Deep Learning - CNN Convolutional Neural Network

basic concept

overview

Convolutional Neural Network (CNN) is a neural network model commonly used in deep learning to process data with a grid structure. It is widely used in image classification, object detection, image generation and other tasks in the field of computer vision.

main idea

The core idea of ​​CNN is to capture the spatial structure information of input data by utilizing local perception and parameter sharing. Compared with the traditional fully connected neural network, CNN introduces convolutional layers and pooling layers in the network structure, thereby reducing the number of parameters and better handling high-dimensional input data.

other concepts

Input Layer : Receives raw images or other forms of input data.
Convolutional Layer : Use convolution operations to extract input features, slide on the input data by setting filters (convolution kernels) and perform convolution operations. In this way, local features such as edges and textures can be learned.
Activation Function : Each convolutional layer is usually followed by a nonlinear activation function, such as ReLU (Rectified Linear Unit), to increase the nonlinear expressiveness of the network.
Pooling Layer : Reduces model complexity by reducing the size of feature maps. A commonly used pooling operation is Max Pooling, which selects the largest eigenvalue within each pooling window as the output.
Fully Connected Layer : Connect the output of the convolutional layer and the pooling layer to the fully connected layer, and use the traditional neural network model to perform tasks such as classification and regression.
Dropout layer : During the training process, the output of some neurons is randomly set to 0 with a certain probability to reduce the overfitting of the model.
Softmax layer : The output layer commonly used in multi-classification problems. The softmax operation is performed in the last layer to convert the output into a probability distribution on the category.

Code and detailed comments

import os

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

# torch.manual_seed(1)    # reproducible

# Hyper Parameters
#  轮次
EPOCH = 1               # train the training data n times, to save time, we just train 1 epoch
# 批大小为50
BATCH_SIZE = 50
# 学习率
LR = 0.001
# 是否下载mnist数据集
DOWNLOAD_MNIST = False


# 下载minist数据集
if not(os.path.exists('./mnist/')) or not os.listdir('./mnist/'):
    # not mnist dir or mnist is empyt dir
    DOWNLOAD_MNIST = True

# torchvision本身就是一个数据库
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,
)

# 输出训练数据尺寸
print(train_data.train_data.size())                 # (60000, 28, 28)
# 输出标签数据尺寸
print(train_data.train_labels.size())               # (60000)
# 展示训练数据集中的第0个图片
plt.imshow(train_data.train_data[0].numpy(), cmap='gray')
# 图片的标题是标签
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)
# 批大小为50，shuffle为True意思是设置为随机
train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)

# pick 2000 samples to speed up testing
test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)
# 使用unsqueeze增加一个维度
test_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.FloatTensor)[:2000]/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__()
        # 快速搭建神经网络
        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 Conv2d, padding=(kernel_size-1)/2 if stride=1
            ),                              # output shape (16, 28, 28)
            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.ReLU(),                      # activation
            nn.MaxPool2d(2),                # output shape (32, 7, 7)
        )
        self.out = nn.Linear(32 * 7 * 7, 10)   # fully connected layer, output 10 classes

    # 前向传播
    def forward(self, x):
        # 第一层卷积
        x = self.conv1(x)
        # 第二层卷积
        x = self.conv2(x)
        x = x.view(x.size(0), -1)           # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
        output = self.out(x)
        return output, x    # return x for visualization


cnn = CNN()
print(cnn)  # net architecture

# 选择优化器
optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)   # optimize all cnn parameters
# 选择损失函数
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 = True
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, (b_x, b_y) in enumerate(train_loader):   # gives batch data, normalize x when iterate train_loader

        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

        if step % 50 == 0:
            test_output, last_layer = cnn(test_x)
            pred_y = torch.max(test_output, 1)[1].data.numpy()
            accuracy = float((pred_y == test_y.data.numpy()).astype(int).sum()) / float(test_y.size(0))
            print('Epoch: ', epoch, '| train loss: %.4f' % loss.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.data.numpy()[:plot_only, :])
                labels = test_y.numpy()[:plot_only]
                plot_with_labels(low_dim_embs, labels)
plt.ioff()

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

operation result