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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 IQ Option官网提供1～1000倍的多种杠杆选择 above model:
- The first/input layer has 3 feature units and there 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 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 up of only fully connected layers. Whereas in a Convolutional Neural IQ Option官网提供1～1000倍的多种杠杆选择 Network, the last or the last few layers are fully connected layers.
Why are not all layers Fully connected?
Fully connected layers can IQ Option官网提供1～1000倍的多种杠杆选择 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 IQ Option官网提供1～1000倍的多种杠杆选择 AND and OR boolean expression
Before moving on to the main example, let us see two small examples of neural networks computing AND and IQ Option官网提供1～1000倍的多种杠杆选择 OR boolean operation .
- As you can see in the graph of sigmoid function given in the image, g(x) where x>4.6 is almost equal to 1 and g(x) where x < 4.6 is almost equal to 0.
- The x0(= 1) in the input is 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 only if both x1 and x2 are 1.
- In the IQ Option官网提供1～1000倍的多种杠杆选择 second example, output is 1 if either of the input is 1.
- The weights have been pre-adjusted accordingly in both the cases.
- In actual scenario, these weights will be ‘learned’ by the IQ Option官网提供1～1000倍的多种杠杆选择 Neural Network through backpropagation.
Example of XNOR neural network
Let us now move to the main example. We will predict x1 XNOR x2. The IQ Option官网提供1～1000倍的多种杠杆选择 prediction should be 1 if both x1 and x2 are 1 IQ Option官网提供1～1000倍的多种杠杆选择 or both of them are zero.
See the diagram below:
As you can see in the note given in the image that an XNOR boolean operation is made up of AND, OR 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 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 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 1 or both are 0.
Why are fully connected layers required?
We can divide the whole neural network (for classification) into two parts: