Acc is not stable

username: YCH
cooked: <p>I set up a simple CNN network, trained through the MNIST data set.But accuracy varies from 99 percent to 10 percent.I don’t know what went wrong.<br>
<strong>deepconvnet.py</strong></p>
<pre><code class="lang-auto">import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as Data
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
import os
from torchvision import datasets, transforms


class DeepConvNet(nn.Module):
    def __init__(self, train_set_x, train_set_y, test_set_x, test_set_y,
                DROPOUT = 0.5, BATH_SIZE = 1000, EPOCH = 20, LEARNING_RATE = 0.01):
        super(DeepConvNet, self).__init__()
        self.conv = nn.Sequential(
            # nn.Conv2d(in_channels = 1, out_channels = 16, kernel_size = 3, padding = 1, padding_mode = 'zeros'),
            nn.Conv2d(in_channels = 1, out_channels = 16, kernel_size = 3, padding = 1),
            nn.ReLU(),
            # nn.Conv2d(in_channels = 16, out_channels = 16, kernel_size = 3, padding = 1, padding_mode = 'zeros'),
            nn.Conv2d(in_channels = 16, out_channels = 16, kernel_size = 3, padding = 1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size = 2, stride = 2),
            nn.Conv2d(in_channels = 16, out_channels = 32, kernel_size = 3, padding = 1, padding_mode = 'zeros'),
            nn.ReLU(),
            nn.Conv2d(in_channels = 32, out_channels = 32, kernel_size = 3, padding = 2, padding_mode = 'zeros'),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size = 2, stride = 2),
            nn.Conv2d(in_channels = 32, out_channels = 64, kernel_size = 3, padding = 1, padding_mode = 'zeros'),
            nn.ReLU(),
            nn.Conv2d(in_channels = 64, out_channels = 64, kernel_size = 3, padding = 1, padding_mode = 'zeros'),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size = 2, stride = 2),
        )
        for m in self.conv:
            if(isinstance(m, nn.Conv2d)):
                nn.init.kaiming_normal_(m.weight, mode='fan_in')
        self.fc = nn.Sequential(
            nn.Linear(in_features = 1024, out_features = 50),
            nn.ReLU(),
            # nn.Dropout(DROPOUT),
            nn.Linear(in_features = 50, out_features = 10),
            nn.ReLU(),
            # nn.Dropout(DROPOUT)
        )
        for m in self.conv:
            if(isinstance(m, nn.Linear)):
                nn.init.kaiming_normal_(m.weight, mode='fan_in')
                m.parameters = m.parameters / 10
        self.train_dataset = Data.TensorDataset(train_set_x, train_set_y)
        self.test_dataset = Data.TensorDataset(test_set_x, test_set_y)
        self.BATH_SIZE = BATH_SIZE
        self.loader_train = Data.DataLoader(
            dataset = self.train_dataset,
            batch_size = BATH_SIZE,
            shuffle = True,
        )
        self.loader_test = Data.DataLoader(
            dataset = self.test_dataset,
            batch_size = len(test_set_y),
            shuffle = True
        )
        self.optimizer = optim.Adam(self.parameters(), lr = LEARNING_RATE, betas = (0.9, 0.999), eps = 1e-8)
        # self.optimizer = optim.SGD(self.parameters(), lr = LEARNING_RATE)
        # self.optimizer = optim.RMSprop(self.parameters(),lr = LEARNING_RATE)
        self.EPOCH = EPOCH

    def forward(self, x):
        conv_out = self.conv(x)
        conv_out = conv_out.view(conv_out.size()[0], -1)
        out = self.fc(conv_out)
        return out
        
    def loss(self, pre_y, label_y):
        # loss = nn.CrossEntropyLoss()
        # Loss = loss(pre_y, label_y)
        # return Loss
        loss = F.log_softmax(pre_y)
        loss = -torch.mean((loss[range(pre_y.shape[0]), label_y]))
        return loss
        
    def train_net(self):
        for eporch in range(self.EPOCH):
            running_loss = 0.0
            for i, data in enumerate(self.loader_train):
                x, y = data
                # print(x.view(1000,-1)[1])
                # print(y.shape)
                # print(i)
                # img = np.transpose(np.array(x[0].numpy()), (1, 2, 0))
                # print(img.shape)
                # plt.imshow(img)
                # plt.show()
                
                self.optimizer.zero_grad()
                # params = list(self.named_parameters())
                # print(params)
                # (name, param) = params[0]
                # print(name)
                # print(param.shape)
                # print(y)
                # os.system("pause")
                outputs = self.forward(x)
                # print(outputs.shape)
                Loss = self.loss(outputs, y)
                Loss.backward()
                # print(param.grad)
                self.optimizer.step()
                running_loss += Loss.item()
                # if (i+1) % self.BATH_SIZE == 0:
                prediction = torch.argmax(outputs, 1)
                correct = (prediction == y).sum().float()
                total = float(len(prediction))
                acc = correct / total
                print(acc)
                print('Epoch: %d, iter: %5d, Loss: %3f Acc: %3f' %(eporch+1, (i+1)*self.BATH_SIZE, running_loss, acc))
                # print(outputs.shape)
                # print(y)


                running_loss = 0.0
                
        torch.save(self.state_dict(),'parameters.pth')            
        print('Finish Train')

    def test(self):
        self.eval()
        for data in self.loader_test:
            # print(data)
            x, y = data
        outputs = self.forward(x)
        # print(outputs)
        Loss = self.loss(outputs, y)
        prediction = torch.argmax(outputs, 1)
        # print(prediction)
        correct = (prediction == y).sum().float()
        total = float(len(prediction))
        acc = correct / total
        print("On test set, the loss is %.3f, Acc is %.3f" %(Loss, acc))            
</code></pre>
<p><strong>traindeepnet.py</strong></p>
<pre><code class="lang-auto">import sys, os
sys.path.append(os.pardir)
import numpy as np
import matplotlib.pyplot as plt
from mnist import load_mnist
import torch
from deepconvnet import DeepConvNet

(x_train,t_train), (x_test,t_test) = load_mnist(flatten=False)

x_train = torch.Tensor(x_train)
t_train = torch.Tensor(t_train).long()
# x_train = torch.Tensor(x_train)[:10000]
# t_train = torch.Tensor(t_train).long()[:10000]
x_test = torch.Tensor(x_test)
t_test = torch.Tensor(t_test).long()


print(x_train.shape)
print(t_train.shape)                                            


Net = DeepConvNet(x_train, t_train, x_test, t_test)
Net.test()
Net.train_net()
Net.test()
</code></pre>
<p><strong>mnist.py</strong></p>
<pre><code class="lang-auto"># coding: utf-8
try:
    import urllib.request
except ImportError:
    raise ImportError('You should use Python 3.x')
import os.path
import gzip
import pickle
import os
import numpy as np


url_base = 'http://yann.lecun.com/exdb/mnist/'
key_file = {
    'train_img':'train-images-idx3-ubyte.gz',
    'train_label':'train-labels-idx1-ubyte.gz',
    'test_img':'t10k-images-idx3-ubyte.gz',
    'test_label':'t10k-labels-idx1-ubyte.gz'
}

dataset_dir = os.path.dirname(os.path.abspath(__file__))
save_file = dataset_dir + "/mnist.pkl"

train_num = 60000
test_num = 10000
img_dim = (1, 28, 28)
img_size = 784


def _download(file_name):
    file_path = dataset_dir + "/" + file_name
    
    if os.path.exists(file_path):
        return

    print("Downloading " + file_name + " ... ")
    urllib.request.urlretrieve(url_base + file_name, file_path)
    print("Done")
    
def download_mnist():
    for v in key_file.values():
       _download(v)
        
def _load_label(file_name):
    file_path = dataset_dir + "/" + file_name
    
    print("Converting " + file_name + " to NumPy Array ...")
    with gzip.open(file_path, 'rb') as f:
            labels = np.frombuffer(f.read(), np.uint8, offset=8)
    print("Done")
    
    return labels

def _load_img(file_name):
    file_path = dataset_dir + "/" + file_name
    
    print("Converting " + file_name + " to NumPy Array ...")    
    with gzip.open(file_path, 'rb') as f:
            data = np.frombuffer(f.read(), np.uint8, offset=16)
    data = data.reshape(-1, img_size)
    print("Done")
    
    return data
    
def _convert_numpy():
    dataset = {}
    dataset['train_img'] =  _load_img(key_file['train_img'])
    dataset['train_label'] = _load_label(key_file['train_label'])    
    dataset['test_img'] = _load_img(key_file['test_img'])
    dataset['test_label'] = _load_label(key_file['test_label'])
    
    return dataset

def init_mnist():
    download_mnist()
    dataset = _convert_numpy()
    print("Creating pickle file ...")
    with open(save_file, 'wb') as f:
        pickle.dump(dataset, f, -1)
    print("Done!")

def _change_one_hot_label(X):
    T = np.zeros((X.size, 10))
    for idx, row in enumerate(T):
        row[X[idx]] = 1
        
    return T
    

def load_mnist(normalize=True, flatten=True, one_hot_label=False):
    """读入MNIST数据集
    
    Parameters
    ----------
    normalize : 将图像的像素值正规化为0.0~1.0
    one_hot_label : 
        one_hot_label为True的情况下，标签作为one-hot数组返回
        one-hot数组是指[0,0,1,0,0,0,0,0,0,0]这样的数组
    flatten : 是否将图像展开为一维数组
    
    Returns
    -------
    (训练图像, 训练标签), (测试图像, 测试标签)
    """
    if not os.path.exists(save_file):
        init_mnist()
        
    with open(save_file, 'rb') as f:
        dataset = pickle.load(f)
    
    if normalize:
        for key in ('train_img', 'test_img'):
            dataset[key] = dataset[key].astype(np.float32)
            dataset[key] /= 255.0
            
    if one_hot_label:
        dataset['train_label'] = _change_one_hot_label(dataset['train_label'])
        dataset['test_label'] = _change_one_hot_label(dataset['test_label'])
    
    if not flatten:
         for key in ('train_img', 'test_img'):
            dataset[key] = dataset[key].reshape(-1, 1, 28, 28)

    return (dataset['train_img'], dataset['train_label']), (dataset['test_img'], dataset['test_label']) 


if __name__ == '__main__':
    init_mnist()
</code></pre>

username: ptrblck
cooked: <p>If your accuracy is already really high (~99%), further training (especially with a high learning rate) might push the model out of the “good” loss landscape and your model would have to be retrained.</p>