神经网络的快速搭建

快速搭建法

逐层加上激励函数直接构造

# method 2
# 快速搭建

net2 = torch.nn.Sequential(
    torch.nn.Linear(1, 10),
    torch.nn.ReLU(),
    torch.nn.Linear(10, 1),
)
print(net2)

out

Sequential(
  (0): Linear(in_features=1, out_features=10, bias=True)
  (1): ReLU()
  (2): Linear(in_features=10, out_features=1, bias=True)
)

批训练

import torch
import torch.utils.data as Data

BATCH_SIZE = 5

x = torch.linspace(1, 10, 10)
y = torch.linspace(10, 1, 10)

torch_dataset = Data.TensorDataset(x, y)
loader = Data.DataLoader(
    dataset=torch_dataset, # 数据集
    batch_size=BATCH_SIZE,
    shuffle=True, # 训练时需不需要打乱
    # num_workers = 2 # 此破电脑不能多线程
)

for epoch in range(3): # 整体训练三次
    for step, (batch_x, batch_y) in enumerate(loader):
        # training
        print('Epoch', epoch, '|Step:', step, '|batch x:',
              batch_x.numpy(), '|batch y:', batch_y.numpy())

out

Epoch 0 |Step: 0 |batch x: [9. 2. 7. 4. 3.] |batch y: [2. 9. 4. 7. 8.]
Epoch 0 |Step: 1 |batch x: [ 8.  1.  5.  6. 10.] |batch y: [ 3. 10.  6.  5.  1.]
Epoch 1 |Step: 0 |batch x: [ 3.  7.  6. 10.  8.] |batch y: [8. 4. 5. 1. 3.]
Epoch 1 |Step: 1 |batch x: [9. 1. 2. 4. 5.] |batch y: [ 2. 10.  9.  7.  6.]
Epoch 2 |Step: 0 |batch x: [10.  5.  4.  8.  7.] |batch y: [1. 6. 7. 3. 4.]
Epoch 2 |Step: 1 |batch x: [1. 9. 3. 6. 2.] |batch y: [10.  2.  8.  5.  9.]

可以用data_loader进行批训练

加速神经网络训练过程

SGD

stochastic Gradient Descent

批量数据进入神经网络训练

Mumentum： $m=b_1\ast m-learningrate\ast dx$

adagrad：$v+=d{x}^2$

结合之后得出RMSProp

$m=b_1\ast m+(1-b_1)\ast dx$ Momentum：下坡

$v=b_2\ast v+(1-b_2)\ast d{x}^2$ AdaGrad：阻力太大，只能沿着下降方向

$w+=-learingrate\ast m/\sqrt{v}$

CNN卷积神经网络

处理图片识别：将图片变成一个个像素，并不是对一个个像素进行处理，而是对一块像素区域进行处理。

采用一个过滤器在图片中不断移动，这叫做卷积

然后输出一个长和宽更小，高度更大的图片

在卷积的过程中，可能会丢失一些信息，则需要进行池化

pytorch实现

我们采用MNIST数据集

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

# Hyper Parameters
EPOCH = 1
BATCH_SIZE = 50
LR = 0.001
DOWNLOAD_MNIST = True

train_data = torchvision.datasets.MNIST(
    root='./mnist',
    train=True,
    transform=torchvision.transforms.ToTensor(), # 改成tensor格式
    download=DOWNLOAD_MNIST
)


# plot one example

print(train_data.train_data.size())
print(train_data.train_labels.size())
plt.imshow(train_data.train_data[0].numpy(), cmap='gray')
plt.title('%i' % train_data.train_labels[0])
plt.show()

输出有一张5

构建cnn卷积神经网络

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

# Hyper Parameters
EPOCH = 1
BATCH_SIZE = 50
LR = 0.001
DOWNLOAD_MNIST = False

train_data = torchvision.datasets.MNIST(
    root='./mnist',
    train=True,
    transform=torchvision.transforms.ToTensor(), # 改成tensor格式
    download=DOWNLOAD_MNIST
)


# plot one example

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

train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)

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]/255
test_y = test_data.test_labels[:2000]

class CNN(nn.Module):
    # 卷积层
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(
                in_channels=1, # 图片的高度
                out_channels=16, # 输出的高度(特征个数)
                kernel_size=5, # filter宽和高都是5(5*5)的扫描形式
                stride=1, # 隔几步跳一次
                padding=2 # 周围围上一圈0， padding = (kernel_size - 1) / 2
            ), # 过滤器 -> (16, 28, 28)
            nn.ReLU(), # 卷积层
            nn.MaxPool2d(kernel_size=2), # 池化层，保留重要特征 ->(16, 14, 14)
        )
        self.conv2 = nn.Sequential( # (16, 14, 14)
            nn.Conv2d(16,32,5,1,2), # ->(32, 14, 14)
            nn.ReLU(),
            nn.MaxPool2d(2) # -> (32, 7, 7)
        )
        self.out = nn.Linear(32 * 7 * 7, 10)

        def forward(self, x):
            x = self.conv1(x)
            x = self.conv2(x) # (batch, 32, 7, 7)
            x = x.view(x.size(0), -1) # (batch, 32 * 7 * 7)

cnn = CNN()
print(cnn)

out

CNN(
  (conv1): Sequential(
    (0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (1): ReLU()
    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (conv2): Sequential(
    (0): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (1): ReLU()
    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (out): Linear(in_features=1568, out_features=10, bias=True)
)

加上优化器与训练之后

"""
View more, visit my tutorial page: https://mofanpy.com/tutorials/
My Youtube Channel: https://www.youtube.com/user/MorvanZhou

Dependencies:
torch: 0.4
torchvision
matplotlib
"""
# library
# standard library
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
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)

# pick 2000 samples to speed up testing
test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)
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__()
        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

    def forward(self, x):
        x = self.conv1(x)
        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

optimizer = torch.optim.Adam(cnn.parameters(), 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 = 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')

out

[7 2 1 0 4 1 4 9 5 9] prediction number
[7 2 1 0 4 1 4 9 5 9] real number