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<p>
We just created two separate
<code>Affine</code>
layers, and each contains its own version of
layers, and each contains its own (randomly initialised) version of
<code>W</code>
and
<code>b</code>

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"page": "Model Building Basics",
"title": "The Model",
"category": "section",
"text": "... Initialising Photon Beams ...The core concept in Flux is the model. A model (or \"layer\") is simply a function with parameters. For example, in plain Julia code, we could define the following function to represent a logistic regression (or simple neural network):W = randn(3,5)\nb = randn(3)\naffine(x) = W * x + b\n\nx1 = rand(5) # [0.581466,0.606507,0.981732,0.488618,0.415414]\ny1 = softmax(affine(x1)) # [0.32676,0.0974173,0.575823]affine is simply a function which takes some vector x1 and outputs a new one y1. For example, x1 could be data from an image and y1 could be predictions about the content of that image. However, affine isn't static. It has parameters W and b, and if we tweak those parameters we'll tweak the result – hopefully to make the predictions more accurate.This is all well and good, but we usually want to have more than one affine layer in our network; writing out the above definition to create new sets of parameters every time would quickly become tedious. For that reason, we want to use a template which creates these functions for us:affine1 = Affine(5, 5)\naffine2 = Affine(5, 5)\n\nsoftmax(affine1(x1)) # [0.167952, 0.186325, 0.176683, 0.238571, 0.23047]\nsoftmax(affine2(x1)) # [0.125361, 0.246448, 0.21966, 0.124596, 0.283935]We just created two separate Affine layers, and each contains its own version of W and b, leading to a different result when called with our data. It's easy to define templates like Affine ourselves (see templates), but Flux provides Affine out of the box, so we'll use that for now."
"text": "... Initialising Photon Beams ...The core concept in Flux is the model. A model (or \"layer\") is simply a function with parameters. For example, in plain Julia code, we could define the following function to represent a logistic regression (or simple neural network):W = randn(3,5)\nb = randn(3)\naffine(x) = W * x + b\n\nx1 = rand(5) # [0.581466,0.606507,0.981732,0.488618,0.415414]\ny1 = softmax(affine(x1)) # [0.32676,0.0974173,0.575823]affine is simply a function which takes some vector x1 and outputs a new one y1. For example, x1 could be data from an image and y1 could be predictions about the content of that image. However, affine isn't static. It has parameters W and b, and if we tweak those parameters we'll tweak the result – hopefully to make the predictions more accurate.This is all well and good, but we usually want to have more than one affine layer in our network; writing out the above definition to create new sets of parameters every time would quickly become tedious. For that reason, we want to use a template which creates these functions for us:affine1 = Affine(5, 5)\naffine2 = Affine(5, 5)\n\nsoftmax(affine1(x1)) # [0.167952, 0.186325, 0.176683, 0.238571, 0.23047]\nsoftmax(affine2(x1)) # [0.125361, 0.246448, 0.21966, 0.124596, 0.283935]We just created two separate Affine layers, and each contains its own (randomly initialised) version of W and b, leading to a different result when called with our data. It's easy to define templates like Affine ourselves (see templates), but Flux provides Affine out of the box, so we'll use that for now."
},
{