You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/10/16 20:53:41 UTC
[GitHub] nswamy closed pull request #12759: Scala Docs - Replace old Symbol
api usages
nswamy closed pull request #12759: Scala Docs - Replace old Symbol api usages
URL: https://github.com/apache/incubator-mxnet/pull/12759
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):
diff --git a/docs/api/scala/model.md b/docs/api/scala/model.md
index ed452cc763e..3e09d15218b 100644
--- a/docs/api/scala/model.md
+++ b/docs/api/scala/model.md
@@ -21,10 +21,10 @@ The following example creates a two-layer neural network.
```scala
// configure a two layer neuralnetwork
val data = Symbol.Variable("data")
- val fc1 = Symbol.FullyConnected(name = "fc1")()(Map("data" -> data, "num_hidden" -> 128))
- val act1 = Symbol.Activation(name = "relu1")()(Map("data" -> fc1, "act_type" -> "relu"))
- val fc2 = Symbol.FullyConnected(name = "fc2")()(Map("data" -> act1, "num_hidden" -> 64))
- val softmax = Symbol.SoftmaxOutput(name = "sm")()(Map("data" -> fc2))
+ val fc1 = Symbol.api.FullyConnected(data, num_hidden = 128, name = "fc1")
+ val act1 = Symbol.api.Activation(Some(fc1), "relu", "relu1")
+ val fc2 = Symbol.api.FullyConnected(Some(act1), num_hidden = 64, name = "fc2")
+ val softmax = Symbol.api.SoftmaxOutput(Some(fc2), name = "sm")
// Construct the FeedForward model and fit on the input training data
val model = FeedForward.newBuilder(softmax)
diff --git a/docs/api/scala/module.md b/docs/api/scala/module.md
index daf2d9747c4..06487f734a3 100644
--- a/docs/api/scala/module.md
+++ b/docs/api/scala/module.md
@@ -12,12 +12,12 @@ To construct a module, refer to the constructors for the module class. For examp
// construct a simple MLP
val data = Symbol.Variable("data")
- val fc1 = Symbol.FullyConnected(name = "fc1")(data)(Map("num_hidden" -> 128))
- val act1 = Symbol.Activation(name = "relu1")(fc1)(Map("act_type" -> "relu"))
- val fc2 = Symbol.FullyConnected(name = "fc2")(act1)(Map("num_hidden" -> 64))
- val act2 = Symbol.Activation(name = "relu2")(fc2)(Map("act_type" -> "relu"))
- val fc3 = Symbol.FullyConnected(name = "fc3")(act2)(Map("num_hidden" -> 10))
- val out = Symbol.SoftmaxOutput(name = "softmax")(fc3)()
+ val fc1 = Symbol.api.FullyConnected(Some(data), num_hidden = 128, name = "fc1")
+ val act1 = Symbol.api.Activation(Some(fc1), "relu", "relu1")
+ val fc2 = Symbol.api.FullyConnected(Some(act1), num_hidden = 64, name = "fc2")
+ val act2 = Symbol.api.Activation(Some(fc2), "relu", "relu2")
+ val fc3 = Symbol.api.FullyConnected(Some(act2), num_hidden = 10, name = "fc3")
+ val out = Symbol.api.SoftmaxOutput(fc3, name = "softmax")
// construct the module
val mod = new Module(out)
diff --git a/docs/api/scala/symbol.md b/docs/api/scala/symbol.md
index 4616a656100..5b73ae5d600 100644
--- a/docs/api/scala/symbol.md
+++ b/docs/api/scala/symbol.md
@@ -22,10 +22,10 @@ The following example configures a two-layer neural network.
```scala
scala> import org.apache.mxnet._
scala> val data = Symbol.Variable("data")
- scala> val fc1 = Symbol.FullyConnected(name = "fc1")()(Map("data" -> data, "num_hidden" -> 128))
- scala> val act1 = Symbol.Activation(name = "relu1")()(Map("data" -> fc1, "act_type" -> "relu"))
- scala> val fc2 = Symbol.FullyConnected(name = "fc2")()(Map("data" -> act1, "num_hidden" -> 64))
- scala> val net = Symbol.SoftmaxOutput(name = "out")()(Map("data" -> fc2))
+ scala> val fc1 = Symbol.api.FullyConnected(Some(data), num_hidden = 128, name = "fc1")
+ scala> val act1 = Symbol.api.Activation(Some(fc1), "relu", "relu1")
+ scala> val fc2 = Symbol.api.FullyConnected(some(act1), num_hidden = 64, name = "fc2")
+ scala> val net = Symbol.api.SoftmaxOutput(Some(fc2), name = "out")
scala> :type net
org.apache.mxnet.Symbol
```
@@ -48,8 +48,7 @@ You can add an attribute to a symbol by providing an attribute dictionary when y
```scala
val data = Symbol.Variable("data", Map("mood"-> "angry"))
- val op = Symbol.Convolution()()(
- Map("data" -> data, "kernel" -> "(1, 1)", "num_filter" -> 1, "mood"-> "so so"))
+ val op = Symbol.api.Convolution(Some(data), kernel = Shape(1, 1), num_filter = 1, attr = Map("mood" -> "so so"))
```
For proper communication with the C++ backend, both the key and values of the attribute dictionary should be strings. To retrieve the attributes, use `attr(key)`:
@@ -114,10 +113,10 @@ To group the symbols together, use the [mxnet.symbol.Group](#mxnet.symbol.Group)
```scala
scala> import org.apache.mxnet._
scala> val data = Symbol.Variable("data")
- scala> val fc1 = Symbol.FullyConnected(name = "fc1")()(Map("data" -> data, "num_hidden" -> 128))
- scala> val act1 = Symbol.Activation(name = "relu1")()(Map("data" -> fc1, "act_type" -> "relu"))
- scala> val fc2 = Symbol.FullyConnected(name = "fc2")()(Map("data" -> act1, "num_hidden" -> 64))
- scala> val net = Symbol.SoftmaxOutput(name = "out")()(Map("data" -> fc2))
+ scala> val fc1 = Symbol.api.FullyConnected(Some(data), num_hidden = 128, name = "fc1")
+ scala> val act1 = Symbol.api.Activation(Some(fc1), "relu", "relu1")
+ scala> val fc2 = Symbol.api.FullyConnected(Some(act1), num_hidden = 64, name = "fc2")
+ scala> val net = Symbol.api.SoftmaxOutput(Some(fc2), name = "out")
scala> val group = Symbol.Group(fc1, net)
scala> group.listOutputs()
IndexedSeq[String] = ArrayBuffer(fc1_output, out_output)
diff --git a/docs/tutorials/scala/mnist.md b/docs/tutorials/scala/mnist.md
index c5c953503dd..79f2129ef0e 100644
--- a/docs/tutorials/scala/mnist.md
+++ b/docs/tutorials/scala/mnist.md
@@ -20,12 +20,12 @@ import org.apache.mxnet.optimizer.SGD
// model definition
val data = Symbol.Variable("data")
-val fc1 = Symbol.FullyConnected(name = "fc1")()(Map("data" -> data, "num_hidden" -> 128))
-val act1 = Symbol.Activation(name = "relu1")()(Map("data" -> fc1, "act_type" -> "relu"))
-val fc2 = Symbol.FullyConnected(name = "fc2")()(Map("data" -> act1, "num_hidden" -> 64))
-val act2 = Symbol.Activation(name = "relu2")()(Map("data" -> fc2, "act_type" -> "relu"))
-val fc3 = Symbol.FullyConnected(name = "fc3")()(Map("data" -> act2, "num_hidden" -> 10))
-val mlp = Symbol.SoftmaxOutput(name = "sm")()(Map("data" -> fc3))
+val fc1 = Symbol.api.FullyConnected(Some(data), num_hidden = 128, name = "fc1")
+val act1 = Symbol.api.Activation(Some(fc1), "relu", "relu1")
+val fc2 = Symbol.api.FullyConnected(Some(act1), num_hidden = 64, name = "fc2")
+val act2 = Symbol.api.Activation(Some(fc2), "relu", "relu2")
+val fc3 = Symbol.api.FullyConnected(Some(act2), num_hidden = 10, name = "fc3")
+val mlp = Symbol.api.SoftmaxOutput(Some(fc3), name = "sm")
```
## Load the Data
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services