More README updates

Author: EmergentOrder

README.md

@@ -44,7 +44,7 @@ val squeezenetBytes = Files.readAllBytes(Paths.get("squeezenet1.1.onnx"))
 val squeezenet = new ORTModelBackend(squeezenetBytes)
 
 val data = Array.fill(1*3*224*224){42f}
-val tensorDenotation: String & Singleton = "SomeTensorType"
+val tensorDenotation: String & Singleton = "Image"
 //In NCHW tensor image format
 val tensorShapeDenotation = "Batch" ##: "Channel" ##: "Height" ##: "Width" ##: TSNil
 val shape = 1 #: 3 #: 224 #: 224 #: SNil
@@ -58,12 +58,13 @@ val imageTensDefaultDenotations = Tensor(data,shape)
 Note that ONNX Tensor content is in row-major order.
 
 ```scala
-val out = squeezenet.fullModel[Float, "T","T" ##: TSNil,1 #: 1000 #: SNil](Tuple(imageTens))
+val out = squeezenet.fullModel[Float, "ImageNetClassification","Batch" ##: "Class" ##: TSNil,1 #: 1000 #: SNil](Tuple(imageTens))
 // val out:
-//  org.emergentorder.onnx.Tensors.Tensor[Float, ("T", "T" ##:
+//  org.emergentorder.onnx.Tensors.Tensor[Float, ("ImageNetClassification", "Batch" ##: "Class" ##:
 //    org.emergentorder.compiletime.TSNil
 //  , 1 #: 1000 #: io.kjaer.compiletime.SNil)] = (Array(0.8230729,
 // ...
+
 //The output shape
 out.shape
 // val res0: Array[Int] = Array(1, 1000)
@@ -95,7 +96,7 @@ val onnxBackend = new ORTOperatorBackendAll()
 
 val longTens = Tensor(Array.fill(1*3*224*224){-42l},tensorDenotation,tensorShapeDenotation,shape)
 // longTens:
-//  org.emergentorder.onnx.Tensors.Tensor[Float, ("T", "T" ##:
+//  org.emergentorder.onnx.Tensors.Tensor[Float, ("Image", "Batch" ##: "Channel" ##: "Height" ##: "Width" ##:
 //    org.emergentorder.compiletime.TSNil
 //  , 1 #: 1000 #: io.kjaer.compiletime.SNil)] = (
 //   Array(
@@ -105,7 +106,7 @@ val longTens = Tensor(Array.fill(1*3*224*224){-42l},tensorDenotation,tensorShape
 
 onnxBackend.AbsV6("abs", longTens)
 // res2:
-//  org.emergentorder.onnx.Tensors.Tensor[Float, ("T", "T" ##:
+//  org.emergentorder.onnx.Tensors.Tensor[Float, ("Image", "Batch" ##: "Channel" ##: "Height" ##: "Width" ##:
 //    org.emergentorder.compiletime.TSNil
 //  , 1 #: 1000 #: io.kjaer.compiletime.SNil)] = ( 
 //   Array(

docs/README.md

@@ -44,7 +44,7 @@ val squeezenetBytes = Files.readAllBytes(Paths.get("squeezenet1.1.onnx"))
 val squeezenet = new ORTModelBackend(squeezenetBytes)
 
 val data = Array.fill(1*3*224*224){42f}
-val tensorDenotation: String & Singleton = "SomeTensorType"
+val tensorDenotation: String & Singleton = "Image"
 //In NCHW tensor image format
 val tensorShapeDenotation = "Batch" ##: "Channel" ##: "Height" ##: "Width" ##: TSNil
 val shape = 1 #: 3 #: 224 #: 224 #: SNil
@@ -58,12 +58,13 @@ val imageTensDefaultDenotations = Tensor(data,shape)
 Note that ONNX Tensor content is in row-major order.
 
 ```scala
-val out = squeezenet.fullModel[Float, "T","T" ##: TSNil,1 #: 1000 #: SNil](Tuple(imageTens))
+val out = squeezenet.fullModel[Float, "ImageNetClassification","Batch" ##: "Class" ##: TSNil,1 #: 1000 #: SNil](Tuple(imageTens))
 // val out:
-//  org.emergentorder.onnx.Tensors.Tensor[Float, ("T", "T" ##:
+//  org.emergentorder.onnx.Tensors.Tensor[Float, ("ImageNetClassification", "Batch" ##: "Class" ##:
 //    org.emergentorder.compiletime.TSNil
 //  , 1 #: 1000 #: io.kjaer.compiletime.SNil)] = (Array(0.8230729,
 // ...
+
 //The output shape
 out.shape
 // val res0: Array[Int] = Array(1, 1000)
@@ -95,7 +96,7 @@ val onnxBackend = new ORTOperatorBackendAll()
 
 val longTens = Tensor(Array.fill(1*3*224*224){-42l},tensorDenotation,tensorShapeDenotation,shape)
 // longTens:
-//  org.emergentorder.onnx.Tensors.Tensor[Float, ("T", "T" ##:
+//  org.emergentorder.onnx.Tensors.Tensor[Float, ("Image", "Batch" ##: "Channel" ##: "Height" ##: "Width" ##:
 //    org.emergentorder.compiletime.TSNil
 //  , 1 #: 1000 #: io.kjaer.compiletime.SNil)] = (
 //   Array(
@@ -105,7 +106,7 @@ val longTens = Tensor(Array.fill(1*3*224*224){-42l},tensorDenotation,tensorShape
 
 onnxBackend.AbsV6("abs", longTens)
 // res2:
-//  org.emergentorder.onnx.Tensors.Tensor[Float, ("T", "T" ##:
+//  org.emergentorder.onnx.Tensors.Tensor[Float, ("Image", "Batch" ##: "Channel" ##: "Height" ##: "Width" ##:
 //    org.emergentorder.compiletime.TSNil
 //  , 1 #: 1000 #: io.kjaer.compiletime.SNil)] = ( 
 //   Array(