I'm not sure I understand the question correctly. But I think the answer is
yes.

Hidden layer in an MLP are fully connected layers.

Fred

On Wed, Nov 9, 2016 at 7:26 AM, Beatriz G. <[email protected]> wrote:

> Hi,
>
> Could be used a independant hidden layer of mlp as a fully connected layer?
>
> regards.
>
>
> El viernes, 13 de marzo de 2015, 18:25:38 (UTC+1), Pascal Lamblin escribió:
>>
>> Hi,
>>
>> On Fri, Mar 13, 2015, Orry Messer wrote:
>> > I don't quite understand the structure of the network after the second
>> > convolution/pooling layer and just before the hidden layer.
>> > I think what is confusing me is the batch aspect of it.
>> > With a batch size of 500, the hidden layer will have 500 units. This
>> much
>> > I'm ok with.
>>
>> That's not right... by coincidence, the batch size in 500, which is
>> also the number of output units of that layer. Each of these units will
>> compute a different value for each of the examples in the batch, so the
>> output of that layer will be (batch_size, n_outputs) or (500, 500).
>>
>> > But what is the input to this hidden layer? In the comments it says
>> that
>> > the hidden layer operates on matrices of size (batch_size, pixel_size),
>> > which in the case of this code
>> > is (500, 800).
>>
>> That's correct.
>>
>> > Does this then mean that each hidden unit has 500*800 inputs?
>>
>> Not really. Each hidden unit has 800 scalar inputs. Each of these inputs
>> will take a different value for each example of the minibatch, and the
>> neuron will also output one value for each example.
>>
>> > And since there are 500 hidden units, does this then mean that there
>> > are a total of 500*(500*800) connections and as many weights to tune
>> > in this layer alone?
>>
>> No, the weights are the same for all the examples of the minibatch.
>>
>> --
>> Pascal
>>
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