fix up tutorial example with right namespaces

- get rid of getting started since it shows up in codox docs

contrib/clojure-package/README.md

@@ -162,8 +162,6 @@ Do `lein run` for the cpu version or `lein run :gpu` for gpu.
 
 To generate api docs, run `lein codox`. The html docs will be generated in the target/docs directory.
 
-_Note: There is an error thrown in the generated code due to some loading issues, but the docs are all still there._
-
 ## Code Coverage
 
 To run the Code Coverage tool. Run `lein cloverage`.

contrib/clojure-package/examples/tutorial/src/tutorial/introduction.clj

@@ -24,7 +24,7 @@
 
 (def arr (ndarray/ones [2 3]))
 
-arr  ;=> #object[ml.dmlc.mxnet.NDArray 0x482401ab "ml.dmlc.mxnet.NDArray@d8902656"]
+arr ;=> #object[org.apache.mxnet.NDArray 0x597d72e "org.apache.mxnet.NDArray@e35c3ba9"]
 
 (ndarray/shape-vec arr) ;=>  [2 3]
 

contrib/clojure-package/examples/tutorial/src/tutorial/module.clj

@@ -60,7 +60,7 @@
       act2 (sym/activation "relu2" {:data fc2 :act-type "relu"})
       fc3 (sym/fully-connected "fc3" {:data act2 :num-hidden 10})
       out (sym/softmax-output "softmax" {:data fc3})]
-  out) ;=> #object[ml.dmlc.mxnet.Symbol 0x2c7b036b "ml.dmlc.mxnet.Symbol@2c7b036b"]
+  out) ;=>#object[org.apache.mxnet.Symbol 0x1f43a406 "org.apache.mxnet.Symbol@1f43a406"]
 
 ;; You can also use as-> for easier threading
 
@@ -94,17 +94,17 @@
 ;;Modules provide high-level APIs for training, predicting, and evaluating. To fit a module, call the `fit` function with some DataIters:
 
 (def mod (m/fit (m/module out) {:train-data train-data :eval-data test-data :num-epoch 1}))
-;; INFO  ml.dmlc.mxnet.module.BaseModule: Epoch[0] Train-accuracy=0.12521666
-;; INFO  ml.dmlc.mxnet.module.BaseModule: Epoch[0] Time cost=7863
-;; INFO  ml.dmlc.mxnet.module.BaseModule: Epoch[0] Validation-accuracy=0.2227
+;; Epoch  0  Train- [accuracy 0.12521666]
+;; Epoch  0  Time cost- 8392
+;; Epoch  0  Validation-  [accuracy 0.2227]
 
 
 ;; You can pass in batch-end callbacks using batch-end-callback and epoch-end callbacks using epoch-end-callback in the `fit-params`. You can also set parameters using functions like in the fit-params like optimizer and eval-metric. To learn more about the fit-params, see the fit-param function options. To predict with a module, call `predict` with a DataIter:
 
 (def results (m/predict mod {:eval-data test-data}))
-(first results) ;=> #object[ml.dmlc.mxnet.NDArray 0x270236e5 "ml.dmlc.mxnet.NDArray@9180e594"]
+(first results) ;=>#object[org.apache.mxnet.NDArray 0x3540b6d3 "org.apache.mxnet.NDArray@a48686ec"]
 
-(first (ndarray/->vec (first results))) ;=> 0.099454574
+(first (ndarray/->vec (first results))) ;=>0.08261358
 
 ;;The module collects and returns all of the prediction results. For more details about the format of the return values, see the documentation for the `predict` function.
 
@@ -136,15 +136,19 @@
 ]))
     (m/save-checkpoint mod {:prefix save-prefix :epoch epoch-num :save-opt-states true})))
 
-;; INFO  ml.dmlc.mxnet.module.Module: Saved checkpoint to my-model-0000.params
-;; INFO  ml.dmlc.mxnet.module.Module: Saved optimizer state to my-model-0000.states
-;; INFO  ml.dmlc.mxnet.module.Module: Saved checkpoint to my-model-0001.params
-;; INFO  ml.dmlc.mxnet.module.Module: Saved optimizer state to my-model-0001.states
+;; INFO  org.apache.mxnet.module.Module: Saved checkpoint to my-model-0000.params
+;; INFO  org.apache.mxnet.module.Module: Saved optimizer state to my-model-0000.states
+;; INFO  org.apache.mxnet.module.Module: Saved checkpoint to my-model-0001.params
+;; INFO  org.apache.mxnet.module.Module: Saved optimizer state to my-model-0001.states
+;; INFO  org.apache.mxnet.module.Module: Saved checkpoint to my-model-0002.params
+;; INFO  org.apache.mxnet.module.Module: Saved optimizer state to my-model-0002.states
+
 
 ;;To load the saved module parameters, call the `load-checkpoint` function:
 
 (def new-mod (m/load-checkpoint {:prefix "my-model" :epoch 1 :load-optimizer-states true}))
-;=> #object[ml.dmlc.mxnet.module.Module 0x352c8590 "ml.dmlc.mxnet.module.Module@352c8590"]
+
+new-mod ;=> #object[org.apache.mxnet.module.Module 0x5304d0f4 "org.apache.mxnet.module.Module@5304d0f4"]
 
 ;;To initialize parameters, Bind the symbols to construct executors first with bind function. Then, initialize the parameters and auxiliary states by calling `init-params` function.
 
@@ -160,23 +164,24 @@
 
 ;; {:arg-params
 ;;  {"fc3_bias"
-;;   #object[ml.dmlc.mxnet.NDArray 0x4fcda4a0 "ml.dmlc.mxnet.NDArray@70276e89"],
+;;   #object[org.apache.mxnet.NDArray 0x39adc3b0 "org.apache.mxnet.NDArray@49caf426"],
 ;;   "fc2_weight"
-;;   #object[ml.dmlc.mxnet.NDArray 0x33651972 "ml.dmlc.mxnet.NDArray@b2a396eb"],
+;;   #object[org.apache.mxnet.NDArray 0x25baf623 "org.apache.mxnet.NDArray@a6c8f9ac"],
 ;;   "fc1_bias"
-;;   #object[ml.dmlc.mxnet.NDArray 0x3ad02326 "ml.dmlc.mxnet.NDArray@b4110d31"],
+;;   #object[org.apache.mxnet.NDArray 0x6e089973 "org.apache.mxnet.NDArray@9f91d6eb"],
 ;;   "fc3_weight"
-;;   #object[ml.dmlc.mxnet.NDArray 0x4c088d9b "ml.dmlc.mxnet.NDArray@19399ebd"],
+;;   #object[org.apache.mxnet.NDArray 0x756fd109 "org.apache.mxnet.NDArray@2dd0fe3c"],
 ;;   "fc2_bias"
-;;   #object[ml.dmlc.mxnet.NDArray 0x3cca519d "ml.dmlc.mxnet.NDArray@61012c"],
+;;   #object[org.apache.mxnet.NDArray 0x1dc69c8b "org.apache.mxnet.NDArray@d128f73d"],
 ;;   "fc1_weight"
-;;   #object[ml.dmlc.mxnet.NDArray 0xea5d61c "ml.dmlc.mxnet.NDArray@b16841b4"]},
+;;   #object[org.apache.mxnet.NDArray 0x20abc769 "org.apache.mxnet.NDArray@b8e1c5e8"]},
 ;;  :aux-params {}}
 
+
 ;;To assign parameter and aux state values, use `set-params` function.
 
 (m/set-params new-mod {:arg-params (m/arg-params new-mod) :aux-params (m/aux-params new-mod)})
- ;=>#object[ml.dmlc.mxnet.module.Module 0x11f34e1 "ml.dmlc.mxnet.module.Module@11f34e1"]
+;=> #object[org.apache.mxnet.module.Module 0x5304d0f4 "org.apache.mxnet.module.Module@5304d0f4"]
 
 ;;To resume training from a saved checkpoint, instead of calling `set-params`, directly call `fit`, passing the loaded parameters, so that `fit` knows to start from those parameters instead of initializing randomly:
 

contrib/clojure-package/examples/tutorial/src/tutorial/symbol.clj

@@ -39,7 +39,7 @@
   (sym/fully-connected "fc2" {:data data :num-hidden 64})
   (sym/softmax-output "out" {:data data}))
 
-net ;=> #object[ml.dmlc.mxnet.Symbol 0x38c72806 "ml.dmlc.mxnet.Symbol@38c72806"]
+net ;=> #object[org.apache.mxnet.Symbol 0x5c78c8c2 "org.apache.mxnet.Symbol@5c78c8c2"] 
 
 ;; Each symbol takes a (unique) string name. NDArray and Symbol both represent a single tensor. Operators represent the computation between tensors. Operators take symbol (or NDArray) as inputs and might also additionally accept other hyperparameters such as the number of hidden neurons (num_hidden) or the activation type (act_type) and produce the output.