Imported Upstream version 1.4.0upstream/1.4.0submit/tizen/20200423.054851

157 files changed, 5692 insertions, 0 deletions

diff --git a/res/ONNXTests/UNIT_Gemm_000/test.pbtxt b/res/ONNXTests/UNIT_Gemm_000/test.pbtxt
new file mode 100644
index 000000000..6fd497f89
--- /dev/null
+++ b/res/ONNXTests/UNIT_Gemm_000/test.pbtxt
@@ -0,0 +1,79 @@
+# This testcase is compatible with ONNX 1.4.1 or newer
+ir_version: 5
+
+opset_import {
+  version: 7
+}
+
+graph {
+  name: "Gemm_000"
+
+ node {
+    input: "input"
+    input: "weight"
+    input: "bias"
+    output: "output"
+    op_type: "Gemm"
+    attribute {
+      name: "alpha"
+      f: 1.5
+      type: FLOAT
+    }
+    attribute {
+      name: "beta"
+      f: 1.5
+      type: FLOAT
+    }
+    attribute {
+      name: "transA"
+      i: 1
+      type: INT
+    }
+  }
+
+# Initializers generated by python helper script:
+#   a = np.ones((2,2), dtype = np.float32)
+#   onnx.numpy_helper.from_array(a)
+#
+  initializer {
+    dims: 3
+    dims: 2
+    data_type: 1
+    name: "weight"
+    raw_data: "\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?"
+  }
+
+  initializer {
+    dims: 2
+    dims: 2
+    data_type: 1
+    name: "bias"
+    raw_data: "\000\000\200?\000\000\200?\000\000\200?\000\000\200?"
+  }
+
+  input {
+    name: "input"
+    type {
+      tensor_type {
+        elem_type: 1 # FLOAT type
+        shape {
+          dim { dim_value: 3 }
+          dim { dim_value: 2 }
+        }
+      }
+    }
+  }
+
+  output {
+    name: "output"
+    type {
+      tensor_type {
+        elem_type: 1 # FLOAT type
+        shape {
+          dim { dim_value: 2 }
+          dim { dim_value: 2 }
+        }
+      }
+    }
+  }
+}
diff --git a/res/ONNXTests/UNIT_Gemm_001/test.pbtxt b/res/ONNXTests/UNIT_Gemm_001/test.pbtxt
new file mode 100644
index 000000000..0371fc250
--- /dev/null
+++ b/res/ONNXTests/UNIT_Gemm_001/test.pbtxt
@@ -0,0 +1,70 @@
+# This testcase is compatible with ONNX 1.4.1 or newer
+ir_version: 5
+
+opset_import {
+  version: 11
+}
+
+graph {
+  name: "Gemm_001"
+
+ node {
+    input: "input"
+    input: "weight"
+    output: "output"
+    op_type: "Gemm"
+    attribute {
+      name: "alpha"
+      f: 1.5
+      type: FLOAT
+    }
+    attribute {
+      name: "beta"
+      f: 1.5
+      type: FLOAT
+    }
+    attribute {
+      name: "transA"
+      i: 1
+      type: INT
+    }
+  }
+
+# Initializers generated by python helper script:
+#   a = np.ones((2,2), dtype = np.float32)
+#   onnx.numpy_helper.from_array(a)
+#
+  initializer {
+    dims: 3
+    dims: 2
+    data_type: 1
+    name: "weight"
+    raw_data: "\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?\000\000\200?"
+  }
+
+  input {
+    name: "input"
+    type {
+      tensor_type {
+        elem_type: 1 # FLOAT type
+        shape {
+          dim { dim_value: 3 }
+          dim { dim_value: 2 }
+        }
+      }
+    }
+  }
+
+  output {
+    name: "output"
+    type {
+      tensor_type {
+        elem_type: 1 # FLOAT type
+        shape {
+          dim { dim_value: 2 }
+          dim { dim_value: 2 }
+        }
+      }
+    }
+  }
+}
diff --git a/res/TensorFlowLiteRecipes/Abs_000/test.recipe b/res/TensorFlowLiteRecipes/Abs_000/test.recipe
new file mode 100644
index 000000000..0603a43fd
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Abs_000/test.recipe
@@ -0,0 +1,17 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operation {
+  type: "Abs"
+  input: "ifm"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Abs_000/test.reverse b/res/TensorFlowLiteRecipes/Abs_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Abs_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Add_000/test.recipe b/res/TensorFlowLiteRecipes/Add_000/test.recipe
new file mode 100644
index 000000000..54018446a
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Add_000/test.recipe
@@ -0,0 +1,27 @@
+operand {
+  name: "ifm1"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+}
+operand {
+  name: "ifm2"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+}
+operation {
+  type: "Add"
+  input: "ifm1"
+  input: "ifm2"
+  output: "ofm"
+  add_options {
+    activation: NONE
+  }
+}
+input: "ifm1"
+input: "ifm2"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Add_000/test.reverse b/res/TensorFlowLiteRecipes/Add_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Add_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Add_U8_000/test.recipe b/res/TensorFlowLiteRecipes/Add_U8_000/test.recipe
new file mode 100644
index 000000000..f4ccc3cc8
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Add_U8_000/test.recipe
@@ -0,0 +1,30 @@
+operand {
+  name: "ifm1"
+  type: UINT8
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operand {
+  name: "ifm2"
+  type: UINT8
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operand {
+  name: "ofm"
+  type: UINT8
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operation {
+  type: "Add"
+  input: "ifm1"
+  input: "ifm2"
+  output: "ofm"
+  add_options {
+    activation: NONE
+  }
+}
+input: "ifm1"
+input: "ifm2"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Add_U8_000/test.reverse b/res/TensorFlowLiteRecipes/Add_U8_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Add_U8_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/ArgMax_000/test.recipe b/res/TensorFlowLiteRecipes/ArgMax_000/test.recipe
new file mode 100644
index 000000000..2883e1853
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ArgMax_000/test.recipe
@@ -0,0 +1,30 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 4 }
+}
+operand {
+  name: "ofm"
+  type: INT64
+  shape { }
+}
+operand {
+  name: "argmax/dim"
+  type: INT32
+  shape { }
+  filler {
+    tag: "explicit"
+    arg: "0"
+  }
+}
+operation {
+  type: "ArgMax"
+  argmax_options {
+    output_type: INT64
+  }
+  input: "ifm"
+  input: "argmax/dim"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/ArgMax_000/test.reverse b/res/TensorFlowLiteRecipes/ArgMax_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ArgMax_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/ArgMax_001/test.recipe b/res/TensorFlowLiteRecipes/ArgMax_001/test.recipe
new file mode 100644
index 000000000..1f3961cae
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ArgMax_001/test.recipe
@@ -0,0 +1,30 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 4 dim: 5 }
+}
+operand {
+  name: "ofm"
+  type: INT64
+  shape { dim: 5 }
+}
+operand {
+  name: "argmax/dim"
+  type: INT32
+  shape { }
+  filler {
+    tag: "explicit"
+    arg: "0"
+  }
+}
+operation {
+  type: "ArgMax"
+  argmax_options {
+    output_type: INT64
+  }
+  input: "ifm"
+  input: "argmax/dim"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/ArgMax_001/test.reverse b/res/TensorFlowLiteRecipes/ArgMax_001/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ArgMax_001/test.reverse
diff --git a/res/TensorFlowLiteRecipes/ArgMax_002/test.recipe b/res/TensorFlowLiteRecipes/ArgMax_002/test.recipe
new file mode 100644
index 000000000..56d951f3d
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ArgMax_002/test.recipe
@@ -0,0 +1,30 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 4 dim: 5 }
+}
+operand {
+  name: "ofm"
+  type: INT64
+  shape { dim: 4 }
+}
+operand {
+  name: "argmax/dim"
+  type: INT32
+  shape { }
+  filler {
+    tag: "explicit"
+    arg: "1"
+  }
+}
+operation {
+  type: "ArgMax"
+  argmax_options {
+    output_type: INT64
+  }
+  input: "ifm"
+  input: "argmax/dim"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/ArgMax_002/test.reverse b/res/TensorFlowLiteRecipes/ArgMax_002/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ArgMax_002/test.reverse
diff --git a/res/TensorFlowLiteRecipes/ArgMax_003/test.recipe b/res/TensorFlowLiteRecipes/ArgMax_003/test.recipe
new file mode 100644
index 000000000..cb34e3824
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ArgMax_003/test.recipe
@@ -0,0 +1,30 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 4 dim: 5 dim: 6 }
+}
+operand {
+  name: "ofm"
+  type: INT64
+  shape { dim: 4 dim: 6 }
+}
+operand {
+  name: "argmax/dim"
+  type: INT32
+  shape { }
+  filler {
+    tag: "explicit"
+    arg: "1"
+  }
+}
+operation {
+  type: "ArgMax"
+  argmax_options {
+    output_type: INT64
+  }
+  input: "ifm"
+  input: "argmax/dim"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/ArgMax_003/test.reverse b/res/TensorFlowLiteRecipes/ArgMax_003/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ArgMax_003/test.reverse
diff --git a/res/TensorFlowLiteRecipes/ArgMax_U8_000/test.recipe b/res/TensorFlowLiteRecipes/ArgMax_U8_000/test.recipe
new file mode 100644
index 000000000..12e2b250b
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ArgMax_U8_000/test.recipe
@@ -0,0 +1,31 @@
+operand {
+  name: "ifm"
+  type: UINT8
+  shape { dim: 4 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operand {
+  name: "ofm"
+  type: INT64
+  shape { }
+}
+operand {
+  name: "argmax/dim"
+  type: INT32
+  shape { }
+  filler {
+    tag: "explicit"
+    arg: "0"
+  }
+}
+operation {
+  type: "ArgMax"
+  argmax_options {
+    output_type: INT64
+  }
+  input: "ifm"
+  input: "argmax/dim"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/ArgMax_U8_000/test.reverse b/res/TensorFlowLiteRecipes/ArgMax_U8_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ArgMax_U8_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/ArgMax_U8_001/test.recipe b/res/TensorFlowLiteRecipes/ArgMax_U8_001/test.recipe
new file mode 100644
index 000000000..78a519304
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ArgMax_U8_001/test.recipe
@@ -0,0 +1,31 @@
+operand {
+  name: "ifm"
+  type: UINT8
+  shape { dim: 4 dim: 5 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operand {
+  name: "ofm"
+  type: INT64
+  shape { dim: 5 }
+}
+operand {
+  name: "argmax/dim"
+  type: INT32
+  shape { }
+  filler {
+    tag: "explicit"
+    arg: "0"
+  }
+}
+operation {
+  type: "ArgMax"
+  argmax_options {
+    output_type: INT64
+  }
+  input: "ifm"
+  input: "argmax/dim"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/ArgMax_U8_001/test.reverse b/res/TensorFlowLiteRecipes/ArgMax_U8_001/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ArgMax_U8_001/test.reverse
diff --git a/res/TensorFlowLiteRecipes/ArgMax_U8_002/test.recipe b/res/TensorFlowLiteRecipes/ArgMax_U8_002/test.recipe
new file mode 100644
index 000000000..3f1e5ec53
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ArgMax_U8_002/test.recipe
@@ -0,0 +1,31 @@
+operand {
+  name: "ifm"
+  type: UINT8
+  shape { dim: 4 dim: 5 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operand {
+  name: "ofm"
+  type: INT64
+  shape { dim: 4 }
+}
+operand {
+  name: "argmax/dim"
+  type: INT32
+  shape { }
+  filler {
+    tag: "explicit"
+    arg: "1"
+  }
+}
+operation {
+  type: "ArgMax"
+  argmax_options {
+    output_type: INT64
+  }
+  input: "ifm"
+  input: "argmax/dim"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/ArgMax_U8_002/test.reverse b/res/TensorFlowLiteRecipes/ArgMax_U8_002/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ArgMax_U8_002/test.reverse
diff --git a/res/TensorFlowLiteRecipes/ArgMax_U8_003/test.recipe b/res/TensorFlowLiteRecipes/ArgMax_U8_003/test.recipe
new file mode 100644
index 000000000..2ef292045
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ArgMax_U8_003/test.recipe
@@ -0,0 +1,31 @@
+operand {
+  name: "ifm"
+  type: UINT8
+  shape { dim: 4 dim: 5 dim: 6 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operand {
+  name: "ofm"
+  type: INT64
+  shape { dim: 4 dim: 6 }
+}
+operand {
+  name: "argmax/dim"
+  type: INT32
+  shape { }
+  filler {
+    tag: "explicit"
+    arg: "1"
+  }
+}
+operation {
+  type: "ArgMax"
+  argmax_options {
+    output_type: INT64
+  }
+  input: "ifm"
+  input: "argmax/dim"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/ArgMax_U8_003/test.reverse b/res/TensorFlowLiteRecipes/ArgMax_U8_003/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ArgMax_U8_003/test.reverse
diff --git a/res/TensorFlowLiteRecipes/AveragePool2D_000/test.recipe b/res/TensorFlowLiteRecipes/AveragePool2D_000/test.recipe
new file mode 100644
index 000000000..746c34334
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/AveragePool2D_000/test.recipe
@@ -0,0 +1,24 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 8 dim: 8 dim: 1 }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 7 dim: 7 dim: 1 }
+}
+operation {
+  type: "AveragePool2D"
+  averagepool2d_options {
+    padding: VALID
+    stride_w: 1
+    stride_h: 1
+    filter_width: 2
+    filter_height: 2
+  }
+  input: "ifm"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/AveragePool2D_000/test.reverse b/res/TensorFlowLiteRecipes/AveragePool2D_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/AveragePool2D_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/BatchToSpaceND_000/test.recipe b/res/TensorFlowLiteRecipes/BatchToSpaceND_000/test.recipe
new file mode 100644
index 000000000..3d7c28cb0
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/BatchToSpaceND_000/test.recipe
@@ -0,0 +1,38 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 4 dim: 1 dim: 1 dim: 1 }
+}
+operand {
+  name: "crops"
+  type: INT32
+  shape { dim: 2 dim: 2 }
+  filler {
+    tag: "explicit"
+    arg: "0" arg: "0"
+    arg: "0" arg: "0"
+  }
+}
+operand {
+  name: "block_shape"
+  type: INT32
+  shape { dim: 2 }
+  filler {
+    tag: "explicit"
+    arg: "2" arg: "2"
+  }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 2 dim: 2 dim: 1 }
+}
+operation {
+  type: "BatchToSpaceND"
+  input: "ifm"
+  input: "block_shape"
+  input: "crops"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/BatchToSpaceND_000/test.reverse b/res/TensorFlowLiteRecipes/BatchToSpaceND_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/BatchToSpaceND_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Concatenation_000/test.recipe b/res/TensorFlowLiteRecipes/Concatenation_000/test.recipe
new file mode 100644
index 000000000..35641bd07
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Concatenation_000/test.recipe
@@ -0,0 +1,28 @@
+operand {
+  name: "ifm1"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 1 }
+}
+operand {
+  name: "ifm2"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 2 }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+}
+operation {
+  type: "Concatenation"
+  concatenation_options {
+    axis: 3
+    activation: NONE
+  }
+  input: "ifm1"
+  input: "ifm2"
+  output: "ofm"
+}
+input: "ifm1"
+input: "ifm2"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Concatenation_000/test.reverse b/res/TensorFlowLiteRecipes/Concatenation_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Concatenation_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Concatenation_U8_000/test.recipe b/res/TensorFlowLiteRecipes/Concatenation_U8_000/test.recipe
new file mode 100644
index 000000000..3ae21e356
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Concatenation_U8_000/test.recipe
@@ -0,0 +1,31 @@
+operand {
+  name: "ifm1"
+  type: UINT8
+  shape { dim: 1 dim: 4 dim: 4 dim: 1 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operand {
+  name: "ifm2"
+  type: UINT8
+  shape { dim: 1 dim: 4 dim: 4 dim: 2 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operand {
+  name: "ofm"
+  type: UINT8
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operation {
+  type: "Concatenation"
+  concatenation_options {
+    axis: 3
+    activation: NONE
+  }
+  input: "ifm1"
+  input: "ifm2"
+  output: "ofm"
+}
+input: "ifm1"
+input: "ifm2"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Conv2D_000/test.recipe b/res/TensorFlowLiteRecipes/Conv2D_000/test.recipe
new file mode 100644
index 000000000..9cf8a0f69
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Conv2D_000/test.recipe
@@ -0,0 +1,44 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operand {
+  name: "ker"
+  type: FLOAT32
+  shape { dim: 1 dim: 1 dim: 1 dim: 2 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "bias"
+  type: FLOAT32
+  shape { dim: 1 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 1 }
+}
+operation {
+  type: "Conv2D"
+  conv2d_options {
+    padding: VALID
+    stride_w: 1
+    stride_h: 1
+  }
+  input: "ifm"
+  input: "ker"
+  input: "bias"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Conv2D_000/test.reverse b/res/TensorFlowLiteRecipes/Conv2D_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Conv2D_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Conv2D_001/test.recipe b/res/TensorFlowLiteRecipes/Conv2D_001/test.recipe
new file mode 100644
index 000000000..bc41a3fc0
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Conv2D_001/test.recipe
@@ -0,0 +1,44 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operand {
+  name: "ker"
+  type: FLOAT32
+  shape { dim: 1 dim: 1 dim: 1 dim: 2 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "bias"
+  type: FLOAT32
+  shape { dim: 1 }
+  filler {
+    tag: "constant"
+    arg: "1.1"
+  }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 1 }
+}
+operation {
+  type: "Conv2D"
+  conv2d_options {
+    padding: VALID
+    stride_w: 1
+    stride_h: 1
+  }
+  input: "ifm"
+  input: "ker"
+  input: "bias"
+  output: "ofm"
+}
+input: "ifm"
+input: "ker"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Conv2D_001/test.reverse b/res/TensorFlowLiteRecipes/Conv2D_001/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Conv2D_001/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Conv2D_002/test.recipe b/res/TensorFlowLiteRecipes/Conv2D_002/test.recipe
new file mode 100644
index 000000000..1901ead3b
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Conv2D_002/test.recipe
@@ -0,0 +1,45 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 1 }
+}
+operand {
+  name: "ker"
+  type: FLOAT32
+  shape { dim: 1 dim: 1 dim: 1 dim: 1 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "bias"
+  type: FLOAT32
+  shape { dim: 1 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 2 dim: 2 dim: 1 }
+}
+operation {
+  type: "Conv2D"
+  conv2d_options {
+    padding: SAME
+    stride_w: 2
+    stride_h: 2
+    activation: RELU
+  }
+  input: "ifm"
+  input: "ker"
+  input: "bias"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Conv2D_U8_000/test.recipe b/res/TensorFlowLiteRecipes/Conv2D_U8_000/test.recipe
new file mode 100644
index 000000000..9a8e47853
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Conv2D_U8_000/test.recipe
@@ -0,0 +1,48 @@
+operand {
+  name: "ifm"
+  type: UINT8
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+  quant { min: 0 max: 1 scale: 0.004 zero_point: 0 }
+}
+operand {
+  name: "ker"
+  type: UINT8
+  shape { dim: 1 dim: 1 dim: 1 dim: 2 }
+  filler {
+    tag: "gaussian"
+    arg: "102"
+    arg: "32"
+  }
+  quant { min: -4 max: 6 scale: 0.039215686 zero_point: 102 }
+}
+operand {
+  name: "bias"
+  type: INT32
+  shape { dim: 1 }
+  filler {
+    tag: "gaussian"
+    arg: "0"
+    arg: "1024"
+  }
+  quant { scale: 0.00015686276310589164 zero_point: 0 }
+}
+operand {
+  name: "ofm"
+  type: UINT8
+  shape { dim: 1 dim: 3 dim: 3 dim: 1 }
+  quant { min: -4 max: 6 scale: 0.039215686 zero_point: 102 }
+}
+operation {
+  type: "Conv2D"
+  conv2d_options {
+    padding: VALID
+    stride_w: 1
+    stride_h: 1
+  }
+  input: "ifm"
+  input: "ker"
+  input: "bias"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Cos_000/test.recipe b/res/TensorFlowLiteRecipes/Cos_000/test.recipe
new file mode 100644
index 000000000..6fa8ac9b8
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Cos_000/test.recipe
@@ -0,0 +1,17 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operation {
+  type: "Cos"
+  input: "ifm"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Cos_000/test.reverse b/res/TensorFlowLiteRecipes/Cos_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Cos_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/DepthwiseConv2D_000/test.recipe b/res/TensorFlowLiteRecipes/DepthwiseConv2D_000/test.recipe
new file mode 100644
index 000000000..17a3b06c7
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/DepthwiseConv2D_000/test.recipe
@@ -0,0 +1,41 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 64 dim: 64 dim: 8 }
+}
+operand {
+  name: "ker"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 8 }
+}
+operand {
+  name: "bias"
+  type: FLOAT32
+  shape { dim: 8 }
+  filler {
+    tag: "constant"
+    arg: "1.1"
+  }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 64 dim: 64 dim: 8 }
+}
+operation {
+  type: "DepthwiseConv2D"
+  depthwiseconv2d_options {
+    padding: SAME
+    stride_w: 1
+    stride_h: 1
+    depth_multiplier: 1
+    activation : RELU6
+  }
+  input: "ifm"
+  input: "ker"
+  input: "bias"
+  output: "ofm"
+}
+input: "ifm"
+input: "ker"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/DepthwiseConv2D_000/test.reverse b/res/TensorFlowLiteRecipes/DepthwiseConv2D_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/DepthwiseConv2D_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/DepthwiseConv2D_U8_000/test.recipe b/res/TensorFlowLiteRecipes/DepthwiseConv2D_U8_000/test.recipe
new file mode 100644
index 000000000..9a4ddf155
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/DepthwiseConv2D_U8_000/test.recipe
@@ -0,0 +1,46 @@
+operand {
+  name: "ifm"
+  type: UINT8
+  shape { dim: 1 dim: 64 dim: 64 dim: 8 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operand {
+  name: "ker"
+  type: UINT8
+  shape { dim: 1 dim: 3 dim: 3 dim: 8 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operand {
+  name: "bias"
+  type: INT32
+  shape { dim: 8 }
+	filler {
+	  tag: "gaussian"
+	  arg: "0"
+	  arg: "1024"
+	}
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operand {
+  name: "ofm"
+  type: UINT8
+  shape { dim: 1 dim: 64 dim: 64 dim: 8 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operation {
+  type: "DepthwiseConv2D"
+  depthwiseconv2d_options {
+    padding: SAME
+    stride_w: 1
+    stride_h: 1
+    depth_multiplier: 1
+    activation : RELU6
+  }
+  input: "ifm"
+  input: "ker"
+  input: "bias"
+  output: "ofm"
+}
+input: "ifm"
+input: "ker"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/DepthwiseConv2D_U8_000/test.reverse b/res/TensorFlowLiteRecipes/DepthwiseConv2D_U8_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/DepthwiseConv2D_U8_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Div_000/test.recipe b/res/TensorFlowLiteRecipes/Div_000/test.recipe
new file mode 100644
index 000000000..4fb76d467
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Div_000/test.recipe
@@ -0,0 +1,27 @@
+operand {
+  name: "ifm1"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+}
+operand {
+  name: "ifm2"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+}
+operation {
+  type: "Div"
+  input: "ifm1"
+  input: "ifm2"
+  output: "ofm"
+  div_options {
+    activation: NONE
+  }
+}
+input: "ifm1"
+input: "ifm2"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Div_000/test.reverse b/res/TensorFlowLiteRecipes/Div_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Div_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Equal_000/test.recipe b/res/TensorFlowLiteRecipes/Equal_000/test.recipe
new file mode 100644
index 000000000..dcc81f9f5
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Equal_000/test.recipe
@@ -0,0 +1,26 @@
+operand {
+  name: "ifm1"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+}
+operand {
+  name: "ifm2"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+}
+operand {
+  name: "ofm"
+  type: BOOL
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+}
+operation {
+  type: "Equal"
+  equal_options {
+  }
+  input: "ifm1"
+  input: "ifm2"
+  output: "ofm"
+}
+input: "ifm1"
+input: "ifm2"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Equal_000/test.reverse b/res/TensorFlowLiteRecipes/Equal_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Equal_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Exp_000/test.recipe b/res/TensorFlowLiteRecipes/Exp_000/test.recipe
new file mode 100644
index 000000000..06e0054db
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Exp_000/test.recipe
@@ -0,0 +1,17 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operation {
+  type: "Exp"
+  input: "ifm"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Exp_000/test.reverse b/res/TensorFlowLiteRecipes/Exp_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Exp_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/FullyConnected_000/test.recipe b/res/TensorFlowLiteRecipes/FullyConnected_000/test.recipe
new file mode 100644
index 000000000..dca4c09f0
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/FullyConnected_000/test.recipe
@@ -0,0 +1,34 @@
+operand {
+  name: "in"
+  type: FLOAT32
+  shape { dim: 1 dim: 64 }
+}
+operand {
+  name: "weight"
+  type: FLOAT32
+  shape { dim: 8 dim: 64 }
+}
+operand {
+  name: "bias"
+  type: FLOAT32
+  shape { dim: 8 }
+}
+operand {
+  name: "out"
+  type: FLOAT32
+  shape { dim: 1 dim: 8 }
+}
+operation {
+  type: "FullyConnected"
+  fullyconnected_options {
+    activation: NONE
+  }
+  input: "in"
+  input: "weight"
+  input: "bias"
+  output: "out"
+}
+input: "in"
+input: "weight"
+input: "bias"
+output: "out"
diff --git a/res/TensorFlowLiteRecipes/FullyConnected_000/test.reverse b/res/TensorFlowLiteRecipes/FullyConnected_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/FullyConnected_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/FullyConnected_001/test.recipe b/res/TensorFlowLiteRecipes/FullyConnected_001/test.recipe
new file mode 100644
index 000000000..e404f759f
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/FullyConnected_001/test.recipe
@@ -0,0 +1,34 @@
+operand {
+  name: "in"
+  type: FLOAT32
+  shape { dim: 1 dim: 1 dim: 1 dim: 4 }
+}
+operand {
+  name: "weight"
+  type: FLOAT32
+  shape { dim: 2 dim: 4 }
+}
+operand {
+  name: "bias"
+  type: FLOAT32
+  shape { dim: 2 }
+}
+operand {
+  name: "out"
+  type: FLOAT32
+  shape { dim: 1 dim: 2 }
+}
+operation {
+  type: "FullyConnected"
+  fullyconnected_options {
+    activation: NONE
+  }
+  input: "in"
+  input: "weight"
+  input: "bias"
+  output: "out"
+}
+input: "in"
+input: "weight"
+input: "bias"
+output: "out"
diff --git a/res/TensorFlowLiteRecipes/FullyConnected_001/test.reverse b/res/TensorFlowLiteRecipes/FullyConnected_001/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/FullyConnected_001/test.reverse
diff --git a/res/TensorFlowLiteRecipes/FullyConnected_U8_000/test.recipe b/res/TensorFlowLiteRecipes/FullyConnected_U8_000/test.recipe
new file mode 100644
index 000000000..3c996218f
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/FullyConnected_U8_000/test.recipe
@@ -0,0 +1,35 @@
+operand {
+  name: "in"
+  type: FLOAT32
+  shape { dim: 1 dim: 64 }
+}
+operand {
+  name: "weight"
+  type: UINT8
+  shape { dim: 8 dim: 64 }
+  quant { min: 0 max: 1 scale: 0.000553869 zero_point: 0 }
+}
+operand {
+  name: "bias"
+  type: FLOAT32
+  shape { dim: 8 }
+}
+operand {
+  name: "out"
+  type: FLOAT32
+  shape { dim: 1 dim: 8 }
+}
+operation {
+  type: "FullyConnected"
+  fullyconnected_options {
+    activation: NONE
+  }
+  input: "in"
+  input: "weight"
+  input: "bias"
+  output: "out"
+}
+input: "in"
+input: "weight"
+input: "bias"
+output: "out"
diff --git a/res/TensorFlowLiteRecipes/FullyConnected_U8_000/test.reverse b/res/TensorFlowLiteRecipes/FullyConnected_U8_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/FullyConnected_U8_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/LogicalNot_000/test.recipe b/res/TensorFlowLiteRecipes/LogicalNot_000/test.recipe
new file mode 100644
index 000000000..da02bd9af
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/LogicalNot_000/test.recipe
@@ -0,0 +1,17 @@
+operand {
+  name: "ifm"
+  type: BOOL
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operand {
+  name: "ofm"
+  type: BOOL
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operation {
+  type: "LogicalNot"
+  input: "ifm"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/LogicalNot_000/test.reverse b/res/TensorFlowLiteRecipes/LogicalNot_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/LogicalNot_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/LogicalOr_000/test.recipe b/res/TensorFlowLiteRecipes/LogicalOr_000/test.recipe
new file mode 100644
index 000000000..636c8b857
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/LogicalOr_000/test.recipe
@@ -0,0 +1,24 @@
+operand {
+  name: "ifm1"
+  type: BOOL
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+}
+operand {
+  name: "ifm2"
+  type: BOOL
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+}
+operand {
+  name: "ofm"
+  type: BOOL
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+}
+operation {
+  type: "LogicalOr"
+  input: "ifm1"
+  input: "ifm2"
+  output: "ofm"
+}
+input: "ifm1"
+input: "ifm2"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/LogicalOr_000/test.reverse b/res/TensorFlowLiteRecipes/LogicalOr_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/LogicalOr_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/MaxPool2D_000/test.recipe b/res/TensorFlowLiteRecipes/MaxPool2D_000/test.recipe
new file mode 100644
index 000000000..718630f08
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/MaxPool2D_000/test.recipe
@@ -0,0 +1,24 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 8 dim: 8 dim: 1 }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 7 dim: 7 dim: 1 }
+}
+operation {
+  type: "MaxPool2D"
+  maxpool2d_options {
+    padding: VALID
+    stride_w: 1
+    stride_h: 1
+    filter_width: 2
+    filter_height: 2
+  }
+  input: "ifm"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/MaxPool2D_000/test.reverse b/res/TensorFlowLiteRecipes/MaxPool2D_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/MaxPool2D_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/MaxPool2D_U8_000/test.recipe b/res/TensorFlowLiteRecipes/MaxPool2D_U8_000/test.recipe
new file mode 100644
index 000000000..a736988e9
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/MaxPool2D_U8_000/test.recipe
@@ -0,0 +1,26 @@
+operand {
+  name: "ifm"
+  type: UINT8
+  shape { dim: 1 dim: 8 dim: 8 dim: 1 }
+  quant { min: 0 max: 1 scale: 0.004 zero_point: 0 }
+}
+operand {
+  name: "ofm"
+  type: UINT8
+  shape { dim: 1 dim: 7 dim: 7 dim: 1 }
+  quant { min: 0 max: 1 scale: 0.004 zero_point: 0 }
+}
+operation {
+  type: "MaxPool2D"
+  maxpool2d_options {
+    padding: VALID
+    stride_w: 1
+    stride_h: 1
+    filter_width: 2
+    filter_height: 2
+  }
+  input: "ifm"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Mean_000/test.recipe b/res/TensorFlowLiteRecipes/Mean_000/test.recipe
new file mode 100644
index 000000000..d383997d3
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Mean_000/test.recipe
@@ -0,0 +1,27 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 8 dim: 8 dim: 4 }
+}
+operand {
+  name: "reduction_indices"
+  type: INT32
+  shape { dim: 1 }
+  filler { tag: "explicit" arg: "-1" }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 8 dim: 8 dim: 1 }
+}
+operation {
+  type: "Mean"
+  mean_options {
+    keep_dims: true
+  }
+  input: "ifm"
+  input: "reduction_indices"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Mean_000/test.reverse b/res/TensorFlowLiteRecipes/Mean_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Mean_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Mul_000/test.recipe b/res/TensorFlowLiteRecipes/Mul_000/test.recipe
new file mode 100644
index 000000000..43ca30dec
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Mul_000/test.recipe
@@ -0,0 +1,27 @@
+operand {
+  name: "ifm1"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+}
+operand {
+  name: "ifm2"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+}
+operation {
+  type: "Mul"
+  input: "ifm1"
+  input: "ifm2"
+  output: "ofm"
+  mul_options {
+    activation: NONE
+  }
+}
+input: "ifm1"
+input: "ifm2"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Mul_U8_000/test.recipe b/res/TensorFlowLiteRecipes/Mul_U8_000/test.recipe
new file mode 100644
index 000000000..2fbf96d29
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Mul_U8_000/test.recipe
@@ -0,0 +1,30 @@
+operand {
+  name: "ifm1"
+  type: UINT8
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+  quant { min: 0 max: 255 scale: 0.5 zero_point: 0 }
+}
+operand {
+  name: "ifm2"
+  type: UINT8
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+  quant { min: 0 max: 255 scale: 0.5 zero_point: 0 }
+}
+operand {
+  name: "ofm"
+  type: UINT8
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+  quant { min: 0 max: 255 scale: 0.5 zero_point: 0 }
+}
+operation {
+  type: "Mul"
+  input: "ifm1"
+  input: "ifm2"
+  output: "ofm"
+  mul_options {
+    activation: NONE
+  }
+}
+input: "ifm1"
+input: "ifm2"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Pack_000/test.recipe b/res/TensorFlowLiteRecipes/Pack_000/test.recipe
new file mode 100644
index 000000000..5d9141d9e
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Pack_000/test.recipe
@@ -0,0 +1,28 @@
+operand {
+  name: "input"
+  type: FLOAT32
+  shape { dim: 2 dim: 4 dim: 3 }
+}
+operand {
+  name: "input_1"
+  type: FLOAT32
+  shape { dim: 2 dim: 4 dim: 3 }
+}
+operand {
+  name: "stack_4d"
+  type: FLOAT32
+  shape { dim: 2 dim: 2 dim: 4 dim: 3 }
+}
+operation {
+  type: "Pack"
+  pack_options {
+    values_count: 2,
+    axis: 1
+  }
+  input: "input"
+  input: "input_1"
+  output: "stack_4d"
+}
+input: "input"
+input: "input_1"
+output: "stack_4d"
diff --git a/res/TensorFlowLiteRecipes/Pack_000/test.reverse b/res/TensorFlowLiteRecipes/Pack_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Pack_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Pack_U8_000/test.recipe b/res/TensorFlowLiteRecipes/Pack_U8_000/test.recipe
new file mode 100644
index 000000000..f00199980
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Pack_U8_000/test.recipe
@@ -0,0 +1,31 @@
+operand {
+  name: "input"
+  type: UINT8
+  shape { dim: 2 dim: 4 dim: 3 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operand {
+  name: "input_1"
+  type: UINT8
+  shape { dim: 2 dim: 4 dim: 3 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operand {
+  name: "stack_4d"
+  type: UINT8
+  shape { dim: 2 dim: 2 dim: 4 dim: 3 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operation {
+  type: "Pack"
+  pack_options {
+    values_count: 2,
+    axis: 1
+  }
+  input: "input"
+  input: "input_1"
+  output: "stack_4d"
+}
+input: "input"
+input: "input_1"
+output: "stack_4d"
diff --git a/res/TensorFlowLiteRecipes/Pack_U8_000/test.reverse b/res/TensorFlowLiteRecipes/Pack_U8_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Pack_U8_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Pad_000/test.recipe b/res/TensorFlowLiteRecipes/Pad_000/test.recipe
new file mode 100644
index 000000000..2cc980b9c
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Pad_000/test.recipe
@@ -0,0 +1,30 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operand {
+  name: "padding"
+  type: INT32
+  shape { dim: 4 dim: 2 }
+  filler {
+    tag: "explicit"
+    arg: "0" arg: "0"
+    arg: "1" arg: "1"
+    arg: "2" arg: "2"
+    arg: "0" arg: "0"
+  }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 5 dim: 7 dim: 2 }
+}
+operation {
+  type: "Pad"
+  input: "ifm"
+  input: "padding"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Pad_000/test.reverse b/res/TensorFlowLiteRecipes/Pad_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Pad_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Pad_U8_000/test.recipe b/res/TensorFlowLiteRecipes/Pad_U8_000/test.recipe
new file mode 100644
index 000000000..7a835bdb8
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Pad_U8_000/test.recipe
@@ -0,0 +1,32 @@
+operand {
+  name: "ifm"
+  type: UINT8
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operand {
+  name: "padding"
+  type: INT32
+  shape { dim: 4 dim: 2 }
+  filler {
+    tag: "explicit"
+    arg: "0" arg: "0"
+    arg: "1" arg: "1"
+    arg: "2" arg: "2"
+    arg: "0" arg: "0"
+  }
+}
+operand {
+  name: "ofm"
+  type: UINT8
+  shape { dim: 1 dim: 5 dim: 7 dim: 2 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operation {
+  type: "Pad"
+  input: "ifm"
+  input: "padding"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Pad_U8_000/test.reverse b/res/TensorFlowLiteRecipes/Pad_U8_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Pad_U8_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Quantization_000/test.recipe b/res/TensorFlowLiteRecipes/Quantization_000/test.recipe
new file mode 100644
index 000000000..be5d222a2
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Quantization_000/test.recipe
@@ -0,0 +1,46 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+  quant { min: 0 max: 128 scale: 2 zero_point: 2 }
+}
+operand {
+  name: "ker"
+  type: FLOAT32
+  shape { dim: 1 dim: 1 dim: 1 dim: 2 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "bias"
+  type: FLOAT32
+  shape { dim: 1 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 1 }
+  quant { min: 0 max: 80 scale: 1.5 zero_point: 3 }
+}
+operation {
+  type: "Conv2D"
+  conv2d_options {
+    padding: VALID
+    stride_w: 1
+    stride_h: 1
+  }
+  input: "ifm"
+  input: "ker"
+  input: "bias"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Quantization_000/test.reverse b/res/TensorFlowLiteRecipes/Quantization_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Quantization_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/ReLU6_000/test.recipe b/res/TensorFlowLiteRecipes/ReLU6_000/test.recipe
new file mode 100644
index 000000000..226593593
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ReLU6_000/test.recipe
@@ -0,0 +1,17 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operation {
+  type: "ReLU6"
+  input: "ifm"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/ReLU6_000/test.reverse b/res/TensorFlowLiteRecipes/ReLU6_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ReLU6_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/ReLU_000/test.recipe b/res/TensorFlowLiteRecipes/ReLU_000/test.recipe
new file mode 100644
index 000000000..8eaa3602f
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ReLU_000/test.recipe
@@ -0,0 +1,17 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operation {
+  type: "ReLU"
+  input: "ifm"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/ReLU_000/test.reverse b/res/TensorFlowLiteRecipes/ReLU_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/ReLU_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Reshape_000/test.recipe b/res/TensorFlowLiteRecipes/Reshape_000/test.recipe
new file mode 100644
index 000000000..cdca58980
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Reshape_000/test.recipe
@@ -0,0 +1,20 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 1 dim: 1 dim: 10 }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 10 }
+}
+operation {
+  type: "Reshape"
+  reshape_options {
+    new_shape: 10
+  }
+  input: "ifm"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Reshape_000/test.reverse b/res/TensorFlowLiteRecipes/Reshape_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Reshape_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Reshape_001/test.recipe b/res/TensorFlowLiteRecipes/Reshape_001/test.recipe
new file mode 100644
index 000000000..bd5213f39
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Reshape_001/test.recipe
@@ -0,0 +1,28 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 1 dim: 1 dim: 10 }
+}
+operand {
+  name: "shape"
+  type: INT32
+  shape { dim: 2 }
+  filler { tag: "explicit" arg: "-1" arg: "10" }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 10 }
+}
+operation {
+  type: "Reshape"
+  reshape_options {
+    new_shape: -1
+    new_shape: 10
+  }
+  input: "ifm"
+  input: "shape"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Reshape_001/test.reverse b/res/TensorFlowLiteRecipes/Reshape_001/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Reshape_001/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Reshape_U8_000/test.recipe b/res/TensorFlowLiteRecipes/Reshape_U8_000/test.recipe
new file mode 100644
index 000000000..5fe10e599
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Reshape_U8_000/test.recipe
@@ -0,0 +1,22 @@
+operand {
+  name: "ifm"
+  type: UINT8
+  shape { dim: 1 dim: 1 dim: 1 dim: 10 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operand {
+  name: "ofm"
+  type: UINT8
+  shape { dim: 10 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operation {
+  type: "Reshape"
+  reshape_options {
+    new_shape: 10
+  }
+  input: "ifm"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Reshape_U8_000/test.reverse b/res/TensorFlowLiteRecipes/Reshape_U8_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Reshape_U8_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Rsqrt_000/test.recipe b/res/TensorFlowLiteRecipes/Rsqrt_000/test.recipe
new file mode 100644
index 000000000..ba16f2b6b
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Rsqrt_000/test.recipe
@@ -0,0 +1,17 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operation {
+  type: "Rsqrt"
+  input: "ifm"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Rsqrt_000/test.reverse b/res/TensorFlowLiteRecipes/Rsqrt_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Rsqrt_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Softmax_000/test.recipe b/res/TensorFlowLiteRecipes/Softmax_000/test.recipe
new file mode 100644
index 000000000..ce9abf555
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Softmax_000/test.recipe
@@ -0,0 +1,20 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operation {
+  type: "Softmax"
+  softmax_options {
+    beta: 0.0
+  }
+  input: "ifm"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Softmax_000/test.reverse b/res/TensorFlowLiteRecipes/Softmax_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Softmax_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Softmax_U8_000/test.recipe b/res/TensorFlowLiteRecipes/Softmax_U8_000/test.recipe
new file mode 100644
index 000000000..a753ca437
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Softmax_U8_000/test.recipe
@@ -0,0 +1,22 @@
+operand {
+  name: "ifm"
+  type: UINT8
+  shape { dim: 1 dim: 1001 }
+  quant { min: -6.02353 max: 5.97647 scale: 0.0470588 zero_point: 128 }
+}
+operand {
+  name: "ofm"
+  type: UINT8
+  shape { dim: 1 dim: 1001 }
+  quant { min: 0 max: 0.996094 scale: 0.00390625 zero_point: 0 }
+}
+operation {
+  type: "Softmax"
+  softmax_options {
+    beta: 1.0
+  }
+  input: "ifm"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Softmax_U8_000/test.reverse b/res/TensorFlowLiteRecipes/Softmax_U8_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Softmax_U8_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Sqrt_000/test.recipe b/res/TensorFlowLiteRecipes/Sqrt_000/test.recipe
new file mode 100644
index 000000000..1754f9a58
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Sqrt_000/test.recipe
@@ -0,0 +1,18 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+  filler { tag: "constant" arg: "3.5" }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+}
+operation {
+  type: "Sqrt"
+  input: "ifm"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Sqrt_000/test.reverse b/res/TensorFlowLiteRecipes/Sqrt_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Sqrt_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Sub_000/test.recipe b/res/TensorFlowLiteRecipes/Sub_000/test.recipe
new file mode 100644
index 000000000..c934bb8aa
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Sub_000/test.recipe
@@ -0,0 +1,27 @@
+operand {
+  name: "ifm1"
+  type: FLOAT32
+  shape { dim: 1 dim: 5 dim:2 dim:3 }
+}
+operand {
+  name: "ifm2"
+  type: FLOAT32
+  shape { dim: 1 dim: 5 dim:2 dim:3 }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 5 dim:2 dim:3 }
+}
+operation {
+  type: "Sub"
+  sub_options {
+    activation: 0
+  }
+  input: "ifm1"
+  input: "ifm2"
+  output: "ofm"
+}
+input: "ifm1"
+input: "ifm2"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Sub_000/test.reverse b/res/TensorFlowLiteRecipes/Sub_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Sub_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Sub_001/test.recipe b/res/TensorFlowLiteRecipes/Sub_001/test.recipe
new file mode 100644
index 000000000..09f46e4f6
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Sub_001/test.recipe
@@ -0,0 +1,42 @@
+operand {
+  name: "ifm1"
+  type: FLOAT32
+  shape { dim: 1 dim: 5 dim:2 dim:3 }
+}
+operand {
+  name: "ifm2"
+  type: FLOAT32
+  shape { dim: 1 dim: 5 dim:2 dim:3 }
+}
+operand {
+  name: "ofm1"
+  type: FLOAT32
+  shape { dim: 1 dim: 5 dim:2 dim:3 }
+}
+operand {
+  name: "ofm2"
+  type: FLOAT32
+  shape { dim: 1 dim: 5 dim:2 dim:3 }
+}
+operation {
+  type: "Sub"
+  sub_options {
+    activation: 0
+  }
+  input: "ifm1"
+  input: "ifm2"
+  output: "ofm1"
+}
+operation {
+  type: "Sub"
+  sub_options {
+    activation: 0
+  }
+  input: "ifm1"
+  input: "ifm2"
+  output: "ofm2"
+}
+input: "ifm1"
+input: "ifm2"
+output: "ofm1"
+output: "ofm2"
diff --git a/res/TensorFlowLiteRecipes/Sub_001/test.reverse b/res/TensorFlowLiteRecipes/Sub_001/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Sub_001/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Sub_U8_000/test.recipe b/res/TensorFlowLiteRecipes/Sub_U8_000/test.recipe
new file mode 100644
index 000000000..eeeb8aacc
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Sub_U8_000/test.recipe
@@ -0,0 +1,30 @@
+operand {
+  name: "ifm1"
+  type: UINT8
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operand {
+  name: "ifm2"
+  type: UINT8
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operand {
+  name: "ofm"
+  type: UINT8
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operation {
+  type: "Sub"
+  input: "ifm1"
+  input: "ifm2"
+  output: "ofm"
+  sub_options {
+    activation: NONE
+  }
+}
+input: "ifm1"
+input: "ifm2"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Sub_U8_000/test.reverse b/res/TensorFlowLiteRecipes/Sub_U8_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Sub_U8_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Transpose_000/test.recipe b/res/TensorFlowLiteRecipes/Transpose_000/test.recipe
new file mode 100644
index 000000000..82a85c13b
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Transpose_000/test.recipe
@@ -0,0 +1,27 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 3 dim: 8 dim: 1 }
+}
+operand {
+  name: "perm"
+  type: INT32
+  shape { dim: 3 }
+  filler { tag: "explicit" arg: "1" arg: "2" arg: "0" }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 8 dim: 1 dim: 3 }
+}
+
+operation {
+  type: "Transpose"
+  transpose_options {
+  }
+  input: "ifm"
+  input: "perm"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Transpose_000/test.reverse b/res/TensorFlowLiteRecipes/Transpose_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Transpose_000/test.reverse
diff --git a/res/TensorFlowLiteSchema/1.13.1/schema.fbs b/res/TensorFlowLiteSchema/1.13.1/schema.fbs
new file mode 100644
index 000000000..980f13b19
--- /dev/null
+++ b/res/TensorFlowLiteSchema/1.13.1/schema.fbs
@@ -0,0 +1,794 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// Revision History
+// Version 0: Initial version.
+// Version 1: Add subgraphs to schema.
+// Version 2: Rename operators to conform to NN API.
+// Version 3: Move buffer data from Model.Subgraph.Tensors to Model.Buffers.
+
+namespace tflite;
+
+// This corresponds to the version.
+file_identifier "TFL3";
+// File extension of any written files.
+file_extension "tflite";
+
+// IMPORTANT: All new members of tables, enums and unions must be added at the
+// end to ensure backwards compatibility.
+
+// The type of data stored in a tensor.
+enum TensorType : byte {
+  FLOAT32 = 0,
+  FLOAT16 = 1,
+  INT32 = 2,
+  UINT8 = 3,
+  INT64 = 4,
+  STRING = 5,
+  BOOL = 6,
+  INT16 = 7,
+  COMPLEX64 = 8,
+  INT8 = 9,
+}
+
+// Custom quantization parameters for experimenting with new quantization
+// techniques.
+table CustomQuantization {
+  custom:[ubyte] (force_align: 16);
+}
+
+// Represents a specific quantization technique's parameters.
+union QuantizationDetails {
+  CustomQuantization,
+}
+
+// Parameters for converting a quantized tensor back to float.
+table QuantizationParameters {
+  // These four parameters are the asymmetric linear quantization parameters.
+  // Given a quantized value q, the corresponding float value f should be:
+  //   f = scale * (q - zero_point)
+  // For other quantization types, the QuantizationDetails below is used.
+  min:[float];  // For importing back into tensorflow.
+  max:[float];  // For importing back into tensorflow.
+  scale:[float];  // For dequantizing the tensor's values.
+  zero_point:[long];
+
+  // If this is not none, the quantization parameters above are ignored and the
+  // value of the QuantizationDetails union below should be used.
+  details:QuantizationDetails;
+}
+
+table Tensor {
+  // The tensor shape. The meaning of each entry is operator-specific but
+  // builtin ops use: [batch size, height, width, number of channels] (That's
+  // Tensorflow's NHWC).
+  shape:[int];
+  type:TensorType;
+  // An index that refers to the buffers table at the root of the model. Or,
+  // if there is no data buffer associated (i.e. intermediate results), then
+  // this is 0 (which refers to an always existent empty buffer).
+  //
+  // The data_buffer itself is an opaque container, with the assumption that the
+  // target device is little-endian. In addition, all builtin operators assume
+  // the memory is ordered such that if `shape` is [4, 3, 2], then index
+  // [i, j, k] maps to data_buffer[i*3*2 + j*2 + k].
+  buffer:uint;
+  name:string;  // For debugging and importing back into tensorflow.
+  quantization:QuantizationParameters;  // Optional.
+
+  is_variable:bool = false;
+}
+
+// A list of builtin operators. Builtin operators are slightly faster than custom
+// ones, but not by much. Moreover, while custom operators accept an opaque
+// object containing configuration parameters, builtins have a predetermined
+// set of acceptable options.
+enum BuiltinOperator : byte {
+  ADD = 0,
+  AVERAGE_POOL_2D = 1,
+  CONCATENATION = 2,
+  CONV_2D = 3,
+  DEPTHWISE_CONV_2D = 4,
+  // DEPTH_TO_SPACE = 5,
+  DEQUANTIZE = 6,
+  EMBEDDING_LOOKUP = 7,
+  FLOOR = 8,
+  FULLY_CONNECTED = 9,
+  HASHTABLE_LOOKUP = 10,
+  L2_NORMALIZATION = 11,
+  L2_POOL_2D = 12,
+  LOCAL_RESPONSE_NORMALIZATION = 13,
+  LOGISTIC = 14,
+  LSH_PROJECTION = 15,
+  LSTM = 16,
+  MAX_POOL_2D = 17,
+  MUL = 18,
+  RELU = 19,
+  // NOTE(aselle): RELU_N1_TO_1 used to be called RELU1, but it was renamed
+  // since different model developers use RELU1 in different ways. Never
+  // create another op called RELU1.
+  RELU_N1_TO_1 = 20,
+  RELU6 = 21,
+  RESHAPE = 22,
+  RESIZE_BILINEAR = 23,
+  RNN = 24,
+  SOFTMAX = 25,
+  SPACE_TO_DEPTH = 26,
+  SVDF = 27,
+  TANH = 28,
+  // TODO(aselle): Consider rename to CONCATENATE_EMBEDDINGS
+  CONCAT_EMBEDDINGS = 29,
+  SKIP_GRAM = 30,
+  CALL = 31,
+  CUSTOM = 32,
+  EMBEDDING_LOOKUP_SPARSE = 33,
+  PAD = 34,
+  UNIDIRECTIONAL_SEQUENCE_RNN = 35,
+  GATHER = 36,
+  BATCH_TO_SPACE_ND = 37,
+  SPACE_TO_BATCH_ND = 38,
+  TRANSPOSE = 39,
+  MEAN = 40,
+  SUB = 41,
+  DIV = 42,
+  SQUEEZE = 43,
+  UNIDIRECTIONAL_SEQUENCE_LSTM = 44,
+  STRIDED_SLICE = 45,
+  BIDIRECTIONAL_SEQUENCE_RNN = 46,
+  EXP = 47,
+  TOPK_V2 = 48,
+  SPLIT = 49,
+  LOG_SOFTMAX = 50,
+  // DELEGATE is a special op type for the operations which are delegated to
+  // other backends.
+  // WARNING: Experimental interface, subject to change
+  DELEGATE = 51,
+  BIDIRECTIONAL_SEQUENCE_LSTM = 52,
+  CAST = 53,
+  PRELU = 54,
+  MAXIMUM = 55,
+  ARG_MAX = 56,
+  MINIMUM = 57,
+  LESS = 58,
+  NEG = 59,
+  PADV2 = 60,
+  GREATER = 61,
+  GREATER_EQUAL = 62,
+  LESS_EQUAL = 63,
+  SELECT = 64,
+  SLICE = 65,
+  SIN = 66,
+  TRANSPOSE_CONV = 67,
+  SPARSE_TO_DENSE = 68,
+  TILE = 69,
+  EXPAND_DIMS = 70,
+  EQUAL = 71,
+  NOT_EQUAL = 72,
+  LOG = 73,
+  SUM = 74,
+  SQRT = 75,
+  RSQRT = 76,
+  SHAPE = 77,
+  POW = 78,
+  ARG_MIN = 79,
+  FAKE_QUANT = 80,
+  REDUCE_PROD = 81,
+  REDUCE_MAX = 82,
+  PACK = 83,
+  LOGICAL_OR = 84,
+  ONE_HOT = 85,
+  LOGICAL_AND = 86,
+  LOGICAL_NOT = 87,
+  UNPACK = 88,
+  REDUCE_MIN = 89,
+  FLOOR_DIV = 90,
+  REDUCE_ANY = 91,
+  SQUARE = 92,
+  ZEROS_LIKE = 93,
+  FILL = 94,
+  FLOOR_MOD = 95,
+  RANGE = 96,
+  RESIZE_NEAREST_NEIGHBOR = 97,
+  LEAKY_RELU = 98,
+  SQUARED_DIFFERENCE = 99,
+  MIRROR_PAD = 100,
+  ABS = 101,
+  SPLIT_V = 102,
+}
+
+// Options for the builtin operators.
+union BuiltinOptions {
+  Conv2DOptions,
+  DepthwiseConv2DOptions,
+  ConcatEmbeddingsOptions,
+  LSHProjectionOptions,
+  Pool2DOptions,
+  SVDFOptions,
+  RNNOptions,
+  FullyConnectedOptions,
+  SoftmaxOptions,
+  ConcatenationOptions,
+  AddOptions,
+  L2NormOptions,
+  LocalResponseNormalizationOptions,
+  LSTMOptions,
+  ResizeBilinearOptions,
+  CallOptions,
+  ReshapeOptions,
+  SkipGramOptions,
+  SpaceToDepthOptions,
+  EmbeddingLookupSparseOptions,
+  MulOptions,
+  PadOptions,
+  GatherOptions,
+  BatchToSpaceNDOptions,
+  SpaceToBatchNDOptions,
+  TransposeOptions,
+  ReducerOptions,
+  SubOptions,
+  DivOptions,
+  SqueezeOptions,
+  SequenceRNNOptions,
+  StridedSliceOptions,
+  ExpOptions,
+  TopKV2Options,
+  SplitOptions,
+  LogSoftmaxOptions,
+  CastOptions,
+  DequantizeOptions,
+  MaximumMinimumOptions,
+  ArgMaxOptions,
+  LessOptions,
+  NegOptions,
+  PadV2Options,
+  GreaterOptions,
+  GreaterEqualOptions,
+  LessEqualOptions,
+  SelectOptions,
+  SliceOptions,
+  TransposeConvOptions,
+  SparseToDenseOptions,
+  TileOptions,
+  ExpandDimsOptions,
+  EqualOptions,
+  NotEqualOptions,
+  ShapeOptions,
+  PowOptions,
+  ArgMinOptions,
+  FakeQuantOptions,
+  PackOptions,
+  LogicalOrOptions,
+  OneHotOptions,
+  LogicalAndOptions,
+  LogicalNotOptions,
+  UnpackOptions,
+  FloorDivOptions,
+  SquareOptions,
+  ZerosLikeOptions,
+  FillOptions,
+  BidirectionalSequenceLSTMOptions,
+  BidirectionalSequenceRNNOptions,
+  UnidirectionalSequenceLSTMOptions,
+  FloorModOptions,
+  RangeOptions,
+  ResizeNearestNeighborOptions,
+  LeakyReluOptions,
+  SquaredDifferenceOptions,
+  MirrorPadOptions,
+  AbsOptions,
+  SplitVOptions,
+}
+
+enum Padding : byte { SAME, VALID }
+
+enum ActivationFunctionType : byte {
+  NONE = 0,
+  RELU = 1,
+  RELU_N1_TO_1 = 2,
+  RELU6 = 3,
+  TANH = 4,
+  SIGN_BIT = 5,
+}
+
+table Conv2DOptions {
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+  fused_activation_function:ActivationFunctionType;
+  dilation_w_factor:int = 1;
+  dilation_h_factor:int = 1;
+}
+
+table Pool2DOptions {
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+  filter_width:int;
+  filter_height:int;
+  fused_activation_function:ActivationFunctionType;
+}
+
+table DepthwiseConv2DOptions {
+  // Parameters for DepthwiseConv version 1 or above.
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+  depth_multiplier:int;
+  fused_activation_function:ActivationFunctionType;
+  // Parameters for DepthwiseConv version 2 or above.
+  dilation_w_factor:int = 1;
+  dilation_h_factor:int = 1;
+}
+
+table ConcatEmbeddingsOptions {
+  num_channels:int;
+  num_columns_per_channel:[int];
+  embedding_dim_per_channel:[int]; // This could be inferred from parameters.
+}
+
+enum LSHProjectionType: byte {
+  UNKNOWN = 0,
+  SPARSE = 1,
+  DENSE = 2,
+}
+
+table LSHProjectionOptions {
+  type: LSHProjectionType;
+}
+
+table SVDFOptions {
+  rank:int;
+  fused_activation_function:ActivationFunctionType;
+}
+
+// An implementation of TensorFlow RNNCell.
+table RNNOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+// An implementation of TensorFlow dynamic_rnn with RNNCell.
+table SequenceRNNOptions {
+  time_major:bool;
+  fused_activation_function:ActivationFunctionType;
+}
+
+// An implementation of TensorFlow bidrectional_dynamic_rnn with RNNCell.
+table BidirectionalSequenceRNNOptions {
+  time_major:bool;
+  fused_activation_function:ActivationFunctionType;
+  merge_outputs: bool;
+}
+
+enum FullyConnectedOptionsWeightsFormat: byte {
+  DEFAULT = 0,
+  SHUFFLED4x16INT8 = 1,
+}
+
+// An implementation of TensorFlow fully_connected (a.k.a Dense) layer.
+table FullyConnectedOptions {
+  // Parameters for FullyConnected version 1 or above.
+  fused_activation_function:ActivationFunctionType;
+
+  // Parameters for FullyConnected version 2 or above.
+  weights_format:FullyConnectedOptionsWeightsFormat = DEFAULT;
+}
+
+table SoftmaxOptions {
+  beta: float;
+}
+
+// An implementation of TensorFlow concat.
+table ConcatenationOptions {
+  axis:int;
+  fused_activation_function:ActivationFunctionType;
+}
+
+table AddOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table MulOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table L2NormOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table LocalResponseNormalizationOptions {
+  radius:int;
+  bias:float;
+  alpha:float;
+  beta:float;
+}
+
+enum LSTMKernelType : byte {
+  // Full LSTM kernel which supports peephole and projection.
+  FULL = 0,
+  // Basic LSTM kernels. Equivalent to TensorFlow BasicLSTMCell.
+  BASIC = 1,
+}
+
+// An implementation of TensorFlow LSTMCell and CoupledInputForgetGateLSTMCell
+table LSTMOptions {
+  // Parameters for LSTM version 1 or above.
+  fused_activation_function:ActivationFunctionType;
+  cell_clip: float; // Optional, 0.0 means no clipping
+  proj_clip: float; // Optional, 0.0 means no clipping
+
+  // Parameters for LSTM version 2 or above.
+  // Basic kernel is only supported in version 2 or above.
+  kernel_type: LSTMKernelType = FULL;
+}
+
+// An implementation of TensorFlow dynamic_rnn with LSTMCell.
+table UnidirectionalSequenceLSTMOptions {
+  fused_activation_function:ActivationFunctionType;
+  cell_clip: float; // Optional, 0.0 means no clipping
+  proj_clip: float; // Optional, 0.0 means no clipping
+
+  // If true then first dimension is sequence, otherwise batch.
+  time_major:bool;
+}
+
+table BidirectionalSequenceLSTMOptions {
+  fused_activation_function:ActivationFunctionType;
+  cell_clip: float; // Optional, 0.0 means no clipping
+  proj_clip: float; // Optional, 0.0 means no clipping
+
+  // If true, store the outputs of both directions into the first output.
+  merge_outputs: bool;
+}
+
+table ResizeBilinearOptions {
+  new_height: int (deprecated);
+  new_width: int (deprecated);
+  align_corners: bool;
+}
+
+table ResizeNearestNeighborOptions {
+  align_corners: bool;
+}
+
+// A call operation options
+table CallOptions {
+  // The subgraph index that needs to be called.
+  subgraph:uint;
+}
+
+table PadOptions {
+}
+
+table PadV2Options {
+}
+
+table ReshapeOptions {
+  new_shape:[int];
+}
+
+table SpaceToBatchNDOptions {
+}
+
+table BatchToSpaceNDOptions {
+}
+
+table SkipGramOptions {
+  ngram_size: int;
+  max_skip_size: int;
+  include_all_ngrams: bool;
+}
+
+table SpaceToDepthOptions {
+  block_size: int;
+}
+
+table SubOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table DivOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table TopKV2Options {
+}
+
+enum CombinerType : byte {
+  SUM = 0,
+  MEAN = 1,
+  SQRTN = 2,
+}
+
+table EmbeddingLookupSparseOptions {
+  combiner:CombinerType;
+}
+
+table GatherOptions {
+  axis: int;
+}
+
+table TransposeOptions {
+}
+
+table ExpOptions {
+}
+
+table ReducerOptions {
+  keep_dims: bool;
+}
+
+table SqueezeOptions {
+  squeeze_dims:[int];
+}
+
+table SplitOptions {
+  num_splits: int;
+}
+
+table SplitVOptions {
+  num_splits: int;
+}
+
+table StridedSliceOptions {
+  begin_mask: int;
+  end_mask: int;
+  ellipsis_mask: int;
+  new_axis_mask: int;
+  shrink_axis_mask: int;
+}
+
+table LogSoftmaxOptions {
+}
+
+table CastOptions {
+  in_data_type: TensorType;
+  out_data_type: TensorType;
+}
+
+table DequantizeOptions {
+}
+
+table MaximumMinimumOptions {
+}
+
+table TileOptions {
+}
+
+table ArgMaxOptions {
+  output_type : TensorType;
+}
+
+table ArgMinOptions {
+  output_type : TensorType;
+}
+
+table GreaterOptions {
+}
+
+table GreaterEqualOptions {
+}
+
+table LessOptions {
+}
+
+table LessEqualOptions {
+}
+
+table NegOptions {
+}
+
+table SelectOptions {
+}
+
+table SliceOptions {
+}
+
+table TransposeConvOptions {
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+}
+
+table ExpandDimsOptions {
+}
+
+table SparseToDenseOptions {
+  validate_indices:bool;
+}
+
+table EqualOptions {
+}
+
+table NotEqualOptions {
+}
+
+table ShapeOptions {
+  // Optional output type of the operation (int32 or int64). Defaults to int32.
+  out_type : TensorType;
+}
+
+table PowOptions {
+}
+
+table FakeQuantOptions {
+  // Parameters supported by version 1:
+  min:float;
+  max:float;
+  num_bits:int;
+
+  // Parameters supported by version 2:
+  narrow_range:bool;
+}
+
+table PackOptions {
+  values_count:int;
+  axis:int;
+}
+
+table LogicalOrOptions {
+}
+
+table OneHotOptions {
+  axis:int;
+}
+
+table AbsOptions {
+}
+
+
+table LogicalAndOptions {
+}
+
+table LogicalNotOptions {
+}
+
+table UnpackOptions {
+  num:int;
+  axis:int;
+}
+
+table FloorDivOptions {
+}
+
+table SquareOptions {
+}
+
+table ZerosLikeOptions {
+}
+
+table FillOptions {
+}
+
+table FloorModOptions {
+}
+
+table RangeOptions {
+}
+
+table LeakyReluOptions {
+  alpha:float;
+}
+
+table SquaredDifferenceOptions {
+}
+
+enum MirrorPadMode : byte {
+  // Doesn't include borders.
+  REFLECT = 0,
+  // Includes borders.
+  SYMMETRIC = 1,
+}
+
+table MirrorPadOptions {
+  mode:MirrorPadMode;
+}
+
+// An OperatorCode can be an enum value (BuiltinOperator) if the operator is a
+// builtin, or a string if the operator is custom.
+table OperatorCode {
+  builtin_code:BuiltinOperator;
+  custom_code:string;
+
+  // The version of the operator. The version need to be bumped whenever new
+  // parameters are introduced into an op.
+  version:int = 1;
+}
+
+enum CustomOptionsFormat : byte {
+  FLEXBUFFERS = 0,
+}
+
+// An operator takes tensors as inputs and outputs. The type of operation being
+// performed is determined by an index into the list of valid OperatorCodes,
+// while the specifics of each operations is configured using builtin_options
+// or custom_options.
+table Operator {
+  // Index into the operator_codes array. Using an integer here avoids
+  // complicate map lookups.
+  opcode_index:uint;
+
+  // Optional input and output tensors are indicated by -1.
+  inputs:[int];
+  outputs:[int];
+
+  builtin_options:BuiltinOptions;
+  custom_options:[ubyte];
+  custom_options_format:CustomOptionsFormat;
+
+  // A list of booleans indicating the input tensors which are being mutated by
+  // this operator.(e.g. used by RNN and LSTM).
+  // For example, if the "inputs" array refers to 5 tensors and the second and
+  // fifth are mutable variables, then this list will contain
+  // [false, true, false, false, true].
+  //
+  // If the list is empty, no variable is mutated in this operator.
+  // The list either has the same length as `inputs`, or is empty.
+  mutating_variable_inputs:[bool];
+}
+
+// The root type, defining a subgraph, which typically represents an entire
+// model.
+table SubGraph {
+  // A list of all tensors used in this subgraph.
+  tensors:[Tensor];
+
+  // Indices of the tensors that are inputs into this subgraph. Note this is
+  // the list of non-static tensors that feed into the subgraph for inference.
+  inputs:[int];
+
+  // Indices of the tensors that are outputs out of this subgraph. Note this is
+  // the list of output tensors that are considered the product of the
+  // subgraph's inference.
+  outputs:[int];
+
+  // All operators, in execution order.
+  operators:[Operator];
+
+  // Name of this subgraph (used for debugging).
+  name:string;
+}
+
+// Table of raw data buffers (used for constant tensors). Referenced by tensors
+// by index. The generous alignment accommodates mmap-friendly data structures.
+table Buffer {
+  data:[ubyte] (force_align: 16);
+}
+
+table Model {
+  // Version of the schema.
+  version:uint;
+
+  // A list of all operator codes used in this model. This is
+  // kept in order because operators carry an index into this
+  // vector.
+  operator_codes:[OperatorCode];
+
+  // All the subgraphs of the model. The 0th is assumed to be the main
+  // model.
+  subgraphs:[SubGraph];
+
+  // A description of the model.
+  description:string;
+
+  // Buffers of the model.
+  // Note the 0th entry of this array must be an empty buffer (sentinel).
+  // This is a convention so that tensors without a buffer can provide 0 as
+  // their buffer.
+  buffers:[Buffer];
+
+  // Metadata about the model.  Indirects into the existings buffers list.
+  metadata_buffer:[int];
+}
+
+root_type Model;
diff --git a/res/TensorFlowLiteSchema/1.14.0/schema.fbs b/res/TensorFlowLiteSchema/1.14.0/schema.fbs
new file mode 100644
index 000000000..b5fc0f31b
--- /dev/null
+++ b/res/TensorFlowLiteSchema/1.14.0/schema.fbs
@@ -0,0 +1,873 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// Revision History
+// Version 0: Initial version.
+// Version 1: Add subgraphs to schema.
+// Version 2: Rename operators to conform to NN API.
+// Version 3: Move buffer data from Model.Subgraph.Tensors to Model.Buffers.
+
+namespace tflite;
+
+// This corresponds to the version.
+file_identifier "TFL3";
+// File extension of any written files.
+file_extension "tflite";
+
+// IMPORTANT: All new members of tables, enums and unions must be added at the
+// end to ensure backwards compatibility.
+
+// The type of data stored in a tensor.
+enum TensorType : byte {
+  FLOAT32 = 0,
+  FLOAT16 = 1,
+  INT32 = 2,
+  UINT8 = 3,
+  INT64 = 4,
+  STRING = 5,
+  BOOL = 6,
+  INT16 = 7,
+  COMPLEX64 = 8,
+  INT8 = 9,
+}
+
+// Custom quantization parameters for experimenting with new quantization
+// techniques.
+table CustomQuantization {
+  custom:[ubyte] (force_align: 16);
+}
+
+// Represents a specific quantization technique's parameters.
+union QuantizationDetails {
+  CustomQuantization,
+}
+
+// Parameters for converting a quantized tensor back to float.
+table QuantizationParameters {
+  // These four parameters are the asymmetric linear quantization parameters.
+  // Given a quantized value q, the corresponding float value f should be:
+  //   f = scale * (q - zero_point)
+  // For other quantization types, the QuantizationDetails below is used.
+  min:[float];  // For importing back into tensorflow.
+  max:[float];  // For importing back into tensorflow.
+  scale:[float];  // For dequantizing the tensor's values.
+  zero_point:[long];
+
+  // If this is not none, the other quantization parameters (i.e. min, max,
+  // scale, zero_point fields above) are ignored and the value of the
+  // QuantizationDetails union should be used.
+  details:QuantizationDetails;
+
+  // Specifies the dimension of the Tensor's shape that the scales and
+  // zero_points correspond to. For example, a tensor t, with dims=[4, 3, 2, 1]
+  // with quantization params:
+  //   scale=[1.0, 2.0, 3.0], zero_point=[1, 2, 3], quantization_dimension=1
+  // will be quantized across the second dimension of t.
+  //   t[:, 0, :, :] will have scale[0]=1.0, zero_point[0]=1
+  //   t[:, 1, :, :] will have scale[1]=2.0, zero_point[0]=2
+  //   t[:, 2, :, :] will have scale[2]=3.0, zero_point[0]=3
+  quantized_dimension:int;
+}
+
+table Tensor {
+  // The tensor shape. The meaning of each entry is operator-specific but
+  // builtin ops use: [batch size, height, width, number of channels] (That's
+  // Tensorflow's NHWC).
+  shape:[int];
+  type:TensorType;
+  // An index that refers to the buffers table at the root of the model. Or,
+  // if there is no data buffer associated (i.e. intermediate results), then
+  // this is 0 (which refers to an always existent empty buffer).
+  //
+  // The data_buffer itself is an opaque container, with the assumption that the
+  // target device is little-endian. In addition, all builtin operators assume
+  // the memory is ordered such that if `shape` is [4, 3, 2], then index
+  // [i, j, k] maps to data_buffer[i*3*2 + j*2 + k].
+  buffer:uint;
+  name:string;  // For debugging and importing back into tensorflow.
+  quantization:QuantizationParameters;  // Optional.
+
+  is_variable:bool = false;
+}
+
+// A list of builtin operators. Builtin operators are slightly faster than custom
+// ones, but not by much. Moreover, while custom operators accept an opaque
+// object containing configuration parameters, builtins have a predetermined
+// set of acceptable options.
+enum BuiltinOperator : byte {
+  ADD = 0,
+  AVERAGE_POOL_2D = 1,
+  CONCATENATION = 2,
+  CONV_2D = 3,
+  DEPTHWISE_CONV_2D = 4,
+  // DEPTH_TO_SPACE = 5,
+  DEQUANTIZE = 6,
+  EMBEDDING_LOOKUP = 7,
+  FLOOR = 8,
+  FULLY_CONNECTED = 9,
+  HASHTABLE_LOOKUP = 10,
+  L2_NORMALIZATION = 11,
+  L2_POOL_2D = 12,
+  LOCAL_RESPONSE_NORMALIZATION = 13,
+  LOGISTIC = 14,
+  LSH_PROJECTION = 15,
+  LSTM = 16,
+  MAX_POOL_2D = 17,
+  MUL = 18,
+  RELU = 19,
+  // NOTE(aselle): RELU_N1_TO_1 used to be called RELU1, but it was renamed
+  // since different model developers use RELU1 in different ways. Never
+  // create another op called RELU1.
+  RELU_N1_TO_1 = 20,
+  RELU6 = 21,
+  RESHAPE = 22,
+  RESIZE_BILINEAR = 23,
+  RNN = 24,
+  SOFTMAX = 25,
+  SPACE_TO_DEPTH = 26,
+  SVDF = 27,
+  TANH = 28,
+  // TODO(aselle): Consider rename to CONCATENATE_EMBEDDINGS
+  CONCAT_EMBEDDINGS = 29,
+  SKIP_GRAM = 30,
+  CALL = 31,
+  CUSTOM = 32,
+  EMBEDDING_LOOKUP_SPARSE = 33,
+  PAD = 34,
+  UNIDIRECTIONAL_SEQUENCE_RNN = 35,
+  GATHER = 36,
+  BATCH_TO_SPACE_ND = 37,
+  SPACE_TO_BATCH_ND = 38,
+  TRANSPOSE = 39,
+  MEAN = 40,
+  SUB = 41,
+  DIV = 42,
+  SQUEEZE = 43,
+  UNIDIRECTIONAL_SEQUENCE_LSTM = 44,
+  STRIDED_SLICE = 45,
+  BIDIRECTIONAL_SEQUENCE_RNN = 46,
+  EXP = 47,
+  TOPK_V2 = 48,
+  SPLIT = 49,
+  LOG_SOFTMAX = 50,
+  // DELEGATE is a special op type for the operations which are delegated to
+  // other backends.
+  // WARNING: Experimental interface, subject to change
+  DELEGATE = 51,
+  BIDIRECTIONAL_SEQUENCE_LSTM = 52,
+  CAST = 53,
+  PRELU = 54,
+  MAXIMUM = 55,
+  ARG_MAX = 56,
+  MINIMUM = 57,
+  LESS = 58,
+  NEG = 59,
+  PADV2 = 60,
+  GREATER = 61,
+  GREATER_EQUAL = 62,
+  LESS_EQUAL = 63,
+  SELECT = 64,
+  SLICE = 65,
+  SIN = 66,
+  TRANSPOSE_CONV = 67,
+  SPARSE_TO_DENSE = 68,
+  TILE = 69,
+  EXPAND_DIMS = 70,
+  EQUAL = 71,
+  NOT_EQUAL = 72,
+  LOG = 73,
+  SUM = 74,
+  SQRT = 75,
+  RSQRT = 76,
+  SHAPE = 77,
+  POW = 78,
+  ARG_MIN = 79,
+  FAKE_QUANT = 80,
+  REDUCE_PROD = 81,
+  REDUCE_MAX = 82,
+  PACK = 83,
+  LOGICAL_OR = 84,
+  ONE_HOT = 85,
+  LOGICAL_AND = 86,
+  LOGICAL_NOT = 87,
+  UNPACK = 88,
+  REDUCE_MIN = 89,
+  FLOOR_DIV = 90,
+  REDUCE_ANY = 91,
+  SQUARE = 92,
+  ZEROS_LIKE = 93,
+  FILL = 94,
+  FLOOR_MOD = 95,
+  RANGE = 96,
+  RESIZE_NEAREST_NEIGHBOR = 97,
+  LEAKY_RELU = 98,
+  SQUARED_DIFFERENCE = 99,
+  MIRROR_PAD = 100,
+  ABS = 101,
+  SPLIT_V = 102,
+  UNIQUE = 103,
+  CEIL = 104,
+  REVERSE_V2 = 105,
+  ADD_N = 106,
+  GATHER_ND = 107,
+  COS = 108,
+  WHERE = 109,
+  RANK = 110,
+  ELU = 111,
+  REVERSE_SEQUENCE = 112,
+  MATRIX_DIAG = 113,
+  QUANTIZE = 114,
+  MATRIX_SET_DIAG = 115,
+  ROUND = 116,
+}
+
+// Options for the builtin operators.
+union BuiltinOptions {
+  Conv2DOptions,
+  DepthwiseConv2DOptions,
+  ConcatEmbeddingsOptions,
+  LSHProjectionOptions,
+  Pool2DOptions,
+  SVDFOptions,
+  RNNOptions,
+  FullyConnectedOptions,
+  SoftmaxOptions,
+  ConcatenationOptions,
+  AddOptions,
+  L2NormOptions,
+  LocalResponseNormalizationOptions,
+  LSTMOptions,
+  ResizeBilinearOptions,
+  CallOptions,
+  ReshapeOptions,
+  SkipGramOptions,
+  SpaceToDepthOptions,
+  EmbeddingLookupSparseOptions,
+  MulOptions,
+  PadOptions,
+  GatherOptions,
+  BatchToSpaceNDOptions,
+  SpaceToBatchNDOptions,
+  TransposeOptions,
+  ReducerOptions,
+  SubOptions,
+  DivOptions,
+  SqueezeOptions,
+  SequenceRNNOptions,
+  StridedSliceOptions,
+  ExpOptions,
+  TopKV2Options,
+  SplitOptions,
+  LogSoftmaxOptions,
+  CastOptions,
+  DequantizeOptions,
+  MaximumMinimumOptions,
+  ArgMaxOptions,
+  LessOptions,
+  NegOptions,
+  PadV2Options,
+  GreaterOptions,
+  GreaterEqualOptions,
+  LessEqualOptions,
+  SelectOptions,
+  SliceOptions,
+  TransposeConvOptions,
+  SparseToDenseOptions,
+  TileOptions,
+  ExpandDimsOptions,
+  EqualOptions,
+  NotEqualOptions,
+  ShapeOptions,
+  PowOptions,
+  ArgMinOptions,
+  FakeQuantOptions,
+  PackOptions,
+  LogicalOrOptions,
+  OneHotOptions,
+  LogicalAndOptions,
+  LogicalNotOptions,
+  UnpackOptions,
+  FloorDivOptions,
+  SquareOptions,
+  ZerosLikeOptions,
+  FillOptions,
+  BidirectionalSequenceLSTMOptions,
+  BidirectionalSequenceRNNOptions,
+  UnidirectionalSequenceLSTMOptions,
+  FloorModOptions,
+  RangeOptions,
+  ResizeNearestNeighborOptions,
+  LeakyReluOptions,
+  SquaredDifferenceOptions,
+  MirrorPadOptions,
+  AbsOptions,
+  SplitVOptions,
+  UniqueOptions,
+  ReverseV2Options,
+  AddNOptions,
+  GatherNdOptions,
+  CosOptions,
+  WhereOptions,
+  RankOptions,
+  ReverseSequenceOptions,
+  MatrixDiagOptions,
+  QuantizeOptions,
+  MatrixSetDiagOptions
+}
+
+enum Padding : byte { SAME, VALID }
+
+enum ActivationFunctionType : byte {
+  NONE = 0,
+  RELU = 1,
+  RELU_N1_TO_1 = 2,
+  RELU6 = 3,
+  TANH = 4,
+  SIGN_BIT = 5,
+}
+
+table Conv2DOptions {
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+  fused_activation_function:ActivationFunctionType;
+  dilation_w_factor:int = 1;
+  dilation_h_factor:int = 1;
+}
+
+table Pool2DOptions {
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+  filter_width:int;
+  filter_height:int;
+  fused_activation_function:ActivationFunctionType;
+}
+
+table DepthwiseConv2DOptions {
+  // Parameters for DepthwiseConv version 1 or above.
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+  depth_multiplier:int;
+  fused_activation_function:ActivationFunctionType;
+  // Parameters for DepthwiseConv version 2 or above.
+  dilation_w_factor:int = 1;
+  dilation_h_factor:int = 1;
+}
+
+table ConcatEmbeddingsOptions {
+  num_channels:int;
+  num_columns_per_channel:[int];
+  embedding_dim_per_channel:[int]; // This could be inferred from parameters.
+}
+
+enum LSHProjectionType: byte {
+  UNKNOWN = 0,
+  SPARSE = 1,
+  DENSE = 2,
+}
+
+table LSHProjectionOptions {
+  type: LSHProjectionType;
+}
+
+table SVDFOptions {
+  rank:int;
+  fused_activation_function:ActivationFunctionType;
+}
+
+// An implementation of TensorFlow RNNCell.
+table RNNOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+// An implementation of TensorFlow dynamic_rnn with RNNCell.
+table SequenceRNNOptions {
+  time_major:bool;
+  fused_activation_function:ActivationFunctionType;
+}
+
+// An implementation of TensorFlow bidrectional_dynamic_rnn with RNNCell.
+table BidirectionalSequenceRNNOptions {
+  time_major:bool;
+  fused_activation_function:ActivationFunctionType;
+  merge_outputs: bool;
+}
+
+enum FullyConnectedOptionsWeightsFormat: byte {
+  DEFAULT = 0,
+  SHUFFLED4x16INT8 = 1,
+}
+
+// An implementation of TensorFlow fully_connected (a.k.a Dense) layer.
+table FullyConnectedOptions {
+  // Parameters for FullyConnected version 1 or above.
+  fused_activation_function:ActivationFunctionType;
+
+  // Parameters for FullyConnected version 2 or above.
+  weights_format:FullyConnectedOptionsWeightsFormat = DEFAULT;
+}
+
+table SoftmaxOptions {
+  beta: float;
+}
+
+// An implementation of TensorFlow concat.
+table ConcatenationOptions {
+  axis:int;
+  fused_activation_function:ActivationFunctionType;
+}
+
+table AddOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table MulOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table L2NormOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table LocalResponseNormalizationOptions {
+  radius:int;
+  bias:float;
+  alpha:float;
+  beta:float;
+}
+
+enum LSTMKernelType : byte {
+  // Full LSTM kernel which supports peephole and projection.
+  FULL = 0,
+  // Basic LSTM kernels. Equivalent to TensorFlow BasicLSTMCell.
+  BASIC = 1,
+}
+
+// An implementation of TensorFlow LSTMCell and CoupledInputForgetGateLSTMCell
+table LSTMOptions {
+  // Parameters for LSTM version 1 or above.
+  fused_activation_function:ActivationFunctionType;
+  cell_clip: float; // Optional, 0.0 means no clipping
+  proj_clip: float; // Optional, 0.0 means no clipping
+
+  // Parameters for LSTM version 2 or above.
+  // Basic kernel is only supported in version 2 or above.
+  kernel_type: LSTMKernelType = FULL;
+}
+
+// An implementation of TensorFlow dynamic_rnn with LSTMCell.
+table UnidirectionalSequenceLSTMOptions {
+  fused_activation_function:ActivationFunctionType;
+  cell_clip: float; // Optional, 0.0 means no clipping
+  proj_clip: float; // Optional, 0.0 means no clipping
+
+  // If true then first dimension is sequence, otherwise batch.
+  time_major:bool;
+}
+
+table BidirectionalSequenceLSTMOptions {
+  // Parameters supported by version 1:
+  fused_activation_function:ActivationFunctionType;
+  cell_clip: float; // Optional, 0.0 means no clipping
+  proj_clip: float; // Optional, 0.0 means no clipping
+
+  // If true, store the outputs of both directions into the first output.
+  merge_outputs: bool;
+
+  // Parameters supported by version 2:
+  // If true then first dimension is sequence, otherwise batch.
+  // Version 1 implementations assumed time_major to be true, so this default
+  // value should never change.
+  time_major: bool = true;
+}
+
+table ResizeBilinearOptions {
+  new_height: int (deprecated);
+  new_width: int (deprecated);
+  align_corners: bool;
+}
+
+table ResizeNearestNeighborOptions {
+  align_corners: bool;
+}
+
+// A call operation options
+table CallOptions {
+  // The subgraph index that needs to be called.
+  subgraph:uint;
+}
+
+table PadOptions {
+}
+
+table PadV2Options {
+}
+
+table ReshapeOptions {
+  new_shape:[int];
+}
+
+table SpaceToBatchNDOptions {
+}
+
+table BatchToSpaceNDOptions {
+}
+
+table SkipGramOptions {
+  ngram_size: int;
+  max_skip_size: int;
+  include_all_ngrams: bool;
+}
+
+table SpaceToDepthOptions {
+  block_size: int;
+}
+
+table SubOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table DivOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table TopKV2Options {
+}
+
+enum CombinerType : byte {
+  SUM = 0,
+  MEAN = 1,
+  SQRTN = 2,
+}
+
+table EmbeddingLookupSparseOptions {
+  combiner:CombinerType;
+}
+
+table GatherOptions {
+  axis: int;
+}
+
+table TransposeOptions {
+}
+
+table ExpOptions {
+}
+
+table CosOptions {
+}
+
+table ReducerOptions {
+  keep_dims: bool;
+}
+
+table SqueezeOptions {
+  squeeze_dims:[int];
+}
+
+table SplitOptions {
+  num_splits: int;
+}
+
+table SplitVOptions {
+  num_splits: int;
+}
+
+table StridedSliceOptions {
+  begin_mask: int;
+  end_mask: int;
+  ellipsis_mask: int;
+  new_axis_mask: int;
+  shrink_axis_mask: int;
+}
+
+table LogSoftmaxOptions {
+}
+
+table CastOptions {
+  in_data_type: TensorType;
+  out_data_type: TensorType;
+}
+
+table DequantizeOptions {
+}
+
+table MaximumMinimumOptions {
+}
+
+table TileOptions {
+}
+
+table ArgMaxOptions {
+  output_type : TensorType;
+}
+
+table ArgMinOptions {
+  output_type : TensorType;
+}
+
+table GreaterOptions {
+}
+
+table GreaterEqualOptions {
+}
+
+table LessOptions {
+}
+
+table LessEqualOptions {
+}
+
+table NegOptions {
+}
+
+table SelectOptions {
+}
+
+table SliceOptions {
+}
+
+table TransposeConvOptions {
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+}
+
+table ExpandDimsOptions {
+}
+
+table SparseToDenseOptions {
+  validate_indices:bool;
+}
+
+table EqualOptions {
+}
+
+table NotEqualOptions {
+}
+
+table ShapeOptions {
+  // Optional output type of the operation (int32 or int64). Defaults to int32.
+  out_type : TensorType;
+}
+
+table RankOptions {
+}
+
+table PowOptions {
+}
+
+table FakeQuantOptions {
+  // Parameters supported by version 1:
+  min:float;
+  max:float;
+  num_bits:int;
+
+  // Parameters supported by version 2:
+  narrow_range:bool;
+}
+
+table PackOptions {
+  values_count:int;
+  axis:int;
+}
+
+table LogicalOrOptions {
+}
+
+table OneHotOptions {
+  axis:int;
+}
+
+table AbsOptions {
+}
+
+
+table LogicalAndOptions {
+}
+
+table LogicalNotOptions {
+}
+
+table UnpackOptions {
+  num:int;
+  axis:int;
+}
+
+table FloorDivOptions {
+}
+
+table SquareOptions {
+}
+
+table ZerosLikeOptions {
+}
+
+table FillOptions {
+}
+
+table FloorModOptions {
+}
+
+table RangeOptions {
+}
+
+table LeakyReluOptions {
+  alpha:float;
+}
+
+table SquaredDifferenceOptions {
+}
+
+enum MirrorPadMode : byte {
+  // Doesn't include borders.
+  REFLECT = 0,
+  // Includes borders.
+  SYMMETRIC = 1,
+}
+
+table MirrorPadOptions {
+  mode:MirrorPadMode;
+}
+
+table UniqueOptions {
+  idx_out_type:TensorType = INT32;
+}
+
+table ReverseV2Options {
+}
+
+table AddNOptions {
+}
+
+table GatherNdOptions {
+}
+
+table WhereOptions {
+}
+
+table ReverseSequenceOptions {
+  seq_dim:int;
+  batch_dim:int = 0;
+}
+
+table MatrixDiagOptions {
+}
+
+table QuantizeOptions {
+}
+
+table MatrixSetDiagOptions {
+}
+
+// An OperatorCode can be an enum value (BuiltinOperator) if the operator is a
+// builtin, or a string if the operator is custom.
+table OperatorCode {
+  builtin_code:BuiltinOperator;
+  custom_code:string;
+
+  // The version of the operator. The version need to be bumped whenever new
+  // parameters are introduced into an op.
+  version:int = 1;
+}
+
+enum CustomOptionsFormat : byte {
+  FLEXBUFFERS = 0,
+}
+
+// An operator takes tensors as inputs and outputs. The type of operation being
+// performed is determined by an index into the list of valid OperatorCodes,
+// while the specifics of each operations is configured using builtin_options
+// or custom_options.
+table Operator {
+  // Index into the operator_codes array. Using an integer here avoids
+  // complicate map lookups.
+  opcode_index:uint;
+
+  // Optional input and output tensors are indicated by -1.
+  inputs:[int];
+  outputs:[int];
+
+  builtin_options:BuiltinOptions;
+  custom_options:[ubyte];
+  custom_options_format:CustomOptionsFormat;
+
+  // A list of booleans indicating the input tensors which are being mutated by
+  // this operator.(e.g. used by RNN and LSTM).
+  // For example, if the "inputs" array refers to 5 tensors and the second and
+  // fifth are mutable variables, then this list will contain
+  // [false, true, false, false, true].
+  //
+  // If the list is empty, no variable is mutated in this operator.
+  // The list either has the same length as `inputs`, or is empty.
+  mutating_variable_inputs:[bool];
+}
+
+// The root type, defining a subgraph, which typically represents an entire
+// model.
+table SubGraph {
+  // A list of all tensors used in this subgraph.
+  tensors:[Tensor];
+
+  // Indices of the tensors that are inputs into this subgraph. Note this is
+  // the list of non-static tensors that feed into the subgraph for inference.
+  inputs:[int];
+
+  // Indices of the tensors that are outputs out of this subgraph. Note this is
+  // the list of output tensors that are considered the product of the
+  // subgraph's inference.
+  outputs:[int];
+
+  // All operators, in execution order.
+  operators:[Operator];
+
+  // Name of this subgraph (used for debugging).
+  name:string;
+}
+
+// Table of raw data buffers (used for constant tensors). Referenced by tensors
+// by index. The generous alignment accommodates mmap-friendly data structures.
+table Buffer {
+  data:[ubyte] (force_align: 16);
+}
+
+table Model {
+  // Version of the schema.
+  version:uint;
+
+  // A list of all operator codes used in this model. This is
+  // kept in order because operators carry an index into this
+  // vector.
+  operator_codes:[OperatorCode];
+
+  // All the subgraphs of the model. The 0th is assumed to be the main
+  // model.
+  subgraphs:[SubGraph];
+
+  // A description of the model.
+  description:string;
+
+  // Buffers of the model.
+  // Note the 0th entry of this array must be an empty buffer (sentinel).
+  // This is a convention so that tensors without a buffer can provide 0 as
+  // their buffer.
+  buffers:[Buffer];
+
+  // Metadata about the model.  Indirects into the existings buffers list.
+  metadata_buffer:[int];
+}
+
+root_type Model;
diff --git a/res/TensorFlowLiteSchema/1.15.2/schema.fbs b/res/TensorFlowLiteSchema/1.15.2/schema.fbs
new file mode 100644
index 000000000..d63386035
--- /dev/null
+++ b/res/TensorFlowLiteSchema/1.15.2/schema.fbs
@@ -0,0 +1,922 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// Revision History
+// Version 0: Initial version.
+// Version 1: Add subgraphs to schema.
+// Version 2: Rename operators to conform to NN API.
+// Version 3: Move buffer data from Model.Subgraph.Tensors to Model.Buffers.
+
+namespace tflite;
+
+// This corresponds to the version.
+file_identifier "TFL3";
+// File extension of any written files.
+file_extension "tflite";
+
+// IMPORTANT: All new members of tables, enums and unions must be added at the
+// end to ensure backwards compatibility.
+
+// The type of data stored in a tensor.
+enum TensorType : byte {
+  FLOAT32 = 0,
+  FLOAT16 = 1,
+  INT32 = 2,
+  UINT8 = 3,
+  INT64 = 4,
+  STRING = 5,
+  BOOL = 6,
+  INT16 = 7,
+  COMPLEX64 = 8,
+  INT8 = 9,
+}
+
+// Custom quantization parameters for experimenting with new quantization
+// techniques.
+table CustomQuantization {
+  custom:[ubyte] (force_align: 16);
+}
+
+// Represents a specific quantization technique's parameters.
+union QuantizationDetails {
+  CustomQuantization,
+}
+
+// Parameters for converting a quantized tensor back to float.
+table QuantizationParameters {
+  // These four parameters are the asymmetric linear quantization parameters.
+  // Given a quantized value q, the corresponding float value f should be:
+  //   f = scale * (q - zero_point)
+  // For other quantization types, the QuantizationDetails below is used.
+  min:[float];  // For importing back into tensorflow.
+  max:[float];  // For importing back into tensorflow.
+  scale:[float];  // For dequantizing the tensor's values.
+  zero_point:[long];
+
+  // If this is not none, the other quantization parameters (i.e. min, max,
+  // scale, zero_point fields above) are ignored and the value of the
+  // QuantizationDetails union should be used.
+  details:QuantizationDetails;
+
+  // Specifies the dimension of the Tensor's shape that the scales and
+  // zero_points correspond to. For example, a tensor t, with dims=[4, 3, 2, 1]
+  // with quantization params:
+  //   scale=[1.0, 2.0, 3.0], zero_point=[1, 2, 3], quantization_dimension=1
+  // will be quantized across the second dimension of t.
+  //   t[:, 0, :, :] will have scale[0]=1.0, zero_point[0]=1
+  //   t[:, 1, :, :] will have scale[1]=2.0, zero_point[0]=2
+  //   t[:, 2, :, :] will have scale[2]=3.0, zero_point[0]=3
+  quantized_dimension:int;
+}
+
+table Tensor {
+  // The tensor shape. The meaning of each entry is operator-specific but
+  // builtin ops use: [batch size, height, width, number of channels] (That's
+  // Tensorflow's NHWC).
+  shape:[int];
+  type:TensorType;
+  // An index that refers to the buffers table at the root of the model. Or,
+  // if there is no data buffer associated (i.e. intermediate results), then
+  // this is 0 (which refers to an always existent empty buffer).
+  //
+  // The data_buffer itself is an opaque container, with the assumption that the
+  // target device is little-endian. In addition, all builtin operators assume
+  // the memory is ordered such that if `shape` is [4, 3, 2], then index
+  // [i, j, k] maps to data_buffer[i*3*2 + j*2 + k].
+  buffer:uint;
+  name:string;  // For debugging and importing back into tensorflow.
+  quantization:QuantizationParameters;  // Optional.
+
+  is_variable:bool = false;
+}
+
+// A list of builtin operators. Builtin operators are slightly faster than custom
+// ones, but not by much. Moreover, while custom operators accept an opaque
+// object containing configuration parameters, builtins have a predetermined
+// set of acceptable options.
+enum BuiltinOperator : byte {
+  ADD = 0,
+  AVERAGE_POOL_2D = 1,
+  CONCATENATION = 2,
+  CONV_2D = 3,
+  DEPTHWISE_CONV_2D = 4,
+  DEPTH_TO_SPACE = 5,
+  DEQUANTIZE = 6,
+  EMBEDDING_LOOKUP = 7,
+  FLOOR = 8,
+  FULLY_CONNECTED = 9,
+  HASHTABLE_LOOKUP = 10,
+  L2_NORMALIZATION = 11,
+  L2_POOL_2D = 12,
+  LOCAL_RESPONSE_NORMALIZATION = 13,
+  LOGISTIC = 14,
+  LSH_PROJECTION = 15,
+  LSTM = 16,
+  MAX_POOL_2D = 17,
+  MUL = 18,
+  RELU = 19,
+  // NOTE(aselle): RELU_N1_TO_1 used to be called RELU1, but it was renamed
+  // since different model developers use RELU1 in different ways. Never
+  // create another op called RELU1.
+  RELU_N1_TO_1 = 20,
+  RELU6 = 21,
+  RESHAPE = 22,
+  RESIZE_BILINEAR = 23,
+  RNN = 24,
+  SOFTMAX = 25,
+  SPACE_TO_DEPTH = 26,
+  SVDF = 27,
+  TANH = 28,
+  // TODO(aselle): Consider rename to CONCATENATE_EMBEDDINGS
+  CONCAT_EMBEDDINGS = 29,
+  SKIP_GRAM = 30,
+  CALL = 31,
+  CUSTOM = 32,
+  EMBEDDING_LOOKUP_SPARSE = 33,
+  PAD = 34,
+  UNIDIRECTIONAL_SEQUENCE_RNN = 35,
+  GATHER = 36,
+  BATCH_TO_SPACE_ND = 37,
+  SPACE_TO_BATCH_ND = 38,
+  TRANSPOSE = 39,
+  MEAN = 40,
+  SUB = 41,
+  DIV = 42,
+  SQUEEZE = 43,
+  UNIDIRECTIONAL_SEQUENCE_LSTM = 44,
+  STRIDED_SLICE = 45,
+  BIDIRECTIONAL_SEQUENCE_RNN = 46,
+  EXP = 47,
+  TOPK_V2 = 48,
+  SPLIT = 49,
+  LOG_SOFTMAX = 50,
+  // DELEGATE is a special op type for the operations which are delegated to
+  // other backends.
+  // WARNING: Experimental interface, subject to change
+  DELEGATE = 51,
+  BIDIRECTIONAL_SEQUENCE_LSTM = 52,
+  CAST = 53,
+  PRELU = 54,
+  MAXIMUM = 55,
+  ARG_MAX = 56,
+  MINIMUM = 57,
+  LESS = 58,
+  NEG = 59,
+  PADV2 = 60,
+  GREATER = 61,
+  GREATER_EQUAL = 62,
+  LESS_EQUAL = 63,
+  SELECT = 64,
+  SLICE = 65,
+  SIN = 66,
+  TRANSPOSE_CONV = 67,
+  SPARSE_TO_DENSE = 68,
+  TILE = 69,
+  EXPAND_DIMS = 70,
+  EQUAL = 71,
+  NOT_EQUAL = 72,
+  LOG = 73,
+  SUM = 74,
+  SQRT = 75,
+  RSQRT = 76,
+  SHAPE = 77,
+  POW = 78,
+  ARG_MIN = 79,
+  FAKE_QUANT = 80,
+  REDUCE_PROD = 81,
+  REDUCE_MAX = 82,
+  PACK = 83,
+  LOGICAL_OR = 84,
+  ONE_HOT = 85,
+  LOGICAL_AND = 86,
+  LOGICAL_NOT = 87,
+  UNPACK = 88,
+  REDUCE_MIN = 89,
+  FLOOR_DIV = 90,
+  REDUCE_ANY = 91,
+  SQUARE = 92,
+  ZEROS_LIKE = 93,
+  FILL = 94,
+  FLOOR_MOD = 95,
+  RANGE = 96,
+  RESIZE_NEAREST_NEIGHBOR = 97,
+  LEAKY_RELU = 98,
+  SQUARED_DIFFERENCE = 99,
+  MIRROR_PAD = 100,
+  ABS = 101,
+  SPLIT_V = 102,
+  UNIQUE = 103,
+  CEIL = 104,
+  REVERSE_V2 = 105,
+  ADD_N = 106,
+  GATHER_ND = 107,
+  COS = 108,
+  WHERE = 109,
+  RANK = 110,
+  ELU = 111,
+  REVERSE_SEQUENCE = 112,
+  MATRIX_DIAG = 113,
+  QUANTIZE = 114,
+  MATRIX_SET_DIAG = 115,
+  ROUND = 116,
+  HARD_SWISH = 117,
+  IF = 118,
+  WHILE = 119,
+}
+
+// Options for the builtin operators.
+union BuiltinOptions {
+  Conv2DOptions,
+  DepthwiseConv2DOptions,
+  ConcatEmbeddingsOptions,
+  LSHProjectionOptions,
+  Pool2DOptions,
+  SVDFOptions,
+  RNNOptions,
+  FullyConnectedOptions,
+  SoftmaxOptions,
+  ConcatenationOptions,
+  AddOptions,
+  L2NormOptions,
+  LocalResponseNormalizationOptions,
+  LSTMOptions,
+  ResizeBilinearOptions,
+  CallOptions,
+  ReshapeOptions,
+  SkipGramOptions,
+  SpaceToDepthOptions,
+  EmbeddingLookupSparseOptions,
+  MulOptions,
+  PadOptions,
+  GatherOptions,
+  BatchToSpaceNDOptions,
+  SpaceToBatchNDOptions,
+  TransposeOptions,
+  ReducerOptions,
+  SubOptions,
+  DivOptions,
+  SqueezeOptions,
+  SequenceRNNOptions,
+  StridedSliceOptions,
+  ExpOptions,
+  TopKV2Options,
+  SplitOptions,
+  LogSoftmaxOptions,
+  CastOptions,
+  DequantizeOptions,
+  MaximumMinimumOptions,
+  ArgMaxOptions,
+  LessOptions,
+  NegOptions,
+  PadV2Options,
+  GreaterOptions,
+  GreaterEqualOptions,
+  LessEqualOptions,
+  SelectOptions,
+  SliceOptions,
+  TransposeConvOptions,
+  SparseToDenseOptions,
+  TileOptions,
+  ExpandDimsOptions,
+  EqualOptions,
+  NotEqualOptions,
+  ShapeOptions,
+  PowOptions,
+  ArgMinOptions,
+  FakeQuantOptions,
+  PackOptions,
+  LogicalOrOptions,
+  OneHotOptions,
+  LogicalAndOptions,
+  LogicalNotOptions,
+  UnpackOptions,
+  FloorDivOptions,
+  SquareOptions,
+  ZerosLikeOptions,
+  FillOptions,
+  BidirectionalSequenceLSTMOptions,
+  BidirectionalSequenceRNNOptions,
+  UnidirectionalSequenceLSTMOptions,
+  FloorModOptions,
+  RangeOptions,
+  ResizeNearestNeighborOptions,
+  LeakyReluOptions,
+  SquaredDifferenceOptions,
+  MirrorPadOptions,
+  AbsOptions,
+  SplitVOptions,
+  UniqueOptions,
+  ReverseV2Options,
+  AddNOptions,
+  GatherNdOptions,
+  CosOptions,
+  WhereOptions,
+  RankOptions,
+  ReverseSequenceOptions,
+  MatrixDiagOptions,
+  QuantizeOptions,
+  MatrixSetDiagOptions,
+  HardSwishOptions,
+  IfOptions,
+  WhileOptions,
+  DepthToSpaceOptions
+}
+
+enum Padding : byte { SAME, VALID }
+
+enum ActivationFunctionType : byte {
+  NONE = 0,
+  RELU = 1,
+  RELU_N1_TO_1 = 2,
+  RELU6 = 3,
+  TANH = 4,
+  SIGN_BIT = 5,
+}
+
+table Conv2DOptions {
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+  fused_activation_function:ActivationFunctionType;
+  dilation_w_factor:int = 1;
+  dilation_h_factor:int = 1;
+}
+
+table Pool2DOptions {
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+  filter_width:int;
+  filter_height:int;
+  fused_activation_function:ActivationFunctionType;
+}
+
+table DepthwiseConv2DOptions {
+  // Parameters for DepthwiseConv version 1 or above.
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+  depth_multiplier:int;
+  fused_activation_function:ActivationFunctionType;
+  // Parameters for DepthwiseConv version 2 or above.
+  dilation_w_factor:int = 1;
+  dilation_h_factor:int = 1;
+}
+
+table ConcatEmbeddingsOptions {
+  num_channels:int;
+  num_columns_per_channel:[int];
+  embedding_dim_per_channel:[int]; // This could be inferred from parameters.
+}
+
+enum LSHProjectionType: byte {
+  UNKNOWN = 0,
+  SPARSE = 1,
+  DENSE = 2,
+}
+
+table LSHProjectionOptions {
+  type: LSHProjectionType;
+}
+
+table SVDFOptions {
+  rank:int;
+  fused_activation_function:ActivationFunctionType;
+}
+
+// An implementation of TensorFlow RNNCell.
+table RNNOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+// An implementation of TensorFlow dynamic_rnn with RNNCell.
+table SequenceRNNOptions {
+  time_major:bool;
+  fused_activation_function:ActivationFunctionType;
+}
+
+// An implementation of TensorFlow bidrectional_dynamic_rnn with RNNCell.
+table BidirectionalSequenceRNNOptions {
+  time_major:bool;
+  fused_activation_function:ActivationFunctionType;
+  merge_outputs: bool;
+}
+
+enum FullyConnectedOptionsWeightsFormat: byte {
+  DEFAULT = 0,
+  SHUFFLED4x16INT8 = 1,
+}
+
+// An implementation of TensorFlow fully_connected (a.k.a Dense) layer.
+table FullyConnectedOptions {
+  // Parameters for FullyConnected version 1 or above.
+  fused_activation_function:ActivationFunctionType;
+
+  // Parameters for FullyConnected version 2 or above.
+  weights_format:FullyConnectedOptionsWeightsFormat = DEFAULT;
+
+  // Parameters for FullyConnected version 5 or above.
+  // If set to true, then the number of dimension is preserved. Furthermore,
+  // all but the last dimension of the input and output shapes will be equal.
+  keep_num_dims: bool;
+}
+
+table SoftmaxOptions {
+  beta: float;
+}
+
+// An implementation of TensorFlow concat.
+table ConcatenationOptions {
+  axis:int;
+  fused_activation_function:ActivationFunctionType;
+}
+
+table AddOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table MulOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table L2NormOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table LocalResponseNormalizationOptions {
+  radius:int;
+  bias:float;
+  alpha:float;
+  beta:float;
+}
+
+enum LSTMKernelType : byte {
+  // Full LSTM kernel which supports peephole and projection.
+  FULL = 0,
+  // Basic LSTM kernels. Equivalent to TensorFlow BasicLSTMCell.
+  BASIC = 1,
+}
+
+// An implementation of TensorFlow LSTMCell and CoupledInputForgetGateLSTMCell
+table LSTMOptions {
+  // Parameters for LSTM version 1 or above.
+  fused_activation_function:ActivationFunctionType;
+  cell_clip: float; // Optional, 0.0 means no clipping
+  proj_clip: float; // Optional, 0.0 means no clipping
+
+  // Parameters for LSTM version 2 or above.
+  // Basic kernel is only supported in version 2 or above.
+  kernel_type: LSTMKernelType = FULL;
+}
+
+// An implementation of TensorFlow dynamic_rnn with LSTMCell.
+table UnidirectionalSequenceLSTMOptions {
+  fused_activation_function:ActivationFunctionType;
+  cell_clip: float; // Optional, 0.0 means no clipping
+  proj_clip: float; // Optional, 0.0 means no clipping
+
+  // If true then first dimension is sequence, otherwise batch.
+  time_major:bool;
+}
+
+table BidirectionalSequenceLSTMOptions {
+  // Parameters supported by version 1:
+  fused_activation_function:ActivationFunctionType;
+  cell_clip: float; // Optional, 0.0 means no clipping
+  proj_clip: float; // Optional, 0.0 means no clipping
+
+  // If true, store the outputs of both directions into the first output.
+  merge_outputs: bool;
+
+  // Parameters supported by version 2:
+  // If true then first dimension is sequence, otherwise batch.
+  // Version 1 implementations assumed time_major to be true, so this default
+  // value should never change.
+  time_major: bool = true;
+}
+
+table ResizeBilinearOptions {
+  new_height: int (deprecated);
+  new_width: int (deprecated);
+  align_corners: bool;
+}
+
+table ResizeNearestNeighborOptions {
+  align_corners: bool;
+}
+
+// A call operation options
+table CallOptions {
+  // The subgraph index that needs to be called.
+  subgraph:uint;
+}
+
+table PadOptions {
+}
+
+table PadV2Options {
+}
+
+table ReshapeOptions {
+  new_shape:[int];
+}
+
+table SpaceToBatchNDOptions {
+}
+
+table BatchToSpaceNDOptions {
+}
+
+table SkipGramOptions {
+  ngram_size: int;
+  max_skip_size: int;
+  include_all_ngrams: bool;
+}
+
+table SpaceToDepthOptions {
+  block_size: int;
+}
+
+table DepthToSpaceOptions {
+  block_size: int;
+}
+
+table SubOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table DivOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table TopKV2Options {
+}
+
+enum CombinerType : byte {
+  SUM = 0,
+  MEAN = 1,
+  SQRTN = 2,
+}
+
+table EmbeddingLookupSparseOptions {
+  combiner:CombinerType;
+}
+
+table GatherOptions {
+  axis: int;
+}
+
+table TransposeOptions {
+}
+
+table ExpOptions {
+}
+
+table CosOptions {
+}
+
+table ReducerOptions {
+  keep_dims: bool;
+}
+
+table SqueezeOptions {
+  squeeze_dims:[int];
+}
+
+table SplitOptions {
+  num_splits: int;
+}
+
+table SplitVOptions {
+  num_splits: int;
+}
+
+table StridedSliceOptions {
+  begin_mask: int;
+  end_mask: int;
+  ellipsis_mask: int;
+  new_axis_mask: int;
+  shrink_axis_mask: int;
+}
+
+table LogSoftmaxOptions {
+}
+
+table CastOptions {
+  in_data_type: TensorType;
+  out_data_type: TensorType;
+}
+
+table DequantizeOptions {
+}
+
+table MaximumMinimumOptions {
+}
+
+table TileOptions {
+}
+
+table ArgMaxOptions {
+  output_type : TensorType;
+}
+
+table ArgMinOptions {
+  output_type : TensorType;
+}
+
+table GreaterOptions {
+}
+
+table GreaterEqualOptions {
+}
+
+table LessOptions {
+}
+
+table LessEqualOptions {
+}
+
+table NegOptions {
+}
+
+table SelectOptions {
+}
+
+table SliceOptions {
+}
+
+table TransposeConvOptions {
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+}
+
+table ExpandDimsOptions {
+}
+
+table SparseToDenseOptions {
+  validate_indices:bool;
+}
+
+table EqualOptions {
+}
+
+table NotEqualOptions {
+}
+
+table ShapeOptions {
+  // Optional output type of the operation (int32 or int64). Defaults to int32.
+  out_type : TensorType;
+}
+
+table RankOptions {
+}
+
+table PowOptions {
+}
+
+table FakeQuantOptions {
+  // Parameters supported by version 1:
+  min:float;
+  max:float;
+  num_bits:int;
+
+  // Parameters supported by version 2:
+  narrow_range:bool;
+}
+
+table PackOptions {
+  values_count:int;
+  axis:int;
+}
+
+table LogicalOrOptions {
+}
+
+table OneHotOptions {
+  axis:int;
+}
+
+table AbsOptions {
+}
+
+
+table HardSwishOptions {
+}
+
+table LogicalAndOptions {
+}
+
+table LogicalNotOptions {
+}
+
+table UnpackOptions {
+  num:int;
+  axis:int;
+}
+
+table FloorDivOptions {
+}
+
+table SquareOptions {
+}
+
+table ZerosLikeOptions {
+}
+
+table FillOptions {
+}
+
+table FloorModOptions {
+}
+
+table RangeOptions {
+}
+
+table LeakyReluOptions {
+  alpha:float;
+}
+
+table SquaredDifferenceOptions {
+}
+
+enum MirrorPadMode : byte {
+  // Doesn't include borders.
+  REFLECT = 0,
+  // Includes borders.
+  SYMMETRIC = 1,
+}
+
+table MirrorPadOptions {
+  mode:MirrorPadMode;
+}
+
+table UniqueOptions {
+  idx_out_type:TensorType = INT32;
+}
+
+table ReverseV2Options {
+}
+
+table AddNOptions {
+}
+
+table GatherNdOptions {
+}
+
+table WhereOptions {
+}
+
+table ReverseSequenceOptions {
+  seq_dim:int;
+  batch_dim:int = 0;
+}
+
+table MatrixDiagOptions {
+}
+
+table QuantizeOptions {
+}
+
+table MatrixSetDiagOptions {
+}
+
+table IfOptions {
+  then_subgraph_index:int;
+  else_subgraph_index:int;
+}
+
+table WhileOptions {
+  cond_subgraph_index:int;
+  body_subgraph_index:int;
+}
+
+// An OperatorCode can be an enum value (BuiltinOperator) if the operator is a
+// builtin, or a string if the operator is custom.
+table OperatorCode {
+  builtin_code:BuiltinOperator;
+  custom_code:string;
+
+  // The version of the operator. The version need to be bumped whenever new
+  // parameters are introduced into an op.
+  version:int = 1;
+}
+
+enum CustomOptionsFormat : byte {
+  FLEXBUFFERS = 0,
+}
+
+// An operator takes tensors as inputs and outputs. The type of operation being
+// performed is determined by an index into the list of valid OperatorCodes,
+// while the specifics of each operations is configured using builtin_options
+// or custom_options.
+table Operator {
+  // Index into the operator_codes array. Using an integer here avoids
+  // complicate map lookups.
+  opcode_index:uint;
+
+  // Optional input and output tensors are indicated by -1.
+  inputs:[int];
+  outputs:[int];
+
+  builtin_options:BuiltinOptions;
+  custom_options:[ubyte];
+  custom_options_format:CustomOptionsFormat;
+
+  // A list of booleans indicating the input tensors which are being mutated by
+  // this operator.(e.g. used by RNN and LSTM).
+  // For example, if the "inputs" array refers to 5 tensors and the second and
+  // fifth are mutable variables, then this list will contain
+  // [false, true, false, false, true].
+  //
+  // If the list is empty, no variable is mutated in this operator.
+  // The list either has the same length as `inputs`, or is empty.
+  mutating_variable_inputs:[bool];
+
+  // A list of indices to the subgraph's "tensors" that are internal to an Op.
+  // Internal tensors are those that do not flow in or out of the operation,
+  // but instead are part of internal computation. As such, the operation's
+  // implementation may manage its memory more efficiently. They are needed
+  // however (i.e. not just an implementation detail) since they are part of the
+  // computation, which may require relevant metadata such as quantization
+  // parameters.
+  intermediates:[int];
+}
+
+// The root type, defining a subgraph, which typically represents an entire
+// model.
+table SubGraph {
+  // A list of all tensors used in this subgraph.
+  tensors:[Tensor];
+
+  // Indices of the tensors that are inputs into this subgraph. Note this is
+  // the list of non-static tensors that feed into the subgraph for inference.
+  inputs:[int];
+
+  // Indices of the tensors that are outputs out of this subgraph. Note this is
+  // the list of output tensors that are considered the product of the
+  // subgraph's inference.
+  outputs:[int];
+
+  // All operators, in execution order.
+  operators:[Operator];
+
+  // Name of this subgraph (used for debugging).
+  name:string;
+}
+
+// Table of raw data buffers (used for constant tensors). Referenced by tensors
+// by index. The generous alignment accommodates mmap-friendly data structures.
+table Buffer {
+  data:[ubyte] (force_align: 16);
+}
+
+table Metadata {
+  // A human readable string to uniquely identify a Metadata.
+  name:string;
+  // An index to the buffers table.
+  buffer:uint;
+}
+
+table Model {
+  // Version of the schema.
+  version:uint;
+
+  // A list of all operator codes used in this model. This is
+  // kept in order because operators carry an index into this
+  // vector.
+  operator_codes:[OperatorCode];
+
+  // All the subgraphs of the model. The 0th is assumed to be the main
+  // model.
+  subgraphs:[SubGraph];
+
+  // A description of the model.
+  description:string;
+
+  // Buffers of the model.
+  // Note the 0th entry of this array must be an empty buffer (sentinel).
+  // This is a convention so that tensors without a buffer can provide 0 as
+  // their buffer.
+  buffers:[Buffer];
+
+  // Metadata about the model. Indirects into the existings buffers list.
+  // Deprecated, prefer to use metadata field.
+  metadata_buffer:[int];
+
+  // Metadata about the model.
+  metadata:[Metadata];
+}
+
+root_type Model;
diff --git a/res/TensorFlowLiteSchema/2.1.0/schema.fbs b/res/TensorFlowLiteSchema/2.1.0/schema.fbs
new file mode 100644
index 000000000..f1fbfc655
--- /dev/null
+++ b/res/TensorFlowLiteSchema/2.1.0/schema.fbs
@@ -0,0 +1,940 @@
+// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// Revision History
+// Version 0: Initial version.
+// Version 1: Add subgraphs to schema.
+// Version 2: Rename operators to conform to NN API.
+// Version 3: Move buffer data from Model.Subgraph.Tensors to Model.Buffers.
+
+namespace tflite;
+
+// This corresponds to the version.
+file_identifier "TFL3";
+// File extension of any written files.
+file_extension "tflite";
+
+// IMPORTANT: All new members of tables, enums and unions must be added at the
+// end to ensure backwards compatibility.
+
+// The type of data stored in a tensor.
+enum TensorType : byte {
+  FLOAT32 = 0,
+  FLOAT16 = 1,
+  INT32 = 2,
+  UINT8 = 3,
+  INT64 = 4,
+  STRING = 5,
+  BOOL = 6,
+  INT16 = 7,
+  COMPLEX64 = 8,
+  INT8 = 9,
+}
+
+// Custom quantization parameters for experimenting with new quantization
+// techniques.
+table CustomQuantization {
+  custom:[ubyte] (force_align: 16);
+}
+
+// Represents a specific quantization technique's parameters.
+union QuantizationDetails {
+  CustomQuantization,
+}
+
+// Parameters for converting a quantized tensor back to float.
+table QuantizationParameters {
+  // These four parameters are the asymmetric linear quantization parameters.
+  // Given a quantized value q, the corresponding float value f should be:
+  //   f = scale * (q - zero_point)
+  // For other quantization types, the QuantizationDetails below is used.
+  min:[float];  // For importing back into tensorflow.
+  max:[float];  // For importing back into tensorflow.
+  scale:[float];  // For dequantizing the tensor's values.
+  zero_point:[long];
+
+  // If this is not none, the other quantization parameters (i.e. min, max,
+  // scale, zero_point fields above) are ignored and the value of the
+  // QuantizationDetails union should be used.
+  details:QuantizationDetails;
+
+  // Specifies the dimension of the Tensor's shape that the scales and
+  // zero_points correspond to. For example, a tensor t, with dims=[4, 3, 2, 1]
+  // with quantization params:
+  //   scale=[1.0, 2.0, 3.0], zero_point=[1, 2, 3], quantization_dimension=1
+  // will be quantized across the second dimension of t.
+  //   t[:, 0, :, :] will have scale[0]=1.0, zero_point[0]=1
+  //   t[:, 1, :, :] will have scale[1]=2.0, zero_point[0]=2
+  //   t[:, 2, :, :] will have scale[2]=3.0, zero_point[0]=3
+  quantized_dimension:int;
+}
+
+table Tensor {
+  // The tensor shape. The meaning of each entry is operator-specific but
+  // builtin ops use: [batch size, height, width, number of channels] (That's
+  // Tensorflow's NHWC).
+  shape:[int];
+  type:TensorType;
+  // An index that refers to the buffers table at the root of the model. Or,
+  // if there is no data buffer associated (i.e. intermediate results), then
+  // this is 0 (which refers to an always existent empty buffer).
+  //
+  // The data_buffer itself is an opaque container, with the assumption that the
+  // target device is little-endian. In addition, all builtin operators assume
+  // the memory is ordered such that if `shape` is [4, 3, 2], then index
+  // [i, j, k] maps to data_buffer[i*3*2 + j*2 + k].
+  buffer:uint;
+  name:string;  // For debugging and importing back into tensorflow.
+  quantization:QuantizationParameters;  // Optional.
+
+  is_variable:bool = false;
+}
+
+// A list of builtin operators. Builtin operators are slightly faster than custom
+// ones, but not by much. Moreover, while custom operators accept an opaque
+// object containing configuration parameters, builtins have a predetermined
+// set of acceptable options.
+enum BuiltinOperator : byte {
+  ADD = 0,
+  AVERAGE_POOL_2D = 1,
+  CONCATENATION = 2,
+  CONV_2D = 3,
+  DEPTHWISE_CONV_2D = 4,
+  DEPTH_TO_SPACE = 5,
+  DEQUANTIZE = 6,
+  EMBEDDING_LOOKUP = 7,
+  FLOOR = 8,
+  FULLY_CONNECTED = 9,
+  HASHTABLE_LOOKUP = 10,
+  L2_NORMALIZATION = 11,
+  L2_POOL_2D = 12,
+  LOCAL_RESPONSE_NORMALIZATION = 13,
+  LOGISTIC = 14,
+  LSH_PROJECTION = 15,
+  LSTM = 16,
+  MAX_POOL_2D = 17,
+  MUL = 18,
+  RELU = 19,
+  // NOTE(aselle): RELU_N1_TO_1 used to be called RELU1, but it was renamed
+  // since different model developers use RELU1 in different ways. Never
+  // create another op called RELU1.
+  RELU_N1_TO_1 = 20,
+  RELU6 = 21,
+  RESHAPE = 22,
+  RESIZE_BILINEAR = 23,
+  RNN = 24,
+  SOFTMAX = 25,
+  SPACE_TO_DEPTH = 26,
+  SVDF = 27,
+  TANH = 28,
+  // TODO(aselle): Consider rename to CONCATENATE_EMBEDDINGS
+  CONCAT_EMBEDDINGS = 29,
+  SKIP_GRAM = 30,
+  CALL = 31,
+  CUSTOM = 32,
+  EMBEDDING_LOOKUP_SPARSE = 33,
+  PAD = 34,
+  UNIDIRECTIONAL_SEQUENCE_RNN = 35,
+  GATHER = 36,
+  BATCH_TO_SPACE_ND = 37,
+  SPACE_TO_BATCH_ND = 38,
+  TRANSPOSE = 39,
+  MEAN = 40,
+  SUB = 41,
+  DIV = 42,
+  SQUEEZE = 43,
+  UNIDIRECTIONAL_SEQUENCE_LSTM = 44,
+  STRIDED_SLICE = 45,
+  BIDIRECTIONAL_SEQUENCE_RNN = 46,
+  EXP = 47,
+  TOPK_V2 = 48,
+  SPLIT = 49,
+  LOG_SOFTMAX = 50,
+  // DELEGATE is a special op type for the operations which are delegated to
+  // other backends.
+  // WARNING: Experimental interface, subject to change
+  DELEGATE = 51,
+  BIDIRECTIONAL_SEQUENCE_LSTM = 52,
+  CAST = 53,
+  PRELU = 54,
+  MAXIMUM = 55,
+  ARG_MAX = 56,
+  MINIMUM = 57,
+  LESS = 58,
+  NEG = 59,
+  PADV2 = 60,
+  GREATER = 61,
+  GREATER_EQUAL = 62,
+  LESS_EQUAL = 63,
+  SELECT = 64,
+  SLICE = 65,
+  SIN = 66,
+  TRANSPOSE_CONV = 67,
+  SPARSE_TO_DENSE = 68,
+  TILE = 69,
+  EXPAND_DIMS = 70,
+  EQUAL = 71,
+  NOT_EQUAL = 72,
+  LOG = 73,
+  SUM = 74,
+  SQRT = 75,
+  RSQRT = 76,
+  SHAPE = 77,
+  POW = 78,
+  ARG_MIN = 79,
+  FAKE_QUANT = 80,
+  REDUCE_PROD = 81,
+  REDUCE_MAX = 82,
+  PACK = 83,
+  LOGICAL_OR = 84,
+  ONE_HOT = 85,
+  LOGICAL_AND = 86,
+  LOGICAL_NOT = 87,
+  UNPACK = 88,
+  REDUCE_MIN = 89,
+  FLOOR_DIV = 90,
+  REDUCE_ANY = 91,
+  SQUARE = 92,
+  ZEROS_LIKE = 93,
+  FILL = 94,
+  FLOOR_MOD = 95,
+  RANGE = 96,
+  RESIZE_NEAREST_NEIGHBOR = 97,
+  LEAKY_RELU = 98,
+  SQUARED_DIFFERENCE = 99,
+  MIRROR_PAD = 100,
+  ABS = 101,
+  SPLIT_V = 102,
+  UNIQUE = 103,
+  CEIL = 104,
+  REVERSE_V2 = 105,
+  ADD_N = 106,
+  GATHER_ND = 107,
+  COS = 108,
+  WHERE = 109,
+  RANK = 110,
+  ELU = 111,
+  REVERSE_SEQUENCE = 112,
+  MATRIX_DIAG = 113,
+  QUANTIZE = 114,
+  MATRIX_SET_DIAG = 115,
+  ROUND = 116,
+  HARD_SWISH = 117,
+  IF = 118,
+  WHILE = 119,
+  NON_MAX_SUPPRESSION_V4 = 120,
+  NON_MAX_SUPPRESSION_V5 = 121,
+  SCATTER_ND = 122
+}
+
+// Options for the builtin operators.
+union BuiltinOptions {
+  Conv2DOptions,
+  DepthwiseConv2DOptions,
+  ConcatEmbeddingsOptions,
+  LSHProjectionOptions,
+  Pool2DOptions,
+  SVDFOptions,
+  RNNOptions,
+  FullyConnectedOptions,
+  SoftmaxOptions,
+  ConcatenationOptions,
+  AddOptions,
+  L2NormOptions,
+  LocalResponseNormalizationOptions,
+  LSTMOptions,
+  ResizeBilinearOptions,
+  CallOptions,
+  ReshapeOptions,
+  SkipGramOptions,
+  SpaceToDepthOptions,
+  EmbeddingLookupSparseOptions,
+  MulOptions,
+  PadOptions,
+  GatherOptions,
+  BatchToSpaceNDOptions,
+  SpaceToBatchNDOptions,
+  TransposeOptions,
+  ReducerOptions,
+  SubOptions,
+  DivOptions,
+  SqueezeOptions,
+  SequenceRNNOptions,
+  StridedSliceOptions,
+  ExpOptions,
+  TopKV2Options,
+  SplitOptions,
+  LogSoftmaxOptions,
+  CastOptions,
+  DequantizeOptions,
+  MaximumMinimumOptions,
+  ArgMaxOptions,
+  LessOptions,
+  NegOptions,
+  PadV2Options,
+  GreaterOptions,
+  GreaterEqualOptions,
+  LessEqualOptions,
+  SelectOptions,
+  SliceOptions,
+  TransposeConvOptions,
+  SparseToDenseOptions,
+  TileOptions,
+  ExpandDimsOptions,
+  EqualOptions,
+  NotEqualOptions,
+  ShapeOptions,
+  PowOptions,
+  ArgMinOptions,
+  FakeQuantOptions,
+  PackOptions,
+  LogicalOrOptions,
+  OneHotOptions,
+  LogicalAndOptions,
+  LogicalNotOptions,
+  UnpackOptions,
+  FloorDivOptions,
+  SquareOptions,
+  ZerosLikeOptions,
+  FillOptions,
+  BidirectionalSequenceLSTMOptions,
+  BidirectionalSequenceRNNOptions,
+  UnidirectionalSequenceLSTMOptions,
+  FloorModOptions,
+  RangeOptions,
+  ResizeNearestNeighborOptions,
+  LeakyReluOptions,
+  SquaredDifferenceOptions,
+  MirrorPadOptions,
+  AbsOptions,
+  SplitVOptions,
+  UniqueOptions,
+  ReverseV2Options,
+  AddNOptions,
+  GatherNdOptions,
+  CosOptions,
+  WhereOptions,
+  RankOptions,
+  ReverseSequenceOptions,
+  MatrixDiagOptions,
+  QuantizeOptions,
+  MatrixSetDiagOptions,
+  HardSwishOptions,
+  IfOptions,
+  WhileOptions,
+  DepthToSpaceOptions,
+  NonMaxSuppressionV4Options,
+  NonMaxSuppressionV5Options,
+  ScatterNdOptions
+}
+
+enum Padding : byte { SAME, VALID }
+
+enum ActivationFunctionType : byte {
+  NONE = 0,
+  RELU = 1,
+  RELU_N1_TO_1 = 2,
+  RELU6 = 3,
+  TANH = 4,
+  SIGN_BIT = 5,
+}
+
+table Conv2DOptions {
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+  fused_activation_function:ActivationFunctionType;
+  dilation_w_factor:int = 1;
+  dilation_h_factor:int = 1;
+}
+
+table Pool2DOptions {
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+  filter_width:int;
+  filter_height:int;
+  fused_activation_function:ActivationFunctionType;
+}
+
+table DepthwiseConv2DOptions {
+  // Parameters for DepthwiseConv version 1 or above.
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+  // `depth_multiplier` is redundant. It's used by CPU kernels in
+  // TensorFlow 2.0 or below, but ignored in versions above.
+  // See comments in lite/c/builtin_op_data.h for more details.
+  depth_multiplier:int;
+  fused_activation_function:ActivationFunctionType;
+  // Parameters for DepthwiseConv version 2 or above.
+  dilation_w_factor:int = 1;
+  dilation_h_factor:int = 1;
+}
+
+table ConcatEmbeddingsOptions {
+  num_channels:int;
+  num_columns_per_channel:[int];
+  embedding_dim_per_channel:[int]; // This could be inferred from parameters.
+}
+
+enum LSHProjectionType: byte {
+  UNKNOWN = 0,
+  SPARSE = 1,
+  DENSE = 2,
+}
+
+table LSHProjectionOptions {
+  type: LSHProjectionType;
+}
+
+table SVDFOptions {
+  rank:int;
+  fused_activation_function:ActivationFunctionType;
+}
+
+// An implementation of TensorFlow RNNCell.
+table RNNOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+// An implementation of TensorFlow dynamic_rnn with RNNCell.
+table SequenceRNNOptions {
+  time_major:bool;
+  fused_activation_function:ActivationFunctionType;
+}
+
+// An implementation of TensorFlow bidrectional_dynamic_rnn with RNNCell.
+table BidirectionalSequenceRNNOptions {
+  time_major:bool;
+  fused_activation_function:ActivationFunctionType;
+  merge_outputs: bool;
+}
+
+enum FullyConnectedOptionsWeightsFormat: byte {
+  DEFAULT = 0,
+  SHUFFLED4x16INT8 = 1,
+}
+
+// An implementation of TensorFlow fully_connected (a.k.a Dense) layer.
+table FullyConnectedOptions {
+  // Parameters for FullyConnected version 1 or above.
+  fused_activation_function:ActivationFunctionType;
+
+  // Parameters for FullyConnected version 2 or above.
+  weights_format:FullyConnectedOptionsWeightsFormat = DEFAULT;
+
+  // Parameters for FullyConnected version 5 or above.
+  // If set to true, then the number of dimension is preserved. Furthermore,
+  // all but the last dimension of the input and output shapes will be equal.
+  keep_num_dims: bool;
+}
+
+table SoftmaxOptions {
+  beta: float;
+}
+
+// An implementation of TensorFlow concat.
+table ConcatenationOptions {
+  axis:int;
+  fused_activation_function:ActivationFunctionType;
+}
+
+table AddOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table MulOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table L2NormOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table LocalResponseNormalizationOptions {
+  radius:int;
+  bias:float;
+  alpha:float;
+  beta:float;
+}
+
+enum LSTMKernelType : byte {
+  // Full LSTM kernel which supports peephole and projection.
+  FULL = 0,
+  // Basic LSTM kernels. Equivalent to TensorFlow BasicLSTMCell.
+  BASIC = 1,
+}
+
+// An implementation of TensorFlow LSTMCell and CoupledInputForgetGateLSTMCell
+table LSTMOptions {
+  // Parameters for LSTM version 1 or above.
+  fused_activation_function:ActivationFunctionType;
+  cell_clip: float; // Optional, 0.0 means no clipping
+  proj_clip: float; // Optional, 0.0 means no clipping
+
+  // Parameters for LSTM version 2 or above.
+  // Basic kernel is only supported in version 2 or above.
+  kernel_type: LSTMKernelType = FULL;
+}
+
+// An implementation of TensorFlow dynamic_rnn with LSTMCell.
+table UnidirectionalSequenceLSTMOptions {
+  fused_activation_function:ActivationFunctionType;
+  cell_clip: float; // Optional, 0.0 means no clipping
+  proj_clip: float; // Optional, 0.0 means no clipping
+
+  // If true then first dimension is sequence, otherwise batch.
+  time_major:bool;
+}
+
+table BidirectionalSequenceLSTMOptions {
+  // Parameters supported by version 1:
+  fused_activation_function:ActivationFunctionType;
+  cell_clip: float; // Optional, 0.0 means no clipping
+  proj_clip: float; // Optional, 0.0 means no clipping
+
+  // If true, store the outputs of both directions into the first output.
+  merge_outputs: bool;
+
+  // Parameters supported by version 2:
+  // If true then first dimension is sequence, otherwise batch.
+  // Version 1 implementations assumed time_major to be true, so this default
+  // value should never change.
+  time_major: bool = true;
+}
+
+table ResizeBilinearOptions {
+  new_height: int (deprecated);
+  new_width: int (deprecated);
+  align_corners: bool;
+}
+
+table ResizeNearestNeighborOptions {
+  align_corners: bool;
+}
+
+// A call operation options
+table CallOptions {
+  // The subgraph index that needs to be called.
+  subgraph:uint;
+}
+
+table PadOptions {
+}
+
+table PadV2Options {
+}
+
+table ReshapeOptions {
+  new_shape:[int];
+}
+
+table SpaceToBatchNDOptions {
+}
+
+table BatchToSpaceNDOptions {
+}
+
+table SkipGramOptions {
+  ngram_size: int;
+  max_skip_size: int;
+  include_all_ngrams: bool;
+}
+
+table SpaceToDepthOptions {
+  block_size: int;
+}
+
+table DepthToSpaceOptions {
+  block_size: int;
+}
+
+table SubOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table DivOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table TopKV2Options {
+}
+
+enum CombinerType : byte {
+  SUM = 0,
+  MEAN = 1,
+  SQRTN = 2,
+}
+
+table EmbeddingLookupSparseOptions {
+  combiner:CombinerType;
+}
+
+table GatherOptions {
+  axis: int;
+}
+
+table TransposeOptions {
+}
+
+table ExpOptions {
+}
+
+table CosOptions {
+}
+
+table ReducerOptions {
+  keep_dims: bool;
+}
+
+table SqueezeOptions {
+  squeeze_dims:[int];
+}
+
+table SplitOptions {
+  num_splits: int;
+}
+
+table SplitVOptions {
+  num_splits: int;
+}
+
+table StridedSliceOptions {
+  begin_mask: int;
+  end_mask: int;
+  ellipsis_mask: int;
+  new_axis_mask: int;
+  shrink_axis_mask: int;
+}
+
+table LogSoftmaxOptions {
+}
+
+table CastOptions {
+  in_data_type: TensorType;
+  out_data_type: TensorType;
+}
+
+table DequantizeOptions {
+}
+
+table MaximumMinimumOptions {
+}
+
+table TileOptions {
+}
+
+table ArgMaxOptions {
+  output_type : TensorType;
+}
+
+table ArgMinOptions {
+  output_type : TensorType;
+}
+
+table GreaterOptions {
+}
+
+table GreaterEqualOptions {
+}
+
+table LessOptions {
+}
+
+table LessEqualOptions {
+}
+
+table NegOptions {
+}
+
+table SelectOptions {
+}
+
+table SliceOptions {
+}
+
+table TransposeConvOptions {
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+}
+
+table ExpandDimsOptions {
+}
+
+table SparseToDenseOptions {
+  validate_indices:bool;
+}
+
+table EqualOptions {
+}
+
+table NotEqualOptions {
+}
+
+table ShapeOptions {
+  // Optional output type of the operation (int32 or int64). Defaults to int32.
+  out_type : TensorType;
+}
+
+table RankOptions {
+}
+
+table PowOptions {
+}
+
+table FakeQuantOptions {
+  // Parameters supported by version 1:
+  min:float;
+  max:float;
+  num_bits:int;
+
+  // Parameters supported by version 2:
+  narrow_range:bool;
+}
+
+table PackOptions {
+  values_count:int;
+  axis:int;
+}
+
+table LogicalOrOptions {
+}
+
+table OneHotOptions {
+  axis:int;
+}
+
+table AbsOptions {
+}
+
+
+table HardSwishOptions {
+}
+
+table LogicalAndOptions {
+}
+
+table LogicalNotOptions {
+}
+
+table UnpackOptions {
+  num:int;
+  axis:int;
+}
+
+table FloorDivOptions {
+}
+
+table SquareOptions {
+}
+
+table ZerosLikeOptions {
+}
+
+table FillOptions {
+}
+
+table FloorModOptions {
+}
+
+table RangeOptions {
+}
+
+table LeakyReluOptions {
+  alpha:float;
+}
+
+table SquaredDifferenceOptions {
+}
+
+enum MirrorPadMode : byte {
+  // Doesn't include borders.
+  REFLECT = 0,
+  // Includes borders.
+  SYMMETRIC = 1,
+}
+
+table MirrorPadOptions {
+  mode:MirrorPadMode;
+}
+
+table UniqueOptions {
+  idx_out_type:TensorType = INT32;
+}
+
+table ReverseV2Options {
+}
+
+table AddNOptions {
+}
+
+table GatherNdOptions {
+}
+
+table WhereOptions {
+}
+
+table ReverseSequenceOptions {
+  seq_dim:int;
+  batch_dim:int = 0;
+}
+
+table MatrixDiagOptions {
+}
+
+table QuantizeOptions {
+}
+
+table MatrixSetDiagOptions {
+}
+
+table IfOptions {
+  then_subgraph_index:int;
+  else_subgraph_index:int;
+}
+
+table WhileOptions {
+  cond_subgraph_index:int;
+  body_subgraph_index:int;
+}
+
+table NonMaxSuppressionV4Options {
+}
+
+table NonMaxSuppressionV5Options {
+}
+
+table ScatterNdOptions {
+}
+
+// An OperatorCode can be an enum value (BuiltinOperator) if the operator is a
+// builtin, or a string if the operator is custom.
+table OperatorCode {
+  builtin_code:BuiltinOperator;
+  custom_code:string;
+
+  // The version of the operator. The version need to be bumped whenever new
+  // parameters are introduced into an op.
+  version:int = 1;
+}
+
+enum CustomOptionsFormat : byte {
+  FLEXBUFFERS = 0,
+}
+
+// An operator takes tensors as inputs and outputs. The type of operation being
+// performed is determined by an index into the list of valid OperatorCodes,
+// while the specifics of each operations is configured using builtin_options
+// or custom_options.
+table Operator {
+  // Index into the operator_codes array. Using an integer here avoids
+  // complicate map lookups.
+  opcode_index:uint;
+
+  // Optional input and output tensors are indicated by -1.
+  inputs:[int];
+  outputs:[int];
+
+  builtin_options:BuiltinOptions;
+  custom_options:[ubyte];
+  custom_options_format:CustomOptionsFormat;
+
+  // A list of booleans indicating the input tensors which are being mutated by
+  // this operator.(e.g. used by RNN and LSTM).
+  // For example, if the "inputs" array refers to 5 tensors and the second and
+  // fifth are mutable variables, then this list will contain
+  // [false, true, false, false, true].
+  //
+  // If the list is empty, no variable is mutated in this operator.
+  // The list either has the same length as `inputs`, or is empty.
+  mutating_variable_inputs:[bool];
+
+  // A list of indices to the subgraph's "tensors" that are internal to an Op.
+  // Internal tensors are those that do not flow in or out of the operation,
+  // but instead are part of internal computation. As such, the operation's
+  // implementation may manage its memory more efficiently. They are needed
+  // however (i.e. not just an implementation detail) since they are part of the
+  // computation, which may require relevant metadata such as quantization
+  // parameters.
+  intermediates:[int];
+}
+
+// The root type, defining a subgraph, which typically represents an entire
+// model.
+table SubGraph {
+  // A list of all tensors used in this subgraph.
+  tensors:[Tensor];
+
+  // Indices of the tensors that are inputs into this subgraph. Note this is
+  // the list of non-static tensors that feed into the subgraph for inference.
+  inputs:[int];
+
+  // Indices of the tensors that are outputs out of this subgraph. Note this is
+  // the list of output tensors that are considered the product of the
+  // subgraph's inference.
+  outputs:[int];
+
+  // All operators, in execution order.
+  operators:[Operator];
+
+  // Name of this subgraph (used for debugging).
+  name:string;
+}
+
+// Table of raw data buffers (used for constant tensors). Referenced by tensors
+// by index. The generous alignment accommodates mmap-friendly data structures.
+table Buffer {
+  data:[ubyte] (force_align: 16);
+}
+
+table Metadata {
+  // A human readable string to uniquely identify a Metadata.
+  name:string;
+  // An index to the buffers table.
+  buffer:uint;
+}
+
+table Model {
+  // Version of the schema.
+  version:uint;
+
+  // A list of all operator codes used in this model. This is
+  // kept in order because operators carry an index into this
+  // vector.
+  operator_codes:[OperatorCode];
+
+  // All the subgraphs of the model. The 0th is assumed to be the main
+  // model.
+  subgraphs:[SubGraph];
+
+  // A description of the model.
+  description:string;
+
+  // Buffers of the model.
+  // Note the 0th entry of this array must be an empty buffer (sentinel).
+  // This is a convention so that tensors without a buffer can provide 0 as
+  // their buffer.
+  buffers:[Buffer];
+
+  // Metadata about the model. Indirects into the existings buffers list.
+  // Deprecated, prefer to use metadata field.
+  metadata_buffer:[int];
+
+  // Metadata about the model.
+  metadata:[Metadata];
+}
+
+root_type Model;
diff --git a/res/TensorFlowLiteSchema/README.md b/res/TensorFlowLiteSchema/README.md
new file mode 100644
index 000000000..b99cfe4a6
--- /dev/null
+++ b/res/TensorFlowLiteSchema/README.md
@@ -0,0 +1,7 @@
+# TensorFlow Lite Schema
+
+A collection of T/F Lite schema files (for each version)
+
+## How to add a new schema?
+
+Update [SCHEMA.lst](SCHEMA.lst) and run "download.sh".
diff --git a/res/TensorFlowLiteSchema/SCHEMA.lst b/res/TensorFlowLiteSchema/SCHEMA.lst
new file mode 100644
index 000000000..f264b0c9f
--- /dev/null
+++ b/res/TensorFlowLiteSchema/SCHEMA.lst
@@ -0,0 +1,5 @@
+VERSION,URL
+1.13.1,https://raw.githubusercontent.com/tensorflow/tensorflow/v1.13.1/tensorflow/lite/schema/schema.fbs
+1.14.0,https://raw.githubusercontent.com/tensorflow/tensorflow/v1.14.0/tensorflow/lite/schema/schema.fbs
+1.15.2,https://raw.githubusercontent.com/tensorflow/tensorflow/v1.15.2/tensorflow/lite/schema/schema.fbs
+2.1.0,https://raw.githubusercontent.com/tensorflow/tensorflow/v2.1.0/tensorflow/lite/schema/schema.fbs
diff --git a/res/TensorFlowLiteSchema/download.sh b/res/TensorFlowLiteSchema/download.sh
new file mode 100755
index 000000000..9a946c3d9
--- /dev/null
+++ b/res/TensorFlowLiteSchema/download.sh
@@ -0,0 +1,9 @@
+#!/bin/bash
+
+while IFS=',' read -r VERSION URL
+do
+  echo "Download ${VERSION} from '${URL}'"
+  mkdir -p "${VERSION}"
+  wget -nv -O "${VERSION}/schema.fbs" "${URL}"
+  echo "Download ${VERSION} from '${URL}' - Done"
+done < <(cat SCHEMA.lst | tail -n +2)
diff --git a/res/TensorFlowPythonExamples/.gitignore b/res/TensorFlowPythonExamples/.gitignore
new file mode 100644
index 000000000..bee8a64b7
--- /dev/null
+++ b/res/TensorFlowPythonExamples/.gitignore
@@ -0,0 +1 @@
+__pycache__
diff --git a/res/TensorFlowPythonExamples/README.md b/res/TensorFlowPythonExamples/README.md
new file mode 100644
index 000000000..63e249b93
--- /dev/null
+++ b/res/TensorFlowPythonExamples/README.md
@@ -0,0 +1,31 @@
+# TensorFlow Python Examples
+
+## Prerequisite
+
+- Python 3.X
+- TensorFlow 1.13.1
+
+## Directory Layout
+
+```
+tfpem.py <- TensorFlow Python Example Manager
+examples/
+  [EXAMPLE NAME]/
+    __init__.py
+```
+
+## HOWTO: Create a Python environment
+
+TBA
+
+## HOWTO: Generate a pbtxt from examples
+
+```
+$ /path/to/python -B <path/to/tfpem.py> [EXAMPLE NAME 1] [EXANMPE NAME 2] ...
+```
+
+NOTE. Add "-B" option not to generate "__pycache__".
+
+## HOWTO: Add a new example
+
+TBA
diff --git a/res/TensorFlowPythonExamples/examples/abs/__init__.py b/res/TensorFlowPythonExamples/examples/abs/__init__.py
new file mode 100755
index 000000000..fd5515595
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/abs/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+abs_ = tf.compat.v1.abs(in_)
diff --git a/res/TensorFlowPythonExamples/examples/add/__init__.py b/res/TensorFlowPythonExamples/examples/add/__init__.py
new file mode 100755
index 000000000..7e283f35f
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/add/__init__.py
@@ -0,0 +1,5 @@
+import tensorflow as tf
+
+lhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+rhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+op_ = tf.compat.v1.add(lhs_, rhs_)
diff --git a/res/TensorFlowPythonExamples/examples/argmax/__init__.py b/res/TensorFlowPythonExamples/examples/argmax/__init__.py
new file mode 100755
index 000000000..059df97f9
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/argmax/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(1, 1), name="Hole")
+op_ = tf.compat.v1.math.argmax(in_)
diff --git a/res/TensorFlowPythonExamples/examples/biasadd/__init__.py b/res/TensorFlowPythonExamples/examples/biasadd/__init__.py
new file mode 100755
index 000000000..eb8a69bc3
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/biasadd/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(1, 1, 2, 3), name="Hole")
+op_ = tf.nn.bias_add(in_, bias=[1.0, 1.0, -1.0], data_format="NHWC")
diff --git a/res/TensorFlowPythonExamples/examples/cos/__init__.py b/res/TensorFlowPythonExamples/examples/cos/__init__.py
new file mode 100755
index 000000000..cfce5d830
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/cos/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+op_ = tf.compat.v1.cos(in_)
diff --git a/res/TensorFlowPythonExamples/examples/div/__init__.py b/res/TensorFlowPythonExamples/examples/div/__init__.py
new file mode 100755
index 000000000..2887771ff
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/div/__init__.py
@@ -0,0 +1,5 @@
+import tensorflow as tf
+
+lhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+rhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+op_ = tf.compat.v1.div(lhs_, rhs_)
diff --git a/res/TensorFlowPythonExamples/examples/elu/__init__.py b/res/TensorFlowPythonExamples/examples/elu/__init__.py
new file mode 100755
index 000000000..b41f65111
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/elu/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(1, 1), name="Hole")
+elu_ = tf.compat.v1.nn.elu(in_)
diff --git a/res/TensorFlowPythonExamples/examples/exp/__init__.py b/res/TensorFlowPythonExamples/examples/exp/__init__.py
new file mode 100644
index 000000000..e83638436
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/exp/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+op_ = tf.compat.v1.exp(in_)
diff --git a/res/TensorFlowPythonExamples/examples/floor/__init__.py b/res/TensorFlowPythonExamples/examples/floor/__init__.py
new file mode 100755
index 000000000..3b3f5bfc3
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/floor/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(1, 1), name="Hole")
+op_ = tf.compat.v1.floor(in_)
diff --git a/res/TensorFlowPythonExamples/examples/floordiv/__init__.py b/res/TensorFlowPythonExamples/examples/floordiv/__init__.py
new file mode 100755
index 000000000..34f413f2b
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/floordiv/__init__.py
@@ -0,0 +1,5 @@
+import tensorflow as tf
+
+lhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+rhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+op_ = tf.compat.v1.floordiv(lhs_, rhs_)
diff --git a/res/TensorFlowPythonExamples/examples/greater/__init__.py b/res/TensorFlowPythonExamples/examples/greater/__init__.py
new file mode 100755
index 000000000..e88f57471
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/greater/__init__.py
@@ -0,0 +1,5 @@
+import tensorflow as tf
+
+lhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+rhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+op_ = tf.compat.v1.greater(lhs_, rhs_)
diff --git a/res/TensorFlowPythonExamples/examples/greater_equal/__init__.py b/res/TensorFlowPythonExamples/examples/greater_equal/__init__.py
new file mode 100755
index 000000000..b15fbd324
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/greater_equal/__init__.py
@@ -0,0 +1,5 @@
+import tensorflow as tf
+
+lhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+rhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+op_ = tf.compat.v1.greater_equal(lhs_, rhs_)
diff --git a/res/TensorFlowPythonExamples/examples/leaky_relu/__init__.py b/res/TensorFlowPythonExamples/examples/leaky_relu/__init__.py
new file mode 100755
index 000000000..d595edbd0
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/leaky_relu/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(1, 1), name="Hole")
+op_ = tf.compat.v1.nn.leaky_relu(in_)
diff --git a/res/TensorFlowPythonExamples/examples/less/__init__.py b/res/TensorFlowPythonExamples/examples/less/__init__.py
new file mode 100755
index 000000000..41ba18c62
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/less/__init__.py
@@ -0,0 +1,5 @@
+import tensorflow as tf
+
+lhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+rhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+op_ = tf.compat.v1.less(lhs_, rhs_)
diff --git a/res/TensorFlowPythonExamples/examples/less_equal/__init__.py b/res/TensorFlowPythonExamples/examples/less_equal/__init__.py
new file mode 100755
index 000000000..d60bf2a73
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/less_equal/__init__.py
@@ -0,0 +1,5 @@
+import tensorflow as tf
+
+lhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+rhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+op_ = tf.compat.v1.less_equal(lhs_, rhs_)
diff --git a/res/TensorFlowPythonExamples/examples/logical_not/__init__.py b/res/TensorFlowPythonExamples/examples/logical_not/__init__.py
new file mode 100755
index 000000000..f1bcc2c8f
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/logical_not/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.bool, shape=(4, 4), name="Hole")
+op_ = tf.compat.v1.logical_not(in_)
diff --git a/res/TensorFlowPythonExamples/examples/logical_or/__init__.py b/res/TensorFlowPythonExamples/examples/logical_or/__init__.py
new file mode 100755
index 000000000..991d61ab9
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/logical_or/__init__.py
@@ -0,0 +1,5 @@
+import tensorflow as tf
+
+lhs_ = tf.compat.v1.placeholder(dtype=tf.bool, shape=(4, 4), name="Hole")
+rhs_ = tf.compat.v1.placeholder(dtype=tf.bool, shape=(4, 4), name="Hole")
+op_ = tf.compat.v1.logical_or(lhs_, rhs_)
diff --git a/res/TensorFlowPythonExamples/examples/matmul/__init__.py b/res/TensorFlowPythonExamples/examples/matmul/__init__.py
new file mode 100755
index 000000000..760241de7
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/matmul/__init__.py
@@ -0,0 +1,5 @@
+import tensorflow as tf
+
+lhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(3, 4), name="Hole")
+rhs_ = tf.compat.v1.constant(dtype=tf.float32, shape=(4, 4), name="Hole", value=1.0)
+op_ = tf.compat.v1.matmul(lhs_, rhs_)
diff --git a/res/TensorFlowPythonExamples/examples/multiply/__init__.py b/res/TensorFlowPythonExamples/examples/multiply/__init__.py
new file mode 100755
index 000000000..da8885660
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/multiply/__init__.py
@@ -0,0 +1,5 @@
+import tensorflow as tf
+
+lhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+rhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+op_ = tf.compat.v1.multiply(lhs_, rhs_)
diff --git a/res/TensorFlowPythonExamples/examples/not_equal/__init__.py b/res/TensorFlowPythonExamples/examples/not_equal/__init__.py
new file mode 100755
index 000000000..95073fe4a
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/not_equal/__init__.py
@@ -0,0 +1,5 @@
+import tensorflow as tf
+
+lhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+rhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+op_ = tf.compat.v1.not_equal(lhs_, rhs_)
diff --git a/res/TensorFlowPythonExamples/examples/pack/__init__.py b/res/TensorFlowPythonExamples/examples/pack/__init__.py
new file mode 100755
index 000000000..609bc9b76
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/pack/__init__.py
@@ -0,0 +1,5 @@
+import tensorflow as tf
+
+in_1 = tf.compat.v1.placeholder(dtype=tf.float32, shape=(2, 3, 4), name="Hole")
+in_2 = tf.compat.v1.placeholder(dtype=tf.float32, shape=(2, 3, 4), name="Hole")
+op_ = tf.compat.v1.stack([in_1, in_2])
diff --git a/res/TensorFlowPythonExamples/examples/pad/__init__.py b/res/TensorFlowPythonExamples/examples/pad/__init__.py
new file mode 100755
index 000000000..ac5cf81fa
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/pad/__init__.py
@@ -0,0 +1,5 @@
+import tensorflow as tf
+
+tensor_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(2, 3), name="Hole")
+paddings_ = tf.compat.v1.constant([[1, 1], [2, 2]], name="Hole")
+op_ = tf.compat.v1.pad(tensor_, paddings_)
diff --git a/res/TensorFlowPythonExamples/examples/pow/__init__.py b/res/TensorFlowPythonExamples/examples/pow/__init__.py
new file mode 100755
index 000000000..960032a84
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/pow/__init__.py
@@ -0,0 +1,5 @@
+import tensorflow as tf
+
+lhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+rhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+op_ = tf.compat.v1.pow(lhs_, rhs_)
diff --git a/res/TensorFlowPythonExamples/examples/prelu/__init__.py b/res/TensorFlowPythonExamples/examples/prelu/__init__.py
new file mode 100755
index 000000000..2ab030265
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/prelu/__init__.py
@@ -0,0 +1,7 @@
+import tensorflow as tf
+from tensorflow.compat.v1.keras import layers
+
+model = tf.compat.v1.keras.Sequential()
+model.add(layers.PReLU())
+# TODO Find a way to freeze Keras model for inference
+model.build((1, 1))
diff --git a/res/TensorFlowPythonExamples/examples/relu/__init__.py b/res/TensorFlowPythonExamples/examples/relu/__init__.py
new file mode 100755
index 000000000..a144a1212
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/relu/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(1, 1), name="Hole")
+op_ = tf.compat.v1.nn.relu(in_)
diff --git a/res/TensorFlowPythonExamples/examples/relu6/__init__.py b/res/TensorFlowPythonExamples/examples/relu6/__init__.py
new file mode 100755
index 000000000..f58ae7c2c
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/relu6/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(1, 1), name="Hole")
+op_ = tf.compat.v1.nn.relu6(in_)
diff --git a/res/TensorFlowPythonExamples/examples/reshape/__init.py__ b/res/TensorFlowPythonExamples/examples/reshape/__init.py__
new file mode 100644
index 000000000..3afe7efa9
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/reshape/__init.py__
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+op_ = tf.compat.v1.reshape(in_, shape=[2,2,2,2])
diff --git a/res/TensorFlowPythonExamples/examples/resize_bilinear/__init__.py b/res/TensorFlowPythonExamples/examples/resize_bilinear/__init__.py
new file mode 100755
index 000000000..422bf1db5
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/resize_bilinear/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(1, 8, 8, 3), name="Hole")
+op_ = tf.compat.v1.image.resize_bilinear(in_, [16, 16])
diff --git a/res/TensorFlowPythonExamples/examples/resize_nearest_neighbor/__init__.py b/res/TensorFlowPythonExamples/examples/resize_nearest_neighbor/__init__.py
new file mode 100755
index 000000000..a14022948
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/resize_nearest_neighbor/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(1, 8, 8, 3), name="Hole")
+op_ = tf.compat.v1.image.resize_nearest_neighbor(in_, [16, 16])
diff --git a/res/TensorFlowPythonExamples/examples/rsqrt/__init__.py b/res/TensorFlowPythonExamples/examples/rsqrt/__init__.py
new file mode 100755
index 000000000..90500bd11
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/rsqrt/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(1, 1), name="Hole")
+op_ = tf.compat.v1.rsqrt(in_)
diff --git a/res/TensorFlowPythonExamples/examples/sigmoid/__init__.py b/res/TensorFlowPythonExamples/examples/sigmoid/__init__.py
new file mode 100755
index 000000000..43328f2cb
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/sigmoid/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(1, 1), name="Hole")
+op_ = tf.compat.v1.nn.sigmoid(in_)
diff --git a/res/TensorFlowPythonExamples/examples/softmax/__init__.py b/res/TensorFlowPythonExamples/examples/softmax/__init__.py
new file mode 100755
index 000000000..5b8d1cdfb
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/softmax/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(1, 1), name="Hole")
+op_ = tf.compat.v1.nn.softmax(in_)
diff --git a/res/TensorFlowPythonExamples/examples/sqrt/__init__.py b/res/TensorFlowPythonExamples/examples/sqrt/__init__.py
new file mode 100755
index 000000000..4aab5da9c
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/sqrt/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(1, 1), name="Hole")
+op_ = tf.compat.v1.sqrt(in_)
diff --git a/res/TensorFlowPythonExamples/examples/subtract/__init__.py b/res/TensorFlowPythonExamples/examples/subtract/__init__.py
new file mode 100755
index 000000000..feb11b12e
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/subtract/__init__.py
@@ -0,0 +1,5 @@
+import tensorflow as tf
+
+lhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+rhs_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(4, 4), name="Hole")
+op_ = tf.compat.v1.subtract(lhs_, rhs_)
diff --git a/res/TensorFlowPythonExamples/examples/tanh/__init__.py b/res/TensorFlowPythonExamples/examples/tanh/__init__.py
new file mode 100755
index 000000000..dd202a78d
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/tanh/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(1, 1), name="Hole")
+op_ = tf.compat.v1.tanh(in_)
diff --git a/res/TensorFlowPythonExamples/examples/yuv_to_rgb/__init__.py b/res/TensorFlowPythonExamples/examples/yuv_to_rgb/__init__.py
new file mode 100755
index 000000000..5230bbac6
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/yuv_to_rgb/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(1, 16, 16, 3), name="Hole")
+op_ = tf.compat.v1.image.yuv_to_rgb(in_)
diff --git a/res/TensorFlowPythonExamples/requirements.txt b/res/TensorFlowPythonExamples/requirements.txt
new file mode 100644
index 000000000..2a938941f
--- /dev/null
+++ b/res/TensorFlowPythonExamples/requirements.txt
@@ -0,0 +1,18 @@
+absl-py==0.9.0
+astor==0.8.1
+gast==0.3.3
+grpcio==1.27.2
+h5py==2.10.0
+Keras-Applications==1.0.8
+Keras-Preprocessing==1.1.0
+Markdown==3.2.1
+mock==4.0.2
+numpy==1.18.2
+pkg-resources==0.0.0
+protobuf==3.11.3
+six==1.14.0
+tensorboard==1.13.1
+tensorflow==1.13.1
+tensorflow-estimator==1.13.0
+termcolor==1.1.0
+Werkzeug==1.0.0
diff --git a/res/TensorFlowPythonExamples/tfpem.py b/res/TensorFlowPythonExamples/tfpem.py
new file mode 100755
index 000000000..514200c19
--- /dev/null
+++ b/res/TensorFlowPythonExamples/tfpem.py
@@ -0,0 +1,25 @@
+# TensorFlow Python Example Manager
+
+import tensorflow as tf
+import importlib
+import argparse
+
+parser = argparse.ArgumentParser(description='Process TensorFlow Python Examples')
+
+parser.add_argument('--mode', metavar='MODE', choices=['pbtxt'], default='pbtxt')
+parser.add_argument('examples', metavar='EXAMPLES', nargs='+')
+
+args = parser.parse_args()
+
+if args.mode == 'pbtxt':
+    for example in args.examples:
+        print("Generate '" + example + ".pbtxt'")
+
+        tf.compat.v1.reset_default_graph()
+        # https://stackoverflow.com/questions/37808866/proper-way-to-dynamically-import-a-module-with-relative-imports
+        importlib.import_module("examples." + example)
+
+        with open(example + ".pbtxt", "w") as f:
+            f.write(str(tf.compat.v1.get_default_graph().as_graph_def(add_shapes=True)))
+
+        print("Generate '" + example + ".pbtxt' - Done")
diff --git a/res/TensorFlowTests/NET_0003/test.py b/res/TensorFlowTests/NET_0003/test.py
index b5bad2dae..b5bad2dae 100644..100755
--- a/res/TensorFlowTests/NET_0003/test.py
+++ b/res/TensorFlowTests/NET_0003/test.py
diff --git a/res/TensorFlowTests/NET_0004/test.py b/res/TensorFlowTests/NET_0004/test.py
index a0c790d79..a0c790d79 100644..100755
--- a/res/TensorFlowTests/NET_0004/test.py
+++ b/res/TensorFlowTests/NET_0004/test.py
diff --git a/res/TensorFlowTests/UNIT_Maximum_000/test.info b/res/TensorFlowTests/UNIT_Maximum_000/test.info
new file mode 100644
index 000000000..f8f74e382
--- /dev/null
+++ b/res/TensorFlowTests/UNIT_Maximum_000/test.info
@@ -0,0 +1,3 @@
+input, input_01:0,  TF_FLOAT,   [1, 3, 3, 1]
+input, input_02:0,  TF_FLOAT,   [1, 3, 3, 1]
+output, maximum:0,  TF_FLOAT,   [1, 3, 3, 1]
diff --git a/res/TensorFlowTests/UNIT_Maximum_000/test.pbtxt b/res/TensorFlowTests/UNIT_Maximum_000/test.pbtxt
new file mode 100644
index 000000000..9ab81b52b
--- /dev/null
+++ b/res/TensorFlowTests/UNIT_Maximum_000/test.pbtxt
@@ -0,0 +1,70 @@
+node {
+  name: "input_01"
+  op: "Placeholder"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "shape"
+    value {
+      shape {
+        dim {
+          size: 1
+        }
+        dim {
+          size: 3
+        }
+        dim {
+          size: 3
+        }
+        dim {
+          size: 1
+        }
+      }
+    }
+  }
+}
+node {
+  name: "input_02"
+  op: "Placeholder"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "shape"
+    value {
+      shape {
+        dim {
+          size: 1
+        }
+        dim {
+          size: 3
+        }
+        dim {
+          size: 3
+        }
+        dim {
+          size: 1
+        }
+      }
+    }
+  }
+}
+node {
+  name: "maximum"
+  op: "Maximum"
+  input: "input_01"
+  input: "input_02"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
diff --git a/res/TensorFlowTests/UNIT_Maximum_001/test.info b/res/TensorFlowTests/UNIT_Maximum_001/test.info
new file mode 100644
index 000000000..24dc2be07
--- /dev/null
+++ b/res/TensorFlowTests/UNIT_Maximum_001/test.info
@@ -0,0 +1,3 @@
+input, input_01:0,  TF_FLOAT,   [1, 3, 3, 5]
+input, input_02:0,  TF_FLOAT,   [1, 1, 1, 5]
+output, maximum:0,  TF_FLOAT,   [1, 3, 3, 5]
diff --git a/res/TensorFlowTests/UNIT_Maximum_001/test.pbtxt b/res/TensorFlowTests/UNIT_Maximum_001/test.pbtxt
new file mode 100644
index 000000000..29e90ab2f
--- /dev/null
+++ b/res/TensorFlowTests/UNIT_Maximum_001/test.pbtxt
@@ -0,0 +1,70 @@
+node {
+  name: "input_01"
+  op: "Placeholder"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "shape"
+    value {
+      shape {
+        dim {
+          size: 1
+        }
+        dim {
+          size: 3
+        }
+        dim {
+          size: 3
+        }
+        dim {
+          size: 5
+        }
+      }
+    }
+  }
+}
+node {
+  name: "input_02"
+  op: "Placeholder"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "shape"
+    value {
+      shape {
+        dim {
+          size: 1
+        }
+        dim {
+          size: 1
+        }
+        dim {
+          size: 1
+        }
+        dim {
+          size: 5
+        }
+      }
+    }
+  }
+}
+node {
+  name: "maximum"
+  op: "Maximum"
+  input: "input_01"
+  input: "input_02"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
diff --git a/res/TensorFlowTests/UNIT_Maximum_002/test.info b/res/TensorFlowTests/UNIT_Maximum_002/test.info
new file mode 100644
index 000000000..44ddd085a
--- /dev/null
+++ b/res/TensorFlowTests/UNIT_Maximum_002/test.info
@@ -0,0 +1,3 @@
+input, input_01:0,  TF_FLOAT,   [1, 3, 3, 5]
+input, input_02:0,  TF_FLOAT,   [5]
+output, maximum:0,  TF_FLOAT,   [1, 3, 3, 5]
diff --git a/res/TensorFlowTests/UNIT_Maximum_002/test.pbtxt b/res/TensorFlowTests/UNIT_Maximum_002/test.pbtxt
new file mode 100644
index 000000000..ca4ae80de
--- /dev/null
+++ b/res/TensorFlowTests/UNIT_Maximum_002/test.pbtxt
@@ -0,0 +1,61 @@
+node {
+  name: "input_01"
+  op: "Placeholder"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "shape"
+    value {
+      shape {
+        dim {
+          size: 1
+        }
+        dim {
+          size: 3
+        }
+        dim {
+          size: 3
+        }
+        dim {
+          size: 5
+        }
+      }
+    }
+  }
+}
+node {
+  name: "input_02"
+  op: "Placeholder"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "shape"
+    value {
+      shape {
+        dim {
+          size: 5
+        }
+      }
+    }
+  }
+}
+node {
+  name: "maximum"
+  op: "Maximum"
+  input: "input_01"
+  input: "input_02"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}