deep-learning - 在每个 epoch 之后训练 MNIST 数据集时如何输出准确度和损失

问题描述

from __future__ import print_function
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from tensorflow.examples.tutorials.mnist import input_data
import torch.optim as optim
import tensorflow.python.util.deprecation as deprecation
deprecation._PRINT_DEPRECATION_WARNINGS = False
import matplotlib.pyplot as plt
%matplotlib inline
from plot import plot_loss_and_acc

mnist = input_data.read_data_sets("MNIST_data", one_hot=False)
batch_size = 250
epoch_num = 10      
lr = 0.0001        
disp_freq = 20


def next_batch(train=True):
    # Reads the next batch of MNIST images and labels and returns them

    if train:
        batch_img, batch_label = mnist.train.next_batch(batch_size)
    else:
        batch_img, batch_label = mnist.test.next_batch(batch_size)

    batch_label = torch.from_numpy(batch_label).long()  # convert the numpy array into torch tensor
    batch_label = Variable(batch_label)         # create a torch variable

    batch_img = torch.from_numpy(batch_img).float()     # convert the numpy array into torch tensor
    batch_img = Variable(batch_img)             # create a torch variable
    return batch_img, batch_label


class MLP(nn.Module):
    def __init__(self, n_features, n_classes):
        super(MLP, self).__init__()
        self.layer1 = nn.Linear(n_features, 128)
        self.layer2 = nn.Linear(128, 128)
        self.layer3 = nn.Linear(128, n_classes)

    def forward(self, x, training=True):
        # a neural network with 2 hidden layers
        # x -> FC -> relu -> dropout -> FC -> relu -> dropout -> FC -> output
        x = F.relu(self.layer1(x))
        x = F.dropout(x, 0.5, training=training)
        x = F.relu(self.layer2(x))
        x = F.dropout(x, 0.5, training=training)
        x = self.layer3(x)
        return x

    def predict(self, x):
        # a function to predict the labels of a batch of inputs
        x = F.softmax(self.forward(x, training=False))
        return x

    def accuracy(self, x, y):
        # a function to calculate the accuracy of label prediction for a batch of inputs
        #   x: a batch of inputs
        #   y: the true labels associated with x
        prediction = self.predict(x)
        maxs, indices = torch.max(prediction, 1)
        acc = 100 * torch.sum(torch.eq(indices.float(), y.float()).float())/y.size()[0]
        print(acc.data)
        return acc.data

# define the neural network (multilayer perceptron)
net = MLP(784, 10)

# calculate the number of batches per epoch
batch_per_ep = mnist.train.num_examples // batch_size

# define the loss (criterion) and create an optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=lr)
print(' ')
print("__________Training__________________")

xArray = []
yLoss = []
yAcc = []
for ep in range(epoch_num):  # epochs loop
    for batch_n in range(batch_per_ep):  # batches loop
        features, labels = next_batch()

        # Reset gradients
        optimizer.zero_grad()

        # Forward pass
        output = net(features)
        loss = criterion(output, labels) 
        # Backward pass and updates
        loss.backward()                 # calculate the gradients (backpropagation)
        optimizer.step()                # update the weights
        if batch_n % disp_freq == 0:
            print('epoch: {} - batch: {}/{} '.format(ep, batch_n, batch_per_ep))
    xArray.append(ep)
    yLoss.append(loss.data)
    #yAcc.append(acc.data)

    print('loss: ', loss.data)
    print('__________________________________')

# test the accuracy on a batch of test data
features, labels = next_batch(train=False)
print("Result")
print('Test accuracy: ', net.accuracy(features, labels))
print('loss: ', loss.data)
accuracy = net.accuracy(features, labels)


#Loss Plot

# plotting the points  
plt.plot(xArray, yLoss) 

# naming the x axis 
plt.xlabel('epoch') 
# naming the y axis 
plt.ylabel('loss') 

# giving a title to my graph 
plt.title('Loss Plot') 

# function to show the plot 
plt.show() 

#Accuracy Plot

# plotting the points  
plt.plot(xArray, yAcc) 

# naming the x axis 
plt.xlabel('epoch') 
# naming the y axis 
plt.ylabel(' accuracy') 

# giving a title to my graph 
plt.title('Accuracy Plot ') 

# function to show the plot 
plt.show()

我想显示我的训练数据集的准确性。我已经设法显示和绘制损失，但我没有设法做到准确。我知道我缺少 1 或 2 行代码，但我不知道该怎么做。

我的意思是，如果我可以像损失一样显示每个时期的准确性，我可以自己进行绘图。

标签： deep-learningpytorch

deep-learning - 在每个 epoch 之后训练 MNIST 数据集时如何输出准确度和损失

问题描述

解决方案

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