Project_CNN_with_softmax_layer_visualisation.py

# coding: utf-8

# In[20]:


import torch
import torchvision
import torchvision.transforms as transforms

########################################################################
# The output of torchvision datasets are PILImage images of range [0, 1].
# We transform them to Tensors of normalized range [-1, 1].

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

########################################################################
# Let us show some of the training images, for fun.

import matplotlib.pyplot as plt
import numpy as np

# functions to show an image


def imshow(img):
    img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))


# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()

# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))


########################################################################
# 2. Define a Convolution Neural Network
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# Copy the neural network from the Neural Networks section before and modify it to
# take 3-channel images (instead of 1-channel images as it was defined).

import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 3)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x1=x
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x,x1


net = Net()

########################################################################
# 3. Define a Loss function and optimizer
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# Let's use a Classification Cross-Entropy loss and SGD with momentum.

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

########################################################################
# 4. Train the network
# ^^^^^^^^^^^^^^^^^^^^
#
# This is when things start to get interesting.
# We simply have to loop over our data iterator, and feed the inputs to the
# network and optimize.

for epoch in range(2):  # loop over the dataset multiple times
    layer1=[]
    l=[]

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs
        inputs, labels = data
        l.append(labels[0])
        l.append(labels[1])
        l.append(labels[2])
        l.append(labels[3])

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs,outputs1 = net(inputs)
        layer1.append(outputs1[0,1,:,:].detach().numpy())
        layer1.append(outputs1[1,1,:,:].detach().numpy())
        layer1.append(outputs1[2,1,:,:].detach().numpy())
        layer1.append(outputs1[3,1,:,:].detach().numpy())
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        #running_loss += loss.item()
        #if i % 2000 == 1999:    # print every 2000 mini-batches
            #print('[%d, %5d] loss: %.3f' %
                  #(epoch + 1, i + 1, running_loss / 2000))
            #running_loss = 0.0

#print('Finished Training')


# In[22]:


train_svm=np.reshape(layer1,(50000,25))


# In[13]:


#np.shape(outputs1)
#outputs1[1,4,:,:]
outputs


# In[29]:


labels


# In[23]:


from sklearn import svm

clf=svm.SVC()
#Train_SVM=[]
#for i in range (0,4):
        #A=outputs1[i].detach().numpy()
        
        #Train_SVM.append(A)
        
clf.fit(train_svm,l)


# In[30]:


testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, 
                                         shuffle=False, num_workers=2)


# In[31]:


test_svm=[]
l=[]
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs,outputs1 = net(images)
        test_svm.append(outputs1[0,1,:,:].detach().numpy())
        l.append(labels)
        


# In[33]:


test_svm=np.reshape(test_svm,(10000,25))


# In[34]:


clf.score(test_svm, l)


# In[37]:


pred_svm=clf.predict(test_svm);
np.shape(pred_svm)


# In[36]:


from sklearn.metrics import confusion_matrix
confusion_matrix(l, pred_svm)


# In[5]:


np.shape(outputs1)


# In[8]:


A=outputs1[1,1]


# In[9]:


np.shape(A)