CNN_Specification.md

CNN Specification

It has 2 parts:

1. cnn
2. mlp

Each one contain a json object.cnn describes convolution layer configuration and mlp describes hidden layer configuration.

• cnn contains a json object with following parameters:

• layers: An Array of json objects.Each one decribes a convolution layer which contains:

• convmat_dim : Dimension of Convolution Weight
• num_filters : No. of Feature maps
• poolsize : Dimension for Max-pooling
• flatten : whether to flatten output or not(true for last layer else false)
• update : true if weight need to updated during training.
• activation : Activation function used by this layer, if not present global activation fuction is used.

• activation : Activation function used by layers (global)
• use_fast : if true program will use pylearn2 library for faster computation (Default Value = false)

• mlp contains a json object with following parameters:

• layers : An Array contain size of hidden layers.
• adv_activation: if maxout/pnorm is used.

• method : 'maxout','pnorm'. In maxout, a pooling of neuron o/p is done based on poolsize. But in pnorm output is normalized after pooling.
• pool_size: pool size
• pnorm_order: order of normalization (in pnorm)

• activation : Activation function used by layers. (if adv_activation is used, it sholud be either 'linear','relu' or 'cappedrelu')

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Also See

• Example
• Types of Activation functions