RELURural Economy and Land Use Programme (Centre for Rural Economy; School of Agriculture, Food and Rural Development; University of Newcastle; Newcastle Upon Tyne, UK)
RELUReal Estate & Land Use
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The output, calculated using a ReLU activation function of the first convolutional layer, is then connected to the neurons in the first subsampling layer with a pool size of 20 and stride of 10 samples, producing 20 feature maps with a length of 659, which are connected to the second convolutional layer.
We use Rectified Linear Units (ReLU) for faster training and apply dropout to prevent from overfitting.
To demonstrate the advantages of using APL units over traditional ReLUs and tangent functions, we compare the results of our deep CNN network model with APL units in hidden layers versus ReLU functions and tangent functions.
Leaky ReLU activations: We use Leaky ReLU activations with alpha [greater than or equal to] 0.5
Subs not used: Stoian Relu, Capusa, Popa, Iacob, Rus, Ciobanu, Torday.
Following the same convention as 50x2_mlp_elu, 50x2_mlp_relu is a 2 hidden layer, 50 hidden nodes per layer feed forward network with ReLU activation applied at each hidden layer, 100x2_mlp_elu is a feed forward network with 2 hidden layers, each having 100 hidden units and ELU activation, and 100x2_mlp_relu is one with ReLU activation.
Fully connected layers are followed by ReLU (Rectified Linear Units) for nonlinear mapping.
The DNN used here to obtain [V.sub.pred] consists of multiple fully connected layers with ReLU [27] hidden neurons.
ReLU (x) = max(0,x) is the nonlinear active function of the neuron.
A 12-layer network structure is developed, which contains 3 convolution layers, 3 ReLU layers, a max pooling layer, and 3 full-connection layers besides the input and output layers.