build based on 23674b2

latest/models/layers.html

@@ -11,4 +11,4 @@ m(5) == 26
 
 m = Chain(Dense(10, 5), Dense(5, 2))
 x = rand(10)
-m(x) == m[2](m[1](x))</code></pre><p><code>Chain</code> also supports indexing and slicing, e.g. <code>m[2]</code> or <code>m[1:end-1]</code>. <code>m[1:3](x)</code> will calculate the output of the first three layers.</p></div><a class="source-link" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c764b74ebaeded69d6d6d94b18a9ee4b810d8c02/src/layers/basic.jl#L1-L16">source</a></section><section class="docstring"><div class="docstring-header"><a class="docstring-binding" id="Flux.Dense" href="#Flux.Dense"><code>Flux.Dense</code></a> — <span class="docstring-category">Type</span>.</div><div><pre><code class="language-none">Dense(in::Integer, out::Integer, σ = identity)</code></pre><p>Creates a traditional <code>Dense</code> layer with parameters <code>W</code> and <code>b</code>.</p><pre><code class="language-none">y = σ.(W * x .+ b)</code></pre><p>The input <code>x</code> must be a vector of length <code>in</code>, or a batch of vectors represented as an <code>in × N</code> matrix. The out <code>y</code> will be a vector or batch of length <code>in</code>.</p></div><a class="source-link" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/c764b74ebaeded69d6d6d94b18a9ee4b810d8c02/src/layers/basic.jl#L38-L47">source</a></section><footer><hr/><a class="previous" href="recurrence.html"><span class="direction">Previous</span><span class="title">Recurrence</span></a><a class="next" href="../training/optimisers.html"><span class="direction">Next</span><span class="title">Optimisers</span></a></footer></article></body></html>
+m(x) == m[2](m[1](x))</code></pre><p><code>Chain</code> also supports indexing and slicing, e.g. <code>m[2]</code> or <code>m[1:end-1]</code>. <code>m[1:3](x)</code> will calculate the output of the first three layers.</p></div><a class="source-link" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/23674b255556d90256490718eed933036e5615fd/src/layers/basic.jl#L1-L16">source</a></section><section class="docstring"><div class="docstring-header"><a class="docstring-binding" id="Flux.Dense" href="#Flux.Dense"><code>Flux.Dense</code></a> — <span class="docstring-category">Type</span>.</div><div><pre><code class="language-none">Dense(in::Integer, out::Integer, σ = identity)</code></pre><p>Creates a traditional <code>Dense</code> layer with parameters <code>W</code> and <code>b</code>.</p><pre><code class="language-none">y = σ.(W * x .+ b)</code></pre><p>The input <code>x</code> must be a vector of length <code>in</code>, or a batch of vectors represented as an <code>in × N</code> matrix. The out <code>y</code> will be a vector or batch of length <code>in</code>.</p></div><a class="source-link" target="_blank" href="https://github.com/FluxML/Flux.jl/blob/23674b255556d90256490718eed933036e5615fd/src/layers/basic.jl#L38-L47">source</a></section><footer><hr/><a class="previous" href="recurrence.html"><span class="direction">Previous</span><span class="title">Recurrence</span></a><a class="next" href="../training/optimisers.html"><span class="direction">Next</span><span class="title">Optimisers</span></a></footer></article></body></html>

latest/search_index.js

@@ -181,7 +181,7 @@ var documenterSearchIndex = {"docs": [
     "page": "Training",
     "title": "Loss Functions",
     "category": "section",
-    "text": "The loss that we defined in basics is completely valid for training. We can also define a loss in terms of some model:m = Chain(\n  Dense(784, 32, σ),\n  Dense(32, 10), softmax)\n\n# Model loss function\nloss(x, y) = Flux.mse(m(x), y)\n\n# later\nFlux.train!(loss, data, opt)The loss will almost always be defined in terms of some cost function that measures the distance of the prediction m(x) from the target y. Flux has several of these built in, like mse for mean squared error or logloss for cross entropy loss, but you can calculate it however you want."
+    "text": "The loss that we defined in basics is completely valid for training. We can also define a loss in terms of some model:m = Chain(\n  Dense(784, 32, σ),\n  Dense(32, 10), softmax)\n\n# Model loss function\nloss(x, y) = Flux.mse(m(x), y)\n\n# later\nFlux.train!(loss, data, opt)The loss will almost always be defined in terms of some cost function that measures the distance of the prediction m(x) from the target y. Flux has several of these built in, like mse for mean squared error or crossentropy for cross entropy loss, but you can calculate it however you want."
 },
 
 {

latest/training/training.html

@@ -14,7 +14,7 @@ ga('send', 'pageview');
 loss(x, y) = Flux.mse(m(x), y)
 
 # later
-Flux.train!(loss, data, opt)</code></pre><p>The loss will almost always be defined in terms of some <em>cost function</em> that measures the distance of the prediction <code>m(x)</code> from the target <code>y</code>. Flux has several of these built in, like <code>mse</code> for mean squared error or <code>logloss</code> for cross entropy loss, but you can calculate it however you want.</p><h2><a class="nav-anchor" id="Datasets-1" href="#Datasets-1">Datasets</a></h2><p>The <code>data</code> argument provides a collection of data to train with (usually a set of inputs <code>x</code> and target outputs <code>y</code>). For example, here&#39;s a dummy data set with only one data point:</p><pre><code class="language-julia">x = rand(784)
+Flux.train!(loss, data, opt)</code></pre><p>The loss will almost always be defined in terms of some <em>cost function</em> that measures the distance of the prediction <code>m(x)</code> from the target <code>y</code>. Flux has several of these built in, like <code>mse</code> for mean squared error or <code>crossentropy</code> for cross entropy loss, but you can calculate it however you want.</p><h2><a class="nav-anchor" id="Datasets-1" href="#Datasets-1">Datasets</a></h2><p>The <code>data</code> argument provides a collection of data to train with (usually a set of inputs <code>x</code> and target outputs <code>y</code>). For example, here&#39;s a dummy data set with only one data point:</p><pre><code class="language-julia">x = rand(784)
 y = rand(10)
 data = [(x, y)]</code></pre><p><code>Flux.train!</code> will call <code>loss(x, y)</code>, calculate gradients, update the weights and then move on to the next data point if there is one. We can train the model on the same data three times:</p><pre><code class="language-julia">data = [(x, y), (x, y), (x, y)]
 # Or equivalently