Gluon 实现 dropout 丢弃法

佚名 6年前 (2019-05-05) 随笔 1924人围观抢沙发百度已收录

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SRE实战互联网时代守护先锋，助力企业售后服务体系运筹帷幄！一键直达领取阿里云限量特价优惠。

hi 以 p 的概率被丢弃，以 1-p 的概率被拉伸，除以 1 - p

import mxnet as mx
import sys
import os
import time
import gluonbook as gb
from mxnet import autograd,init
from mxnet import nd,gluon
from mxnet.gluon import data as gdata,nn
from mxnet.gluon import loss as gloss


‘‘‘
# 模型参数
num_inputs, num_outputs, num_hiddens1, num_hiddens2 = 784,10,256,256

W1 = nd.random.normal(scale=0.01,shape=(num_inputs,num_hiddens1))
b1 = nd.zeros(num_hiddens1)

W2 = nd.random.normal(scale=0.01,shape=(num_hiddens1,num_hiddens2))
b2 = nd.zeros(num_hiddens2)

W3 = nd.random.normal(scale=0.01,shape=(num_hiddens2,num_outputs))
b3 = nd.zeros(num_outputs)

params = [W1,b1,W2,b2,W3,b3]

for param in params:
    param.attach_grad()

# 定义网络

‘‘‘
# 读取数据
# fashionMNIST 28*28 转为224*224
def load_data_fashion_mnist(batch_size, resize=None, root=os.path.join(
        ‘~‘, ‘.mxnet‘, ‘datasets‘, ‘fashion-mnist‘)):
    root = os.path.expanduser(root)  # 展开用户路径 ‘~‘。
    transformer = []
    if resize:
        transformer += [gdata.vision.transforms.Resize(resize)]
    transformer += [gdata.vision.transforms.ToTensor()]
    transformer = gdata.vision.transforms.Compose(transformer)
    mnist_train = gdata.vision.FashionMNIST(root=root, train=True)
    mnist_test = gdata.vision.FashionMNIST(root=root, train=False)
    num_workers = 0 if sys.platform.startswith(‘win32‘) else 4
    train_iter = gdata.DataLoader(
        mnist_train.transform_first(transformer), batch_size, shuffle=True,
        num_workers=num_workers)
    test_iter = gdata.DataLoader(
        mnist_test.transform_first(transformer), batch_size, shuffle=False,
        num_workers=num_workers)
    return train_iter, test_iter


# 定义网络
drop_prob1,drop_prob2 = 0.2,0.5
# Gluon版
net = nn.Sequential()
net.add(nn.Dense(256,activation="relu"),
        nn.Dropout(drop_prob1),
        nn.Dense(256,activation="relu"),
        nn.Dropout(drop_prob2),
        nn.Dense(10)
        )
net.initialize(init.Normal(sigma=0.01))



# 训练模型

def accuracy(y_hat, y):
    return (y_hat.argmax(axis=1) == y.astype(‘float32‘)).mean().asscalar()
def evaluate_accuracy(data_iter, net):
    acc = 0
    for X, y in data_iter:
        acc += accuracy(net(X), y)
    return acc / len(data_iter)


def train(net, train_iter, test_iter, loss, num_epochs, batch_size,
              params=None, lr=None, trainer=None):
    for epoch in range(num_epochs):
        train_l_sum = 0
        train_acc_sum = 0
        for X, y in train_iter:
            with autograd.record():
                y_hat = net(X)
                l = loss(y_hat, y)
            l.backward()
            if trainer is None:
                gb.sgd(params, lr, batch_size)
            else:
                trainer.step(batch_size)  # 下一节将用到。
            train_l_sum += l.mean().asscalar()
            train_acc_sum += accuracy(y_hat, y)
        test_acc = evaluate_accuracy(test_iter, net)
        print(‘epoch %d, loss %.4f, train acc %.3f, test acc %.3f‘
              % (epoch + 1, train_l_sum / len(train_iter),
                 train_acc_sum / len(train_iter), test_acc))


num_epochs = 5
lr = 0.5
batch_size = 256
loss = gloss.SoftmaxCrossEntropyLoss()
train_iter, test_iter = load_data_fashion_mnist(batch_size)

trainer = gluon.Trainer(net.collect_params(),‘sgd‘,{‘learning_rate‘:lr})
train(net,train_iter,test_iter,loss,num_epochs,batch_size,None,None,trainer)