零基础学习人工智能—Python—Pytorch学习（十一）

前言

本文主要介绍tensorboard的使用。

tensorboard是一个可视化的，支持人工智能学习的一个工具。

tensorboard的官方地址：https://www.tensorflow.org/tensorboard

本文内容来自视频教程16课，个人感觉对于tensorboard讲的非常好。

Tensorboard的使用

使用代码如下：

import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import sys
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter

# pip install tensorboard 安装 tensorboard
# 启动 tensorboard 启动成功的话，地址是http://localhost:6006/
# logdir要等于 SummaryWriter('runs/mnist1')的入参地址
# tensorboard --logdir=C:\Project\python_test\github\PythonTest\PythonTest\PythonTest\pytorchTutorial\runs
# tensorboard的官方地址：https://www.tensorflow.org/tensorboard

############## TENSORBOARD ########################
writer = SummaryWriter('runs/mnist1')
###################################################

# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Hyper-parameters
input_size = 784 # 28x28
hidden_size = 500
num_classes = 10
num_epochs = 1
batch_size = 64
learning_rate = 0.001

# MNIST dataset
train_dataset = torchvision.datasets.MNIST(root='./data',
                                           train=True,
                                           transform=transforms.ToTensor(),
                                           download=True)

test_dataset = torchvision.datasets.MNIST(root='./data',
                                          train=False,
                                          transform=transforms.ToTensor())

# Data loader
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batch_size,
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=batch_size,
                                          shuffle=False)

examples = iter(test_loader)
example_data, example_targets = next(examples)

for i in range(6):
    plt.subplot(2,3,i+1)
    plt.imshow(example_data[i][0], cmap='gray')
#plt.show()

############## TENSORBOARD ########################
img_grid = torchvision.utils.make_grid(example_data)
writer.add_image('mnist_images', img_grid)
#writer.close()
#sys.exit()
###################################################

# Fully connected neural network with one hidden layer
class NeuralNet(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(NeuralNet, self).__init__()
        self.input_size = input_size
        self.l1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.l2 = nn.Linear(hidden_size, num_classes)  

    def forward(self, x):
        out = self.l1(x)
        out = self.relu(out)
        out = self.l2(out)
        # no activation and no softmax at the end
        return out

model = NeuralNet(input_size, hidden_size, num_classes).to(device)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  

############## TENSORBOARD ########################
writer.add_graph(model, example_data.reshape(-1, 28*28).to(device))
#writer.close()
#sys.exit()
###################################################

# Train the model
running_loss = 0.0
running_correct = 0
n_total_steps = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # origin shape: [100, 1, 28, 28]
        # resized: [100, 784]
        images = images.reshape(-1, 28*28).to(device)
        labels = labels.to(device)

        # Forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)

        # Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

        _, predicted = torch.max(outputs.data, 1)
        running_correct += (predicted == labels).sum().item()
        if (i+1) % 100 == 0:
            print (f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.4f}')
            ############## TENSORBOARD ########################
            writer.add_scalar('training loss', running_loss / 100, epoch * n_total_steps + i)
            running_accuracy = running_correct / 100 / predicted.size(0)
            writer.add_scalar('accuracy', running_accuracy, epoch * n_total_steps + i)
            running_correct = 0
            running_loss = 0.0
            ###################################################

# Test the model
# In test phase, we don't need to compute gradients (for memory efficiency)
class_labels = []
class_preds = []
with torch.no_grad():
    n_correct = 0
    n_samples = 0
    for images, labels in test_loader:
        images = images.reshape(-1, 28*28).to(device)
        labels = labels.to(device)
        outputs = model(images)
        # max returns (value ,index)
        values, predicted = torch.max(outputs.data, 1)
        n_samples += labels.size(0)
        n_correct += (predicted == labels).sum().item()

        class_probs_batch = [F.softmax(output, dim=0) for output in outputs]

        class_preds.append(class_probs_batch)
        class_labels.append(labels)

    # 10000, 10, and 10000, 1
    # stack concatenates tensors along a new dimension
    # cat concatenates tensors in the given dimension
    class_preds = torch.cat([torch.stack(batch) for batch in class_preds])
    class_labels = torch.cat(class_labels)

    acc = 100.0 * n_correct / n_samples
    print(f'Accuracy of the network on the 10000 test images: {acc} %')

    ############## TENSORBOARD ########################
    classes = range(10)
    for i in classes:
        labels_i = class_labels == i
        preds_i = class_preds[:, i]
        writer.add_pr_curve(str(i), labels_i, preds_i, global_step=0)
        writer.close()
    ###################################################

运行 http://localhost:6006 ,可以得到下图，可以根据图中的曲线等信息进行分析学习结果。

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零基础学习人工智能—Python—Pytorch学习—全集

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