diff --git a/latest/models/layers.html b/latest/models/layers.html index fd75a068..8f405353 100644 --- a/latest/models/layers.html +++ b/latest/models/layers.html @@ -11,16 +11,16 @@ m(5) == 26 m = Chain(Dense(10, 5), Dense(5, 2)) x = rand(10) -m(x) == m[2](m[1](x))

Chain also supports indexing and slicing, e.g. m[2] or m[1:end-1]. m[1:3](x) will calculate the output of the first three layers.

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Flux.Dense — Type.
Dense(in::Integer, out::Integer, σ = identity)

Creates a traditional Dense layer with parameters W and b.

y = σ.(W * x .+ b)

The input x must be a vector of length in, or a batch of vectors represented as an in × N matrix. The out y will be a vector or batch of length out.

julia> d = Dense(5, 2)
+m(x) == m[2](m[1](x))

Chain also supports indexing and slicing, e.g. m[2] or m[1:end-1]. m[1:3](x) will calculate the output of the first three layers.

source
Flux.Dense — Type.
Dense(in::Integer, out::Integer, σ = identity)

Creates a traditional Dense layer with parameters W and b.

y = σ.(W * x .+ b)

The input x must be a vector of length in, or a batch of vectors represented as an in × N matrix. The out y will be a vector or batch of length out.

julia> d = Dense(5, 2)
 Dense(5, 2)
 
 julia> d(rand(5))
 Tracked 2-element Array{Float64,1}:
   0.00257447
-  -0.00449443
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Recurrent Cells

Much like the core layers above, but can be used to process sequence data (as well as other kinds of structured data).

Flux.RNN — Function.
RNN(in::Integer, out::Integer, σ = tanh)

The most basic recurrent layer; essentially acts as a Dense layer, but with the output fed back into the input each time step.

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Flux.LSTM — Function.
LSTM(in::Integer, out::Integer, σ = tanh)

Long Short Term Memory recurrent layer. Behaves like an RNN but generally exhibits a longer memory span over sequences.

See this article for a good overview of the internals.

source
Flux.Recur — Type.
Recur(cell)

Recur takes a recurrent cell and makes it stateful, managing the hidden state in the background. cell should be a model of the form:

h, y = cell(h, x...)

For example, here's a recurrent network that keeps a running total of its inputs.

accum(h, x) = (h+x, x)
+  -0.00449443
source

Recurrent Cells

Much like the core layers above, but can be used to process sequence data (as well as other kinds of structured data).

Flux.RNN — Function.
RNN(in::Integer, out::Integer, σ = tanh)

The most basic recurrent layer; essentially acts as a Dense layer, but with the output fed back into the input each time step.

source
Flux.LSTM — Function.
LSTM(in::Integer, out::Integer, σ = tanh)

Long Short Term Memory recurrent layer. Behaves like an RNN but generally exhibits a longer memory span over sequences.

See this article for a good overview of the internals.

source
Flux.Recur — Type.
Recur(cell)

Recur takes a recurrent cell and makes it stateful, managing the hidden state in the background. cell should be a model of the form:

h, y = cell(h, x...)

For example, here's a recurrent network that keeps a running total of its inputs.

accum(h, x) = (h+x, x)
 rnn = Flux.Recur(accum, 0)
 rnn(2) # 2
 rnn(3) # 3
 rnn.state # 5
 rnn.(1:10) # apply to a sequence
-rnn.state # 60
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Activation Functions

Non-linearities that go between layers of your model. Most of these functions are defined in NNlib but are available by default in Flux.

Note that, unless otherwise stated, activation functions operate on scalars. To apply them to an array you can call σ.(xs), relu.(xs) and so on.

NNlib.σ — Function.
σ(x) = 1 / (1 + exp(-x))

Classic sigmoid activation function.

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NNlib.relu — Function.
relu(x) = max(0, x)

Rectified Linear Unit activation function.

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NNlib.leakyrelu — Function.
leakyrelu(x) = max(0.01x, x)

Leaky Rectified Linear Unit activation function.

You can also specify the coefficient explicitly, e.g. leakyrelu(x, 0.01).

source
NNlib.elu — Function.
elu(x; α = 1) = x > 0 ? x : α * (exp(x) - one(x)

Exponential Linear Unit activation function. See Fast and Accurate Deep Network Learning by Exponential Linear Units

source
NNlib.swish — Function.
swish(x) = x * σ(x)

Self-gated actvation function.

See Swish: a Self-Gated Activation Function.

source
+rnn.state # 60source

Activation Functions

Non-linearities that go between layers of your model. Most of these functions are defined in NNlib but are available by default in Flux.

Note that, unless otherwise stated, activation functions operate on scalars. To apply them to an array you can call σ.(xs), relu.(xs) and so on.

NNlib.σ — Function.
σ(x) = 1 / (1 + exp(-x))

Classic sigmoid activation function.

source
NNlib.relu — Function.
relu(x) = max(0, x)

Rectified Linear Unit activation function.

source
NNlib.leakyrelu — Function.
leakyrelu(x) = max(0.01x, x)

Leaky Rectified Linear Unit activation function.

You can also specify the coefficient explicitly, e.g. leakyrelu(x, 0.01).

source
NNlib.elu — Function.
elu(x; α = 1) = x > 0 ? x : α * (exp(x) - one(x)

Exponential Linear Unit activation function. See Fast and Accurate Deep Network Learning by Exponential Linear Units

source
NNlib.swish — Function.
swish(x) = x * σ(x)

Self-gated actvation function.

See Swish: a Self-Gated Activation Function.

source
diff --git a/latest/models/recurrence.html b/latest/models/recurrence.html index 9b2eb2d9..100984eb 100644 --- a/latest/models/recurrence.html +++ b/latest/models/recurrence.html @@ -39,4 +39,4 @@ m = Flux.Recur(rnn, h) y = m(x)

The Recur wrapper stores the state between runs in the m.state field.

If you use the RNN(10, 5) constructor – as opposed to RNNCell – you'll see that it's simply a wrapped cell.

julia> RNN(10, 5)
 Recur(RNNCell(Dense(15, 5)))

Sequences

Often we want to work with sequences of inputs, rather than individual xs.

seq = [rand(10) for i = 1:10]

With Recur, applying our model to each element of a sequence is trivial:

m.(seq) # returns a list of 5-element vectors

This works even when we've chain recurrent layers into a larger model.

m = Chain(LSTM(10, 15), Dense(15, 5))
-m.(seq)

Truncating Gradients

By default, calculating the gradients in a recurrent layer involves the entire history. For example, if we call the model on 100 inputs, calling back! will calculate the gradient for those 100 calls. If we then calculate another 10 inputs we have to calculate 110 gradients – this accumulates and quickly becomes expensive.

To avoid this we can truncate the gradient calculation, forgetting the history.

truncate!(m)

Calling truncate! wipes the slate clean, so we can call the model with more inputs without building up an expensive gradient computation.

+m.(seq)

Truncating Gradients

By default, calculating the gradients in a recurrent layer involves the entire history. For example, if we call the model on 100 inputs, calling back! will calculate the gradient for those 100 calls. If we then calculate another 10 inputs we have to calculate 110 gradients – this accumulates and quickly becomes expensive.

To avoid this we can truncate the gradient calculation, forgetting the history.

truncate!(m)

Calling truncate! wipes the slate clean, so we can call the model with more inputs without building up an expensive gradient computation.

truncate! makes sense when you are working with multiple chunks of a large sequence, but we may also want to work with a set of independent sequences. In this case the hidden state should be completely reset to its original value, throwing away any accumulated information. reset! does this for you.

diff --git a/latest/search_index.js b/latest/search_index.js index 8fc1c962..84c05611 100644 --- a/latest/search_index.js +++ b/latest/search_index.js @@ -109,7 +109,7 @@ var documenterSearchIndex = {"docs": [ "page": "Recurrence", "title": "Truncating Gradients", "category": "section", - "text": "By default, calculating the gradients in a recurrent layer involves the entire history. For example, if we call the model on 100 inputs, calling back! will calculate the gradient for those 100 calls. If we then calculate another 10 inputs we have to calculate 110 gradients – this accumulates and quickly becomes expensive.To avoid this we can truncate the gradient calculation, forgetting the history.truncate!(m)Calling truncate! wipes the slate clean, so we can call the model with more inputs without building up an expensive gradient computation." + "text": "By default, calculating the gradients in a recurrent layer involves the entire history. For example, if we call the model on 100 inputs, calling back! will calculate the gradient for those 100 calls. If we then calculate another 10 inputs we have to calculate 110 gradients – this accumulates and quickly becomes expensive.To avoid this we can truncate the gradient calculation, forgetting the history.truncate!(m)Calling truncate! wipes the slate clean, so we can call the model with more inputs without building up an expensive gradient computation.truncate! makes sense when you are working with multiple chunks of a large sequence, but we may also want to work with a set of independent sequences. In this case the hidden state should be completely reset to its original value, throwing away any accumulated information. reset! does this for you." }, {