Fully connected layer
A fully connected layer multiplies the input by a weight matrix and then adds a bias vector.
returns a fully connected layer and specifies the layer
= fullyConnectedLayer(outputSize
)OutputSize
property.
sets the optional Parameters and Initialization,
Learn Rate and Regularization, and
layer
= fullyConnectedLayer(outputSize
,Name,Value
)Name
properties using name-value pairs. For
example, fullyConnectedLayer(10,'Name','fc1')
creates a fully
connected layer with an output size of 10 and the name 'fc1'
.
You can specify multiple name-value pairs. Enclose each property name in single
quotes.
[1] Glorot, Xavier, and Yoshua Bengio. "Understanding the Difficulty of Training Deep Feedforward Neural Networks." In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 249–356. Sardinia, Italy: AISTATS, 2010.
[2] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification." In Proceedings of the 2015 IEEE International Conference on Computer Vision, 1026–1034. Washington, DC: IEEE Computer Vision Society, 2015.
[3] Saxe, Andrew M., James L. McClelland, and Surya Ganguli. "Exact solutions to the nonlinear dynamics of learning in deep linear neural networks." arXiv preprint arXiv:1312.6120 (2013).
batchNormalizationLayer
| convolution2dLayer
| Deep Network
Designer | reluLayer
| trainNetwork