classfication_cnn.py

"""
View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/
My Youtube Channel: https://www.youtube.com/user/MorvanZhou
Dependencies:
torch: 0.1.11
torchvision
matplotlib
"""
# library
# standard library
import os

# third-party library
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.utils.data as Data
import torchvision
import matplotlib.pyplot as plt

# torch.manual_seed(1)    # reproducible

# Hyper Parameters
EPOCH = 1               # train the training data n times, to save time, we just train 1 epoch
BATCH_SIZE = 50
LR = 0.001              # learning rate
DOWNLOAD_MNIST = False


# Mnist digits dataset
if not(os.path.exists('./mnist/')) or not os.listdir('./mnist/'):
    # not mnist dir or mnist is empyt dir
    DOWNLOAD_MNIST = True

train_data = torchvision.datasets.MNIST(
    root='./mnist/',
    train=True,                                     # this is training data
    transform=torchvision.transforms.ToTensor(),    # Converts a PIL.Image or numpy.ndarray to
                                                    # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
    download=DOWNLOAD_MNIST,
)

# plot one example
print(train_data.train_data.size())                 # (60000, 28, 28)
print(train_data.train_labels.size())               # (60000)
# plt.imshow(train_data.train_data[0].numpy(), cmap='gray')
# plt.title('%i' % train_data.train_labels[0])
# plt.show()

# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)
train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)

# convert test data into Variable, pick 2000 samples to speed up testing
test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)
test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1), volatile=True).type(torch.FloatTensor)[:2000].cuda()/255.  # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)
test_y = test_data.test_labels[:2000]


class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Sequential(         # input shape (1, 28, 28)
            nn.Conv2d(
                in_channels=1,              # input height
                out_channels=16,            # n_filters
                kernel_size=5,              # filter size
                stride=1,                   # filter movement/step
                padding=2,                  # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1
            ),                              # output shape (16, 28, 28)
            nn.BatchNorm2d(16),
            nn.ReLU(),                      # activation
            nn.MaxPool2d(kernel_size=2),    # choose max value in 2x2 area, output shape (16, 14, 14)
        )
        self.conv2 = nn.Sequential(         # input shape (16, 14, 14)
            nn.Conv2d(16, 32, 5, 1, 2),     # output shape (32, 14, 14)
            nn.BatchNorm2d(32),
            nn.ReLU(),                      # activation
            nn.MaxPool2d(2),                # output shape (32, 7, 7)
        )
        self.out = nn.Linear(32 * 7 * 7, 10)   # fully connected layer, output 10 classes
        # self._fix_layer()

    def train(self, mode=True):
        """Sets the module in training mode.

        This has any effect only on modules such as Dropout or BatchNorm.

        Returns:
            Module: self
        """
        self.training = mode
        for module in self.children():
            module.train(mode)
        for layer in [ ]:
            layer.eval()
        return self

    def _freeze_module(self, module):
        for p in module.parameters():
            p.requires_grad = False

    def _fix_layer(self, ):
        for layer in [self.conv1,]:
            self._freeze_module(layer)

    def forward(self, x):
        # x = self.conv1(x)
        # # print(self.data.detach().size(), x.size())
        # device=torch.device('cuda')
        # data = torch.zeros_like(x, dtype=torch.float32, device=device, requires_grad=True)
        # data.detach()[:,:8,:,:] = x[:,:8,:,:]
        # # print(self.data.size())
        # x1 = self.conv2(data)
        # x2 = x1.view(x1.size(0), -1)           # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
        # output = self.out(x2)

        x = self.conv1(x)
        x = self.conv2(x)
        tmp = torch.zeros_like(x, dtype=torch.float32, device=torch.device('cuda'))
        tmp[:,:,:,:] = x[:,:,:,:]
        x2 = tmp.view(x.size(0), -1)  # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
        output = self.out(x2)
        data=torch.zeros_like(output, dtype=torch.float32, device=torch.device('cuda'))
        data[:,:] = output[:,:]
        # print(x.requires_grad, x2.requires_grad, data.requires_grad)
        return data, x2    # return x for visualization


cnn = CNN().cuda()
cnn.train()
print(cnn) # net architecture
for name, params in cnn.named_parameters():
    print(name, params.size())
trainable_params = [p for p in cnn.parameters() if p.requires_grad]
# print([p.keys() for p in trainable_params])
# for name, module in cnn.named_children():
#     print(name, module)
for name, p in cnn.named_parameters():
    print(name, p.requires_grad)
optimizer = torch.optim.Adam(trainable_params, lr=LR)   # optimize all cnn parameters
loss_func = nn.CrossEntropyLoss()                       # the target label is not one-hotted

# following function (plot_with_labels) is for visualization, can be ignored if not interested
from matplotlib import cm
try: from sklearn.manifold import TSNE; HAS_SK = False
except: HAS_SK = False; print('Please install sklearn for layer visualization')
def plot_with_labels(lowDWeights, labels):
    plt.cla()
    X, Y = lowDWeights[:, 0], lowDWeights[:, 1]
    for x, y, s in zip(X, Y, labels):
        c = cm.rainbow(int(255 * s / 9)); plt.text(x, y, s, backgroundcolor=c, fontsize=9)
    plt.xlim(X.min(), X.max()); plt.ylim(Y.min(), Y.max()); plt.title('Visualize last layer'); plt.show(); plt.pause(0.01)

plt.ion()
# training and testing
for epoch in range(EPOCH):
    for step, (x, y) in enumerate(train_loader):   # gives batch data, normalize x when iterate train_loader
        b_x = Variable(x).cuda()   # batch x
        b_y = Variable(y).cuda()   # batch y

        output = cnn(b_x)[0]               # cnn output
        loss = loss_func(output, b_y)   # cross entropy loss
        optimizer.zero_grad()           # clear gradients for this training step
        loss.backward()                 # backpropagation, compute gradients
        optimizer.step()                # apply gradients

        x = x.cpu().data.numpy()
        if step % 50 == 0:
            test_output, last_layer = cnn(test_x)
            pred_y = torch.max(test_output, 1)[1].cpu().data.squeeze().numpy()
            accuracy = float((pred_y == test_y.data.numpy()).astype(int).sum()) / float(test_y.size(0))
            print('Epoch: ', epoch, '| train loss: %.4f' % loss.cpu().data.numpy(), '| test accuracy: %.2f' % accuracy)
            if HAS_SK:
                # Visualization of trained flatten layer (T-SNE)
                tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
                plot_only = 500
                low_dim_embs = tsne.fit_transform(last_layer.cpu().data.numpy()[:plot_only, :])
                labels = test_y.cpu().numpy()[:plot_only]
                plot_with_labels(low_dim_embs, labels)
plt.ioff()

# print 10 predictions from test data
cnn.eval()
test_output, _ = cnn(test_x[:10])
pred_y = torch.max(test_output, 1)[1].cpu().data.numpy().squeeze()
print(pred_y, 'prediction number')
print(test_y[:10].cpu().numpy(), 'real number')