-You now know understand enough to take a look at the +You now know enough to take a look at the logistic regression diff --git a/latest/models/debugging.html b/latest/models/debugging.html index 4d9d063e..38c86d4b 100644 --- a/latest/models/debugging.html +++ b/latest/models/debugging.html @@ -107,7 +107,7 @@ Debugging
- + diff --git a/latest/models/recurrent.html b/latest/models/recurrent.html index 799ceceb..69ba9a8f 100644 --- a/latest/models/recurrent.html +++ b/latest/models/recurrent.html @@ -107,7 +107,7 @@ Recurrence - + diff --git a/latest/search_index.js b/latest/search_index.js index f742e47e..418705b5 100644 --- a/latest/search_index.js +++ b/latest/search_index.js @@ -53,7 +53,7 @@ var documenterSearchIndex = {"docs": [ "page": "First Steps", "title": "Combining Models", "category": "section", - "text": "... Inflating Graviton Zeppelins ...A more complex model usually involves many basic layers like affine, where we use the output of one layer as the input to the next:mymodel1(x) = softmax(affine2(σ(affine1(x))))\nmymodel1(x1) # [0.187935, 0.232237, 0.169824, 0.230589, 0.179414]This syntax is again a little unwieldy for larger networks, so Flux provides another template of sorts to create the function for us:mymodel2 = Chain(affine1, σ, affine2, softmax)\nmymodel2(x2) # [0.187935, 0.232237, 0.169824, 0.230589, 0.179414]mymodel2 is exactly equivalent to mymodel1 because it simply calls the provided functions in sequence. We don't have to predefine the affine layers and can also write this as:mymodel3 = Chain(\n Affine(5, 5), σ,\n Affine(5, 5), softmax)You now know understand enough to take a look at the logistic regression example, if you haven't already." + "text": "... Inflating Graviton Zeppelins ...A more complex model usually involves many basic layers like affine, where we use the output of one layer as the input to the next:mymodel1(x) = softmax(affine2(σ(affine1(x))))\nmymodel1(x1) # [0.187935, 0.232237, 0.169824, 0.230589, 0.179414]This syntax is again a little unwieldy for larger networks, so Flux provides another template of sorts to create the function for us:mymodel2 = Chain(affine1, σ, affine2, softmax)\nmymodel2(x2) # [0.187935, 0.232237, 0.169824, 0.230589, 0.179414]mymodel2 is exactly equivalent to mymodel1 because it simply calls the provided functions in sequence. We don't have to predefine the affine layers and can also write this as:mymodel3 = Chain(\n Affine(5, 5), σ,\n Affine(5, 5), softmax)You now know enough to take a look at the logistic regression example, if you haven't already." }, {