文章作者：Tyan
博客：noahsnail.com | CSDN | 简书
注：本文为李沐大神的《动手学深度学习》的课程笔记！
注意：mxnet随机种子设为1时，loss一直为nan，经测试，种子为2时，jupyter-notebook有时会出现nan，但在命令行执行python文件多次都不会出现nan。
获取数据
# 导入mxnet
import mxnet as mx
# 设置随机种子
mx.random.seed(2)
from mxnet import gluon
from mxnet import ndarray as nd
from mxnet import autograd
# 数据预处理
def transform(data, label):
return data.astype('float32') / 255, label.astype('float32')
# 加载训练数据
mnist_train = gluon.data.vision.FashionMNIST(train=True, transform=transform)
# 加载测试数据
mnist_test = gluon.data.vision.FashionMNIST(train=False, transform=transform)
# 取出单条数据
data, label = mnist_train[0]
# 查看数据
('example shape: ', data.shape, 'label:', label)
('example shape: ', (28L, 28L, 1L), 'label:', 2.0)
数据可视化
import matplotlib.pyplot as plt
# 显示图像
def show_images(images):
# 获得图像的数量
n = images.shape[0]
# 绘制子图
_, figs = plt.subplots(1, n, figsize=(15, 15))
# 遍历绘制图像
for i in xrange(n):
# 显示图像
figs[i].imshow(images[i].reshape((28, 28)).asnumpy())
# 显示灰度图
#figs[i].imshow(images[i].reshape((28, 28)).asnumpy(), cmap="gray")
# 不显示坐标轴
figs[i].axes.get_xaxis().set_visible(False)
figs[i].axes.get_yaxis().set_visible(False)
plt.show()
# 获取图像对应的文本标签
def get_text_labels(labels):
# 图像标签对应的文本
text_labels = [
't-shirt', 'trouser', 'pullover', 'dress,', 'coat',
'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot'
]
# 返回图像标签对应的文本
return [text_labels[int(i)] for i in labels]
# 取出训练集的前9条数据
data, label = mnist_train[0:9]
# 显示数据
show_images(data)
# 显示标签
print get_text_labels(label)
Figure
['pullover', 'ankle boot', 'shirt', 't-shirt', 'dress,', 'coat', 'coat', 'sandal', 'coat']
数据读取
# 批数据大小
batch_size = 256
# 读取训练数据
train_data = gluon.data.DataLoader(mnist_train, batch_size, shuffle=True)
# 读取测试数据
test_data = gluon.data.DataLoader(mnist_test, batch_size, shuffle=False)
初始化模型参数
# 输入数据大小
num_inputs = 28 * 28
# 输出数据大小, 分为10类
num_outputs = 10
# 随机初始化权重
W = nd.random_normal(shape=(num_inputs, num_outputs))
# 随机初始化偏置
b = nd.random_normal(shape=num_outputs)
# 参数数组
params = [W, b]
# 需要计算梯度, 添加自动求导
for param in params:
param.attach_grad()
定义模型
# 定义softmax
def softmax(X):
# 计算e^x
exp = nd.exp(X)
# 假设exp是矩阵，这里对行进行求和，并要求保留axis 1, 就是返回 (nrows, 1) 形状的矩阵
partition = exp.sum(axis=1, keepdims=True)
# 对exp进行归一化
return exp / partition
# 测试softmax
# 随机初始化数据
X = nd.random_normal(shape=(2, 5))
# 求softmax
X_prob = softmax(X)
# 输出结果
print X_prob
# 对每行概率求和, 如果和为1, 说明没问题
print X_prob.sum(axis=1)
[[ 0.06774449 0.5180918 0.1474141 0.11459844 0.1521512 ]
[ 0.23102701 0.47666225 0.10536087 0.09706162 0.08988826]]
<NDArray 2x5 @cpu(0)>
[ 1. 1.]
<NDArray 2 @cpu(0)>
# 定义模型
def net(X):
return softmax(nd.dot(X.reshape((-1, num_inputs)), W) + b)
交叉熵损失函数
# 定义交叉熵损失
def cross_entropy(yhat, y):
return -nd.pick(nd.log(yhat), y)
计算精度
# 计算分类准确率
def accuracy(output, label):
return nd.mean(output.argmax(axis=1) == label).asscalar()
# 评估整个数据集的分类精度
def evaluate_accuracy(data_iterator, net):
# 最终的准确率
acc = 0.0
# 遍历测试数据集
for data, label in data_iterator:
output = net(data)
# 累加准确率
acc += accuracy(output, label)
# 返回平均准确率
return acc / len(data_iterator)
# 测试随机初始化参数的分类准确率
evaluate_accuracy(test_data, net)
0.15156249999999999
训练
# 定义SGD
def SGD(params, lr):
# 对参数进行梯度下降
for param in params:
# 这样写不会创建新的param, 而是会写在原来的param里, 新的param没有梯度
param[:] = param - lr * param.grad
# 学习率
learning_rate = 0.1
# 定义迭代周期
epochs = 5
# 训练
for epoch in range(5):
# 训练损失
train_loss = 0.0
# 训练集准确率
train_acc = 0.0
# 迭代巡礼
for data, label in train_data:
# 记录梯度
with autograd.record():
# 计算输出
output = net(data)
# 计算损失
loss = cross_entropy(output, label)
# 反向传播求梯度
loss.backward()
# 将梯度做平均，这样学习率会对batch size不那么敏感, 避免学习率与batch_size耦合
SGD(params, learning_rate / batch_size)
# 总的训练损失
train_loss += nd.mean(loss).asscalar()
# 总的训练准确率
train_acc += accuracy(output, label)
# 测试集的准确率
test_acc = evaluate_accuracy(test_data, net)
print("Epoch %d. Loss: %f, Train acc %f, Test acc %f" % (
epoch, train_loss / len(train_data), train_acc / len(train_data), test_acc))
Epoch 0. Loss: 3.725043, Train acc 0.487916, Test acc 0.602344
Epoch 1. Loss: 1.965692, Train acc 0.632812, Test acc 0.666895
Epoch 2. Loss: 1.630362, Train acc 0.677349, Test acc 0.691016
Epoch 3. Loss: 1.450728, Train acc 0.701690, Test acc 0.710352
Epoch 4. Loss: 1.329035, Train acc 0.717996, Test acc 0.720410
- learning_rate = 1
Epoch 0. Loss: 18.803541, Train acc 0.081366, Test acc 0.085840
Epoch 1. Loss: 18.806381, Train acc 0.081394, Test acc 0.085840
Epoch 2. Loss: 18.795504, Train acc 0.081449, Test acc 0.085840
Epoch 3. Loss: 18.798995, Train acc 0.081505, Test acc 0.085840
Epoch 4. Loss: 18.794157, Train acc 0.081505, Test acc 0.085840
- learning_rate = 0.01
Epoch 0. Loss: 10.406917, Train acc 0.115165, Test acc 0.169629
Epoch 1. Loss: 5.512807, Train acc 0.246349, Test acc 0.304590
Epoch 2. Loss: 3.911078, Train acc 0.373232, Test acc 0.412695
Epoch 3. Loss: 3.218969, Train acc 0.454189, Test acc 0.471191
Epoch 4. Loss: 2.855459, Train acc 0.502371, Test acc 0.509082
预测
# 从测试集中取数据
data, label = mnist_test[0:9]
# 显示图片及真实标签
show_images(data)
print 'True labels: '
print get_text_labels(label)
# 预测标签
predicted_labels = net(data).argmax(axis=1)
print 'Predicted labels: '
print get_text_labels(predicted_labels.asnumpy())
Figure
True labels:
['t-shirt', 'trouser', 'pullover', 'pullover', 'dress,', 'pullover', 'bag', 'shirt', 'sandal']
Predicted labels:
['shirt', 'trouser', 'pullover', 'pullover', 'dress,', 'bag', 'bag', 'shirt', 'sandal']
