Electron microscopy
 
Fully Connected Layers (FC) in Deep Learning
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Table 3829. Hidden layer in deep learning neural network.

Application example   Hidden Layer
Feedforward neural network for image classification Details
  • Number of Nodes: Let's say we have one hidden layer with 128 nodes. The number of nodes in the hidden layer is a hyperparameter that can be adjusted based on the problem and complexity of the task.
  • Activation Function: ReLU (Rectified Linear Unit) activation function is commonly used in hidden layers. So, ReLU activation is applied to the output of each node in this layer.
Variables /nodes 128 nodes (ReLU activation)
Included layers Convolutional Layer (CONV)
Pooling Layer (e.g., MaxPooling)
Convolutional Layer (CONV)
Pooling Layer
Fully Connected Layer (FC)
Energy usage 99% energy are used by Convolutional Layer (CONV) and Fully Connected Layer (FC)
* Feedforward neural network for image classification: this builds a neural network to classify handwritten digits from the MNIST dataset, where each image is a 28x28 pixel grayscale image of a handwritten digit (0 through 9).

 

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