LEARNING APPARATUS, RECORDING MEDIUM, AND LEARNING METHOD

• US 20170147921A1
• Filed: 11/10/2016
• Published: 05/25/2017
• Est. Priority Date: 11/24/2015
• Status: Abandoned Application

• Current Assignee: Ricoh Company Limited
• Original Assignee: Ryosuke Kasahara
• Inventors: KASAHARA, Ryosuke
• Application Number:
  US15/348,165
• Publication Number:
  US 20170147921A1
• Time in Patent Office: Days
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 3/084 Backpropagation, e.g. using gradient descent

Abstract

A learning apparatus includes: a learning performing unit configured to learn parameters of a multilayer neural network with regularization; a determining unit configured to determine whether learning has progressed; and a changing unit configured to reduce effect of the regularization in response to the determining unit determining that the learning has progressed.

Claims

1. A learning apparatus comprising:

a learning performing unit configured to learn parameters of a multilayer neural network with regularization;

a determining unit configured to determine whether learning has progressed; and

a changing unit configured to reduce effect of the regularization in response to the determining unit determining that the learning has progressed.

2. The learning apparatus according to claim 1, wherein the changing unit is configured to reduce a learning rate of the learning while reducing the effect of the regularization in response to the determining unit determining that the learning has progressed.

3. The learning apparatus according to claim 1, wherein the changing unit is configured to reduce a regularization parameter to reduce the effect of the regularization, the regularization parameter being a coefficient of a regularization term used in the regularization.

4. The learning apparatus according to claim 1, wherein the changing unit is configured to reduce a rate of dropout to reduce the effect of the regularization.

5. The learning apparatus according to claim 1, wherein the changing unit is configured to reduce a rate of dropconnect to reduce the effect of the regularization.

6. The learning apparatus according to claim 1, wherein the multilayer neural network is a convolutional neural network.

7. The learning apparatus according to claim 1, wherein the multilayer neural network is a stacked autoencoder.

8. The learning apparatus according to claim 1, wherein the multilayer neural network is a recurrent neural network.

9. The learning apparatus according to claim 1, wherein the learning performing unit is configured to learn the parameters by stochastic gradient descent.

10. A non-transitory computer-readable recording medium including a program causing a computer to execute:

learning parameters of a multilayer neural network with regularization;

determining whether learning has progressed; and

reducing effect of the regularization in response to determining that the learning has progressed.

11. A learning method performed by a learning apparatus, the learning method comprising:

learning parameters of a multilayer neural network with regularization;

determining whether learning has progressed; and

reducing effect of the regularization in response to determining that the learning has progressed.