"""
This is an implementation of Convolution Neural Network with multi conv layer.
"""
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F #233
import torch.optim as optim
from torchvision import datasets, transforms
import numpy as np
from PIL import Image
[docs]class Net(nn.Module):
def __init__(self, in_channel1 = 1, out_channel1 = 32, out_channel2 = 64, H = 28, W = 28):
super(Net, self).__init__()
self.H = H
self.W = W
self.out_channel2 = out_channel2
## define two convolutional layers
self.conv1 = nn.Conv2d(in_channels = in_channel1,
out_channels = out_channel1,
kernel_size = 5,
stride= 1,
padding = (2,2))
self.conv2 = nn.Conv2d(in_channels = out_channel1,
out_channels = out_channel2,
kernel_size = 5,
stride = 1,
padding = (2,2))
## define two linear layers
self.fc1 = nn.Linear(int(H/4)*int(W/4)* out_channel2, 1024)
self.fc2 = nn.Linear(1024, 10)
def forward(self, x):
self.layers[0] = F.relu(self.conv1(x))
self.layers[1] = F.max_pool2d(x, 2, 2)
self.layers[2] = F.relu(self.conv2(x))
self.layers[3] = F.max_pool2d(x, 2, 2)
self.layers[4] = x.view(-1, int(self.H/4) * int(self.W/4) * self.out_channel2)
self.layers[5] = F.relu(self.fc1(x))
self.layers[6] = self.fc2(x)
return F.log_softmax(layers[6], dim=1)
#def get_logits(self, x):
#x = F.relu(self.conv1(x))
#x = F.max_pool2d(x, 2, 2)
#x = F.relu(self.conv2(x))
#x = F.max_pool2d(x, 2, 2)
#x = x.view(-1, 4* 4 * 50)
#x = F.relu(self.fc1(x))
#x = self.fc2(x)
#return x
[docs]def train(model, device, train_loader, optimizer, epoch):
"""train.
Parameters
----------
model :
model
device :
device
train_loader :
train_loader
optimizer :
optimizer
epoch :
epoch
"""
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
#print every 10
if batch_idx % 10 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
[docs]def test(model, device, test_loader):
"""test.
Parameters
----------
model :
model
device :
device
test_loader :
test_loader
"""
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))