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, pp. 249-256. 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 IEEE international conference on computer vision, pp. 1026-1034. 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