TensorFlow: import tensorflow as tf
from d2l import tensorflow as d2l
def dropout_layer(X, dropout):
    assert 0 <= dropout <= 1
    if dropout == 1:
        return tf.zeros_like(X)
    if dropout == 0:
        return X
    mask = tf.random.uniform(shape=tf.shape(X), minval=0, maxval=1) < 1 - dropout
    return tf.cast(mask, dtype=tf.float32) * X / (1.0 - dropout)
X = tf.reshape(tf.range(16, dtype=tf.float32), (2, 8))
num_outputs, num_hiddens1, num_hiddens2 = 10, 256, 256
dropout1, dropout2 = 0.2, 0.5
class Net(tf.keras.Model):
    def __init__(self, num_outputs, num_hiddens1, num_hiddens2):
        super().__init__()
        self.input_layer = tf.keras.layers.Flatten()
        self.hidden1 = tf.keras.layers.Dense(num_hiddens1, activation='relu')
        self.hidden2 = tf.keras.layers.Dense(num_hiddens2, activation='relu')
        self.output_layer = tf.keras.layers.Dense(num_outputs)
    def call(self, inputs, training=None):
        x = self.input_layer(inputs)
        x = self.hidden1(x)
        if training:
            x = dropout_layer(x, dropout1)
        x = self.hidden2(x)
        if training:
            x = dropout_layer(x, dropout2)
        x = self.output_layer(x)
        return x
net = Net(num_outputs, num_hiddens1, num_hiddens2)
num_epochs, lr, batch_size = 10, 0.5, 256
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
trainer = tf.keras.optimizers.SGD(learning_rate=lr)
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)
net = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(256, activation=tf.nn.relu),
    tf.keras.layers.Dropout(dropout1),
    tf.keras.layers.Dense(256, activation=tf.nn.relu),
    tf.keras.layers.Dropout(dropout2),
    tf.keras.layers.Dense(10),
])
trainer = tf.keras.optimizers.SGD(learning_rate=lr)
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)

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)