All Courses
AI Career Planner
1:1 Coaching/Mentoring
Job Board
SQL
HTML/CSS/JS
Coding
Settings
LogoutThe aim of the Fully connected layer is to use the high-level feature of the input image produced by convolutional and pooling layers for classifying the input image into various classes based on the training dataset.
Fully connected means that every neuron in the previous layer is connected to each and every neuron in the next layer. The Sum of output probabilities from the Fully connected layer is 1, fully connected using a softmax activation function in the output layer.
The softmax function takes a vector of arbitrary real-valued scores and transforms it into a vector of values between 0 and 1 that sums to 1.
Working
It works like an ANN, assigning random weights to each synapse, the input layer is weight-adjusted and put into an activation function. The output of this is then compared to the true values and the error generated is back-propagated, i.e. the weights are re-calculated and repeat all the processes. This is done until the error or cost function is minimized.