.. _sec_mlp_concise:
Concise Implementation of Multilayer Perceptrons
================================================
As you might expect, by relying on the high-level APIs, we can implement
MLPs even more concisely.
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from mxnet import gluon, init, npx
from mxnet.gluon import nn
from d2l import mxnet as d2l
npx.set_np()
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import torch
from torch import nn
from d2l import torch as d2l
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import tensorflow as tf
from d2l import tensorflow as d2l
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Model
-----
As compared with our concise implementation of softmax regression
implementation (:numref:`sec_softmax_concise`), the only difference is
that we add *two* fully-connected layers (previously, we added *one*).
The first is our hidden layer, which contains 256 hidden units and
applies the ReLU activation function. The second is our output layer.
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net = nn.Sequential()
net.add(nn.Dense(256, activation='relu'),
nn.Dense(10))
net.initialize(init.Normal(sigma=0.01))
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net = nn.Sequential(nn.Flatten(),
nn.Linear(784, 256),
nn.ReLU(),
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);
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net = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(256, activation='relu'),
tf.keras.layers.Dense(10)])
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The training loop is exactly the same as when we implemented softmax
regression. This modularity enables us to separate matters concerning
the model architecture from orthogonal considerations.
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batch_size, lr, num_epochs = 256, 0.1, 10
loss = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr})
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)
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batch_size, lr, num_epochs = 256, 0.1, 10
loss = nn.CrossEntropyLoss(reduction='none')
trainer = torch.optim.SGD(net.parameters(), lr=lr)
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)
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batch_size, lr, num_epochs = 256, 0.1, 10
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
trainer = tf.keras.optimizers.SGD(learning_rate=lr)
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)
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Summary
-------
- Using high-level APIs, we can implement MLPs much more concisely.
- For the same classification problem, the implementation of an MLP is
the same as that of softmax regression except for additional hidden
layers with activation functions.
Exercises
---------
1. Try adding different numbers of hidden layers (you may also modify
the learning rate). What setting works best?
2. Try out different activation functions. Which one works best?
3. Try different schemes for initializing the weights. What method works
best?
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`Discussions `__
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`Discussions `__
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`Discussions `__
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