![]() Scale(1, 4, abs2 bias=false) # 4 parameters You may specify the function init, turn off trainable bias with bias=false, or provide the array(s) explicitly. ![]() The learnable scale & bias are initialised init(size.) and zeros32(size.), with init=ones32 by default. * instead of matrix multiplication * of Dense. Scale(size::Integer., σ=identity bias=true, init=ones32)Ĭreate an element-wise layer, whose forward pass is given by: y = σ.(scale. Julia> Flux.Bilinear(rand(4,8,16), false, tanh) # first dim of weight is the outputīilinear((8, 16) => 4, tanh bias=false) # 512 parameters source Flux.Scale - Type ) # used as the recombinator, with skip as the second input Julia> B(x,y) = B((x,y)) # two inputs, may be given as a tupleĬhain(Dense(5 => 20, tanh), Dense(20 => 9, tanh)), Julia> B(x) |> size # interactions based on one input Either of these may be provided explicitly.Įxamples julia> x, y = randn(Float32, 5, 32), randn(Float32, 5, 32) By default the bias vector is zeros(Float32, out), option bias=false will switch off trainable bias. The initialisation works as for Dense layer, with W = init(out, in1, in2). If the two input sizes are the same, in1 = in2, then you may write Bilinear(in => out, σ). The two inputs may also be provided as a tuple, B((x, y)) = B(x, y), which is accepted as the input to a Chain. If the second input y is not given, it is taken to be equal to x, i.e. If x and y are matrices, then each column of the output z = B(x, y) is of this form, with B the Bilinear layer. Its output, given vectors x & y, is another vector z with, for all i ∈ 1:out: z = σ(x' * W * y + bias) Params(]) source Flux.Bilinear - Typeīilinear((in1, in2) => out, σ=identity bias=true, init=glorot_uniform)Ĭreates a layer which is fully connected between two inputs and the output, and otherwise similar to Dense. Julia> Flux.params(d1) # no trainable bias Julia> d1 = Dense(ones(2, 5), false, tanh) # using provided weight matrixĭense(5 => 2, tanh bias=false) # 10 parameters Julia> d(rand32(5, 6, 4, 64)) |> size # treated as three batch dimensions The weight matrix and/or the bias vector (of length out) may also be provided explicitly. The initialisation of the weight matrix is W = init(out, in), calling the function given to keyword init, with default glorot_uniform. ![]() Keyword bias=false will switch off trainable bias for the layer. The out y will be a vector of length out, or a batch with size(y) = (out, size(x).) The input x should be a vector of length in, or batch of vectors represented as an in × N matrix, or any array with size(x,1) = in. Fully Connected Flux.Dense - Typeĭense(in => out, σ=identity bias=true, init=glorot_uniform)Ĭreate a traditional fully connected layer, whose forward pass is given by: y = σ.(W * x. The section on dataflow layers introduces others like this. It is annotated with which means that params will see the contents, and gpu will move their arrays to the GPU.īy contrast, Chain itself contains no parameters, but connects other layers together. The keyword bias=false will turn this off, i.e. The bias vector is always initialised Flux.zeros32. All make a CPU array, moved later with gpu if desired. That is, init(2,3,4) should create an array of this size. It take an init keyword, which accepts a function acting like rand. Because this broadcast can be fused with other operations, doing so is more efficient than applying the activation function separately. It contains an an activation function, which is broadcasted over the output. These core layers form the foundation of almost all neural networks. If you started at the beginning of the guide, then you have already met the basic Dense layer, and seen Chain for combining layers.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |