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mnist_data.py
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mnist_data.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import torch
import torchvision
from torchvision import transforms,datasets
# In[2]:
train = datasets.MNIST("",train=True,download=False,transform=transforms.Compose([transforms.ToTensor()]))
test = datasets.MNIST("",train=False,download=False,transform=transforms.Compose([transforms.ToTensor()]))
# In[3]:
trainset = torch.utils.data.DataLoader(train,batch_size=10,shuffle=True)
testset = torch.utils.data.DataLoader(test,batch_size=10,shuffle=True)
# In[4]:
for data in trainset:
print(data)
break
# In[5]:
x , y = data[0][0],data[1][0]
y
# In[6]:
import matplotlib.pyplot as plt
plt.imshow(data[0][0].view(28,28))
plt.show()
# In[7]:
total = 0
counter_dict = {0:0,1:0,2:0,3:0,4:0,5:0,6:0,7:0,8:0,9:0}
for data in trainset:
xs,ys = data
for y in ys:
counter_dict[int(y)] += 1
total += 1
print(counter_dict)
# In[8]:
ys
# In[9]:
total
# In[10]:
for i in counter_dict:
print(f"{i} : {counter_dict[i]/total*100}")
# In[11]:
import torch.nn as nn
import torch.nn.functional as F
# In[12]:
class Net(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(28*28,64)
self.fc2 = nn.Linear(64,64)
self.fc3 = nn.Linear(64,64)
self.fc4 = nn.Linear(64,10)
def forward(self,x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
x = self.fc4(x)
return F.log_softmax(x,dim=1)
net = Net()
print(net)
# In[13]:
X = torch.rand((28,28))
X = X.view(1,28*28)
X.shape
# In[14]:
output = net(X)
output
# In[15]:
import torch.optim as optim
optimizet = optim.Adam(net.parameters(),lr=0.001)
EPOCHS = 3
for epoch in range(EPOCHS):
for data in trainset:
X,y = data
net.zero_grad()
output = net(X.view(-1,28*28))
loss = F.nll_loss(output,y)
loss.backward()
optimizet.step()
print(loss)
# In[17]:
correct = 0
total = 0
with torch.no_grad():
for data in trainset:
X,y = data
output = net(X.view(-1,28*28))
for idx,i in enumerate(output):
if torch.argmax(i)==y[idx]:
correct += 1
total +=1
print("Accuracy: ",round(correct/total,3))
# In[30]:
import matplotlib.pyplot as plt
plt.imshow(X[4].view(28,28))
plt.show()
# In[31]:
print(torch.argmax(net(X[4].view(-1,28*28))[0]))
# In[ ]: