PyTorch: import torch
from torch import nn
from d2l import torch as d2l
def dropout_layer(X, dropout):
    assert 0 <= dropout <= 1
    if dropout == 1:
        return torch.zeros_like(X)
    if dropout == 0:
        return X
    mask = (torch.rand(X.shape) > dropout).float()
    return mask * X / (1.0 - dropout)
X= torch.arange(16, dtype = torch.float32).reshape((2, 8))
num_inputs, num_outputs, num_hiddens1, num_hiddens2 = 784, 10, 256, 256
dropout1, dropout2 = 0.2, 0.5
class Net(nn.Module):
    def __init__(self, num_inputs, num_outputs, num_hiddens1, num_hiddens2, is_training = True):
        super(Net, self).__init__()
        self.num_inputs = num_inputs
        self.training = is_training
        self.lin1 = nn.Linear(num_inputs, num_hiddens1)
        self.lin2 = nn.Linear(num_hiddens1, num_hiddens2)
        self.lin3 = nn.Linear(num_hiddens2, num_outputs)
        self.relu = nn.ReLU()
    def forward(self, X):
        H1 = self.relu(self.lin1(X.reshape((-1, self.num_inputs))))
        if self.training == True:
            H1 = dropout_layer(H1, dropout1)
        H2 = self.relu(self.lin2(H1))
        if self.training == True:
            H2 = dropout_layer(H2, dropout2)
        out = self.lin3(H2)
        return out
net = Net(num_inputs, num_outputs, num_hiddens1, num_hiddens2)
num_epochs, lr, batch_size = 10, 0.5, 256
loss = nn.CrossEntropyLoss(reduction='none')
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
trainer = torch.optim.SGD(net.parameters(), lr=lr)
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)
net = nn.Sequential(nn.Flatten(),
        nn.Linear(784, 256),
        nn.ReLU(),
        nn.Dropout(dropout1),
        nn.Linear(256, 256),
        nn.ReLU(),
        nn.Dropout(dropout2),
        nn.Linear(256, 10))
def init_weights(m):
    if type(m) == nn.Linear:
        nn.init.normal_(m.weight, std=0.01)
net.apply(init_weights);
trainer = torch.optim.SGD(net.parameters(), lr=lr)
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)

MXNet: from mxnet import autograd, gluon, init, np, npx
from mxnet.gluon import nn
from d2l import mxnet as d2l
npx.set_np()
def dropout_layer(X, dropout):
    assert 0 <= dropout <= 1
    if dropout == 1:
        return np.zeros_like(X)
    if dropout == 0:
        return X
    mask = np.random.uniform(0, 1, X.shape) > dropout
    return mask.astype(np.float32) * X / (1.0 - dropout)
X = np.arange(16).reshape(2, 8)
num_inputs, num_outputs, num_hiddens1, num_hiddens2 = 784, 10, 256, 256
W1 = np.random.normal(scale=0.01, size=(num_inputs, num_hiddens1))
b1 = np.zeros(num_hiddens1)
W2 = np.random.normal(scale=0.01, size=(num_hiddens1, num_hiddens2))
b2 = np.zeros(num_hiddens2)
W3 = np.random.normal(scale=0.01, size=(num_hiddens2, num_outputs))
b3 = np.zeros(num_outputs)
params = [W1, b1, W2, b2, W3, b3]
for param in params:
    param.attach_grad()
dropout1, dropout2 = 0.2, 0.5
def net(X):
    X = X.reshape(-1, num_inputs)
    H1 = npx.relu(np.dot(X, W1) + b1)
    if autograd.is_training():
        H1 = dropout_layer(H1, dropout1)
    H2 = npx.relu(np.dot(H1, W2) + b2)
    if autograd.is_training():
        H2 = dropout_layer(H2, dropout2)
    return np.dot(H2, W3) + b3
num_epochs, lr, batch_size = 10, 0.5, 256
loss = gluon.loss.SoftmaxCrossEntropyLoss()
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, lambda batch_size: d2l.sgd(params, lr, batch_size))
net = nn.Sequential()
net.add(nn.Dense(256, activation="relu"),
        nn.Dropout(dropout1),
        nn.Dense(256, activation="relu"),
        nn.Dropout(dropout2),
        nn.Dense(10))
net.initialize(init.Normal(sigma=0.01))
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr})
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)