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Fully Connected Layer: The brute force layer of a Machine Learning model
Fully Connected layers in a neural networks are those layers where all the inputs from one layer are connected to every activation unit of the next layer. In most popular machine learning models, the last few layers are full connected layers which IQ Option官网提供1～1000倍的多种杠杆选择 compiles the data extracted by previous layers to form the final output. It is the second most time consuming layer second to Convolution Layer.
The diagram below clarifies the statement.
In the above model:
- The first/input layer has 3 feature units and there IQ Option官网提供1～1000倍的多种杠杆选择 are 4 activation units in the next hidden layer.
- The 1's in each layer are bias units.
- a01, a02 and a03 are input values to the neural network.They are IQ Option官网提供1～1000倍的多种杠杆选择 basically features of the training example.
- The 4 activation units of first hidden layer is connected to all 3 activation units of second hidden layer The weights/parameters connect the two layers.
So the activation units would be like this:
Theta00, theta01 etc. are weights in the above picture. A conventional neural network is made IQ Option官网提供1～1000倍的多种杠杆选择 up of only fully connected layers. Whereas in a Convolutional Neural Network, the last or the last few layers are fully connected layers.IQ Option官网提供1～1000倍的多种杠杆选择
Why are not all layers Fully connected?
Fully connected layers can be seen as a brute force approach whereas there are approaches like the convolutional layer which reduces the input to concerned features only
Examples of working of fully connected layers
Let’s take a simple example of a Neural network made up of fully connected layers.
Example of AND and OR boolean expression
Before moving on to the main example, let us see two small examples of neural networks computing AND and OR boolean operation .
- As you can see in the graph of sigmoid function given in the image, g(x)IQ Option官网提供1～1000倍的多种杠杆选择 where x>4.6 is almost equal to 1 and g(x) IQ Option官网提供1～1000倍的多种杠杆选择 where x < 4.6 is almost equal to 0.
- The x0(= 1) in the input is IQ Option官网提供1～1000倍的多种杠杆选择 the bias unit. Every layer has a bias unit.
- h(subscript theta) is the output value and is equal to g(-30 + 20x1 +20x2) in AND operation.
- As you can see in the first example, the output will be 1 IQ Option官网提供1～1000倍的多种杠杆选择 only if both x1 and x2 are 1.
- In the second example, output is 1 if either of the input is 1.
- The weights have been pre-adjusted accordingly in both the cases.IQ Option官网提供1～1000倍的多种杠杆选择
- In actual scenario, these weights will be ‘learned’ by the Neural Network through backpropagation.
Example of XNOR neural network
Let us now move IQ Option官网提供1～1000倍的多种杠杆选择 to the main example. We will predict x1 XNOR x2. The prediction should be 1 if both x1 and x2 are 1 or both of them are zero.
See the diagram below:
IQ Option官网提供1～1000倍的多种杠杆选择 IQ Option官网提供1～1000倍的多种杠杆选择
As you can see in the note given in the image that an XNOR boolean operation is made up of AND, OR and NOR boolean operation. The weights have been adjusted for all the three boolean operations.
In the table you can see that the output is 1 only if either both x1 and x2 are 1 or both are 0.
Why are fully connected layers required?
We can divide the whole neural network (for classification) into two parts: