I want to recreate the result of this paper. They use the term convolutional ply for the neural network they apply on the audio spectogram. I am not sure I understand what a convolutional ply is, and how it differs from an ordinary convolutional neural network (cnn).
The paper states this as being the difference:
A convolution ply differs from a standard, fully connected hidden
layer in two important aspects, however. First, each convolutional
unit receives input only from a local area of the input. This means
that each unit represents some features of a local region of the
input. Second, the units of the convolution ply can themselves be
organized into a number of feature maps, where all units in the same
feature map share the same weights but receive input from different
locations of the lower layer
Which to me sound like a ordinary cnn network. What is the difference?
Related
When coding a convolutional neural network I am unsure of where to start with the convolutional layer. When different convolutional filters are used to produce different feature maps does that mean that the filters have different sizes (for example, 3x3, 2x2 etc.) ?
In most examples which is a good indication of how to go about coding a convolutional neural network, you will find to start with 1 convolutional layer and pass layer sizes, 3x3 window, input data features.
model.add(Conv2D(layer_size, (3,3), input_shape = x.shape[1:]))
The filter sizes usually only differ in the max pooling layer e.g 2x2.
model.add(MaxPooling2D(pool_size=(2,2)))
Layer sizes are usually selected from range layer_size = [32, 64,128] and you can do the same to experiment with different convolution_layers = [1,2,3]
I've never seen different kernel sizes for the filters in the same layer, although it is possible to do so, is not a default option the frameworks I have used. What makes filters yield different feature maps are the weights.
Along different layers different kernel sizes are used because the idea of the convolutional networks is to gradually reduce dimensionality through downsampling layers (max pooling for example), so in deep levels you have smaller feature maps and a smaller filter keeps it convolutional and less fully connected (having a kernel the same size as the image is equivalent to have a dense layer).
If you're starting with convolutionals I recommend you to play with this interactive visualization of a CNN, it helped me with a lot of concepts.
I mean to ask that can I have a neural network classifier with a large number of layers without fully connected layers?
Yes, you can make a fully convolutional classifier, one example is SqueezeNet.
The basic working principle is that at the end of the network you insert a convolutional layer with C output channels, where C is the number of classes. Then you proceed to apply global average pooling, which will produce a 1D vector of C elements (independent of input feature map width/height), and you can apply the softmax function to that vector to produce output class probabilities.
related to How to Create CaffeDB training data for siamese networks out of image directory
If I have N labels. How can I enforce, that the feature vector of size N right before the contrastive loss layer represents some kind of probability for each class? Or comes that automatically with the siamese net design?
If you only use contrastive loss in a Siamese network, there is no way of forcing the net to classify into the correct label - because the net is only trained using "same/not same" information and does not know the semantics of the different classes.
What you can do is train with multiple loss layers.
You should aim at training a feature representation that is reach enough for your domain, so that looking at the trained feature vector of some input (in some high dimension) you should be able to easily classify that input to the correct class. Moreover, given that feature representation of two inputs one should be able to easily say if they are "same" or "not same".
Therefore, I recommend that you train your deep network with two loss layer with "bottom" as the output of one of the "InnerProduct" layers. One loss is the contrastive loss. The other loss should have another "InnerProduct" layer with num_output: N and a "SoftmaxWithLoss" layer.
A similar concept was used in this work:
Sun, Chen, Wang and Tang Deep Learning Face Representation by Joint Identification-Verification NIPS 2014.
I have trained a feedforward neural network in Matlab. Now I have to implement this neural network in C language (or simulate the model in Matlab using mathematical equations, without using direct functions). How do I do that? I know that I have to take the weights and bias and activation function. What else is required?
There is no point in representing it as a mathematical function because it won't save you any computations.
Indeed all you need is the weights, biases, activation and your architecture. I'm assuming it is a simple feedforward network as you said, you need to implement some kind of matrix multiplication and addition in C. Also, you'll need to implement the activation function. After that, you're ready to go. Your feed forward NN is ready to be implemented. If the C code will not be used for training, it won't be necessary to implement the backpropagation algorithm in C.
A feedforward layer would be implemented as follows:
Output = Activation_function(Input * weights + bias)
Where,
Input: (1 x number_of_input_parameters_for_this_layer)
Weights: (number_of_input_parameters_for_this_layer x number_of_neurons_for_this_layer)
Bias: (1 x number_of_neurons_for_this_layer)
Output: (1 x number_of_neurons_for_this_layer)
The output of a layer is the input to the next layer.
After some days of searching, I have found the following webpage to be very useful http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/
The picture below shows a simple feedforward neural network. Picture taken from the above website.
In this figure, the circles denote the inputs to the network. The circles labeled “+1” are called bias units, and correspond to the intercept term. The leftmost layer of the network is called the input layer, and the rightmost layer the output layer (which, in this example, has only one node). The middle layer of nodes is called the hidden layer, because its values are not observed in the training set. In this example, the neural network has 3 input units (not counting the bias unit), 3 hidden units, and 1 output unit.
The mathematical equations representing this feedforward network are
This neural network has parameters (W,b)=(W(l),b(l),W(2),b(2)), where we write Wij(l) to denote the parameter (or weight) associated with the connection between unit j in layer l, and unit i in layer l+1. (Note the order of the indices.) Also, bi(l) is the bias associated with unit i in layer l+1.
So, from the trained model, as Mido mentioned in his answer, we have to take the input weight matrix which is W(1), the layer weight matrix which is W(2), biases, hidden layer transfer function and output layer transfer function. After this, use the above equations to estimate the output hW,b(x). A popular transfer function used for a regression problem is tan-sigmoid transfer function in the hidden layer and linear transfer function in the output layer.
Those who use Matlab, these links are highly useful
try to simulate neural network in Matlab by myself
Neural network in MATLAB
Programming a Basic Neural Network from scratch in MATLAB
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I'm new to the topic of neural networks. I came across the two terms convolutional neural network and recurrent neural network.
I'm wondering if these two terms are referring to the same thing, or, if not, what would be the difference between them?
Difference between CNN and RNN are as follows:
CNN:
CNN takes a fixed size inputs and generates fixed-size outputs.
CNN is a type of feed-forward artificial neural network - are variations of multilayer perceptrons which are designed to use minimal amounts of preprocessing.
CNNs use connectivity pattern between its neurons and is inspired by the organization of the animal visual cortex, whose individual neurons are arranged in such a way that they respond to overlapping regions tiling the visual field.
CNNs are ideal for images and video processing.
RNN:
RNN can handle arbitrary input/output lengths.
RNN unlike feedforward neural networks - can use their internal memory to process arbitrary sequences of inputs.
Recurrent neural networks use time-series information. i.e. what I spoke last will impact what I will speak next.
RNNs are ideal for text and speech analysis.
Convolutional neural networks (CNN) are designed to recognize images. It has convolutions inside, which see the edges of an object recognized on the image. Recurrent neural networks (RNN) are designed to recognize sequences, for example, a speech signal or a text. The recurrent network has cycles inside that implies the presence of short memory in the net. We have applied CNN as well as RNN choosing an appropriate machine learning algorithm to classify EEG signals for BCI: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
These architectures are completely different, so it is rather hard to say "what is the difference", as the only thing in common is the fact, that they are both neural networks.
Convolutional networks are networks with overlapping "reception fields" performing convolution tasks.
Recurrent networks are networks with recurrent connections (going in the opposite direction of the "normal" signal flow) which form cycles in the network's topology.
Apart from others, in CNN we generally use a 2d squared sliding window along an axis and convolute (with original input 2d image) to identify patterns.
In RNN we use previously calculated memory. If you are interested you can see, LSTM (Long Short-Term Memory) which is a special kind of RNN.
Both CNN and RNN have one point in common, as they detect patterns and sequences, that is you can't shuffle your single input data bits.
Convolutional neural networks (CNNs) for computer vision, and recurrent neural networks (RNNs) for natural language processing.
Although this can be applied in other areas, RNNs have the advantage of networks that can have signals travelling in both directions by introducing loops in the network.
Feedback networks are powerful and can get extremely complicated. Computations derived from the previous input are fed back into the network, which gives them a kind of memory. Feedback networks are dynamic: their state is changing continuously until they reach an equilibrium point.
First, we need to know that recursive NN is different from recurrent NN.
By wiki's definition,
A recursive neural network (RNN) is a kind of deep neural network created by applying the same set of weights recursively over a structure
In this sense, CNN is a type of Recursive NN.
On the other hand, recurrent NN is a type of recursive NN based on time difference.
Therefore, in my opinion, CNN and recurrent NN are different but both are derived from recursive NN.
This is the difference between CNN and RNN
Convolutional Neural NEtwork:
In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. ... They have applications in image and video recognition, recommender systems, image classification, medical image analysis, and natural language processing.
Recurrent Neural Networks:
A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs.
It is more helpful to describe the convolution and recurrent layers first.
Convolution layer:
Includes input, one or more filters (as well as subsampling).
The input can be one-dimensional or n-dimensional (n>1), for example, it can be a two-dimensional image. One or more filters are also defined in each layer. Inputs are convolving with each filter. The method of convolution is almost similar to the convolution of filters in image processing. In general, the purpose of this section is to extract the features of each filter from the input. The output of each convolution is called a feature map.
For example, a filter is considered for horizontal edges, and the result of its convolution with the input is the extraction of the horizontal edges of the input image. Usually, in practice and especially in the first layers, a large number of filters (for example, 60 filters in one layer) are defined. Also, after convolution, the subsampling operation is usually performed, for example, their maximum or average of each of the two neighborhood values is selected.
The convolution layer allows important features and patterns to be extracted from the input. And delete input data dependencies (linear and nonlinear).
[The following figure shows an example of the use of convolutional layers and pattern extraction for classification.][1]
[1]: https://i.stack.imgur.com/HS4U0.png [Kalhor, A. (2020). Classification and Regression NNs. Lecture.]
Advantages of convolutional layers:
Able to remove correlations and reduce input dimensions
Network generalization is increasing
Network robustness increases against changes because it extracts key features
Very powerful and widely used in supervised learning
...
Recurrent layers:
In these layers, the output of the current layer or the output of the next layers can also be used as the input of the layer. It also can receive time series as input.
The output without using the recurrent layer is as follows (a simple example):
y = f(W * x)
Where x is input, W is weight and f is the activator function.
But in recurrent networks it can be as follows:
y = f(W * x)
y = f(W * y)
y = f(W * y)
... until convergence
This means that in these networks the generated output can be used as an input and thus have memory networks. Some types of recurrent networks are Discrete Hopfield Net and Recurrent Auto-Associative NET, which are simple networks or complex networks such as LSTM.
An example is shown in the image below.
Advantages of Recurrent Layers:
They have memory capability
They can use time series as input.
They can use the generated output for later use.
Very used in machine translation, voice recognition, image description
...
Networks that use convolutional layers are called convolutional networks (CNN). Similarly, networks that use recurrent layers are called recurrent networks. It is also possible to use both layers in a network according to the desired application!