# 8.6. Residual Networks (ResNet) and ResNeXt¶ Open the notebook in SageMaker Studio Lab

As we design ever deeper networks it becomes imperative to understand how adding layers can increase the complexity and expressiveness of the network. Even more important is the ability to design networks where adding layers makes networks strictly more expressive rather than just different. To make some progress we need a bit of mathematics.

```
import torch
from torch import nn
from torch.nn import functional as F
from d2l import torch as d2l
```

```
from mxnet import init, np, npx
from mxnet.gluon import nn
from d2l import mxnet as d2l
npx.set_np()
```

```
import jax
from flax import linen as nn
from jax import numpy as jnp
from d2l import jax as d2l
```

```
import tensorflow as tf
from d2l import tensorflow as d2l
```

## 8.6.1. Function Classes¶

Consider \(\mathcal{F}\), the class of functions that a specific network architecture (together with learning rates and other hyperparameter settings) can reach. That is, for all \(f \in \mathcal{F}\) there exists some set of parameters (e.g., weights and biases) that can be obtained through training on a suitable dataset. Let’s assume that \(f^*\) is the “truth” function that we really would like to find. If it is in \(\mathcal{F}\), we are in good shape but typically we will not be quite so lucky. Instead, we will try to find some \(f^*_\mathcal{F}\) which is our best bet within \(\mathcal{F}\). For instance, given a dataset with features \(\mathbf{X}\) and labels \(\mathbf{y}\), we might try finding it by solving the following optimization problem:

We know that regularization (Morozov, 1984, Tikhonov and Arsenin, 1977) may control complexity of \(\mathcal{F}\) and achieve consistency, so a larger size of training data generally leads to better \(f^*_\mathcal{F}\). It is only reasonable to assume that if we design a different and more powerful architecture \(\mathcal{F}'\) we should arrive at a better outcome. In other words, we would expect that \(f^*_{\mathcal{F}'}\) is “better” than \(f^*_{\mathcal{F}}\). However, if \(\mathcal{F} \not\subseteq \mathcal{F}'\) there is no guarantee that this should even happen. In fact, \(f^*_{\mathcal{F}'}\) might well be worse. As illustrated by Fig. 8.6.1, for non-nested function classes, a larger function class does not always move closer to the “truth” function \(f^*\). For instance, on the left of Fig. 8.6.1, though \(\mathcal{F}_3\) is closer to \(f^*\) than \(\mathcal{F}_1\), \(\mathcal{F}_6\) moves away and there is no guarantee that further increasing the complexity can reduce the distance from \(f^*\). With nested function classes where \(\mathcal{F}_1 \subseteq \cdots \subseteq \mathcal{F}_6\) on the right of Fig. 8.6.1, we can avoid the aforementioned issue from the non-nested function classes.

Thus, only if larger function classes contain the smaller ones are we guaranteed that increasing them strictly increases the expressive power of the network. For deep neural networks, if we can train the newly-added layer into an identity function \(f(\mathbf{x}) = \mathbf{x}\), the new model will be as effective as the original model. As the new model may get a better solution to fit the training dataset, the added layer might make it easier to reduce training errors.

This is the question that He *et al.* (2016) considered
when working on very deep computer vision models. At the heart of their
proposed *residual network* (*ResNet*) is the idea that every additional
layer should more easily contain the identity function as one of its
elements. These considerations are rather profound but they led to a
surprisingly simple solution, a *residual block*. With it, ResNet won
the ImageNet Large Scale Visual Recognition Challenge in 2015. The
design had a profound influence on how to build deep neural networks.
For instance, residual blocks have been added to recurrent networks
(Kim *et al.*, 2017, Prakash *et al.*, 2016). Likewise, Transformers
(Vaswani *et al.*, 2017) use them to stack many layers
of networks efficiently. It is also used in graph neural networks
(Kipf and Welling, 2016) and, as a basic concept, it has been used
extensively in computer vision
(Redmon and Farhadi, 2018, Ren *et al.*, 2015). Note that
residual networks are predated by highway networks
(Srivastava *et al.*, 2015) that share some of the motivation,
albeit without the elegant parametrization around the identity function.

## 8.6.2. Residual Blocks¶

Let’s focus on a local part of a neural network, as depicted in
Fig. 8.6.2. Denote the input by \(\mathbf{x}\).
We assume that \(f(\mathbf{x})\), the desired underlying mapping we
want to obtain by learning, is to be used as input to the activation
function on the top. On the left, the portion within the dotted-line box
must directly learn \(f(\mathbf{x})\). On the right, the portion
within the dotted-line box needs to learn the *residual mapping*
\(g(\mathbf{x}) = f(\mathbf{x}) - \mathbf{x}\), which is how the
residual block derives its name. If the identity mapping
\(f(\mathbf{x}) = \mathbf{x}\) is the desired underlying mapping,
the residual mapping amounts to \(g(\mathbf{x}) = 0\) and it is thus
easier to learn: we only need to push the weights and biases of the
upper weight layer (e.g., fully connected layer and convolutional layer)
within the dotted-line box to zero. The right figure illustrates the
*residual block* of ResNet, where the solid line carrying the layer
input \(\mathbf{x}\) to the addition operator is called a *residual
connection* (or *shortcut connection*). With residual blocks, inputs can
forward propagate faster through the residual connections across layers.
In fact, the residual block can be thought of as a special case of the
multi-branch Inception block: it has two branches one of which is the
identity mapping.

ResNet has VGG’s full \(3\times 3\) convolutional layer design. The residual block has two \(3\times 3\) convolutional layers with the same number of output channels. Each convolutional layer is followed by a batch normalization layer and a ReLU activation function. Then, we skip these two convolution operations and add the input directly before the final ReLU activation function. This kind of design requires that the output of the two convolutional layers has to be of the same shape as the input, so that they can be added together. If we want to change the number of channels, we need to introduce an additional \(1\times 1\) convolutional layer to transform the input into the desired shape for the addition operation. Let’s have a look at the code below.

```
class Residual(nn.Module): #@save
"""The Residual block of ResNet models."""
def __init__(self, num_channels, use_1x1conv=False, strides=1):
super().__init__()
self.conv1 = nn.LazyConv2d(num_channels, kernel_size=3, padding=1,
stride=strides)
self.conv2 = nn.LazyConv2d(num_channels, kernel_size=3, padding=1)
if use_1x1conv:
self.conv3 = nn.LazyConv2d(num_channels, kernel_size=1,
stride=strides)
else:
self.conv3 = None
self.bn1 = nn.LazyBatchNorm2d()
self.bn2 = nn.LazyBatchNorm2d()
def forward(self, X):
Y = F.relu(self.bn1(self.conv1(X)))
Y = self.bn2(self.conv2(Y))
if self.conv3:
X = self.conv3(X)
Y += X
return F.relu(Y)
```

```
class Residual(nn.Block): #@save
"""The Residual block of ResNet models."""
def __init__(self, num_channels, use_1x1conv=False, strides=1, **kwargs):
super().__init__(**kwargs)
self.conv1 = nn.Conv2D(num_channels, kernel_size=3, padding=1,
strides=strides)
self.conv2 = nn.Conv2D(num_channels, kernel_size=3, padding=1)
if use_1x1conv:
self.conv3 = nn.Conv2D(num_channels, kernel_size=1,
strides=strides)
else:
self.conv3 = None
self.bn1 = nn.BatchNorm()
self.bn2 = nn.BatchNorm()
def forward(self, X):
Y = npx.relu(self.bn1(self.conv1(X)))
Y = self.bn2(self.conv2(Y))
if self.conv3:
X = self.conv3(X)
return npx.relu(Y + X)
```

```
class Residual(nn.Module): #@save
"""The Residual block of ResNet models."""
num_channels: int
use_1x1conv: bool = False
strides: tuple = (1, 1)
training: bool = True
def setup(self):
self.conv1 = nn.Conv(self.num_channels, kernel_size=(3, 3),
padding='same', strides=self.strides)
self.conv2 = nn.Conv(self.num_channels, kernel_size=(3, 3),
padding='same')
if self.use_1x1conv:
self.conv3 = nn.Conv(self.num_channels, kernel_size=(1, 1),
strides=self.strides)
else:
self.conv3 = None
self.bn1 = nn.BatchNorm(not self.training)
self.bn2 = nn.BatchNorm(not self.training)
def __call__(self, X):
Y = nn.relu(self.bn1(self.conv1(X)))
Y = self.bn2(self.conv2(Y))
if self.conv3:
X = self.conv3(X)
Y += X
return nn.relu(Y)
```

```
class Residual(tf.keras.Model): #@save
"""The Residual block of ResNet models."""
def __init__(self, num_channels, use_1x1conv=False, strides=1):
super().__init__()
self.conv1 = tf.keras.layers.Conv2D(num_channels, padding='same',
kernel_size=3, strides=strides)
self.conv2 = tf.keras.layers.Conv2D(num_channels, kernel_size=3,
padding='same')
self.conv3 = None
if use_1x1conv:
self.conv3 = tf.keras.layers.Conv2D(num_channels, kernel_size=1,
strides=strides)
self.bn1 = tf.keras.layers.BatchNormalization()
self.bn2 = tf.keras.layers.BatchNormalization()
def call(self, X):
Y = tf.keras.activations.relu(self.bn1(self.conv1(X)))
Y = self.bn2(self.conv2(Y))
if self.conv3 is not None:
X = self.conv3(X)
Y += X
return tf.keras.activations.relu(Y)
```

This code generates two types of networks: one where we add the input to
the output before applying the ReLU nonlinearity whenever
`use_1x1conv=False`

; and one where we adjust channels and resolution
by means of a \(1 \times 1\) convolution before adding.
Fig. 8.6.3 illustrates this.

Now let’s look at a situation where the input and output are of the same shape, where \(1 \times 1\) convolution is not needed.

```
blk = Residual(3)
X = torch.randn(4, 3, 6, 6)
blk(X).shape
```

```
torch.Size([4, 3, 6, 6])
```

```
blk = Residual(3)
blk.initialize()
X = np.random.randn(4, 3, 6, 6)
blk(X).shape
```

```
[22:49:23] ../src/storage/storage.cc:196: Using Pooled (Naive) StorageManager for CPU
```

```
(4, 3, 6, 6)
```

```
blk = Residual(3)
X = jax.random.normal(d2l.get_key(), (4, 6, 6, 3))
blk.init_with_output(d2l.get_key(), X)[0].shape
```

```
(4, 6, 6, 3)
```

```
blk = Residual(3)
X = tf.random.normal((4, 6, 6, 3))
Y = blk(X)
Y.shape
```

```
TensorShape([4, 6, 6, 3])
```

We also have the option to halve the output height and width while
increasing the number of output channels. In this case we use
\(1 \times 1\) convolutions via `use_1x1conv=True`

. This comes in
handy at the beginning of each ResNet block to reduce the spatial
dimensionality via `strides=2`

.

```
blk = Residual(6, use_1x1conv=True, strides=2)
blk(X).shape
```

```
torch.Size([4, 6, 3, 3])
```

```
blk = Residual(6, use_1x1conv=True, strides=2)
blk.initialize()
blk(X).shape
```

```
(4, 6, 3, 3)
```

```
blk = Residual(6, use_1x1conv=True, strides=(2, 2))
blk.init_with_output(d2l.get_key(), X)[0].shape
```

```
(4, 3, 3, 6)
```

```
blk = Residual(6, use_1x1conv=True, strides=2)
blk(X).shape
```

```
TensorShape([4, 3, 3, 6])
```

## 8.6.3. ResNet Model¶

The first two layers of ResNet are the same as those of the GoogLeNet we described before: the \(7\times 7\) convolutional layer with 64 output channels and a stride of 2 is followed by the \(3\times 3\) max-pooling layer with a stride of 2. The difference is the batch normalization layer added after each convolutional layer in ResNet.

```
class ResNet(d2l.Classifier):
def b1(self):
return nn.Sequential(
nn.LazyConv2d(64, kernel_size=7, stride=2, padding=3),
nn.LazyBatchNorm2d(), nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
```

```
class ResNet(d2l.Classifier):
def b1(self):
net = nn.Sequential()
net.add(nn.Conv2D(64, kernel_size=7, strides=2, padding=3),
nn.BatchNorm(), nn.Activation('relu'),
nn.MaxPool2D(pool_size=3, strides=2, padding=1))
return net
```

```
class ResNet(d2l.Classifier):
arch: tuple
lr: float = 0.1
num_classes: int = 10
training: bool = True
def setup(self):
self.net = self.create_net()
def b1(self):
return nn.Sequential([
nn.Conv(64, kernel_size=(7, 7), strides=(2, 2), padding='same'),
nn.BatchNorm(not self.training), nn.relu,
lambda x: nn.max_pool(x, window_shape=(3, 3), strides=(2, 2),
padding='same')])
```

```
class ResNet(d2l.Classifier):
def b1(self):
return tf.keras.models.Sequential([
tf.keras.layers.Conv2D(64, kernel_size=7, strides=2,
padding='same'),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.Activation('relu'),
tf.keras.layers.MaxPool2D(pool_size=3, strides=2,
padding='same')])
```

GoogLeNet uses four modules made up of Inception blocks. However, ResNet uses four modules made up of residual blocks, each of which uses several residual blocks with the same number of output channels. The number of channels in the first module is the same as the number of input channels. Since a max-pooling layer with a stride of 2 has already been used, it is not necessary to reduce the height and width. In the first residual block for each of the subsequent modules, the number of channels is doubled compared with that of the previous module, and the height and width are halved.

```
@d2l.add_to_class(ResNet)
def block(self, num_residuals, num_channels, first_block=False):
blk = []
for i in range(num_residuals):
if i == 0 and not first_block:
blk.append(Residual(num_channels, use_1x1conv=True, strides=2))
else:
blk.append(Residual(num_channels))
return nn.Sequential(*blk)
```

```
@d2l.add_to_class(ResNet)
def block(self, num_residuals, num_channels, first_block=False):
blk = nn.Sequential()
for i in range(num_residuals):
if i == 0 and not first_block:
blk.add(Residual(num_channels, use_1x1conv=True, strides=2))
else:
blk.add(Residual(num_channels))
return blk
```

```
@d2l.add_to_class(ResNet)
def block(self, num_residuals, num_channels, first_block=False):
blk = []
for i in range(num_residuals):
if i == 0 and not first_block:
blk.append(Residual(num_channels, use_1x1conv=True,
strides=(2, 2), training=self.training))
else:
blk.append(Residual(num_channels, training=self.training))
return nn.Sequential(blk)
```

```
@d2l.add_to_class(ResNet)
def block(self, num_residuals, num_channels, first_block=False):
blk = tf.keras.models.Sequential()
for i in range(num_residuals):
if i == 0 and not first_block:
blk.add(Residual(num_channels, use_1x1conv=True, strides=2))
else:
blk.add(Residual(num_channels))
return blk
```

Then, we add all the modules to ResNet. Here, two residual blocks are used for each module. Lastly, just like GoogLeNet, we add a global average pooling layer, followed by the fully connected layer output.

```
@d2l.add_to_class(ResNet)
def __init__(self, arch, lr=0.1, num_classes=10):
super(ResNet, self).__init__()
self.save_hyperparameters()
self.net = nn.Sequential(self.b1())
for i, b in enumerate(arch):
self.net.add_module(f'b{i+2}', self.block(*b, first_block=(i==0)))
self.net.add_module('last', nn.Sequential(
nn.AdaptiveAvgPool2d((1, 1)), nn.Flatten(),
nn.LazyLinear(num_classes)))
self.net.apply(d2l.init_cnn)
```

```
@d2l.add_to_class(ResNet)
def __init__(self, arch, lr=0.1, num_classes=10):
super(ResNet, self).__init__()
self.save_hyperparameters()
self.net = nn.Sequential()
self.net.add(self.b1())
for i, b in enumerate(arch):
self.net.add(self.block(*b, first_block=(i==0)))
self.net.add(nn.GlobalAvgPool2D(), nn.Dense(num_classes))
self.net.initialize(init.Xavier())
```

```
@d2l.add_to_class(ResNet)
def create_net(self):
net = nn.Sequential([self.b1()])
for i, b in enumerate(self.arch):
net.layers.extend([self.block(*b, first_block=(i==0))])
net.layers.extend([nn.Sequential([
# Flax does not provide a GlobalAvg2D layer
lambda x: nn.avg_pool(x, window_shape=x.shape[1:3],
strides=x.shape[1:3], padding='valid'),
lambda x: x.reshape((x.shape[0], -1)),
nn.Dense(self.num_classes)])])
return net
```

```
@d2l.add_to_class(ResNet)
def __init__(self, arch, lr=0.1, num_classes=10):
super(ResNet, self).__init__()
self.save_hyperparameters()
self.net = tf.keras.models.Sequential(self.b1())
for i, b in enumerate(arch):
self.net.add(self.block(*b, first_block=(i==0)))
self.net.add(tf.keras.models.Sequential([
tf.keras.layers.GlobalAvgPool2D(),
tf.keras.layers.Dense(units=num_classes)]))
```

There are four convolutional layers in each module (excluding the \(1\times 1\) convolutional layer). Together with the first \(7\times 7\) convolutional layer and the final fully connected layer, there are 18 layers in total. Therefore, this model is commonly known as ResNet-18. By configuring different numbers of channels and residual blocks in the module, we can create different ResNet models, such as the deeper 152-layer ResNet-152. Although the main architecture of ResNet is similar to that of GoogLeNet, ResNet’s structure is simpler and easier to modify. All these factors have resulted in the rapid and widespread use of ResNet. Fig. 8.6.4 depicts the full ResNet-18.

Before training ResNet, let’s observe how the input shape changes across different modules in ResNet. As in all the previous architectures, the resolution decreases while the number of channels increases up until the point where a global average pooling layer aggregates all features.

```
class ResNet18(ResNet):
def __init__(self, lr=0.1, num_classes=10):
super().__init__(((2, 64), (2, 128), (2, 256), (2, 512)),
lr, num_classes)
ResNet18().layer_summary((1, 1, 96, 96))
```

```
Sequential output shape: torch.Size([1, 64, 24, 24])
Sequential output shape: torch.Size([1, 64, 24, 24])
Sequential output shape: torch.Size([1, 128, 12, 12])
Sequential output shape: torch.Size([1, 256, 6, 6])
Sequential output shape: torch.Size([1, 512, 3, 3])
Sequential output shape: torch.Size([1, 10])
```

```
class ResNet18(ResNet):
def __init__(self, lr=0.1, num_classes=10):
super().__init__(((2, 64), (2, 128), (2, 256), (2, 512)),
lr, num_classes)
ResNet18().layer_summary((1, 1, 96, 96))
```

```
Sequential output shape: (1, 64, 24, 24)
Sequential output shape: (1, 64, 24, 24)
Sequential output shape: (1, 128, 12, 12)
Sequential output shape: (1, 256, 6, 6)
Sequential output shape: (1, 512, 3, 3)
GlobalAvgPool2D output shape: (1, 512, 1, 1)
Dense output shape: (1, 10)
```

```
class ResNet18(ResNet):
arch: tuple = ((2, 64), (2, 128), (2, 256), (2, 512))
lr: float = 0.1
num_classes: int = 10
ResNet18(training=False).layer_summary((1, 96, 96, 1))
```

```
Sequential output shape: (1, 24, 24, 64)
Sequential output shape: (1, 24, 24, 64)
Sequential output shape: (1, 12, 12, 128)
Sequential output shape: (1, 6, 6, 256)
Sequential output shape: (1, 3, 3, 512)
Sequential output shape: (1, 10)
```

```
class ResNet18(ResNet):
def __init__(self, lr=0.1, num_classes=10):
super().__init__(((2, 64), (2, 128), (2, 256), (2, 512)),
lr, num_classes)
ResNet18().layer_summary((1, 96, 96, 1))
```

```
Sequential output shape: (1, 24, 24, 64)
Sequential output shape: (1, 24, 24, 64)
Sequential output shape: (1, 12, 12, 128)
Sequential output shape: (1, 6, 6, 256)
Sequential output shape: (1, 3, 3, 512)
Sequential output shape: (1, 10)
```

## 8.6.4. Training¶

We train ResNet on the Fashion-MNIST dataset, just like before. ResNet is quite a powerful and flexible architecture. The plot capturing training and validation loss illustrates a significant gap between both graphs, with the training loss being considerably lower. For a network of this flexibility, more training data would offer distinct benefit in closing the gap and improving accuracy.

```
model = ResNet18(lr=0.01)
trainer = d2l.Trainer(max_epochs=10, num_gpus=1)
data = d2l.FashionMNIST(batch_size=128, resize=(96, 96))
model.apply_init([next(iter(data.get_dataloader(True)))[0]], d2l.init_cnn)
trainer.fit(model, data)
```

```
model = ResNet18(lr=0.01)
trainer = d2l.Trainer(max_epochs=10, num_gpus=1)
data = d2l.FashionMNIST(batch_size=128, resize=(96, 96))
trainer.fit(model, data)
```

```
model = ResNet18(lr=0.01)
trainer = d2l.Trainer(max_epochs=10, num_gpus=1)
data = d2l.FashionMNIST(batch_size=128, resize=(96, 96))
trainer.fit(model, data)
```

```
trainer = d2l.Trainer(max_epochs=10)
data = d2l.FashionMNIST(batch_size=128, resize=(96, 96))
with d2l.try_gpu():
model = ResNet18(lr=0.01)
trainer.fit(model, data)
```

## 8.6.5. ResNeXt¶

One of the challenges one encounters in the design of ResNet is the trade-off between nonlinearity and dimensionality within a given block. That is, we could add more nonlinearity by increasing the number of layers, or by increasing the width of the convolutions. An alternative strategy is to increase the number of channels that can carry information between blocks. Unfortunately, the latter comes with a quadratic penalty since the computational cost of ingesting \(c_\textrm{i}\) channels and emitting \(c_\textrm{o}\) channels is proportional to \(\mathcal{O}(c_\textrm{i} \cdot c_\textrm{o})\) (see our discussion in Section 7.4).

We can take some inspiration from the Inception block of
Fig. 8.4.1 which has information flowing through the
block in separate groups. Applying the idea of multiple independent
groups to the ResNet block of Fig. 8.6.3 led to the
design of ResNeXt (Xie *et al.*, 2017). Different from
the smorgasbord of transformations in Inception, ResNeXt adopts the
*same* transformation in all branches, thus minimizing the need for
manual tuning of each branch.

Breaking up a convolution from \(c_\textrm{i}\) to
\(c_\textrm{o}\) channels into one of \(g\) groups of size
\(c_\textrm{i}/g\) generating \(g\) outputs of size
\(c_\textrm{o}/g\) is called, quite fittingly, a *grouped
convolution*. The computational cost (proportionally) is reduced from
\(\mathcal{O}(c_\textrm{i} \cdot c_\textrm{o})\) to
\(\mathcal{O}(g \cdot (c_\textrm{i}/g) \cdot (c_\textrm{o}/g)) = \mathcal{O}(c_\textrm{i} \cdot c_\textrm{o} / g)\),
i.e., it is \(g\) times faster. Even better, the number of
parameters needed to generate the output is also reduced from a
\(c_\textrm{i} \times c_\textrm{o}\) matrix to \(g\) smaller
matrices of size \((c_\textrm{i}/g) \times (c_\textrm{o}/g)\), again
a \(g\) times reduction. In what follows we assume that both
\(c_\textrm{i}\) and \(c_\textrm{o}\) are divisible by
\(g\).

The only challenge in this design is that no information is exchanged between the \(g\) groups. The ResNeXt block of Fig. 8.6.5 amends this in two ways: the grouped convolution with a \(3 \times 3\) kernel is sandwiched in between two \(1 \times 1\) convolutions. The second one serves double duty in changing the number of channels back. The benefit is that we only pay the \(\mathcal{O}(c \cdot b)\) cost for \(1 \times 1\) kernels and can make do with an \(\mathcal{O}(b^2 / g)\) cost for \(3 \times 3\) kernels. Similar to the residual block implementation in Section 8.6.2, the residual connection is replaced (thus generalized) by a \(1 \times 1\) convolution.

The right-hand figure in Fig. 8.6.5 provides a much
more concise summary of the resulting network block. It will also play a
major role in the design of generic modern CNNs in
Section 8.8. Note that the idea of grouped convolutions
dates back to the implementation of AlexNet
(Krizhevsky *et al.*, 2012). When distributing the
network across two GPUs with limited memory, the implementation treated
each GPU as its own channel with no ill effects.

The following implementation of the `ResNeXtBlock`

class takes as
argument `groups`

(\(g\)), with `bot_channels`

(\(b\))
intermediate (bottleneck) channels. Lastly, when we need to reduce the
height and width of the representation, we add a stride of \(2\) by
setting `use_1x1conv=True, strides=2`

.

```
class ResNeXtBlock(nn.Module): #@save
"""The ResNeXt block."""
def __init__(self, num_channels, groups, bot_mul, use_1x1conv=False,
strides=1):
super().__init__()
bot_channels = int(round(num_channels * bot_mul))
self.conv1 = nn.LazyConv2d(bot_channels, kernel_size=1, stride=1)
self.conv2 = nn.LazyConv2d(bot_channels, kernel_size=3,
stride=strides, padding=1,
groups=bot_channels//groups)
self.conv3 = nn.LazyConv2d(num_channels, kernel_size=1, stride=1)
self.bn1 = nn.LazyBatchNorm2d()
self.bn2 = nn.LazyBatchNorm2d()
self.bn3 = nn.LazyBatchNorm2d()
if use_1x1conv:
self.conv4 = nn.LazyConv2d(num_channels, kernel_size=1,
stride=strides)
self.bn4 = nn.LazyBatchNorm2d()
else:
self.conv4 = None
def forward(self, X):
Y = F.relu(self.bn1(self.conv1(X)))
Y = F.relu(self.bn2(self.conv2(Y)))
Y = self.bn3(self.conv3(Y))
if self.conv4:
X = self.bn4(self.conv4(X))
return F.relu(Y + X)
```

```
class ResNeXtBlock(nn.Block): #@save
"""The ResNeXt block."""
def __init__(self, num_channels, groups, bot_mul,
use_1x1conv=False, strides=1, **kwargs):
super().__init__(**kwargs)
bot_channels = int(round(num_channels * bot_mul))
self.conv1 = nn.Conv2D(bot_channels, kernel_size=1, padding=0,
strides=1)
self.conv2 = nn.Conv2D(bot_channels, kernel_size=3, padding=1,
strides=strides, groups=bot_channels//groups)
self.conv3 = nn.Conv2D(num_channels, kernel_size=1, padding=0,
strides=1)
self.bn1 = nn.BatchNorm()
self.bn2 = nn.BatchNorm()
self.bn3 = nn.BatchNorm()
if use_1x1conv:
self.conv4 = nn.Conv2D(num_channels, kernel_size=1,
strides=strides)
self.bn4 = nn.BatchNorm()
else:
self.conv4 = None
def forward(self, X):
Y = npx.relu(self.bn1(self.conv1(X)))
Y = npx.relu(self.bn2(self.conv2(Y)))
Y = self.bn3(self.conv3(Y))
if self.conv4:
X = self.bn4(self.conv4(X))
return npx.relu(Y + X)
```

```
class ResNeXtBlock(nn.Module): #@save
"""The ResNeXt block."""
num_channels: int
groups: int
bot_mul: int
use_1x1conv: bool = False
strides: tuple = (1, 1)
training: bool = True
def setup(self):
bot_channels = int(round(self.num_channels * self.bot_mul))
self.conv1 = nn.Conv(bot_channels, kernel_size=(1, 1),
strides=(1, 1))
self.conv2 = nn.Conv(bot_channels, kernel_size=(3, 3),
strides=self.strides, padding='same',
feature_group_count=bot_channels//self.groups)
self.conv3 = nn.Conv(self.num_channels, kernel_size=(1, 1),
strides=(1, 1))
self.bn1 = nn.BatchNorm(not self.training)
self.bn2 = nn.BatchNorm(not self.training)
self.bn3 = nn.BatchNorm(not self.training)
if self.use_1x1conv:
self.conv4 = nn.Conv(self.num_channels, kernel_size=(1, 1),
strides=self.strides)
self.bn4 = nn.BatchNorm(not self.training)
else:
self.conv4 = None
def __call__(self, X):
Y = nn.relu(self.bn1(self.conv1(X)))
Y = nn.relu(self.bn2(self.conv2(Y)))
Y = self.bn3(self.conv3(Y))
if self.conv4:
X = self.bn4(self.conv4(X))
return nn.relu(Y + X)
```

```
class ResNeXtBlock(tf.keras.Model): #@save
"""The ResNeXt block."""
def __init__(self, num_channels, groups, bot_mul, use_1x1conv=False,
strides=1):
super().__init__()
bot_channels = int(round(num_channels * bot_mul))
self.conv1 = tf.keras.layers.Conv2D(bot_channels, 1, strides=1)
self.conv2 = tf.keras.layers.Conv2D(bot_channels, 3, strides=strides,
padding="same",
groups=bot_channels//groups)
self.conv3 = tf.keras.layers.Conv2D(num_channels, 1, strides=1)
self.bn1 = tf.keras.layers.BatchNormalization()
self.bn2 = tf.keras.layers.BatchNormalization()
self.bn3 = tf.keras.layers.BatchNormalization()
if use_1x1conv:
self.conv4 = tf.keras.layers.Conv2D(num_channels, 1,
strides=strides)
self.bn4 = tf.keras.layers.BatchNormalization()
else:
self.conv4 = None
def call(self, X):
Y = tf.keras.activations.relu(self.bn1(self.conv1(X)))
Y = tf.keras.activations.relu(self.bn2(self.conv2(Y)))
Y = self.bn3(self.conv3(Y))
if self.conv4:
X = self.bn4(self.conv4(X))
return tf.keras.activations.relu(Y + X)
```

Its use is entirely analogous to that of the `ResNetBlock`

discussed
previously. For instance, when using (`use_1x1conv=False, strides=1`

),
the input and output are of the same shape. Alternatively, setting
`use_1x1conv=True, strides=2`

halves the output height and width.

```
blk = ResNeXtBlock(32, 16, 1)
X = torch.randn(4, 32, 96, 96)
blk(X).shape
```

```
torch.Size([4, 32, 96, 96])
```

```
blk = ResNeXtBlock(32, 16, 1)
blk.initialize()
X = np.random.randn(4, 32, 96, 96)
blk(X).shape
```

```
(4, 32, 96, 96)
```

```
blk = ResNeXtBlock(32, 16, 1)
X = jnp.zeros((4, 96, 96, 32))
blk.init_with_output(d2l.get_key(), X)[0].shape
```

```
(4, 96, 96, 32)
```

```
blk = ResNeXtBlock(32, 16, 1)
X = tf.random.normal((4, 96, 96, 32))
Y = blk(X)
Y.shape
```

```
TensorShape([4, 96, 96, 32])
```

## 8.6.6. Summary and Discussion¶

Nested function classes are desirable since they allow us to obtain
strictly *more powerful* rather than also subtly *different* function
classes when adding capacity. One way of accomplishing this is by
letting additional layers to simply pass through the input to the
output. Residual connections allow for this. As a consequence, this
changes the inductive bias from simple functions being of the form
\(f(\mathbf{x}) = 0\) to simple functions looking like
\(f(\mathbf{x}) = \mathbf{x}\).

The residual mapping can learn the identity function more easily, such
as pushing parameters in the weight layer to zero. We can train an
effective *deep* neural network by having residual blocks. Inputs can
forward propagate faster through the residual connections across layers.
As a consequence, we can thus train much deeper networks. For instance,
the original ResNet paper (He *et al.*, 2016) allowed for up
to 152 layers. Another benefit of residual networks is that it allows us
to add layers, initialized as the identity function, *during* the
training process. After all, the default behavior of a layer is to let
the data pass through unchanged. This can accelerate the training of
very large networks in some cases.

Prior to residual connections, bypassing paths with gating units were
introduced to effectively train highway networks with over 100 layers
(Srivastava *et al.*, 2015). Using identity functions as bypassing
paths, ResNet performed remarkably well on multiple computer vision
tasks. Residual connections had a major influence on the design of
subsequent deep neural networks, of either convolutional or sequential
nature. As we will introduce later, the Transformer architecture
(Vaswani *et al.*, 2017) adopts residual connections
(together with other design choices) and is pervasive in areas as
diverse as language, vision, speech, and reinforcement learning.

ResNeXt is an example for how the design of convolutional neural
networks has evolved over time: by being more frugal with computation
and trading it off against the size of the activations (number of
channels), it allows for faster and more accurate networks at lower
cost. An alternative way of viewing grouped convolutions is to think of
a block-diagonal matrix for the convolutional weights. Note that there
are quite a few such “tricks” that lead to more efficient networks. For
instance, ShiftNet (Wu *et al.*, 2018) mimicks the effects of a
\(3 \times 3\) convolution, simply by adding shifted activations to
the channels, offering increased function complexity, this time without
any computational cost.

A common feature of the designs we have discussed so far is that the
network design is fairly manual, primarily relying on the ingenuity of
the designer to find the “right” network hyperparameters. While clearly
feasible, it is also very costly in terms of human time and there is no
guarantee that the outcome is optimal in any sense. In
Section 8.8 we will discuss a number of strategies for
obtaining high quality networks in a more automated fashion. In
particular, we will review the notion of *network design spaces* that
led to the RegNetX/Y models
(Radosavovic *et al.*, 2020).

## 8.6.7. Exercises¶

What are the major differences between the Inception block in Fig. 8.4.1 and the residual block? How do they compare in terms of computation, accuracy, and the classes of functions they can describe?

Refer to Table 1 in the ResNet paper (He

*et al.*, 2016) to implement different variants of the network.For deeper networks, ResNet introduces a “bottleneck” architecture to reduce model complexity. Try to implement it.

In subsequent versions of ResNet, the authors changed the “convolution, batch normalization, and activation” structure to the “batch normalization, activation, and convolution” structure. Make this improvement yourself. See Figure 1 in He

*et al.*(2016) for details.Why can’t we just increase the complexity of functions without bound, even if the function classes are nested?