As is followed by a nonlinear activation: Yj(l)=f(?ikijXxi(l-1)+bj)

As shown
in Fig. 3, the lowest level of this architecture is the input layer with 2D nxn images as our inputs. With
local receptive fields, upper layer neurons extract some elementary and complex
visual features. Each convolutional layer (Fig. 3) is composed of multiple
feature maps, which are constructed by convolving inputs with different filters.
In other words, the value of each unit in a feature map is the result depending
on a local receptive field in the previous layer and the filter. This is
followed by a nonlinear activation:

Yj(l)=f(?ikijXxi(l-1)+bj)     (4)

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!

order now

Where Yj(l) is
the jth output for
the ith convolution layer Ci ; f(.)  is
a nonlinear function (most recent implementations use a scaled hyperbolic
tangent function as the nonlinear activation function:

 f (x)=1.7159.tanh(2x/3)). Kij is a trainable filter
(or kernel) in the filter bank that convolves with the feature map xi(l-1) from
the previous layer to produce a new feature map in the current layer. The
symbol X represents a discrete convolution operator and bj is a bias. Note that each filter Kij can connect to
all or a portion of feature maps in the previous layer reduces the spatial
resolution of the feature map. In general, each unit in the sub-sampling layer
is constructed by averaging a 2×2 area in the feature map or by max pooling
over a small region. The key parameters to be decided are weights between
layers, which are normally trained by standard back propagation procedures and
a gradient descent algorithm with mean squared-error as the loss function.
Alternatively, training deep CNN architectures can be unsupervised. Here in we
review a particular method for unsupervised training of CNNs: predictive sparse
decomposition (PSD) The idea is to approximate inputs X with a linear
combination of some basic and sparse functions.


where W is a matrix with a
linear basis set, Z is a sparse coefficient matrix, D is a
diagonal gain matrix and K is the filter bank with predictor parameters.
The goal is to find the optimal basis function sets W and the filter
bank K that minimize the reconstruction error (the first term in Eq. 5)
with a sparse representation (the second term), and the code prediction error
simultaneously (the third term in Eq. 5, measuring the difference between the
predicted code and actual code, preserves invariance for certain distortions).
PSD can be trained with a feed-forward encoder to learn the filter bank and
also the pooling together.

summary, inspired by biological processes CNN algorithms learn a hierarchical
feature representation by utilizing strategies like local receptive fields,
shared weights, and sub sampling. Each
filter bank can be trained with either supervised or unsupervised methods. A
CNN is capable of learning good feature hierarchies automatically and providing
some degree of translational and distortional invariances.38



multilayer perceptron (MLP) is a
class of feed-forward
neural network. An MLP consists of  atleast three layers of nodes. Except for the
input nodes, each node is a neuron that uses a nonlinear activation
function. MLP utilizes a supervised
learning technique called back-propagation
for training.39
Its multiple layers and non-linear activation distinguish MLP from a linear perceptron.
It can distinguish data that is not linearly

MLP consists of three or more layers (an input layer and an output layer with
one or more hidden layers) of
non linearly-activating nodes making it a deep
neural network.
Since MLPs are fully connected, each node in one layer connects with a certain
weight w i j {displaystyle w_{ij}} wij to every node in
the following layer.

If a multilayer perceptron has a linear activation
function in all neurons, that is, a linear function that maps the weighted inputs to the output of each neuron, then linear algebra shows that any number of layers can be
reduced to a two-layer input-output model. In MLPs some neurons use a nonlinear activation function that
was developed to model the frequency of action potentials, or firing, of biological neurons.

The two
common activation functions are both sigmoids, and are described by

y(vi) =tanh(vi)
and y(vi)=(1+e-vi)-1  y ( v i ) = tanh ? ( v i )     and     y ( v
i ) = ( 1 + e ? v i ) ? 1 {displaystyle y(v_{i})= anh(v_{i})~~{ extrm

The first is
a hyperbolic
tangent that ranges from -1 to 1, while the other is the logistic function, which is similar in shape but ranges from 0
to 1. Here y i {displaystyle y_{i}} vi  is the output of the  i {displaystyle i} ith
node (neuron) and v i {displaystyle v_{i}} vi  is 
the weighted sum of the input connections.

Learning occurs in the perceptron by changing
connection weights after each piece of data is processed, based on the amount
of error in the output compared to the expected result. This is an example of supervised
learning, and is carried out through back-propagation,
a generalization of the least mean squares algorithm in the linear perceptron.

We represent the error in output node j
{displaystyle j} j in the n {displaystyle n} nth data
point  by

e j ( n ) = d j ( n ) ? y j ( n ) {displaystyle
where d {displaystyle d} d is the
target value and y {displaystyle y} y is the value produced by the
perceptron. The node weights are adjusted based on corrections that minimize
the error in the entire output, given by

E ( n ) = 1 2 ? j e j 2 ( n ) {displaystyle
{mathcal {E}}(n)={frac {1}{2}}sum _{j}e_{j}^{2}(n)} ?(n)=


Using gradient descent, the
change in each weight is

?wji(n)= -?

where y i {displaystyle y_{i}} yi  is the output of the previous neuron and ?  ? {displaystyle eta } is the learning rate, which is selected to
ensure that the weights quickly converge to a response, without oscillations.

The derivative to be calculated depends on the induced local field v j
{displaystyle v_{j}}vj, which itself varies. It is
easy to prove that for an output node this derivative can be simplified to

= ej(n) ? (vj(n))? ? E ( n
) ? v j ( n ) = e j ( n ) ? ? ( v j ( n ) ) {displaystyle -{frac {partial
{mathcal {E}}(n)}{partial v_{j}(n)}}=e_{j}(n)phi ^{prime }(v_{j}(n))}

Where ?  ? ? {displaystyle phi ^{prime }} is
the derivative of the activation function described above, which itself does
not vary. The analysis is more difficult for the change in weights to a hidden
node, but it can be shown that the relevant derivative is

? E ( n ) ? v j ( n ) = ? ? ( v j ( n ) ) ? k ? ? E ( n ) ? v k ( n ) w k j ( n
) {displaystyle -{frac {partial {mathcal {E}}(n)}{partial v_{j}(n)}}=phi
^{prime }(v_{j}(n))sum _{k}-{frac {partial {mathcal {E}}(n)}{partial

= ?(vj(n))?k

wkj(n)? ? E ( n
) ? v j ( n ) = e j ( n ) ? ? ( v j ( n ) ) {displaystyle -{frac {partial
{mathcal {E}}(n)}{partial v_{j}(n)}}=e_{j}(n)phi ^{prime }(v_{j}(n))}


This depends on
the change in weights of the k {displaystyle k} kth
nodes, which represent the output layer. So to change the hidden layer weights,
the output layer weights change according to the derivative of the activation
function, and so this algorithm represents a back-propagation of the activation