.. _chapter_nin:
Network in Network (NiN)
========================
LeNet, AlexNet, and VGG all share a common design pattern: extract
features exploiting *spatial* structure via a sequence of convolutions
and pooling layers and then post-process the representations via
fully-connected layers. The improvements upon LeNet by AlexNet and VGG
mainly lie in how these later networks widen and deepen these two
modules. Alternatively, one could imagine using fully-connected layers
earlier in the process. However, a careless use of dense layers might
give up the the spatial structure of the representation entirely,
Network in Network (NiN) blocks offer an alternative. They were proposed
in :cite:`Lin.Chen.Yan.2013` based on a very simple insight—to use an
MLP on the channels for each pixel separately.
NiN Blocks
----------
Recall that the inputs and outputs of convolutional layers consist of
four-dimensional arrays with axes corresponding to the batch, channel,
height, and width. Also recall that the inputs and outputs of
fully-connected layers are typically two-dimensional arrays
corresponding to the batch, and features. The idea of behind NiN is to
apply a fully-connected layer at each pixel location (for each height
and width). If we tie the weights across each spatial location, we could
think of this as a :math:`1\times 1` convolutional layer (as described
in :numref:`chapter_channels`) or as a fully-connected layer acting
indepdendently on each pixel location. Another way to view this is to
think of each element in the spatial dimension (height and width) as
equivalent to an example and the channel as equivalent to a feature. The
figure below illustrates the main structural differences between NiN and
AlexNet, VGG, and other networks.
.. figure:: ../img/nin.svg
The figure on the left shows the network structure of AlexNet and
VGG, and the figure on the right shows the network structure of NiN.
The NiN block consists of one convolutional layer followed by two
:math:`1\times 1` convolutional layers that act as per-pixel
fully-connected layers with ReLU activations. The convolution width of
the first layer is typically set by the user. The subsequent widths are
fixed to :math:`1 \times 1`.
.. code:: python
import d2l
from mxnet import gluon, nd
from mxnet.gluon import nn
def nin_block(num_channels, kernel_size, strides, padding):
blk = nn.Sequential()
blk.add(nn.Conv2D(num_channels, kernel_size, strides, padding, activation='relu'),
nn.Conv2D(num_channels, kernel_size=1, activation='relu'),
nn.Conv2D(num_channels, kernel_size=1, activation='relu'))
return blk
NiN Model
---------
The original NiN network was proposed shortly after AlexNet and clearly
draws some inspiration. NiN uses convolutional layers with window shapes
of :math:`11\times 11`, :math:`5\times 5`, and :math:`3\times 3`, and
the corresponding numbers of output channels are the same as in AlexNet.
Each NiN block is followed by a maximum pooling layer with a stride of 2
and a window shape of :math:`3\times 3`.
Once significant difference between NiN and AlexNet is that NiN avoids
dense connections altogether. Instead, NiN uses an NiN block with a
number of output channels equal to the number of label classes, followed
by a *global* average pooling layer, yielding a vector of logits. One
advantage of NiN’s design is that it significantly reduces the number of
required model parameters. However, in practice, this design sometimes
requires increased model training time.
.. code:: python
net = nn.Sequential()
net.add(nin_block(96, kernel_size=11, strides=4, padding=0),
nn.MaxPool2D(pool_size=3, strides=2),
nin_block(256, kernel_size=5, strides=1, padding=2),
nn.MaxPool2D(pool_size=3, strides=2),
nin_block(384, kernel_size=3, strides=1, padding=1),
nn.MaxPool2D(pool_size=3, strides=2),
nn.Dropout(0.5),
# There are 10 label classes
nin_block(10, kernel_size=3, strides=1, padding=1),
# The global average pooling layer automatically sets the window shape
# to the height and width of the input
nn.GlobalAvgPool2D(),
# Transform the four-dimensional output into two-dimensional output
# with a shape of (batch size, 10)
nn.Flatten())
We create a data example to see the output shape of each block.
.. code:: python
X = nd.random.uniform(shape=(1, 1, 224, 224))
net.initialize()
for layer in net:
X = layer(X)
print(layer.name, 'output shape:\t', X.shape)
.. parsed-literal::
:class: output
sequential1 output shape: (1, 96, 54, 54)
pool0 output shape: (1, 96, 26, 26)
sequential2 output shape: (1, 256, 26, 26)
pool1 output shape: (1, 256, 12, 12)
sequential3 output shape: (1, 384, 12, 12)
pool2 output shape: (1, 384, 5, 5)
dropout0 output shape: (1, 384, 5, 5)
sequential4 output shape: (1, 10, 5, 5)
pool3 output shape: (1, 10, 1, 1)
flatten0 output shape: (1, 10)
Data Acquisition and Training
-----------------------------
As before we use Fashion-MNIST to train the model. NiN’s training is
similar to that for AlexNet and VGG, but it often uses a larger learning
rate.
.. code:: python
lr, num_epochs, batch_size = 0.1, 10, 128
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)
d2l.train_ch5(net, train_iter, test_iter, num_epochs, lr)
.. parsed-literal::
:class: output
loss 0.360, train acc 0.867, test acc 0.870
2995.5 exampes/sec on gpu(0)
.. figure:: output_nin_2871e8_7_1.svg
Summary
-------
- NiN uses blocks consisting of a convolutional layer and multiple
:math:`1\times 1` convolutional layer. This can be used within the
convolutional stack to allow for more per-pixel nonlinearity.
- NiN removes the fully connected layers and replaces them with global
average pooling (i.e. summing over all locations) after reducing the
number of channels to the desired number of outputs (e.g. 10 for
Fashion-MNIST).
- Removing the dense layers reduces overfitting. NiN has dramatically
fewer parameters.
- The NiN design influenced many subsequent convolutional neural
networks designs.
Exercises
---------
1. Tune the hyper-parameters to improve the classification accuracy.
2. Why are there two :math:`1\times 1` convolutional layers in the NiN
block? Remove one of them, and then observe and analyze the
experimental phenomena.
3. Calculate the resource usage for NiN
- What is the number of parameters?
- What is the amount of computation?
- What is the amount of memory needed during training?
- What is the amount of memory needed during inference?
4. What are possible problems with reducing the
:math:`384 \times 5 \times 5` representation to a
:math:`10 \times 5 \times 5` representation in one step?
Scan the QR Code to `Discuss `__
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.. |image0| image:: ../img/qr_nin.svg