200 - First percepton with pytorch#
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First perception on MNIST database.
Note: install tqdm if not
installed: !pip install tqdm
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
print("torch", torch.__version__)
from torchvision import datasets, transforms
from tqdm import tqdm
torch 1.5.0+cpu
%matplotlib inline
BATCH_SIZE = 64
TEST_BATCH_SIZE = 64
DATA_DIR = 'data/'
USE_CUDA = True
N_EPOCHS = 100
train_loader = torch.utils.data.DataLoader(
datasets.MNIST(DATA_DIR, train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=BATCH_SIZE, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST(DATA_DIR, train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=TEST_BATCH_SIZE, shuffle=True)
data, target = next(i for i in train_loader)
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(28*28, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = x.view(-1, 28*28)
x = torch.tanh(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x, dim=-1)
model = Net()
if USE_CUDA:
try:
model = model.cuda()
except Exception as e:
print(e)
USE_CUDA = False
N_EPOCHS = 5
Torch not compiled with CUDA enabled
optimizer = optim.Adam(model.parameters())
def train(epoch, verbose=True):
model.train()
losses = []
loader = tqdm(train_loader, total=len(train_loader))
for batch_idx, (data, target) in enumerate(loader):
if USE_CUDA:
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
losses.append(float(loss.data.item()))
if verbose and batch_idx % 100 == 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()))
return np.mean(losses)
def test(verbose=True):
model.eval()
test_loss = 0
correct = 0
for data, target in test_loader:
if USE_CUDA:
data, target = data.cuda(), target.cuda()
data, target = Variable(data, volatile=True), Variable(target)
output = model(data)
test_loss += F.nll_loss(output, target, reduction='sum').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()
test_loss /= len(test_loader.dataset)
if verbose:
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
return [float(test_loss), correct]
perfs = []
for epoch in range(1, N_EPOCHS + 1):
t0 = time.time()
train_loss = train(epoch, verbose=False)
test_loss, correct = test(verbose=False)
perfs.append([epoch, train_loss, test_loss,
correct, len(test_loader.dataset), time.time() - t0])
print("epoch {}: train loss {:.4f}, test loss {:.4f}, accuracy {}/{} in {:.2f}s".format(*perfs[-1]))
100%|██████████| 938/938 [00:18<00:00, 52.70it/s] c:python372_x64libsite-packagesipykernel_launcher.py:8: UserWarning: volatile was removed and now has no effect. Use with torch.no_grad(): instead.
epoch 1: train loss 0.4866, test loss 0.2546, accuracy 9259/10000 in 20.77s
100%|██████████| 938/938 [00:18<00:00, 51.27it/s]
epoch 2: train loss 0.3374, test loss 0.2236, accuracy 9337/10000 in 20.57s
100%|██████████| 938/938 [00:18<00:00, 51.55it/s]
epoch 3: train loss 0.3091, test loss 0.2040, accuracy 9392/10000 in 20.66s
100%|██████████| 938/938 [00:18<00:00, 50.80it/s]
epoch 4: train loss 0.2951, test loss 0.2035, accuracy 9382/10000 in 20.70s
100%|██████████| 938/938 [00:18<00:00, 50.85it/s]
epoch 5: train loss 0.2848, test loss 0.1877, accuracy 9439/10000 in 20.77s
df_perfs = pd.DataFrame(perfs, columns=["epoch", "train_loss", "test_loss",
"accuracy", "n_test", "time"])
df_perfs
epoch | train_loss | test_loss | accuracy | n_test | time | |
---|---|---|---|---|---|---|
0 | 1 | 0.486636 | 0.254567 | 9259 | 10000 | 20.770426 |
1 | 2 | 0.337395 | 0.223594 | 9337 | 10000 | 20.567967 |
2 | 3 | 0.309106 | 0.204043 | 9392 | 10000 | 20.660723 |
3 | 4 | 0.295136 | 0.203547 | 9382 | 10000 | 20.696622 |
4 | 5 | 0.284773 | 0.187705 | 9439 | 10000 | 20.769429 |
df_perfs[["train_loss", "test_loss"]].plot();
df_perfs[["train_loss", "test_loss"]].plot(ylim=(0, 0.2));