Notebook 14: Ising Phases using Convolutional Neural Networks in PyTorch¶

Learning Goal¶

The primary goal of this notebooks is to learn how to implement a Convolutional Neural Network (CNN) using the powerful PyTorch package. It also introduces core concepts for CNNs such as convolutional and pooling layers and padding.

Overview¶

In this notebook, we will write a simple convolutional neural network (CNN) in Pytorch for classifying phases for the Ising Model. We will consider perhaps the simplest CNN: a single convolutional layer with depth $N \in \{1,5,10,20,50\}$. This will introduce the power of the Pytorch framework to make dynamic graphs.

We will use this notebook to characterize samples drawn from the 2D Ising model at various temperatures. This is the same dataset that has been used in all earlier examples. Recall that the critical temperature for the Ising model is $T_c=2.26$.

In [1]:
from __future__ import print_function, division
import os,sys
import numpy as np
import torch # pytorch package, allows using GPUs
# fix seed
seed=17
np.random.seed(seed)
torch.manual_seed(seed)

Out[1]:
<torch._C.Generator at 0x10fee8bd0>

Structure of the Procedure¶

Constructing a Deep Neural Network to solve ML problems is a multiple-stage process. Quite generally, one can identify the key steps as follows:

• step 1: Load and process the data
• step 2: Define the model and its architecture
• step 3: Choose the optimizer and the cost function
• step 4: Train the model
• step 5: Evaluate the model performance on the unseen test data
• step 6: Modify the hyperparameters to optimize performance for the specific data set

Below, we sometimes combine some of these steps together for convenience.

Step 1: Load and Process the Ising Dataset¶

We start by defining the dataset class for Pytorch.

We have three types of samples in the Ising dataset: samples drawn from deep in the disordered phase, samples drawn from the ordered phase, and samples drawn from near the critical phase which we do not use for training. The goal is to classify whether a sample comes from $T>T_c$ or $T<T_c$.

There is standard way to load data when using the PyTorch package, which we discussed in the DNN example for the SUSY dataset. Here, we just switch to the 2D-Ising data instead.

To proceed, download the Ising dataset and insert the proper path_to_data in the script.

In [2]:
from torchvision import datasets # load data

class Ising_Dataset(torch.utils.data.Dataset):
"""Ising pytorch dataset."""

def __init__(self, data_type, transform=False):
"""
Args:
data_type (string): train, test or critical: creates data_loader
transform (callable, optional): Optional transform to be applied on a sample.

"""

from sklearn.model_selection import train_test_split
import collections
import pickle as pickle

L=40 # linear system size
T=np.linspace(0.25,4.0,16) # temperatures
T_c=2.26 # critical temperature in the TD limit

# path to data directory
path_to_data=os.path.expanduser('~')+'/Dropbox/MachineLearningReview/Datasets/isingMC/'

file_name = "Ising2DFM_reSample_L40_T=All.pkl" # this file contains 16*10000 samples taken in T=np.arange(0.25,4.0001,0.25)
data = pickle.load(open(path_to_data+file_name,'rb')) # pickle reads the file and returns the Python object (1D array, compressed bits)
data = np.unpackbits(data).reshape(-1, 1600) # Decompress array and reshape for convenience
data=data.astype('int')
data[np.where(data==0)]=-1 # map 0 state to -1 (Ising variable can take values +/-1)

file_name = "Ising2DFM_reSample_L40_T=All_labels.pkl" # this file contains 16*10000 samples taken in T=np.arange(0.25,4.0001,0.25)
labels = pickle.load(open(path_to_data+file_name,'rb')) # pickle reads the file and returns the Python object (here just a 1D array with the binary labels)

# divide data into ordered, critical and disordered

X_ordered=data[:70000,:]
Y_ordered=labels[:70000]

X_critical=data[70000:100000,:]
Y_critical=labels[70000:100000]

X_disordered=data[100000:,:]
Y_disordered=labels[100000:]

del data,labels
# define training, critical and test data sets
X=np.concatenate((X_ordered,X_disordered)) #np.concatenate((X_ordered,X_critical,X_disordered))
Y=np.concatenate((Y_ordered,Y_disordered)) #np.concatenate((Y_ordered,Y_critical,Y_disordered))

# pick random data points from ordered and disordered states to create the training and test sets
X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,train_size=0.8)

if data_type=='train':
X=X_train
Y=Y_train
print("Training on 80 percent of examples")

if data_type=='test':
X=X_test
Y=Y_test
print("Testing on 20 percent of examples")

if data_type=='critical':
X=X_critical
Y=Y_critical
print("Predicting on %i critical examples"%len(Y_critical))

# reshape data back to original 2D-array form
X=X.reshape(X.shape[0],40,40)

# these are necessary attributes in dataset class and must be assigned
self.data=(X,Y)
self.transform = transform

# override __len__ and __getitem__ of the Dataset() class

def __len__(self):
return len(self.data[1])

def __getitem__(self, idx):

sample=(self.data[0][idx,...],self.data[1][idx])
if self.transform:
sample=self.transform(sample)

return sample

# kwargs:  CUDA arguments, if enabled
# load and noralise train,test, and data
Ising_Dataset(data_type='train'),
batch_size=args.batch_size, shuffle=True, **kwargs)

Ising_Dataset(data_type='test'),
batch_size=args.test_batch_size, shuffle=True, **kwargs)

Ising_Dataset(data_type='critical'),
batch_size=args.test_batch_size, shuffle=True, **kwargs)



Step 2: Define the Neural Net and its Architecture¶

Similar to the discussion in the SUSY DNN notebook, we then define the architecture of the neural net in the model class which contains the forward function method that tells us how to produce the output given some input. The backpropagaiton algorithm is implemented automatically by the Pytorch package.

Recall that a CNN is composed of convolutional layers, max-pool layer, often followed by a fully connected layer and then the classifier. In the architecture below, we start with a convolutional layer that takes as an input a layer with $D_{in}=1$ with height and width $H=W=40$, a receptive field or filter size of $2 \times 2$, and depth $N$ (there are $N$ layers). We also add a padding of zeros on both sides of the image. This convolutional layer can be summarized by the four numbers $[N,D_{in},H,W]=[N,1,41,41]$. This is then fed into a $2 \times 2$ maxpool layer which results in layer of size $[N,1,20,20]$. This layer is then hooked up to a linear layer that takes as an [input,output] of the form [N*20*20*1,2] since there are $2$ classes (corresponding to the ordered and disordered phases). We use a logistic (softmax) classifier as the output and train using various optimizers.

In [3]:
import torch.nn as nn # construct NN

class model(nn.Module):
# create convolutional net
def __init__(self, N=10, L=40):
# inherit attributes and methods of nn.Module
super(model, self).__init__()
# create convolutional layer with input depth 1 and output depth N
self.conv1 = nn.Conv2d(1, N, kernel_size=2, padding=1)
# batch norm layer takes Depth
self.bn1=nn.BatchNorm2d(N)
# create fully connected layer after maxpool operation reduced 40->18
self.fc1 = nn.Linear(20*20*N, 2)
self.N=N
self.L=L
print("The number of neurons in CNN layer is %i"%(N))

def forward(self, x):
#Unsqueeze command indicates one channel and turns x.shape from (:,40,40) to (:,1, 40,40)
x=F.relu(self.conv1(torch.unsqueeze(x,1).float()))
#print(x.shape)  often useful to look at shapes for debugging
x = F.max_pool2d(x,2)
#print(x.shape)
x=self.bn1(x) # largely unnecessary and here just for pedagogical purposes
return F.log_softmax(self.fc1(x.view(-1,20*20*self.N)), dim=1)


Define the train and test functions¶

These are very standard functions for going over data to train and evaluate the model.

Since we will be testing the CNN performance on both the test and the critical data, the test function accepts two arguments: data_loader and verbose to allow control over the input data and the printing messages.

In [4]:
def train(epoch):
# these are very standard functions for going over data to train

CNN.train() # effects Dropout and BatchNorm layers
for batch_idx, (data, target) in enumerate(train_loader):
if args.cuda:
data, target = data.cuda(), target.cuda()
output = CNN(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
if batch_idx % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
100. * batch_idx / len(train_loader), loss.item()))

# these are very standard functions for evaluating data

CNN.eval() # effects Dropout and BatchNorm layers
test_loss = 0
correct = 0
if args.cuda:
data, target = data.cuda(), target.cuda()
output = CNN(data)
test_loss += F.nll_loss(output, target, size_average=False).item() # sum up batch loss
pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
correct += pred.eq(target.data.view_as(pred)).cpu().sum().item()

print('\n'+verbose+' set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
return(accuracy)


Define Model Parameters¶

Next we define the training settings. This proceeds in the same way as in Notebook 13 on the SUSY dataset, except we now also show how to turn on the cuda library option of PyTorch which enables parallel coputations (whenever resources for this are available).

In [5]:
import argparse # handles arguments
import sys; sys.argv=['']; del sys # required to use parser in jupyter notebooks

# training settings
parser = argparse.ArgumentParser(description='PyTorch Convmodel Ising Example')
help='input batch size for training (default: 64)')
help='input batch size for testing (default: 1000)')
help='number of epochs to train (default: 10)')
help='learning rate (default: 0.01)')
help='SGD momentum (default: 0.5)')
help='disables CUDA training')
help='random seed (default: 1)')
help='how many batches to wait before logging training status')
args = parser.parse_args()
args.epochs=5
args.cuda = not args.no_cuda and torch.cuda.is_available()

torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)

cuda_kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}


Steps 3+4+5: Choose the Optimizer and the Cost Function. Train and Evaluate the Model.¶

In [6]:
import torch.nn.functional as F # implements forward and backward definitions of an autograd operation
import torch.optim as optim # different update rules such as SGD, Nesterov-SGD, Adam, RMSProp, etc

test_array=[]
critical_array=[]

# create array of depth of convolutional layer
N_array=[1,5,10,20,50]

# loop over depths
for N in N_array:
CNN = model(N=N)
if args.cuda:
CNN.cuda()

# negative log-likelihood (nll) loss for training: takes class labels NOT one-hot vectors!
criterion = F.nll_loss
# define SGD optimizer
optimizer = optim.SGD(CNN.parameters(), lr=args.lr, momentum=args.momentum)
#optimizer = optim.Adam(DNN.parameters(), lr=0.001, betas=(0.9, 0.999))

# train the CNN and test its performance at each epoch
for epoch in range(1, args.epochs + 1):
train(epoch)
if epoch==args.epochs:
else:
print(test_array)
print(critical_array)

Training on 80 percent of examples
Testing on 20 percent of examples
Predicting on 30000 critical examples
The number of neurons in CNN layer is 1
Train Epoch: 1 [0/104000 (0%)]	Loss: 0.662742
Train Epoch: 1 [640/104000 (1%)]	Loss: 0.109889
Train Epoch: 1 [1280/104000 (1%)]	Loss: 0.045462
Train Epoch: 1 [1920/104000 (2%)]	Loss: 0.050722
Train Epoch: 1 [2560/104000 (2%)]	Loss: 0.020955
Train Epoch: 1 [3200/104000 (3%)]	Loss: 0.016692
Train Epoch: 1 [3840/104000 (4%)]	Loss: 0.019543
Train Epoch: 1 [4480/104000 (4%)]	Loss: 0.012761
Train Epoch: 1 [5120/104000 (5%)]	Loss: 0.017204
Train Epoch: 1 [5760/104000 (6%)]	Loss: 0.011533
Train Epoch: 1 [6400/104000 (6%)]	Loss: 0.007559
Train Epoch: 1 [7040/104000 (7%)]	Loss: 0.003841
Train Epoch: 1 [7680/104000 (7%)]	Loss: 0.004554
Train Epoch: 1 [8320/104000 (8%)]	Loss: 0.008697
Train Epoch: 1 [8960/104000 (9%)]	Loss: 0.005727
Train Epoch: 1 [9600/104000 (9%)]	Loss: 0.016714
Train Epoch: 1 [10240/104000 (10%)]	Loss: 0.002938
Train Epoch: 1 [10880/104000 (10%)]	Loss: 0.009886
Train Epoch: 1 [11520/104000 (11%)]	Loss: 0.006929
Train Epoch: 1 [12160/104000 (12%)]	Loss: 0.002550
Train Epoch: 1 [12800/104000 (12%)]	Loss: 0.008572
Train Epoch: 1 [13440/104000 (13%)]	Loss: 0.002893
Train Epoch: 1 [14080/104000 (14%)]	Loss: 0.001733
Train Epoch: 1 [14720/104000 (14%)]	Loss: 0.009416
Train Epoch: 1 [15360/104000 (15%)]	Loss: 0.002594
Train Epoch: 1 [16000/104000 (15%)]	Loss: 0.006668
Train Epoch: 1 [16640/104000 (16%)]	Loss: 0.001514
Train Epoch: 1 [17280/104000 (17%)]	Loss: 0.005911
Train Epoch: 1 [17920/104000 (17%)]	Loss: 0.004004
Train Epoch: 1 [18560/104000 (18%)]	Loss: 0.004119
Train Epoch: 1 [19200/104000 (18%)]	Loss: 0.015166
Train Epoch: 1 [19840/104000 (19%)]	Loss: 0.003167
Train Epoch: 1 [20480/104000 (20%)]	Loss: 0.002569
Train Epoch: 1 [21120/104000 (20%)]	Loss: 0.003589
Train Epoch: 1 [21760/104000 (21%)]	Loss: 0.002717
Train Epoch: 1 [22400/104000 (22%)]	Loss: 0.004898
Train Epoch: 1 [23040/104000 (22%)]	Loss: 0.002880
Train Epoch: 1 [23680/104000 (23%)]	Loss: 0.004688
Train Epoch: 1 [24320/104000 (23%)]	Loss: 0.000671
Train Epoch: 1 [24960/104000 (24%)]	Loss: 0.004286
Train Epoch: 1 [25600/104000 (25%)]	Loss: 0.001073
Train Epoch: 1 [26240/104000 (25%)]	Loss: 0.002328
Train Epoch: 1 [26880/104000 (26%)]	Loss: 0.002988
Train Epoch: 1 [27520/104000 (26%)]	Loss: 0.001294
Train Epoch: 1 [28160/104000 (27%)]	Loss: 0.052510
Train Epoch: 1 [28800/104000 (28%)]	Loss: 0.000678
Train Epoch: 1 [29440/104000 (28%)]	Loss: 0.000652
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Train Epoch: 1 [30720/104000 (30%)]	Loss: 0.012102
Train Epoch: 1 [31360/104000 (30%)]	Loss: 0.000991
Train Epoch: 1 [32000/104000 (31%)]	Loss: 0.002256
Train Epoch: 1 [32640/104000 (31%)]	Loss: 0.004379
Train Epoch: 1 [33280/104000 (32%)]	Loss: 0.001981
Train Epoch: 1 [33920/104000 (33%)]	Loss: 0.001887
Train Epoch: 1 [34560/104000 (33%)]	Loss: 0.004612
Train Epoch: 1 [35200/104000 (34%)]	Loss: 0.005662
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Train Epoch: 1 [36480/104000 (35%)]	Loss: 0.001433
Train Epoch: 1 [37120/104000 (36%)]	Loss: 0.002003
Train Epoch: 1 [37760/104000 (36%)]	Loss: 0.000672
Train Epoch: 1 [38400/104000 (37%)]	Loss: 0.001759
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Train Epoch: 1 [39680/104000 (38%)]	Loss: 0.001550
Train Epoch: 1 [40320/104000 (39%)]	Loss: 0.001455
Train Epoch: 1 [40960/104000 (39%)]	Loss: 0.000544
Train Epoch: 1 [41600/104000 (40%)]	Loss: 0.004700
Train Epoch: 1 [42240/104000 (41%)]	Loss: 0.000772
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Train Epoch: 1 [44160/104000 (42%)]	Loss: 0.001542
Train Epoch: 1 [44800/104000 (43%)]	Loss: 0.009389
Train Epoch: 1 [45440/104000 (44%)]	Loss: 0.000525
Train Epoch: 1 [46080/104000 (44%)]	Loss: 0.000306
Train Epoch: 1 [46720/104000 (45%)]	Loss: 0.007553
Train Epoch: 1 [47360/104000 (46%)]	Loss: 0.000785
Train Epoch: 1 [48000/104000 (46%)]	Loss: 0.003900
Train Epoch: 1 [48640/104000 (47%)]	Loss: 0.001991
Train Epoch: 1 [49280/104000 (47%)]	Loss: 0.002793
Train Epoch: 1 [49920/104000 (48%)]	Loss: 0.020791
Train Epoch: 1 [50560/104000 (49%)]	Loss: 0.000786
Train Epoch: 1 [51200/104000 (49%)]	Loss: 0.000717
Train Epoch: 1 [51840/104000 (50%)]	Loss: 0.000714
Train Epoch: 1 [52480/104000 (50%)]	Loss: 0.001216
Train Epoch: 1 [53120/104000 (51%)]	Loss: 0.000183
Train Epoch: 1 [53760/104000 (52%)]	Loss: 0.000735
Train Epoch: 1 [54400/104000 (52%)]	Loss: 0.002544
Train Epoch: 1 [55040/104000 (53%)]	Loss: 0.000421
Train Epoch: 1 [55680/104000 (54%)]	Loss: 0.000800
Train Epoch: 1 [56320/104000 (54%)]	Loss: 0.008846
Train Epoch: 1 [56960/104000 (55%)]	Loss: 0.000468
Train Epoch: 1 [57600/104000 (55%)]	Loss: 0.001781
Train Epoch: 1 [58240/104000 (56%)]	Loss: 0.000885
Train Epoch: 1 [58880/104000 (57%)]	Loss: 0.001745
Train Epoch: 1 [59520/104000 (57%)]	Loss: 0.000826
Train Epoch: 1 [60160/104000 (58%)]	Loss: 0.000531
Train Epoch: 1 [60800/104000 (58%)]	Loss: 0.003100
Train Epoch: 1 [61440/104000 (59%)]	Loss: 0.000649
Train Epoch: 1 [62080/104000 (60%)]	Loss: 0.000953
Train Epoch: 1 [62720/104000 (60%)]	Loss: 0.000340
Train Epoch: 1 [63360/104000 (61%)]	Loss: 0.003273
Train Epoch: 1 [64000/104000 (62%)]	Loss: 0.001740
Train Epoch: 1 [64640/104000 (62%)]	Loss: 0.000066
Train Epoch: 1 [65280/104000 (63%)]	Loss: 0.001644
Train Epoch: 1 [65920/104000 (63%)]	Loss: 0.001525
Train Epoch: 1 [66560/104000 (64%)]	Loss: 0.000233
Train Epoch: 1 [67200/104000 (65%)]	Loss: 0.001457
Train Epoch: 1 [67840/104000 (65%)]	Loss: 0.000194
Train Epoch: 1 [68480/104000 (66%)]	Loss: 0.001445
Train Epoch: 1 [69120/104000 (66%)]	Loss: 0.002877
Train Epoch: 1 [69760/104000 (67%)]	Loss: 0.000257
Train Epoch: 1 [70400/104000 (68%)]	Loss: 0.000617
Train Epoch: 1 [71040/104000 (68%)]	Loss: 0.000587
Train Epoch: 1 [71680/104000 (69%)]	Loss: 0.000208
Train Epoch: 1 [72320/104000 (70%)]	Loss: 0.001745
Train Epoch: 1 [72960/104000 (70%)]	Loss: 0.000251
Train Epoch: 1 [73600/104000 (71%)]	Loss: 0.000186
Train Epoch: 1 [74240/104000 (71%)]	Loss: 0.000555
Train Epoch: 1 [74880/104000 (72%)]	Loss: 0.000174
Train Epoch: 1 [75520/104000 (73%)]	Loss: 0.000444
Train Epoch: 1 [76160/104000 (73%)]	Loss: 0.000899
Train Epoch: 1 [76800/104000 (74%)]	Loss: 0.000435
Train Epoch: 1 [77440/104000 (74%)]	Loss: 0.000337
Train Epoch: 1 [78080/104000 (75%)]	Loss: 0.001668
Train Epoch: 1 [78720/104000 (76%)]	Loss: 0.000330
Train Epoch: 1 [79360/104000 (76%)]	Loss: 0.000514
Train Epoch: 1 [80000/104000 (77%)]	Loss: 0.006597
Train Epoch: 1 [80640/104000 (78%)]	Loss: 0.001635
Train Epoch: 1 [81280/104000 (78%)]	Loss: 0.000408
Train Epoch: 1 [81920/104000 (79%)]	Loss: 0.000226
Train Epoch: 1 [82560/104000 (79%)]	Loss: 0.001308
Train Epoch: 1 [83200/104000 (80%)]	Loss: 0.000930
Train Epoch: 1 [83840/104000 (81%)]	Loss: 0.002042
Train Epoch: 1 [84480/104000 (81%)]	Loss: 0.000472
Train Epoch: 1 [85120/104000 (82%)]	Loss: 0.000097
Train Epoch: 1 [85760/104000 (82%)]	Loss: 0.000263
Train Epoch: 1 [86400/104000 (83%)]	Loss: 0.000370
Train Epoch: 1 [87040/104000 (84%)]	Loss: 0.000702
Train Epoch: 1 [87680/104000 (84%)]	Loss: 0.000267
Train Epoch: 1 [88320/104000 (85%)]	Loss: 0.000295
Train Epoch: 1 [88960/104000 (86%)]	Loss: 0.000098
Train Epoch: 1 [89600/104000 (86%)]	Loss: 0.000813
Train Epoch: 1 [90240/104000 (87%)]	Loss: 0.001157
Train Epoch: 1 [90880/104000 (87%)]	Loss: 0.000578
Train Epoch: 1 [91520/104000 (88%)]	Loss: 0.005544
Train Epoch: 1 [92160/104000 (89%)]	Loss: 0.000640
Train Epoch: 1 [92800/104000 (89%)]	Loss: 0.000296
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Train Epoch: 1 [94080/104000 (90%)]	Loss: 0.000388
Train Epoch: 1 [94720/104000 (91%)]	Loss: 0.002296
Train Epoch: 1 [95360/104000 (92%)]	Loss: 0.002912
Train Epoch: 1 [96000/104000 (92%)]	Loss: 0.000297
Train Epoch: 1 [96640/104000 (93%)]	Loss: 0.000208
Train Epoch: 1 [97280/104000 (94%)]	Loss: 0.001088
Train Epoch: 1 [97920/104000 (94%)]	Loss: 0.000779
Train Epoch: 1 [98560/104000 (95%)]	Loss: 0.000636
Train Epoch: 1 [99200/104000 (95%)]	Loss: 0.000509
Train Epoch: 1 [99840/104000 (96%)]	Loss: 0.001388
Train Epoch: 1 [100480/104000 (97%)]	Loss: 0.002664
Train Epoch: 1 [101120/104000 (97%)]	Loss: 0.000189
Train Epoch: 1 [101760/104000 (98%)]	Loss: 0.000681
Train Epoch: 1 [102400/104000 (98%)]	Loss: 0.000616
Train Epoch: 1 [103040/104000 (99%)]	Loss: 0.000326
Train Epoch: 1 [103680/104000 (100%)]	Loss: 0.000554

/Users/mgbukov/miniconda3/envs/mlreview/lib/python3.6/site-packages/torch/nn/functional.py:52: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
warnings.warn(warning.format(ret))

Test set: Average loss: 0.0005, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.8165, Accuracy: 23875/30000 (80%)

Train Epoch: 2 [0/104000 (0%)]	Loss: 0.000207
Train Epoch: 2 [640/104000 (1%)]	Loss: 0.003293
Train Epoch: 2 [1280/104000 (1%)]	Loss: 0.002911
Train Epoch: 2 [1920/104000 (2%)]	Loss: 0.000456
Train Epoch: 2 [2560/104000 (2%)]	Loss: 0.000537
Train Epoch: 2 [3200/104000 (3%)]	Loss: 0.000169
Train Epoch: 2 [3840/104000 (4%)]	Loss: 0.004852
Train Epoch: 2 [4480/104000 (4%)]	Loss: 0.000377
Train Epoch: 2 [5120/104000 (5%)]	Loss: 0.000677
Train Epoch: 2 [5760/104000 (6%)]	Loss: 0.000874
Train Epoch: 2 [6400/104000 (6%)]	Loss: 0.000330
Train Epoch: 2 [7040/104000 (7%)]	Loss: 0.000055
Train Epoch: 2 [7680/104000 (7%)]	Loss: 0.000904
Train Epoch: 2 [8320/104000 (8%)]	Loss: 0.000891
Train Epoch: 2 [8960/104000 (9%)]	Loss: 0.001143
Train Epoch: 2 [9600/104000 (9%)]	Loss: 0.000845
Train Epoch: 2 [10240/104000 (10%)]	Loss: 0.000624
Train Epoch: 2 [10880/104000 (10%)]	Loss: 0.000672
Train Epoch: 2 [11520/104000 (11%)]	Loss: 0.000998
Train Epoch: 2 [12160/104000 (12%)]	Loss: 0.000145
Train Epoch: 2 [12800/104000 (12%)]	Loss: 0.000117
Train Epoch: 2 [13440/104000 (13%)]	Loss: 0.000100
Train Epoch: 2 [14080/104000 (14%)]	Loss: 0.000311
Train Epoch: 2 [14720/104000 (14%)]	Loss: 0.001324
Train Epoch: 2 [15360/104000 (15%)]	Loss: 0.000712
Train Epoch: 2 [16000/104000 (15%)]	Loss: 0.000173
Train Epoch: 2 [16640/104000 (16%)]	Loss: 0.000301
Train Epoch: 2 [17280/104000 (17%)]	Loss: 0.000548
Train Epoch: 2 [17920/104000 (17%)]	Loss: 0.000567
Train Epoch: 2 [18560/104000 (18%)]	Loss: 0.000097
Train Epoch: 2 [19200/104000 (18%)]	Loss: 0.001975
Train Epoch: 2 [19840/104000 (19%)]	Loss: 0.000741
Train Epoch: 2 [20480/104000 (20%)]	Loss: 0.000132
Train Epoch: 2 [21120/104000 (20%)]	Loss: 0.000875
Train Epoch: 2 [21760/104000 (21%)]	Loss: 0.003284
Train Epoch: 2 [22400/104000 (22%)]	Loss: 0.000269
Train Epoch: 2 [23040/104000 (22%)]	Loss: 0.000087
Train Epoch: 2 [23680/104000 (23%)]	Loss: 0.000139
Train Epoch: 2 [24320/104000 (23%)]	Loss: 0.001205
Train Epoch: 2 [24960/104000 (24%)]	Loss: 0.000231
Train Epoch: 2 [25600/104000 (25%)]	Loss: 0.000141
Train Epoch: 2 [26240/104000 (25%)]	Loss: 0.000569
Train Epoch: 2 [26880/104000 (26%)]	Loss: 0.000188
Train Epoch: 2 [27520/104000 (26%)]	Loss: 0.000104
Train Epoch: 2 [28160/104000 (27%)]	Loss: 0.000142
Train Epoch: 2 [28800/104000 (28%)]	Loss: 0.000027
Train Epoch: 2 [29440/104000 (28%)]	Loss: 0.000069
Train Epoch: 2 [30080/104000 (29%)]	Loss: 0.001534
Train Epoch: 2 [30720/104000 (30%)]	Loss: 0.000342
Train Epoch: 2 [31360/104000 (30%)]	Loss: 0.000902
Train Epoch: 2 [32000/104000 (31%)]	Loss: 0.000623
Train Epoch: 2 [32640/104000 (31%)]	Loss: 0.000091
Train Epoch: 2 [33280/104000 (32%)]	Loss: 0.000142
Train Epoch: 2 [33920/104000 (33%)]	Loss: 0.003602
Train Epoch: 2 [34560/104000 (33%)]	Loss: 0.000527
Train Epoch: 2 [35200/104000 (34%)]	Loss: 0.000956
Train Epoch: 2 [35840/104000 (34%)]	Loss: 0.000588
Train Epoch: 2 [36480/104000 (35%)]	Loss: 0.001144
Train Epoch: 2 [37120/104000 (36%)]	Loss: 0.000418
Train Epoch: 2 [37760/104000 (36%)]	Loss: 0.002153
Train Epoch: 2 [38400/104000 (37%)]	Loss: 0.000243
Train Epoch: 2 [39040/104000 (38%)]	Loss: 0.001616
Train Epoch: 2 [39680/104000 (38%)]	Loss: 0.000081
Train Epoch: 2 [40320/104000 (39%)]	Loss: 0.000097
Train Epoch: 2 [40960/104000 (39%)]	Loss: 0.000208
Train Epoch: 2 [41600/104000 (40%)]	Loss: 0.000639
Train Epoch: 2 [42240/104000 (41%)]	Loss: 0.001528
Train Epoch: 2 [42880/104000 (41%)]	Loss: 0.000136
Train Epoch: 2 [43520/104000 (42%)]	Loss: 0.000272
Train Epoch: 2 [44160/104000 (42%)]	Loss: 0.000405
Train Epoch: 2 [44800/104000 (43%)]	Loss: 0.000983
Train Epoch: 2 [45440/104000 (44%)]	Loss: 0.000348
Train Epoch: 2 [46080/104000 (44%)]	Loss: 0.000270
Train Epoch: 2 [46720/104000 (45%)]	Loss: 0.001273
Train Epoch: 2 [47360/104000 (46%)]	Loss: 0.000122
Train Epoch: 2 [48000/104000 (46%)]	Loss: 0.000065
Train Epoch: 2 [48640/104000 (47%)]	Loss: 0.000083
Train Epoch: 2 [49280/104000 (47%)]	Loss: 0.000129
Train Epoch: 2 [49920/104000 (48%)]	Loss: 0.000709
Train Epoch: 2 [50560/104000 (49%)]	Loss: 0.000526
Train Epoch: 2 [51200/104000 (49%)]	Loss: 0.000125
Train Epoch: 2 [51840/104000 (50%)]	Loss: 0.000151
Train Epoch: 2 [52480/104000 (50%)]	Loss: 0.000048
Train Epoch: 2 [53120/104000 (51%)]	Loss: 0.000072
Train Epoch: 2 [53760/104000 (52%)]	Loss: 0.000176
Train Epoch: 2 [54400/104000 (52%)]	Loss: 0.001160
Train Epoch: 2 [55040/104000 (53%)]	Loss: 0.003954
Train Epoch: 2 [55680/104000 (54%)]	Loss: 0.004039
Train Epoch: 2 [56320/104000 (54%)]	Loss: 0.000845
Train Epoch: 2 [56960/104000 (55%)]	Loss: 0.000327
Train Epoch: 2 [57600/104000 (55%)]	Loss: 0.000085
Train Epoch: 2 [58240/104000 (56%)]	Loss: 0.000056
Train Epoch: 2 [58880/104000 (57%)]	Loss: 0.000969
Train Epoch: 2 [59520/104000 (57%)]	Loss: 0.000158
Train Epoch: 2 [60160/104000 (58%)]	Loss: 0.000294
Train Epoch: 2 [60800/104000 (58%)]	Loss: 0.000255
Train Epoch: 2 [61440/104000 (59%)]	Loss: 0.000088
Train Epoch: 2 [62080/104000 (60%)]	Loss: 0.003106
Train Epoch: 2 [62720/104000 (60%)]	Loss: 0.004540
Train Epoch: 2 [63360/104000 (61%)]	Loss: 0.000265
Train Epoch: 2 [64000/104000 (62%)]	Loss: 0.000957
Train Epoch: 2 [64640/104000 (62%)]	Loss: 0.000135
Train Epoch: 2 [65280/104000 (63%)]	Loss: 0.000137
Train Epoch: 2 [65920/104000 (63%)]	Loss: 0.000109
Train Epoch: 2 [66560/104000 (64%)]	Loss: 0.000603
Train Epoch: 2 [67200/104000 (65%)]	Loss: 0.000102
Train Epoch: 2 [67840/104000 (65%)]	Loss: 0.000302
Train Epoch: 2 [68480/104000 (66%)]	Loss: 0.000063
Train Epoch: 2 [69120/104000 (66%)]	Loss: 0.000650
Train Epoch: 2 [69760/104000 (67%)]	Loss: 0.000157
Train Epoch: 2 [70400/104000 (68%)]	Loss: 0.000306
Train Epoch: 2 [71040/104000 (68%)]	Loss: 0.033093
Train Epoch: 2 [71680/104000 (69%)]	Loss: 0.000443
Train Epoch: 2 [72320/104000 (70%)]	Loss: 0.000308
Train Epoch: 2 [72960/104000 (70%)]	Loss: 0.000392
Train Epoch: 2 [73600/104000 (71%)]	Loss: 0.000295
Train Epoch: 2 [74240/104000 (71%)]	Loss: 0.001193
Train Epoch: 2 [74880/104000 (72%)]	Loss: 0.003020
Train Epoch: 2 [75520/104000 (73%)]	Loss: 0.000096
Train Epoch: 2 [76160/104000 (73%)]	Loss: 0.000202
Train Epoch: 2 [76800/104000 (74%)]	Loss: 0.000464
Train Epoch: 2 [77440/104000 (74%)]	Loss: 0.000096
Train Epoch: 2 [78080/104000 (75%)]	Loss: 0.000068
Train Epoch: 2 [78720/104000 (76%)]	Loss: 0.002312
Train Epoch: 2 [79360/104000 (76%)]	Loss: 0.000055
Train Epoch: 2 [80000/104000 (77%)]	Loss: 0.000171
Train Epoch: 2 [80640/104000 (78%)]	Loss: 0.002382
Train Epoch: 2 [81280/104000 (78%)]	Loss: 0.000563
Train Epoch: 2 [81920/104000 (79%)]	Loss: 0.000178
Train Epoch: 2 [82560/104000 (79%)]	Loss: 0.000115
Train Epoch: 2 [83200/104000 (80%)]	Loss: 0.000178
Train Epoch: 2 [83840/104000 (81%)]	Loss: 0.000097
Train Epoch: 2 [84480/104000 (81%)]	Loss: 0.000127
Train Epoch: 2 [85120/104000 (82%)]	Loss: 0.001672
Train Epoch: 2 [85760/104000 (82%)]	Loss: 0.000080
Train Epoch: 2 [86400/104000 (83%)]	Loss: 0.000045
Train Epoch: 2 [87040/104000 (84%)]	Loss: 0.000213
Train Epoch: 2 [87680/104000 (84%)]	Loss: 0.000210
Train Epoch: 2 [88320/104000 (85%)]	Loss: 0.000272
Train Epoch: 2 [88960/104000 (86%)]	Loss: 0.001581
Train Epoch: 2 [89600/104000 (86%)]	Loss: 0.000713
Train Epoch: 2 [90240/104000 (87%)]	Loss: 0.000070
Train Epoch: 2 [90880/104000 (87%)]	Loss: 0.002180
Train Epoch: 2 [91520/104000 (88%)]	Loss: 0.000037
Train Epoch: 2 [92160/104000 (89%)]	Loss: 0.000250
Train Epoch: 2 [92800/104000 (89%)]	Loss: 0.000103
Train Epoch: 2 [93440/104000 (90%)]	Loss: 0.000222
Train Epoch: 2 [94080/104000 (90%)]	Loss: 0.000026
Train Epoch: 2 [94720/104000 (91%)]	Loss: 0.000986
Train Epoch: 2 [95360/104000 (92%)]	Loss: 0.000350
Train Epoch: 2 [96000/104000 (92%)]	Loss: 0.000084
Train Epoch: 2 [96640/104000 (93%)]	Loss: 0.000553
Train Epoch: 2 [97280/104000 (94%)]	Loss: 0.000681
Train Epoch: 2 [97920/104000 (94%)]	Loss: 0.000453
Train Epoch: 2 [98560/104000 (95%)]	Loss: 0.000199
Train Epoch: 2 [99200/104000 (95%)]	Loss: 0.000310
Train Epoch: 2 [99840/104000 (96%)]	Loss: 0.000116
Train Epoch: 2 [100480/104000 (97%)]	Loss: 0.000248
Train Epoch: 2 [101120/104000 (97%)]	Loss: 0.000102
Train Epoch: 2 [101760/104000 (98%)]	Loss: 0.000298
Train Epoch: 2 [102400/104000 (98%)]	Loss: 0.000583
Train Epoch: 2 [103040/104000 (99%)]	Loss: 0.000026
Train Epoch: 2 [103680/104000 (100%)]	Loss: 0.000239

Test set: Average loss: 0.0003, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.9904, Accuracy: 23634/30000 (79%)

Train Epoch: 3 [0/104000 (0%)]	Loss: 0.000187
Train Epoch: 3 [640/104000 (1%)]	Loss: 0.000054
Train Epoch: 3 [1280/104000 (1%)]	Loss: 0.000154
Train Epoch: 3 [1920/104000 (2%)]	Loss: 0.000851
Train Epoch: 3 [2560/104000 (2%)]	Loss: 0.000049
Train Epoch: 3 [3200/104000 (3%)]	Loss: 0.000274
Train Epoch: 3 [3840/104000 (4%)]	Loss: 0.000528
Train Epoch: 3 [4480/104000 (4%)]	Loss: 0.000440
Train Epoch: 3 [5120/104000 (5%)]	Loss: 0.001590
Train Epoch: 3 [5760/104000 (6%)]	Loss: 0.003739
Train Epoch: 3 [6400/104000 (6%)]	Loss: 0.000099
Train Epoch: 3 [7040/104000 (7%)]	Loss: 0.000564
Train Epoch: 3 [7680/104000 (7%)]	Loss: 0.000268
Train Epoch: 3 [8320/104000 (8%)]	Loss: 0.000250
Train Epoch: 3 [8960/104000 (9%)]	Loss: 0.000432
Train Epoch: 3 [9600/104000 (9%)]	Loss: 0.000188
Train Epoch: 3 [10240/104000 (10%)]	Loss: 0.000163
Train Epoch: 3 [10880/104000 (10%)]	Loss: 0.000407
Train Epoch: 3 [11520/104000 (11%)]	Loss: 0.001478
Train Epoch: 3 [12160/104000 (12%)]	Loss: 0.000351
Train Epoch: 3 [12800/104000 (12%)]	Loss: 0.000769
Train Epoch: 3 [13440/104000 (13%)]	Loss: 0.000069
Train Epoch: 3 [14080/104000 (14%)]	Loss: 0.000273
Train Epoch: 3 [14720/104000 (14%)]	Loss: 0.000319
Train Epoch: 3 [15360/104000 (15%)]	Loss: 0.000328
Train Epoch: 3 [16000/104000 (15%)]	Loss: 0.000319
Train Epoch: 3 [16640/104000 (16%)]	Loss: 0.003195
Train Epoch: 3 [17280/104000 (17%)]	Loss: 0.020422
Train Epoch: 3 [17920/104000 (17%)]	Loss: 0.001275
Train Epoch: 3 [18560/104000 (18%)]	Loss: 0.000046
Train Epoch: 3 [19200/104000 (18%)]	Loss: 0.000498
Train Epoch: 3 [19840/104000 (19%)]	Loss: 0.000359
Train Epoch: 3 [20480/104000 (20%)]	Loss: 0.000036
Train Epoch: 3 [21120/104000 (20%)]	Loss: 0.000650
Train Epoch: 3 [21760/104000 (21%)]	Loss: 0.000145
Train Epoch: 3 [22400/104000 (22%)]	Loss: 0.000065
Train Epoch: 3 [23040/104000 (22%)]	Loss: 0.000142
Train Epoch: 3 [23680/104000 (23%)]	Loss: 0.000053
Train Epoch: 3 [24320/104000 (23%)]	Loss: 0.000696
Train Epoch: 3 [24960/104000 (24%)]	Loss: 0.000080
Train Epoch: 3 [25600/104000 (25%)]	Loss: 0.000205
Train Epoch: 3 [26240/104000 (25%)]	Loss: 0.000185
Train Epoch: 3 [26880/104000 (26%)]	Loss: 0.000382
Train Epoch: 3 [27520/104000 (26%)]	Loss: 0.000008
Train Epoch: 3 [28160/104000 (27%)]	Loss: 0.000297
Train Epoch: 3 [28800/104000 (28%)]	Loss: 0.000176
Train Epoch: 3 [29440/104000 (28%)]	Loss: 0.000226
Train Epoch: 3 [30080/104000 (29%)]	Loss: 0.000032
Train Epoch: 3 [30720/104000 (30%)]	Loss: 0.000534
Train Epoch: 3 [31360/104000 (30%)]	Loss: 0.000174
Train Epoch: 3 [32000/104000 (31%)]	Loss: 0.000824
Train Epoch: 3 [32640/104000 (31%)]	Loss: 0.000051
Train Epoch: 3 [33280/104000 (32%)]	Loss: 0.000113
Train Epoch: 3 [33920/104000 (33%)]	Loss: 0.001166
Train Epoch: 3 [34560/104000 (33%)]	Loss: 0.000498
Train Epoch: 3 [35200/104000 (34%)]	Loss: 0.000053
Train Epoch: 3 [35840/104000 (34%)]	Loss: 0.000401
Train Epoch: 3 [36480/104000 (35%)]	Loss: 0.000713
Train Epoch: 3 [37120/104000 (36%)]	Loss: 0.000182
Train Epoch: 3 [37760/104000 (36%)]	Loss: 0.000007
Train Epoch: 3 [38400/104000 (37%)]	Loss: 0.000010
Train Epoch: 3 [39040/104000 (38%)]	Loss: 0.000439
Train Epoch: 3 [39680/104000 (38%)]	Loss: 0.000041
Train Epoch: 3 [40320/104000 (39%)]	Loss: 0.000068
Train Epoch: 3 [40960/104000 (39%)]	Loss: 0.000030
Train Epoch: 3 [41600/104000 (40%)]	Loss: 0.000021
Train Epoch: 3 [42240/104000 (41%)]	Loss: 0.000017
Train Epoch: 3 [42880/104000 (41%)]	Loss: 0.000019
Train Epoch: 3 [43520/104000 (42%)]	Loss: 0.000061
Train Epoch: 3 [44160/104000 (42%)]	Loss: 0.000012
Train Epoch: 3 [44800/104000 (43%)]	Loss: 0.000309
Train Epoch: 3 [45440/104000 (44%)]	Loss: 0.000012
Train Epoch: 3 [46080/104000 (44%)]	Loss: 0.000395
Train Epoch: 3 [46720/104000 (45%)]	Loss: 0.000037
Train Epoch: 3 [47360/104000 (46%)]	Loss: 0.000115
Train Epoch: 3 [48000/104000 (46%)]	Loss: 0.000219
Train Epoch: 3 [48640/104000 (47%)]	Loss: 0.000003
Train Epoch: 3 [49280/104000 (47%)]	Loss: 0.000043
Train Epoch: 3 [49920/104000 (48%)]	Loss: 0.000033
Train Epoch: 3 [50560/104000 (49%)]	Loss: 0.000105
Train Epoch: 3 [51200/104000 (49%)]	Loss: 0.000077
Train Epoch: 3 [51840/104000 (50%)]	Loss: 0.000032
Train Epoch: 3 [52480/104000 (50%)]	Loss: 0.000009
Train Epoch: 3 [53120/104000 (51%)]	Loss: 0.000001
Train Epoch: 3 [53760/104000 (52%)]	Loss: 0.000005
Train Epoch: 3 [54400/104000 (52%)]	Loss: 0.000008
Train Epoch: 3 [55040/104000 (53%)]	Loss: 0.000023
Train Epoch: 3 [55680/104000 (54%)]	Loss: 0.000157
Train Epoch: 3 [56320/104000 (54%)]	Loss: 0.000024
Train Epoch: 3 [56960/104000 (55%)]	Loss: 0.000004
Train Epoch: 3 [57600/104000 (55%)]	Loss: 0.000002
Train Epoch: 3 [58240/104000 (56%)]	Loss: 0.000016
Train Epoch: 3 [58880/104000 (57%)]	Loss: 0.000006
Train Epoch: 3 [59520/104000 (57%)]	Loss: 0.000004
Train Epoch: 3 [60160/104000 (58%)]	Loss: 0.000003
Train Epoch: 3 [60800/104000 (58%)]	Loss: 0.000039
Train Epoch: 3 [61440/104000 (59%)]	Loss: 0.009346
Train Epoch: 3 [62080/104000 (60%)]	Loss: 0.000003
Train Epoch: 3 [62720/104000 (60%)]	Loss: 0.000013
Train Epoch: 3 [63360/104000 (61%)]	Loss: 0.000208
Train Epoch: 3 [64000/104000 (62%)]	Loss: 0.000003
Train Epoch: 3 [64640/104000 (62%)]	Loss: 0.000024
Train Epoch: 3 [65280/104000 (63%)]	Loss: 0.000003
Train Epoch: 3 [65920/104000 (63%)]	Loss: 0.000018
Train Epoch: 3 [66560/104000 (64%)]	Loss: 0.000025
Train Epoch: 3 [67200/104000 (65%)]	Loss: 0.000717
Train Epoch: 3 [67840/104000 (65%)]	Loss: 0.000052
Train Epoch: 3 [68480/104000 (66%)]	Loss: 0.000007
Train Epoch: 3 [69120/104000 (66%)]	Loss: 0.000030
Train Epoch: 3 [69760/104000 (67%)]	Loss: 0.000991
Train Epoch: 3 [70400/104000 (68%)]	Loss: 0.000052
Train Epoch: 3 [71040/104000 (68%)]	Loss: 0.000017
Train Epoch: 3 [71680/104000 (69%)]	Loss: 0.000007
Train Epoch: 3 [72320/104000 (70%)]	Loss: 0.000091
Train Epoch: 3 [72960/104000 (70%)]	Loss: 0.000005
Train Epoch: 3 [73600/104000 (71%)]	Loss: 0.000018
Train Epoch: 3 [74240/104000 (71%)]	Loss: 0.000002
Train Epoch: 3 [74880/104000 (72%)]	Loss: 0.000006
Train Epoch: 3 [75520/104000 (73%)]	Loss: 0.000011
Train Epoch: 3 [76160/104000 (73%)]	Loss: 0.000001
Train Epoch: 3 [76800/104000 (74%)]	Loss: 0.000004
Train Epoch: 3 [77440/104000 (74%)]	Loss: 0.000010
Train Epoch: 3 [78080/104000 (75%)]	Loss: 0.000007
Train Epoch: 3 [78720/104000 (76%)]	Loss: 0.000012
Train Epoch: 3 [79360/104000 (76%)]	Loss: 0.000001
Train Epoch: 3 [80000/104000 (77%)]	Loss: 0.000004
Train Epoch: 3 [80640/104000 (78%)]	Loss: 0.000002
Train Epoch: 3 [81280/104000 (78%)]	Loss: 0.000075
Train Epoch: 3 [81920/104000 (79%)]	Loss: 0.000005
Train Epoch: 3 [82560/104000 (79%)]	Loss: 0.000022
Train Epoch: 3 [83200/104000 (80%)]	Loss: 0.000009
Train Epoch: 3 [83840/104000 (81%)]	Loss: 0.000004
Train Epoch: 3 [84480/104000 (81%)]	Loss: 0.000002
Train Epoch: 3 [85120/104000 (82%)]	Loss: 0.000005
Train Epoch: 3 [85760/104000 (82%)]	Loss: 0.000133
Train Epoch: 3 [86400/104000 (83%)]	Loss: 0.000140
Train Epoch: 3 [87040/104000 (84%)]	Loss: 0.000006
Train Epoch: 3 [87680/104000 (84%)]	Loss: 0.000036
Train Epoch: 3 [88320/104000 (85%)]	Loss: 0.000010
Train Epoch: 3 [88960/104000 (86%)]	Loss: 0.000005
Train Epoch: 3 [89600/104000 (86%)]	Loss: 0.000006
Train Epoch: 3 [90240/104000 (87%)]	Loss: 0.000026
Train Epoch: 3 [90880/104000 (87%)]	Loss: 0.000003
Train Epoch: 3 [91520/104000 (88%)]	Loss: 0.000098
Train Epoch: 3 [92160/104000 (89%)]	Loss: 0.000007
Train Epoch: 3 [92800/104000 (89%)]	Loss: 0.000002
Train Epoch: 3 [93440/104000 (90%)]	Loss: 0.000005
Train Epoch: 3 [94080/104000 (90%)]	Loss: 0.000001
Train Epoch: 3 [94720/104000 (91%)]	Loss: 0.000005
Train Epoch: 3 [95360/104000 (92%)]	Loss: 0.000036
Train Epoch: 3 [96000/104000 (92%)]	Loss: 0.000001
Train Epoch: 3 [96640/104000 (93%)]	Loss: 0.000001
Train Epoch: 3 [97280/104000 (94%)]	Loss: 0.000008
Train Epoch: 3 [97920/104000 (94%)]	Loss: 0.000013
Train Epoch: 3 [98560/104000 (95%)]	Loss: 0.000003
Train Epoch: 3 [99200/104000 (95%)]	Loss: 0.000030
Train Epoch: 3 [99840/104000 (96%)]	Loss: 0.000005
Train Epoch: 3 [100480/104000 (97%)]	Loss: 0.000014
Train Epoch: 3 [101120/104000 (97%)]	Loss: 0.000025
Train Epoch: 3 [101760/104000 (98%)]	Loss: 0.000002
Train Epoch: 3 [102400/104000 (98%)]	Loss: 0.000003
Train Epoch: 3 [103040/104000 (99%)]	Loss: 0.000034
Train Epoch: 3 [103680/104000 (100%)]	Loss: 0.000004

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.3871, Accuracy: 25795/30000 (86%)

Train Epoch: 4 [0/104000 (0%)]	Loss: 0.000001
Train Epoch: 4 [640/104000 (1%)]	Loss: 0.000017
Train Epoch: 4 [1280/104000 (1%)]	Loss: 0.000013
Train Epoch: 4 [1920/104000 (2%)]	Loss: 0.000007
Train Epoch: 4 [2560/104000 (2%)]	Loss: 0.000003
Train Epoch: 4 [3200/104000 (3%)]	Loss: 0.000014
Train Epoch: 4 [3840/104000 (4%)]	Loss: 0.000000
Train Epoch: 4 [4480/104000 (4%)]	Loss: 0.000014
Train Epoch: 4 [5120/104000 (5%)]	Loss: 0.000003
Train Epoch: 4 [5760/104000 (6%)]	Loss: 0.000009
Train Epoch: 4 [6400/104000 (6%)]	Loss: 0.000011
Train Epoch: 4 [7040/104000 (7%)]	Loss: 0.000000
Train Epoch: 4 [7680/104000 (7%)]	Loss: 0.000046
Train Epoch: 4 [8320/104000 (8%)]	Loss: 0.000006
Train Epoch: 4 [8960/104000 (9%)]	Loss: 0.000004
Train Epoch: 4 [9600/104000 (9%)]	Loss: 0.000064
Train Epoch: 4 [10240/104000 (10%)]	Loss: 0.000001
Train Epoch: 4 [10880/104000 (10%)]	Loss: 0.000859
Train Epoch: 4 [11520/104000 (11%)]	Loss: 0.000001
Train Epoch: 4 [12160/104000 (12%)]	Loss: 0.000021
Train Epoch: 4 [12800/104000 (12%)]	Loss: 0.000045
Train Epoch: 4 [13440/104000 (13%)]	Loss: 0.000004
Train Epoch: 4 [14080/104000 (14%)]	Loss: 0.000212
Train Epoch: 4 [14720/104000 (14%)]	Loss: 0.000001
Train Epoch: 4 [15360/104000 (15%)]	Loss: 0.000002
Train Epoch: 4 [16000/104000 (15%)]	Loss: 0.000001
Train Epoch: 4 [16640/104000 (16%)]	Loss: 0.000008
Train Epoch: 4 [17280/104000 (17%)]	Loss: 0.000005
Train Epoch: 4 [17920/104000 (17%)]	Loss: 0.000027
Train Epoch: 4 [18560/104000 (18%)]	Loss: 0.000003
Train Epoch: 4 [19200/104000 (18%)]	Loss: 0.000002
Train Epoch: 4 [19840/104000 (19%)]	Loss: 0.000001
Train Epoch: 4 [20480/104000 (20%)]	Loss: 0.000003
Train Epoch: 4 [21120/104000 (20%)]	Loss: 0.000002
Train Epoch: 4 [21760/104000 (21%)]	Loss: 0.000001
Train Epoch: 4 [22400/104000 (22%)]	Loss: 0.000008
Train Epoch: 4 [23040/104000 (22%)]	Loss: 0.000005
Train Epoch: 4 [23680/104000 (23%)]	Loss: 0.000005
Train Epoch: 4 [24320/104000 (23%)]	Loss: 0.000003
Train Epoch: 4 [24960/104000 (24%)]	Loss: 0.000004
Train Epoch: 4 [25600/104000 (25%)]	Loss: 0.000001
Train Epoch: 4 [26240/104000 (25%)]	Loss: 0.000001
Train Epoch: 4 [26880/104000 (26%)]	Loss: 0.000003
Train Epoch: 4 [27520/104000 (26%)]	Loss: 0.000001
Train Epoch: 4 [28160/104000 (27%)]	Loss: 0.000083
Train Epoch: 4 [28800/104000 (28%)]	Loss: 0.000001
Train Epoch: 4 [29440/104000 (28%)]	Loss: 0.000563
Train Epoch: 4 [30080/104000 (29%)]	Loss: 0.000001
Train Epoch: 4 [30720/104000 (30%)]	Loss: 0.000002
Train Epoch: 4 [31360/104000 (30%)]	Loss: 0.000005
Train Epoch: 4 [32000/104000 (31%)]	Loss: 0.000008
Train Epoch: 4 [32640/104000 (31%)]	Loss: 0.000002
Train Epoch: 4 [33280/104000 (32%)]	Loss: 0.000008
Train Epoch: 4 [33920/104000 (33%)]	Loss: 0.000001
Train Epoch: 4 [34560/104000 (33%)]	Loss: 0.000133
Train Epoch: 4 [35200/104000 (34%)]	Loss: 0.000001
Train Epoch: 4 [35840/104000 (34%)]	Loss: 0.000011
Train Epoch: 4 [36480/104000 (35%)]	Loss: 0.000002
Train Epoch: 4 [37120/104000 (36%)]	Loss: 0.000004
Train Epoch: 4 [37760/104000 (36%)]	Loss: 0.000001
Train Epoch: 4 [38400/104000 (37%)]	Loss: 0.000270
Train Epoch: 4 [39040/104000 (38%)]	Loss: 0.000004
Train Epoch: 4 [39680/104000 (38%)]	Loss: 0.000012
Train Epoch: 4 [40320/104000 (39%)]	Loss: 0.000037
Train Epoch: 4 [40960/104000 (39%)]	Loss: 0.000005
Train Epoch: 4 [41600/104000 (40%)]	Loss: 0.000005
Train Epoch: 4 [42240/104000 (41%)]	Loss: 0.000011
Train Epoch: 4 [42880/104000 (41%)]	Loss: 0.000001
Train Epoch: 4 [43520/104000 (42%)]	Loss: 0.000015
Train Epoch: 4 [44160/104000 (42%)]	Loss: 0.000001
Train Epoch: 4 [44800/104000 (43%)]	Loss: 0.000009
Train Epoch: 4 [45440/104000 (44%)]	Loss: 0.000007
Train Epoch: 4 [46080/104000 (44%)]	Loss: 0.000002
Train Epoch: 4 [46720/104000 (45%)]	Loss: 0.000009
Train Epoch: 4 [47360/104000 (46%)]	Loss: 0.000006
Train Epoch: 4 [48000/104000 (46%)]	Loss: 0.000002
Train Epoch: 4 [48640/104000 (47%)]	Loss: 0.000010
Train Epoch: 4 [49280/104000 (47%)]	Loss: 0.000001
Train Epoch: 4 [49920/104000 (48%)]	Loss: 0.000002
Train Epoch: 4 [50560/104000 (49%)]	Loss: 0.000003
Train Epoch: 4 [51200/104000 (49%)]	Loss: 0.000001
Train Epoch: 4 [51840/104000 (50%)]	Loss: 0.000008
Train Epoch: 4 [52480/104000 (50%)]	Loss: 0.000012
Train Epoch: 4 [53120/104000 (51%)]	Loss: 0.000158
Train Epoch: 4 [53760/104000 (52%)]	Loss: 0.000009
Train Epoch: 4 [54400/104000 (52%)]	Loss: 0.000003
Train Epoch: 4 [55040/104000 (53%)]	Loss: 0.000001
Train Epoch: 4 [55680/104000 (54%)]	Loss: 0.000001
Train Epoch: 4 [56320/104000 (54%)]	Loss: 0.000002
Train Epoch: 4 [56960/104000 (55%)]	Loss: 0.000002
Train Epoch: 4 [57600/104000 (55%)]	Loss: 0.000003
Train Epoch: 4 [58240/104000 (56%)]	Loss: 0.000008
Train Epoch: 4 [58880/104000 (57%)]	Loss: 0.000008
Train Epoch: 4 [59520/104000 (57%)]	Loss: 0.000013
Train Epoch: 4 [60160/104000 (58%)]	Loss: 0.000002
Train Epoch: 4 [60800/104000 (58%)]	Loss: 0.000002
Train Epoch: 4 [61440/104000 (59%)]	Loss: 0.000019
Train Epoch: 4 [62080/104000 (60%)]	Loss: 0.000004
Train Epoch: 4 [62720/104000 (60%)]	Loss: 0.000002
Train Epoch: 4 [63360/104000 (61%)]	Loss: 0.000003
Train Epoch: 4 [64000/104000 (62%)]	Loss: 0.000003
Train Epoch: 4 [64640/104000 (62%)]	Loss: 0.000004
Train Epoch: 4 [65280/104000 (63%)]	Loss: 0.000002
Train Epoch: 4 [65920/104000 (63%)]	Loss: 0.000004
Train Epoch: 4 [66560/104000 (64%)]	Loss: 0.000001
Train Epoch: 4 [67200/104000 (65%)]	Loss: 0.000007
Train Epoch: 4 [67840/104000 (65%)]	Loss: 0.000030
Train Epoch: 4 [68480/104000 (66%)]	Loss: 0.000031
Train Epoch: 4 [69120/104000 (66%)]	Loss: 0.000087
Train Epoch: 4 [69760/104000 (67%)]	Loss: 0.000059
Train Epoch: 4 [70400/104000 (68%)]	Loss: 0.000084
Train Epoch: 4 [71040/104000 (68%)]	Loss: 0.000006
Train Epoch: 4 [71680/104000 (69%)]	Loss: 0.000003
Train Epoch: 4 [72320/104000 (70%)]	Loss: 0.000064
Train Epoch: 4 [72960/104000 (70%)]	Loss: 0.000001
Train Epoch: 4 [73600/104000 (71%)]	Loss: 0.000004
Train Epoch: 4 [74240/104000 (71%)]	Loss: 0.000004
Train Epoch: 4 [74880/104000 (72%)]	Loss: 0.000000
Train Epoch: 4 [75520/104000 (73%)]	Loss: 0.000001
Train Epoch: 4 [76160/104000 (73%)]	Loss: 0.000034
Train Epoch: 4 [76800/104000 (74%)]	Loss: 0.000062
Train Epoch: 4 [77440/104000 (74%)]	Loss: 0.000008
Train Epoch: 4 [78080/104000 (75%)]	Loss: 0.000015
Train Epoch: 4 [78720/104000 (76%)]	Loss: 0.000000
Train Epoch: 4 [79360/104000 (76%)]	Loss: 0.000002
Train Epoch: 4 [80000/104000 (77%)]	Loss: 0.000001
Train Epoch: 4 [80640/104000 (78%)]	Loss: 0.000000
Train Epoch: 4 [81280/104000 (78%)]	Loss: 0.000008
Train Epoch: 4 [81920/104000 (79%)]	Loss: 0.000007
Train Epoch: 4 [82560/104000 (79%)]	Loss: 0.000003
Train Epoch: 4 [83200/104000 (80%)]	Loss: 0.000015
Train Epoch: 4 [83840/104000 (81%)]	Loss: 0.000003
Train Epoch: 4 [84480/104000 (81%)]	Loss: 0.000007
Train Epoch: 4 [85120/104000 (82%)]	Loss: 0.000001
Train Epoch: 4 [85760/104000 (82%)]	Loss: 0.000002
Train Epoch: 4 [86400/104000 (83%)]	Loss: 0.000004
Train Epoch: 4 [87040/104000 (84%)]	Loss: 0.000227
Train Epoch: 4 [87680/104000 (84%)]	Loss: 0.000003
Train Epoch: 4 [88320/104000 (85%)]	Loss: 0.000003
Train Epoch: 4 [88960/104000 (86%)]	Loss: 0.000005
Train Epoch: 4 [89600/104000 (86%)]	Loss: 0.000004
Train Epoch: 4 [90240/104000 (87%)]	Loss: 0.000001
Train Epoch: 4 [90880/104000 (87%)]	Loss: 0.000002
Train Epoch: 4 [91520/104000 (88%)]	Loss: 0.000001
Train Epoch: 4 [92160/104000 (89%)]	Loss: 0.000001
Train Epoch: 4 [92800/104000 (89%)]	Loss: 0.000053
Train Epoch: 4 [93440/104000 (90%)]	Loss: 0.000002
Train Epoch: 4 [94080/104000 (90%)]	Loss: 0.000013
Train Epoch: 4 [94720/104000 (91%)]	Loss: 0.000020
Train Epoch: 4 [95360/104000 (92%)]	Loss: 0.000001
Train Epoch: 4 [96000/104000 (92%)]	Loss: 0.000009
Train Epoch: 4 [96640/104000 (93%)]	Loss: 0.000001
Train Epoch: 4 [97280/104000 (94%)]	Loss: 0.000074
Train Epoch: 4 [97920/104000 (94%)]	Loss: 0.000001
Train Epoch: 4 [98560/104000 (95%)]	Loss: 0.000016
Train Epoch: 4 [99200/104000 (95%)]	Loss: 0.000002
Train Epoch: 4 [99840/104000 (96%)]	Loss: 0.000001
Train Epoch: 4 [100480/104000 (97%)]	Loss: 0.000003
Train Epoch: 4 [101120/104000 (97%)]	Loss: 0.000004
Train Epoch: 4 [101760/104000 (98%)]	Loss: 0.000006
Train Epoch: 4 [102400/104000 (98%)]	Loss: 0.000001
Train Epoch: 4 [103040/104000 (99%)]	Loss: 0.000000
Train Epoch: 4 [103680/104000 (100%)]	Loss: 0.000006

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.3498, Accuracy: 25909/30000 (86%)

Train Epoch: 5 [0/104000 (0%)]	Loss: 0.000037
Train Epoch: 5 [640/104000 (1%)]	Loss: 0.000001
Train Epoch: 5 [1280/104000 (1%)]	Loss: 0.000001
Train Epoch: 5 [1920/104000 (2%)]	Loss: 0.000003
Train Epoch: 5 [2560/104000 (2%)]	Loss: 0.000002
Train Epoch: 5 [3200/104000 (3%)]	Loss: 0.000003
Train Epoch: 5 [3840/104000 (4%)]	Loss: 0.000001
Train Epoch: 5 [4480/104000 (4%)]	Loss: 0.000009
Train Epoch: 5 [5120/104000 (5%)]	Loss: 0.000000
Train Epoch: 5 [5760/104000 (6%)]	Loss: 0.000002
Train Epoch: 5 [6400/104000 (6%)]	Loss: 0.000002
Train Epoch: 5 [7040/104000 (7%)]	Loss: 0.000004
Train Epoch: 5 [7680/104000 (7%)]	Loss: 0.000001
Train Epoch: 5 [8320/104000 (8%)]	Loss: 0.000001
Train Epoch: 5 [8960/104000 (9%)]	Loss: 0.000002
Train Epoch: 5 [9600/104000 (9%)]	Loss: 0.000003
Train Epoch: 5 [10240/104000 (10%)]	Loss: 0.000002
Train Epoch: 5 [10880/104000 (10%)]	Loss: 0.000000
Train Epoch: 5 [11520/104000 (11%)]	Loss: 0.000002
Train Epoch: 5 [12160/104000 (12%)]	Loss: 0.000158
Train Epoch: 5 [12800/104000 (12%)]	Loss: 0.000001
Train Epoch: 5 [13440/104000 (13%)]	Loss: 0.000003
Train Epoch: 5 [14080/104000 (14%)]	Loss: 0.000004
Train Epoch: 5 [14720/104000 (14%)]	Loss: 0.000007
Train Epoch: 5 [15360/104000 (15%)]	Loss: 0.000001
Train Epoch: 5 [16000/104000 (15%)]	Loss: 0.000001
Train Epoch: 5 [16640/104000 (16%)]	Loss: 0.000001
Train Epoch: 5 [17280/104000 (17%)]	Loss: 0.000002
Train Epoch: 5 [17920/104000 (17%)]	Loss: 0.000001
Train Epoch: 5 [18560/104000 (18%)]	Loss: 0.000005
Train Epoch: 5 [19200/104000 (18%)]	Loss: 0.000001
Train Epoch: 5 [19840/104000 (19%)]	Loss: 0.000003
Train Epoch: 5 [20480/104000 (20%)]	Loss: 0.000004
Train Epoch: 5 [21120/104000 (20%)]	Loss: 0.000001
Train Epoch: 5 [21760/104000 (21%)]	Loss: 0.000001
Train Epoch: 5 [22400/104000 (22%)]	Loss: 0.000002
Train Epoch: 5 [23040/104000 (22%)]	Loss: 0.000003
Train Epoch: 5 [23680/104000 (23%)]	Loss: 0.000002
Train Epoch: 5 [24320/104000 (23%)]	Loss: 0.000052
Train Epoch: 5 [24960/104000 (24%)]	Loss: 0.000039
Train Epoch: 5 [25600/104000 (25%)]	Loss: 0.000011
Train Epoch: 5 [26240/104000 (25%)]	Loss: 0.000009
Train Epoch: 5 [26880/104000 (26%)]	Loss: 0.000010
Train Epoch: 5 [27520/104000 (26%)]	Loss: 0.000046
Train Epoch: 5 [28160/104000 (27%)]	Loss: 0.000002
Train Epoch: 5 [28800/104000 (28%)]	Loss: 0.000004
Train Epoch: 5 [29440/104000 (28%)]	Loss: 0.000001
Train Epoch: 5 [30080/104000 (29%)]	Loss: 0.000001
Train Epoch: 5 [30720/104000 (30%)]	Loss: 0.000042
Train Epoch: 5 [31360/104000 (30%)]	Loss: 0.000001
Train Epoch: 5 [32000/104000 (31%)]	Loss: 0.000003
Train Epoch: 5 [32640/104000 (31%)]	Loss: 0.000012
Train Epoch: 5 [33280/104000 (32%)]	Loss: 0.000004
Train Epoch: 5 [33920/104000 (33%)]	Loss: 0.000002
Train Epoch: 5 [34560/104000 (33%)]	Loss: 0.000000
Train Epoch: 5 [35200/104000 (34%)]	Loss: 0.000011
Train Epoch: 5 [35840/104000 (34%)]	Loss: 0.000001
Train Epoch: 5 [36480/104000 (35%)]	Loss: 0.000000
Train Epoch: 5 [37120/104000 (36%)]	Loss: 0.000679
Train Epoch: 5 [37760/104000 (36%)]	Loss: 0.000003
Train Epoch: 5 [38400/104000 (37%)]	Loss: 0.000006
Train Epoch: 5 [39040/104000 (38%)]	Loss: 0.000004
Train Epoch: 5 [39680/104000 (38%)]	Loss: 0.000001
Train Epoch: 5 [40320/104000 (39%)]	Loss: 0.000001
Train Epoch: 5 [40960/104000 (39%)]	Loss: 0.000001
Train Epoch: 5 [41600/104000 (40%)]	Loss: 0.000000
Train Epoch: 5 [42240/104000 (41%)]	Loss: 0.000044
Train Epoch: 5 [42880/104000 (41%)]	Loss: 0.000001
Train Epoch: 5 [43520/104000 (42%)]	Loss: 0.000002
Train Epoch: 5 [44160/104000 (42%)]	Loss: 0.000007
Train Epoch: 5 [44800/104000 (43%)]	Loss: 0.000002
Train Epoch: 5 [45440/104000 (44%)]	Loss: 0.000005
Train Epoch: 5 [46080/104000 (44%)]	Loss: 0.000006
Train Epoch: 5 [46720/104000 (45%)]	Loss: 0.000011
Train Epoch: 5 [47360/104000 (46%)]	Loss: 0.000000
Train Epoch: 5 [48000/104000 (46%)]	Loss: 0.000001
Train Epoch: 5 [48640/104000 (47%)]	Loss: 0.000001
Train Epoch: 5 [49280/104000 (47%)]	Loss: 0.000013
Train Epoch: 5 [49920/104000 (48%)]	Loss: 0.000007
Train Epoch: 5 [50560/104000 (49%)]	Loss: 0.000001
Train Epoch: 5 [51200/104000 (49%)]	Loss: 0.000008
Train Epoch: 5 [51840/104000 (50%)]	Loss: 0.000000
Train Epoch: 5 [52480/104000 (50%)]	Loss: 0.000003
Train Epoch: 5 [53120/104000 (51%)]	Loss: 0.000002
Train Epoch: 5 [53760/104000 (52%)]	Loss: 0.000004
Train Epoch: 5 [54400/104000 (52%)]	Loss: 0.000007
Train Epoch: 5 [55040/104000 (53%)]	Loss: 0.000002
Train Epoch: 5 [55680/104000 (54%)]	Loss: 0.000001
Train Epoch: 5 [56320/104000 (54%)]	Loss: 0.000000
Train Epoch: 5 [56960/104000 (55%)]	Loss: 0.000001
Train Epoch: 5 [57600/104000 (55%)]	Loss: 0.000002
Train Epoch: 5 [58240/104000 (56%)]	Loss: 0.000004
Train Epoch: 5 [58880/104000 (57%)]	Loss: 0.000007
Train Epoch: 5 [59520/104000 (57%)]	Loss: 0.000001
Train Epoch: 5 [60160/104000 (58%)]	Loss: 0.000002
Train Epoch: 5 [60800/104000 (58%)]	Loss: 0.000002
Train Epoch: 5 [61440/104000 (59%)]	Loss: 0.000192
Train Epoch: 5 [62080/104000 (60%)]	Loss: 0.000000
Train Epoch: 5 [62720/104000 (60%)]	Loss: 0.000002
Train Epoch: 5 [63360/104000 (61%)]	Loss: 0.000002
Train Epoch: 5 [64000/104000 (62%)]	Loss: 0.000004
Train Epoch: 5 [64640/104000 (62%)]	Loss: 0.000029
Train Epoch: 5 [65280/104000 (63%)]	Loss: 0.000001
Train Epoch: 5 [65920/104000 (63%)]	Loss: 0.000000
Train Epoch: 5 [66560/104000 (64%)]	Loss: 0.000004
Train Epoch: 5 [67200/104000 (65%)]	Loss: 0.000002
Train Epoch: 5 [67840/104000 (65%)]	Loss: 0.000031
Train Epoch: 5 [68480/104000 (66%)]	Loss: 0.000002
Train Epoch: 5 [69120/104000 (66%)]	Loss: 0.000003
Train Epoch: 5 [69760/104000 (67%)]	Loss: 0.000001
Train Epoch: 5 [70400/104000 (68%)]	Loss: 0.000001
Train Epoch: 5 [71040/104000 (68%)]	Loss: 0.000006
Train Epoch: 5 [71680/104000 (69%)]	Loss: 0.000002
Train Epoch: 5 [72320/104000 (70%)]	Loss: 0.000006
Train Epoch: 5 [72960/104000 (70%)]	Loss: 0.000001
Train Epoch: 5 [73600/104000 (71%)]	Loss: 0.000001
Train Epoch: 5 [74240/104000 (71%)]	Loss: 0.000004
Train Epoch: 5 [74880/104000 (72%)]	Loss: 0.000001
Train Epoch: 5 [75520/104000 (73%)]	Loss: 0.000001
Train Epoch: 5 [76160/104000 (73%)]	Loss: 0.000003
Train Epoch: 5 [76800/104000 (74%)]	Loss: 0.000061
Train Epoch: 5 [77440/104000 (74%)]	Loss: 0.000007
Train Epoch: 5 [78080/104000 (75%)]	Loss: 0.000001
Train Epoch: 5 [78720/104000 (76%)]	Loss: 0.000005
Train Epoch: 5 [79360/104000 (76%)]	Loss: 0.000003
Train Epoch: 5 [80000/104000 (77%)]	Loss: 0.000009
Train Epoch: 5 [80640/104000 (78%)]	Loss: 0.000017
Train Epoch: 5 [81280/104000 (78%)]	Loss: 0.000051
Train Epoch: 5 [81920/104000 (79%)]	Loss: 0.000001
Train Epoch: 5 [82560/104000 (79%)]	Loss: 0.000002
Train Epoch: 5 [83200/104000 (80%)]	Loss: 0.000005
Train Epoch: 5 [83840/104000 (81%)]	Loss: 0.000006
Train Epoch: 5 [84480/104000 (81%)]	Loss: 0.000007
Train Epoch: 5 [85120/104000 (82%)]	Loss: 0.000006
Train Epoch: 5 [85760/104000 (82%)]	Loss: 0.000060
Train Epoch: 5 [86400/104000 (83%)]	Loss: 0.000002
Train Epoch: 5 [87040/104000 (84%)]	Loss: 0.000003
Train Epoch: 5 [87680/104000 (84%)]	Loss: 0.000021
Train Epoch: 5 [88320/104000 (85%)]	Loss: 0.000002
Train Epoch: 5 [88960/104000 (86%)]	Loss: 0.000003
Train Epoch: 5 [89600/104000 (86%)]	Loss: 0.000031
Train Epoch: 5 [90240/104000 (87%)]	Loss: 0.000000
Train Epoch: 5 [90880/104000 (87%)]	Loss: 0.000003
Train Epoch: 5 [91520/104000 (88%)]	Loss: 0.000001
Train Epoch: 5 [92160/104000 (89%)]	Loss: 0.000001
Train Epoch: 5 [92800/104000 (89%)]	Loss: 0.000001
Train Epoch: 5 [93440/104000 (90%)]	Loss: 0.000002
Train Epoch: 5 [94080/104000 (90%)]	Loss: 0.000001
Train Epoch: 5 [94720/104000 (91%)]	Loss: 0.000001
Train Epoch: 5 [95360/104000 (92%)]	Loss: 0.000001
Train Epoch: 5 [96000/104000 (92%)]	Loss: 0.000006
Train Epoch: 5 [96640/104000 (93%)]	Loss: 0.000064
Train Epoch: 5 [97280/104000 (94%)]	Loss: 0.000003
Train Epoch: 5 [97920/104000 (94%)]	Loss: 0.000001
Train Epoch: 5 [98560/104000 (95%)]	Loss: 0.000021
Train Epoch: 5 [99200/104000 (95%)]	Loss: 0.000017
Train Epoch: 5 [99840/104000 (96%)]	Loss: 0.000006
Train Epoch: 5 [100480/104000 (97%)]	Loss: 0.000000
Train Epoch: 5 [101120/104000 (97%)]	Loss: 0.000002
Train Epoch: 5 [101760/104000 (98%)]	Loss: 0.000014
Train Epoch: 5 [102400/104000 (98%)]	Loss: 0.000002
Train Epoch: 5 [103040/104000 (99%)]	Loss: 0.000006
Train Epoch: 5 [103680/104000 (100%)]	Loss: 0.000003

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.2745, Accuracy: 26568/30000 (89%)

[100.0]
[88.56]
The number of neurons in CNN layer is 5
Train Epoch: 1 [0/104000 (0%)]	Loss: 0.735635
Train Epoch: 1 [640/104000 (1%)]	Loss: 0.006729
Train Epoch: 1 [1280/104000 (1%)]	Loss: 0.008744
Train Epoch: 1 [1920/104000 (2%)]	Loss: 0.003856
Train Epoch: 1 [2560/104000 (2%)]	Loss: 0.002027
Train Epoch: 1 [3200/104000 (3%)]	Loss: 0.001791
Train Epoch: 1 [3840/104000 (4%)]	Loss: 0.001974
Train Epoch: 1 [4480/104000 (4%)]	Loss: 0.002818
Train Epoch: 1 [5120/104000 (5%)]	Loss: 0.001130
Train Epoch: 1 [5760/104000 (6%)]	Loss: 0.001281
Train Epoch: 1 [6400/104000 (6%)]	Loss: 0.001820
Train Epoch: 1 [7040/104000 (7%)]	Loss: 0.000577
Train Epoch: 1 [7680/104000 (7%)]	Loss: 0.002283
Train Epoch: 1 [8320/104000 (8%)]	Loss: 0.001235
Train Epoch: 1 [8960/104000 (9%)]	Loss: 0.000694
Train Epoch: 1 [9600/104000 (9%)]	Loss: 0.001370
Train Epoch: 1 [10240/104000 (10%)]	Loss: 0.000621
Train Epoch: 1 [10880/104000 (10%)]	Loss: 0.000582
Train Epoch: 1 [11520/104000 (11%)]	Loss: 0.002235
Train Epoch: 1 [12160/104000 (12%)]	Loss: 0.000568
Train Epoch: 1 [12800/104000 (12%)]	Loss: 0.000624
Train Epoch: 1 [13440/104000 (13%)]	Loss: 0.000438
Train Epoch: 1 [14080/104000 (14%)]	Loss: 0.000685
Train Epoch: 1 [14720/104000 (14%)]	Loss: 0.000693
Train Epoch: 1 [15360/104000 (15%)]	Loss: 0.000425
Train Epoch: 1 [16000/104000 (15%)]	Loss: 0.000955
Train Epoch: 1 [16640/104000 (16%)]	Loss: 0.000375
Train Epoch: 1 [17280/104000 (17%)]	Loss: 0.001304
Train Epoch: 1 [17920/104000 (17%)]	Loss: 0.000319
Train Epoch: 1 [18560/104000 (18%)]	Loss: 0.000427
Train Epoch: 1 [19200/104000 (18%)]	Loss: 0.000714
Train Epoch: 1 [19840/104000 (19%)]	Loss: 0.000396
Train Epoch: 1 [20480/104000 (20%)]	Loss: 0.000221
Train Epoch: 1 [21120/104000 (20%)]	Loss: 0.001221
Train Epoch: 1 [21760/104000 (21%)]	Loss: 0.000625
Train Epoch: 1 [22400/104000 (22%)]	Loss: 0.000173
Train Epoch: 1 [23040/104000 (22%)]	Loss: 0.000290
Train Epoch: 1 [23680/104000 (23%)]	Loss: 0.000173
Train Epoch: 1 [24320/104000 (23%)]	Loss: 0.000257
Train Epoch: 1 [24960/104000 (24%)]	Loss: 0.000416
Train Epoch: 1 [25600/104000 (25%)]	Loss: 0.000329
Train Epoch: 1 [26240/104000 (25%)]	Loss: 0.001052
Train Epoch: 1 [26880/104000 (26%)]	Loss: 0.000258
Train Epoch: 1 [27520/104000 (26%)]	Loss: 0.000182
Train Epoch: 1 [28160/104000 (27%)]	Loss: 0.000222
Train Epoch: 1 [28800/104000 (28%)]	Loss: 0.001760
Train Epoch: 1 [29440/104000 (28%)]	Loss: 0.000554
Train Epoch: 1 [30080/104000 (29%)]	Loss: 0.000418
Train Epoch: 1 [30720/104000 (30%)]	Loss: 0.000221
Train Epoch: 1 [31360/104000 (30%)]	Loss: 0.000220
Train Epoch: 1 [32000/104000 (31%)]	Loss: 0.000295
Train Epoch: 1 [32640/104000 (31%)]	Loss: 0.000210
Train Epoch: 1 [33280/104000 (32%)]	Loss: 0.000116
Train Epoch: 1 [33920/104000 (33%)]	Loss: 0.002035
Train Epoch: 1 [34560/104000 (33%)]	Loss: 0.000297
Train Epoch: 1 [35200/104000 (34%)]	Loss: 0.000158
Train Epoch: 1 [35840/104000 (34%)]	Loss: 0.000380
Train Epoch: 1 [36480/104000 (35%)]	Loss: 0.000463
Train Epoch: 1 [37120/104000 (36%)]	Loss: 0.000155
Train Epoch: 1 [37760/104000 (36%)]	Loss: 0.000535
Train Epoch: 1 [38400/104000 (37%)]	Loss: 0.000258
Train Epoch: 1 [39040/104000 (38%)]	Loss: 0.000307
Train Epoch: 1 [39680/104000 (38%)]	Loss: 0.000672
Train Epoch: 1 [40320/104000 (39%)]	Loss: 0.000382
Train Epoch: 1 [40960/104000 (39%)]	Loss: 0.000181
Train Epoch: 1 [41600/104000 (40%)]	Loss: 0.000159
Train Epoch: 1 [42240/104000 (41%)]	Loss: 0.000101
Train Epoch: 1 [42880/104000 (41%)]	Loss: 0.000263
Train Epoch: 1 [43520/104000 (42%)]	Loss: 0.000222
Train Epoch: 1 [44160/104000 (42%)]	Loss: 0.000363
Train Epoch: 1 [44800/104000 (43%)]	Loss: 0.000188
Train Epoch: 1 [45440/104000 (44%)]	Loss: 0.000522
Train Epoch: 1 [46080/104000 (44%)]	Loss: 0.000156
Train Epoch: 1 [46720/104000 (45%)]	Loss: 0.000356
Train Epoch: 1 [47360/104000 (46%)]	Loss: 0.000110
Train Epoch: 1 [48000/104000 (46%)]	Loss: 0.000104
Train Epoch: 1 [48640/104000 (47%)]	Loss: 0.001513
Train Epoch: 1 [49280/104000 (47%)]	Loss: 0.000110
Train Epoch: 1 [49920/104000 (48%)]	Loss: 0.000910
Train Epoch: 1 [50560/104000 (49%)]	Loss: 0.000097
Train Epoch: 1 [51200/104000 (49%)]	Loss: 0.000221
Train Epoch: 1 [51840/104000 (50%)]	Loss: 0.000094
Train Epoch: 1 [52480/104000 (50%)]	Loss: 0.000126
Train Epoch: 1 [53120/104000 (51%)]	Loss: 0.000117
Train Epoch: 1 [53760/104000 (52%)]	Loss: 0.000074
Train Epoch: 1 [54400/104000 (52%)]	Loss: 0.000081
Train Epoch: 1 [55040/104000 (53%)]	Loss: 0.000126
Train Epoch: 1 [55680/104000 (54%)]	Loss: 0.000236
Train Epoch: 1 [56320/104000 (54%)]	Loss: 0.000757
Train Epoch: 1 [56960/104000 (55%)]	Loss: 0.000106
Train Epoch: 1 [57600/104000 (55%)]	Loss: 0.000110
Train Epoch: 1 [58240/104000 (56%)]	Loss: 0.000213
Train Epoch: 1 [58880/104000 (57%)]	Loss: 0.002330
Train Epoch: 1 [59520/104000 (57%)]	Loss: 0.000128
Train Epoch: 1 [60160/104000 (58%)]	Loss: 0.000242
Train Epoch: 1 [60800/104000 (58%)]	Loss: 0.000098
Train Epoch: 1 [61440/104000 (59%)]	Loss: 0.000131
Train Epoch: 1 [62080/104000 (60%)]	Loss: 0.000151
Train Epoch: 1 [62720/104000 (60%)]	Loss: 0.000059
Train Epoch: 1 [63360/104000 (61%)]	Loss: 0.000225
Train Epoch: 1 [64000/104000 (62%)]	Loss: 0.000086
Train Epoch: 1 [64640/104000 (62%)]	Loss: 0.000091
Train Epoch: 1 [65280/104000 (63%)]	Loss: 0.000537
Train Epoch: 1 [65920/104000 (63%)]	Loss: 0.000686
Train Epoch: 1 [66560/104000 (64%)]	Loss: 0.000159
Train Epoch: 1 [67200/104000 (65%)]	Loss: 0.000342
Train Epoch: 1 [67840/104000 (65%)]	Loss: 0.000073
Train Epoch: 1 [68480/104000 (66%)]	Loss: 0.000064
Train Epoch: 1 [69120/104000 (66%)]	Loss: 0.000098
Train Epoch: 1 [69760/104000 (67%)]	Loss: 0.000095
Train Epoch: 1 [70400/104000 (68%)]	Loss: 0.000273
Train Epoch: 1 [71040/104000 (68%)]	Loss: 0.000096
Train Epoch: 1 [71680/104000 (69%)]	Loss: 0.000077
Train Epoch: 1 [72320/104000 (70%)]	Loss: 0.000106
Train Epoch: 1 [72960/104000 (70%)]	Loss: 0.000131
Train Epoch: 1 [73600/104000 (71%)]	Loss: 0.000170
Train Epoch: 1 [74240/104000 (71%)]	Loss: 0.000123
Train Epoch: 1 [74880/104000 (72%)]	Loss: 0.000081
Train Epoch: 1 [75520/104000 (73%)]	Loss: 0.000088
Train Epoch: 1 [76160/104000 (73%)]	Loss: 0.000166
Train Epoch: 1 [76800/104000 (74%)]	Loss: 0.000057
Train Epoch: 1 [77440/104000 (74%)]	Loss: 0.000100
Train Epoch: 1 [78080/104000 (75%)]	Loss: 0.000197
Train Epoch: 1 [78720/104000 (76%)]	Loss: 0.000060
Train Epoch: 1 [79360/104000 (76%)]	Loss: 0.000106
Train Epoch: 1 [80000/104000 (77%)]	Loss: 0.000139
Train Epoch: 1 [80640/104000 (78%)]	Loss: 0.000069
Train Epoch: 1 [81280/104000 (78%)]	Loss: 0.000104
Train Epoch: 1 [81920/104000 (79%)]	Loss: 0.000082
Train Epoch: 1 [82560/104000 (79%)]	Loss: 0.000159
Train Epoch: 1 [83200/104000 (80%)]	Loss: 0.000061
Train Epoch: 1 [83840/104000 (81%)]	Loss: 0.000215
Train Epoch: 1 [84480/104000 (81%)]	Loss: 0.000723
Train Epoch: 1 [85120/104000 (82%)]	Loss: 0.000050
Train Epoch: 1 [85760/104000 (82%)]	Loss: 0.000138
Train Epoch: 1 [86400/104000 (83%)]	Loss: 0.000088
Train Epoch: 1 [87040/104000 (84%)]	Loss: 0.000291
Train Epoch: 1 [87680/104000 (84%)]	Loss: 0.000090
Train Epoch: 1 [88320/104000 (85%)]	Loss: 0.000047
Train Epoch: 1 [88960/104000 (86%)]	Loss: 0.000045
Train Epoch: 1 [89600/104000 (86%)]	Loss: 0.000162
Train Epoch: 1 [90240/104000 (87%)]	Loss: 0.000181
Train Epoch: 1 [90880/104000 (87%)]	Loss: 0.000334
Train Epoch: 1 [91520/104000 (88%)]	Loss: 0.000049
Train Epoch: 1 [92160/104000 (89%)]	Loss: 0.000166
Train Epoch: 1 [92800/104000 (89%)]	Loss: 0.000054
Train Epoch: 1 [93440/104000 (90%)]	Loss: 0.000352
Train Epoch: 1 [94080/104000 (90%)]	Loss: 0.000066
Train Epoch: 1 [94720/104000 (91%)]	Loss: 0.000109
Train Epoch: 1 [95360/104000 (92%)]	Loss: 0.000052
Train Epoch: 1 [96000/104000 (92%)]	Loss: 0.000066
Train Epoch: 1 [96640/104000 (93%)]	Loss: 0.000053
Train Epoch: 1 [97280/104000 (94%)]	Loss: 0.000082
Train Epoch: 1 [97920/104000 (94%)]	Loss: 0.000039
Train Epoch: 1 [98560/104000 (95%)]	Loss: 0.000065
Train Epoch: 1 [99200/104000 (95%)]	Loss: 0.000068
Train Epoch: 1 [99840/104000 (96%)]	Loss: 0.000054
Train Epoch: 1 [100480/104000 (97%)]	Loss: 0.000082
Train Epoch: 1 [101120/104000 (97%)]	Loss: 0.000055
Train Epoch: 1 [101760/104000 (98%)]	Loss: 0.000100
Train Epoch: 1 [102400/104000 (98%)]	Loss: 0.000185
Train Epoch: 1 [103040/104000 (99%)]	Loss: 0.000271
Train Epoch: 1 [103680/104000 (100%)]	Loss: 0.000036

Test set: Average loss: 0.0001, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.2616, Accuracy: 26099/30000 (87%)

Train Epoch: 2 [0/104000 (0%)]	Loss: 0.000091
Train Epoch: 2 [640/104000 (1%)]	Loss: 0.000039
Train Epoch: 2 [1280/104000 (1%)]	Loss: 0.000085
Train Epoch: 2 [1920/104000 (2%)]	Loss: 0.000152
Train Epoch: 2 [2560/104000 (2%)]	Loss: 0.000086
Train Epoch: 2 [3200/104000 (3%)]	Loss: 0.000064
Train Epoch: 2 [3840/104000 (4%)]	Loss: 0.000059
Train Epoch: 2 [4480/104000 (4%)]	Loss: 0.000649
Train Epoch: 2 [5120/104000 (5%)]	Loss: 0.000282
Train Epoch: 2 [5760/104000 (6%)]	Loss: 0.000036
Train Epoch: 2 [6400/104000 (6%)]	Loss: 0.000047
Train Epoch: 2 [7040/104000 (7%)]	Loss: 0.000039
Train Epoch: 2 [7680/104000 (7%)]	Loss: 0.000091
Train Epoch: 2 [8320/104000 (8%)]	Loss: 0.000032
Train Epoch: 2 [8960/104000 (9%)]	Loss: 0.000316
Train Epoch: 2 [9600/104000 (9%)]	Loss: 0.000034
Train Epoch: 2 [10240/104000 (10%)]	Loss: 0.000067
Train Epoch: 2 [10880/104000 (10%)]	Loss: 0.000373
Train Epoch: 2 [11520/104000 (11%)]	Loss: 0.000094
Train Epoch: 2 [12160/104000 (12%)]	Loss: 0.000035
Train Epoch: 2 [12800/104000 (12%)]	Loss: 0.000582
Train Epoch: 2 [13440/104000 (13%)]	Loss: 0.000043
Train Epoch: 2 [14080/104000 (14%)]	Loss: 0.000256
Train Epoch: 2 [14720/104000 (14%)]	Loss: 0.000051
Train Epoch: 2 [15360/104000 (15%)]	Loss: 0.000048
Train Epoch: 2 [16000/104000 (15%)]	Loss: 0.000040
Train Epoch: 2 [16640/104000 (16%)]	Loss: 0.000048
Train Epoch: 2 [17280/104000 (17%)]	Loss: 0.000190
Train Epoch: 2 [17920/104000 (17%)]	Loss: 0.000038
Train Epoch: 2 [18560/104000 (18%)]	Loss: 0.000502
Train Epoch: 2 [19200/104000 (18%)]	Loss: 0.000037
Train Epoch: 2 [19840/104000 (19%)]	Loss: 0.000034
Train Epoch: 2 [20480/104000 (20%)]	Loss: 0.000313
Train Epoch: 2 [21120/104000 (20%)]	Loss: 0.000083
Train Epoch: 2 [21760/104000 (21%)]	Loss: 0.000061
Train Epoch: 2 [22400/104000 (22%)]	Loss: 0.000113
Train Epoch: 2 [23040/104000 (22%)]	Loss: 0.000027
Train Epoch: 2 [23680/104000 (23%)]	Loss: 0.000078
Train Epoch: 2 [24320/104000 (23%)]	Loss: 0.000094
Train Epoch: 2 [24960/104000 (24%)]	Loss: 0.000041
Train Epoch: 2 [25600/104000 (25%)]	Loss: 0.000130
Train Epoch: 2 [26240/104000 (25%)]	Loss: 0.000056
Train Epoch: 2 [26880/104000 (26%)]	Loss: 0.002189
Train Epoch: 2 [27520/104000 (26%)]	Loss: 0.000205
Train Epoch: 2 [28160/104000 (27%)]	Loss: 0.000045
Train Epoch: 2 [28800/104000 (28%)]	Loss: 0.000070
Train Epoch: 2 [29440/104000 (28%)]	Loss: 0.000073
Train Epoch: 2 [30080/104000 (29%)]	Loss: 0.000064
Train Epoch: 2 [30720/104000 (30%)]	Loss: 0.000055
Train Epoch: 2 [31360/104000 (30%)]	Loss: 0.000139
Train Epoch: 2 [32000/104000 (31%)]	Loss: 0.000033
Train Epoch: 2 [32640/104000 (31%)]	Loss: 0.000054
Train Epoch: 2 [33280/104000 (32%)]	Loss: 0.000057
Train Epoch: 2 [33920/104000 (33%)]	Loss: 0.000062
Train Epoch: 2 [34560/104000 (33%)]	Loss: 0.000061
Train Epoch: 2 [35200/104000 (34%)]	Loss: 0.000050
Train Epoch: 2 [35840/104000 (34%)]	Loss: 0.000143
Train Epoch: 2 [36480/104000 (35%)]	Loss: 0.000057
Train Epoch: 2 [37120/104000 (36%)]	Loss: 0.000041
Train Epoch: 2 [37760/104000 (36%)]	Loss: 0.000286
Train Epoch: 2 [38400/104000 (37%)]	Loss: 0.000020
Train Epoch: 2 [39040/104000 (38%)]	Loss: 0.000036
Train Epoch: 2 [39680/104000 (38%)]	Loss: 0.000020
Train Epoch: 2 [40320/104000 (39%)]	Loss: 0.000025
Train Epoch: 2 [40960/104000 (39%)]	Loss: 0.000055
Train Epoch: 2 [41600/104000 (40%)]	Loss: 0.000046
Train Epoch: 2 [42240/104000 (41%)]	Loss: 0.000083
Train Epoch: 2 [42880/104000 (41%)]	Loss: 0.000153
Train Epoch: 2 [43520/104000 (42%)]	Loss: 0.000138
Train Epoch: 2 [44160/104000 (42%)]	Loss: 0.000025
Train Epoch: 2 [44800/104000 (43%)]	Loss: 0.000027
Train Epoch: 2 [45440/104000 (44%)]	Loss: 0.000072
Train Epoch: 2 [46080/104000 (44%)]	Loss: 0.000040
Train Epoch: 2 [46720/104000 (45%)]	Loss: 0.000063
Train Epoch: 2 [47360/104000 (46%)]	Loss: 0.000047
Train Epoch: 2 [48000/104000 (46%)]	Loss: 0.000028
Train Epoch: 2 [48640/104000 (47%)]	Loss: 0.000244
Train Epoch: 2 [49280/104000 (47%)]	Loss: 0.000033
Train Epoch: 2 [49920/104000 (48%)]	Loss: 0.000054
Train Epoch: 2 [50560/104000 (49%)]	Loss: 0.000039
Train Epoch: 2 [51200/104000 (49%)]	Loss: 0.000052
Train Epoch: 2 [51840/104000 (50%)]	Loss: 0.000031
Train Epoch: 2 [52480/104000 (50%)]	Loss: 0.000042
Train Epoch: 2 [53120/104000 (51%)]	Loss: 0.000025
Train Epoch: 2 [53760/104000 (52%)]	Loss: 0.000221
Train Epoch: 2 [54400/104000 (52%)]	Loss: 0.000047
Train Epoch: 2 [55040/104000 (53%)]	Loss: 0.000020
Train Epoch: 2 [55680/104000 (54%)]	Loss: 0.000355
Train Epoch: 2 [56320/104000 (54%)]	Loss: 0.000055
Train Epoch: 2 [56960/104000 (55%)]	Loss: 0.000035
Train Epoch: 2 [57600/104000 (55%)]	Loss: 0.000031
Train Epoch: 2 [58240/104000 (56%)]	Loss: 0.000079
Train Epoch: 2 [58880/104000 (57%)]	Loss: 0.000090
Train Epoch: 2 [59520/104000 (57%)]	Loss: 0.000287
Train Epoch: 2 [60160/104000 (58%)]	Loss: 0.000035
Train Epoch: 2 [60800/104000 (58%)]	Loss: 0.000029
Train Epoch: 2 [61440/104000 (59%)]	Loss: 0.000204
Train Epoch: 2 [62080/104000 (60%)]	Loss: 0.000019
Train Epoch: 2 [62720/104000 (60%)]	Loss: 0.000139
Train Epoch: 2 [63360/104000 (61%)]	Loss: 0.000047
Train Epoch: 2 [64000/104000 (62%)]	Loss: 0.000029
Train Epoch: 2 [64640/104000 (62%)]	Loss: 0.000207
Train Epoch: 2 [65280/104000 (63%)]	Loss: 0.000116
Train Epoch: 2 [65920/104000 (63%)]	Loss: 0.000029
Train Epoch: 2 [66560/104000 (64%)]	Loss: 0.000065
Train Epoch: 2 [67200/104000 (65%)]	Loss: 0.000086
Train Epoch: 2 [67840/104000 (65%)]	Loss: 0.000135
Train Epoch: 2 [68480/104000 (66%)]	Loss: 0.000037
Train Epoch: 2 [69120/104000 (66%)]	Loss: 0.000017
Train Epoch: 2 [69760/104000 (67%)]	Loss: 0.000020
Train Epoch: 2 [70400/104000 (68%)]	Loss: 0.000073
Train Epoch: 2 [71040/104000 (68%)]	Loss: 0.000063
Train Epoch: 2 [71680/104000 (69%)]	Loss: 0.000035
Train Epoch: 2 [72320/104000 (70%)]	Loss: 0.000089
Train Epoch: 2 [72960/104000 (70%)]	Loss: 0.000064
Train Epoch: 2 [73600/104000 (71%)]	Loss: 0.000063
Train Epoch: 2 [74240/104000 (71%)]	Loss: 0.000041
Train Epoch: 2 [74880/104000 (72%)]	Loss: 0.000083
Train Epoch: 2 [75520/104000 (73%)]	Loss: 0.000061
Train Epoch: 2 [76160/104000 (73%)]	Loss: 0.000040
Train Epoch: 2 [76800/104000 (74%)]	Loss: 0.000108
Train Epoch: 2 [77440/104000 (74%)]	Loss: 0.000168
Train Epoch: 2 [78080/104000 (75%)]	Loss: 0.000098
Train Epoch: 2 [78720/104000 (76%)]	Loss: 0.000092
Train Epoch: 2 [79360/104000 (76%)]	Loss: 0.000039
Train Epoch: 2 [80000/104000 (77%)]	Loss: 0.000044
Train Epoch: 2 [80640/104000 (78%)]	Loss: 0.000038
Train Epoch: 2 [81280/104000 (78%)]	Loss: 0.000030
Train Epoch: 2 [81920/104000 (79%)]	Loss: 0.000176
Train Epoch: 2 [82560/104000 (79%)]	Loss: 0.000016
Train Epoch: 2 [83200/104000 (80%)]	Loss: 0.000231
Train Epoch: 2 [83840/104000 (81%)]	Loss: 0.000051
Train Epoch: 2 [84480/104000 (81%)]	Loss: 0.000034
Train Epoch: 2 [85120/104000 (82%)]	Loss: 0.000025
Train Epoch: 2 [85760/104000 (82%)]	Loss: 0.000033
Train Epoch: 2 [86400/104000 (83%)]	Loss: 0.000022
Train Epoch: 2 [87040/104000 (84%)]	Loss: 0.000084
Train Epoch: 2 [87680/104000 (84%)]	Loss: 0.000041
Train Epoch: 2 [88320/104000 (85%)]	Loss: 0.000130
Train Epoch: 2 [88960/104000 (86%)]	Loss: 0.000398
Train Epoch: 2 [89600/104000 (86%)]	Loss: 0.000035
Train Epoch: 2 [90240/104000 (87%)]	Loss: 0.000026
Train Epoch: 2 [90880/104000 (87%)]	Loss: 0.000026
Train Epoch: 2 [91520/104000 (88%)]	Loss: 0.000019
Train Epoch: 2 [92160/104000 (89%)]	Loss: 0.000027
Train Epoch: 2 [92800/104000 (89%)]	Loss: 0.000099
Train Epoch: 2 [93440/104000 (90%)]	Loss: 0.000032
Train Epoch: 2 [94080/104000 (90%)]	Loss: 0.000036
Train Epoch: 2 [94720/104000 (91%)]	Loss: 0.000061
Train Epoch: 2 [95360/104000 (92%)]	Loss: 0.000081
Train Epoch: 2 [96000/104000 (92%)]	Loss: 0.000016
Train Epoch: 2 [96640/104000 (93%)]	Loss: 0.000015
Train Epoch: 2 [97280/104000 (94%)]	Loss: 0.000020
Train Epoch: 2 [97920/104000 (94%)]	Loss: 0.000035
Train Epoch: 2 [98560/104000 (95%)]	Loss: 0.000047
Train Epoch: 2 [99200/104000 (95%)]	Loss: 0.000043
Train Epoch: 2 [99840/104000 (96%)]	Loss: 0.000189
Train Epoch: 2 [100480/104000 (97%)]	Loss: 0.000012
Train Epoch: 2 [101120/104000 (97%)]	Loss: 0.000154
Train Epoch: 2 [101760/104000 (98%)]	Loss: 0.000015
Train Epoch: 2 [102400/104000 (98%)]	Loss: 0.000015
Train Epoch: 2 [103040/104000 (99%)]	Loss: 0.000222
Train Epoch: 2 [103680/104000 (100%)]	Loss: 0.000062

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.2827, Accuracy: 25898/30000 (86%)

Train Epoch: 3 [0/104000 (0%)]	Loss: 0.000062
Train Epoch: 3 [640/104000 (1%)]	Loss: 0.000032
Train Epoch: 3 [1280/104000 (1%)]	Loss: 0.000027
Train Epoch: 3 [1920/104000 (2%)]	Loss: 0.000102
Train Epoch: 3 [2560/104000 (2%)]	Loss: 0.000049
Train Epoch: 3 [3200/104000 (3%)]	Loss: 0.000057
Train Epoch: 3 [3840/104000 (4%)]	Loss: 0.000086
Train Epoch: 3 [4480/104000 (4%)]	Loss: 0.000031
Train Epoch: 3 [5120/104000 (5%)]	Loss: 0.000087
Train Epoch: 3 [5760/104000 (6%)]	Loss: 0.000037
Train Epoch: 3 [6400/104000 (6%)]	Loss: 0.000068
Train Epoch: 3 [7040/104000 (7%)]	Loss: 0.000019
Train Epoch: 3 [7680/104000 (7%)]	Loss: 0.000057
Train Epoch: 3 [8320/104000 (8%)]	Loss: 0.000036
Train Epoch: 3 [8960/104000 (9%)]	Loss: 0.000053
Train Epoch: 3 [9600/104000 (9%)]	Loss: 0.000274
Train Epoch: 3 [10240/104000 (10%)]	Loss: 0.000014
Train Epoch: 3 [10880/104000 (10%)]	Loss: 0.000030
Train Epoch: 3 [11520/104000 (11%)]	Loss: 0.000088
Train Epoch: 3 [12160/104000 (12%)]	Loss: 0.000022
Train Epoch: 3 [12800/104000 (12%)]	Loss: 0.000152
Train Epoch: 3 [13440/104000 (13%)]	Loss: 0.000109
Train Epoch: 3 [14080/104000 (14%)]	Loss: 0.000042
Train Epoch: 3 [14720/104000 (14%)]	Loss: 0.000021
Train Epoch: 3 [15360/104000 (15%)]	Loss: 0.000037
Train Epoch: 3 [16000/104000 (15%)]	Loss: 0.000025
Train Epoch: 3 [16640/104000 (16%)]	Loss: 0.000032
Train Epoch: 3 [17280/104000 (17%)]	Loss: 0.000028
Train Epoch: 3 [17920/104000 (17%)]	Loss: 0.000023
Train Epoch: 3 [18560/104000 (18%)]	Loss: 0.000031
Train Epoch: 3 [19200/104000 (18%)]	Loss: 0.000041
Train Epoch: 3 [19840/104000 (19%)]	Loss: 0.000016
Train Epoch: 3 [20480/104000 (20%)]	Loss: 0.000025
Train Epoch: 3 [21120/104000 (20%)]	Loss: 0.000094
Train Epoch: 3 [21760/104000 (21%)]	Loss: 0.000069
Train Epoch: 3 [22400/104000 (22%)]	Loss: 0.000120
Train Epoch: 3 [23040/104000 (22%)]	Loss: 0.000043
Train Epoch: 3 [23680/104000 (23%)]	Loss: 0.000028
Train Epoch: 3 [24320/104000 (23%)]	Loss: 0.000026
Train Epoch: 3 [24960/104000 (24%)]	Loss: 0.000016
Train Epoch: 3 [25600/104000 (25%)]	Loss: 0.000033
Train Epoch: 3 [26240/104000 (25%)]	Loss: 0.000031
Train Epoch: 3 [26880/104000 (26%)]	Loss: 0.000011
Train Epoch: 3 [27520/104000 (26%)]	Loss: 0.000067
Train Epoch: 3 [28160/104000 (27%)]	Loss: 0.000037
Train Epoch: 3 [28800/104000 (28%)]	Loss: 0.000104
Train Epoch: 3 [29440/104000 (28%)]	Loss: 0.000057
Train Epoch: 3 [30080/104000 (29%)]	Loss: 0.000020
Train Epoch: 3 [30720/104000 (30%)]	Loss: 0.000024
Train Epoch: 3 [31360/104000 (30%)]	Loss: 0.000063
Train Epoch: 3 [32000/104000 (31%)]	Loss: 0.000027
Train Epoch: 3 [32640/104000 (31%)]	Loss: 0.000036
Train Epoch: 3 [33280/104000 (32%)]	Loss: 0.000017
Train Epoch: 3 [33920/104000 (33%)]	Loss: 0.000062
Train Epoch: 3 [34560/104000 (33%)]	Loss: 0.000018
Train Epoch: 3 [35200/104000 (34%)]	Loss: 0.000043
Train Epoch: 3 [35840/104000 (34%)]	Loss: 0.000016
Train Epoch: 3 [36480/104000 (35%)]	Loss: 0.000019
Train Epoch: 3 [37120/104000 (36%)]	Loss: 0.000100
Train Epoch: 3 [37760/104000 (36%)]	Loss: 0.000013
Train Epoch: 3 [38400/104000 (37%)]	Loss: 0.000170
Train Epoch: 3 [39040/104000 (38%)]	Loss: 0.000072
Train Epoch: 3 [39680/104000 (38%)]	Loss: 0.000037
Train Epoch: 3 [40320/104000 (39%)]	Loss: 0.000023
Train Epoch: 3 [40960/104000 (39%)]	Loss: 0.000012
Train Epoch: 3 [41600/104000 (40%)]	Loss: 0.000027
Train Epoch: 3 [42240/104000 (41%)]	Loss: 0.000034
Train Epoch: 3 [42880/104000 (41%)]	Loss: 0.000018
Train Epoch: 3 [43520/104000 (42%)]	Loss: 0.000017
Train Epoch: 3 [44160/104000 (42%)]	Loss: 0.000143
Train Epoch: 3 [44800/104000 (43%)]	Loss: 0.000163
Train Epoch: 3 [45440/104000 (44%)]	Loss: 0.000014
Train Epoch: 3 [46080/104000 (44%)]	Loss: 0.000017
Train Epoch: 3 [46720/104000 (45%)]	Loss: 0.000014
Train Epoch: 3 [47360/104000 (46%)]	Loss: 0.000045
Train Epoch: 3 [48000/104000 (46%)]	Loss: 0.000134
Train Epoch: 3 [48640/104000 (47%)]	Loss: 0.000019
Train Epoch: 3 [49280/104000 (47%)]	Loss: 0.000125
Train Epoch: 3 [49920/104000 (48%)]	Loss: 0.000016
Train Epoch: 3 [50560/104000 (49%)]	Loss: 0.000064
Train Epoch: 3 [51200/104000 (49%)]	Loss: 0.000034
Train Epoch: 3 [51840/104000 (50%)]	Loss: 0.000051
Train Epoch: 3 [52480/104000 (50%)]	Loss: 0.000149
Train Epoch: 3 [53120/104000 (51%)]	Loss: 0.000018
Train Epoch: 3 [53760/104000 (52%)]	Loss: 0.000008
Train Epoch: 3 [54400/104000 (52%)]	Loss: 0.000020
Train Epoch: 3 [55040/104000 (53%)]	Loss: 0.000005
Train Epoch: 3 [55680/104000 (54%)]	Loss: 0.000012
Train Epoch: 3 [56320/104000 (54%)]	Loss: 0.000017
Train Epoch: 3 [56960/104000 (55%)]	Loss: 0.000024
Train Epoch: 3 [57600/104000 (55%)]	Loss: 0.000100
Train Epoch: 3 [58240/104000 (56%)]	Loss: 0.000027
Train Epoch: 3 [58880/104000 (57%)]	Loss: 0.000125
Train Epoch: 3 [59520/104000 (57%)]	Loss: 0.000013
Train Epoch: 3 [60160/104000 (58%)]	Loss: 0.000011
Train Epoch: 3 [60800/104000 (58%)]	Loss: 0.000019
Train Epoch: 3 [61440/104000 (59%)]	Loss: 0.000019
Train Epoch: 3 [62080/104000 (60%)]	Loss: 0.000015
Train Epoch: 3 [62720/104000 (60%)]	Loss: 0.000013
Train Epoch: 3 [63360/104000 (61%)]	Loss: 0.000028
Train Epoch: 3 [64000/104000 (62%)]	Loss: 0.000008
Train Epoch: 3 [64640/104000 (62%)]	Loss: 0.000013
Train Epoch: 3 [65280/104000 (63%)]	Loss: 0.000024
Train Epoch: 3 [65920/104000 (63%)]	Loss: 0.000040
Train Epoch: 3 [66560/104000 (64%)]	Loss: 0.000021
Train Epoch: 3 [67200/104000 (65%)]	Loss: 0.000017
Train Epoch: 3 [67840/104000 (65%)]	Loss: 0.000027
Train Epoch: 3 [68480/104000 (66%)]	Loss: 0.000102
Train Epoch: 3 [69120/104000 (66%)]	Loss: 0.000017
Train Epoch: 3 [69760/104000 (67%)]	Loss: 0.000042
Train Epoch: 3 [70400/104000 (68%)]	Loss: 0.000033
Train Epoch: 3 [71040/104000 (68%)]	Loss: 0.000131
Train Epoch: 3 [71680/104000 (69%)]	Loss: 0.000275
Train Epoch: 3 [72320/104000 (70%)]	Loss: 0.000014
Train Epoch: 3 [72960/104000 (70%)]	Loss: 0.000013
Train Epoch: 3 [73600/104000 (71%)]	Loss: 0.000013
Train Epoch: 3 [74240/104000 (71%)]	Loss: 0.000365
Train Epoch: 3 [74880/104000 (72%)]	Loss: 0.000345
Train Epoch: 3 [75520/104000 (73%)]	Loss: 0.000024
Train Epoch: 3 [76160/104000 (73%)]	Loss: 0.000019
Train Epoch: 3 [76800/104000 (74%)]	Loss: 0.000037
Train Epoch: 3 [77440/104000 (74%)]	Loss: 0.000045
Train Epoch: 3 [78080/104000 (75%)]	Loss: 0.000022
Train Epoch: 3 [78720/104000 (76%)]	Loss: 0.000131
Train Epoch: 3 [79360/104000 (76%)]	Loss: 0.000036
Train Epoch: 3 [80000/104000 (77%)]	Loss: 0.000440
Train Epoch: 3 [80640/104000 (78%)]	Loss: 0.000014
Train Epoch: 3 [81280/104000 (78%)]	Loss: 0.000013
Train Epoch: 3 [81920/104000 (79%)]	Loss: 0.000020
Train Epoch: 3 [82560/104000 (79%)]	Loss: 0.000081
Train Epoch: 3 [83200/104000 (80%)]	Loss: 0.000016
Train Epoch: 3 [83840/104000 (81%)]	Loss: 0.000031
Train Epoch: 3 [84480/104000 (81%)]	Loss: 0.000012
Train Epoch: 3 [85120/104000 (82%)]	Loss: 0.000022
Train Epoch: 3 [85760/104000 (82%)]	Loss: 0.000040
Train Epoch: 3 [86400/104000 (83%)]	Loss: 0.000138
Train Epoch: 3 [87040/104000 (84%)]	Loss: 0.000037
Train Epoch: 3 [87680/104000 (84%)]	Loss: 0.000013
Train Epoch: 3 [88320/104000 (85%)]	Loss: 0.000011
Train Epoch: 3 [88960/104000 (86%)]	Loss: 0.000017
Train Epoch: 3 [89600/104000 (86%)]	Loss: 0.000011
Train Epoch: 3 [90240/104000 (87%)]	Loss: 0.000108
Train Epoch: 3 [90880/104000 (87%)]	Loss: 0.000011
Train Epoch: 3 [91520/104000 (88%)]	Loss: 0.000022
Train Epoch: 3 [92160/104000 (89%)]	Loss: 0.000009
Train Epoch: 3 [92800/104000 (89%)]	Loss: 0.000128
Train Epoch: 3 [93440/104000 (90%)]	Loss: 0.000032
Train Epoch: 3 [94080/104000 (90%)]	Loss: 0.000076
Train Epoch: 3 [94720/104000 (91%)]	Loss: 0.000048
Train Epoch: 3 [95360/104000 (92%)]	Loss: 0.000016
Train Epoch: 3 [96000/104000 (92%)]	Loss: 0.000017
Train Epoch: 3 [96640/104000 (93%)]	Loss: 0.000045
Train Epoch: 3 [97280/104000 (94%)]	Loss: 0.000012
Train Epoch: 3 [97920/104000 (94%)]	Loss: 0.000064
Train Epoch: 3 [98560/104000 (95%)]	Loss: 0.000007
Train Epoch: 3 [99200/104000 (95%)]	Loss: 0.000150
Train Epoch: 3 [99840/104000 (96%)]	Loss: 0.000035
Train Epoch: 3 [100480/104000 (97%)]	Loss: 0.000023
Train Epoch: 3 [101120/104000 (97%)]	Loss: 0.000030
Train Epoch: 3 [101760/104000 (98%)]	Loss: 0.000011
Train Epoch: 3 [102400/104000 (98%)]	Loss: 0.000008
Train Epoch: 3 [103040/104000 (99%)]	Loss: 0.000104
Train Epoch: 3 [103680/104000 (100%)]	Loss: 0.000076

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.2302, Accuracy: 26714/30000 (89%)

Train Epoch: 4 [0/104000 (0%)]	Loss: 0.000044
Train Epoch: 4 [640/104000 (1%)]	Loss: 0.000011
Train Epoch: 4 [1280/104000 (1%)]	Loss: 0.000048
Train Epoch: 4 [1920/104000 (2%)]	Loss: 0.000016
Train Epoch: 4 [2560/104000 (2%)]	Loss: 0.000070
Train Epoch: 4 [3200/104000 (3%)]	Loss: 0.000233
Train Epoch: 4 [3840/104000 (4%)]	Loss: 0.000013
Train Epoch: 4 [4480/104000 (4%)]	Loss: 0.000017
Train Epoch: 4 [5120/104000 (5%)]	Loss: 0.000011
Train Epoch: 4 [5760/104000 (6%)]	Loss: 0.000047
Train Epoch: 4 [6400/104000 (6%)]	Loss: 0.000021
Train Epoch: 4 [7040/104000 (7%)]	Loss: 0.000015
Train Epoch: 4 [7680/104000 (7%)]	Loss: 0.000010
Train Epoch: 4 [8320/104000 (8%)]	Loss: 0.000016
Train Epoch: 4 [8960/104000 (9%)]	Loss: 0.000012
Train Epoch: 4 [9600/104000 (9%)]	Loss: 0.000017
Train Epoch: 4 [10240/104000 (10%)]	Loss: 0.000005
Train Epoch: 4 [10880/104000 (10%)]	Loss: 0.000027
Train Epoch: 4 [11520/104000 (11%)]	Loss: 0.000047
Train Epoch: 4 [12160/104000 (12%)]	Loss: 0.000008
Train Epoch: 4 [12800/104000 (12%)]	Loss: 0.000239
Train Epoch: 4 [13440/104000 (13%)]	Loss: 0.000015
Train Epoch: 4 [14080/104000 (14%)]	Loss: 0.000012
Train Epoch: 4 [14720/104000 (14%)]	Loss: 0.000044
Train Epoch: 4 [15360/104000 (15%)]	Loss: 0.000010
Train Epoch: 4 [16000/104000 (15%)]	Loss: 0.000021
Train Epoch: 4 [16640/104000 (16%)]	Loss: 0.000028
Train Epoch: 4 [17280/104000 (17%)]	Loss: 0.000018
Train Epoch: 4 [17920/104000 (17%)]	Loss: 0.000043
Train Epoch: 4 [18560/104000 (18%)]	Loss: 0.000007
Train Epoch: 4 [19200/104000 (18%)]	Loss: 0.000013
Train Epoch: 4 [19840/104000 (19%)]	Loss: 0.000008
Train Epoch: 4 [20480/104000 (20%)]	Loss: 0.000022
Train Epoch: 4 [21120/104000 (20%)]	Loss: 0.000032
Train Epoch: 4 [21760/104000 (21%)]	Loss: 0.000032
Train Epoch: 4 [22400/104000 (22%)]	Loss: 0.000028
Train Epoch: 4 [23040/104000 (22%)]	Loss: 0.000020
Train Epoch: 4 [23680/104000 (23%)]	Loss: 0.000015
Train Epoch: 4 [24320/104000 (23%)]	Loss: 0.000085
Train Epoch: 4 [24960/104000 (24%)]	Loss: 0.000023
Train Epoch: 4 [25600/104000 (25%)]	Loss: 0.000100
Train Epoch: 4 [26240/104000 (25%)]	Loss: 0.000014
Train Epoch: 4 [26880/104000 (26%)]	Loss: 0.000042
Train Epoch: 4 [27520/104000 (26%)]	Loss: 0.000014
Train Epoch: 4 [28160/104000 (27%)]	Loss: 0.001163
Train Epoch: 4 [28800/104000 (28%)]	Loss: 0.000009
Train Epoch: 4 [29440/104000 (28%)]	Loss: 0.000016
Train Epoch: 4 [30080/104000 (29%)]	Loss: 0.000017
Train Epoch: 4 [30720/104000 (30%)]	Loss: 0.000026
Train Epoch: 4 [31360/104000 (30%)]	Loss: 0.000020
Train Epoch: 4 [32000/104000 (31%)]	Loss: 0.000164
Train Epoch: 4 [32640/104000 (31%)]	Loss: 0.000022
Train Epoch: 4 [33280/104000 (32%)]	Loss: 0.000019
Train Epoch: 4 [33920/104000 (33%)]	Loss: 0.000013
Train Epoch: 4 [34560/104000 (33%)]	Loss: 0.000015
Train Epoch: 4 [35200/104000 (34%)]	Loss: 0.000013
Train Epoch: 4 [35840/104000 (34%)]	Loss: 0.000013
Train Epoch: 4 [36480/104000 (35%)]	Loss: 0.000010
Train Epoch: 4 [37120/104000 (36%)]	Loss: 0.000013
Train Epoch: 4 [37760/104000 (36%)]	Loss: 0.000013
Train Epoch: 4 [38400/104000 (37%)]	Loss: 0.000019
Train Epoch: 4 [39040/104000 (38%)]	Loss: 0.000032
Train Epoch: 4 [39680/104000 (38%)]	Loss: 0.000092
Train Epoch: 4 [40320/104000 (39%)]	Loss: 0.000040
Train Epoch: 4 [40960/104000 (39%)]	Loss: 0.000012
Train Epoch: 4 [41600/104000 (40%)]	Loss: 0.000007
Train Epoch: 4 [42240/104000 (41%)]	Loss: 0.000022
Train Epoch: 4 [42880/104000 (41%)]	Loss: 0.000031
Train Epoch: 4 [43520/104000 (42%)]	Loss: 0.000011
Train Epoch: 4 [44160/104000 (42%)]	Loss: 0.000020
Train Epoch: 4 [44800/104000 (43%)]	Loss: 0.000069
Train Epoch: 4 [45440/104000 (44%)]	Loss: 0.000010
Train Epoch: 4 [46080/104000 (44%)]	Loss: 0.000019
Train Epoch: 4 [46720/104000 (45%)]	Loss: 0.000154
Train Epoch: 4 [47360/104000 (46%)]	Loss: 0.000017
Train Epoch: 4 [48000/104000 (46%)]	Loss: 0.000012
Train Epoch: 4 [48640/104000 (47%)]	Loss: 0.000029
Train Epoch: 4 [49280/104000 (47%)]	Loss: 0.000184
Train Epoch: 4 [49920/104000 (48%)]	Loss: 0.000031
Train Epoch: 4 [50560/104000 (49%)]	Loss: 0.000011
Train Epoch: 4 [51200/104000 (49%)]	Loss: 0.000022
Train Epoch: 4 [51840/104000 (50%)]	Loss: 0.000031
Train Epoch: 4 [52480/104000 (50%)]	Loss: 0.000010
Train Epoch: 4 [53120/104000 (51%)]	Loss: 0.000016
Train Epoch: 4 [53760/104000 (52%)]	Loss: 0.000020
Train Epoch: 4 [54400/104000 (52%)]	Loss: 0.000019
Train Epoch: 4 [55040/104000 (53%)]	Loss: 0.000046
Train Epoch: 4 [55680/104000 (54%)]	Loss: 0.000026
Train Epoch: 4 [56320/104000 (54%)]	Loss: 0.000038
Train Epoch: 4 [56960/104000 (55%)]	Loss: 0.000046
Train Epoch: 4 [57600/104000 (55%)]	Loss: 0.000009
Train Epoch: 4 [58240/104000 (56%)]	Loss: 0.000021
Train Epoch: 4 [58880/104000 (57%)]	Loss: 0.000009
Train Epoch: 4 [59520/104000 (57%)]	Loss: 0.000015
Train Epoch: 4 [60160/104000 (58%)]	Loss: 0.000008
Train Epoch: 4 [60800/104000 (58%)]	Loss: 0.000025
Train Epoch: 4 [61440/104000 (59%)]	Loss: 0.000094
Train Epoch: 4 [62080/104000 (60%)]	Loss: 0.000016
Train Epoch: 4 [62720/104000 (60%)]	Loss: 0.000012
Train Epoch: 4 [63360/104000 (61%)]	Loss: 0.000015
Train Epoch: 4 [64000/104000 (62%)]	Loss: 0.000009
Train Epoch: 4 [64640/104000 (62%)]	Loss: 0.000030
Train Epoch: 4 [65280/104000 (63%)]	Loss: 0.000016
Train Epoch: 4 [65920/104000 (63%)]	Loss: 0.000013
Train Epoch: 4 [66560/104000 (64%)]	Loss: 0.000016
Train Epoch: 4 [67200/104000 (65%)]	Loss: 0.000012
Train Epoch: 4 [67840/104000 (65%)]	Loss: 0.000020
Train Epoch: 4 [68480/104000 (66%)]	Loss: 0.000016
Train Epoch: 4 [69120/104000 (66%)]	Loss: 0.000011
Train Epoch: 4 [69760/104000 (67%)]	Loss: 0.000009
Train Epoch: 4 [70400/104000 (68%)]	Loss: 0.000031
Train Epoch: 4 [71040/104000 (68%)]	Loss: 0.000011
Train Epoch: 4 [71680/104000 (69%)]	Loss: 0.000090
Train Epoch: 4 [72320/104000 (70%)]	Loss: 0.000005
Train Epoch: 4 [72960/104000 (70%)]	Loss: 0.000012
Train Epoch: 4 [73600/104000 (71%)]	Loss: 0.000011
Train Epoch: 4 [74240/104000 (71%)]	Loss: 0.000013
Train Epoch: 4 [74880/104000 (72%)]	Loss: 0.000009
Train Epoch: 4 [75520/104000 (73%)]	Loss: 0.000018
Train Epoch: 4 [76160/104000 (73%)]	Loss: 0.000129
Train Epoch: 4 [76800/104000 (74%)]	Loss: 0.000013
Train Epoch: 4 [77440/104000 (74%)]	Loss: 0.000029
Train Epoch: 4 [78080/104000 (75%)]	Loss: 0.000009
Train Epoch: 4 [78720/104000 (76%)]	Loss: 0.000013
Train Epoch: 4 [79360/104000 (76%)]	Loss: 0.000047
Train Epoch: 4 [80000/104000 (77%)]	Loss: 0.000168
Train Epoch: 4 [80640/104000 (78%)]	Loss: 0.000032
Train Epoch: 4 [81280/104000 (78%)]	Loss: 0.000010
Train Epoch: 4 [81920/104000 (79%)]	Loss: 0.000206
Train Epoch: 4 [82560/104000 (79%)]	Loss: 0.000019
Train Epoch: 4 [83200/104000 (80%)]	Loss: 0.000007
Train Epoch: 4 [83840/104000 (81%)]	Loss: 0.000030
Train Epoch: 4 [84480/104000 (81%)]	Loss: 0.000020
Train Epoch: 4 [85120/104000 (82%)]	Loss: 0.000006
Train Epoch: 4 [85760/104000 (82%)]	Loss: 0.000145
Train Epoch: 4 [86400/104000 (83%)]	Loss: 0.000008
Train Epoch: 4 [87040/104000 (84%)]	Loss: 0.000009
Train Epoch: 4 [87680/104000 (84%)]	Loss: 0.000023
Train Epoch: 4 [88320/104000 (85%)]	Loss: 0.000037
Train Epoch: 4 [88960/104000 (86%)]	Loss: 0.000027
Train Epoch: 4 [89600/104000 (86%)]	Loss: 0.000007
Train Epoch: 4 [90240/104000 (87%)]	Loss: 0.000008
Train Epoch: 4 [90880/104000 (87%)]	Loss: 0.000007
Train Epoch: 4 [91520/104000 (88%)]	Loss: 0.000005
Train Epoch: 4 [92160/104000 (89%)]	Loss: 0.000027
Train Epoch: 4 [92800/104000 (89%)]	Loss: 0.000008
Train Epoch: 4 [93440/104000 (90%)]	Loss: 0.000006
Train Epoch: 4 [94080/104000 (90%)]	Loss: 0.000006
Train Epoch: 4 [94720/104000 (91%)]	Loss: 0.000043
Train Epoch: 4 [95360/104000 (92%)]	Loss: 0.000011
Train Epoch: 4 [96000/104000 (92%)]	Loss: 0.000014
Train Epoch: 4 [96640/104000 (93%)]	Loss: 0.000015
Train Epoch: 4 [97280/104000 (94%)]	Loss: 0.000606
Train Epoch: 4 [97920/104000 (94%)]	Loss: 0.000010
Train Epoch: 4 [98560/104000 (95%)]	Loss: 0.000019
Train Epoch: 4 [99200/104000 (95%)]	Loss: 0.000034
Train Epoch: 4 [99840/104000 (96%)]	Loss: 0.000071
Train Epoch: 4 [100480/104000 (97%)]	Loss: 0.000017
Train Epoch: 4 [101120/104000 (97%)]	Loss: 0.000009
Train Epoch: 4 [101760/104000 (98%)]	Loss: 0.000028
Train Epoch: 4 [102400/104000 (98%)]	Loss: 0.000041
Train Epoch: 4 [103040/104000 (99%)]	Loss: 0.000083
Train Epoch: 4 [103680/104000 (100%)]	Loss: 0.000031

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.2675, Accuracy: 26228/30000 (87%)

Train Epoch: 5 [0/104000 (0%)]	Loss: 0.000007
Train Epoch: 5 [640/104000 (1%)]	Loss: 0.000041
Train Epoch: 5 [1280/104000 (1%)]	Loss: 0.000008
Train Epoch: 5 [1920/104000 (2%)]	Loss: 0.000036
Train Epoch: 5 [2560/104000 (2%)]	Loss: 0.000021
Train Epoch: 5 [3200/104000 (3%)]	Loss: 0.000008
Train Epoch: 5 [3840/104000 (4%)]	Loss: 0.000028
Train Epoch: 5 [4480/104000 (4%)]	Loss: 0.000006
Train Epoch: 5 [5120/104000 (5%)]	Loss: 0.000010
Train Epoch: 5 [5760/104000 (6%)]	Loss: 0.000012
Train Epoch: 5 [6400/104000 (6%)]	Loss: 0.000018
Train Epoch: 5 [7040/104000 (7%)]	Loss: 0.000009
Train Epoch: 5 [7680/104000 (7%)]	Loss: 0.000039
Train Epoch: 5 [8320/104000 (8%)]	Loss: 0.000024
Train Epoch: 5 [8960/104000 (9%)]	Loss: 0.000007
Train Epoch: 5 [9600/104000 (9%)]	Loss: 0.000015
Train Epoch: 5 [10240/104000 (10%)]	Loss: 0.000010
Train Epoch: 5 [10880/104000 (10%)]	Loss: 0.000049
Train Epoch: 5 [11520/104000 (11%)]	Loss: 0.000212
Train Epoch: 5 [12160/104000 (12%)]	Loss: 0.000006
Train Epoch: 5 [12800/104000 (12%)]	Loss: 0.000050
Train Epoch: 5 [13440/104000 (13%)]	Loss: 0.000028
Train Epoch: 5 [14080/104000 (14%)]	Loss: 0.000006
Train Epoch: 5 [14720/104000 (14%)]	Loss: 0.000069
Train Epoch: 5 [15360/104000 (15%)]	Loss: 0.000053
Train Epoch: 5 [16000/104000 (15%)]	Loss: 0.000056
Train Epoch: 5 [16640/104000 (16%)]	Loss: 0.000007
Train Epoch: 5 [17280/104000 (17%)]	Loss: 0.000014
Train Epoch: 5 [17920/104000 (17%)]	Loss: 0.000007
Train Epoch: 5 [18560/104000 (18%)]	Loss: 0.000039
Train Epoch: 5 [19200/104000 (18%)]	Loss: 0.000129
Train Epoch: 5 [19840/104000 (19%)]	Loss: 0.000129
Train Epoch: 5 [20480/104000 (20%)]	Loss: 0.000007
Train Epoch: 5 [21120/104000 (20%)]	Loss: 0.000004
Train Epoch: 5 [21760/104000 (21%)]	Loss: 0.000152
Train Epoch: 5 [22400/104000 (22%)]	Loss: 0.000016
Train Epoch: 5 [23040/104000 (22%)]	Loss: 0.000135
Train Epoch: 5 [23680/104000 (23%)]	Loss: 0.000028
Train Epoch: 5 [24320/104000 (23%)]	Loss: 0.000009
Train Epoch: 5 [24960/104000 (24%)]	Loss: 0.000038
Train Epoch: 5 [25600/104000 (25%)]	Loss: 0.000011
Train Epoch: 5 [26240/104000 (25%)]	Loss: 0.000037
Train Epoch: 5 [26880/104000 (26%)]	Loss: 0.000010
Train Epoch: 5 [27520/104000 (26%)]	Loss: 0.000148
Train Epoch: 5 [28160/104000 (27%)]	Loss: 0.000010
Train Epoch: 5 [28800/104000 (28%)]	Loss: 0.000007
Train Epoch: 5 [29440/104000 (28%)]	Loss: 0.000074
Train Epoch: 5 [30080/104000 (29%)]	Loss: 0.000012
Train Epoch: 5 [30720/104000 (30%)]	Loss: 0.000011
Train Epoch: 5 [31360/104000 (30%)]	Loss: 0.000004
Train Epoch: 5 [32000/104000 (31%)]	Loss: 0.000009
Train Epoch: 5 [32640/104000 (31%)]	Loss: 0.000005
Train Epoch: 5 [33280/104000 (32%)]	Loss: 0.000016
Train Epoch: 5 [33920/104000 (33%)]	Loss: 0.000018
Train Epoch: 5 [34560/104000 (33%)]	Loss: 0.000005
Train Epoch: 5 [35200/104000 (34%)]	Loss: 0.000099
Train Epoch: 5 [35840/104000 (34%)]	Loss: 0.000007
Train Epoch: 5 [36480/104000 (35%)]	Loss: 0.000010
Train Epoch: 5 [37120/104000 (36%)]	Loss: 0.000011
Train Epoch: 5 [37760/104000 (36%)]	Loss: 0.000010
Train Epoch: 5 [38400/104000 (37%)]	Loss: 0.000005
Train Epoch: 5 [39040/104000 (38%)]	Loss: 0.000011
Train Epoch: 5 [39680/104000 (38%)]	Loss: 0.000092
Train Epoch: 5 [40320/104000 (39%)]	Loss: 0.000021
Train Epoch: 5 [40960/104000 (39%)]	Loss: 0.000015
Train Epoch: 5 [41600/104000 (40%)]	Loss: 0.000048
Train Epoch: 5 [42240/104000 (41%)]	Loss: 0.000017
Train Epoch: 5 [42880/104000 (41%)]	Loss: 0.000011
Train Epoch: 5 [43520/104000 (42%)]	Loss: 0.000023
Train Epoch: 5 [44160/104000 (42%)]	Loss: 0.000005
Train Epoch: 5 [44800/104000 (43%)]	Loss: 0.000006
Train Epoch: 5 [45440/104000 (44%)]	Loss: 0.000006
Train Epoch: 5 [46080/104000 (44%)]	Loss: 0.000045
Train Epoch: 5 [46720/104000 (45%)]	Loss: 0.000008
Train Epoch: 5 [47360/104000 (46%)]	Loss: 0.000102
Train Epoch: 5 [48000/104000 (46%)]	Loss: 0.000014
Train Epoch: 5 [48640/104000 (47%)]	Loss: 0.000264
Train Epoch: 5 [49280/104000 (47%)]	Loss: 0.000121
Train Epoch: 5 [49920/104000 (48%)]	Loss: 0.000008
Train Epoch: 5 [50560/104000 (49%)]	Loss: 0.000014
Train Epoch: 5 [51200/104000 (49%)]	Loss: 0.000006
Train Epoch: 5 [51840/104000 (50%)]	Loss: 0.000008
Train Epoch: 5 [52480/104000 (50%)]	Loss: 0.000027
Train Epoch: 5 [53120/104000 (51%)]	Loss: 0.000009
Train Epoch: 5 [53760/104000 (52%)]	Loss: 0.000010
Train Epoch: 5 [54400/104000 (52%)]	Loss: 0.000007
Train Epoch: 5 [55040/104000 (53%)]	Loss: 0.000025
Train Epoch: 5 [55680/104000 (54%)]	Loss: 0.000319
Train Epoch: 5 [56320/104000 (54%)]	Loss: 0.000014
Train Epoch: 5 [56960/104000 (55%)]	Loss: 0.000090
Train Epoch: 5 [57600/104000 (55%)]	Loss: 0.000015
Train Epoch: 5 [58240/104000 (56%)]	Loss: 0.000012
Train Epoch: 5 [58880/104000 (57%)]	Loss: 0.000005
Train Epoch: 5 [59520/104000 (57%)]	Loss: 0.000005
Train Epoch: 5 [60160/104000 (58%)]	Loss: 0.000009
Train Epoch: 5 [60800/104000 (58%)]	Loss: 0.000004
Train Epoch: 5 [61440/104000 (59%)]	Loss: 0.000007
Train Epoch: 5 [62080/104000 (60%)]	Loss: 0.000011
Train Epoch: 5 [62720/104000 (60%)]	Loss: 0.000056
Train Epoch: 5 [63360/104000 (61%)]	Loss: 0.000004
Train Epoch: 5 [64000/104000 (62%)]	Loss: 0.000020
Train Epoch: 5 [64640/104000 (62%)]	Loss: 0.000007
Train Epoch: 5 [65280/104000 (63%)]	Loss: 0.000007
Train Epoch: 5 [65920/104000 (63%)]	Loss: 0.000006
Train Epoch: 5 [66560/104000 (64%)]	Loss: 0.000005
Train Epoch: 5 [67200/104000 (65%)]	Loss: 0.000010
Train Epoch: 5 [67840/104000 (65%)]	Loss: 0.000008
Train Epoch: 5 [68480/104000 (66%)]	Loss: 0.000003
Train Epoch: 5 [69120/104000 (66%)]	Loss: 0.000007
Train Epoch: 5 [69760/104000 (67%)]	Loss: 0.000008
Train Epoch: 5 [70400/104000 (68%)]	Loss: 0.000041
Train Epoch: 5 [71040/104000 (68%)]	Loss: 0.000101
Train Epoch: 5 [71680/104000 (69%)]	Loss: 0.000037
Train Epoch: 5 [72320/104000 (70%)]	Loss: 0.000152
Train Epoch: 5 [72960/104000 (70%)]	Loss: 0.000005
Train Epoch: 5 [73600/104000 (71%)]	Loss: 0.000027
Train Epoch: 5 [74240/104000 (71%)]	Loss: 0.000075
Train Epoch: 5 [74880/104000 (72%)]	Loss: 0.000018
Train Epoch: 5 [75520/104000 (73%)]	Loss: 0.000016
Train Epoch: 5 [76160/104000 (73%)]	Loss: 0.000011
Train Epoch: 5 [76800/104000 (74%)]	Loss: 0.000030
Train Epoch: 5 [77440/104000 (74%)]	Loss: 0.000010
Train Epoch: 5 [78080/104000 (75%)]	Loss: 0.000016
Train Epoch: 5 [78720/104000 (76%)]	Loss: 0.000020
Train Epoch: 5 [79360/104000 (76%)]	Loss: 0.000009
Train Epoch: 5 [80000/104000 (77%)]	Loss: 0.000004
Train Epoch: 5 [80640/104000 (78%)]	Loss: 0.000022
Train Epoch: 5 [81280/104000 (78%)]	Loss: 0.000017
Train Epoch: 5 [81920/104000 (79%)]	Loss: 0.000007
Train Epoch: 5 [82560/104000 (79%)]	Loss: 0.000021
Train Epoch: 5 [83200/104000 (80%)]	Loss: 0.000077
Train Epoch: 5 [83840/104000 (81%)]	Loss: 0.000032
Train Epoch: 5 [84480/104000 (81%)]	Loss: 0.000082
Train Epoch: 5 [85120/104000 (82%)]	Loss: 0.000011
Train Epoch: 5 [85760/104000 (82%)]	Loss: 0.000005
Train Epoch: 5 [86400/104000 (83%)]	Loss: 0.000006
Train Epoch: 5 [87040/104000 (84%)]	Loss: 0.000044
Train Epoch: 5 [87680/104000 (84%)]	Loss: 0.000006
Train Epoch: 5 [88320/104000 (85%)]	Loss: 0.000008
Train Epoch: 5 [88960/104000 (86%)]	Loss: 0.000003
Train Epoch: 5 [89600/104000 (86%)]	Loss: 0.000012
Train Epoch: 5 [90240/104000 (87%)]	Loss: 0.000006
Train Epoch: 5 [90880/104000 (87%)]	Loss: 0.000006
Train Epoch: 5 [91520/104000 (88%)]	Loss: 0.000016
Train Epoch: 5 [92160/104000 (89%)]	Loss: 0.000060
Train Epoch: 5 [92800/104000 (89%)]	Loss: 0.000008
Train Epoch: 5 [93440/104000 (90%)]	Loss: 0.000006
Train Epoch: 5 [94080/104000 (90%)]	Loss: 0.000015
Train Epoch: 5 [94720/104000 (91%)]	Loss: 0.000014
Train Epoch: 5 [95360/104000 (92%)]	Loss: 0.000018
Train Epoch: 5 [96000/104000 (92%)]	Loss: 0.000006
Train Epoch: 5 [96640/104000 (93%)]	Loss: 0.000009
Train Epoch: 5 [97280/104000 (94%)]	Loss: 0.000008
Train Epoch: 5 [97920/104000 (94%)]	Loss: 0.000025
Train Epoch: 5 [98560/104000 (95%)]	Loss: 0.000006
Train Epoch: 5 [99200/104000 (95%)]	Loss: 0.000008
Train Epoch: 5 [99840/104000 (96%)]	Loss: 0.000038
Train Epoch: 5 [100480/104000 (97%)]	Loss: 0.000004
Train Epoch: 5 [101120/104000 (97%)]	Loss: 0.000020
Train Epoch: 5 [101760/104000 (98%)]	Loss: 0.000006
Train Epoch: 5 [102400/104000 (98%)]	Loss: 0.000007
Train Epoch: 5 [103040/104000 (99%)]	Loss: 0.000044
Train Epoch: 5 [103680/104000 (100%)]	Loss: 0.000092

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.1840, Accuracy: 27434/30000 (91%)

[100.0, 100.0]
[88.56, 91.44666666666667]
The number of neurons in CNN layer is 10
Train Epoch: 1 [0/104000 (0%)]	Loss: 0.552990
Train Epoch: 1 [640/104000 (1%)]	Loss: 0.007941
Train Epoch: 1 [1280/104000 (1%)]	Loss: 0.004193
Train Epoch: 1 [1920/104000 (2%)]	Loss: 0.003162
Train Epoch: 1 [2560/104000 (2%)]	Loss: 0.002266
Train Epoch: 1 [3200/104000 (3%)]	Loss: 0.004642
Train Epoch: 1 [3840/104000 (4%)]	Loss: 0.001600
Train Epoch: 1 [4480/104000 (4%)]	Loss: 0.001422
Train Epoch: 1 [5120/104000 (5%)]	Loss: 0.001106
Train Epoch: 1 [5760/104000 (6%)]	Loss: 0.001111
Train Epoch: 1 [6400/104000 (6%)]	Loss: 0.002817
Train Epoch: 1 [7040/104000 (7%)]	Loss: 0.000887
Train Epoch: 1 [7680/104000 (7%)]	Loss: 0.000959
Train Epoch: 1 [8320/104000 (8%)]	Loss: 0.002973
Train Epoch: 1 [8960/104000 (9%)]	Loss: 0.000815
Train Epoch: 1 [9600/104000 (9%)]	Loss: 0.000668
Train Epoch: 1 [10240/104000 (10%)]	Loss: 0.000793
Train Epoch: 1 [10880/104000 (10%)]	Loss: 0.000626
Train Epoch: 1 [11520/104000 (11%)]	Loss: 0.000544
Train Epoch: 1 [12160/104000 (12%)]	Loss: 0.000493
Train Epoch: 1 [12800/104000 (12%)]	Loss: 0.001317
Train Epoch: 1 [13440/104000 (13%)]	Loss: 0.000536
Train Epoch: 1 [14080/104000 (14%)]	Loss: 0.000696
Train Epoch: 1 [14720/104000 (14%)]	Loss: 0.000400
Train Epoch: 1 [15360/104000 (15%)]	Loss: 0.000392
Train Epoch: 1 [16000/104000 (15%)]	Loss: 0.000243
Train Epoch: 1 [16640/104000 (16%)]	Loss: 0.000226
Train Epoch: 1 [17280/104000 (17%)]	Loss: 0.000268
Train Epoch: 1 [17920/104000 (17%)]	Loss: 0.000282
Train Epoch: 1 [18560/104000 (18%)]	Loss: 0.000365
Train Epoch: 1 [19200/104000 (18%)]	Loss: 0.000205
Train Epoch: 1 [19840/104000 (19%)]	Loss: 0.000307
Train Epoch: 1 [20480/104000 (20%)]	Loss: 0.000428
Train Epoch: 1 [21120/104000 (20%)]	Loss: 0.000367
Train Epoch: 1 [21760/104000 (21%)]	Loss: 0.000241
Train Epoch: 1 [22400/104000 (22%)]	Loss: 0.000208
Train Epoch: 1 [23040/104000 (22%)]	Loss: 0.000198
Train Epoch: 1 [23680/104000 (23%)]	Loss: 0.000603
Train Epoch: 1 [24320/104000 (23%)]	Loss: 0.000469
Train Epoch: 1 [24960/104000 (24%)]	Loss: 0.000194
Train Epoch: 1 [25600/104000 (25%)]	Loss: 0.000243
Train Epoch: 1 [26240/104000 (25%)]	Loss: 0.000303
Train Epoch: 1 [26880/104000 (26%)]	Loss: 0.000310
Train Epoch: 1 [27520/104000 (26%)]	Loss: 0.000347
Train Epoch: 1 [28160/104000 (27%)]	Loss: 0.000274
Train Epoch: 1 [28800/104000 (28%)]	Loss: 0.000292
Train Epoch: 1 [29440/104000 (28%)]	Loss: 0.000304
Train Epoch: 1 [30080/104000 (29%)]	Loss: 0.000176
Train Epoch: 1 [30720/104000 (30%)]	Loss: 0.000490
Train Epoch: 1 [31360/104000 (30%)]	Loss: 0.000162
Train Epoch: 1 [32000/104000 (31%)]	Loss: 0.000216
Train Epoch: 1 [32640/104000 (31%)]	Loss: 0.000513
Train Epoch: 1 [33280/104000 (32%)]	Loss: 0.000212
Train Epoch: 1 [33920/104000 (33%)]	Loss: 0.000553
Train Epoch: 1 [34560/104000 (33%)]	Loss: 0.000168
Train Epoch: 1 [35200/104000 (34%)]	Loss: 0.000135
Train Epoch: 1 [35840/104000 (34%)]	Loss: 0.000179
Train Epoch: 1 [36480/104000 (35%)]	Loss: 0.000207
Train Epoch: 1 [37120/104000 (36%)]	Loss: 0.000681
Train Epoch: 1 [37760/104000 (36%)]	Loss: 0.000148
Train Epoch: 1 [38400/104000 (37%)]	Loss: 0.000171
Train Epoch: 1 [39040/104000 (38%)]	Loss: 0.000230
Train Epoch: 1 [39680/104000 (38%)]	Loss: 0.000408
Train Epoch: 1 [40320/104000 (39%)]	Loss: 0.000163
Train Epoch: 1 [40960/104000 (39%)]	Loss: 0.000141
Train Epoch: 1 [41600/104000 (40%)]	Loss: 0.000120
Train Epoch: 1 [42240/104000 (41%)]	Loss: 0.000175
Train Epoch: 1 [42880/104000 (41%)]	Loss: 0.000258
Train Epoch: 1 [43520/104000 (42%)]	Loss: 0.000279
Train Epoch: 1 [44160/104000 (42%)]	Loss: 0.000507
Train Epoch: 1 [44800/104000 (43%)]	Loss: 0.000255
Train Epoch: 1 [45440/104000 (44%)]	Loss: 0.000092
Train Epoch: 1 [46080/104000 (44%)]	Loss: 0.000383
Train Epoch: 1 [46720/104000 (45%)]	Loss: 0.000096
Train Epoch: 1 [47360/104000 (46%)]	Loss: 0.000065
Train Epoch: 1 [48000/104000 (46%)]	Loss: 0.000147
Train Epoch: 1 [48640/104000 (47%)]	Loss: 0.000246
Train Epoch: 1 [49280/104000 (47%)]	Loss: 0.000117
Train Epoch: 1 [49920/104000 (48%)]	Loss: 0.000432
Train Epoch: 1 [50560/104000 (49%)]	Loss: 0.000127
Train Epoch: 1 [51200/104000 (49%)]	Loss: 0.000390
Train Epoch: 1 [51840/104000 (50%)]	Loss: 0.000071
Train Epoch: 1 [52480/104000 (50%)]	Loss: 0.000879
Train Epoch: 1 [53120/104000 (51%)]	Loss: 0.000102
Train Epoch: 1 [53760/104000 (52%)]	Loss: 0.000092
Train Epoch: 1 [54400/104000 (52%)]	Loss: 0.000086
Train Epoch: 1 [55040/104000 (53%)]	Loss: 0.000228
Train Epoch: 1 [55680/104000 (54%)]	Loss: 0.000131
Train Epoch: 1 [56320/104000 (54%)]	Loss: 0.000146
Train Epoch: 1 [56960/104000 (55%)]	Loss: 0.000064
Train Epoch: 1 [57600/104000 (55%)]	Loss: 0.000046
Train Epoch: 1 [58240/104000 (56%)]	Loss: 0.000090
Train Epoch: 1 [58880/104000 (57%)]	Loss: 0.000709
Train Epoch: 1 [59520/104000 (57%)]	Loss: 0.000289
Train Epoch: 1 [60160/104000 (58%)]	Loss: 0.000095
Train Epoch: 1 [60800/104000 (58%)]	Loss: 0.000108
Train Epoch: 1 [61440/104000 (59%)]	Loss: 0.000055
Train Epoch: 1 [62080/104000 (60%)]	Loss: 0.000140
Train Epoch: 1 [62720/104000 (60%)]	Loss: 0.000154
Train Epoch: 1 [63360/104000 (61%)]	Loss: 0.000128
Train Epoch: 1 [64000/104000 (62%)]	Loss: 0.000132
Train Epoch: 1 [64640/104000 (62%)]	Loss: 0.000061
Train Epoch: 1 [65280/104000 (63%)]	Loss: 0.000073
Train Epoch: 1 [65920/104000 (63%)]	Loss: 0.000036
Train Epoch: 1 [66560/104000 (64%)]	Loss: 0.000077
Train Epoch: 1 [67200/104000 (65%)]	Loss: 0.000076
Train Epoch: 1 [67840/104000 (65%)]	Loss: 0.000640
Train Epoch: 1 [68480/104000 (66%)]	Loss: 0.000084
Train Epoch: 1 [69120/104000 (66%)]	Loss: 0.000177
Train Epoch: 1 [69760/104000 (67%)]	Loss: 0.000173
Train Epoch: 1 [70400/104000 (68%)]	Loss: 0.000070
Train Epoch: 1 [71040/104000 (68%)]	Loss: 0.000653
Train Epoch: 1 [71680/104000 (69%)]	Loss: 0.000108
Train Epoch: 1 [72320/104000 (70%)]	Loss: 0.000086
Train Epoch: 1 [72960/104000 (70%)]	Loss: 0.000351
Train Epoch: 1 [73600/104000 (71%)]	Loss: 0.001071
Train Epoch: 1 [74240/104000 (71%)]	Loss: 0.000356
Train Epoch: 1 [74880/104000 (72%)]	Loss: 0.000096
Train Epoch: 1 [75520/104000 (73%)]	Loss: 0.000082
Train Epoch: 1 [76160/104000 (73%)]	Loss: 0.000059
Train Epoch: 1 [76800/104000 (74%)]	Loss: 0.000364
Train Epoch: 1 [77440/104000 (74%)]	Loss: 0.000427
Train Epoch: 1 [78080/104000 (75%)]	Loss: 0.000066
Train Epoch: 1 [78720/104000 (76%)]	Loss: 0.000175
Train Epoch: 1 [79360/104000 (76%)]	Loss: 0.002394
Train Epoch: 1 [80000/104000 (77%)]	Loss: 0.000264
Train Epoch: 1 [80640/104000 (78%)]	Loss: 0.000103
Train Epoch: 1 [81280/104000 (78%)]	Loss: 0.000052
Train Epoch: 1 [81920/104000 (79%)]	Loss: 0.000079
Train Epoch: 1 [82560/104000 (79%)]	Loss: 0.000042
Train Epoch: 1 [83200/104000 (80%)]	Loss: 0.000111
Train Epoch: 1 [83840/104000 (81%)]	Loss: 0.000067
Train Epoch: 1 [84480/104000 (81%)]	Loss: 0.000086
Train Epoch: 1 [85120/104000 (82%)]	Loss: 0.000658
Train Epoch: 1 [85760/104000 (82%)]	Loss: 0.000236
Train Epoch: 1 [86400/104000 (83%)]	Loss: 0.000054
Train Epoch: 1 [87040/104000 (84%)]	Loss: 0.000112
Train Epoch: 1 [87680/104000 (84%)]	Loss: 0.000034
Train Epoch: 1 [88320/104000 (85%)]	Loss: 0.000068
Train Epoch: 1 [88960/104000 (86%)]	Loss: 0.000254
Train Epoch: 1 [89600/104000 (86%)]	Loss: 0.000058
Train Epoch: 1 [90240/104000 (87%)]	Loss: 0.000086
Train Epoch: 1 [90880/104000 (87%)]	Loss: 0.000078
Train Epoch: 1 [91520/104000 (88%)]	Loss: 0.000045
Train Epoch: 1 [92160/104000 (89%)]	Loss: 0.000104
Train Epoch: 1 [92800/104000 (89%)]	Loss: 0.000059
Train Epoch: 1 [93440/104000 (90%)]	Loss: 0.000071
Train Epoch: 1 [94080/104000 (90%)]	Loss: 0.000035
Train Epoch: 1 [94720/104000 (91%)]	Loss: 0.000094
Train Epoch: 1 [95360/104000 (92%)]	Loss: 0.000089
Train Epoch: 1 [96000/104000 (92%)]	Loss: 0.000071
Train Epoch: 1 [96640/104000 (93%)]	Loss: 0.000202
Train Epoch: 1 [97280/104000 (94%)]	Loss: 0.000061
Train Epoch: 1 [97920/104000 (94%)]	Loss: 0.000074
Train Epoch: 1 [98560/104000 (95%)]	Loss: 0.000062
Train Epoch: 1 [99200/104000 (95%)]	Loss: 0.000194
Train Epoch: 1 [99840/104000 (96%)]	Loss: 0.000070
Train Epoch: 1 [100480/104000 (97%)]	Loss: 0.000037
Train Epoch: 1 [101120/104000 (97%)]	Loss: 0.000226
Train Epoch: 1 [101760/104000 (98%)]	Loss: 0.000044
Train Epoch: 1 [102400/104000 (98%)]	Loss: 0.000114
Train Epoch: 1 [103040/104000 (99%)]	Loss: 0.000205
Train Epoch: 1 [103680/104000 (100%)]	Loss: 0.000196

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.2509, Accuracy: 26222/30000 (87%)

Train Epoch: 2 [0/104000 (0%)]	Loss: 0.000063
Train Epoch: 2 [640/104000 (1%)]	Loss: 0.000021
Train Epoch: 2 [1280/104000 (1%)]	Loss: 0.000061
Train Epoch: 2 [1920/104000 (2%)]	Loss: 0.000033
Train Epoch: 2 [2560/104000 (2%)]	Loss: 0.000050
Train Epoch: 2 [3200/104000 (3%)]	Loss: 0.000162
Train Epoch: 2 [3840/104000 (4%)]	Loss: 0.000088
Train Epoch: 2 [4480/104000 (4%)]	Loss: 0.000035
Train Epoch: 2 [5120/104000 (5%)]	Loss: 0.000032
Train Epoch: 2 [5760/104000 (6%)]	Loss: 0.000040
Train Epoch: 2 [6400/104000 (6%)]	Loss: 0.000162
Train Epoch: 2 [7040/104000 (7%)]	Loss: 0.000177
Train Epoch: 2 [7680/104000 (7%)]	Loss: 0.000214
Train Epoch: 2 [8320/104000 (8%)]	Loss: 0.000180
Train Epoch: 2 [8960/104000 (9%)]	Loss: 0.000165
Train Epoch: 2 [9600/104000 (9%)]	Loss: 0.000072
Train Epoch: 2 [10240/104000 (10%)]	Loss: 0.000162
Train Epoch: 2 [10880/104000 (10%)]	Loss: 0.000123
Train Epoch: 2 [11520/104000 (11%)]	Loss: 0.001040
Train Epoch: 2 [12160/104000 (12%)]	Loss: 0.000254
Train Epoch: 2 [12800/104000 (12%)]	Loss: 0.000028
Train Epoch: 2 [13440/104000 (13%)]	Loss: 0.000101
Train Epoch: 2 [14080/104000 (14%)]	Loss: 0.000040
Train Epoch: 2 [14720/104000 (14%)]	Loss: 0.000044
Train Epoch: 2 [15360/104000 (15%)]	Loss: 0.000090
Train Epoch: 2 [16000/104000 (15%)]	Loss: 0.000027
Train Epoch: 2 [16640/104000 (16%)]	Loss: 0.000049
Train Epoch: 2 [17280/104000 (17%)]	Loss: 0.000255
Train Epoch: 2 [17920/104000 (17%)]	Loss: 0.000031
Train Epoch: 2 [18560/104000 (18%)]	Loss: 0.000058
Train Epoch: 2 [19200/104000 (18%)]	Loss: 0.000033
Train Epoch: 2 [19840/104000 (19%)]	Loss: 0.000069
Train Epoch: 2 [20480/104000 (20%)]	Loss: 0.000062
Train Epoch: 2 [21120/104000 (20%)]	Loss: 0.000122
Train Epoch: 2 [21760/104000 (21%)]	Loss: 0.000029
Train Epoch: 2 [22400/104000 (22%)]	Loss: 0.000041
Train Epoch: 2 [23040/104000 (22%)]	Loss: 0.000041
Train Epoch: 2 [23680/104000 (23%)]	Loss: 0.000193
Train Epoch: 2 [24320/104000 (23%)]	Loss: 0.000052
Train Epoch: 2 [24960/104000 (24%)]	Loss: 0.000081
Train Epoch: 2 [25600/104000 (25%)]	Loss: 0.000166
Train Epoch: 2 [26240/104000 (25%)]	Loss: 0.000352
Train Epoch: 2 [26880/104000 (26%)]	Loss: 0.000046
Train Epoch: 2 [27520/104000 (26%)]	Loss: 0.000048
Train Epoch: 2 [28160/104000 (27%)]	Loss: 0.000032
Train Epoch: 2 [28800/104000 (28%)]	Loss: 0.000053
Train Epoch: 2 [29440/104000 (28%)]	Loss: 0.000113
Train Epoch: 2 [30080/104000 (29%)]	Loss: 0.000038
Train Epoch: 2 [30720/104000 (30%)]	Loss: 0.000189
Train Epoch: 2 [31360/104000 (30%)]	Loss: 0.000024
Train Epoch: 2 [32000/104000 (31%)]	Loss: 0.000050
Train Epoch: 2 [32640/104000 (31%)]	Loss: 0.000031
Train Epoch: 2 [33280/104000 (32%)]	Loss: 0.000033
Train Epoch: 2 [33920/104000 (33%)]	Loss: 0.000019
Train Epoch: 2 [34560/104000 (33%)]	Loss: 0.000031
Train Epoch: 2 [35200/104000 (34%)]	Loss: 0.000018
Train Epoch: 2 [35840/104000 (34%)]	Loss: 0.000035
Train Epoch: 2 [36480/104000 (35%)]	Loss: 0.000073
Train Epoch: 2 [37120/104000 (36%)]	Loss: 0.000027
Train Epoch: 2 [37760/104000 (36%)]	Loss: 0.000078
Train Epoch: 2 [38400/104000 (37%)]	Loss: 0.000035
Train Epoch: 2 [39040/104000 (38%)]	Loss: 0.000155
Train Epoch: 2 [39680/104000 (38%)]	Loss: 0.000087
Train Epoch: 2 [40320/104000 (39%)]	Loss: 0.000079
Train Epoch: 2 [40960/104000 (39%)]	Loss: 0.000032
Train Epoch: 2 [41600/104000 (40%)]	Loss: 0.000029
Train Epoch: 2 [42240/104000 (41%)]	Loss: 0.000381
Train Epoch: 2 [42880/104000 (41%)]	Loss: 0.000389
Train Epoch: 2 [43520/104000 (42%)]	Loss: 0.000030
Train Epoch: 2 [44160/104000 (42%)]	Loss: 0.000078
Train Epoch: 2 [44800/104000 (43%)]	Loss: 0.000212
Train Epoch: 2 [45440/104000 (44%)]	Loss: 0.000054
Train Epoch: 2 [46080/104000 (44%)]	Loss: 0.000039
Train Epoch: 2 [46720/104000 (45%)]	Loss: 0.000423
Train Epoch: 2 [47360/104000 (46%)]	Loss: 0.000092
Train Epoch: 2 [48000/104000 (46%)]	Loss: 0.000078
Train Epoch: 2 [48640/104000 (47%)]	Loss: 0.000043
Train Epoch: 2 [49280/104000 (47%)]	Loss: 0.000024
Train Epoch: 2 [49920/104000 (48%)]	Loss: 0.000043
Train Epoch: 2 [50560/104000 (49%)]	Loss: 0.000034
Train Epoch: 2 [51200/104000 (49%)]	Loss: 0.000050
Train Epoch: 2 [51840/104000 (50%)]	Loss: 0.000021
Train Epoch: 2 [52480/104000 (50%)]	Loss: 0.000034
Train Epoch: 2 [53120/104000 (51%)]	Loss: 0.000017
Train Epoch: 2 [53760/104000 (52%)]	Loss: 0.000113
Train Epoch: 2 [54400/104000 (52%)]	Loss: 0.000026
Train Epoch: 2 [55040/104000 (53%)]	Loss: 0.000175
Train Epoch: 2 [55680/104000 (54%)]	Loss: 0.000030
Train Epoch: 2 [56320/104000 (54%)]	Loss: 0.000022
Train Epoch: 2 [56960/104000 (55%)]	Loss: 0.000033
Train Epoch: 2 [57600/104000 (55%)]	Loss: 0.000039
Train Epoch: 2 [58240/104000 (56%)]	Loss: 0.000300
Train Epoch: 2 [58880/104000 (57%)]	Loss: 0.000085
Train Epoch: 2 [59520/104000 (57%)]	Loss: 0.000020
Train Epoch: 2 [60160/104000 (58%)]	Loss: 0.000019
Train Epoch: 2 [60800/104000 (58%)]	Loss: 0.000030
Train Epoch: 2 [61440/104000 (59%)]	Loss: 0.000020
Train Epoch: 2 [62080/104000 (60%)]	Loss: 0.000033
Train Epoch: 2 [62720/104000 (60%)]	Loss: 0.000115
Train Epoch: 2 [63360/104000 (61%)]	Loss: 0.000040
Train Epoch: 2 [64000/104000 (62%)]	Loss: 0.000037
Train Epoch: 2 [64640/104000 (62%)]	Loss: 0.000128
Train Epoch: 2 [65280/104000 (63%)]	Loss: 0.000017
Train Epoch: 2 [65920/104000 (63%)]	Loss: 0.000029
Train Epoch: 2 [66560/104000 (64%)]	Loss: 0.000038
Train Epoch: 2 [67200/104000 (65%)]	Loss: 0.000085
Train Epoch: 2 [67840/104000 (65%)]	Loss: 0.000018
Train Epoch: 2 [68480/104000 (66%)]	Loss: 0.000035
Train Epoch: 2 [69120/104000 (66%)]	Loss: 0.000018
Train Epoch: 2 [69760/104000 (67%)]	Loss: 0.000039
Train Epoch: 2 [70400/104000 (68%)]	Loss: 0.000032
Train Epoch: 2 [71040/104000 (68%)]	Loss: 0.000022
Train Epoch: 2 [71680/104000 (69%)]	Loss: 0.000065
Train Epoch: 2 [72320/104000 (70%)]	Loss: 0.000018
Train Epoch: 2 [72960/104000 (70%)]	Loss: 0.000109
Train Epoch: 2 [73600/104000 (71%)]	Loss: 0.000032
Train Epoch: 2 [74240/104000 (71%)]	Loss: 0.000052
Train Epoch: 2 [74880/104000 (72%)]	Loss: 0.000016
Train Epoch: 2 [75520/104000 (73%)]	Loss: 0.000040
Train Epoch: 2 [76160/104000 (73%)]	Loss: 0.000012
Train Epoch: 2 [76800/104000 (74%)]	Loss: 0.000019
Train Epoch: 2 [77440/104000 (74%)]	Loss: 0.000031
Train Epoch: 2 [78080/104000 (75%)]	Loss: 0.000105
Train Epoch: 2 [78720/104000 (76%)]	Loss: 0.000023
Train Epoch: 2 [79360/104000 (76%)]	Loss: 0.000011
Train Epoch: 2 [80000/104000 (77%)]	Loss: 0.000022
Train Epoch: 2 [80640/104000 (78%)]	Loss: 0.000539
Train Epoch: 2 [81280/104000 (78%)]	Loss: 0.000010
Train Epoch: 2 [81920/104000 (79%)]	Loss: 0.000049
Train Epoch: 2 [82560/104000 (79%)]	Loss: 0.000022
Train Epoch: 2 [83200/104000 (80%)]	Loss: 0.000019
Train Epoch: 2 [83840/104000 (81%)]	Loss: 0.000025
Train Epoch: 2 [84480/104000 (81%)]	Loss: 0.000018
Train Epoch: 2 [85120/104000 (82%)]	Loss: 0.000029
Train Epoch: 2 [85760/104000 (82%)]	Loss: 0.000024
Train Epoch: 2 [86400/104000 (83%)]	Loss: 0.000064
Train Epoch: 2 [87040/104000 (84%)]	Loss: 0.000017
Train Epoch: 2 [87680/104000 (84%)]	Loss: 0.000039
Train Epoch: 2 [88320/104000 (85%)]	Loss: 0.000042
Train Epoch: 2 [88960/104000 (86%)]	Loss: 0.000021
Train Epoch: 2 [89600/104000 (86%)]	Loss: 0.000062
Train Epoch: 2 [90240/104000 (87%)]	Loss: 0.000053
Train Epoch: 2 [90880/104000 (87%)]	Loss: 0.000026
Train Epoch: 2 [91520/104000 (88%)]	Loss: 0.000050
Train Epoch: 2 [92160/104000 (89%)]	Loss: 0.000041
Train Epoch: 2 [92800/104000 (89%)]	Loss: 0.000024
Train Epoch: 2 [93440/104000 (90%)]	Loss: 0.000024
Train Epoch: 2 [94080/104000 (90%)]	Loss: 0.000011
Train Epoch: 2 [94720/104000 (91%)]	Loss: 0.000027
Train Epoch: 2 [95360/104000 (92%)]	Loss: 0.000136
Train Epoch: 2 [96000/104000 (92%)]	Loss: 0.000015
Train Epoch: 2 [96640/104000 (93%)]	Loss: 0.000066
Train Epoch: 2 [97280/104000 (94%)]	Loss: 0.000221
Train Epoch: 2 [97920/104000 (94%)]	Loss: 0.000067
Train Epoch: 2 [98560/104000 (95%)]	Loss: 0.000014
Train Epoch: 2 [99200/104000 (95%)]	Loss: 0.000091
Train Epoch: 2 [99840/104000 (96%)]	Loss: 0.000065
Train Epoch: 2 [100480/104000 (97%)]	Loss: 0.000011
Train Epoch: 2 [101120/104000 (97%)]	Loss: 0.000034
Train Epoch: 2 [101760/104000 (98%)]	Loss: 0.000043
Train Epoch: 2 [102400/104000 (98%)]	Loss: 0.000216
Train Epoch: 2 [103040/104000 (99%)]	Loss: 0.000035
Train Epoch: 2 [103680/104000 (100%)]	Loss: 0.000032

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.2632, Accuracy: 26118/30000 (87%)

Train Epoch: 3 [0/104000 (0%)]	Loss: 0.000013
Train Epoch: 3 [640/104000 (1%)]	Loss: 0.000040
Train Epoch: 3 [1280/104000 (1%)]	Loss: 0.000020
Train Epoch: 3 [1920/104000 (2%)]	Loss: 0.000028
Train Epoch: 3 [2560/104000 (2%)]	Loss: 0.000020
Train Epoch: 3 [3200/104000 (3%)]	Loss: 0.000021
Train Epoch: 3 [3840/104000 (4%)]	Loss: 0.000022
Train Epoch: 3 [4480/104000 (4%)]	Loss: 0.000078
Train Epoch: 3 [5120/104000 (5%)]	Loss: 0.000067
Train Epoch: 3 [5760/104000 (6%)]	Loss: 0.000024
Train Epoch: 3 [6400/104000 (6%)]	Loss: 0.000128
Train Epoch: 3 [7040/104000 (7%)]	Loss: 0.000016
Train Epoch: 3 [7680/104000 (7%)]	Loss: 0.000036
Train Epoch: 3 [8320/104000 (8%)]	Loss: 0.000023
Train Epoch: 3 [8960/104000 (9%)]	Loss: 0.000034
Train Epoch: 3 [9600/104000 (9%)]	Loss: 0.000033
Train Epoch: 3 [10240/104000 (10%)]	Loss: 0.000048
Train Epoch: 3 [10880/104000 (10%)]	Loss: 0.000074
Train Epoch: 3 [11520/104000 (11%)]	Loss: 0.000023
Train Epoch: 3 [12160/104000 (12%)]	Loss: 0.000009
Train Epoch: 3 [12800/104000 (12%)]	Loss: 0.000012
Train Epoch: 3 [13440/104000 (13%)]	Loss: 0.000043
Train Epoch: 3 [14080/104000 (14%)]	Loss: 0.000050
Train Epoch: 3 [14720/104000 (14%)]	Loss: 0.000025
Train Epoch: 3 [15360/104000 (15%)]	Loss: 0.000066
Train Epoch: 3 [16000/104000 (15%)]	Loss: 0.000021
Train Epoch: 3 [16640/104000 (16%)]	Loss: 0.000028
Train Epoch: 3 [17280/104000 (17%)]	Loss: 0.000069
Train Epoch: 3 [17920/104000 (17%)]	Loss: 0.000577
Train Epoch: 3 [18560/104000 (18%)]	Loss: 0.000056
Train Epoch: 3 [19200/104000 (18%)]	Loss: 0.000051
Train Epoch: 3 [19840/104000 (19%)]	Loss: 0.000024
Train Epoch: 3 [20480/104000 (20%)]	Loss: 0.000044
Train Epoch: 3 [21120/104000 (20%)]	Loss: 0.000008
Train Epoch: 3 [21760/104000 (21%)]	Loss: 0.000084
Train Epoch: 3 [22400/104000 (22%)]	Loss: 0.000015
Train Epoch: 3 [23040/104000 (22%)]	Loss: 0.000048
Train Epoch: 3 [23680/104000 (23%)]	Loss: 0.000087
Train Epoch: 3 [24320/104000 (23%)]	Loss: 0.000081
Train Epoch: 3 [24960/104000 (24%)]	Loss: 0.000085
Train Epoch: 3 [25600/104000 (25%)]	Loss: 0.000049
Train Epoch: 3 [26240/104000 (25%)]	Loss: 0.000016
Train Epoch: 3 [26880/104000 (26%)]	Loss: 0.000944
Train Epoch: 3 [27520/104000 (26%)]	Loss: 0.000015
Train Epoch: 3 [28160/104000 (27%)]	Loss: 0.000020
Train Epoch: 3 [28800/104000 (28%)]	Loss: 0.000038
Train Epoch: 3 [29440/104000 (28%)]	Loss: 0.000048
Train Epoch: 3 [30080/104000 (29%)]	Loss: 0.000017
Train Epoch: 3 [30720/104000 (30%)]	Loss: 0.000014
Train Epoch: 3 [31360/104000 (30%)]	Loss: 0.000011
Train Epoch: 3 [32000/104000 (31%)]	Loss: 0.000208
Train Epoch: 3 [32640/104000 (31%)]	Loss: 0.000019
Train Epoch: 3 [33280/104000 (32%)]	Loss: 0.000092
Train Epoch: 3 [33920/104000 (33%)]	Loss: 0.000065
Train Epoch: 3 [34560/104000 (33%)]	Loss: 0.000018
Train Epoch: 3 [35200/104000 (34%)]	Loss: 0.000012
Train Epoch: 3 [35840/104000 (34%)]	Loss: 0.000012
Train Epoch: 3 [36480/104000 (35%)]	Loss: 0.000495
Train Epoch: 3 [37120/104000 (36%)]	Loss: 0.000007
Train Epoch: 3 [37760/104000 (36%)]	Loss: 0.000019
Train Epoch: 3 [38400/104000 (37%)]	Loss: 0.000009
Train Epoch: 3 [39040/104000 (38%)]	Loss: 0.000019
Train Epoch: 3 [39680/104000 (38%)]	Loss: 0.000014
Train Epoch: 3 [40320/104000 (39%)]	Loss: 0.000032
Train Epoch: 3 [40960/104000 (39%)]	Loss: 0.000062
Train Epoch: 3 [41600/104000 (40%)]	Loss: 0.000005
Train Epoch: 3 [42240/104000 (41%)]	Loss: 0.000022
Train Epoch: 3 [42880/104000 (41%)]	Loss: 0.000134
Train Epoch: 3 [43520/104000 (42%)]	Loss: 0.000119
Train Epoch: 3 [44160/104000 (42%)]	Loss: 0.000040
Train Epoch: 3 [44800/104000 (43%)]	Loss: 0.000053
Train Epoch: 3 [45440/104000 (44%)]	Loss: 0.000101
Train Epoch: 3 [46080/104000 (44%)]	Loss: 0.000548
Train Epoch: 3 [46720/104000 (45%)]	Loss: 0.000008
Train Epoch: 3 [47360/104000 (46%)]	Loss: 0.000022
Train Epoch: 3 [48000/104000 (46%)]	Loss: 0.000016
Train Epoch: 3 [48640/104000 (47%)]	Loss: 0.000016
Train Epoch: 3 [49280/104000 (47%)]	Loss: 0.000215
Train Epoch: 3 [49920/104000 (48%)]	Loss: 0.000015
Train Epoch: 3 [50560/104000 (49%)]	Loss: 0.000349
Train Epoch: 3 [51200/104000 (49%)]	Loss: 0.000023
Train Epoch: 3 [51840/104000 (50%)]	Loss: 0.000019
Train Epoch: 3 [52480/104000 (50%)]	Loss: 0.000029
Train Epoch: 3 [53120/104000 (51%)]	Loss: 0.000146
Train Epoch: 3 [53760/104000 (52%)]	Loss: 0.000016
Train Epoch: 3 [54400/104000 (52%)]	Loss: 0.000019
Train Epoch: 3 [55040/104000 (53%)]	Loss: 0.000020
Train Epoch: 3 [55680/104000 (54%)]	Loss: 0.000048
Train Epoch: 3 [56320/104000 (54%)]	Loss: 0.000187
Train Epoch: 3 [56960/104000 (55%)]	Loss: 0.000015
Train Epoch: 3 [57600/104000 (55%)]	Loss: 0.000008
Train Epoch: 3 [58240/104000 (56%)]	Loss: 0.000007
Train Epoch: 3 [58880/104000 (57%)]	Loss: 0.000012
Train Epoch: 3 [59520/104000 (57%)]	Loss: 0.000012
Train Epoch: 3 [60160/104000 (58%)]	Loss: 0.000100
Train Epoch: 3 [60800/104000 (58%)]	Loss: 0.000092
Train Epoch: 3 [61440/104000 (59%)]	Loss: 0.000009
Train Epoch: 3 [62080/104000 (60%)]	Loss: 0.000222
Train Epoch: 3 [62720/104000 (60%)]	Loss: 0.000231
Train Epoch: 3 [63360/104000 (61%)]	Loss: 0.000017
Train Epoch: 3 [64000/104000 (62%)]	Loss: 0.000010
Train Epoch: 3 [64640/104000 (62%)]	Loss: 0.000045
Train Epoch: 3 [65280/104000 (63%)]	Loss: 0.000136
Train Epoch: 3 [65920/104000 (63%)]	Loss: 0.000020
Train Epoch: 3 [66560/104000 (64%)]	Loss: 0.000018
Train Epoch: 3 [67200/104000 (65%)]	Loss: 0.000019
Train Epoch: 3 [67840/104000 (65%)]	Loss: 0.000028
Train Epoch: 3 [68480/104000 (66%)]	Loss: 0.000013
Train Epoch: 3 [69120/104000 (66%)]	Loss: 0.000025
Train Epoch: 3 [69760/104000 (67%)]	Loss: 0.000026
Train Epoch: 3 [70400/104000 (68%)]	Loss: 0.000013
Train Epoch: 3 [71040/104000 (68%)]	Loss: 0.000233
Train Epoch: 3 [71680/104000 (69%)]	Loss: 0.000016
Train Epoch: 3 [72320/104000 (70%)]	Loss: 0.000092
Train Epoch: 3 [72960/104000 (70%)]	Loss: 0.000115
Train Epoch: 3 [73600/104000 (71%)]	Loss: 0.000017
Train Epoch: 3 [74240/104000 (71%)]	Loss: 0.000011
Train Epoch: 3 [74880/104000 (72%)]	Loss: 0.000066
Train Epoch: 3 [75520/104000 (73%)]	Loss: 0.000013
Train Epoch: 3 [76160/104000 (73%)]	Loss: 0.000048
Train Epoch: 3 [76800/104000 (74%)]	Loss: 0.000009
Train Epoch: 3 [77440/104000 (74%)]	Loss: 0.000016
Train Epoch: 3 [78080/104000 (75%)]	Loss: 0.000031
Train Epoch: 3 [78720/104000 (76%)]	Loss: 0.000025
Train Epoch: 3 [79360/104000 (76%)]	Loss: 0.000018
Train Epoch: 3 [80000/104000 (77%)]	Loss: 0.000115
Train Epoch: 3 [80640/104000 (78%)]	Loss: 0.000021
Train Epoch: 3 [81280/104000 (78%)]	Loss: 0.000046
Train Epoch: 3 [81920/104000 (79%)]	Loss: 0.000012
Train Epoch: 3 [82560/104000 (79%)]	Loss: 0.000011
Train Epoch: 3 [83200/104000 (80%)]	Loss: 0.000027
Train Epoch: 3 [83840/104000 (81%)]	Loss: 0.000042
Train Epoch: 3 [84480/104000 (81%)]	Loss: 0.000056
Train Epoch: 3 [85120/104000 (82%)]	Loss: 0.000012
Train Epoch: 3 [85760/104000 (82%)]	Loss: 0.000045
Train Epoch: 3 [86400/104000 (83%)]	Loss: 0.000017
Train Epoch: 3 [87040/104000 (84%)]	Loss: 0.000027
Train Epoch: 3 [87680/104000 (84%)]	Loss: 0.000012
Train Epoch: 3 [88320/104000 (85%)]	Loss: 0.000058
Train Epoch: 3 [88960/104000 (86%)]	Loss: 0.000018
Train Epoch: 3 [89600/104000 (86%)]	Loss: 0.000019
Train Epoch: 3 [90240/104000 (87%)]	Loss: 0.000014
Train Epoch: 3 [90880/104000 (87%)]	Loss: 0.000009
Train Epoch: 3 [91520/104000 (88%)]	Loss: 0.000008
Train Epoch: 3 [92160/104000 (89%)]	Loss: 0.000008
Train Epoch: 3 [92800/104000 (89%)]	Loss: 0.000094
Train Epoch: 3 [93440/104000 (90%)]	Loss: 0.000103
Train Epoch: 3 [94080/104000 (90%)]	Loss: 0.000040
Train Epoch: 3 [94720/104000 (91%)]	Loss: 0.000068
Train Epoch: 3 [95360/104000 (92%)]	Loss: 0.000033
Train Epoch: 3 [96000/104000 (92%)]	Loss: 0.000008
Train Epoch: 3 [96640/104000 (93%)]	Loss: 0.000018
Train Epoch: 3 [97280/104000 (94%)]	Loss: 0.000122
Train Epoch: 3 [97920/104000 (94%)]	Loss: 0.000055
Train Epoch: 3 [98560/104000 (95%)]	Loss: 0.000018
Train Epoch: 3 [99200/104000 (95%)]	Loss: 0.000033
Train Epoch: 3 [99840/104000 (96%)]	Loss: 0.000014
Train Epoch: 3 [100480/104000 (97%)]	Loss: 0.000009
Train Epoch: 3 [101120/104000 (97%)]	Loss: 0.000012
Train Epoch: 3 [101760/104000 (98%)]	Loss: 0.000015
Train Epoch: 3 [102400/104000 (98%)]	Loss: 0.000009
Train Epoch: 3 [103040/104000 (99%)]	Loss: 0.000008
Train Epoch: 3 [103680/104000 (100%)]	Loss: 0.000037

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.1820, Accuracy: 27467/30000 (92%)

Train Epoch: 4 [0/104000 (0%)]	Loss: 0.000022
Train Epoch: 4 [640/104000 (1%)]	Loss: 0.000015
Train Epoch: 4 [1280/104000 (1%)]	Loss: 0.000029
Train Epoch: 4 [1920/104000 (2%)]	Loss: 0.000010
Train Epoch: 4 [2560/104000 (2%)]	Loss: 0.000053
Train Epoch: 4 [3200/104000 (3%)]	Loss: 0.000009
Train Epoch: 4 [3840/104000 (4%)]	Loss: 0.000012
Train Epoch: 4 [4480/104000 (4%)]	Loss: 0.000034
Train Epoch: 4 [5120/104000 (5%)]	Loss: 0.000009
Train Epoch: 4 [5760/104000 (6%)]	Loss: 0.000009
Train Epoch: 4 [6400/104000 (6%)]	Loss: 0.000095
Train Epoch: 4 [7040/104000 (7%)]	Loss: 0.000007
Train Epoch: 4 [7680/104000 (7%)]	Loss: 0.000044
Train Epoch: 4 [8320/104000 (8%)]	Loss: 0.000015
Train Epoch: 4 [8960/104000 (9%)]	Loss: 0.000021
Train Epoch: 4 [9600/104000 (9%)]	Loss: 0.000014
Train Epoch: 4 [10240/104000 (10%)]	Loss: 0.000030
Train Epoch: 4 [10880/104000 (10%)]	Loss: 0.000013
Train Epoch: 4 [11520/104000 (11%)]	Loss: 0.000021
Train Epoch: 4 [12160/104000 (12%)]	Loss: 0.000007
Train Epoch: 4 [12800/104000 (12%)]	Loss: 0.000043
Train Epoch: 4 [13440/104000 (13%)]	Loss: 0.000027
Train Epoch: 4 [14080/104000 (14%)]	Loss: 0.000010
Train Epoch: 4 [14720/104000 (14%)]	Loss: 0.000092
Train Epoch: 4 [15360/104000 (15%)]	Loss: 0.000012
Train Epoch: 4 [16000/104000 (15%)]	Loss: 0.000026
Train Epoch: 4 [16640/104000 (16%)]	Loss: 0.001358
Train Epoch: 4 [17280/104000 (17%)]	Loss: 0.000007
Train Epoch: 4 [17920/104000 (17%)]	Loss: 0.000006
Train Epoch: 4 [18560/104000 (18%)]	Loss: 0.000019
Train Epoch: 4 [19200/104000 (18%)]	Loss: 0.000008
Train Epoch: 4 [19840/104000 (19%)]	Loss: 0.000014
Train Epoch: 4 [20480/104000 (20%)]	Loss: 0.000023
Train Epoch: 4 [21120/104000 (20%)]	Loss: 0.000009
Train Epoch: 4 [21760/104000 (21%)]	Loss: 0.000013
Train Epoch: 4 [22400/104000 (22%)]	Loss: 0.000009
Train Epoch: 4 [23040/104000 (22%)]	Loss: 0.000014
Train Epoch: 4 [23680/104000 (23%)]	Loss: 0.000013
Train Epoch: 4 [24320/104000 (23%)]	Loss: 0.000003
Train Epoch: 4 [24960/104000 (24%)]	Loss: 0.000009
Train Epoch: 4 [25600/104000 (25%)]	Loss: 0.000012
Train Epoch: 4 [26240/104000 (25%)]	Loss: 0.000015
Train Epoch: 4 [26880/104000 (26%)]	Loss: 0.000040
Train Epoch: 4 [27520/104000 (26%)]	Loss: 0.000005
Train Epoch: 4 [28160/104000 (27%)]	Loss: 0.000010
Train Epoch: 4 [28800/104000 (28%)]	Loss: 0.000088
Train Epoch: 4 [29440/104000 (28%)]	Loss: 0.000006
Train Epoch: 4 [30080/104000 (29%)]	Loss: 0.000020
Train Epoch: 4 [30720/104000 (30%)]	Loss: 0.000025
Train Epoch: 4 [31360/104000 (30%)]	Loss: 0.000025
Train Epoch: 4 [32000/104000 (31%)]	Loss: 0.000009
Train Epoch: 4 [32640/104000 (31%)]	Loss: 0.000311
Train Epoch: 4 [33280/104000 (32%)]	Loss: 0.000019
Train Epoch: 4 [33920/104000 (33%)]	Loss: 0.000017
Train Epoch: 4 [34560/104000 (33%)]	Loss: 0.000316
Train Epoch: 4 [35200/104000 (34%)]	Loss: 0.000131
Train Epoch: 4 [35840/104000 (34%)]	Loss: 0.000007
Train Epoch: 4 [36480/104000 (35%)]	Loss: 0.000014
Train Epoch: 4 [37120/104000 (36%)]	Loss: 0.000032
Train Epoch: 4 [37760/104000 (36%)]	Loss: 0.000016
Train Epoch: 4 [38400/104000 (37%)]	Loss: 0.000193
Train Epoch: 4 [39040/104000 (38%)]	Loss: 0.000029
Train Epoch: 4 [39680/104000 (38%)]	Loss: 0.000016
Train Epoch: 4 [40320/104000 (39%)]	Loss: 0.000020
Train Epoch: 4 [40960/104000 (39%)]	Loss: 0.000008
Train Epoch: 4 [41600/104000 (40%)]	Loss: 0.000033
Train Epoch: 4 [42240/104000 (41%)]	Loss: 0.001253
Train Epoch: 4 [42880/104000 (41%)]	Loss: 0.000018
Train Epoch: 4 [43520/104000 (42%)]	Loss: 0.000051
Train Epoch: 4 [44160/104000 (42%)]	Loss: 0.000005
Train Epoch: 4 [44800/104000 (43%)]	Loss: 0.000011
Train Epoch: 4 [45440/104000 (44%)]	Loss: 0.000654
Train Epoch: 4 [46080/104000 (44%)]	Loss: 0.000014
Train Epoch: 4 [46720/104000 (45%)]	Loss: 0.000011
Train Epoch: 4 [47360/104000 (46%)]	Loss: 0.000005
Train Epoch: 4 [48000/104000 (46%)]	Loss: 0.000018
Train Epoch: 4 [48640/104000 (47%)]	Loss: 0.000007
Train Epoch: 4 [49280/104000 (47%)]	Loss: 0.000016
Train Epoch: 4 [49920/104000 (48%)]	Loss: 0.000006
Train Epoch: 4 [50560/104000 (49%)]	Loss: 0.000016
Train Epoch: 4 [51200/104000 (49%)]	Loss: 0.000064
Train Epoch: 4 [51840/104000 (50%)]	Loss: 0.000010
Train Epoch: 4 [52480/104000 (50%)]	Loss: 0.000099
Train Epoch: 4 [53120/104000 (51%)]	Loss: 0.000012
Train Epoch: 4 [53760/104000 (52%)]	Loss: 0.000008
Train Epoch: 4 [54400/104000 (52%)]	Loss: 0.000005
Train Epoch: 4 [55040/104000 (53%)]	Loss: 0.000014
Train Epoch: 4 [55680/104000 (54%)]	Loss: 0.000014
Train Epoch: 4 [56320/104000 (54%)]	Loss: 0.000025
Train Epoch: 4 [56960/104000 (55%)]	Loss: 0.000009
Train Epoch: 4 [57600/104000 (55%)]	Loss: 0.000051
Train Epoch: 4 [58240/104000 (56%)]	Loss: 0.000021
Train Epoch: 4 [58880/104000 (57%)]	Loss: 0.000023
Train Epoch: 4 [59520/104000 (57%)]	Loss: 0.000148
Train Epoch: 4 [60160/104000 (58%)]	Loss: 0.000017
Train Epoch: 4 [60800/104000 (58%)]	Loss: 0.000008
Train Epoch: 4 [61440/104000 (59%)]	Loss: 0.000024
Train Epoch: 4 [62080/104000 (60%)]	Loss: 0.000053
Train Epoch: 4 [62720/104000 (60%)]	Loss: 0.000018
Train Epoch: 4 [63360/104000 (61%)]	Loss: 0.000008
Train Epoch: 4 [64000/104000 (62%)]	Loss: 0.000004
Train Epoch: 4 [64640/104000 (62%)]	Loss: 0.000016
Train Epoch: 4 [65280/104000 (63%)]	Loss: 0.000179
Train Epoch: 4 [65920/104000 (63%)]	Loss: 0.000004
Train Epoch: 4 [66560/104000 (64%)]	Loss: 0.000008
Train Epoch: 4 [67200/104000 (65%)]	Loss: 0.000181
Train Epoch: 4 [67840/104000 (65%)]	Loss: 0.000023
Train Epoch: 4 [68480/104000 (66%)]	Loss: 0.000011
Train Epoch: 4 [69120/104000 (66%)]	Loss: 0.000017
Train Epoch: 4 [69760/104000 (67%)]	Loss: 0.000010
Train Epoch: 4 [70400/104000 (68%)]	Loss: 0.000009
Train Epoch: 4 [71040/104000 (68%)]	Loss: 0.000028
Train Epoch: 4 [71680/104000 (69%)]	Loss: 0.000007
Train Epoch: 4 [72320/104000 (70%)]	Loss: 0.000010
Train Epoch: 4 [72960/104000 (70%)]	Loss: 0.000251
Train Epoch: 4 [73600/104000 (71%)]	Loss: 0.000011
Train Epoch: 4 [74240/104000 (71%)]	Loss: 0.000032
Train Epoch: 4 [74880/104000 (72%)]	Loss: 0.000009
Train Epoch: 4 [75520/104000 (73%)]	Loss: 0.000013
Train Epoch: 4 [76160/104000 (73%)]	Loss: 0.000013
Train Epoch: 4 [76800/104000 (74%)]	Loss: 0.000015
Train Epoch: 4 [77440/104000 (74%)]	Loss: 0.000022
Train Epoch: 4 [78080/104000 (75%)]	Loss: 0.000012
Train Epoch: 4 [78720/104000 (76%)]	Loss: 0.000006
Train Epoch: 4 [79360/104000 (76%)]	Loss: 0.000086
Train Epoch: 4 [80000/104000 (77%)]	Loss: 0.000016
Train Epoch: 4 [80640/104000 (78%)]	Loss: 0.000022
Train Epoch: 4 [81280/104000 (78%)]	Loss: 0.000047
Train Epoch: 4 [81920/104000 (79%)]	Loss: 0.000026
Train Epoch: 4 [82560/104000 (79%)]	Loss: 0.000019
Train Epoch: 4 [83200/104000 (80%)]	Loss: 0.000036
Train Epoch: 4 [83840/104000 (81%)]	Loss: 0.000004
Train Epoch: 4 [84480/104000 (81%)]	Loss: 0.000112
Train Epoch: 4 [85120/104000 (82%)]	Loss: 0.000016
Train Epoch: 4 [85760/104000 (82%)]	Loss: 0.000007
Train Epoch: 4 [86400/104000 (83%)]	Loss: 0.000023
Train Epoch: 4 [87040/104000 (84%)]	Loss: 0.000045
Train Epoch: 4 [87680/104000 (84%)]	Loss: 0.000008
Train Epoch: 4 [88320/104000 (85%)]	Loss: 0.000012
Train Epoch: 4 [88960/104000 (86%)]	Loss: 0.000013
Train Epoch: 4 [89600/104000 (86%)]	Loss: 0.000023
Train Epoch: 4 [90240/104000 (87%)]	Loss: 0.000009
Train Epoch: 4 [90880/104000 (87%)]	Loss: 0.000028
Train Epoch: 4 [91520/104000 (88%)]	Loss: 0.000011
Train Epoch: 4 [92160/104000 (89%)]	Loss: 0.000034
Train Epoch: 4 [92800/104000 (89%)]	Loss: 0.000008
Train Epoch: 4 [93440/104000 (90%)]	Loss: 0.000011
Train Epoch: 4 [94080/104000 (90%)]	Loss: 0.000192
Train Epoch: 4 [94720/104000 (91%)]	Loss: 0.000012
Train Epoch: 4 [95360/104000 (92%)]	Loss: 0.000024
Train Epoch: 4 [96000/104000 (92%)]	Loss: 0.000111
Train Epoch: 4 [96640/104000 (93%)]	Loss: 0.000016
Train Epoch: 4 [97280/104000 (94%)]	Loss: 0.000021
Train Epoch: 4 [97920/104000 (94%)]	Loss: 0.000007
Train Epoch: 4 [98560/104000 (95%)]	Loss: 0.000025
Train Epoch: 4 [99200/104000 (95%)]	Loss: 0.000022
Train Epoch: 4 [99840/104000 (96%)]	Loss: 0.000005
Train Epoch: 4 [100480/104000 (97%)]	Loss: 0.000006
Train Epoch: 4 [101120/104000 (97%)]	Loss: 0.000007
Train Epoch: 4 [101760/104000 (98%)]	Loss: 0.000011
Train Epoch: 4 [102400/104000 (98%)]	Loss: 0.000016
Train Epoch: 4 [103040/104000 (99%)]	Loss: 0.000003
Train Epoch: 4 [103680/104000 (100%)]	Loss: 0.000007

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.1927, Accuracy: 27318/30000 (91%)

Train Epoch: 5 [0/104000 (0%)]	Loss: 0.000032
Train Epoch: 5 [640/104000 (1%)]	Loss: 0.000012
Train Epoch: 5 [1280/104000 (1%)]	Loss: 0.000008
Train Epoch: 5 [1920/104000 (2%)]	Loss: 0.000012
Train Epoch: 5 [2560/104000 (2%)]	Loss: 0.000034
Train Epoch: 5 [3200/104000 (3%)]	Loss: 0.000011
Train Epoch: 5 [3840/104000 (4%)]	Loss: 0.000511
Train Epoch: 5 [4480/104000 (4%)]	Loss: 0.000013
Train Epoch: 5 [5120/104000 (5%)]	Loss: 0.000025
Train Epoch: 5 [5760/104000 (6%)]	Loss: 0.000085
Train Epoch: 5 [6400/104000 (6%)]	Loss: 0.000022
Train Epoch: 5 [7040/104000 (7%)]	Loss: 0.000012
Train Epoch: 5 [7680/104000 (7%)]	Loss: 0.000009
Train Epoch: 5 [8320/104000 (8%)]	Loss: 0.000037
Train Epoch: 5 [8960/104000 (9%)]	Loss: 0.000015
Train Epoch: 5 [9600/104000 (9%)]	Loss: 0.000009
Train Epoch: 5 [10240/104000 (10%)]	Loss: 0.000005
Train Epoch: 5 [10880/104000 (10%)]	Loss: 0.000008
Train Epoch: 5 [11520/104000 (11%)]	Loss: 0.000008
Train Epoch: 5 [12160/104000 (12%)]	Loss: 0.000013
Train Epoch: 5 [12800/104000 (12%)]	Loss: 0.000008
Train Epoch: 5 [13440/104000 (13%)]	Loss: 0.000267
Train Epoch: 5 [14080/104000 (14%)]	Loss: 0.000008
Train Epoch: 5 [14720/104000 (14%)]	Loss: 0.000086
Train Epoch: 5 [15360/104000 (15%)]	Loss: 0.000017
Train Epoch: 5 [16000/104000 (15%)]	Loss: 0.000002
Train Epoch: 5 [16640/104000 (16%)]	Loss: 0.000006
Train Epoch: 5 [17280/104000 (17%)]	Loss: 0.000010
Train Epoch: 5 [17920/104000 (17%)]	Loss: 0.000093
Train Epoch: 5 [18560/104000 (18%)]	Loss: 0.000016
Train Epoch: 5 [19200/104000 (18%)]	Loss: 0.000021
Train Epoch: 5 [19840/104000 (19%)]	Loss: 0.000009
Train Epoch: 5 [20480/104000 (20%)]	Loss: 0.000015
Train Epoch: 5 [21120/104000 (20%)]	Loss: 0.000011
Train Epoch: 5 [21760/104000 (21%)]	Loss: 0.000011
Train Epoch: 5 [22400/104000 (22%)]	Loss: 0.000007
Train Epoch: 5 [23040/104000 (22%)]	Loss: 0.000007
Train Epoch: 5 [23680/104000 (23%)]	Loss: 0.000043
Train Epoch: 5 [24320/104000 (23%)]	Loss: 0.000021
Train Epoch: 5 [24960/104000 (24%)]	Loss: 0.000037
Train Epoch: 5 [25600/104000 (25%)]	Loss: 0.000008
Train Epoch: 5 [26240/104000 (25%)]	Loss: 0.000005
Train Epoch: 5 [26880/104000 (26%)]	Loss: 0.000007
Train Epoch: 5 [27520/104000 (26%)]	Loss: 0.000005
Train Epoch: 5 [28160/104000 (27%)]	Loss: 0.000008
Train Epoch: 5 [28800/104000 (28%)]	Loss: 0.000009
Train Epoch: 5 [29440/104000 (28%)]	Loss: 0.000230
Train Epoch: 5 [30080/104000 (29%)]	Loss: 0.000009
Train Epoch: 5 [30720/104000 (30%)]	Loss: 0.000009
Train Epoch: 5 [31360/104000 (30%)]	Loss: 0.000020
Train Epoch: 5 [32000/104000 (31%)]	Loss: 0.000012
Train Epoch: 5 [32640/104000 (31%)]	Loss: 0.000021
Train Epoch: 5 [33280/104000 (32%)]	Loss: 0.000021
Train Epoch: 5 [33920/104000 (33%)]	Loss: 0.000004
Train Epoch: 5 [34560/104000 (33%)]	Loss: 0.000009
Train Epoch: 5 [35200/104000 (34%)]	Loss: 0.000006
Train Epoch: 5 [35840/104000 (34%)]	Loss: 0.000004
Train Epoch: 5 [36480/104000 (35%)]	Loss: 0.000016
Train Epoch: 5 [37120/104000 (36%)]	Loss: 0.000024
Train Epoch: 5 [37760/104000 (36%)]	Loss: 0.000008
Train Epoch: 5 [38400/104000 (37%)]	Loss: 0.000023
Train Epoch: 5 [39040/104000 (38%)]	Loss: 0.000018
Train Epoch: 5 [39680/104000 (38%)]	Loss: 0.000010
Train Epoch: 5 [40320/104000 (39%)]	Loss: 0.000038
Train Epoch: 5 [40960/104000 (39%)]	Loss: 0.000005
Train Epoch: 5 [41600/104000 (40%)]	Loss: 0.000008
Train Epoch: 5 [42240/104000 (41%)]	Loss: 0.000023
Train Epoch: 5 [42880/104000 (41%)]	Loss: 0.000008
Train Epoch: 5 [43520/104000 (42%)]	Loss: 0.000018
Train Epoch: 5 [44160/104000 (42%)]	Loss: 0.000009
Train Epoch: 5 [44800/104000 (43%)]	Loss: 0.000018
Train Epoch: 5 [45440/104000 (44%)]	Loss: 0.000013
Train Epoch: 5 [46080/104000 (44%)]	Loss: 0.000010
Train Epoch: 5 [46720/104000 (45%)]	Loss: 0.000215
Train Epoch: 5 [47360/104000 (46%)]	Loss: 0.000012
Train Epoch: 5 [48000/104000 (46%)]	Loss: 0.000015
Train Epoch: 5 [48640/104000 (47%)]	Loss: 0.000012
Train Epoch: 5 [49280/104000 (47%)]	Loss: 0.000007
Train Epoch: 5 [49920/104000 (48%)]	Loss: 0.000006
Train Epoch: 5 [50560/104000 (49%)]	Loss: 0.000025
Train Epoch: 5 [51200/104000 (49%)]	Loss: 0.000068
Train Epoch: 5 [51840/104000 (50%)]	Loss: 0.000014
Train Epoch: 5 [52480/104000 (50%)]	Loss: 0.000015
Train Epoch: 5 [53120/104000 (51%)]	Loss: 0.000009
Train Epoch: 5 [53760/104000 (52%)]	Loss: 0.000012
Train Epoch: 5 [54400/104000 (52%)]	Loss: 0.000026
Train Epoch: 5 [55040/104000 (53%)]	Loss: 0.000012
Train Epoch: 5 [55680/104000 (54%)]	Loss: 0.000070
Train Epoch: 5 [56320/104000 (54%)]	Loss: 0.000070
Train Epoch: 5 [56960/104000 (55%)]	Loss: 0.000069
Train Epoch: 5 [57600/104000 (55%)]	Loss: 0.000009
Train Epoch: 5 [58240/104000 (56%)]	Loss: 0.000005
Train Epoch: 5 [58880/104000 (57%)]	Loss: 0.000012
Train Epoch: 5 [59520/104000 (57%)]	Loss: 0.000022
Train Epoch: 5 [60160/104000 (58%)]	Loss: 0.000007
Train Epoch: 5 [60800/104000 (58%)]	Loss: 0.000007
Train Epoch: 5 [61440/104000 (59%)]	Loss: 0.000076
Train Epoch: 5 [62080/104000 (60%)]	Loss: 0.000020
Train Epoch: 5 [62720/104000 (60%)]	Loss: 0.000012
Train Epoch: 5 [63360/104000 (61%)]	Loss: 0.000086
Train Epoch: 5 [64000/104000 (62%)]	Loss: 0.000006
Train Epoch: 5 [64640/104000 (62%)]	Loss: 0.000009
Train Epoch: 5 [65280/104000 (63%)]	Loss: 0.000005
Train Epoch: 5 [65920/104000 (63%)]	Loss: 0.000019
Train Epoch: 5 [66560/104000 (64%)]	Loss: 0.000099
Train Epoch: 5 [67200/104000 (65%)]	Loss: 0.000127
Train Epoch: 5 [67840/104000 (65%)]	Loss: 0.000017
Train Epoch: 5 [68480/104000 (66%)]	Loss: 0.000021
Train Epoch: 5 [69120/104000 (66%)]	Loss: 0.000006
Train Epoch: 5 [69760/104000 (67%)]	Loss: 0.000007
Train Epoch: 5 [70400/104000 (68%)]	Loss: 0.000006
Train Epoch: 5 [71040/104000 (68%)]	Loss: 0.000022
Train Epoch: 5 [71680/104000 (69%)]	Loss: 0.000122
Train Epoch: 5 [72320/104000 (70%)]	Loss: 0.000021
Train Epoch: 5 [72960/104000 (70%)]	Loss: 0.000012
Train Epoch: 5 [73600/104000 (71%)]	Loss: 0.000145
Train Epoch: 5 [74240/104000 (71%)]	Loss: 0.000236
Train Epoch: 5 [74880/104000 (72%)]	Loss: 0.000004
Train Epoch: 5 [75520/104000 (73%)]	Loss: 0.000013
Train Epoch: 5 [76160/104000 (73%)]	Loss: 0.000009
Train Epoch: 5 [76800/104000 (74%)]	Loss: 0.000038
Train Epoch: 5 [77440/104000 (74%)]	Loss: 0.000065
Train Epoch: 5 [78080/104000 (75%)]	Loss: 0.000014
Train Epoch: 5 [78720/104000 (76%)]	Loss: 0.000017
Train Epoch: 5 [79360/104000 (76%)]	Loss: 0.000002
Train Epoch: 5 [80000/104000 (77%)]	Loss: 0.000004
Train Epoch: 5 [80640/104000 (78%)]	Loss: 0.000010
Train Epoch: 5 [81280/104000 (78%)]	Loss: 0.000005
Train Epoch: 5 [81920/104000 (79%)]	Loss: 0.000016
Train Epoch: 5 [82560/104000 (79%)]	Loss: 0.000016
Train Epoch: 5 [83200/104000 (80%)]	Loss: 0.000031
Train Epoch: 5 [83840/104000 (81%)]	Loss: 0.000007
Train Epoch: 5 [84480/104000 (81%)]	Loss: 0.000036
Train Epoch: 5 [85120/104000 (82%)]	Loss: 0.000009
Train Epoch: 5 [85760/104000 (82%)]	Loss: 0.000003
Train Epoch: 5 [86400/104000 (83%)]	Loss: 0.000023
Train Epoch: 5 [87040/104000 (84%)]	Loss: 0.000134
Train Epoch: 5 [87680/104000 (84%)]	Loss: 0.000017
Train Epoch: 5 [88320/104000 (85%)]	Loss: 0.000004
Train Epoch: 5 [88960/104000 (86%)]	Loss: 0.000014
Train Epoch: 5 [89600/104000 (86%)]	Loss: 0.000060
Train Epoch: 5 [90240/104000 (87%)]	Loss: 0.000010
Train Epoch: 5 [90880/104000 (87%)]	Loss: 0.000008
Train Epoch: 5 [91520/104000 (88%)]	Loss: 0.000006
Train Epoch: 5 [92160/104000 (89%)]	Loss: 0.000013
Train Epoch: 5 [92800/104000 (89%)]	Loss: 0.000006
Train Epoch: 5 [93440/104000 (90%)]	Loss: 0.000010
Train Epoch: 5 [94080/104000 (90%)]	Loss: 0.000060
Train Epoch: 5 [94720/104000 (91%)]	Loss: 0.000156
Train Epoch: 5 [95360/104000 (92%)]	Loss: 0.000115
Train Epoch: 5 [96000/104000 (92%)]	Loss: 0.000048
Train Epoch: 5 [96640/104000 (93%)]	Loss: 0.000006
Train Epoch: 5 [97280/104000 (94%)]	Loss: 0.000009
Train Epoch: 5 [97920/104000 (94%)]	Loss: 0.000012
Train Epoch: 5 [98560/104000 (95%)]	Loss: 0.000006
Train Epoch: 5 [99200/104000 (95%)]	Loss: 0.000010
Train Epoch: 5 [99840/104000 (96%)]	Loss: 0.000007
Train Epoch: 5 [100480/104000 (97%)]	Loss: 0.000010
Train Epoch: 5 [101120/104000 (97%)]	Loss: 0.000033
Train Epoch: 5 [101760/104000 (98%)]	Loss: 0.000037
Train Epoch: 5 [102400/104000 (98%)]	Loss: 0.000013
Train Epoch: 5 [103040/104000 (99%)]	Loss: 0.000003
Train Epoch: 5 [103680/104000 (100%)]	Loss: 0.000123

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.2115, Accuracy: 27050/30000 (90%)

[100.0, 100.0, 100.0]
[88.56, 91.44666666666667, 90.16666666666667]
The number of neurons in CNN layer is 20
Train Epoch: 1 [0/104000 (0%)]	Loss: 0.838881
Train Epoch: 1 [640/104000 (1%)]	Loss: 0.000247
Train Epoch: 1 [1280/104000 (1%)]	Loss: 0.000319
Train Epoch: 1 [1920/104000 (2%)]	Loss: 0.000412
Train Epoch: 1 [2560/104000 (2%)]	Loss: 0.000413
Train Epoch: 1 [3200/104000 (3%)]	Loss: 0.000391
Train Epoch: 1 [3840/104000 (4%)]	Loss: 0.000342
Train Epoch: 1 [4480/104000 (4%)]	Loss: 0.000258
Train Epoch: 1 [5120/104000 (5%)]	Loss: 0.000206
Train Epoch: 1 [5760/104000 (6%)]	Loss: 0.000161
Train Epoch: 1 [6400/104000 (6%)]	Loss: 0.000135
Train Epoch: 1 [7040/104000 (7%)]	Loss: 0.000136
Train Epoch: 1 [7680/104000 (7%)]	Loss: 0.000528
Train Epoch: 1 [8320/104000 (8%)]	Loss: 0.000241
Train Epoch: 1 [8960/104000 (9%)]	Loss: 0.000707
Train Epoch: 1 [9600/104000 (9%)]	Loss: 0.000771
Train Epoch: 1 [10240/104000 (10%)]	Loss: 0.000273
Train Epoch: 1 [10880/104000 (10%)]	Loss: 0.000157
Train Epoch: 1 [11520/104000 (11%)]	Loss: 0.000274
Train Epoch: 1 [12160/104000 (12%)]	Loss: 0.000260
Train Epoch: 1 [12800/104000 (12%)]	Loss: 0.000103
Train Epoch: 1 [13440/104000 (13%)]	Loss: 0.000208
Train Epoch: 1 [14080/104000 (14%)]	Loss: 0.000094
Train Epoch: 1 [14720/104000 (14%)]	Loss: 0.000089
Train Epoch: 1 [15360/104000 (15%)]	Loss: 0.001090
Train Epoch: 1 [16000/104000 (15%)]	Loss: 0.000184
Train Epoch: 1 [16640/104000 (16%)]	Loss: 0.000120
Train Epoch: 1 [17280/104000 (17%)]	Loss: 0.000158
Train Epoch: 1 [17920/104000 (17%)]	Loss: 0.000283
Train Epoch: 1 [18560/104000 (18%)]	Loss: 0.001772
Train Epoch: 1 [19200/104000 (18%)]	Loss: 0.000266
Train Epoch: 1 [19840/104000 (19%)]	Loss: 0.000424
Train Epoch: 1 [20480/104000 (20%)]	Loss: 0.000118
Train Epoch: 1 [21120/104000 (20%)]	Loss: 0.000107
Train Epoch: 1 [21760/104000 (21%)]	Loss: 0.000129
Train Epoch: 1 [22400/104000 (22%)]	Loss: 0.000481
Train Epoch: 1 [23040/104000 (22%)]	Loss: 0.000154
Train Epoch: 1 [23680/104000 (23%)]	Loss: 0.001279
Train Epoch: 1 [24320/104000 (23%)]	Loss: 0.001023
Train Epoch: 1 [24960/104000 (24%)]	Loss: 0.000088
Train Epoch: 1 [25600/104000 (25%)]	Loss: 0.000109
Train Epoch: 1 [26240/104000 (25%)]	Loss: 0.000168
Train Epoch: 1 [26880/104000 (26%)]	Loss: 0.000079
Train Epoch: 1 [27520/104000 (26%)]	Loss: 0.000115
Train Epoch: 1 [28160/104000 (27%)]	Loss: 0.000096
Train Epoch: 1 [28800/104000 (28%)]	Loss: 0.000067
Train Epoch: 1 [29440/104000 (28%)]	Loss: 0.000063
Train Epoch: 1 [30080/104000 (29%)]	Loss: 0.000077
Train Epoch: 1 [30720/104000 (30%)]	Loss: 0.000138
Train Epoch: 1 [31360/104000 (30%)]	Loss: 0.000242
Train Epoch: 1 [32000/104000 (31%)]	Loss: 0.000136
Train Epoch: 1 [32640/104000 (31%)]	Loss: 0.000498
Train Epoch: 1 [33280/104000 (32%)]	Loss: 0.000327
Train Epoch: 1 [33920/104000 (33%)]	Loss: 0.000224
Train Epoch: 1 [34560/104000 (33%)]	Loss: 0.000677
Train Epoch: 1 [35200/104000 (34%)]	Loss: 0.000079
Train Epoch: 1 [35840/104000 (34%)]	Loss: 0.000067
Train Epoch: 1 [36480/104000 (35%)]	Loss: 0.000086
Train Epoch: 1 [37120/104000 (36%)]	Loss: 0.000132
Train Epoch: 1 [37760/104000 (36%)]	Loss: 0.000195
Train Epoch: 1 [38400/104000 (37%)]	Loss: 0.000085
Train Epoch: 1 [39040/104000 (38%)]	Loss: 0.000071
Train Epoch: 1 [39680/104000 (38%)]	Loss: 0.000099
Train Epoch: 1 [40320/104000 (39%)]	Loss: 0.000136
Train Epoch: 1 [40960/104000 (39%)]	Loss: 0.000041
Train Epoch: 1 [41600/104000 (40%)]	Loss: 0.000178
Train Epoch: 1 [42240/104000 (41%)]	Loss: 0.000059
Train Epoch: 1 [42880/104000 (41%)]	Loss: 0.000080
Train Epoch: 1 [43520/104000 (42%)]	Loss: 0.000081
Train Epoch: 1 [44160/104000 (42%)]	Loss: 0.000036
Train Epoch: 1 [44800/104000 (43%)]	Loss: 0.000212
Train Epoch: 1 [45440/104000 (44%)]	Loss: 0.000143
Train Epoch: 1 [46080/104000 (44%)]	Loss: 0.000042
Train Epoch: 1 [46720/104000 (45%)]	Loss: 0.000037
Train Epoch: 1 [47360/104000 (46%)]	Loss: 0.000304
Train Epoch: 1 [48000/104000 (46%)]	Loss: 0.000308
Train Epoch: 1 [48640/104000 (47%)]	Loss: 0.000110
Train Epoch: 1 [49280/104000 (47%)]	Loss: 0.000160
Train Epoch: 1 [49920/104000 (48%)]	Loss: 0.000273
Train Epoch: 1 [50560/104000 (49%)]	Loss: 0.001155
Train Epoch: 1 [51200/104000 (49%)]	Loss: 0.000160
Train Epoch: 1 [51840/104000 (50%)]	Loss: 0.000068
Train Epoch: 1 [52480/104000 (50%)]	Loss: 0.000114
Train Epoch: 1 [53120/104000 (51%)]	Loss: 0.000130
Train Epoch: 1 [53760/104000 (52%)]	Loss: 0.000039
Train Epoch: 1 [54400/104000 (52%)]	Loss: 0.000042
Train Epoch: 1 [55040/104000 (53%)]	Loss: 0.000034
Train Epoch: 1 [55680/104000 (54%)]	Loss: 0.000041
Train Epoch: 1 [56320/104000 (54%)]	Loss: 0.000199
Train Epoch: 1 [56960/104000 (55%)]	Loss: 0.000083
Train Epoch: 1 [57600/104000 (55%)]	Loss: 0.000072
Train Epoch: 1 [58240/104000 (56%)]	Loss: 0.000156
Train Epoch: 1 [58880/104000 (57%)]	Loss: 0.000081
Train Epoch: 1 [59520/104000 (57%)]	Loss: 0.000045
Train Epoch: 1 [60160/104000 (58%)]	Loss: 0.000072
Train Epoch: 1 [60800/104000 (58%)]	Loss: 0.000081
Train Epoch: 1 [61440/104000 (59%)]	Loss: 0.000658
Train Epoch: 1 [62080/104000 (60%)]	Loss: 0.000097
Train Epoch: 1 [62720/104000 (60%)]	Loss: 0.000073
Train Epoch: 1 [63360/104000 (61%)]	Loss: 0.000036
Train Epoch: 1 [64000/104000 (62%)]	Loss: 0.000048
Train Epoch: 1 [64640/104000 (62%)]	Loss: 0.000230
Train Epoch: 1 [65280/104000 (63%)]	Loss: 0.000046
Train Epoch: 1 [65920/104000 (63%)]	Loss: 0.000088
Train Epoch: 1 [66560/104000 (64%)]	Loss: 0.000138
Train Epoch: 1 [67200/104000 (65%)]	Loss: 0.000102
Train Epoch: 1 [67840/104000 (65%)]	Loss: 0.000067
Train Epoch: 1 [68480/104000 (66%)]	Loss: 0.000061
Train Epoch: 1 [69120/104000 (66%)]	Loss: 0.000054
Train Epoch: 1 [69760/104000 (67%)]	Loss: 0.000094
Train Epoch: 1 [70400/104000 (68%)]	Loss: 0.000031
Train Epoch: 1 [71040/104000 (68%)]	Loss: 0.000020
Train Epoch: 1 [71680/104000 (69%)]	Loss: 0.000097
Train Epoch: 1 [72320/104000 (70%)]	Loss: 0.000029
Train Epoch: 1 [72960/104000 (70%)]	Loss: 0.000068
Train Epoch: 1 [73600/104000 (71%)]	Loss: 0.000026
Train Epoch: 1 [74240/104000 (71%)]	Loss: 0.000280
Train Epoch: 1 [74880/104000 (72%)]	Loss: 0.000192
Train Epoch: 1 [75520/104000 (73%)]	Loss: 0.000072
Train Epoch: 1 [76160/104000 (73%)]	Loss: 0.000275
Train Epoch: 1 [76800/104000 (74%)]	Loss: 0.000050
Train Epoch: 1 [77440/104000 (74%)]	Loss: 0.000067
Train Epoch: 1 [78080/104000 (75%)]	Loss: 0.000064
Train Epoch: 1 [78720/104000 (76%)]	Loss: 0.000075
Train Epoch: 1 [79360/104000 (76%)]	Loss: 0.000049
Train Epoch: 1 [80000/104000 (77%)]	Loss: 0.000106
Train Epoch: 1 [80640/104000 (78%)]	Loss: 0.000031
Train Epoch: 1 [81280/104000 (78%)]	Loss: 0.000327
Train Epoch: 1 [81920/104000 (79%)]	Loss: 0.000087
Train Epoch: 1 [82560/104000 (79%)]	Loss: 0.000025
Train Epoch: 1 [83200/104000 (80%)]	Loss: 0.000041
Train Epoch: 1 [83840/104000 (81%)]	Loss: 0.000026
Train Epoch: 1 [84480/104000 (81%)]	Loss: 0.000110
Train Epoch: 1 [85120/104000 (82%)]	Loss: 0.000016
Train Epoch: 1 [85760/104000 (82%)]	Loss: 0.000031
Train Epoch: 1 [86400/104000 (83%)]	Loss: 0.000027
Train Epoch: 1 [87040/104000 (84%)]	Loss: 0.000044
Train Epoch: 1 [87680/104000 (84%)]	Loss: 0.000048
Train Epoch: 1 [88320/104000 (85%)]	Loss: 0.000054
Train Epoch: 1 [88960/104000 (86%)]	Loss: 0.000034
Train Epoch: 1 [89600/104000 (86%)]	Loss: 0.000043
Train Epoch: 1 [90240/104000 (87%)]	Loss: 0.000054
Train Epoch: 1 [90880/104000 (87%)]	Loss: 0.000130
Train Epoch: 1 [91520/104000 (88%)]	Loss: 0.000110
Train Epoch: 1 [92160/104000 (89%)]	Loss: 0.000069
Train Epoch: 1 [92800/104000 (89%)]	Loss: 0.000036
Train Epoch: 1 [93440/104000 (90%)]	Loss: 0.000028
Train Epoch: 1 [94080/104000 (90%)]	Loss: 0.000022
Train Epoch: 1 [94720/104000 (91%)]	Loss: 0.000058
Train Epoch: 1 [95360/104000 (92%)]	Loss: 0.000016
Train Epoch: 1 [96000/104000 (92%)]	Loss: 0.000045
Train Epoch: 1 [96640/104000 (93%)]	Loss: 0.000174
Train Epoch: 1 [97280/104000 (94%)]	Loss: 0.000091
Train Epoch: 1 [97920/104000 (94%)]	Loss: 0.000419
Train Epoch: 1 [98560/104000 (95%)]	Loss: 0.000254
Train Epoch: 1 [99200/104000 (95%)]	Loss: 0.000016
Train Epoch: 1 [99840/104000 (96%)]	Loss: 0.000049
Train Epoch: 1 [100480/104000 (97%)]	Loss: 0.000031
Train Epoch: 1 [101120/104000 (97%)]	Loss: 0.006004
Train Epoch: 1 [101760/104000 (98%)]	Loss: 0.000035
Train Epoch: 1 [102400/104000 (98%)]	Loss: 0.000055
Train Epoch: 1 [103040/104000 (99%)]	Loss: 0.000103
Train Epoch: 1 [103680/104000 (100%)]	Loss: 0.000018

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.2020, Accuracy: 27074/30000 (90%)

Train Epoch: 2 [0/104000 (0%)]	Loss: 0.000029
Train Epoch: 2 [640/104000 (1%)]	Loss: 0.000105
Train Epoch: 2 [1280/104000 (1%)]	Loss: 0.000028
Train Epoch: 2 [1920/104000 (2%)]	Loss: 0.000515
Train Epoch: 2 [2560/104000 (2%)]	Loss: 0.000029
Train Epoch: 2 [3200/104000 (3%)]	Loss: 0.000050
Train Epoch: 2 [3840/104000 (4%)]	Loss: 0.000240
Train Epoch: 2 [4480/104000 (4%)]	Loss: 0.000127
Train Epoch: 2 [5120/104000 (5%)]	Loss: 0.000032
Train Epoch: 2 [5760/104000 (6%)]	Loss: 0.000113
Train Epoch: 2 [6400/104000 (6%)]	Loss: 0.000029
Train Epoch: 2 [7040/104000 (7%)]	Loss: 0.000015
Train Epoch: 2 [7680/104000 (7%)]	Loss: 0.000020
Train Epoch: 2 [8320/104000 (8%)]	Loss: 0.000091
Train Epoch: 2 [8960/104000 (9%)]	Loss: 0.000032
Train Epoch: 2 [9600/104000 (9%)]	Loss: 0.000016
Train Epoch: 2 [10240/104000 (10%)]	Loss: 0.000034
Train Epoch: 2 [10880/104000 (10%)]	Loss: 0.000012
Train Epoch: 2 [11520/104000 (11%)]	Loss: 0.000044
Train Epoch: 2 [12160/104000 (12%)]	Loss: 0.000037
Train Epoch: 2 [12800/104000 (12%)]	Loss: 0.000054
Train Epoch: 2 [13440/104000 (13%)]	Loss: 0.000017
Train Epoch: 2 [14080/104000 (14%)]	Loss: 0.000048
Train Epoch: 2 [14720/104000 (14%)]	Loss: 0.000028
Train Epoch: 2 [15360/104000 (15%)]	Loss: 0.000021
Train Epoch: 2 [16000/104000 (15%)]	Loss: 0.000016
Train Epoch: 2 [16640/104000 (16%)]	Loss: 0.000027
Train Epoch: 2 [17280/104000 (17%)]	Loss: 0.000095
Train Epoch: 2 [17920/104000 (17%)]	Loss: 0.000018
Train Epoch: 2 [18560/104000 (18%)]	Loss: 0.000121
Train Epoch: 2 [19200/104000 (18%)]	Loss: 0.000019
Train Epoch: 2 [19840/104000 (19%)]	Loss: 0.000008
Train Epoch: 2 [20480/104000 (20%)]	Loss: 0.000017
Train Epoch: 2 [21120/104000 (20%)]	Loss: 0.000089
Train Epoch: 2 [21760/104000 (21%)]	Loss: 0.000056
Train Epoch: 2 [22400/104000 (22%)]	Loss: 0.000010
Train Epoch: 2 [23040/104000 (22%)]	Loss: 0.000024
Train Epoch: 2 [23680/104000 (23%)]	Loss: 0.000093
Train Epoch: 2 [24320/104000 (23%)]	Loss: 0.000022
Train Epoch: 2 [24960/104000 (24%)]	Loss: 0.000027
Train Epoch: 2 [25600/104000 (25%)]	Loss: 0.000089
Train Epoch: 2 [26240/104000 (25%)]	Loss: 0.000019
Train Epoch: 2 [26880/104000 (26%)]	Loss: 0.000258
Train Epoch: 2 [27520/104000 (26%)]	Loss: 0.000018
Train Epoch: 2 [28160/104000 (27%)]	Loss: 0.000007
Train Epoch: 2 [28800/104000 (28%)]	Loss: 0.000018
Train Epoch: 2 [29440/104000 (28%)]	Loss: 0.000057
Train Epoch: 2 [30080/104000 (29%)]	Loss: 0.000011
Train Epoch: 2 [30720/104000 (30%)]	Loss: 0.000012
Train Epoch: 2 [31360/104000 (30%)]	Loss: 0.000017
Train Epoch: 2 [32000/104000 (31%)]	Loss: 0.000051
Train Epoch: 2 [32640/104000 (31%)]	Loss: 0.000011
Train Epoch: 2 [33280/104000 (32%)]	Loss: 0.000019
Train Epoch: 2 [33920/104000 (33%)]	Loss: 0.000753
Train Epoch: 2 [34560/104000 (33%)]	Loss: 0.000037
Train Epoch: 2 [35200/104000 (34%)]	Loss: 0.000026
Train Epoch: 2 [35840/104000 (34%)]	Loss: 0.000099
Train Epoch: 2 [36480/104000 (35%)]	Loss: 0.000037
Train Epoch: 2 [37120/104000 (36%)]	Loss: 0.000019
Train Epoch: 2 [37760/104000 (36%)]	Loss: 0.000008
Train Epoch: 2 [38400/104000 (37%)]	Loss: 0.000019
Train Epoch: 2 [39040/104000 (38%)]	Loss: 0.000011
Train Epoch: 2 [39680/104000 (38%)]	Loss: 0.000110
Train Epoch: 2 [40320/104000 (39%)]	Loss: 0.000026
Train Epoch: 2 [40960/104000 (39%)]	Loss: 0.000008
Train Epoch: 2 [41600/104000 (40%)]	Loss: 0.000137
Train Epoch: 2 [42240/104000 (41%)]	Loss: 0.000023
Train Epoch: 2 [42880/104000 (41%)]	Loss: 0.000011
Train Epoch: 2 [43520/104000 (42%)]	Loss: 0.000045
Train Epoch: 2 [44160/104000 (42%)]	Loss: 0.000020
Train Epoch: 2 [44800/104000 (43%)]	Loss: 0.000013
Train Epoch: 2 [45440/104000 (44%)]	Loss: 0.000048
Train Epoch: 2 [46080/104000 (44%)]	Loss: 0.000009
Train Epoch: 2 [46720/104000 (45%)]	Loss: 0.000013
Train Epoch: 2 [47360/104000 (46%)]	Loss: 0.000101
Train Epoch: 2 [48000/104000 (46%)]	Loss: 0.000010
Train Epoch: 2 [48640/104000 (47%)]	Loss: 0.000063
Train Epoch: 2 [49280/104000 (47%)]	Loss: 0.000170
Train Epoch: 2 [49920/104000 (48%)]	Loss: 0.000005
Train Epoch: 2 [50560/104000 (49%)]	Loss: 0.000034
Train Epoch: 2 [51200/104000 (49%)]	Loss: 0.000011
Train Epoch: 2 [51840/104000 (50%)]	Loss: 0.000045
Train Epoch: 2 [52480/104000 (50%)]	Loss: 0.000013
Train Epoch: 2 [53120/104000 (51%)]	Loss: 0.000018
Train Epoch: 2 [53760/104000 (52%)]	Loss: 0.000685
Train Epoch: 2 [54400/104000 (52%)]	Loss: 0.000035
Train Epoch: 2 [55040/104000 (53%)]	Loss: 0.000171
Train Epoch: 2 [55680/104000 (54%)]	Loss: 0.000015
Train Epoch: 2 [56320/104000 (54%)]	Loss: 0.000009
Train Epoch: 2 [56960/104000 (55%)]	Loss: 0.000020
Train Epoch: 2 [57600/104000 (55%)]	Loss: 0.000031
Train Epoch: 2 [58240/104000 (56%)]	Loss: 0.000010
Train Epoch: 2 [58880/104000 (57%)]	Loss: 0.000042
Train Epoch: 2 [59520/104000 (57%)]	Loss: 0.000016
Train Epoch: 2 [60160/104000 (58%)]	Loss: 0.000031
Train Epoch: 2 [60800/104000 (58%)]	Loss: 0.000074
Train Epoch: 2 [61440/104000 (59%)]	Loss: 0.000042
Train Epoch: 2 [62080/104000 (60%)]	Loss: 0.000018
Train Epoch: 2 [62720/104000 (60%)]	Loss: 0.000044
Train Epoch: 2 [63360/104000 (61%)]	Loss: 0.000040
Train Epoch: 2 [64000/104000 (62%)]	Loss: 0.000017
Train Epoch: 2 [64640/104000 (62%)]	Loss: 0.000026
Train Epoch: 2 [65280/104000 (63%)]	Loss: 0.000050
Train Epoch: 2 [65920/104000 (63%)]	Loss: 0.000022
Train Epoch: 2 [66560/104000 (64%)]	Loss: 0.000013
Train Epoch: 2 [67200/104000 (65%)]	Loss: 0.000011
Train Epoch: 2 [67840/104000 (65%)]	Loss: 0.000015
Train Epoch: 2 [68480/104000 (66%)]	Loss: 0.000009
Train Epoch: 2 [69120/104000 (66%)]	Loss: 0.000185
Train Epoch: 2 [69760/104000 (67%)]	Loss: 0.000013
Train Epoch: 2 [70400/104000 (68%)]	Loss: 0.000013
Train Epoch: 2 [71040/104000 (68%)]	Loss: 0.000051
Train Epoch: 2 [71680/104000 (69%)]	Loss: 0.000679
Train Epoch: 2 [72320/104000 (70%)]	Loss: 0.000024
Train Epoch: 2 [72960/104000 (70%)]	Loss: 0.000019
Train Epoch: 2 [73600/104000 (71%)]	Loss: 0.000051
Train Epoch: 2 [74240/104000 (71%)]	Loss: 0.000061
Train Epoch: 2 [74880/104000 (72%)]	Loss: 0.000019
Train Epoch: 2 [75520/104000 (73%)]	Loss: 0.000027
Train Epoch: 2 [76160/104000 (73%)]	Loss: 0.000077
Train Epoch: 2 [76800/104000 (74%)]	Loss: 0.000009
Train Epoch: 2 [77440/104000 (74%)]	Loss: 0.000010
Train Epoch: 2 [78080/104000 (75%)]	Loss: 0.000012
Train Epoch: 2 [78720/104000 (76%)]	Loss: 0.000013
Train Epoch: 2 [79360/104000 (76%)]	Loss: 0.000029
Train Epoch: 2 [80000/104000 (77%)]	Loss: 0.000015
Train Epoch: 2 [80640/104000 (78%)]	Loss: 0.000034
Train Epoch: 2 [81280/104000 (78%)]	Loss: 0.000028
Train Epoch: 2 [81920/104000 (79%)]	Loss: 0.000027
Train Epoch: 2 [82560/104000 (79%)]	Loss: 0.000008
Train Epoch: 2 [83200/104000 (80%)]	Loss: 0.000008
Train Epoch: 2 [83840/104000 (81%)]	Loss: 0.000009
Train Epoch: 2 [84480/104000 (81%)]	Loss: 0.000221
Train Epoch: 2 [85120/104000 (82%)]	Loss: 0.000295
Train Epoch: 2 [85760/104000 (82%)]	Loss: 0.000021
Train Epoch: 2 [86400/104000 (83%)]	Loss: 0.000078
Train Epoch: 2 [87040/104000 (84%)]	Loss: 0.000030
Train Epoch: 2 [87680/104000 (84%)]	Loss: 0.000041
Train Epoch: 2 [88320/104000 (85%)]	Loss: 0.000014
Train Epoch: 2 [88960/104000 (86%)]	Loss: 0.000192
Train Epoch: 2 [89600/104000 (86%)]	Loss: 0.000028
Train Epoch: 2 [90240/104000 (87%)]	Loss: 0.000109
Train Epoch: 2 [90880/104000 (87%)]	Loss: 0.000029
Train Epoch: 2 [91520/104000 (88%)]	Loss: 0.000008
Train Epoch: 2 [92160/104000 (89%)]	Loss: 0.000020
Train Epoch: 2 [92800/104000 (89%)]	Loss: 0.000016
Train Epoch: 2 [93440/104000 (90%)]	Loss: 0.000058
Train Epoch: 2 [94080/104000 (90%)]	Loss: 0.000028
Train Epoch: 2 [94720/104000 (91%)]	Loss: 0.000011
Train Epoch: 2 [95360/104000 (92%)]	Loss: 0.000010
Train Epoch: 2 [96000/104000 (92%)]	Loss: 0.000033
Train Epoch: 2 [96640/104000 (93%)]	Loss: 0.000015
Train Epoch: 2 [97280/104000 (94%)]	Loss: 0.000029
Train Epoch: 2 [97920/104000 (94%)]	Loss: 0.000041
Train Epoch: 2 [98560/104000 (95%)]	Loss: 0.000035
Train Epoch: 2 [99200/104000 (95%)]	Loss: 0.000008
Train Epoch: 2 [99840/104000 (96%)]	Loss: 0.000015
Train Epoch: 2 [100480/104000 (97%)]	Loss: 0.000085
Train Epoch: 2 [101120/104000 (97%)]	Loss: 0.000006
Train Epoch: 2 [101760/104000 (98%)]	Loss: 0.000015
Train Epoch: 2 [102400/104000 (98%)]	Loss: 0.000065
Train Epoch: 2 [103040/104000 (99%)]	Loss: 0.000008
Train Epoch: 2 [103680/104000 (100%)]	Loss: 0.000034

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.2114, Accuracy: 26974/30000 (90%)

Train Epoch: 3 [0/104000 (0%)]	Loss: 0.000035
Train Epoch: 3 [640/104000 (1%)]	Loss: 0.000016
Train Epoch: 3 [1280/104000 (1%)]	Loss: 0.000010
Train Epoch: 3 [1920/104000 (2%)]	Loss: 0.000018
Train Epoch: 3 [2560/104000 (2%)]	Loss: 0.000031
Train Epoch: 3 [3200/104000 (3%)]	Loss: 0.000017
Train Epoch: 3 [3840/104000 (4%)]	Loss: 0.000069
Train Epoch: 3 [4480/104000 (4%)]	Loss: 0.000030
Train Epoch: 3 [5120/104000 (5%)]	Loss: 0.000021
Train Epoch: 3 [5760/104000 (6%)]	Loss: 0.000013
Train Epoch: 3 [6400/104000 (6%)]	Loss: 0.000048
Train Epoch: 3 [7040/104000 (7%)]	Loss: 0.000009
Train Epoch: 3 [7680/104000 (7%)]	Loss: 0.000009
Train Epoch: 3 [8320/104000 (8%)]	Loss: 0.000017
Train Epoch: 3 [8960/104000 (9%)]	Loss: 0.000008
Train Epoch: 3 [9600/104000 (9%)]	Loss: 0.000014
Train Epoch: 3 [10240/104000 (10%)]	Loss: 0.000019
Train Epoch: 3 [10880/104000 (10%)]	Loss: 0.000035
Train Epoch: 3 [11520/104000 (11%)]	Loss: 0.000179
Train Epoch: 3 [12160/104000 (12%)]	Loss: 0.000009
Train Epoch: 3 [12800/104000 (12%)]	Loss: 0.000013
Train Epoch: 3 [13440/104000 (13%)]	Loss: 0.000016
Train Epoch: 3 [14080/104000 (14%)]	Loss: 0.000052
Train Epoch: 3 [14720/104000 (14%)]	Loss: 0.000011
Train Epoch: 3 [15360/104000 (15%)]	Loss: 0.000129
Train Epoch: 3 [16000/104000 (15%)]	Loss: 0.000133
Train Epoch: 3 [16640/104000 (16%)]	Loss: 0.000011
Train Epoch: 3 [17280/104000 (17%)]	Loss: 0.000008
Train Epoch: 3 [17920/104000 (17%)]	Loss: 0.000282
Train Epoch: 3 [18560/104000 (18%)]	Loss: 0.000007
Train Epoch: 3 [19200/104000 (18%)]	Loss: 0.000007
Train Epoch: 3 [19840/104000 (19%)]	Loss: 0.000020
Train Epoch: 3 [20480/104000 (20%)]	Loss: 0.000005
Train Epoch: 3 [21120/104000 (20%)]	Loss: 0.000010
Train Epoch: 3 [21760/104000 (21%)]	Loss: 0.000008
Train Epoch: 3 [22400/104000 (22%)]	Loss: 0.000017
Train Epoch: 3 [23040/104000 (22%)]	Loss: 0.000009
Train Epoch: 3 [23680/104000 (23%)]	Loss: 0.000065
Train Epoch: 3 [24320/104000 (23%)]	Loss: 0.000013
Train Epoch: 3 [24960/104000 (24%)]	Loss: 0.000007
Train Epoch: 3 [25600/104000 (25%)]	Loss: 0.000021
Train Epoch: 3 [26240/104000 (25%)]	Loss: 0.000007
Train Epoch: 3 [26880/104000 (26%)]	Loss: 0.000016
Train Epoch: 3 [27520/104000 (26%)]	Loss: 0.000013
Train Epoch: 3 [28160/104000 (27%)]	Loss: 0.000008
Train Epoch: 3 [28800/104000 (28%)]	Loss: 0.000011
Train Epoch: 3 [29440/104000 (28%)]	Loss: 0.000034
Train Epoch: 3 [30080/104000 (29%)]	Loss: 0.000070
Train Epoch: 3 [30720/104000 (30%)]	Loss: 0.000006
Train Epoch: 3 [31360/104000 (30%)]	Loss: 0.000009
Train Epoch: 3 [32000/104000 (31%)]	Loss: 0.000007
Train Epoch: 3 [32640/104000 (31%)]	Loss: 0.000016
Train Epoch: 3 [33280/104000 (32%)]	Loss: 0.000020
Train Epoch: 3 [33920/104000 (33%)]	Loss: 0.000021
Train Epoch: 3 [34560/104000 (33%)]	Loss: 0.000019
Train Epoch: 3 [35200/104000 (34%)]	Loss: 0.000008
Train Epoch: 3 [35840/104000 (34%)]	Loss: 0.000005
Train Epoch: 3 [36480/104000 (35%)]	Loss: 0.000010
Train Epoch: 3 [37120/104000 (36%)]	Loss: 0.000046
Train Epoch: 3 [37760/104000 (36%)]	Loss: 0.000043
Train Epoch: 3 [38400/104000 (37%)]	Loss: 0.000015
Train Epoch: 3 [39040/104000 (38%)]	Loss: 0.000008
Train Epoch: 3 [39680/104000 (38%)]	Loss: 0.000008
Train Epoch: 3 [40320/104000 (39%)]	Loss: 0.000031
Train Epoch: 3 [40960/104000 (39%)]	Loss: 0.000158
Train Epoch: 3 [41600/104000 (40%)]	Loss: 0.000085
Train Epoch: 3 [42240/104000 (41%)]	Loss: 0.000009
Train Epoch: 3 [42880/104000 (41%)]	Loss: 0.000013
Train Epoch: 3 [43520/104000 (42%)]	Loss: 0.000014
Train Epoch: 3 [44160/104000 (42%)]	Loss: 0.000016
Train Epoch: 3 [44800/104000 (43%)]	Loss: 0.000107
Train Epoch: 3 [45440/104000 (44%)]	Loss: 0.000022
Train Epoch: 3 [46080/104000 (44%)]	Loss: 0.000008
Train Epoch: 3 [46720/104000 (45%)]	Loss: 0.000007
Train Epoch: 3 [47360/104000 (46%)]	Loss: 0.000066
Train Epoch: 3 [48000/104000 (46%)]	Loss: 0.000015
Train Epoch: 3 [48640/104000 (47%)]	Loss: 0.000012
Train Epoch: 3 [49280/104000 (47%)]	Loss: 0.000007
Train Epoch: 3 [49920/104000 (48%)]	Loss: 0.000021
Train Epoch: 3 [50560/104000 (49%)]	Loss: 0.000006
Train Epoch: 3 [51200/104000 (49%)]	Loss: 0.000009
Train Epoch: 3 [51840/104000 (50%)]	Loss: 0.000077
Train Epoch: 3 [52480/104000 (50%)]	Loss: 0.000013
Train Epoch: 3 [53120/104000 (51%)]	Loss: 0.000008
Train Epoch: 3 [53760/104000 (52%)]	Loss: 0.000008
Train Epoch: 3 [54400/104000 (52%)]	Loss: 0.000018
Train Epoch: 3 [55040/104000 (53%)]	Loss: 0.000012
Train Epoch: 3 [55680/104000 (54%)]	Loss: 0.000075
Train Epoch: 3 [56320/104000 (54%)]	Loss: 0.000010
Train Epoch: 3 [56960/104000 (55%)]	Loss: 0.000018
Train Epoch: 3 [57600/104000 (55%)]	Loss: 0.000010
Train Epoch: 3 [58240/104000 (56%)]	Loss: 0.000127
Train Epoch: 3 [58880/104000 (57%)]	Loss: 0.000006
Train Epoch: 3 [59520/104000 (57%)]	Loss: 0.000004
Train Epoch: 3 [60160/104000 (58%)]	Loss: 0.000019
Train Epoch: 3 [60800/104000 (58%)]	Loss: 0.000042
Train Epoch: 3 [61440/104000 (59%)]	Loss: 0.000031
Train Epoch: 3 [62080/104000 (60%)]	Loss: 0.000004
Train Epoch: 3 [62720/104000 (60%)]	Loss: 0.000119
Train Epoch: 3 [63360/104000 (61%)]	Loss: 0.000048
Train Epoch: 3 [64000/104000 (62%)]	Loss: 0.000023
Train Epoch: 3 [64640/104000 (62%)]	Loss: 0.000011
Train Epoch: 3 [65280/104000 (63%)]	Loss: 0.000023
Train Epoch: 3 [65920/104000 (63%)]	Loss: 0.000021
Train Epoch: 3 [66560/104000 (64%)]	Loss: 0.000063
Train Epoch: 3 [67200/104000 (65%)]	Loss: 0.000072
Train Epoch: 3 [67840/104000 (65%)]	Loss: 0.000012
Train Epoch: 3 [68480/104000 (66%)]	Loss: 0.000014
Train Epoch: 3 [69120/104000 (66%)]	Loss: 0.000016
Train Epoch: 3 [69760/104000 (67%)]	Loss: 0.000072
Train Epoch: 3 [70400/104000 (68%)]	Loss: 0.000012
Train Epoch: 3 [71040/104000 (68%)]	Loss: 0.000011
Train Epoch: 3 [71680/104000 (69%)]	Loss: 0.000021
Train Epoch: 3 [72320/104000 (70%)]	Loss: 0.000011
Train Epoch: 3 [72960/104000 (70%)]	Loss: 0.000006
Train Epoch: 3 [73600/104000 (71%)]	Loss: 0.000006
Train Epoch: 3 [74240/104000 (71%)]	Loss: 0.000035
Train Epoch: 3 [74880/104000 (72%)]	Loss: 0.000010
Train Epoch: 3 [75520/104000 (73%)]	Loss: 0.000009
Train Epoch: 3 [76160/104000 (73%)]	Loss: 0.000009
Train Epoch: 3 [76800/104000 (74%)]	Loss: 0.000086
Train Epoch: 3 [77440/104000 (74%)]	Loss: 0.000047
Train Epoch: 3 [78080/104000 (75%)]	Loss: 0.000012
Train Epoch: 3 [78720/104000 (76%)]	Loss: 0.000030
Train Epoch: 3 [79360/104000 (76%)]	Loss: 0.000054
Train Epoch: 3 [80000/104000 (77%)]	Loss: 0.000047
Train Epoch: 3 [80640/104000 (78%)]	Loss: 0.000015
Train Epoch: 3 [81280/104000 (78%)]	Loss: 0.000043
Train Epoch: 3 [81920/104000 (79%)]	Loss: 0.000006
Train Epoch: 3 [82560/104000 (79%)]	Loss: 0.000030
Train Epoch: 3 [83200/104000 (80%)]	Loss: 0.000012
Train Epoch: 3 [83840/104000 (81%)]	Loss: 0.000007
Train Epoch: 3 [84480/104000 (81%)]	Loss: 0.000006
Train Epoch: 3 [85120/104000 (82%)]	Loss: 0.000010
Train Epoch: 3 [85760/104000 (82%)]	Loss: 0.000011
Train Epoch: 3 [86400/104000 (83%)]	Loss: 0.000014
Train Epoch: 3 [87040/104000 (84%)]	Loss: 0.000008
Train Epoch: 3 [87680/104000 (84%)]	Loss: 0.000024
Train Epoch: 3 [88320/104000 (85%)]	Loss: 0.000018
Train Epoch: 3 [88960/104000 (86%)]	Loss: 0.000089
Train Epoch: 3 [89600/104000 (86%)]	Loss: 0.000048
Train Epoch: 3 [90240/104000 (87%)]	Loss: 0.000016
Train Epoch: 3 [90880/104000 (87%)]	Loss: 0.000011
Train Epoch: 3 [91520/104000 (88%)]	Loss: 0.000006
Train Epoch: 3 [92160/104000 (89%)]	Loss: 0.000028
Train Epoch: 3 [92800/104000 (89%)]	Loss: 0.000079
Train Epoch: 3 [93440/104000 (90%)]	Loss: 0.000007
Train Epoch: 3 [94080/104000 (90%)]	Loss: 0.000007
Train Epoch: 3 [94720/104000 (91%)]	Loss: 0.000003
Train Epoch: 3 [95360/104000 (92%)]	Loss: 0.000012
Train Epoch: 3 [96000/104000 (92%)]	Loss: 0.000007
Train Epoch: 3 [96640/104000 (93%)]	Loss: 0.000005
Train Epoch: 3 [97280/104000 (94%)]	Loss: 0.000006
Train Epoch: 3 [97920/104000 (94%)]	Loss: 0.000008
Train Epoch: 3 [98560/104000 (95%)]	Loss: 0.000005
Train Epoch: 3 [99200/104000 (95%)]	Loss: 0.000012
Train Epoch: 3 [99840/104000 (96%)]	Loss: 0.000010
Train Epoch: 3 [100480/104000 (97%)]	Loss: 0.000010
Train Epoch: 3 [101120/104000 (97%)]	Loss: 0.000023
Train Epoch: 3 [101760/104000 (98%)]	Loss: 0.000023
Train Epoch: 3 [102400/104000 (98%)]	Loss: 0.000019
Train Epoch: 3 [103040/104000 (99%)]	Loss: 0.000008
Train Epoch: 3 [103680/104000 (100%)]	Loss: 0.000052

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.2591, Accuracy: 26312/30000 (88%)

Train Epoch: 4 [0/104000 (0%)]	Loss: 0.000011
Train Epoch: 4 [640/104000 (1%)]	Loss: 0.000014
Train Epoch: 4 [1280/104000 (1%)]	Loss: 0.000014
Train Epoch: 4 [1920/104000 (2%)]	Loss: 0.000007
Train Epoch: 4 [2560/104000 (2%)]	Loss: 0.000013
Train Epoch: 4 [3200/104000 (3%)]	Loss: 0.000009
Train Epoch: 4 [3840/104000 (4%)]	Loss: 0.000006
Train Epoch: 4 [4480/104000 (4%)]	Loss: 0.000232
Train Epoch: 4 [5120/104000 (5%)]	Loss: 0.000019
Train Epoch: 4 [5760/104000 (6%)]	Loss: 0.000023
Train Epoch: 4 [6400/104000 (6%)]	Loss: 0.000005
Train Epoch: 4 [7040/104000 (7%)]	Loss: 0.000014
Train Epoch: 4 [7680/104000 (7%)]	Loss: 0.000010
Train Epoch: 4 [8320/104000 (8%)]	Loss: 0.000088
Train Epoch: 4 [8960/104000 (9%)]	Loss: 0.000037
Train Epoch: 4 [9600/104000 (9%)]	Loss: 0.000008
Train Epoch: 4 [10240/104000 (10%)]	Loss: 0.000023
Train Epoch: 4 [10880/104000 (10%)]	Loss: 0.000021
Train Epoch: 4 [11520/104000 (11%)]	Loss: 0.000008
Train Epoch: 4 [12160/104000 (12%)]	Loss: 0.000062
Train Epoch: 4 [12800/104000 (12%)]	Loss: 0.000174
Train Epoch: 4 [13440/104000 (13%)]	Loss: 0.000005
Train Epoch: 4 [14080/104000 (14%)]	Loss: 0.000019
Train Epoch: 4 [14720/104000 (14%)]	Loss: 0.000234
Train Epoch: 4 [15360/104000 (15%)]	Loss: 0.000008
Train Epoch: 4 [16000/104000 (15%)]	Loss: 0.000007
Train Epoch: 4 [16640/104000 (16%)]	Loss: 0.000005
Train Epoch: 4 [17280/104000 (17%)]	Loss: 0.000007
Train Epoch: 4 [17920/104000 (17%)]	Loss: 0.000048
Train Epoch: 4 [18560/104000 (18%)]	Loss: 0.000016
Train Epoch: 4 [19200/104000 (18%)]	Loss: 0.000005
Train Epoch: 4 [19840/104000 (19%)]	Loss: 0.000004
Train Epoch: 4 [20480/104000 (20%)]	Loss: 0.000050
Train Epoch: 4 [21120/104000 (20%)]	Loss: 0.000004
Train Epoch: 4 [21760/104000 (21%)]	Loss: 0.000017
Train Epoch: 4 [22400/104000 (22%)]	Loss: 0.000107
Train Epoch: 4 [23040/104000 (22%)]	Loss: 0.000007
Train Epoch: 4 [23680/104000 (23%)]	Loss: 0.000007
Train Epoch: 4 [24320/104000 (23%)]	Loss: 0.000073
Train Epoch: 4 [24960/104000 (24%)]	Loss: 0.000004
Train Epoch: 4 [25600/104000 (25%)]	Loss: 0.000052
Train Epoch: 4 [26240/104000 (25%)]	Loss: 0.000008
Train Epoch: 4 [26880/104000 (26%)]	Loss: 0.000021
Train Epoch: 4 [27520/104000 (26%)]	Loss: 0.000006
Train Epoch: 4 [28160/104000 (27%)]	Loss: 0.000038
Train Epoch: 4 [28800/104000 (28%)]	Loss: 0.000030
Train Epoch: 4 [29440/104000 (28%)]	Loss: 0.000009
Train Epoch: 4 [30080/104000 (29%)]	Loss: 0.000024
Train Epoch: 4 [30720/104000 (30%)]	Loss: 0.000007
Train Epoch: 4 [31360/104000 (30%)]	Loss: 0.000018
Train Epoch: 4 [32000/104000 (31%)]	Loss: 0.000010
Train Epoch: 4 [32640/104000 (31%)]	Loss: 0.000007
Train Epoch: 4 [33280/104000 (32%)]	Loss: 0.000004
Train Epoch: 4 [33920/104000 (33%)]	Loss: 0.000004
Train Epoch: 4 [34560/104000 (33%)]	Loss: 0.000003
Train Epoch: 4 [35200/104000 (34%)]	Loss: 0.000005
Train Epoch: 4 [35840/104000 (34%)]	Loss: 0.000014
Train Epoch: 4 [36480/104000 (35%)]	Loss: 0.000164
Train Epoch: 4 [37120/104000 (36%)]	Loss: 0.000014
Train Epoch: 4 [37760/104000 (36%)]	Loss: 0.000007
Train Epoch: 4 [38400/104000 (37%)]	Loss: 0.000030
Train Epoch: 4 [39040/104000 (38%)]	Loss: 0.000005
Train Epoch: 4 [39680/104000 (38%)]	Loss: 0.000012
Train Epoch: 4 [40320/104000 (39%)]	Loss: 0.000009
Train Epoch: 4 [40960/104000 (39%)]	Loss: 0.000049
Train Epoch: 4 [41600/104000 (40%)]	Loss: 0.000006
Train Epoch: 4 [42240/104000 (41%)]	Loss: 0.000011
Train Epoch: 4 [42880/104000 (41%)]	Loss: 0.000004
Train Epoch: 4 [43520/104000 (42%)]	Loss: 0.000008
Train Epoch: 4 [44160/104000 (42%)]	Loss: 0.000014
Train Epoch: 4 [44800/104000 (43%)]	Loss: 0.000010
Train Epoch: 4 [45440/104000 (44%)]	Loss: 0.000005
Train Epoch: 4 [46080/104000 (44%)]	Loss: 0.000009
Train Epoch: 4 [46720/104000 (45%)]	Loss: 0.000004
Train Epoch: 4 [47360/104000 (46%)]	Loss: 0.000004
Train Epoch: 4 [48000/104000 (46%)]	Loss: 0.000007
Train Epoch: 4 [48640/104000 (47%)]	Loss: 0.000008
Train Epoch: 4 [49280/104000 (47%)]	Loss: 0.000095
Train Epoch: 4 [49920/104000 (48%)]	Loss: 0.000013
Train Epoch: 4 [50560/104000 (49%)]	Loss: 0.000014
Train Epoch: 4 [51200/104000 (49%)]	Loss: 0.000009
Train Epoch: 4 [51840/104000 (50%)]	Loss: 0.000049
Train Epoch: 4 [52480/104000 (50%)]	Loss: 0.000016
Train Epoch: 4 [53120/104000 (51%)]	Loss: 0.000009
Train Epoch: 4 [53760/104000 (52%)]	Loss: 0.000007
Train Epoch: 4 [54400/104000 (52%)]	Loss: 0.000007
Train Epoch: 4 [55040/104000 (53%)]	Loss: 0.000116
Train Epoch: 4 [55680/104000 (54%)]	Loss: 0.000004
Train Epoch: 4 [56320/104000 (54%)]	Loss: 0.000007
Train Epoch: 4 [56960/104000 (55%)]	Loss: 0.000013
Train Epoch: 4 [57600/104000 (55%)]	Loss: 0.000011
Train Epoch: 4 [58240/104000 (56%)]	Loss: 0.000013
Train Epoch: 4 [58880/104000 (57%)]	Loss: 0.000005
Train Epoch: 4 [59520/104000 (57%)]	Loss: 0.000028
Train Epoch: 4 [60160/104000 (58%)]	Loss: 0.000005
Train Epoch: 4 [60800/104000 (58%)]	Loss: 0.000034
Train Epoch: 4 [61440/104000 (59%)]	Loss: 0.000006
Train Epoch: 4 [62080/104000 (60%)]	Loss: 0.000023
Train Epoch: 4 [62720/104000 (60%)]	Loss: 0.000006
Train Epoch: 4 [63360/104000 (61%)]	Loss: 0.000006
Train Epoch: 4 [64000/104000 (62%)]	Loss: 0.000006
Train Epoch: 4 [64640/104000 (62%)]	Loss: 0.000011
Train Epoch: 4 [65280/104000 (63%)]	Loss: 0.000011
Train Epoch: 4 [65920/104000 (63%)]	Loss: 0.000006
Train Epoch: 4 [66560/104000 (64%)]	Loss: 0.000030
Train Epoch: 4 [67200/104000 (65%)]	Loss: 0.000004
Train Epoch: 4 [67840/104000 (65%)]	Loss: 0.000005
Train Epoch: 4 [68480/104000 (66%)]	Loss: 0.000010
Train Epoch: 4 [69120/104000 (66%)]	Loss: 0.000015
Train Epoch: 4 [69760/104000 (67%)]	Loss: 0.000004
Train Epoch: 4 [70400/104000 (68%)]	Loss: 0.000012
Train Epoch: 4 [71040/104000 (68%)]	Loss: 0.000024
Train Epoch: 4 [71680/104000 (69%)]	Loss: 0.000081
Train Epoch: 4 [72320/104000 (70%)]	Loss: 0.000004
Train Epoch: 4 [72960/104000 (70%)]	Loss: 0.000009
Train Epoch: 4 [73600/104000 (71%)]	Loss: 0.000017
Train Epoch: 4 [74240/104000 (71%)]	Loss: 0.000035
Train Epoch: 4 [74880/104000 (72%)]	Loss: 0.000089
Train Epoch: 4 [75520/104000 (73%)]	Loss: 0.000005
Train Epoch: 4 [76160/104000 (73%)]	Loss: 0.000008
Train Epoch: 4 [76800/104000 (74%)]	Loss: 0.000008
Train Epoch: 4 [77440/104000 (74%)]	Loss: 0.000010
Train Epoch: 4 [78080/104000 (75%)]	Loss: 0.000013
Train Epoch: 4 [78720/104000 (76%)]	Loss: 0.000014
Train Epoch: 4 [79360/104000 (76%)]	Loss: 0.000064
Train Epoch: 4 [80000/104000 (77%)]	Loss: 0.000233
Train Epoch: 4 [80640/104000 (78%)]	Loss: 0.000006
Train Epoch: 4 [81280/104000 (78%)]	Loss: 0.000103
Train Epoch: 4 [81920/104000 (79%)]	Loss: 0.000003
Train Epoch: 4 [82560/104000 (79%)]	Loss: 0.000009
Train Epoch: 4 [83200/104000 (80%)]	Loss: 0.000061
Train Epoch: 4 [83840/104000 (81%)]	Loss: 0.000067
Train Epoch: 4 [84480/104000 (81%)]	Loss: 0.000004
Train Epoch: 4 [85120/104000 (82%)]	Loss: 0.000003
Train Epoch: 4 [85760/104000 (82%)]	Loss: 0.000018
Train Epoch: 4 [86400/104000 (83%)]	Loss: 0.000004
Train Epoch: 4 [87040/104000 (84%)]	Loss: 0.000019
Train Epoch: 4 [87680/104000 (84%)]	Loss: 0.000004
Train Epoch: 4 [88320/104000 (85%)]	Loss: 0.000081
Train Epoch: 4 [88960/104000 (86%)]	Loss: 0.000031
Train Epoch: 4 [89600/104000 (86%)]	Loss: 0.000005
Train Epoch: 4 [90240/104000 (87%)]	Loss: 0.000009
Train Epoch: 4 [90880/104000 (87%)]	Loss: 0.000009
Train Epoch: 4 [91520/104000 (88%)]	Loss: 0.000006
Train Epoch: 4 [92160/104000 (89%)]	Loss: 0.000010
Train Epoch: 4 [92800/104000 (89%)]	Loss: 0.000007
Train Epoch: 4 [93440/104000 (90%)]	Loss: 0.000014
Train Epoch: 4 [94080/104000 (90%)]	Loss: 0.000004
Train Epoch: 4 [94720/104000 (91%)]	Loss: 0.000014
Train Epoch: 4 [95360/104000 (92%)]	Loss: 0.000002
Train Epoch: 4 [96000/104000 (92%)]	Loss: 0.000009
Train Epoch: 4 [96640/104000 (93%)]	Loss: 0.000005
Train Epoch: 4 [97280/104000 (94%)]	Loss: 0.000002
Train Epoch: 4 [97920/104000 (94%)]	Loss: 0.000010
Train Epoch: 4 [98560/104000 (95%)]	Loss: 0.000021
Train Epoch: 4 [99200/104000 (95%)]	Loss: 0.000141
Train Epoch: 4 [99840/104000 (96%)]	Loss: 0.000010
Train Epoch: 4 [100480/104000 (97%)]	Loss: 0.000076
Train Epoch: 4 [101120/104000 (97%)]	Loss: 0.000013
Train Epoch: 4 [101760/104000 (98%)]	Loss: 0.000016
Train Epoch: 4 [102400/104000 (98%)]	Loss: 0.000010
Train Epoch: 4 [103040/104000 (99%)]	Loss: 0.000018
Train Epoch: 4 [103680/104000 (100%)]	Loss: 0.000008

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.3767, Accuracy: 24775/30000 (83%)

Train Epoch: 5 [0/104000 (0%)]	Loss: 0.000031
Train Epoch: 5 [640/104000 (1%)]	Loss: 0.000005
Train Epoch: 5 [1280/104000 (1%)]	Loss: 0.000005
Train Epoch: 5 [1920/104000 (2%)]	Loss: 0.000002
Train Epoch: 5 [2560/104000 (2%)]	Loss: 0.000023
Train Epoch: 5 [3200/104000 (3%)]	Loss: 0.000010
Train Epoch: 5 [3840/104000 (4%)]	Loss: 0.000008
Train Epoch: 5 [4480/104000 (4%)]	Loss: 0.000003
Train Epoch: 5 [5120/104000 (5%)]	Loss: 0.000037
Train Epoch: 5 [5760/104000 (6%)]	Loss: 0.000203
Train Epoch: 5 [6400/104000 (6%)]	Loss: 0.000017
Train Epoch: 5 [7040/104000 (7%)]	Loss: 0.000013
Train Epoch: 5 [7680/104000 (7%)]	Loss: 0.000004
Train Epoch: 5 [8320/104000 (8%)]	Loss: 0.000015
Train Epoch: 5 [8960/104000 (9%)]	Loss: 0.000015
Train Epoch: 5 [9600/104000 (9%)]	Loss: 0.000012
Train Epoch: 5 [10240/104000 (10%)]	Loss: 0.000010
Train Epoch: 5 [10880/104000 (10%)]	Loss: 0.000012
Train Epoch: 5 [11520/104000 (11%)]	Loss: 0.000009
Train Epoch: 5 [12160/104000 (12%)]	Loss: 0.000044
Train Epoch: 5 [12800/104000 (12%)]	Loss: 0.000008
Train Epoch: 5 [13440/104000 (13%)]	Loss: 0.000007
Train Epoch: 5 [14080/104000 (14%)]	Loss: 0.000004
Train Epoch: 5 [14720/104000 (14%)]	Loss: 0.000002
Train Epoch: 5 [15360/104000 (15%)]	Loss: 0.000009
Train Epoch: 5 [16000/104000 (15%)]	Loss: 0.000021
Train Epoch: 5 [16640/104000 (16%)]	Loss: 0.000008
Train Epoch: 5 [17280/104000 (17%)]	Loss: 0.000050
Train Epoch: 5 [17920/104000 (17%)]	Loss: 0.000018
Train Epoch: 5 [18560/104000 (18%)]	Loss: 0.000006
Train Epoch: 5 [19200/104000 (18%)]	Loss: 0.000005
Train Epoch: 5 [19840/104000 (19%)]	Loss: 0.000004
Train Epoch: 5 [20480/104000 (20%)]	Loss: 0.000008
Train Epoch: 5 [21120/104000 (20%)]	Loss: 0.000010
Train Epoch: 5 [21760/104000 (21%)]	Loss: 0.000006
Train Epoch: 5 [22400/104000 (22%)]	Loss: 0.000014
Train Epoch: 5 [23040/104000 (22%)]	Loss: 0.000005
Train Epoch: 5 [23680/104000 (23%)]	Loss: 0.000004
Train Epoch: 5 [24320/104000 (23%)]	Loss: 0.000003
Train Epoch: 5 [24960/104000 (24%)]	Loss: 0.000056
Train Epoch: 5 [25600/104000 (25%)]	Loss: 0.000129
Train Epoch: 5 [26240/104000 (25%)]	Loss: 0.000009
Train Epoch: 5 [26880/104000 (26%)]	Loss: 0.000006
Train Epoch: 5 [27520/104000 (26%)]	Loss: 0.000022
Train Epoch: 5 [28160/104000 (27%)]	Loss: 0.000006
Train Epoch: 5 [28800/104000 (28%)]	Loss: 0.000012
Train Epoch: 5 [29440/104000 (28%)]	Loss: 0.000008
Train Epoch: 5 [30080/104000 (29%)]	Loss: 0.000037
Train Epoch: 5 [30720/104000 (30%)]	Loss: 0.000003
Train Epoch: 5 [31360/104000 (30%)]	Loss: 0.000004
Train Epoch: 5 [32000/104000 (31%)]	Loss: 0.000018
Train Epoch: 5 [32640/104000 (31%)]	Loss: 0.000014
Train Epoch: 5 [33280/104000 (32%)]	Loss: 0.000010
Train Epoch: 5 [33920/104000 (33%)]	Loss: 0.000005
Train Epoch: 5 [34560/104000 (33%)]	Loss: 0.000004
Train Epoch: 5 [35200/104000 (34%)]	Loss: 0.000007
Train Epoch: 5 [35840/104000 (34%)]	Loss: 0.000004
Train Epoch: 5 [36480/104000 (35%)]	Loss: 0.000015
Train Epoch: 5 [37120/104000 (36%)]	Loss: 0.000008
Train Epoch: 5 [37760/104000 (36%)]	Loss: 0.000004
Train Epoch: 5 [38400/104000 (37%)]	Loss: 0.000010
Train Epoch: 5 [39040/104000 (38%)]	Loss: 0.000002
Train Epoch: 5 [39680/104000 (38%)]	Loss: 0.000051
Train Epoch: 5 [40320/104000 (39%)]	Loss: 0.000012
Train Epoch: 5 [40960/104000 (39%)]	Loss: 0.000006
Train Epoch: 5 [41600/104000 (40%)]	Loss: 0.000031
Train Epoch: 5 [42240/104000 (41%)]	Loss: 0.000008
Train Epoch: 5 [42880/104000 (41%)]	Loss: 0.000015
Train Epoch: 5 [43520/104000 (42%)]	Loss: 0.000006
Train Epoch: 5 [44160/104000 (42%)]	Loss: 0.000035
Train Epoch: 5 [44800/104000 (43%)]	Loss: 0.000011
Train Epoch: 5 [45440/104000 (44%)]	Loss: 0.000012
Train Epoch: 5 [46080/104000 (44%)]	Loss: 0.000006
Train Epoch: 5 [46720/104000 (45%)]	Loss: 0.000009
Train Epoch: 5 [47360/104000 (46%)]	Loss: 0.000037
Train Epoch: 5 [48000/104000 (46%)]	Loss: 0.000002
Train Epoch: 5 [48640/104000 (47%)]	Loss: 0.000005
Train Epoch: 5 [49280/104000 (47%)]	Loss: 0.000002
Train Epoch: 5 [49920/104000 (48%)]	Loss: 0.000012
Train Epoch: 5 [50560/104000 (49%)]	Loss: 0.000006
Train Epoch: 5 [51200/104000 (49%)]	Loss: 0.000061
Train Epoch: 5 [51840/104000 (50%)]	Loss: 0.000006
Train Epoch: 5 [52480/104000 (50%)]	Loss: 0.000004
Train Epoch: 5 [53120/104000 (51%)]	Loss: 0.000059
Train Epoch: 5 [53760/104000 (52%)]	Loss: 0.000010
Train Epoch: 5 [54400/104000 (52%)]	Loss: 0.000003
Train Epoch: 5 [55040/104000 (53%)]	Loss: 0.000016
Train Epoch: 5 [55680/104000 (54%)]	Loss: 0.000005
Train Epoch: 5 [56320/104000 (54%)]	Loss: 0.000006
Train Epoch: 5 [56960/104000 (55%)]	Loss: 0.000227
Train Epoch: 5 [57600/104000 (55%)]	Loss: 0.000008
Train Epoch: 5 [58240/104000 (56%)]	Loss: 0.000008
Train Epoch: 5 [58880/104000 (57%)]	Loss: 0.000014
Train Epoch: 5 [59520/104000 (57%)]	Loss: 0.000002
Train Epoch: 5 [60160/104000 (58%)]	Loss: 0.000005
Train Epoch: 5 [60800/104000 (58%)]	Loss: 0.000031
Train Epoch: 5 [61440/104000 (59%)]	Loss: 0.000004
Train Epoch: 5 [62080/104000 (60%)]	Loss: 0.000013
Train Epoch: 5 [62720/104000 (60%)]	Loss: 0.000020
Train Epoch: 5 [63360/104000 (61%)]	Loss: 0.000035
Train Epoch: 5 [64000/104000 (62%)]	Loss: 0.000007
Train Epoch: 5 [64640/104000 (62%)]	Loss: 0.000008
Train Epoch: 5 [65280/104000 (63%)]	Loss: 0.000005
Train Epoch: 5 [65920/104000 (63%)]	Loss: 0.000034
Train Epoch: 5 [66560/104000 (64%)]	Loss: 0.000004
Train Epoch: 5 [67200/104000 (65%)]	Loss: 0.000054
Train Epoch: 5 [67840/104000 (65%)]	Loss: 0.000040
Train Epoch: 5 [68480/104000 (66%)]	Loss: 0.000004
Train Epoch: 5 [69120/104000 (66%)]	Loss: 0.000003
Train Epoch: 5 [69760/104000 (67%)]	Loss: 0.000023
Train Epoch: 5 [70400/104000 (68%)]	Loss: 0.000005
Train Epoch: 5 [71040/104000 (68%)]	Loss: 0.000037
Train Epoch: 5 [71680/104000 (69%)]	Loss: 0.000005
Train Epoch: 5 [72320/104000 (70%)]	Loss: 0.000007
Train Epoch: 5 [72960/104000 (70%)]	Loss: 0.000005
Train Epoch: 5 [73600/104000 (71%)]	Loss: 0.000004
Train Epoch: 5 [74240/104000 (71%)]	Loss: 0.000008
Train Epoch: 5 [74880/104000 (72%)]	Loss: 0.000015
Train Epoch: 5 [75520/104000 (73%)]	Loss: 0.000004
Train Epoch: 5 [76160/104000 (73%)]	Loss: 0.000005
Train Epoch: 5 [76800/104000 (74%)]	Loss: 0.000004
Train Epoch: 5 [77440/104000 (74%)]	Loss: 0.000003
Train Epoch: 5 [78080/104000 (75%)]	Loss: 0.000013
Train Epoch: 5 [78720/104000 (76%)]	Loss: 0.000002
Train Epoch: 5 [79360/104000 (76%)]	Loss: 0.000189
Train Epoch: 5 [80000/104000 (77%)]	Loss: 0.000044
Train Epoch: 5 [80640/104000 (78%)]	Loss: 0.000009
Train Epoch: 5 [81280/104000 (78%)]	Loss: 0.000002
Train Epoch: 5 [81920/104000 (79%)]	Loss: 0.000008
Train Epoch: 5 [82560/104000 (79%)]	Loss: 0.000002
Train Epoch: 5 [83200/104000 (80%)]	Loss: 0.000004
Train Epoch: 5 [83840/104000 (81%)]	Loss: 0.000019
Train Epoch: 5 [84480/104000 (81%)]	Loss: 0.000004
Train Epoch: 5 [85120/104000 (82%)]	Loss: 0.000004
Train Epoch: 5 [85760/104000 (82%)]	Loss: 0.000009
Train Epoch: 5 [86400/104000 (83%)]	Loss: 0.000012
Train Epoch: 5 [87040/104000 (84%)]	Loss: 0.000016
Train Epoch: 5 [87680/104000 (84%)]	Loss: 0.000008
Train Epoch: 5 [88320/104000 (85%)]	Loss: 0.000025
Train Epoch: 5 [88960/104000 (86%)]	Loss: 0.000006
Train Epoch: 5 [89600/104000 (86%)]	Loss: 0.000398
Train Epoch: 5 [90240/104000 (87%)]	Loss: 0.000003
Train Epoch: 5 [90880/104000 (87%)]	Loss: 0.000003
Train Epoch: 5 [91520/104000 (88%)]	Loss: 0.000008
Train Epoch: 5 [92160/104000 (89%)]	Loss: 0.000012
Train Epoch: 5 [92800/104000 (89%)]	Loss: 0.000009
Train Epoch: 5 [93440/104000 (90%)]	Loss: 0.000018
Train Epoch: 5 [94080/104000 (90%)]	Loss: 0.000004
Train Epoch: 5 [94720/104000 (91%)]	Loss: 0.000007
Train Epoch: 5 [95360/104000 (92%)]	Loss: 0.000002
Train Epoch: 5 [96000/104000 (92%)]	Loss: 0.000007
Train Epoch: 5 [96640/104000 (93%)]	Loss: 0.000005
Train Epoch: 5 [97280/104000 (94%)]	Loss: 0.000012
Train Epoch: 5 [97920/104000 (94%)]	Loss: 0.000004
Train Epoch: 5 [98560/104000 (95%)]	Loss: 0.000007
Train Epoch: 5 [99200/104000 (95%)]	Loss: 0.000391
Train Epoch: 5 [99840/104000 (96%)]	Loss: 0.000004
Train Epoch: 5 [100480/104000 (97%)]	Loss: 0.000006
Train Epoch: 5 [101120/104000 (97%)]	Loss: 0.000467
Train Epoch: 5 [101760/104000 (98%)]	Loss: 0.000007
Train Epoch: 5 [102400/104000 (98%)]	Loss: 0.000004
Train Epoch: 5 [103040/104000 (99%)]	Loss: 0.000067
Train Epoch: 5 [103680/104000 (100%)]	Loss: 0.000057

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.3028, Accuracy: 25782/30000 (86%)

[100.0, 100.0, 100.0, 100.0]
[88.56, 91.44666666666667, 90.16666666666667, 85.94]
The number of neurons in CNN layer is 50
Train Epoch: 1 [0/104000 (0%)]	Loss: 1.178634
Train Epoch: 1 [640/104000 (1%)]	Loss: 0.000044
Train Epoch: 1 [1280/104000 (1%)]	Loss: 0.000000
Train Epoch: 1 [1920/104000 (2%)]	Loss: 0.000000
Train Epoch: 1 [2560/104000 (2%)]	Loss: 0.000002
Train Epoch: 1 [3200/104000 (3%)]	Loss: 0.000001
Train Epoch: 1 [3840/104000 (4%)]	Loss: 0.000000
Train Epoch: 1 [4480/104000 (4%)]	Loss: 0.000000
Train Epoch: 1 [5120/104000 (5%)]	Loss: 0.000000
Train Epoch: 1 [5760/104000 (6%)]	Loss: 0.000000
Train Epoch: 1 [6400/104000 (6%)]	Loss: 0.000000
Train Epoch: 1 [7040/104000 (7%)]	Loss: 0.000010
Train Epoch: 1 [7680/104000 (7%)]	Loss: 0.000000
Train Epoch: 1 [8320/104000 (8%)]	Loss: 0.000000
Train Epoch: 1 [8960/104000 (9%)]	Loss: 0.000000
Train Epoch: 1 [9600/104000 (9%)]	Loss: 0.000001
Train Epoch: 1 [10240/104000 (10%)]	Loss: 0.000000
Train Epoch: 1 [10880/104000 (10%)]	Loss: 0.000000
Train Epoch: 1 [11520/104000 (11%)]	Loss: 0.000000
Train Epoch: 1 [12160/104000 (12%)]	Loss: 0.000000
Train Epoch: 1 [12800/104000 (12%)]	Loss: 0.000000
Train Epoch: 1 [13440/104000 (13%)]	Loss: 0.000002
Train Epoch: 1 [14080/104000 (14%)]	Loss: 0.000000
Train Epoch: 1 [14720/104000 (14%)]	Loss: 0.000000
Train Epoch: 1 [15360/104000 (15%)]	Loss: 0.000000
Train Epoch: 1 [16000/104000 (15%)]	Loss: 0.000000
Train Epoch: 1 [16640/104000 (16%)]	Loss: 0.000000
Train Epoch: 1 [17280/104000 (17%)]	Loss: 0.000000
Train Epoch: 1 [17920/104000 (17%)]	Loss: 0.000000
Train Epoch: 1 [18560/104000 (18%)]	Loss: 0.000000
Train Epoch: 1 [19200/104000 (18%)]	Loss: 0.000000
Train Epoch: 1 [19840/104000 (19%)]	Loss: 0.000000
Train Epoch: 1 [20480/104000 (20%)]	Loss: 0.000000
Train Epoch: 1 [21120/104000 (20%)]	Loss: 0.000000
Train Epoch: 1 [21760/104000 (21%)]	Loss: 0.000000
Train Epoch: 1 [22400/104000 (22%)]	Loss: 0.000000
Train Epoch: 1 [23040/104000 (22%)]	Loss: 0.000000
Train Epoch: 1 [23680/104000 (23%)]	Loss: 0.000000
Train Epoch: 1 [24320/104000 (23%)]	Loss: 0.000000
Train Epoch: 1 [24960/104000 (24%)]	Loss: 0.000003
Train Epoch: 1 [25600/104000 (25%)]	Loss: 0.000000
Train Epoch: 1 [26240/104000 (25%)]	Loss: 0.000000
Train Epoch: 1 [26880/104000 (26%)]	Loss: 0.000000
Train Epoch: 1 [27520/104000 (26%)]	Loss: 0.000000
Train Epoch: 1 [28160/104000 (27%)]	Loss: 0.000004
Train Epoch: 1 [28800/104000 (28%)]	Loss: 0.000010
Train Epoch: 1 [29440/104000 (28%)]	Loss: 0.000000
Train Epoch: 1 [30080/104000 (29%)]	Loss: 0.000000
Train Epoch: 1 [30720/104000 (30%)]	Loss: 0.000000
Train Epoch: 1 [31360/104000 (30%)]	Loss: 0.000000
Train Epoch: 1 [32000/104000 (31%)]	Loss: 0.000000
Train Epoch: 1 [32640/104000 (31%)]	Loss: 0.000000
Train Epoch: 1 [33280/104000 (32%)]	Loss: 0.000000
Train Epoch: 1 [33920/104000 (33%)]	Loss: 0.000000
Train Epoch: 1 [34560/104000 (33%)]	Loss: 0.000000
Train Epoch: 1 [35200/104000 (34%)]	Loss: 0.000000
Train Epoch: 1 [35840/104000 (34%)]	Loss: 0.000000
Train Epoch: 1 [36480/104000 (35%)]	Loss: 0.000000
Train Epoch: 1 [37120/104000 (36%)]	Loss: 0.000000
Train Epoch: 1 [37760/104000 (36%)]	Loss: 0.000000
Train Epoch: 1 [38400/104000 (37%)]	Loss: 0.000000
Train Epoch: 1 [39040/104000 (38%)]	Loss: 0.000000
Train Epoch: 1 [39680/104000 (38%)]	Loss: 0.000000
Train Epoch: 1 [40320/104000 (39%)]	Loss: 0.000003
Train Epoch: 1 [40960/104000 (39%)]	Loss: 0.000000
Train Epoch: 1 [41600/104000 (40%)]	Loss: 0.000000
Train Epoch: 1 [42240/104000 (41%)]	Loss: 0.000000
Train Epoch: 1 [42880/104000 (41%)]	Loss: 0.000000
Train Epoch: 1 [43520/104000 (42%)]	Loss: 0.000000
Train Epoch: 1 [44160/104000 (42%)]	Loss: 0.000000
Train Epoch: 1 [44800/104000 (43%)]	Loss: 0.000000
Train Epoch: 1 [45440/104000 (44%)]	Loss: 0.000001
Train Epoch: 1 [46080/104000 (44%)]	Loss: 0.000000
Train Epoch: 1 [46720/104000 (45%)]	Loss: 0.000000
Train Epoch: 1 [47360/104000 (46%)]	Loss: 0.000000
Train Epoch: 1 [48000/104000 (46%)]	Loss: 0.000000
Train Epoch: 1 [48640/104000 (47%)]	Loss: 0.000003
Train Epoch: 1 [49280/104000 (47%)]	Loss: 0.000000
Train Epoch: 1 [49920/104000 (48%)]	Loss: 0.000000
Train Epoch: 1 [50560/104000 (49%)]	Loss: 0.000000
Train Epoch: 1 [51200/104000 (49%)]	Loss: 0.000000
Train Epoch: 1 [51840/104000 (50%)]	Loss: 0.000000
Train Epoch: 1 [52480/104000 (50%)]	Loss: 0.000000
Train Epoch: 1 [53120/104000 (51%)]	Loss: 0.000000
Train Epoch: 1 [53760/104000 (52%)]	Loss: 0.000000
Train Epoch: 1 [54400/104000 (52%)]	Loss: 0.000000
Train Epoch: 1 [55040/104000 (53%)]	Loss: 0.000001
Train Epoch: 1 [55680/104000 (54%)]	Loss: 0.000000
Train Epoch: 1 [56320/104000 (54%)]	Loss: 0.000000
Train Epoch: 1 [56960/104000 (55%)]	Loss: 0.000000
Train Epoch: 1 [57600/104000 (55%)]	Loss: 0.000000
Train Epoch: 1 [58240/104000 (56%)]	Loss: 0.000000
Train Epoch: 1 [58880/104000 (57%)]	Loss: 0.000000
Train Epoch: 1 [59520/104000 (57%)]	Loss: 0.000000
Train Epoch: 1 [60160/104000 (58%)]	Loss: 0.000000
Train Epoch: 1 [60800/104000 (58%)]	Loss: 0.000000
Train Epoch: 1 [61440/104000 (59%)]	Loss: 0.000000
Train Epoch: 1 [62080/104000 (60%)]	Loss: 0.000000
Train Epoch: 1 [62720/104000 (60%)]	Loss: 0.000000
Train Epoch: 1 [63360/104000 (61%)]	Loss: 0.000000
Train Epoch: 1 [64000/104000 (62%)]	Loss: 0.000000
Train Epoch: 1 [64640/104000 (62%)]	Loss: 0.000000
Train Epoch: 1 [65280/104000 (63%)]	Loss: 0.000000
Train Epoch: 1 [65920/104000 (63%)]	Loss: 0.000000
Train Epoch: 1 [66560/104000 (64%)]	Loss: 0.000000
Train Epoch: 1 [67200/104000 (65%)]	Loss: 0.000000
Train Epoch: 1 [67840/104000 (65%)]	Loss: 0.000000
Train Epoch: 1 [68480/104000 (66%)]	Loss: 0.000000
Train Epoch: 1 [69120/104000 (66%)]	Loss: 0.000000
Train Epoch: 1 [69760/104000 (67%)]	Loss: 0.000000
Train Epoch: 1 [70400/104000 (68%)]	Loss: 0.000000
Train Epoch: 1 [71040/104000 (68%)]	Loss: 0.000000
Train Epoch: 1 [71680/104000 (69%)]	Loss: 0.000000
Train Epoch: 1 [72320/104000 (70%)]	Loss: 0.000000
Train Epoch: 1 [72960/104000 (70%)]	Loss: 0.000000
Train Epoch: 1 [73600/104000 (71%)]	Loss: 0.000003
Train Epoch: 1 [74240/104000 (71%)]	Loss: 0.000000
Train Epoch: 1 [74880/104000 (72%)]	Loss: 0.000000
Train Epoch: 1 [75520/104000 (73%)]	Loss: 0.000000
Train Epoch: 1 [76160/104000 (73%)]	Loss: 0.000002
Train Epoch: 1 [76800/104000 (74%)]	Loss: 0.000000
Train Epoch: 1 [77440/104000 (74%)]	Loss: 0.000000
Train Epoch: 1 [78080/104000 (75%)]	Loss: 0.000000
Train Epoch: 1 [78720/104000 (76%)]	Loss: 0.000000
Train Epoch: 1 [79360/104000 (76%)]	Loss: 0.000000
Train Epoch: 1 [80000/104000 (77%)]	Loss: 0.000000
Train Epoch: 1 [80640/104000 (78%)]	Loss: 0.000000
Train Epoch: 1 [81280/104000 (78%)]	Loss: 0.000001
Train Epoch: 1 [81920/104000 (79%)]	Loss: 0.000000
Train Epoch: 1 [82560/104000 (79%)]	Loss: 0.000000
Train Epoch: 1 [83200/104000 (80%)]	Loss: 0.000000
Train Epoch: 1 [83840/104000 (81%)]	Loss: 0.000000
Train Epoch: 1 [84480/104000 (81%)]	Loss: 0.000000
Train Epoch: 1 [85120/104000 (82%)]	Loss: 0.000000
Train Epoch: 1 [85760/104000 (82%)]	Loss: 0.000000
Train Epoch: 1 [86400/104000 (83%)]	Loss: 0.000000
Train Epoch: 1 [87040/104000 (84%)]	Loss: 0.000000
Train Epoch: 1 [87680/104000 (84%)]	Loss: 0.000000
Train Epoch: 1 [88320/104000 (85%)]	Loss: 0.000000
Train Epoch: 1 [88960/104000 (86%)]	Loss: 0.000000
Train Epoch: 1 [89600/104000 (86%)]	Loss: 0.000000
Train Epoch: 1 [90240/104000 (87%)]	Loss: 0.000000
Train Epoch: 1 [90880/104000 (87%)]	Loss: 0.000000
Train Epoch: 1 [91520/104000 (88%)]	Loss: 0.000001
Train Epoch: 1 [92160/104000 (89%)]	Loss: 0.000000
Train Epoch: 1 [92800/104000 (89%)]	Loss: 0.000000
Train Epoch: 1 [93440/104000 (90%)]	Loss: 0.000000
Train Epoch: 1 [94080/104000 (90%)]	Loss: 0.000000
Train Epoch: 1 [94720/104000 (91%)]	Loss: 0.000000
Train Epoch: 1 [95360/104000 (92%)]	Loss: 0.000000
Train Epoch: 1 [96000/104000 (92%)]	Loss: 0.000000
Train Epoch: 1 [96640/104000 (93%)]	Loss: 0.000000
Train Epoch: 1 [97280/104000 (94%)]	Loss: 0.000000
Train Epoch: 1 [97920/104000 (94%)]	Loss: 0.000000
Train Epoch: 1 [98560/104000 (95%)]	Loss: 0.000000
Train Epoch: 1 [99200/104000 (95%)]	Loss: 0.000000
Train Epoch: 1 [99840/104000 (96%)]	Loss: 0.000000
Train Epoch: 1 [100480/104000 (97%)]	Loss: 0.000000
Train Epoch: 1 [101120/104000 (97%)]	Loss: 0.000000
Train Epoch: 1 [101760/104000 (98%)]	Loss: 0.000000
Train Epoch: 1 [102400/104000 (98%)]	Loss: 0.000000
Train Epoch: 1 [103040/104000 (99%)]	Loss: 0.000000
Train Epoch: 1 [103680/104000 (100%)]	Loss: 0.000000

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 1.3950, Accuracy: 24668/30000 (82%)

Train Epoch: 2 [0/104000 (0%)]	Loss: 0.000000
Train Epoch: 2 [640/104000 (1%)]	Loss: 0.000000
Train Epoch: 2 [1280/104000 (1%)]	Loss: 0.000000
Train Epoch: 2 [1920/104000 (2%)]	Loss: 0.000000
Train Epoch: 2 [2560/104000 (2%)]	Loss: 0.000000
Train Epoch: 2 [3200/104000 (3%)]	Loss: 0.000000
Train Epoch: 2 [3840/104000 (4%)]	Loss: 0.000000
Train Epoch: 2 [4480/104000 (4%)]	Loss: 0.000000
Train Epoch: 2 [5120/104000 (5%)]	Loss: 0.000000
Train Epoch: 2 [5760/104000 (6%)]	Loss: 0.000000
Train Epoch: 2 [6400/104000 (6%)]	Loss: 0.000000
Train Epoch: 2 [7040/104000 (7%)]	Loss: 0.000000
Train Epoch: 2 [7680/104000 (7%)]	Loss: 0.000000
Train Epoch: 2 [8320/104000 (8%)]	Loss: 0.000000
Train Epoch: 2 [8960/104000 (9%)]	Loss: 0.000000
Train Epoch: 2 [9600/104000 (9%)]	Loss: 0.000000
Train Epoch: 2 [10240/104000 (10%)]	Loss: 0.000000
Train Epoch: 2 [10880/104000 (10%)]	Loss: 0.000000
Train Epoch: 2 [11520/104000 (11%)]	Loss: 0.000000
Train Epoch: 2 [12160/104000 (12%)]	Loss: 0.000000
Train Epoch: 2 [12800/104000 (12%)]	Loss: 0.000000
Train Epoch: 2 [13440/104000 (13%)]	Loss: 0.000000
Train Epoch: 2 [14080/104000 (14%)]	Loss: 0.000000
Train Epoch: 2 [14720/104000 (14%)]	Loss: 0.000000
Train Epoch: 2 [15360/104000 (15%)]	Loss: 0.000000
Train Epoch: 2 [16000/104000 (15%)]	Loss: 0.000000
Train Epoch: 2 [16640/104000 (16%)]	Loss: 0.000000
Train Epoch: 2 [17280/104000 (17%)]	Loss: 0.000000
Train Epoch: 2 [17920/104000 (17%)]	Loss: 0.000000
Train Epoch: 2 [18560/104000 (18%)]	Loss: 0.000000
Train Epoch: 2 [19200/104000 (18%)]	Loss: 0.000000
Train Epoch: 2 [19840/104000 (19%)]	Loss: 0.000000
Train Epoch: 2 [20480/104000 (20%)]	Loss: 0.000000
Train Epoch: 2 [21120/104000 (20%)]	Loss: 0.000000
Train Epoch: 2 [21760/104000 (21%)]	Loss: 0.000000
Train Epoch: 2 [22400/104000 (22%)]	Loss: 0.000000
Train Epoch: 2 [23040/104000 (22%)]	Loss: 0.000000
Train Epoch: 2 [23680/104000 (23%)]	Loss: 0.000000
Train Epoch: 2 [24320/104000 (23%)]	Loss: 0.000000
Train Epoch: 2 [24960/104000 (24%)]	Loss: 0.000000
Train Epoch: 2 [25600/104000 (25%)]	Loss: 0.000000
Train Epoch: 2 [26240/104000 (25%)]	Loss: 0.000000
Train Epoch: 2 [26880/104000 (26%)]	Loss: 0.000000
Train Epoch: 2 [27520/104000 (26%)]	Loss: 0.000000
Train Epoch: 2 [28160/104000 (27%)]	Loss: 0.000000
Train Epoch: 2 [28800/104000 (28%)]	Loss: 0.000000
Train Epoch: 2 [29440/104000 (28%)]	Loss: 0.000000
Train Epoch: 2 [30080/104000 (29%)]	Loss: 0.000000
Train Epoch: 2 [30720/104000 (30%)]	Loss: 0.000000
Train Epoch: 2 [31360/104000 (30%)]	Loss: 0.000000
Train Epoch: 2 [32000/104000 (31%)]	Loss: 0.000000
Train Epoch: 2 [32640/104000 (31%)]	Loss: 0.000000
Train Epoch: 2 [33280/104000 (32%)]	Loss: 0.000000
Train Epoch: 2 [33920/104000 (33%)]	Loss: 0.000000
Train Epoch: 2 [34560/104000 (33%)]	Loss: 0.000000
Train Epoch: 2 [35200/104000 (34%)]	Loss: 0.000000
Train Epoch: 2 [35840/104000 (34%)]	Loss: 0.000000
Train Epoch: 2 [36480/104000 (35%)]	Loss: 0.000000
Train Epoch: 2 [37120/104000 (36%)]	Loss: 0.000000
Train Epoch: 2 [37760/104000 (36%)]	Loss: 0.000000
Train Epoch: 2 [38400/104000 (37%)]	Loss: 0.000001
Train Epoch: 2 [39040/104000 (38%)]	Loss: 0.000000
Train Epoch: 2 [39680/104000 (38%)]	Loss: 0.000000
Train Epoch: 2 [40320/104000 (39%)]	Loss: 0.000000
Train Epoch: 2 [40960/104000 (39%)]	Loss: 0.000000
Train Epoch: 2 [41600/104000 (40%)]	Loss: 0.000000
Train Epoch: 2 [42240/104000 (41%)]	Loss: 0.000000
Train Epoch: 2 [42880/104000 (41%)]	Loss: 0.000000
Train Epoch: 2 [43520/104000 (42%)]	Loss: 0.000000
Train Epoch: 2 [44160/104000 (42%)]	Loss: 0.000000
Train Epoch: 2 [44800/104000 (43%)]	Loss: 0.000000
Train Epoch: 2 [45440/104000 (44%)]	Loss: 0.000000
Train Epoch: 2 [46080/104000 (44%)]	Loss: 0.000000
Train Epoch: 2 [46720/104000 (45%)]	Loss: 0.000000
Train Epoch: 2 [47360/104000 (46%)]	Loss: 0.000000
Train Epoch: 2 [48000/104000 (46%)]	Loss: 0.000000
Train Epoch: 2 [48640/104000 (47%)]	Loss: 0.000000
Train Epoch: 2 [49280/104000 (47%)]	Loss: 0.000000
Train Epoch: 2 [49920/104000 (48%)]	Loss: 0.000000
Train Epoch: 2 [50560/104000 (49%)]	Loss: 0.000000
Train Epoch: 2 [51200/104000 (49%)]	Loss: 0.000000
Train Epoch: 2 [51840/104000 (50%)]	Loss: 0.000000
Train Epoch: 2 [52480/104000 (50%)]	Loss: 0.000000
Train Epoch: 2 [53120/104000 (51%)]	Loss: 0.000000
Train Epoch: 2 [53760/104000 (52%)]	Loss: 0.000000
Train Epoch: 2 [54400/104000 (52%)]	Loss: 0.000000
Train Epoch: 2 [55040/104000 (53%)]	Loss: 0.000000
Train Epoch: 2 [55680/104000 (54%)]	Loss: 0.000000
Train Epoch: 2 [56320/104000 (54%)]	Loss: 0.000000
Train Epoch: 2 [56960/104000 (55%)]	Loss: 0.000000
Train Epoch: 2 [57600/104000 (55%)]	Loss: 0.000000
Train Epoch: 2 [58240/104000 (56%)]	Loss: 0.000000
Train Epoch: 2 [58880/104000 (57%)]	Loss: 0.000000
Train Epoch: 2 [59520/104000 (57%)]	Loss: 0.000000
Train Epoch: 2 [60160/104000 (58%)]	Loss: 0.000000
Train Epoch: 2 [60800/104000 (58%)]	Loss: 0.000000
Train Epoch: 2 [61440/104000 (59%)]	Loss: 0.000000
Train Epoch: 2 [62080/104000 (60%)]	Loss: 0.000000
Train Epoch: 2 [62720/104000 (60%)]	Loss: 0.000000
Train Epoch: 2 [63360/104000 (61%)]	Loss: 0.000000
Train Epoch: 2 [64000/104000 (62%)]	Loss: 0.000000
Train Epoch: 2 [64640/104000 (62%)]	Loss: 0.000000
Train Epoch: 2 [65280/104000 (63%)]	Loss: 0.000000
Train Epoch: 2 [65920/104000 (63%)]	Loss: 0.000000
Train Epoch: 2 [66560/104000 (64%)]	Loss: 0.000000
Train Epoch: 2 [67200/104000 (65%)]	Loss: 0.000000
Train Epoch: 2 [67840/104000 (65%)]	Loss: 0.000000
Train Epoch: 2 [68480/104000 (66%)]	Loss: 0.000000
Train Epoch: 2 [69120/104000 (66%)]	Loss: 0.000000
Train Epoch: 2 [69760/104000 (67%)]	Loss: 0.000000
Train Epoch: 2 [70400/104000 (68%)]	Loss: 0.000000
Train Epoch: 2 [71040/104000 (68%)]	Loss: 0.000000
Train Epoch: 2 [71680/104000 (69%)]	Loss: 0.000000
Train Epoch: 2 [72320/104000 (70%)]	Loss: 0.000000
Train Epoch: 2 [72960/104000 (70%)]	Loss: 0.000000
Train Epoch: 2 [73600/104000 (71%)]	Loss: 0.000000
Train Epoch: 2 [74240/104000 (71%)]	Loss: 0.000000
Train Epoch: 2 [74880/104000 (72%)]	Loss: 0.000000
Train Epoch: 2 [75520/104000 (73%)]	Loss: 0.000000
Train Epoch: 2 [76160/104000 (73%)]	Loss: 0.000000
Train Epoch: 2 [76800/104000 (74%)]	Loss: 0.000000
Train Epoch: 2 [77440/104000 (74%)]	Loss: 0.000000
Train Epoch: 2 [78080/104000 (75%)]	Loss: 0.000000
Train Epoch: 2 [78720/104000 (76%)]	Loss: 0.000000
Train Epoch: 2 [79360/104000 (76%)]	Loss: 0.000000
Train Epoch: 2 [80000/104000 (77%)]	Loss: 0.000000
Train Epoch: 2 [80640/104000 (78%)]	Loss: 0.000000
Train Epoch: 2 [81280/104000 (78%)]	Loss: 0.000000
Train Epoch: 2 [81920/104000 (79%)]	Loss: 0.000000
Train Epoch: 2 [82560/104000 (79%)]	Loss: 0.000000
Train Epoch: 2 [83200/104000 (80%)]	Loss: 0.000000
Train Epoch: 2 [83840/104000 (81%)]	Loss: 0.000000
Train Epoch: 2 [84480/104000 (81%)]	Loss: 0.000000
Train Epoch: 2 [85120/104000 (82%)]	Loss: 0.000000
Train Epoch: 2 [85760/104000 (82%)]	Loss: 0.000000
Train Epoch: 2 [86400/104000 (83%)]	Loss: 0.000000
Train Epoch: 2 [87040/104000 (84%)]	Loss: 0.000000
Train Epoch: 2 [87680/104000 (84%)]	Loss: 0.000000
Train Epoch: 2 [88320/104000 (85%)]	Loss: 0.000000
Train Epoch: 2 [88960/104000 (86%)]	Loss: 0.000000
Train Epoch: 2 [89600/104000 (86%)]	Loss: 0.000000
Train Epoch: 2 [90240/104000 (87%)]	Loss: 0.000001
Train Epoch: 2 [90880/104000 (87%)]	Loss: 0.000000
Train Epoch: 2 [91520/104000 (88%)]	Loss: 0.000000
Train Epoch: 2 [92160/104000 (89%)]	Loss: 0.000000
Train Epoch: 2 [92800/104000 (89%)]	Loss: 0.000000
Train Epoch: 2 [93440/104000 (90%)]	Loss: 0.000000
Train Epoch: 2 [94080/104000 (90%)]	Loss: 0.000000
Train Epoch: 2 [94720/104000 (91%)]	Loss: 0.000000
Train Epoch: 2 [95360/104000 (92%)]	Loss: 0.000000
Train Epoch: 2 [96000/104000 (92%)]	Loss: 0.000000
Train Epoch: 2 [96640/104000 (93%)]	Loss: 0.000000
Train Epoch: 2 [97280/104000 (94%)]	Loss: 0.000000
Train Epoch: 2 [97920/104000 (94%)]	Loss: 0.000000
Train Epoch: 2 [98560/104000 (95%)]	Loss: 0.000000
Train Epoch: 2 [99200/104000 (95%)]	Loss: 0.000000
Train Epoch: 2 [99840/104000 (96%)]	Loss: 0.000000
Train Epoch: 2 [100480/104000 (97%)]	Loss: 0.000000
Train Epoch: 2 [101120/104000 (97%)]	Loss: 0.000000
Train Epoch: 2 [101760/104000 (98%)]	Loss: 0.000000
Train Epoch: 2 [102400/104000 (98%)]	Loss: 0.000000
Train Epoch: 2 [103040/104000 (99%)]	Loss: 0.000000
Train Epoch: 2 [103680/104000 (100%)]	Loss: 0.000000

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.7656, Accuracy: 25707/30000 (86%)

Train Epoch: 3 [0/104000 (0%)]	Loss: 0.000000
Train Epoch: 3 [640/104000 (1%)]	Loss: 0.000000
Train Epoch: 3 [1280/104000 (1%)]	Loss: 0.000000
Train Epoch: 3 [1920/104000 (2%)]	Loss: 0.000000
Train Epoch: 3 [2560/104000 (2%)]	Loss: 0.000000
Train Epoch: 3 [3200/104000 (3%)]	Loss: 0.000000
Train Epoch: 3 [3840/104000 (4%)]	Loss: 0.000010
Train Epoch: 3 [4480/104000 (4%)]	Loss: 0.000000
Train Epoch: 3 [5120/104000 (5%)]	Loss: 0.000000
Train Epoch: 3 [5760/104000 (6%)]	Loss: 0.000000
Train Epoch: 3 [6400/104000 (6%)]	Loss: 0.000000
Train Epoch: 3 [7040/104000 (7%)]	Loss: 0.000000
Train Epoch: 3 [7680/104000 (7%)]	Loss: 0.000000
Train Epoch: 3 [8320/104000 (8%)]	Loss: 0.000000
Train Epoch: 3 [8960/104000 (9%)]	Loss: 0.000000
Train Epoch: 3 [9600/104000 (9%)]	Loss: 0.000000
Train Epoch: 3 [10240/104000 (10%)]	Loss: 0.000000
Train Epoch: 3 [10880/104000 (10%)]	Loss: 0.000000
Train Epoch: 3 [11520/104000 (11%)]	Loss: 0.000000
Train Epoch: 3 [12160/104000 (12%)]	Loss: 0.000000
Train Epoch: 3 [12800/104000 (12%)]	Loss: 0.000000
Train Epoch: 3 [13440/104000 (13%)]	Loss: 0.000000
Train Epoch: 3 [14080/104000 (14%)]	Loss: 0.000000
Train Epoch: 3 [14720/104000 (14%)]	Loss: 0.000000
Train Epoch: 3 [15360/104000 (15%)]	Loss: 0.000000
Train Epoch: 3 [16000/104000 (15%)]	Loss: 0.000000
Train Epoch: 3 [16640/104000 (16%)]	Loss: 0.000000
Train Epoch: 3 [17280/104000 (17%)]	Loss: 0.000000
Train Epoch: 3 [17920/104000 (17%)]	Loss: 0.000000
Train Epoch: 3 [18560/104000 (18%)]	Loss: 0.000000
Train Epoch: 3 [19200/104000 (18%)]	Loss: 0.000000
Train Epoch: 3 [19840/104000 (19%)]	Loss: 0.000000
Train Epoch: 3 [20480/104000 (20%)]	Loss: 0.000000
Train Epoch: 3 [21120/104000 (20%)]	Loss: 0.000000
Train Epoch: 3 [21760/104000 (21%)]	Loss: 0.000000
Train Epoch: 3 [22400/104000 (22%)]	Loss: 0.000000
Train Epoch: 3 [23040/104000 (22%)]	Loss: 0.000000
Train Epoch: 3 [23680/104000 (23%)]	Loss: 0.000000
Train Epoch: 3 [24320/104000 (23%)]	Loss: 0.000000
Train Epoch: 3 [24960/104000 (24%)]	Loss: 0.000000
Train Epoch: 3 [25600/104000 (25%)]	Loss: 0.000000
Train Epoch: 3 [26240/104000 (25%)]	Loss: 0.000000
Train Epoch: 3 [26880/104000 (26%)]	Loss: 0.000000
Train Epoch: 3 [27520/104000 (26%)]	Loss: 0.000000
Train Epoch: 3 [28160/104000 (27%)]	Loss: 0.000000
Train Epoch: 3 [28800/104000 (28%)]	Loss: 0.000000
Train Epoch: 3 [29440/104000 (28%)]	Loss: 0.000000
Train Epoch: 3 [30080/104000 (29%)]	Loss: 0.000000
Train Epoch: 3 [30720/104000 (30%)]	Loss: 0.000000
Train Epoch: 3 [31360/104000 (30%)]	Loss: 0.000000
Train Epoch: 3 [32000/104000 (31%)]	Loss: 0.000000
Train Epoch: 3 [32640/104000 (31%)]	Loss: 0.000000
Train Epoch: 3 [33280/104000 (32%)]	Loss: 0.000000
Train Epoch: 3 [33920/104000 (33%)]	Loss: 0.000002
Train Epoch: 3 [34560/104000 (33%)]	Loss: 0.000000
Train Epoch: 3 [35200/104000 (34%)]	Loss: 0.000000
Train Epoch: 3 [35840/104000 (34%)]	Loss: 0.000000
Train Epoch: 3 [36480/104000 (35%)]	Loss: 0.000000
Train Epoch: 3 [37120/104000 (36%)]	Loss: 0.000000
Train Epoch: 3 [37760/104000 (36%)]	Loss: 0.000000
Train Epoch: 3 [38400/104000 (37%)]	Loss: 0.000002
Train Epoch: 3 [39040/104000 (38%)]	Loss: 0.000000
Train Epoch: 3 [39680/104000 (38%)]	Loss: 0.000000
Train Epoch: 3 [40320/104000 (39%)]	Loss: 0.000000
Train Epoch: 3 [40960/104000 (39%)]	Loss: 0.000000
Train Epoch: 3 [41600/104000 (40%)]	Loss: 0.000000
Train Epoch: 3 [42240/104000 (41%)]	Loss: 0.000000
Train Epoch: 3 [42880/104000 (41%)]	Loss: 0.000000
Train Epoch: 3 [43520/104000 (42%)]	Loss: 0.000000
Train Epoch: 3 [44160/104000 (42%)]	Loss: 0.000000
Train Epoch: 3 [44800/104000 (43%)]	Loss: 0.000000
Train Epoch: 3 [45440/104000 (44%)]	Loss: 0.000000
Train Epoch: 3 [46080/104000 (44%)]	Loss: 0.000000
Train Epoch: 3 [46720/104000 (45%)]	Loss: 0.000000
Train Epoch: 3 [47360/104000 (46%)]	Loss: 0.000000
Train Epoch: 3 [48000/104000 (46%)]	Loss: 0.000000
Train Epoch: 3 [48640/104000 (47%)]	Loss: 0.000000
Train Epoch: 3 [49280/104000 (47%)]	Loss: 0.000000
Train Epoch: 3 [49920/104000 (48%)]	Loss: 0.000000
Train Epoch: 3 [50560/104000 (49%)]	Loss: 0.000000
Train Epoch: 3 [51200/104000 (49%)]	Loss: 0.000000
Train Epoch: 3 [51840/104000 (50%)]	Loss: 0.000000
Train Epoch: 3 [52480/104000 (50%)]	Loss: 0.000000
Train Epoch: 3 [53120/104000 (51%)]	Loss: 0.000000
Train Epoch: 3 [53760/104000 (52%)]	Loss: 0.000000
Train Epoch: 3 [54400/104000 (52%)]	Loss: 0.000000
Train Epoch: 3 [55040/104000 (53%)]	Loss: 0.000000
Train Epoch: 3 [55680/104000 (54%)]	Loss: 0.000000
Train Epoch: 3 [56320/104000 (54%)]	Loss: 0.000035
Train Epoch: 3 [56960/104000 (55%)]	Loss: 0.000000
Train Epoch: 3 [57600/104000 (55%)]	Loss: 0.000000
Train Epoch: 3 [58240/104000 (56%)]	Loss: 0.000000
Train Epoch: 3 [58880/104000 (57%)]	Loss: 0.000000
Train Epoch: 3 [59520/104000 (57%)]	Loss: 0.000000
Train Epoch: 3 [60160/104000 (58%)]	Loss: 0.000000
Train Epoch: 3 [60800/104000 (58%)]	Loss: 0.000000
Train Epoch: 3 [61440/104000 (59%)]	Loss: 0.000000
Train Epoch: 3 [62080/104000 (60%)]	Loss: 0.000000
Train Epoch: 3 [62720/104000 (60%)]	Loss: 0.000000
Train Epoch: 3 [63360/104000 (61%)]	Loss: 0.000552
Train Epoch: 3 [64000/104000 (62%)]	Loss: 0.000000
Train Epoch: 3 [64640/104000 (62%)]	Loss: 0.000000
Train Epoch: 3 [65280/104000 (63%)]	Loss: 0.000000
Train Epoch: 3 [65920/104000 (63%)]	Loss: 0.000000
Train Epoch: 3 [66560/104000 (64%)]	Loss: 0.000000
Train Epoch: 3 [67200/104000 (65%)]	Loss: 0.000000
Train Epoch: 3 [67840/104000 (65%)]	Loss: 0.000000
Train Epoch: 3 [68480/104000 (66%)]	Loss: 0.000000
Train Epoch: 3 [69120/104000 (66%)]	Loss: 0.000000
Train Epoch: 3 [69760/104000 (67%)]	Loss: 0.000000
Train Epoch: 3 [70400/104000 (68%)]	Loss: 0.000000
Train Epoch: 3 [71040/104000 (68%)]	Loss: 0.000000
Train Epoch: 3 [71680/104000 (69%)]	Loss: 0.000000
Train Epoch: 3 [72320/104000 (70%)]	Loss: 0.000000
Train Epoch: 3 [72960/104000 (70%)]	Loss: 0.000000
Train Epoch: 3 [73600/104000 (71%)]	Loss: 0.000000
Train Epoch: 3 [74240/104000 (71%)]	Loss: 0.000000
Train Epoch: 3 [74880/104000 (72%)]	Loss: 0.000000
Train Epoch: 3 [75520/104000 (73%)]	Loss: 0.000000
Train Epoch: 3 [76160/104000 (73%)]	Loss: 0.000000
Train Epoch: 3 [76800/104000 (74%)]	Loss: 0.000000
Train Epoch: 3 [77440/104000 (74%)]	Loss: 0.000000
Train Epoch: 3 [78080/104000 (75%)]	Loss: 0.000000
Train Epoch: 3 [78720/104000 (76%)]	Loss: 0.000000
Train Epoch: 3 [79360/104000 (76%)]	Loss: 0.000000
Train Epoch: 3 [80000/104000 (77%)]	Loss: 0.000000
Train Epoch: 3 [80640/104000 (78%)]	Loss: 0.000000
Train Epoch: 3 [81280/104000 (78%)]	Loss: 0.000000
Train Epoch: 3 [81920/104000 (79%)]	Loss: 0.000000
Train Epoch: 3 [82560/104000 (79%)]	Loss: 0.000000
Train Epoch: 3 [83200/104000 (80%)]	Loss: 0.000000
Train Epoch: 3 [83840/104000 (81%)]	Loss: 0.000000
Train Epoch: 3 [84480/104000 (81%)]	Loss: 0.000000
Train Epoch: 3 [85120/104000 (82%)]	Loss: 0.000000
Train Epoch: 3 [85760/104000 (82%)]	Loss: 0.000000
Train Epoch: 3 [86400/104000 (83%)]	Loss: 0.000000
Train Epoch: 3 [87040/104000 (84%)]	Loss: 0.000000
Train Epoch: 3 [87680/104000 (84%)]	Loss: 0.000000
Train Epoch: 3 [88320/104000 (85%)]	Loss: 0.000000
Train Epoch: 3 [88960/104000 (86%)]	Loss: 0.000000
Train Epoch: 3 [89600/104000 (86%)]	Loss: 0.000003
Train Epoch: 3 [90240/104000 (87%)]	Loss: 0.000000
Train Epoch: 3 [90880/104000 (87%)]	Loss: 0.000000
Train Epoch: 3 [91520/104000 (88%)]	Loss: 0.000000
Train Epoch: 3 [92160/104000 (89%)]	Loss: 0.000000
Train Epoch: 3 [92800/104000 (89%)]	Loss: 0.000000
Train Epoch: 3 [93440/104000 (90%)]	Loss: 0.000000
Train Epoch: 3 [94080/104000 (90%)]	Loss: 0.000000
Train Epoch: 3 [94720/104000 (91%)]	Loss: 0.000000
Train Epoch: 3 [95360/104000 (92%)]	Loss: 0.000000
Train Epoch: 3 [96000/104000 (92%)]	Loss: 0.000000
Train Epoch: 3 [96640/104000 (93%)]	Loss: 0.000000
Train Epoch: 3 [97280/104000 (94%)]	Loss: 0.000000
Train Epoch: 3 [97920/104000 (94%)]	Loss: 0.000000
Train Epoch: 3 [98560/104000 (95%)]	Loss: 0.000000
Train Epoch: 3 [99200/104000 (95%)]	Loss: 0.000000
Train Epoch: 3 [99840/104000 (96%)]	Loss: 0.000000
Train Epoch: 3 [100480/104000 (97%)]	Loss: 0.000000
Train Epoch: 3 [101120/104000 (97%)]	Loss: 0.000000
Train Epoch: 3 [101760/104000 (98%)]	Loss: 0.000000
Train Epoch: 3 [102400/104000 (98%)]	Loss: 0.000000
Train Epoch: 3 [103040/104000 (99%)]	Loss: 0.000000
Train Epoch: 3 [103680/104000 (100%)]	Loss: 0.000000

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.6615, Accuracy: 26003/30000 (87%)

Train Epoch: 4 [0/104000 (0%)]	Loss: 0.000000
Train Epoch: 4 [640/104000 (1%)]	Loss: 0.000000
Train Epoch: 4 [1280/104000 (1%)]	Loss: 0.000000
Train Epoch: 4 [1920/104000 (2%)]	Loss: 0.000000
Train Epoch: 4 [2560/104000 (2%)]	Loss: 0.000000
Train Epoch: 4 [3200/104000 (3%)]	Loss: 0.000000
Train Epoch: 4 [3840/104000 (4%)]	Loss: 0.000000
Train Epoch: 4 [4480/104000 (4%)]	Loss: 0.000000
Train Epoch: 4 [5120/104000 (5%)]	Loss: 0.000000
Train Epoch: 4 [5760/104000 (6%)]	Loss: 0.000000
Train Epoch: 4 [6400/104000 (6%)]	Loss: 0.000000
Train Epoch: 4 [7040/104000 (7%)]	Loss: 0.000000
Train Epoch: 4 [7680/104000 (7%)]	Loss: 0.000000
Train Epoch: 4 [8320/104000 (8%)]	Loss: 0.000000
Train Epoch: 4 [8960/104000 (9%)]	Loss: 0.000000
Train Epoch: 4 [9600/104000 (9%)]	Loss: 0.000000
Train Epoch: 4 [10240/104000 (10%)]	Loss: 0.000000
Train Epoch: 4 [10880/104000 (10%)]	Loss: 0.000000
Train Epoch: 4 [11520/104000 (11%)]	Loss: 0.000000
Train Epoch: 4 [12160/104000 (12%)]	Loss: 0.000000
Train Epoch: 4 [12800/104000 (12%)]	Loss: 0.000000
Train Epoch: 4 [13440/104000 (13%)]	Loss: 0.000000
Train Epoch: 4 [14080/104000 (14%)]	Loss: 0.000000
Train Epoch: 4 [14720/104000 (14%)]	Loss: 0.000000
Train Epoch: 4 [15360/104000 (15%)]	Loss: 0.000000
Train Epoch: 4 [16000/104000 (15%)]	Loss: 0.000000
Train Epoch: 4 [16640/104000 (16%)]	Loss: 0.000000
Train Epoch: 4 [17280/104000 (17%)]	Loss: 0.000000
Train Epoch: 4 [17920/104000 (17%)]	Loss: 0.000000
Train Epoch: 4 [18560/104000 (18%)]	Loss: 0.000000
Train Epoch: 4 [19200/104000 (18%)]	Loss: 0.000007
Train Epoch: 4 [19840/104000 (19%)]	Loss: 0.000000
Train Epoch: 4 [20480/104000 (20%)]	Loss: 0.000000
Train Epoch: 4 [21120/104000 (20%)]	Loss: 0.000000
Train Epoch: 4 [21760/104000 (21%)]	Loss: 0.000000
Train Epoch: 4 [22400/104000 (22%)]	Loss: 0.000000
Train Epoch: 4 [23040/104000 (22%)]	Loss: 0.000000
Train Epoch: 4 [23680/104000 (23%)]	Loss: 0.000000
Train Epoch: 4 [24320/104000 (23%)]	Loss: 0.000000
Train Epoch: 4 [24960/104000 (24%)]	Loss: 0.000000
Train Epoch: 4 [25600/104000 (25%)]	Loss: 0.000000
Train Epoch: 4 [26240/104000 (25%)]	Loss: 0.000000
Train Epoch: 4 [26880/104000 (26%)]	Loss: 0.000000
Train Epoch: 4 [27520/104000 (26%)]	Loss: 0.000000
Train Epoch: 4 [28160/104000 (27%)]	Loss: 0.000000
Train Epoch: 4 [28800/104000 (28%)]	Loss: 0.000000
Train Epoch: 4 [29440/104000 (28%)]	Loss: 0.000000
Train Epoch: 4 [30080/104000 (29%)]	Loss: 0.000000
Train Epoch: 4 [30720/104000 (30%)]	Loss: 0.000000
Train Epoch: 4 [31360/104000 (30%)]	Loss: 0.000000
Train Epoch: 4 [32000/104000 (31%)]	Loss: 0.000000
Train Epoch: 4 [32640/104000 (31%)]	Loss: 0.000000
Train Epoch: 4 [33280/104000 (32%)]	Loss: 0.000000
Train Epoch: 4 [33920/104000 (33%)]	Loss: 0.000000
Train Epoch: 4 [34560/104000 (33%)]	Loss: 0.000000
Train Epoch: 4 [35200/104000 (34%)]	Loss: 0.000000
Train Epoch: 4 [35840/104000 (34%)]	Loss: 0.000000
Train Epoch: 4 [36480/104000 (35%)]	Loss: 0.000000
Train Epoch: 4 [37120/104000 (36%)]	Loss: 0.000000
Train Epoch: 4 [37760/104000 (36%)]	Loss: 0.000000
Train Epoch: 4 [38400/104000 (37%)]	Loss: 0.000000
Train Epoch: 4 [39040/104000 (38%)]	Loss: 0.000000
Train Epoch: 4 [39680/104000 (38%)]	Loss: 0.000000
Train Epoch: 4 [40320/104000 (39%)]	Loss: 0.000000
Train Epoch: 4 [40960/104000 (39%)]	Loss: 0.000000
Train Epoch: 4 [41600/104000 (40%)]	Loss: 0.000000
Train Epoch: 4 [42240/104000 (41%)]	Loss: 0.000000
Train Epoch: 4 [42880/104000 (41%)]	Loss: 0.000000
Train Epoch: 4 [43520/104000 (42%)]	Loss: 0.000000
Train Epoch: 4 [44160/104000 (42%)]	Loss: 0.000000
Train Epoch: 4 [44800/104000 (43%)]	Loss: 0.000000
Train Epoch: 4 [45440/104000 (44%)]	Loss: 0.000000
Train Epoch: 4 [46080/104000 (44%)]	Loss: 0.000000
Train Epoch: 4 [46720/104000 (45%)]	Loss: 0.000000
Train Epoch: 4 [47360/104000 (46%)]	Loss: 0.000000
Train Epoch: 4 [48000/104000 (46%)]	Loss: 0.000000
Train Epoch: 4 [48640/104000 (47%)]	Loss: 0.000000
Train Epoch: 4 [49280/104000 (47%)]	Loss: 0.000000
Train Epoch: 4 [49920/104000 (48%)]	Loss: 0.000000
Train Epoch: 4 [50560/104000 (49%)]	Loss: 0.000000
Train Epoch: 4 [51200/104000 (49%)]	Loss: 0.000000
Train Epoch: 4 [51840/104000 (50%)]	Loss: 0.000000
Train Epoch: 4 [52480/104000 (50%)]	Loss: 0.000000
Train Epoch: 4 [53120/104000 (51%)]	Loss: 0.000000
Train Epoch: 4 [53760/104000 (52%)]	Loss: 0.000000
Train Epoch: 4 [54400/104000 (52%)]	Loss: 0.000001
Train Epoch: 4 [55040/104000 (53%)]	Loss: 0.000000
Train Epoch: 4 [55680/104000 (54%)]	Loss: 0.000000
Train Epoch: 4 [56320/104000 (54%)]	Loss: 0.000000
Train Epoch: 4 [56960/104000 (55%)]	Loss: 0.000000
Train Epoch: 4 [57600/104000 (55%)]	Loss: 0.000003
Train Epoch: 4 [58240/104000 (56%)]	Loss: 0.000000
Train Epoch: 4 [58880/104000 (57%)]	Loss: 0.000000
Train Epoch: 4 [59520/104000 (57%)]	Loss: 0.000000
Train Epoch: 4 [60160/104000 (58%)]	Loss: 0.000000
Train Epoch: 4 [60800/104000 (58%)]	Loss: 0.000000
Train Epoch: 4 [61440/104000 (59%)]	Loss: 0.000000
Train Epoch: 4 [62080/104000 (60%)]	Loss: 0.000000
Train Epoch: 4 [62720/104000 (60%)]	Loss: 0.000000
Train Epoch: 4 [63360/104000 (61%)]	Loss: 0.000000
Train Epoch: 4 [64000/104000 (62%)]	Loss: 0.000000
Train Epoch: 4 [64640/104000 (62%)]	Loss: 0.000000
Train Epoch: 4 [65280/104000 (63%)]	Loss: 0.000000
Train Epoch: 4 [65920/104000 (63%)]	Loss: 0.000000
Train Epoch: 4 [66560/104000 (64%)]	Loss: 0.000000
Train Epoch: 4 [67200/104000 (65%)]	Loss: 0.000000
Train Epoch: 4 [67840/104000 (65%)]	Loss: 0.000000
Train Epoch: 4 [68480/104000 (66%)]	Loss: 0.000000
Train Epoch: 4 [69120/104000 (66%)]	Loss: 0.000000
Train Epoch: 4 [69760/104000 (67%)]	Loss: 0.000000
Train Epoch: 4 [70400/104000 (68%)]	Loss: 0.000000
Train Epoch: 4 [71040/104000 (68%)]	Loss: 0.000000
Train Epoch: 4 [71680/104000 (69%)]	Loss: 0.000000
Train Epoch: 4 [72320/104000 (70%)]	Loss: 0.000000
Train Epoch: 4 [72960/104000 (70%)]	Loss: 0.000000
Train Epoch: 4 [73600/104000 (71%)]	Loss: 0.000000
Train Epoch: 4 [74240/104000 (71%)]	Loss: 0.000000
Train Epoch: 4 [74880/104000 (72%)]	Loss: 0.000000
Train Epoch: 4 [75520/104000 (73%)]	Loss: 0.000000
Train Epoch: 4 [76160/104000 (73%)]	Loss: 0.000000
Train Epoch: 4 [76800/104000 (74%)]	Loss: 0.000000
Train Epoch: 4 [77440/104000 (74%)]	Loss: 0.000000
Train Epoch: 4 [78080/104000 (75%)]	Loss: 0.000000
Train Epoch: 4 [78720/104000 (76%)]	Loss: 0.000000
Train Epoch: 4 [79360/104000 (76%)]	Loss: 0.000000
Train Epoch: 4 [80000/104000 (77%)]	Loss: 0.000000
Train Epoch: 4 [80640/104000 (78%)]	Loss: 0.000000
Train Epoch: 4 [81280/104000 (78%)]	Loss: 0.000000
Train Epoch: 4 [81920/104000 (79%)]	Loss: 0.000000
Train Epoch: 4 [82560/104000 (79%)]	Loss: 0.000000
Train Epoch: 4 [83200/104000 (80%)]	Loss: 0.000000
Train Epoch: 4 [83840/104000 (81%)]	Loss: 0.000000
Train Epoch: 4 [84480/104000 (81%)]	Loss: 0.000000
Train Epoch: 4 [85120/104000 (82%)]	Loss: 0.000000
Train Epoch: 4 [85760/104000 (82%)]	Loss: 0.000000
Train Epoch: 4 [86400/104000 (83%)]	Loss: 0.000000
Train Epoch: 4 [87040/104000 (84%)]	Loss: 0.000000
Train Epoch: 4 [87680/104000 (84%)]	Loss: 0.000000
Train Epoch: 4 [88320/104000 (85%)]	Loss: 0.000000
Train Epoch: 4 [88960/104000 (86%)]	Loss: 0.000000
Train Epoch: 4 [89600/104000 (86%)]	Loss: 0.000000
Train Epoch: 4 [90240/104000 (87%)]	Loss: 0.000000
Train Epoch: 4 [90880/104000 (87%)]	Loss: 0.000000
Train Epoch: 4 [91520/104000 (88%)]	Loss: 0.000000
Train Epoch: 4 [92160/104000 (89%)]	Loss: 0.000000
Train Epoch: 4 [92800/104000 (89%)]	Loss: 0.000000
Train Epoch: 4 [93440/104000 (90%)]	Loss: 0.000000
Train Epoch: 4 [94080/104000 (90%)]	Loss: 0.000000
Train Epoch: 4 [94720/104000 (91%)]	Loss: 0.000000
Train Epoch: 4 [95360/104000 (92%)]	Loss: 0.000000
Train Epoch: 4 [96000/104000 (92%)]	Loss: 0.000000
Train Epoch: 4 [96640/104000 (93%)]	Loss: 0.000000
Train Epoch: 4 [97280/104000 (94%)]	Loss: 0.000000
Train Epoch: 4 [97920/104000 (94%)]	Loss: 0.000000
Train Epoch: 4 [98560/104000 (95%)]	Loss: 0.000000
Train Epoch: 4 [99200/104000 (95%)]	Loss: 0.000000
Train Epoch: 4 [99840/104000 (96%)]	Loss: 0.000000
Train Epoch: 4 [100480/104000 (97%)]	Loss: 0.000000
Train Epoch: 4 [101120/104000 (97%)]	Loss: 0.000000
Train Epoch: 4 [101760/104000 (98%)]	Loss: 0.000000
Train Epoch: 4 [102400/104000 (98%)]	Loss: 0.000000
Train Epoch: 4 [103040/104000 (99%)]	Loss: 0.000000
Train Epoch: 4 [103680/104000 (100%)]	Loss: 0.000000

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.4680, Accuracy: 26754/30000 (89%)

Train Epoch: 5 [0/104000 (0%)]	Loss: 0.000000
Train Epoch: 5 [640/104000 (1%)]	Loss: 0.000000
Train Epoch: 5 [1280/104000 (1%)]	Loss: 0.000000
Train Epoch: 5 [1920/104000 (2%)]	Loss: 0.000000
Train Epoch: 5 [2560/104000 (2%)]	Loss: 0.000000
Train Epoch: 5 [3200/104000 (3%)]	Loss: 0.000000
Train Epoch: 5 [3840/104000 (4%)]	Loss: 0.000000
Train Epoch: 5 [4480/104000 (4%)]	Loss: 0.000000
Train Epoch: 5 [5120/104000 (5%)]	Loss: 0.000000
Train Epoch: 5 [5760/104000 (6%)]	Loss: 0.000000
Train Epoch: 5 [6400/104000 (6%)]	Loss: 0.000000
Train Epoch: 5 [7040/104000 (7%)]	Loss: 0.000000
Train Epoch: 5 [7680/104000 (7%)]	Loss: 0.000033
Train Epoch: 5 [8320/104000 (8%)]	Loss: 0.000000
Train Epoch: 5 [8960/104000 (9%)]	Loss: 0.000000
Train Epoch: 5 [9600/104000 (9%)]	Loss: 0.000000
Train Epoch: 5 [10240/104000 (10%)]	Loss: 0.000000
Train Epoch: 5 [10880/104000 (10%)]	Loss: 0.000000
Train Epoch: 5 [11520/104000 (11%)]	Loss: 0.000000
Train Epoch: 5 [12160/104000 (12%)]	Loss: 0.000000
Train Epoch: 5 [12800/104000 (12%)]	Loss: 0.000000
Train Epoch: 5 [13440/104000 (13%)]	Loss: 0.000000
Train Epoch: 5 [14080/104000 (14%)]	Loss: 0.000000
Train Epoch: 5 [14720/104000 (14%)]	Loss: 0.000000
Train Epoch: 5 [15360/104000 (15%)]	Loss: 0.000000
Train Epoch: 5 [16000/104000 (15%)]	Loss: 0.000000
Train Epoch: 5 [16640/104000 (16%)]	Loss: 0.000000
Train Epoch: 5 [17280/104000 (17%)]	Loss: 0.000000
Train Epoch: 5 [17920/104000 (17%)]	Loss: 0.000000
Train Epoch: 5 [18560/104000 (18%)]	Loss: 0.000000
Train Epoch: 5 [19200/104000 (18%)]	Loss: 0.000000
Train Epoch: 5 [19840/104000 (19%)]	Loss: 0.000000
Train Epoch: 5 [20480/104000 (20%)]	Loss: 0.000000
Train Epoch: 5 [21120/104000 (20%)]	Loss: 0.000000
Train Epoch: 5 [21760/104000 (21%)]	Loss: 0.000000
Train Epoch: 5 [22400/104000 (22%)]	Loss: 0.000000
Train Epoch: 5 [23040/104000 (22%)]	Loss: 0.000000
Train Epoch: 5 [23680/104000 (23%)]	Loss: 0.000000
Train Epoch: 5 [24320/104000 (23%)]	Loss: 0.000000
Train Epoch: 5 [24960/104000 (24%)]	Loss: 0.000000
Train Epoch: 5 [25600/104000 (25%)]	Loss: 0.000000
Train Epoch: 5 [26240/104000 (25%)]	Loss: 0.000000
Train Epoch: 5 [26880/104000 (26%)]	Loss: 0.000000
Train Epoch: 5 [27520/104000 (26%)]	Loss: 0.000000
Train Epoch: 5 [28160/104000 (27%)]	Loss: 0.000000
Train Epoch: 5 [28800/104000 (28%)]	Loss: 0.000000
Train Epoch: 5 [29440/104000 (28%)]	Loss: 0.000000
Train Epoch: 5 [30080/104000 (29%)]	Loss: 0.000000
Train Epoch: 5 [30720/104000 (30%)]	Loss: 0.000000
Train Epoch: 5 [31360/104000 (30%)]	Loss: 0.000000
Train Epoch: 5 [32000/104000 (31%)]	Loss: 0.000000
Train Epoch: 5 [32640/104000 (31%)]	Loss: 0.000000
Train Epoch: 5 [33280/104000 (32%)]	Loss: 0.000000
Train Epoch: 5 [33920/104000 (33%)]	Loss: 0.000001
Train Epoch: 5 [34560/104000 (33%)]	Loss: 0.000000
Train Epoch: 5 [35200/104000 (34%)]	Loss: 0.000000
Train Epoch: 5 [35840/104000 (34%)]	Loss: 0.000000
Train Epoch: 5 [36480/104000 (35%)]	Loss: 0.000000
Train Epoch: 5 [37120/104000 (36%)]	Loss: 0.000000
Train Epoch: 5 [37760/104000 (36%)]	Loss: 0.000000
Train Epoch: 5 [38400/104000 (37%)]	Loss: 0.000000
Train Epoch: 5 [39040/104000 (38%)]	Loss: 0.000000
Train Epoch: 5 [39680/104000 (38%)]	Loss: 0.000000
Train Epoch: 5 [40320/104000 (39%)]	Loss: 0.000000
Train Epoch: 5 [40960/104000 (39%)]	Loss: 0.000000
Train Epoch: 5 [41600/104000 (40%)]	Loss: 0.000000
Train Epoch: 5 [42240/104000 (41%)]	Loss: 0.000000
Train Epoch: 5 [42880/104000 (41%)]	Loss: 0.000000
Train Epoch: 5 [43520/104000 (42%)]	Loss: 0.000000
Train Epoch: 5 [44160/104000 (42%)]	Loss: 0.000000
Train Epoch: 5 [44800/104000 (43%)]	Loss: 0.000000
Train Epoch: 5 [45440/104000 (44%)]	Loss: 0.000000
Train Epoch: 5 [46080/104000 (44%)]	Loss: 0.000000
Train Epoch: 5 [46720/104000 (45%)]	Loss: 0.000000
Train Epoch: 5 [47360/104000 (46%)]	Loss: 0.000000
Train Epoch: 5 [48000/104000 (46%)]	Loss: 0.000000
Train Epoch: 5 [48640/104000 (47%)]	Loss: 0.000000
Train Epoch: 5 [49280/104000 (47%)]	Loss: 0.000000
Train Epoch: 5 [49920/104000 (48%)]	Loss: 0.000000
Train Epoch: 5 [50560/104000 (49%)]	Loss: 0.000000
Train Epoch: 5 [51200/104000 (49%)]	Loss: 0.000000
Train Epoch: 5 [51840/104000 (50%)]	Loss: 0.000000
Train Epoch: 5 [52480/104000 (50%)]	Loss: 0.000000
Train Epoch: 5 [53120/104000 (51%)]	Loss: 0.000000
Train Epoch: 5 [53760/104000 (52%)]	Loss: 0.000000
Train Epoch: 5 [54400/104000 (52%)]	Loss: 0.000000
Train Epoch: 5 [55040/104000 (53%)]	Loss: 0.000000
Train Epoch: 5 [55680/104000 (54%)]	Loss: 0.000000
Train Epoch: 5 [56320/104000 (54%)]	Loss: 0.000000
Train Epoch: 5 [56960/104000 (55%)]	Loss: 0.000000
Train Epoch: 5 [57600/104000 (55%)]	Loss: 0.000000
Train Epoch: 5 [58240/104000 (56%)]	Loss: 0.000000
Train Epoch: 5 [58880/104000 (57%)]	Loss: 0.000000
Train Epoch: 5 [59520/104000 (57%)]	Loss: 0.000000
Train Epoch: 5 [60160/104000 (58%)]	Loss: 0.000000
Train Epoch: 5 [60800/104000 (58%)]	Loss: 0.000000
Train Epoch: 5 [61440/104000 (59%)]	Loss: 0.000000
Train Epoch: 5 [62080/104000 (60%)]	Loss: 0.000000
Train Epoch: 5 [62720/104000 (60%)]	Loss: 0.000000
Train Epoch: 5 [63360/104000 (61%)]	Loss: 0.000000
Train Epoch: 5 [64000/104000 (62%)]	Loss: 0.000000
Train Epoch: 5 [64640/104000 (62%)]	Loss: 0.000000
Train Epoch: 5 [65280/104000 (63%)]	Loss: 0.000000
Train Epoch: 5 [65920/104000 (63%)]	Loss: 0.000000
Train Epoch: 5 [66560/104000 (64%)]	Loss: 0.000000
Train Epoch: 5 [67200/104000 (65%)]	Loss: 0.000000
Train Epoch: 5 [67840/104000 (65%)]	Loss: 0.000000
Train Epoch: 5 [68480/104000 (66%)]	Loss: 0.000000
Train Epoch: 5 [69120/104000 (66%)]	Loss: 0.000000
Train Epoch: 5 [69760/104000 (67%)]	Loss: 0.000000
Train Epoch: 5 [70400/104000 (68%)]	Loss: 0.000000
Train Epoch: 5 [71040/104000 (68%)]	Loss: 0.000000
Train Epoch: 5 [71680/104000 (69%)]	Loss: 0.000000
Train Epoch: 5 [72320/104000 (70%)]	Loss: 0.000000
Train Epoch: 5 [72960/104000 (70%)]	Loss: 0.000000
Train Epoch: 5 [73600/104000 (71%)]	Loss: 0.000000
Train Epoch: 5 [74240/104000 (71%)]	Loss: 0.000000
Train Epoch: 5 [74880/104000 (72%)]	Loss: 0.000000
Train Epoch: 5 [75520/104000 (73%)]	Loss: 0.000000
Train Epoch: 5 [76160/104000 (73%)]	Loss: 0.000000
Train Epoch: 5 [76800/104000 (74%)]	Loss: 0.000000
Train Epoch: 5 [77440/104000 (74%)]	Loss: 0.000000
Train Epoch: 5 [78080/104000 (75%)]	Loss: 0.000000
Train Epoch: 5 [78720/104000 (76%)]	Loss: 0.000000
Train Epoch: 5 [79360/104000 (76%)]	Loss: 0.000000
Train Epoch: 5 [80000/104000 (77%)]	Loss: 0.000000
Train Epoch: 5 [80640/104000 (78%)]	Loss: 0.000000
Train Epoch: 5 [81280/104000 (78%)]	Loss: 0.000000
Train Epoch: 5 [81920/104000 (79%)]	Loss: 0.000000
Train Epoch: 5 [82560/104000 (79%)]	Loss: 0.000000
Train Epoch: 5 [83200/104000 (80%)]	Loss: 0.000000
Train Epoch: 5 [83840/104000 (81%)]	Loss: 0.000000
Train Epoch: 5 [84480/104000 (81%)]	Loss: 0.000000
Train Epoch: 5 [85120/104000 (82%)]	Loss: 0.000000
Train Epoch: 5 [85760/104000 (82%)]	Loss: 0.000000
Train Epoch: 5 [86400/104000 (83%)]	Loss: 0.000000
Train Epoch: 5 [87040/104000 (84%)]	Loss: 0.000000
Train Epoch: 5 [87680/104000 (84%)]	Loss: 0.000000
Train Epoch: 5 [88320/104000 (85%)]	Loss: 0.000000
Train Epoch: 5 [88960/104000 (86%)]	Loss: 0.000000
Train Epoch: 5 [89600/104000 (86%)]	Loss: 0.000000
Train Epoch: 5 [90240/104000 (87%)]	Loss: 0.000000
Train Epoch: 5 [90880/104000 (87%)]	Loss: 0.000000
Train Epoch: 5 [91520/104000 (88%)]	Loss: 0.000000
Train Epoch: 5 [92160/104000 (89%)]	Loss: 0.000000
Train Epoch: 5 [92800/104000 (89%)]	Loss: 0.000000
Train Epoch: 5 [93440/104000 (90%)]	Loss: 0.000000
Train Epoch: 5 [94080/104000 (90%)]	Loss: 0.000000
Train Epoch: 5 [94720/104000 (91%)]	Loss: 0.000000
Train Epoch: 5 [95360/104000 (92%)]	Loss: 0.000000
Train Epoch: 5 [96000/104000 (92%)]	Loss: 0.000000
Train Epoch: 5 [96640/104000 (93%)]	Loss: 0.000000
Train Epoch: 5 [97280/104000 (94%)]	Loss: 0.000000
Train Epoch: 5 [97920/104000 (94%)]	Loss: 0.000000
Train Epoch: 5 [98560/104000 (95%)]	Loss: 0.000000
Train Epoch: 5 [99200/104000 (95%)]	Loss: 0.000000
Train Epoch: 5 [99840/104000 (96%)]	Loss: 0.000000
Train Epoch: 5 [100480/104000 (97%)]	Loss: 0.000000
Train Epoch: 5 [101120/104000 (97%)]	Loss: 0.000000
Train Epoch: 5 [101760/104000 (98%)]	Loss: 0.000000
Train Epoch: 5 [102400/104000 (98%)]	Loss: 0.000000
Train Epoch: 5 [103040/104000 (99%)]	Loss: 0.000000
Train Epoch: 5 [103680/104000 (100%)]	Loss: 0.000000

Test set: Average loss: 0.0000, Accuracy: 26000/26000 (100%)

Critical set: Average loss: 0.7019, Accuracy: 25847/30000 (86%)

[100.0, 100.0, 100.0, 100.0, 100.0]
[88.56, 91.44666666666667, 90.16666666666667, 85.94, 86.15666666666667]


Plot Accuracy¶

Let us now plot the accuracy of our calculation. Notice that even with a convolutional layer of depth 1 (one set of weights and a single bias!) we can get a 100% accuracy on the test set. We do less well on the critical data (somewhere between 80-90%) with lots of fluctuations from training run to training to run. Again, this shows you the incredible power and (some of the limitations) of all these ML methods. If the dataset we care about (critical region) is not exactly the dataset we train on, our accuracy can be significantly diminished.

In [7]:
from matplotlib import pyplot as plt

## Print the result for different N
%matplotlib inline

plt.plot(N_array, test_array, 'r-*', label="test")
plt.plot(N_array, critical_array, 'b-s', label="critical")
plt.ylim(60,110)
plt.xlabel('Depth of hidden layer', fontsize=24)
plt.xticks(N_array)
plt.ylabel('Accuracy', fontsize=24)
plt.legend(loc='best', fontsize=24)
plt.tick_params(axis='both', which='major', labelsize=24)
plt.tight_layout()
plt.show()


Exercises¶

• Do Step 6: modify the hyperparameters to optimize performance for the specific data set
• The strides used above do not account for the periodic boundary conditions. Define your own stride function in PyTorch to incorporate periodic boundary conditions