model: tensorflow: dnnc: Updated docstring with examples

Signed-off-by: John Andersen <[email protected]>

Author: pdxjohnny

model/tensorflow/dffml_model_tensorflow/dnnc.py

@@ -328,59 +328,34 @@ class DNNClassifierModel(Model):
     """
     Implemented using Tensorflow's DNNClassifier.
 
-    .. code-block:: console
-
-        $ wget http://download.tensorflow.org/data/iris_training.csv
-        $ wget http://download.tensorflow.org/data/iris_test.csv
-        $ head iris_training.csv
-        $ sed -i 's/.*setosa,versicolor,virginica/SepalLength,SepalWidth,PetalLength,PetalWidth,classification/g' *.csv
-        $ head iris_training.csv
-        $ dffml train \\
-            -model tfdnnc \\
-            -model-epochs 3000 \\
-            -model-steps 20000 \\
-            -model-predict classification:int:1 \\
-            -model-classifications 0 1 2 \\
-            -model-clstype int \\
-            -sources iris=csv \\
-            -source-filename iris_training.csv \\
-            -model-features \\
-              SepalLength:float:1 \\
-              SepalWidth:float:1 \\
-              PetalLength:float:1 \\
-              PetalWidth:float:1 \\
-            -log debug
-        ... lots of output ...
-        $ dffml accuracy \\
-            -model tfdnnc \\
-            -model-predict classification:int:1 \\
-            -model-classifications 0 1 2 \\
-            -model-clstype int \\
-            -sources iris=csv \\
-            -source-filename iris_test.csv \\
-            -model-features \\
-              SepalLength:float:1 \\
-              SepalWidth:float:1 \\
-              PetalLength:float:1 \\
-              PetalWidth:float:1 \\
-            -log critical
+    First we create the training and testing datasets
+
+    .. literalinclude:: /../model/tensorflow/examples/tfdnnc/train_data.sh
+
+    .. literalinclude:: /../model/tensorflow/examples/tfdnnc/test_data.sh
+
+    Train the model
+
+    .. literalinclude:: /../model/tensorflow/examples/tfdnnc/train.sh
+
+    Assess the accuracy
+
+    .. literalinclude:: /../model/tensorflow/examples/tfdnnc/accuracy.sh
+
+    Output
+
+    .. code-block::
+
         0.99996233782
-        $ dffml predict all \\
-            -model tfdnnc \\
-            -model-predict classification:int:1 \\
-            -model-classifications 0 1 2 \\
-            -model-clstype int \\
-            -sources iris=csv \\
-            -source-filename iris_test.csv \\
-            -model-features \\
-              SepalLength:float:1 \\
-              SepalWidth:float:1 \\
-              PetalLength:float:1 \\
-              PetalWidth:float:1 \\
-            -caching \\
-            -log critical \\
-          > results.json
-        $ head -n 33 results.json
+
+    Make a prediction
+
+    .. literalinclude:: /../model/tensorflow/examples/tfdnnc/predict.sh
+
+    Output
+
+    .. code-block:: json
+
         [
             {
                 "extra": {},
@@ -401,25 +376,11 @@ class DNNClassifierModel(Model):
                 },
                 "key": "0"
             },
-            {
-                "extra": {},
-                "features": {
-                    "PetalLength": 5.4,
-                    "PetalWidth": 2.1,
-                    "SepalLength": 6.9,
-                    "SepalWidth": 3.1,
-                    "classification": 2
-                },
-                "last_updated": "2019-07-31T02:00:12Z",
-                "prediction": {
-                    "classification":
-                    {
-                        "confidence": 0.9999984502792358,
-                        "value": 2
-                    }
-                },
-                "key": "1"
-            },
+        ]
+
+    Example usage of Tensorflow DNNClassifier model using python API
+
+    .. literalinclude:: /../model/tensorflow/examples/tfdnnc/tfdnnc.py
 
     """