Given:
let weights = [0.5;0.4;0.3]
let X = [[2;3;4];[7;3;2];[5;3;6]]
what I want is:
wX = [(0.5)*[2;3;4];(0.4)*[7;3;2];(0.3)*[5;3;6]]
would like to know an elegant way to do this with lists as well as with arrays. Additional optimization information is welcome
You write about a list of lists, but your code shows a list of tuples. Taking the liberty to adjust for that, a solution would be
let weights = [0.5;0.4;0.3]
let X = [[2;3;4];[7;3;2];[5;3;6]]
X
|> List.map2 (fun w x ->
x
|> List.map (fun xi ->
(float xi) * w
)
) weights
Depending on how comfortable you are with the syntax, you may prefer a oneliner like
List.map2 (fun w x -> List.map (float >> (*) w) x) weights X
The same library functions exist for sequences (Seq.map2, Seq.map) and arrays (in the Array module).
This is much more than an answer to the specific question but after a chat in the comments and learning that the question was specifically a part of a neural network in F# I am posting this which covers the question and implements the feedforward part of a neural network. It makes use of MathNet Numerics
This code is an F# translation of part of the Python code from Neural Networks and Deep Learning.
def backprop(self, x, y):
"""Return a tuple ``(nabla_b, nabla_w)`` representing the
gradient for the cost function C_x. ``nabla_b`` and
``nabla_w`` are layer-by-layer lists of numpy arrays, similar
to ``self.biases`` and ``self.weights``."""
nabla_b = [np.zeros(b.shape) for b in self.biases]
nabla_w = [np.zeros(w.shape) for w in self.weights]
# feedforward
activation = x
activations = [x] # list to store all the activations, layer by layer
zs = [] # list to store all the z vectors, layer by layer
for b, w in zip(self.biases, self.weights):
z = np.dot(w, activation)+b
zs.append(z)
activation = sigmoid(z)
activations.append(activation)
module NeuralNetwork1 =
//# Third-party libraries
open MathNet.Numerics.Distributions // Normal.Sample
open MathNet.Numerics.LinearAlgebra // Matrix
type Network(sizes : int array) =
let mutable (_biases : Matrix<double> list) = []
let mutable (_weights : Matrix<double> list) = []
member __.Biases
with get() = _biases
and set value =
_biases <- value
member __.Weights
with get() = _weights
and set value =
_weights <- value
member __.Backprop (x : Matrix<double>) (y : Matrix<double>) =
// Note: There is a separate member for feedforward. This one is only used within Backprop
// Note: In the text layers are numbered from 1 to n with 1 being the input and n being the output
// In the code layers are numbered from 0 to n-1 with 0 being the input and n-1 being the output
// Layers
// 1 2 3 Text
// 0 1 2 Code
// 784 -> 30 -> 10
let feedforward () : (Matrix<double> list * Matrix<double> list) =
let (bw : (Matrix<double> * Matrix<double>) list) = List.zip __.Biases __.Weights
let rec feedfowardInner layer activation zs activations =
match layer with
| x when x < (__.NumLayers - 1) ->
let (bias, weight) = bw.[layer]
let z = weight * activation + bias
let activation = __.Sigmoid z
feedfowardInner (layer + 1) activation (z :: zs) (activation :: activations)
| _ ->
// Normally with recursive functions that build list for returning
// the final list(s) would be reversed before returning.
// However since the returned list will be accessed in reverse order
// for the backpropagation step, we leave them in the reverse order.
(zs, activations)
feedfowardInner 0 x [] [x]
In weight * activation * is an overloaded operator operating on Matrix<double>
Related back to your example data and using MathNet Numerics Arithmetics
let weights = [0.5;0.4;0.3]
let X = [[2;3;4];[7;3;2];[5;3;6]]
first the values for X need to be converted to float
let x1 = [[2.0;3.0;4.0];[7.0;3.0;2.0];[5.0;3;0;6;0]]
Now notice that x1 is a matrix and weights is a vector
so we can just multiply
let wx1 = weights * x1
Since the way I validated the code was a bit more than most I will explain it so that you don't have doubts to its validity.
When working with Neural Networks and in particular mini-batches, the starting numbers for the weights and biases are random and the generation of the mini-batches is also done randomly.
I know the original Python code was valid and I was able to run it successfully and get the same results as indicated in the book, meaning that the initial successes were within a couple of percent of the book and the graphs of the success were the same. I did this for several runs and several configurations of the neural network as discussed in the book. Then I ran the F# code and achieved the same graphs.
I also copied the starting random number sets from the Python code into the F# code so that while the data generated was random, both the Python and F# code used the same starting numbers, of which there are thousands. I then single stepped both the Python and F# code to verify that each individual function was returning a comparable float value, e.g. I put a break point on each line and made sure I checked each one. This actually took a few days because I had to write export and import code and massage the data from Python to F#.
See: How to determine type of nested data structures in Python?
I also tried a variation where I replaced the F# list with Linked list, but found no increase in speed, e.g. LinkedList<Matrix<double>>. Was an interesting exercise.
If I understand correctly,
let wX = weights |> List.map (fun w ->
X |> List.map (fun (a, b, c) ->
w * float a,
w * float b,
w * float c))
This is an alternate way to achieve this using Math.Net: https://numerics.mathdotnet.com/Matrix.html#Arithmetics
