CNN结构

CNN图层

VGG-16

CNN由处理视觉信息的层组成。CNN首先接收输入图像，然后将其传递通过这些层。有几种不同类型的层:最常用的层：卷积，池化和完全连接的层。
首先，让我们来看看完整的CNN架构; 下面是一个名为VGG-16的网络，它经过培训可识别各种图像类别。它接收图像作为输入，并输出该图像的预测类。

VGG-16

在PyTorch定义图层

对于卷积神经网络，由一些列简单的层组成：

卷积层
最大池化层
全连接(线性)层

要在PyTorch定义神经网络，创建并命名一个新的神经网络类，在函数init中定义网络层。
注意：在训练期间，PyTorch将能够通过跟踪网络的前馈行为并使用autograd来计算网络中权重的更新来执行反向传播。

import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):

    def __init__(self, n_classes):
        super(Net, self).__init__()

        # 1 input image channel (grayscale), 32 output channels/feature maps
        # 5x5 square convolution kernel
        self.conv1 = nn.Conv2d(1, 32, 5)

        # maxpool layer
        # pool with kernel_size=2, stride=2
        self.pool = nn.MaxPool2d(2, 2)

        # fully-connected layer
        # 32*4 input size to account for the downsampled image size after pooling
        # num_classes outputs (for n_classes of image data)
        self.fc1 = nn.Linear(32*4, n_classes)

    # define the feedforward behavior
    def forward(self, x):
        # one conv/relu + pool layers
        x = self.pool(F.relu(self.conv1(x)))

        # prep for linear layer by flattening the feature maps into feature vectors
        x = x.view(x.size(0), -1)
        # linear layer 
        x = F.relu(self.fc1(x))

        # final output
        return x

# instantiate and print your Net
n_classes = 20 # example number of classes
net = Net(n_classes)
print(net)

可视化四个滤波器输出

mport cv2
import matplotlib.pyplot as plt
%matplotlib inline

# TODO: Feel free to try out your own images here by changing img_path
# to a file path to another image on your computer!
img_path = 'images/udacity_sdc.png'

# load color image 
bgr_img = cv2.imread(img_path)
# convert to grayscale
gray_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2GRAY)

# normalize, rescale entries to lie in [0,1]
gray_img = gray_img.astype("float32")/255

# plot image
plt.imshow(gray_img, cmap='gray')
plt.show()

定义滤波器

import numpy as np

## TODO: Feel free to modify the numbers here, to try out another filter!
filter_vals = np.array([[-1, -1, 1, 1], [-1, -1, 1, 1], [-1, -1, 1, 1], [-1, -1, 1, 1]])

print('Filter shape: ', filter_vals.shape)
# define four filters
filter_1 = filter_vals
filter_2 = -filter_1
filter_3 = filter_1.T
filter_4 = -filter_3
filters = np.array([filter_1, filter_2, filter_3, filter_4])

# For an example, print out the values of filter 1
print('Filter 1: \n', filter_1)

### do not modify the code below this line ###

# visualize all four filters
fig = plt.figure(figsize=(10, 5))
for i in range(4):
    ax = fig.add_subplot(1, 4, i+1, xticks=[], yticks=[])
    ax.imshow(filters[i], cmap='gray')
    ax.set_title('Filter %s' % str(i+1))
    width, height = filters[i].shape
    for x in range(width):
        for y in range(height):
            ax.annotate(str(filters[i][x][y]), xy=(y,x),
                        horizontalalignment='center',
                        verticalalignment='center',
                        color='white' if filters[i][x][y]<0 else 'black')

初始化单个卷积层，使其包含创建的所有过滤器

import torch
import torch.nn as nn
import torch.nn.functional as F

    
# define a neural network with a single convolutional layer with four filters
class Net(nn.Module):
    
    def __init__(self, weight):
        super(Net, self).__init__()
        # initializes the weights of the convolutional layer to be the weights of the 4 defined filters
        k_height, k_width = weight.shape[2:]
        # assumes there are 4 grayscale filters
        self.conv = nn.Conv2d(1, 4, kernel_size=(k_height, k_width), bias=False)
        self.conv.weight = torch.nn.Parameter(weight)

    def forward(self, x):
        # calculates the output of a convolutional layer
        # pre- and post-activation
        conv_x = self.conv(x)
        activated_x = F.relu(conv_x)
        
        # returns both layers
        return conv_x, activated_x
    
# instantiate the model and set the weights
weight = torch.from_numpy(filters).unsqueeze(1).type(torch.FloatTensor)
model = Net(weight)

# print out the layer in the network
print(model)

可视化每个过滤器的输出¶
首先，我们将定义一个辅助函数viz_layer，它接受一个特定的图层和多个过滤器（可选参数），并在图像通过后显示该图层的输出。

# helper function for visualizing the output of a given layer
# default number of filters is 4
def viz_layer(layer, n_filters= 4):
    fig = plt.figure(figsize=(20, 20))
    
    for i in range(n_filters):
        ax = fig.add_subplot(1, n_filters, i+1, xticks=[], yticks=[])
        # grab layer outputs
        ax.imshow(np.squeeze(layer[0,i].data.numpy()), cmap='gray')
        ax.set_title('Output %s' % str(i+1))

# plot original image
plt.imshow(gray_img, cmap='gray')

# visualize all filters
fig = plt.figure(figsize=(12, 6))
fig.subplots_adjust(left=0, right=1.5, bottom=0.8, top=1, hspace=0.05, wspace=0.05)
for i in range(4):
    ax = fig.add_subplot(1, 4, i+1, xticks=[], yticks=[])
    ax.imshow(filters[i], cmap='gray')
    ax.set_title('Filter %s' % str(i+1))

    
# convert the image into an input Tensor
gray_img_tensor = torch.from_numpy(gray_img).unsqueeze(0).unsqueeze(1)

# get the convolutional layer (pre and post activation)
conv_layer, activated_layer = model(gray_img_tensor)

# visualize the output of a conv layer
viz_layer(conv_layer)

# visualize the output of an activated conv layer
viz_layer(activated_layer)

池化层

在几个卷积层（+ ReLu）之后，在VGG-16网络中，会有一个最大化层。

池化层会获取图像（通常是过滤后的图像）并输出该图像的缩小版本
池化层会降低输入的维度
最大池化层会查看输入图像中的区域（如下图所示的4x4像素区域），并选择在新的缩小区域中保留该区域中的最大像素值。
Maxpooling是CNN中最常见的池化层类型，但也有其他类型，如平均池化。

全连接层

在一系列卷积和池化层之后的完全连接的层。注意它们的扁平形状
完全连接层的工作是将它看到的输入连接到所需的输出形式。通常，这意味着将图像特征矩阵转换为尺寸为1xC的特征向量，其中C是类的数量。例如，假设我们将图像分类为十个类，您可以为完全连接的图层提供一组[池化，激活]特征图作为输入，并告诉它使用这些特征的组合（将它们相乘，添加它们，将它们组合起来等）输出10项长特征向量。该向量将来自特征映射的信息压缩为单个特征向量。

softmax

网络中看到的最后一层是SOFTMAX功能。softmax函数可以将任何值向量作为输入，并返回相同长度的向量，其值均在范围（0,1）中，并且这些值一起将加起来为1

Train in PyTorch

一旦你加载了一个训练数据集，接下来你的工作将是定义一个CNN网并训练它来对这组图像进行分类。

Loss and Optimizer

要训练一个模型，你需要通过选择一个损失函数和优化器来定义它是如何训练的。这些函数决定了模型在其训练时如何更新其参数，并且可以影响模型收敛的速度。
对于这样的分类问题，通常使用交叉熵损失，这可以在如下代码中定义：criterion = nn.CrossEntropyLoss()。PyTorch还包括一些标准随机优化，如随机梯度下降和亚当。鼓励你尝试不同的优化器，看看你的模型是如何响应这些选择的。

通常，我们通过训练数据集来训练多个时期或周期的任何网络。下面是训练函数在迭代训练数据集时执行的步骤：

准备所有输入图像和标签数据以进行培训
通过网络传递输入（正向传递）
计算损失（预测类与正确标签的距离）
将梯度传播回网络参数（向后传递）
更新权重（参数更新）

CNN for Classification

在 _ init_ 定义层

conv / pool层可以像这样定义（在init中）：

# 1 input image channel (for grayscale images), 32 output channels/feature maps, 3x3 square convolution kernel
self.conv1 = nn.Conv2d(1, 32, 3)
# 通道1，输出32，卷积核3*3
# maxpool that uses a square window of kernel_size=2, stride=2
self.pool = nn.MaxPool2d(2, 2)

# our basic libraries
import torch
import torchvision

# data loading and transforming
from torchvision.datasets import FashionMNIST
from torch.utils.data import DataLoader
from torchvision import transforms

# The output of torchvision datasets are PILImage images of range [0, 1]. 
# We transform them to Tensors for input into a CNN

## Define a transform to read the data in as a tensor
data_transform = transforms.ToTensor()

# choose the training and test datasets
train_data = FashionMNIST(root='./data', train=True,
                                   download=True, transform=data_transform)

test_data = FashionMNIST(root='./data', train=False,
                                  download=True, transform=data_transform)


# Print out some stats about the training and test data
print('Train data, number of images: ', len(train_data))
print('Test data, number of images: ', len(test_data))

# prepare data loaders, set the batch_size
## TODO: you can try changing the batch_size to be larger or smaller
## when you get to training your network, see how batch_size affects the loss
batch_size = 20

train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=True)

# specify the image classes
classes = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 
           'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

该单元格遍历训练数据集，使用dataiter.next（）加载随机批次的图像/标签数据。然后，它在2 x batch_size / 2网格中绘制一批图像和标签。

import numpy as np
import matplotlib.pyplot as plt

%matplotlib inline
    
# obtain one batch of training images
dataiter = iter(train_loader)
images, labels = dataiter.next()
images = images.numpy()

# plot the images in the batch, along with the corresponding labels
fig = plt.figure(figsize=(25, 4))
for idx in np.arange(batch_size):
    ax = fig.add_subplot(2, batch_size/2, idx+1, xticks=[], yticks=[])
    ax.imshow(np.squeeze(images[idx]), cmap='gray')
    ax.set_title(classes[labels[idx]])

对于卷积神经网络，我们将使用一系列简单的层：

卷积层
最大池化层数
完全连接（线性）层

flattening

回想一下，要从卷积/池化层的输出移动到线性层，必须先将提取的特征展平为矢量。如果您使用了深度学习库Keras，您可能已经看过Flatten（）完成此操作，而在PyTorch中，您可以使用x = x.view（x.size（0）， - 1）展平输入x。

import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        
        # 1 input image channel (grayscale), 10 output channels/feature maps
        # 3x3 square convolution kernel
        ## output size = (W-F)/S +1 = (28-3)/1 +1 = 26
        # the output Tensor for one image, will have the dimensions: (10, 26, 26)
        # after one pool layer, this becomes (10, 13, 13)
        self.conv1 = nn.Conv2d(1, 10, 3)
        
        # maxpool layer
        # pool with kernel_size=2, stride=2
        self.pool = nn.MaxPool2d(2, 2)
        
        # second conv layer: 10 inputs, 20 outputs, 3x3 conv
        ## output size = (W-F)/S +1 = (13-3)/1 +1 = 11
        # the output tensor will have dimensions: (20, 11, 11)
        # after another pool layer this becomes (20, 5, 5); 5.5 is rounded down
        self.conv2 = nn.Conv2d(10, 20, 3)
        
        # 20 outputs * the 5*5 filtered/pooled map size
        # 10 output channels (for the 10 classes)
        self.fc1 = nn.Linear(20*5*5, 10)
        

    # define the feedforward behavior
    def forward(self, x):
        # two conv/relu + pool layers
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))

        # prep for linear layer
        # flatten the inputs into a vector
        x = x.view(x.size(0), -1)
        
        # one linear layer
        x = F.relu(self.fc1(x))
        # a softmax layer to convert the 10 outputs into a distribution of class scores
        x = F.log_softmax(x, dim=1)
        
        # final output
        return x

# instantiate and print your Net
net = Net()
print(net)

TODO: Specify the loss function and optimizer

import torch.optim as optim

## TODO: specify loss function 
# cross entropy loss combines softmax and nn.NLLLoss() in one single class.
criterion = nn.NLLLoss()

## TODO: specify optimizer 
# stochastic gradient descent with a small learning rate
optimizer = optim.SGD(net.parameters(), lr=0.001)

A note on accuracy

before training

correct = 0
total = 0

# Iterate through test dataset
for images, labels in test_loader:

    # forward pass to get outputs
    # the outputs are a series of class scores
    outputs = net(images)

    # get the predicted class from the maximum value in the output-list of class scores
    _, predicted = torch.max(outputs.data, 1)

    # count up total number of correct labels
    # for which the predicted and true labels are equal
    total += labels.size(0)
    correct += (predicted == labels).sum()

# calculate the accuracy
# to convert `correct` from a Tensor into a scalar, use .item()
accuracy = 100.0 * correct.item() / total

# print it out!
print('Accuracy before training: ', accuracy)

Here are the steps that this training function performs as it iterates over the training dataset:

Zero's the gradients to prepare for a forward pass
Passes the input through the network (forward pass)
Computes the loss (how far is the predicted classes are from the correct labels)
Propagates gradients back into the network’s parameters (backward pass)
Updates the weights (parameter update)
Prints out the calculated loss

def train(n_epochs):
    
    loss_over_time = [] # to track the loss as the network trains
    
    for epoch in range(n_epochs):  # loop over the dataset multiple times
        
        running_loss = 0.0
        
        for batch_i, data in enumerate(train_loader):
            # get the input images and their corresponding labels
            inputs, labels = data

            # zero the parameter (weight) gradients
            optimizer.zero_grad()

            # forward pass to get outputs
            outputs = net(inputs)

            # calculate the loss
            loss = criterion(outputs, labels)

            # backward pass to calculate the parameter gradients
            loss.backward()

            # update the parameters
            optimizer.step()

            # print loss statistics
            # to convert loss into a scalar and add it to running_loss, we use .item()
            running_loss += loss.item()
            
            if batch_i % 1000 == 999:    # print every 1000 batches
                avg_loss = running_loss/1000
                # record and print the avg loss over the 1000 batches
                loss_over_time.append(avg_loss)
                print('Epoch: {}, Batch: {}, Avg. Loss: {}'.format(epoch + 1, batch_i+1, avg_loss))
                running_loss = 0.0

    print('Finished Training')
    return loss_over_time
# define the number of epochs to train for
n_epochs = 30 # start small to see if your model works, initially

# call train and record the loss over time
training_loss = train(n_epochs)

Visualizing the loss

# visualize the loss as the network trained
plt.plot(training_loss)
plt.xlabel('1000\'s of batches')
plt.ylabel('loss')
plt.ylim(0, 2.5) # consistent scale
plt.show()

Test

# initialize tensor and lists to monitor test loss and accuracy
test_loss = torch.zeros(1)
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))

# set the module to evaluation mode
net.eval()

for batch_i, data in enumerate(test_loader):
    
    # get the input images and their corresponding labels
    inputs, labels = data
    
    # forward pass to get outputs
    outputs = net(inputs)

    # calculate the loss
    loss = criterion(outputs, labels)
            
    # update average test loss 
    test_loss = test_loss + ((torch.ones(1) / (batch_i + 1)) * (loss.data - test_loss))
    
    # get the predicted class from the maximum value in the output-list of class scores
    _, predicted = torch.max(outputs.data, 1)
    
    # compare predictions to true label
    # this creates a `correct` Tensor that holds the number of correctly classified images in a batch
    correct = np.squeeze(predicted.eq(labels.data.view_as(predicted)))
    
    # calculate test accuracy for *each* object class
    # we get the scalar value of correct items for a class, by calling `correct[i].item()`
    for i in range(batch_size):
        label = labels.data[i]
        class_correct[label] += correct[i].item()
        class_total[label] += 1

print('Test Loss: {:.6f}\n'.format(test_loss.numpy()[0]))

for i in range(10):
    if class_total[i] > 0:
        print('Test Accuracy of %5s: %2d%% (%2d/%2d)' % (
            classes[i], 100 * class_correct[i] / class_total[i],
            np.sum(class_correct[i]), np.sum(class_total[i])))
    else:
        print('Test Accuracy of %5s: N/A (no training examples)' % (classes[i]))

        
print('\nTest Accuracy (Overall): %2d%% (%2d/%2d)' % (
    100. * np.sum(class_correct) / np.sum(class_total),
    np.sum(class_correct), np.sum(class_total)))

保存模型

# Saving the model
model_dir = 'saved_models/'
model_name = 'fashion_net_simple.pt'

# after training, save your model parameters in the dir 'saved_models'
# when you're ready, un-comment the line below
torch.save(net.state_dict(), model_dir+model_name)

可视化feature map

Load the data

# our basic libraries
import torch
import torchvision

# data loading and transforming
from torchvision.datasets import FashionMNIST
from torch.utils.data import DataLoader
from torchvision import transforms

# The output of torchvision datasets are PILImage images of range [0, 1]. 
# We transform them to Tensors for input into a CNN

## Define a transform to read the data in as a tensor
data_transform = transforms.ToTensor()

test_data = FashionMNIST(root='./data', train=False,
                                  download=True, transform=data_transform)


# Print out some stats about the test data
print('Test data, number of images: ', len(test_data))

# prepare data loaders, set the batch_size
## TODO: you can try changing the batch_size to be larger or smaller
## when you get to training your network, see how batch_size affects the loss
batch_size = 20

test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=True)

# specify the image classes
classes = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 
           'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

import numpy as np
import matplotlib.pyplot as plt

%matplotlib inline
    
# obtain one batch of training images
dataiter = iter(test_loader)
images, labels = dataiter.next()
images = images.numpy()

# plot the images in the batch, along with the corresponding labels
fig = plt.figure(figsize=(25, 4))
for idx in np.arange(batch_size):
    ax = fig.add_subplot(2, batch_size/2, idx+1, xticks=[], yticks=[])
    ax.imshow(np.squeeze(images[idx]), cmap='gray')
    ax.set_title(classes[labels[idx]])

Define the network architecture

The various layers that make up any neural network are documented, here. For a convolutional neural network, we'll use a simple series of layers:

Convolutional layers
Maxpooling layers
Fully-connected (linear) layers

import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        
        # 1 input image channel (grayscale), 10 output channels/feature maps
        # 3x3 square convolution kernel
        ## output size = (W-F)/S +1 = (28-3)/1 +1 = 26
        # the output Tensor for one image, will have the dimensions: (10, 26, 26)
        # after one pool layer, this becomes (10, 13, 13)
        self.conv1 = nn.Conv2d(1, 10, 3)
        
        # maxpool layer
        # pool with kernel_size=2, stride=2
        self.pool = nn.MaxPool2d(2, 2)
        
        # second conv layer: 10 inputs, 20 outputs, 3x3 conv
        ## output size = (W-F)/S +1 = (13-3)/1 +1 = 11
        # the output tensor will have dimensions: (20, 11, 11)
        # after another pool layer this becomes (20, 5, 5); 5.5 is rounded down
        self.conv2 = nn.Conv2d(10, 20, 3)
        
        # 20 outputs * the 5*5 filtered/pooled map size
        self.fc1 = nn.Linear(20*5*5, 50)
        
        # dropout with p=0.4
        self.fc1_drop = nn.Dropout(p=0.4)
        
        # finally, create 10 output channels (for the 10 classes)
        self.fc2 = nn.Linear(50, 10)

    # define the feedforward behavior
    def forward(self, x):
        # two conv/relu + pool layers
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))

        # prep for linear layer
        # this line of code is the equivalent of Flatten in Keras
        x = x.view(x.size(0), -1)
        
        # two linear layers with dropout in between
        x = F.relu(self.fc1(x))
        x = self.fc1_drop(x)
        x = self.fc2(x)
        
        # final output
        return x

# instantiate your Net
net = Net()

# load the net parameters by name
net.load_state_dict(torch.load('saved_models/fashion_net_ex.pt'))

print(net)

import torch.optim as optim

## TODO: specify loss function
# using cross entropy whcih combines softmax and NLL loss
criterion = nn.CrossEntropyLoss()

## TODO: specify optimizer 
# stochastic gradient descent with a small learning rate AND some momentum
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# Calculate accuracy before training
correct = 0
total = 0

# Iterate through test dataset
for images, labels in test_loader:

    # forward pass to get outputs
    # the outputs are a series of class scores
    outputs = net(images)

    # get the predicted class from the maximum value in the output-list of class scores
    _, predicted = torch.max(outputs.data, 1)

    # count up total number of correct labels
    # for which the predicted and true labels are equal
    total += labels.size(0)
    correct += (predicted == labels).sum()

# calculate the accuracy
# to convert `correct` from a Tensor into a scalar, use .item()
accuracy = 100.0 * correct.item() / total

# print it out!
print('Accuracy before training: ', accuracy)

Train

def train(n_epochs):
    
    loss_over_time = [] # to track the loss as the network trains
    
    for epoch in range(n_epochs):  # loop over the dataset multiple times
        
        running_loss = 0.0
        
        for batch_i, data in enumerate(train_loader):
            # get the input images and their corresponding labels
            inputs, labels = data

            # zero the parameter (weight) gradients
            optimizer.zero_grad()

            # forward pass to get outputs
            outputs = net(inputs)

            # calculate the loss
            loss = criterion(outputs, labels)

            # backward pass to calculate the parameter gradients
            loss.backward()

            # update the parameters
            optimizer.step()

            # print loss statistics
            # to convert loss into a scalar and add it to running_loss, we use .item()
            running_loss += loss.item()
            
            if batch_i % 1000 == 999:    # print every 1000 batches
                avg_loss = running_loss/1000
                # record and print the avg loss over the 1000 batches
                loss_over_time.append(avg_loss)
                print('Epoch: {}, Batch: {}, Avg. Loss: {}'.format(epoch + 1, batch_i+1, avg_loss))
                running_loss = 0.0

    print('Finished Training')
    return loss_over_time

# define the number of epochs to train for
n_epochs = 30 # start small to see if your model works, initially

# call train
training_loss = train(n_epochs)

可视化LOSS

# visualize the loss as the network trained
plt.plot(training_loss)
plt.xlabel('1000\'s of batches')
plt.ylabel('loss')
plt.ylim(0, 2.5) # consistent scale
plt.show()

Test

# initialize tensor and lists to monitor test loss and accuracy
test_loss = torch.zeros(1)
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))

# set the module to evaluation mode
net.eval()

for batch_i, data in enumerate(test_loader):
    
    # get the input images and their corresponding labels
    inputs, labels = data
    
    # forward pass to get outputs
    outputs = net(inputs)

    # calculate the loss
    loss = criterion(outputs, labels)
            
    # update average test loss 
    test_loss = test_loss + ((torch.ones(1) / (batch_i + 1)) * (loss.data - test_loss))
    
    # get the predicted class from the maximum value in the output-list of class scores
    _, predicted = torch.max(outputs.data, 1)
    
    # compare predictions to true label
    # this creates a `correct` Tensor that holds the number of correctly classified images in a batch
    correct = np.squeeze(predicted.eq(labels.data.view_as(predicted)))
    
    # calculate test accuracy for *each* object class
    # we get the scalar value of correct items for a class, by calling `correct[i].item()`
    for i in range(batch_size):
        label = labels.data[i]
        class_correct[label] += correct[i].item()
        class_total[label] += 1

print('Test Loss: {:.6f}\n'.format(test_loss.numpy()[0]))

for i in range(10):
    if class_total[i] > 0:
        print('Test Accuracy of %5s: %2d%% (%2d/%2d)' % (
            classes[i], 100 * class_correct[i] / class_total[i],
            np.sum(class_correct[i]), np.sum(class_total[i])))
    else:
        print('Test Accuracy of %5s: N/A (no training examples)' % (classes[i]))

        
print('\nTest Accuracy (Overall): %2d%% (%2d/%2d)' % (
    100. * np.sum(class_correct) / np.sum(class_total),
    np.sum(class_correct), np.sum(class_total)))

# Saving the model
model_dir = 'saved_models/'
model_name = 'fashion_net_ex.pt'

# after training, save your model parameters in the dir 'saved_models'
# when you're ready, un-comment the line below
torch.save(net.state_dict(), model_dir+model_name)

CNN层和特征可视化

CNN结构

CNN图层

VGG-16

在PyTorch定义图层

可视化四个滤波器输出

定义滤波器

池化层

全连接层

softmax

Train in PyTorch

Loss and Optimizer

CNN for Classification

在 _ init_ 定义层

flattening

TODO: Specify the loss function and optimizer

A note on accuracy

before training

Visualizing the loss

Test

保存模型

可视化feature map

Load the data

Define the network architecture

Train

可视化LOSS

Test

你可能感兴趣的:(CNN层和特征可视化)