.. _210ConvolutionMNISTrst: ================================================== 210 - First convolution network (CNN) with pytorch ================================================== .. only:: html **Links:** :download:`notebook <210_Convolution_MNIST.ipynb>`, :downloadlink:`html <210_Convolution_MNIST2html.html>`, :download:`PDF <210_Convolution_MNIST.pdf>`, :download:`python <210_Convolution_MNIST.py>`, :downloadlink:`slides <210_Convolution_MNIST.slides.html>`, :githublink:`GitHub|_doc/notebooks/101/210_Convolution_MNIST.ipynb|*` First convolution network on MNIST database. **Note:** install `tqdm `__ if not installed: ``!pip install tqdm`` .. code:: ipython3 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 .. parsed-literal:: torch 1.5.0+cpu .. code:: ipython3 %matplotlib inline .. code:: ipython3 BATCH_SIZE = 64 TEST_BATCH_SIZE = 64 DATA_DIR = 'data/' USE_CUDA = True N_EPOCHS = 50 .. code:: ipython3 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) .. code:: ipython3 class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) x = x.view(-1, 320) x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x, dim=-1) .. code:: ipython3 model = Net() if USE_CUDA: try: model = model.cuda() except Exception as e: print(e) USE_CUDA = False N_EPOCHS = 3 .. parsed-literal:: Torch not compiled with CUDA enabled .. code:: ipython3 optimizer = optim.Adam(model.parameters()) .. code:: ipython3 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.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) .. code:: ipython3 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() with torch.no_grad(): data = Variable(data) target = 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] .. code:: ipython3 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])) .. parsed-literal:: 100%|██████████| 938/938 [00:43<00:00, 23.07it/s] .. parsed-literal:: epoch 1: train loss 0.5491, test loss 0.1113, accuracy 9673/10000 in 46.74s .. parsed-literal:: 100%|██████████| 938/938 [00:39<00:00, 25.41it/s] .. parsed-literal:: epoch 2: train loss 0.2752, test loss 0.0783, accuracy 9759/10000 in 42.01s .. parsed-literal:: 100%|██████████| 938/938 [00:38<00:00, 24.14it/s] .. parsed-literal:: epoch 3: train loss 0.2232, test loss 0.0682, accuracy 9800/10000 in 41.69s .. code:: ipython3 df_perfs = pd.DataFrame(perfs, columns=["epoch", "train_loss", "test_loss", "accuracy", "n_test", "time"]) df_perfs .. raw:: html
epoch train_loss test_loss accuracy n_test time
0 1 0.549102 0.111298 9673 10000 46.742926
1 2 0.275170 0.078345 9759 10000 42.009596
2 3 0.223219 0.068236 9800 10000 41.691449
.. code:: ipython3 df_perfs[["train_loss", "test_loss"]].plot(); .. image:: 210_Convolution_MNIST_13_0.png .. code:: ipython3 df_perfs[["train_loss", "test_loss"]].plot(ylim=(0, 0.2)); .. image:: 210_Convolution_MNIST_14_0.png