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  • Gluon 实现 dropout 丢弃法

    http://www.mamicode.com/info-detail-2537502.html

    多层感知机中:

    技术分享图片

    hi 以 p 的概率被丢弃,以 1-p 的概率被拉伸,除以  1 - p

    技术分享图片

    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)

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    Gluon 实现 dropout 丢弃法

    标签:com   24*   shuff   dom   hat   normal   sgd   rom   step   

    原文地址:https://www.cnblogs.com/TreeDream/p/10045913.html

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  • 原文地址:https://www.cnblogs.com/jukan/p/10814806.html
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