4, 1400] loss: 0.435 Accuracy: 77.94 %
[5, 200] loss: 0.251 Accuracy: 79.96 %
[5, 400] loss: 0.263 Accuracy: 80.21 %
[5, 600] loss: 0.305 Accuracy: 79.52 %
[5, 800] loss: 0.325 Accuracy: 79.28 %
[5, 1000] loss: 0.328 Accuracy: 79.60 %
[5, 1200] loss: 0.310 Accuracy: 80.36 %
[5, 1400] loss: 0.321 Accuracy: 79.35 %
[6, 200] loss: 0.197 Accuracy: 80.52 %
[6, 400] loss: 0.175 Accuracy: 81.41 %
[6, 600] loss: 0.205 Accuracy: 79.99 %
[6, 800] loss: 0.225 Accuracy: 80.46 %
[6, 1000] loss: 0.226 Accuracy: 81.30 %
[6, 1200] loss: 0.268 Accuracy: 80.72 %
[6, 1400] loss: 0.260 Accuracy: 80.55 %
[7, 200] loss: 0.137 Accuracy: 81.70 %
[7, 400] loss: 0.154 Accuracy: 80.79 %
[7, 600] loss: 0.159 Accuracy: 81.09 %
[7, 800] loss: 0.163 Accuracy: 80.51 %
[7, 1000] loss: 0.181 Accuracy: 81.27 %
[7, 1200] loss: 0.188 Accuracy: 81.19 %
[7, 1400] loss: 0.175 Accuracy: 81.94 %
[8, 200] loss: 0.097 Accuracy: 81.12 %
[8, 400] loss: 0.127 Accuracy: 80.91 %
[8, 600] loss: 0.122 Accuracy: 81.28 %
[8, 800] loss: 0.136 Accuracy: 81.21 %
[8, 1000] loss: 0.128 Accuracy: 81.71 %
[8, 1200] loss: 0.144 Accuracy: 81.51 %
[8, 1400] loss: 0.152 Accuracy: 81.56 %
[9, 200] loss: 0.079 Accuracy: 82.23 %
[9, 400] loss: 0.082 Accuracy: 81.96 %
[9, 600] loss: 0.082 Accuracy: 81.99 %
[9, 800] loss: 0.088 Accuracy: 81.79 %
[9, 1000] loss: 0.095 Accuracy: 81.77 %
[9, 1200] loss: 0.105 Accuracy: 82.10 %
[9, 1400] loss: 0.119 Accuracy: 82.12 %
[10, 200] loss: 0.068 Accuracy: 82.85 %
[10, 400] loss: 0.054 Accuracy: 82.08 %
[10, 600] loss: 0.075 Accuracy: 81.81 %
[10, 800] loss: 0.077 Accuracy: 81.26 %
[10, 1000] loss: 0.088 Accuracy: 81.52 %
[10, 1200] loss: 0.092 Accuracy: 82.67 %
[10, 1400] loss: 0.086 Accuracy: 81.33 %
[11, 200] loss: 0.058 Accuracy: 82.81 %
[11, 400] loss: 0.054 Accuracy: 82.56 %
[11, 600] loss: 0.061 Accuracy: 82.24 %
[11, 800] loss: 0.076 Accuracy: 82.50 %
[11, 1000] loss: 0.073 Accuracy: 82.36 %
[11, 1200] loss: 0.058 Accuracy: 82.78 %
[11, 1400] loss: 0.081 Accuracy: 81.89 %
[12, 200] loss: 0.052 Accuracy: 82.33 %
[12, 400] loss: 0.034 Accuracy: 82.74 %
[12, 600] loss: 0.039 Accuracy: 82.18 %
[12, 800] loss: 0.049 Accuracy: 82.51 %
[12, 1000] loss: 0.054 Accuracy: 82.29 %
[12, 1200] loss: 0.051 Accuracy: 83.02 %
[12, 1400] loss: 0.058 Accuracy: 82.70 %
[13, 200] loss: 0.053 Accuracy: 82.71 %
[13, 400] loss: 0.060 Accuracy: 82.67 %
[13, 600] loss: 0.043 Accuracy: 82.62 %
[13, 800] loss: 0.049 Accuracy: 82.43 %
[13, 1000] loss: 0.051 Accuracy: 82.64 %
[13, 1200] loss: 0.064 Accuracy: 82.29 %
[13, 1400] loss: 0.060 Accuracy: 82.71 %
[14, 200] loss: 0.039 Accuracy: 82.99 %
[14, 400] loss: 0.031 Accuracy: 82.65 %
[14, 600] loss: 0.029 Accuracy: 83.03 %
[14, 800] loss: 0.029 Accuracy: 83.56 %
[14, 1000] loss: 0.036 Accuracy: 83.31 %
[14, 1200] loss: 0.035 Accuracy: 83.16 %
[14, 1400] loss: 0.050 Accuracy: 81.60 %
[15, 200] loss: 0.029 Accuracy: 83.00 %
[15, 400] loss: 0.020 Accuracy: 83.58 %
[15, 600] loss: 0.021 Accuracy: 83.13 %
[15, 800] loss: 0.030 Accuracy: 82.34 %
[15, 1000] loss: 0.030 Accuracy: 82.31 %
[15, 1200] loss: 0.028 Accuracy: 82.54 %
[15, 1400] loss: 0.038 Accuracy: 82.27 %
[16, 200] loss: 0.027 Accuracy: 82.22 %
[16, 400] loss: 0.027 Accuracy: 82.48 %
[16, 600] loss: 0.029 Accuracy: 82.61 %
[16, 800] loss: 0.034 Accuracy: 82.41 %
[16, 1000] loss: 0.043 Accuracy: 82.86 %
[16, 1200] loss: 0.034 Accuracy: 83.38 %
[16, 1400] loss: 0.035 Accuracy: 83.11 %
[17, 200] loss: 0.022 Accuracy: 83.67 %
[17, 400] loss: 0.024 Accuracy: 82.72 %
[17, 600] loss: 0.023 Accuracy: 82.82 %
[17, 800] loss: 0.016 Accuracy: 83.68 %
[17, 1000] loss: 0.019 Accuracy: 83.34 %
[17, 1200] loss: 0.025 Accuracy: 82.77 %
[17, 1400] loss: 0.034 Accuracy: 83.47 %
[18, 200] loss: 0.021 Accuracy: 83.69 %
[18, 400] loss: 0.020 Accuracy: 83.29 %
[18, 600] loss: 0.014 Accuracy: 83.81 %
[18, 800] loss: 0.020 Accuracy: 83.58 %
[18, 1000] loss: 0.028 Accuracy: 82.57 %
[18, 1200] loss: 0.029 Accuracy: 82.51 %
[18, 1400] loss: 0.030 Accuracy: 82.37 %
[19, 200] loss: 0.022 Accuracy: 83.79 %
[19, 400] loss: 0.012 Accuracy: 83.80 %
[19, 600] loss: 0.012 Accuracy: 83.77 %
[19, 800] loss: 0.017 Accuracy: 83.51 %
[19, 1000] loss: 0.016 Accuracy: 83.54 %
[19, 1200] loss: 0.011 Accuracy: 83.88 %
[19, 1400] loss: 0.011 Accuracy: 83.56 %
[20, 200] loss: 0.018 Accuracy: 82.86 %
[20, 400] loss: 0.023 Accuracy: 83.04 %
[20, 600] loss: 0.026 Accuracy: 83.26 %
[20, 800] loss: 0.020 Accuracy: 82.70 %
[20, 1000] loss: 0.016 Accuracy: 83.13 %
[20, 1200] loss: 0.021 Accuracy: 82.92 %
[20, 1400] loss: 0.029 Accuracy: 82.57 %
Finished Training
Accuracy: 83.03 %
GroundTruth: cat ship ship plane
Predicted: cat ship ship plane
Accuracy of plane : 87 %
Accuracy of car : 93 %
Accuracy of bird : 76 %
Accuracy of cat : 59 %
Accuracy of deer : 80 %
Accuracy of dog : 77 %
Accuracy of frog : 85 %
Accuracy of horse : 85 %
Accuracy of ship : 92 %
Accuracy of truck : 93 %
elapsed time: 2412.977 [sec]
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