diff --git a/.gitignore b/.gitignore
index 3af67b1..8ff543c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,3 +2,4 @@
 *.jl.cov
 *.jl.mem
 /docs/build/
+.vscode/*
\ No newline at end of file
diff --git a/Manifest.toml b/Manifest.toml
index f45eecf..9d76cef 100644
--- a/Manifest.toml
+++ b/Manifest.toml
@@ -1,2 +1,412 @@
 # This file is machine-generated - editing it directly is not advised
 
+[[AbstractFFTs]]
+deps = ["LinearAlgebra"]
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+[[Base64]]
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+
+[[BinaryProvider]]
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+
+[[CUDAnative]]
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+[[CuArrays]]
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+[[DiffResults]]
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+[[Distributed]]
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+
+[[ExprTools]]
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+[[FillArrays]]
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+[[FixedPointNumbers]]
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+
+[[Flux]]
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+git-tree-sha1 = "eb5801eea6294851dc2b16b20669f91776e79a3b"
+pinned = true
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+[[FoldingTrees]]
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+
+[[ForwardDiff]]
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+[[Logging]]
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diff --git a/Project.toml b/Project.toml
index 6be9d2a..22070f4 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,12 @@
 name = "GreenFlux"
 uuid = "ccad5352-7643-4eb2-b711-e9c298e87bf0"
 authors = ["Eduardo Cueto Mendoza"]
-version = "0.1.0"
+version = "0.0.4"
+
+[deps]
+CUDAapi = "3895d2a7-ec45-59b8-82bb-cfc6a382f9b3"
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
 julia = "1"
diff --git a/src/GreenFlux.jl b/src/GreenFlux.jl
index aec582f..e01e64e 100644
--- a/src/GreenFlux.jl
+++ b/src/GreenFlux.jl
@@ -1,5 +1,24 @@
 module GreenFlux
 
-# Write your package code here.
+using Flux: Chain, Recur, Dense, Conv, MaxPool, GlobalMaxPool, MeanPool, GlobalMeanPool,
+            DepthwiseConv, ConvTranspose, CrossCor, GRUCell, LSTMCell,RNNCell, Maxout
+using Flux: params, outdims, sigmoid, rrelu, elu, celu, softsign, softplus, tanh, gelu, 
+            hardsigmoid, logsigmoid,swish, selu, softmax, logsoftmax, hardtanh,
+            leakyrelu, relu6, lisht, tanhshrink, logcosh, mish, relu, trelu,
+            softshrink, identity
+using Statistics: mean
+using CUDAapi: has_cuda_gpu 
 
+export avgpowerdraw, modelflops
+
+include("power/powerdraw.jl")
+include("neflops/measureutils.jl")
+include("neflops/layerflops.jl")
+include("neflops/gradientflops.jl")
+include("neflops/modelflops.jl")
+
+function __init__()
+   @info "Finished loading GreenFlux..." 
 end
+
+end # module
diff --git a/src/neflops/gradientflops.jl b/src/neflops/gradientflops.jl
new file mode 100644
index 0000000..5cd2a5b
--- /dev/null
+++ b/src/neflops/gradientflops.jl
@@ -0,0 +1,206 @@
+"""
+    gradientflops(layer, input)::Float64
+
+Calculates the number of non-embedding Floating Point Operations `neFLOPs` for most layers.
+`layer` should be any of the [Flux](https://github.com/FluxML/Flux.jl) model layers except
+`GlobalMaxPool` and `GlobalMeanPool`.
+
+# Example
+```julia
+layer = Conv((2, 2), 1=>16, relu)
+input = (4,4)
+layerflops(Conv((2, 2), 1=>16, relu),(4,4))
+```
+"""
+function gradientflops(layer::Dense, input::Tuple)
+    x = 0; y = 0; Fm = 1
+    if length(input) == 3
+        x,y,Fn = input
+        xo, yo = outdims(layer,(x,y))
+    elseif length(input) == 2
+        x,y,_ = input
+        xo, yo = outdims(layer,(x,y))
+    else
+        error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
+    end
+    bi = length(layer.b)
+    noofoper = gradientoperations(layer)
+    return convert(Float64, ((xo*yo+bi)*noofoper)*Fm), (xo,yo)
+end
+
+function gradientflops(layer::Maxout,input::Tuple)
+    x = 0; y = 0; Fm = 1
+    if length(input) == 3
+        x,y,Fm = input
+        xo, yo = outdims(layer,(x,y))
+    elseif length(input) == 2
+        x,y,_ = input
+        xo, yo = outdims(layer,(x,y))
+    else
+        error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
+    end
+    a,b = layer.over
+    return convert(Float64,xo*yo*Fm), (x,1)
+end
+
+
+function gradientflops(layer::Conv, input::Tuple)
+    _,_,_,outputfeaturemaps = size(layer.weight)
+    x = 0; y = 0; Fm = 1
+    if length(input) == 3
+        x,y,Fm = input
+        xo, yo = outdims(layer,(x,y))
+    elseif length(input) == 2
+        x,y,_ = input
+        xo, yo = outdims(layer,(x,y))
+    else
+        error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
+    end
+    noofoper = gradientoperations(layer)
+    if size(layer.bias) == ()
+        return convert(Float64,xo*yo*noofoper*Fm), (xo,yo,outputfeaturemaps)
+    else
+        return convert(Float64,((xo*yo+size(layer.bias))*noofoper)*Fm), (xo,yo,outputfeaturemaps)
+    end
+end
+
+function gradientflops(layer::MaxPool, input::Tuple)
+    kernelsizex,kernelsizey = layer.k
+    x = 0; y = 0; Fm = 1
+    if length(input) == 3
+        x,y,Fm = input
+        xo, yo = outdims(layer,(x,y))
+    elseif length(input) == 2
+        x,y,_ = input
+        xo, yo = outdims(layer,(x,y))
+    else
+        error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
+    end
+    outx, outy = outdims(layer,(x,y))
+    return convert(Float64,outx*outy*Fm), (outx,outy)
+end
+
+gradientflops(layer::GlobalMaxPool, input::Tuple) = error("Must be implemented in a future release")
+
+function gradientflops(layer::MeanPool, input::Tuple)
+    kernelsizex,kernelsizey = layer.k
+    x = 0; y = 0; Fm = 1
+    if length(input) == 3
+        x,y,Fm = input
+        xo, yo = outdims(layer,(x,y))
+    elseif length(input) == 2
+        x,y,_ = input
+        xo, yo = outdims(layer,(x,y))
+    else
+        error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
+    end
+    outx, outy = outdims(layer,(x,y))
+    return convert(Float64,outx*outy*Fm), (outx,outy)
+end
+
+gradientflops(layer::GlobalMeanPool, input::Tuple) = error("Must be implemented in a future release")
+
+function gradientflops(layer::DepthwiseConv, input::Tuple)
+    kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
+    x = 0; y = 0; Fm = 1
+    if length(input) == 3
+        x,y,Fm = input
+        xo, yo = outdims(layer,(x,y))
+    elseif length(input) == 2
+        x,y,_ = input
+        xo, yo = outdims(layer,(x,y))
+    else
+        error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
+    end
+    outx, outy = outdims(layer,(x,y))
+    noofoper = gradientoperations(layer)
+    if size(layer.bias) == ()
+        return convert(Float64,kernelsizex*kernelsizey*noofoper*Fm), (outx,outy)
+    else
+        return convert(Float64,kernelsizex*kernelsizey*noofoper*Fm + (length(layer.bias)-1)), (outx,outy)
+    end
+end
+
+function gradientflops(layer::ConvTranspose, input::Tuple)
+    kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
+    x = 0; y = 0; Fm = 1
+    if length(input) == 3
+        x,y,Fm = input
+        xo, yo = outdims(layer,(x,y))
+    elseif length(input) == 2
+        x,y,_ = input
+        xo, yo = outdims(layer,(x,y))
+    else
+        error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
+    end
+    outx, outy = outdims(layer,(x,y))
+    noofoper = gradientoperations(layer)
+    if size(layer.bias) == ()
+        return convert(Float64,kernelsizex*kernelsizey*noofoper*Fm), (outx,outy)
+    else
+        return convert(Float64,kernelsizex*kernelsizey*noofoper*Fm + (length(layer.bias)-1)), (outx,outy)
+    end
+end
+
+function gradientflops(layer::CrossCor, input::Tuple)
+    kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
+    x = 0; y = 0; Fm = 1
+    if length(input) == 3
+        x,y,Fm = input
+        xo, yo = outdims(layer,(x,y))
+    elseif length(input) == 2
+        x,y,_ = input
+        xo, yo = outdims(layer,(x,y))
+    else
+        error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
+    end
+    outx, outy = outdims(layer,(x,y))
+    noofoper = gradientoperations(layer)
+    if size(layer.bias) == ()
+        return convert(Float64,kernelsizex*kernelsizey*noofoper*Fm), (outx,outy)
+    else
+        return convert(Float64,kernelsizex*kernelsizey*noofoper*Fm + (length(layer.bias)-1)), (outx,outy)
+    end
+end
+
+function gradientflops(layer::Recur{T}, input::Tuple) where {T <: RNNCell}
+    inM,inN = input
+    WhM,WhN = size(layer.cell.Wh)
+    WiM,WiN = size(layer.cell.Wi)
+    hM = length(layer.cell.h)
+    noofoper = activationoperations(layer)
+    if size(layer.cell.b) == ()
+        return convert(Float64,((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM)*noofoper)), (hM,1)
+    else
+        bM = length(layer.cell.b)
+        return convert(Float64,((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + bM)*noofoper)), (bM,1)
+    end
+end
+
+function gradientflops(layer::Recur{T}, input::Tuple) where {T <: LSTMCell}
+    inM,inN = input
+    WhM,WhN = size(layer.cell.Wh)
+    WiM,WiN = size(layer.cell.Wi)
+    hM = length(layer.cell.h)
+    noofoper = 3
+    if size(layer.cell.b) == ()
+        return convert(Float64,((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+(3*hM)+((hM*noofoper)+hM)), (hM,1)
+    else
+        bM = length(layer.cell.b)
+        return convert(Float64,((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+(3*bM)+((bM*noofoper)+bM)), (bM,1)
+    end
+end
+
+function gradientflops(layer::Recur{T}, input::Tuple) where {T <: GRUCell}
+    inM,inN = input
+    WhM,WhN = size(layer.cell.Wh)
+    WiM,WiN = size(layer.cell.Wi)
+    hM = length(layer.cell.h)
+    noofoper = 3
+    if size(layer.cell.b) == ()
+        return convert(Float64,((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+(3*hM)+((hM*noofoper)+hM)), (hM,1)
+    else
+        bM = length(layer.cell.b)
+        return convert(Float64,((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*(WiN+hM)-inM + 2*bM)*noofoper)+(4bM)), (bM,1)
+    end
+end
diff --git a/src/neflops/layerflops.jl b/src/neflops/layerflops.jl
new file mode 100644
index 0000000..46bfd6f
--- /dev/null
+++ b/src/neflops/layerflops.jl
@@ -0,0 +1,223 @@
+"""
+    layerflops(layer, input)::Float64
+
+Calculates the number of non-embedding Floating Point Operations `neFLOPs` for most layers.
+`layer` should be any of the [Flux](https://github.com/FluxML/Flux.jl) model layers except
+`GlobalMaxPool` and `GlobalMeanPool`.
+
+# Example
+```julia
+layer = Conv((2, 2), 1=>16, relu)
+input = (4,4)
+layerflops(Conv((2, 2), 1=>16, relu),(4,4))
+```
+
+"""
+function layerflops(layer::Dense,input::Tuple)
+    N,M = size(layer.W)
+    Mi = 0; Ni = 0; Fm = 1; out = 0
+    if length(input) == 3
+        Mi,Ni,Fm = input
+        out = outdims(layer,(Mi,Ni))
+    elseif length(input) == 2
+        Mi,Ni = input
+        Fm = 1
+        out = outdims(layer,input)
+    else
+        error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
+    end
+    bi = length(layer.b)
+    if length(out) < 2
+        out = (out[1],1)
+    end
+    noofopers = activationoperations(layer)
+    return convert(Float64,((2*Mi*N - M)+bi)*noofopers*Fm), out
+end
+
+function layerflops(layer::Maxout,input::Tuple)
+    i = 0; j = 0; Fm = 1
+    if length(input) == 3
+        i,j,Fm = input
+    elseif length(input) == 2
+        i,j = input
+        Fm = 1
+    else
+        error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
+    end
+    a,b = layer.over
+    return convert(Float64,a*b*j*i*Fm), (j,1)
+end
+
+
+function layerflops(layer::Conv, input::Tuple)
+    kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
+    x = 0; y = 0
+    if length(input) == 3
+        x, y, _ = input
+    elseif length(input) == 2
+        x, y = input
+    else
+        error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
+    end
+    outx, outy = outdims(layer,(x,y))
+    noofoper = activationoperations(layer)
+    if size(layer.bias) == ()
+        return convert(Float64,inputfeaturemaps*(kernelsizex*kernelsizey+(kernelsizex*kernelsizey-1))*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
+    else
+        return convert(Float64,(2*kernelsizex*kernelsizey)*inputfeaturemaps*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
+    end
+end
+
+function layerflops(layer::MaxPool, input::Tuple)
+    Fm = 1
+    if length(input) == 3
+        _,_,Fm = input
+    elseif length(input) == 2
+        _,_ = input
+        Fm = 1
+    else
+        error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
+    end
+    kernelsizex,kernelsizey = layer.k
+    outx, outy = outdims(layer,input)
+    return convert(Float64,kernelsizex*kernelsizey*outx*outy*Fm), (outx,outy)
+end
+
+layerflops(layer::GlobalMaxPool, input::Tuple) = error("Must be implemented in a future release")
+
+function layerflops(layer::MeanPool, input::Tuple)
+    Fm = 1
+    if length(input) == 3
+        _,_,Fm = input
+    elseif length(input) == 2
+        _,_ = input
+        Fm = 1
+    else
+        error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
+    end
+    kernelsizex,kernelsizey = layer.k
+    outx, outy = outdims(layer,input)
+    return convert(Float64,kernelsizex*kernelsizey*outx*outy*Fm), (outx,outy)
+end
+
+layerflops(layer::GlobalMeanPool, input::Tuple) = error("Must be implemented in a future release")
+
+function layerflops(layer::DepthwiseConv, input::Tuple)
+    kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
+    x = 0; y = 0
+    if length(input) == 3
+        x, y, _ = input
+    elseif length(input) == 2
+        x, y = input
+    else
+        error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
+    end
+    outx, outy = outdims(layer,(x,y))
+    noofoper = activationoperations(layer)
+    if size(layer.bias) == ()
+        return convert(Float64,inputfeaturemaps*(kernelsizex*kernelsizey+(kernelsizex*kernelsizey-1))*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
+    else
+        return convert(Float64,(2*kernelsizex*kernelsizey)*inputfeaturemaps*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
+    end
+end
+
+function layerflops(layer::ConvTranspose, input::Tuple)
+    kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
+    x = 0; y = 0
+    if length(input) == 3
+        x, y, _ = input
+    elseif length(input) == 2
+        x, y = input
+    else
+        error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
+    end
+    outx, outy = outdims(layer,(x,y))
+    noofoper = activationoperations(layer)
+    if size(layer.bias) == ()
+        return convert(Float64,inputfeaturemaps*(kernelsizex*kernelsizey+(kernelsizex*kernelsizey-1))*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
+    else
+        return convert(Float64,(2*kernelsizex*kernelsizey)*inputfeaturemaps*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
+    end
+end
+
+function layerflops(layer::CrossCor, input::Tuple)
+    kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
+    x = 0; y = 0
+    if length(input) == 3
+        x, y, _ = input
+    elseif length(input) == 2
+        x, y = input
+    else
+        error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
+    end
+    outx, outy = outdims(layer,(x,y))
+    noofoper = activationoperations(layer)
+    if size(layer.bias) == ()
+        return convert(Float64,inputfeaturemaps*(kernelsizex*kernelsizey+(kernelsizex*kernelsizey-1))*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
+    else
+        return convert(Float64,(2*kernelsizex*kernelsizey)*inputfeaturemaps*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
+    end
+end
+
+function layerflops(layer::Recur{T}, input::Tuple) where {T <: RNNCell}
+    inM = 0; inN = 0; Fm = 1
+    if length(input) == 3
+        inM,inN, Fm = input
+    elseif length(input) == 2
+        inM,inN = input
+    else
+        error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
+    end
+    WhM,WhN = size(layer.cell.Wh)
+    WiM,WiN = size(layer.cell.Wi)
+    hM = length(layer.cell.h)
+    noofoper = activationoperations(layer)
+    if size(layer.cell.b) == ()
+        return convert(Float64,(((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM)*noofoper))*Fm), (hM,1)
+    else
+        bM = length(layer.cell.b)
+        return convert(Float64,(((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + bM)*noofoper))*Fm), (bM,1)
+    end
+end
+
+function layerflops(layer::Recur{T}, input::Tuple) where {T <: LSTMCell}
+    inM = 0; inN = 0; Fm = 1
+    if length(input) == 3
+        inM,inN, Fm = input
+    elseif length(input) == 2
+        inM,inN = input
+    else
+        error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
+    end
+    WhM,WhN = size(layer.cell.Wh)
+    WiM,WiN = size(layer.cell.Wi)
+    hM = length(layer.cell.h)
+    noofoper = 3
+    if size(layer.cell.b) == ()
+        return convert(Float64,(((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+(3*hM)+((hM*noofoper)+hM))*Fm), (hM,1)
+    else
+        bM = length(layer.cell.b)
+        return convert(Float64,(((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+(3*bM)+((bM*noofoper)+bM))*Fm), (bM,1)
+    end
+end
+
+function layerflops(layer::Recur{T}, input::Tuple) where {T <: GRUCell}
+    inM = 0; inN = 0; Fm = 1
+    if length(input) == 3
+        inM,inN, Fm = input
+    elseif length(input) == 2
+        inM,inN = input
+    else
+        error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
+    end
+    WhM,WhN = size(layer.cell.Wh)
+    WiM,WiN = size(layer.cell.Wi)
+    hM = length(layer.cell.h)
+    noofoper = 3
+    if size(layer.cell.b) == ()
+        return convert(Float64,(((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+(3*hM)+((hM*noofoper)+hM))*Fm), (hM,1)
+    else
+        bM = length(layer.cell.b)
+        return convert(Float64,(((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*(WiN+hM)-inM + 2*bM)*noofoper)+(4bM))*Fm), (bM,1)
+    end
+end
diff --git a/src/neflops/measureutils.jl b/src/neflops/measureutils.jl
new file mode 100644
index 0000000..6273319
--- /dev/null
+++ b/src/neflops/measureutils.jl
@@ -0,0 +1,149 @@
+"""
+    activationoperations(layer)::Int
+    
+Outputs the approximate mumber of operations for a Flux activation function.
+
+```julia
+layer = Conv(weight = weight,
+    σ = sigmoid)
+
+activationoperations(Conv(weight = weight,
+    σ = sigmoid))
+```
+"""
+function activationoperations(layer::Recur)
+    layer = recurrentunpack(layer)
+    activation = layer.σ
+    if activation == sigmoid || activation == rrelu || activation == elu || activation == celu || 
+        activation == softsign || activation == softplus || activation == tanh
+        noofoper = 3 
+    elseif activation == gelu
+        noofoper = 8
+    elseif activation == hardsigmoid || activation == logsigmoid || activation == swish ||
+            activation == selu || activation == softmax || activation == logsoftmax
+        noofoper = 4
+    elseif activation == hardtanh || activation == leakyrelu || activation == relu6 || 
+            activation == lisht || activation == tanhshrink
+        noofoper = 2
+    elseif activation == logcosh || activation == mish
+        noofoper = 5
+    elseif activation == relu || activation == trelu || activation == softshrink
+        noofoper = 1
+    elseif activation == identity
+        noofoper = 0
+    else
+        @info "Unkown activation type defaulting to identity"
+        return 0
+    end
+    return noofoper
+end
+
+
+function activationoperations(layer)
+    activation = layer.σ
+    if activation == sigmoid || activation == rrelu || activation == elu || activation == celu || 
+        activation == softsign || activation == softplus || activation == tanh
+        noofoper = 4 
+    elseif activation == gelu
+        noofoper = 9
+    elseif activation == hardsigmoid || activation == logsigmoid || activation == swish ||
+            activation == selu || activation == softmax || activation == logsoftmax
+        noofoper = 5
+    elseif activation == hardtanh || activation == leakyrelu || activation == relu6 || 
+            activation == lisht || activation == tanhshrink
+        noofoper = 3
+    elseif activation == logcosh || activation == mish
+        noofoper = 6
+    elseif activation == relu || activation == trelu || activation == softshrink
+        noofoper = 2
+    elseif activation == identity
+        noofoper = 1
+    else
+        @info "Unkown activation type defaulting to identity"
+        return 1
+    end
+    return noofoper
+end
+
+"""
+    gradientoperations(layer)::Int
+    
+Outputs the approximate mumber of operations for the gradient of a Flux activation 
+function.
+
+```julia
+layer = Conv(weight = weight,
+    σ = sigmoid)
+
+gradientoperations(Conv(weight = weight,
+    σ = sigmoid))
+```
+"""
+function gradientoperations(layer::Recur)
+    layer = recurrentunpack(layer)
+    activation = layer.σ
+    if activation == sigmoid || activation == rrelu || activation == elu || activation == celu || 
+        activation == softsign || activation == softplus || activation == tanh
+        noofoper = 4 
+    elseif activation == gelu
+        noofoper = 8
+    elseif activation == hardsigmoid || activation == logsigmoid || activation == swish ||
+            activation == selu || activation == softmax || activation == logsoftmax
+        noofoper = 4
+    elseif activation == hardtanh || activation == leakyrelu || activation == relu6 || 
+            activation == lisht || activation == tanhshrink
+        noofoper = 2
+    elseif activation == logcosh || activation == mish
+        noofoper = 5
+    elseif activation == relu || activation == trelu || activation == softshrink
+        noofoper = 1
+    elseif activation == identity
+        noofoper = 0
+    else
+        @info "Unkown activation type defaulting to identity"
+        return 1
+    end
+    return noofoper
+end
+
+function gradientoperations(layer)
+    activation = layer.σ
+    if activation == sigmoid || activation == rrelu || activation == elu || activation == celu || 
+        activation == softsign || activation == softplus || activation == tanh
+        noofoper = 4 
+    elseif activation == gelu
+        noofoper = 8
+    elseif activation == hardsigmoid || activation == logsigmoid || activation == swish ||
+            activation == selu || activation == softmax || activation == logsoftmax
+        noofoper = 4
+    elseif activation == hardtanh || activation == leakyrelu || activation == relu6 || 
+            activation == lisht || activation == tanhshrink
+        noofoper = 2
+    elseif activation == logcosh || activation == mish
+        noofoper = 5
+    elseif activation == relu || activation == trelu || activation == softshrink
+        noofoper = 1
+    elseif activation == identity
+        noofoper = 0
+    else
+        @info "Unkown activation type defaulting to identity"
+        return 1
+    end
+    return noofoper
+end
+
+function recurrentunpack(layer::Recur)
+    return layer.cell
+end
+
+islayer(::Any) = false
+islayer(::Recur) = true
+islayer(::Dense) = true
+islayer(::Conv) = true
+islayer(::MaxPool) = true
+islayer(::MeanPool) = true
+islayer(::DepthwiseConv) = true
+islayer(::ConvTranspose) = true
+islayer(::CrossCor) = true
+islayer(::Maxout) = true
+             
\ No newline at end of file
diff --git a/src/neflops/modelflops.jl b/src/neflops/modelflops.jl
new file mode 100644
index 0000000..9f04fb4
--- /dev/null
+++ b/src/neflops/modelflops.jl
@@ -0,0 +1,44 @@
+"""
+    modelflops(model::Chain)::Float64
+    
+Calculates the approximate number of Floating Point Operations that the model will require
+
+
+```julia
+weight = rand(Float64, 3, 3, 5)
+bias = zeros(Float64, 5)
+Conv(weight = weight,
+    bias = bias,
+    σ = sigmoid)
+```
+"""
+function modelflops(model::Chain,inputsize::Tuple,samplesize::Float64,batchsize::Float64)
+    x = 0; y = 0; Fm = 1
+    if length(inputsize) == 3
+        x,y,Fm = inputsize
+    elseif length(inputsize) == 2
+        x,y = inputsize
+    end
+    modellayers = collect(model)
+    nelayers = Array{Any,1}()
+    layeroutput = Array{Tuple,1}()
+    outsizes = Array{Tuple,1}()
+    lossandgradient = 1
+    for ml in modellayers
+        if islayer(ml)
+            push!(nelayers,ml)
+        end
+    end
+    output = (0,0)
+    for mli in 1:length(nelayers)
+        if mli == 1
+            noflops, output = layerflops(nelayers[mli],inputsize)
+            layeroutput = vcat(layeroutput, noflops)
+        else
+            noflops, output = layerflops(nelayers[mli],output)
+            layeroutput = vcat(layeroutput, noflops)
+        end
+    end
+    numberoflayers = length(layeroutput)
+    return sum(layeroutput) * samplesize + lossandgradient*batchsize
+end
diff --git a/src/power/powerdraw.jl b/src/power/powerdraw.jl
new file mode 100644
index 0000000..c97fe96
--- /dev/null
+++ b/src/power/powerdraw.jl
@@ -0,0 +1,138 @@
+"""
+    gpupowerdraw()::Float64
+
+The function uses Linux `nvidia-smi` package to sample and get the average electricity
+draw of the GPUs.
+"""
+function gpupowerdraw()
+    if has_cuda_gpu()
+        gpucommand = `nvidia-smi`
+        usage = Array{Any}(undef,60)
+        cap = Array{Any}(undef,60)
+        nogpus = 0
+
+        for count in 1:60
+            smis = Array{Any}[]
+            smiss = Array{Any}[]
+            gpus = Array{Any}[]
+            powerdraw = Array{Float64}[]
+            powercap = Array{Float64}[]
+
+            smi = read(gpucommand, String);
+            smi = split(smi, "\n")
+            for s in smi
+                push!(smis,split(s, " "))
+            end
+            for s in smis
+                push!(smiss,filter(x->x≠"",s))
+            end
+            for strings in smiss
+                if length(strings) > 5 && strings[6] == "/" && strings[10] == "/"
+                    push!(gpus,strings)
+                end
+            end
+            
+            nogpus = length(gpus)
+             
+            for g in gpus
+                usagestr = ""
+                capstr = ""
+                if g[5] == "N/A"
+                    usagestr = "0.0"
+                else
+                    usagestr = usagestr * g[5]
+                end
+                if g[7] == "N/A"
+                    capstr = "0.0"
+                else
+                    capstr = capstr * g[7]
+                end
+                powerdraw = vcat(powerdraw, parse(Float64,usagestr))
+                powercap = vcat(powercap, parse(Float64,capstr))
+            end
+            usage[count] = mean(powerdraw)
+            cap[count] = mean(powercap)
+            
+            sleep(1)
+        end
+        return nogpus, mean(usage), mean(cap)
+    else
+        @info "This computer does not have acces to a GPU passing to CPU and RAM computations"
+    end
+end
+
+
+"""
+    cpupowerdraw()::Float64
+
+This function uses the Linux `powerstat` utility to get the average CPU energy cost.
+"""
+function cpupowerdraw()
+    cpucommand = `powerstat -R -n -d0`
+    try
+        cpu = read(cpucommand, String);
+        cpu = split(cpu,"\n")
+        cpu = cpu[66][60:64]
+        
+        return parse(Float64,cpu)
+    catch e
+        @info "powerstat not installed in your computer"
+    end
+end
+
+
+#TODO: further fine tune the model
+"""
+    rampowerdraw()::Float64
+
+[Approximate RAM Power Draw](https://www.jedec.org/) the values are provided by the JEDEC we just take the 
+ratio of activated memory against the unactivated for the maximum power value and convert it 
+to hours.
+"""
+function rampowerdraw()
+    ramcommand = `free`
+    powerused = Array{Float64}(undef,60)
+    for count in 1:60
+        ram = read(ramcommand, String);
+        ram = split(ram,"\n")
+        ram = split(ram[2]," ")
+        filter!(x->x≠"",ram)
+        usedram = parse(Float64,ram[3])
+        totalram = parse(Float64,ram[2])
+        powerused[count] = ((usedram*1.575)/totalram)*1.904
+        sleep(1)
+    end
+    return mean(powerused)
+end
+
+
+#TODO: modify the code to work in Windows.
+"""
+    avgpowerdraw()::Float64
+
+[Average Power Draw](https://arxiv.org/abs/1906.02243) where `pc` is the average power
+draw (in watts) from all CPU sockets during training, let `pr` be the average power draw from all
+DRAM (main memory) sockets, let `pg` be the average power draw of the GPUs during training and `g` 
+the number of available gpus.
+
+`apd = 1.58*t*(pc + pr + g*pg)/1000`
+
+returns the average power consumption in kWh.
+"""
+function avgpowerdraw()
+    if has_cuda_gpu()
+        starttime = time()
+        g, pg, _ = gpupowerdraw()
+        pc = cpupowerdraw()
+        pr = rampowerdraw() 
+        endtime = time()
+        elapsedtime = (endtime - starttime)/3600
+        return 1.58*elapsedtime*(pc + pr + g*pg)/1000    
+    else
+        pc = cpupowerdraw()
+        pr = rampowerdraw() 
+        endtime = time()
+        elapsedtime = (endtime - starttime)/3600
+        return 1.58*elapsedtime*(pc + pr)/1000   
+    end
+end