a convolutional net with regression output

[Stone]

hi! I am very naive here, thanks a bunch for the help

what I am trying to do is to build a convolutional net, but with regression output (not softmax classification); the network structure might look like : the input is a square image, but output is a continuous value

the input space is specified as:

input_space: !obj:pylearn2.space.Conv2DSpace {

            shape: [8, 8],

            num_channels: 1

        }, 

but the last layer is a !obj:pylearn2.models.mlp.LinearGaussian 

but I got the errors:  "ValueError: The value of a VectorSpace batch must be 2D, got 3 dimensions"

I guess the last layer requires "vector space"?  

How to resolve this problem?  using CompositeSpace? what yaml will look like?

mega thanks

[Stone]

any thoughts?

[Frédéric Bastien]

Pylearn2 isn't supported anymore. You can check Lasagne, Keras, Blocks that are ML library on top of Theano.

Fred

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