动手学深度学习(三)——丢弃法(gluon)

文章作者:Tyan
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注:本文为李沐大神的《动手学深度学习》的课程笔记!

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import mxnet as mx
from mxnet import nd
from mxnet import gluon
from mxnet import autograd
from mxnet.gluon import nn
from utils import load_data_fashion_mnist, accuracy, evaluate_accuracy

定义模型并添加丢弃层

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# 定义模型
net = nn.Sequential()
# 丢弃概率
drop_prob1 = 0.2
drop_prob2 = 0.5
# 添加层
with net.name_scope():
# 将输入数据展开
net.add(nn.Flatten())
# 第一个全连接层
net.add(nn.Dense(256, activation="relu"))
# 添加丢弃层
net.add(nn.Dropout(drop_prob1))
# 第二个全连接层
net.add(nn.Dense(256, activation="relu"))
# 添加丢弃层
net.add(nn.Dropout(drop_prob2))
# 定义输出层
net.add(nn.Dense(10))
# 初始化模型参数
net.initialize()

读取数据并训练

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# 批数据大小
batch_size = 256
# 加载数据
train_data, test_data = load_data_fashion_mnist(batch_size)
# 优化
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.5})
# 定义交叉熵损失
softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
# 训练
for epoch in range(5):
# 训练损失
train_loss = 0.0
# 训练准确率
train_acc = 0.0
# 迭代训练
for data, label in train_data:
with autograd.record():
# 计算输出
output = net(data)
# 计算损失
loss = softmax_cross_entropy(output, label)
# 梯度反向传播
loss.backward()
# 更新梯度
trainer.step(batch_size)
# 记录训练损失
train_loss += nd.mean(loss).asscalar()
# 记录训练准确率
train_acc += accuracy(output, label)
# 计算测试准确率
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: 0.817475, Train acc 0.697349, Test acc 0.778145
Epoch 1. Loss: 0.515098, Train acc 0.810831, Test acc 0.847456
Epoch 2. Loss: 0.458402, Train acc 0.833450, Test acc 0.823918
Epoch 3. Loss: 0.419452, Train acc 0.846554, Test acc 0.862079
Epoch 4. Loss: 0.396483, Train acc 0.854067, Test acc 0.874499
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