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The MLP consists of three or more layers (an input and an output layer with one or more hidden layers) of nonlinearly-activating nodes. Since MLPs are fully connected, each node in one layer connects with a certain weight w i j {\displaystyle w_{ij}} to every node in the following layer.
FC = fully connected layer (with ReLU activation) Linear = fully connected layer (without activation) DO = dropout; It used the non-saturating ReLU activation function, which trained better than tanh and sigmoid. [1] Because the network did not fit onto a single Nvidia GTX 580 3GB GPU, it was split into two halves, one on each GPU. [1]: Section 3.2
The Convolutional layer [4] is typically used for image analysis tasks. In this layer, the network detects edges, textures, and patterns. The outputs from this layer are then fed into a fully-connected layer for further processing. See also: CNN model. The Pooling layer [5] is used to reduce the size of data input.
The layer that produces the ultimate result is the output layer. In between them are zero or more hidden layers. Single layer and unlayered networks are also used. Between two layers, multiple connection patterns are possible. They can be 'fully connected', with every neuron in one layer connecting to every neuron in the next layer.
The bottom layer of inputs is not always considered a real neural network layer. A multilayer perceptron (MLP) is a misnomer for a modern feedforward artificial neural network, consisting of fully connected neurons (hence the synonym sometimes used of fully connected network (FCN)), often with a nonlinear kind of activation function, organized ...
Kurt Hornik , Maxwell Stinchcombe, and Halbert White showed in 1989 that multilayer feed-forward networks with as few as one hidden layer are universal approximators. [1] Hornik also showed in 1991 [ 4 ] that it is not the specific choice of the activation function but rather the multilayer feed-forward architecture itself that gives neural ...
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1994 LeNet was a larger version of 1989 LeNet designed to fit the larger MNIST database. It had more feature maps in its convolutional layers, and had an additional layer of hidden units, fully connected to both the last convolutional layer and to the output units. It has 2 convolutions, 2 average poolings, and 2 fully connected layers.