Imported Upstream version 1.10.0upstream/1.10.0submit/tizen/20201028.104702submit/tizen/20201028.031836accepted/tizen/unified/20201029.124827

37 files changed, 1346 insertions, 6 deletions

diff --git a/res/TensorFlowLiteRecipes/Add_002/test.recipe b/res/TensorFlowLiteRecipes/Add_002/test.recipe
new file mode 100644
index 000000000..12ba8000b
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Add_002/test.recipe
@@ -0,0 +1,32 @@
+operand {
+  name: "ifm1"
+  type: FLOAT32
+  shape { dim: 1 dim: 2 dim: 2 dim: 3 }
+}
+operand {
+  name: "ifm2"
+  type: FLOAT32
+  shape { dim: 1 dim: 2 dim: 2 dim: 3 }
+  filler {
+    tag: "explicit"
+    arg: "1" arg: "2" arg: "-3" arg: "-4"
+    arg: "-5" arg: "6" arg: "-7" arg: "8"
+    arg: "4" arg: "-2" arg: "3" arg: "-1"
+  }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 2 dim: 2 dim: 3 }
+}
+operation {
+  type: "Add"
+  input: "ifm1"
+  input: "ifm2"
+  output: "ofm"
+  add_options {
+    activation: NONE
+  }
+}
+input: "ifm1"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Add_002/test.reverse b/res/TensorFlowLiteRecipes/Add_002/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Add_002/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Concatenation_001/test.recipe b/res/TensorFlowLiteRecipes/Concatenation_001/test.recipe
new file mode 100644
index 000000000..211976c8c
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Concatenation_001/test.recipe
@@ -0,0 +1,32 @@
+operand {
+  name: "ifm1"
+  type: FLOAT32
+  shape { dim: 1 dim: 2 dim: 2 dim: 1 }
+}
+operand {
+  name: "ifm2"
+  type: FLOAT32
+  shape { dim: 1 dim: 2 dim: 2 dim: 2 }
+  filler {
+    tag: "explicit"
+    arg: "1" arg: "2" arg: "-3" arg: "-4"
+    arg: "-5" arg: "6" arg: "-7" arg: "8"
+  }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 2 dim: 2 dim: 3 }
+}
+operation {
+  type: "Concatenation"
+  concatenation_options {
+    axis: 3
+    activation: NONE
+  }
+  input: "ifm1"
+  input: "ifm2"
+  output: "ofm"
+}
+input: "ifm1"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Concatenation_001/test.reverse b/res/TensorFlowLiteRecipes/Concatenation_001/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Concatenation_001/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Dequantize_000/test.recipe b/res/TensorFlowLiteRecipes/Dequantize_000/test.recipe
new file mode 100644
index 000000000..bbd3220c9
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Dequantize_000/test.recipe
@@ -0,0 +1,18 @@
+operand {
+  name: "ifm"
+  type: UINT8
+  shape { dim: 4 }
+  quant { min: 0 max: 255 scale: 1.0 zero_point: 0 }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 4 }
+}
+operation {
+  type: "Dequantize"
+  input: "ifm"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Dequantize_000/test.reverse b/res/TensorFlowLiteRecipes/Dequantize_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Dequantize_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/FullyConnected_004/test.recipe b/res/TensorFlowLiteRecipes/FullyConnected_004/test.recipe
new file mode 100644
index 000000000..b89eabeeb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/FullyConnected_004/test.recipe
@@ -0,0 +1,69 @@
+operand {
+  name: "in"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 }
+}
+operand {
+  name: "weight"
+  type: FLOAT32
+  shape { dim: 4 dim: 4 }
+  filler {
+    tag: "explicit"
+    arg: "1" arg: "0" arg: "0" arg: "4"
+    arg: "2" arg: "3" arg: "0" arg: "0"
+    arg: "5" arg: "0" arg: "0" arg: "6"
+  }
+  sparsity {
+      traversal_order { dim: 0 dim: 1 dim: 2 dim: 3 }
+      block_map { dim: 0 dim: 1 }
+      dim_metadata {
+          format: DENSE
+          dense_size: 2
+      }
+      dim_metadata {
+          format: SPARSE_CSR
+          array_segments {
+              dim: 0 dim: 2 dim: 3
+              type: UINT8VEC
+          }
+          array_indices {
+              dim: 0 dim: 1 dim: 1
+              type: UINT8VEC
+          }
+      }
+      dim_metadata {
+          format: DENSE
+          dense_size: 2
+      }
+      dim_metadata {
+          format: DENSE
+          dense_size: 2
+      }
+  }
+}
+operand {
+  name: "bias"
+  type: FLOAT32
+  shape { dim: 4 }
+  filler {
+    tag: "explicit"
+    arg: "1" arg: "-2" arg: "-3" arg: "4"
+  }
+}
+operand {
+  name: "out"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 }
+}
+operation {
+  type: "FullyConnected"
+  fullyconnected_options {
+    activation: NONE
+  }
+  input: "in"
+  input: "weight"
+  input: "bias"
+  output: "out"
+}
+input: "in"
+output: "out"
diff --git a/res/TensorFlowLiteRecipes/FullyConnected_004/test.reverse b/res/TensorFlowLiteRecipes/FullyConnected_004/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/FullyConnected_004/test.reverse
diff --git a/res/TensorFlowLiteRecipes/FullyConnected_005/test.recipe b/res/TensorFlowLiteRecipes/FullyConnected_005/test.recipe
new file mode 100644
index 000000000..0aa1dfa77
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/FullyConnected_005/test.recipe
@@ -0,0 +1,43 @@
+operand {
+  name: "in"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 }
+}
+operand {
+  name: "weight"
+  type: FLOAT32
+  shape { dim: 4 dim: 4 }
+  filler {
+    tag: "explicit"
+    arg: "1" arg: "0" arg: "2" arg: "3"
+    arg: "0" arg: "4" arg: "0" arg: "0"
+    arg: "0" arg: "0" arg: "5" arg: "0"
+    arg: "0" arg: "0" arg: "0" arg: "6"
+  }
+}
+operand {
+  name: "bias"
+  type: FLOAT32
+  shape { dim: 4 }
+  filler {
+    tag: "explicit"
+    arg: "1" arg: "-2" arg: "-3" arg: "4"
+  }
+}
+operand {
+  name: "out"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 }
+}
+operation {
+  type: "FullyConnected"
+  fullyconnected_options {
+    activation: NONE
+  }
+  input: "in"
+  input: "weight"
+  input: "bias"
+  output: "out"
+}
+input: "in"
+output: "out"
diff --git a/res/TensorFlowLiteRecipes/LogSoftmax_U8_000/test.recipe b/res/TensorFlowLiteRecipes/LogSoftmax_U8_000/test.recipe
new file mode 100644
index 000000000..d960567e8
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/LogSoftmax_U8_000/test.recipe
@@ -0,0 +1,21 @@
+operand {
+  name: "ifm"
+  type: UINT8
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+  quant { min: -4.952 max: 4.939 scale: 0.0388 zero_point: 128 }
+}
+operand {
+  name: "ofm"
+  type: UINT8
+  shape { dim: 1 dim: 3 dim: 3 dim: 2 }
+  quant { min: -15.9375 max: 0 scale: 0.0625 zero_point: 255 }
+}
+operation {
+  type: "LogSoftmax"
+  log_softmax_options {
+  }
+  input: "ifm"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/LogSoftmax_U8_000/test.reverse b/res/TensorFlowLiteRecipes/LogSoftmax_U8_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/LogSoftmax_U8_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Mul_001/test.recipe b/res/TensorFlowLiteRecipes/Mul_001/test.recipe
new file mode 100644
index 000000000..18c19ff19
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Mul_001/test.recipe
@@ -0,0 +1,32 @@
+operand {
+  name: "ifm1"
+  type: FLOAT32
+  shape { dim: 1 dim: 2 dim: 2 dim: 3 }
+}
+operand {
+  name: "ifm2"
+  type: FLOAT32
+  shape { dim: 1 dim: 2 dim: 2 dim: 3 }
+  filler {
+    tag: "explicit"
+    arg: "1" arg: "2" arg: "-3" arg: "-4"
+    arg: "-5" arg: "6" arg: "-7" arg: "8"
+    arg: "4" arg: "-2" arg: "3" arg: "-1"
+  }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 2 dim: 2 dim: 3 }
+}
+operation {
+  type: "Mul"
+  input: "ifm1"
+  input: "ifm2"
+  output: "ofm"
+  mul_options {
+    activation: NONE
+  }
+}
+input: "ifm1"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/Mul_001/test.reverse b/res/TensorFlowLiteRecipes/Mul_001/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Mul_001/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Net_TConv_Add_000/test.recipe b/res/TensorFlowLiteRecipes/Net_TConv_Add_000/test.recipe
new file mode 100644
index 000000000..b3247f24f
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Net_TConv_Add_000/test.recipe
@@ -0,0 +1,113 @@
+operand {
+  name: "filter"
+  type: FLOAT32
+  shape {
+    dim: 1
+    dim: 3
+    dim: 3
+    dim: 2
+  }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "0.1"
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "Addition"
+  type: FLOAT32
+  shape {
+    dim: 1
+    dim: 4
+    dim: 4
+    dim: 1
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "Addition_add_param"
+  type: FLOAT32
+  shape {
+    dim: 1
+  }
+  filler {
+    tag: "explicit"
+    arg: "-2.04724"
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "Hole"
+  type: FLOAT32
+  shape {
+    dim: 1
+    dim: 2
+    dim: 2
+    dim: 2
+  }
+  quant {
+    min: 0
+    max: 255
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "conv2d_transpose"
+  type: FLOAT32
+  shape {
+    dim: 1
+    dim: 4
+    dim: 4
+    dim: 1
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "input_size"
+  type: INT32
+  shape {
+    dim: 4
+  }
+  filler {
+    tag: "explicit"
+    arg: "1"
+    arg: "4"
+    arg: "4"
+    arg: "1"
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operation {
+  type: "TransposeConv"
+  input: "input_size"
+  input: "filter"
+  input: "Hole"
+  output: "conv2d_transpose"
+  transpose_conv_options {
+    padding: VALID
+    stride_w: 1
+    stride_h: 1
+  }
+}
+operation {
+  type: "Add"
+  input: "conv2d_transpose"
+  input: "Addition_add_param"
+  output: "Addition"
+  add_options {
+    activation: NONE
+  }
+}
+input: "Hole"
+output: "Addition"
diff --git a/res/TensorFlowLiteRecipes/Net_TConv_Add_000/test.rule b/res/TensorFlowLiteRecipes/Net_TConv_Add_000/test.rule
new file mode 100644
index 000000000..894d642a3
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Net_TConv_Add_000/test.rule
@@ -0,0 +1,6 @@
+# To check if Add op is fused to Transposed Convolution op
+
+RULE    "VERIFY_FILE_FORMAT"      $(verify_file_format) '=' 1
+
+RULE    "TCONV_EXIST"             $(op_count TRANSPOSE_CONV) '=' 1
+RULE    "NO_ADD"                  $(op_count ADD) '=' 0
diff --git a/res/TensorFlowLiteRecipes/Net_TConv_Add_001/test.recipe b/res/TensorFlowLiteRecipes/Net_TConv_Add_001/test.recipe
new file mode 100644
index 000000000..89a344f0e
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Net_TConv_Add_001/test.recipe
@@ -0,0 +1,119 @@
+operand {
+  name: "filter"
+  type: FLOAT32
+  shape {
+    dim: 1
+    dim: 3
+    dim: 3
+    dim: 2
+  }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "0.1"
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "Addition"
+  type: FLOAT32
+  shape {
+    dim: 1
+    dim: 4
+    dim: 4
+    dim: 1
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "Addition_add_param"
+  type: FLOAT32
+  shape {
+    dim: 1
+    dim: 4
+    dim: 4
+    dim: 1
+  }
+  filler {
+    tag: "explicit"
+    arg: "1" arg: "2" arg: "3" arg: "4"
+    arg: "-1" arg: "-2" arg: "-3" arg: "-4"
+    arg: "1" arg: "2" arg: "3" arg: "4"
+    arg: "-1" arg: "-2" arg: "-3" arg: "-4"
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "Hole"
+  type: FLOAT32
+  shape {
+    dim: 1
+    dim: 2
+    dim: 2
+    dim: 2
+  }
+  quant {
+    min: 0
+    max: 255
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "conv2d_transpose"
+  type: FLOAT32
+  shape {
+    dim: 1
+    dim: 4
+    dim: 4
+    dim: 1
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "input_size"
+  type: INT32
+  shape {
+    dim: 4
+  }
+  filler {
+    tag: "explicit"
+    arg: "1"
+    arg: "4"
+    arg: "4"
+    arg: "1"
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operation {
+  type: "TransposeConv"
+  input: "input_size"
+  input: "filter"
+  input: "Hole"
+  output: "conv2d_transpose"
+  transpose_conv_options {
+    padding: VALID
+    stride_w: 1
+    stride_h: 1
+  }
+}
+operation {
+  type: "Add"
+  input: "conv2d_transpose"
+  input: "Addition_add_param"
+  output: "Addition"
+  add_options {
+    activation: NONE
+  }
+}
+input: "Hole"
+output: "Addition"
diff --git a/res/TensorFlowLiteRecipes/Net_TConv_Add_001/test.rule b/res/TensorFlowLiteRecipes/Net_TConv_Add_001/test.rule
new file mode 100644
index 000000000..86afc47f6
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Net_TConv_Add_001/test.rule
@@ -0,0 +1,6 @@
+# To check if Add op is not fused to Transposed Convolution op
+
+RULE    "VERIFY_FILE_FORMAT"      $(verify_file_format) '=' 1
+
+RULE    "TCONV_EXIST"             $(op_count TRANSPOSE_CONV) '=' 1
+RULE    "NO_FUSION"               $(op_count ADD) '=' 1
diff --git a/res/TensorFlowLiteRecipes/Net_TConv_Add_002/test.recipe b/res/TensorFlowLiteRecipes/Net_TConv_Add_002/test.recipe
new file mode 100644
index 000000000..cfea30653
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Net_TConv_Add_002/test.recipe
@@ -0,0 +1,113 @@
+operand {
+  name: "filter"
+  type: FLOAT32
+  shape {
+    dim: 1
+    dim: 3
+    dim: 3
+    dim: 2
+  }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "0.1"
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "Addition"
+  type: FLOAT32
+  shape {
+    dim: 1
+    dim: 4
+    dim: 4
+    dim: 1
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "Addition_add_param"
+  type: FLOAT32
+  shape {
+    dim: 1
+  }
+  filler {
+    tag: "explicit"
+    arg: "-2.04724"
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "Hole"
+  type: FLOAT32
+  shape {
+    dim: 1
+    dim: 2
+    dim: 2
+    dim: 2
+  }
+  quant {
+    min: 0
+    max: 255
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "conv2d_transpose"
+  type: FLOAT32
+  shape {
+    dim: 1
+    dim: 4
+    dim: 4
+    dim: 1
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "input_size"
+  type: INT32
+  shape {
+    dim: 4
+  }
+  filler {
+    tag: "explicit"
+    arg: "1"
+    arg: "4"
+    arg: "4"
+    arg: "1"
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operation {
+  type: "TransposeConv"
+  input: "input_size"
+  input: "filter"
+  input: "Hole"
+  output: "conv2d_transpose"
+  transpose_conv_options {
+    padding: VALID
+    stride_w: 1
+    stride_h: 1
+  }
+}
+operation {
+  type: "Add"
+  input: "Addition_add_param"
+  input: "conv2d_transpose"
+  output: "Addition"
+  add_options {
+    activation: NONE
+  }
+}
+input: "Hole"
+output: "Addition"
diff --git a/res/TensorFlowLiteRecipes/Net_TConv_Add_002/test.rule b/res/TensorFlowLiteRecipes/Net_TConv_Add_002/test.rule
new file mode 100644
index 000000000..894d642a3
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Net_TConv_Add_002/test.rule
@@ -0,0 +1,6 @@
+# To check if Add op is fused to Transposed Convolution op
+
+RULE    "VERIFY_FILE_FORMAT"      $(verify_file_format) '=' 1
+
+RULE    "TCONV_EXIST"             $(op_count TRANSPOSE_CONV) '=' 1
+RULE    "NO_ADD"                  $(op_count ADD) '=' 0
diff --git a/res/TensorFlowLiteRecipes/Net_TConv_BN_001/test.recipe b/res/TensorFlowLiteRecipes/Net_TConv_BN_001/test.recipe
new file mode 100644
index 000000000..babf5af4e
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Net_TConv_BN_001/test.recipe
@@ -0,0 +1,149 @@
+operand {
+  name: "Const_transposed"
+  type: FLOAT32
+  shape {
+    dim: 1
+    dim: 3
+    dim: 3
+    dim: 2
+  }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "0.1"
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "FusedBatchNormV3"
+  type: FLOAT32
+  shape {
+    dim: 1
+    dim: 4
+    dim: 4
+    dim: 1
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "FusedBatchNormV3_add_param"
+  type: FLOAT32
+  shape {
+    dim: 1
+  }
+  filler {
+    tag: "explicit"
+    arg: "-2.04724"
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "FusedBatchNormV3_mul_0"
+  type: FLOAT32
+  shape {
+    dim: 1
+    dim: 4
+    dim: 4
+    dim: 1
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "FusedBatchNormV3_mul_0_param"
+  type: FLOAT32
+  shape {
+    dim: 1
+  }
+  filler {
+    tag: "explicit"
+    arg: "2.00834"
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "Hole"
+  type: FLOAT32
+  shape {
+    dim: 1
+    dim: 2
+    dim: 2
+    dim: 2
+  }
+  quant {
+    min: 0
+    max: 255
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "conv2d_transpose"
+  type: FLOAT32
+  shape {
+    dim: 1
+    dim: 4
+    dim: 4
+    dim: 1
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operand {
+  name: "conv2d_transpose/input_sizes"
+  type: INT32
+  shape {
+    dim: 4
+  }
+  filler {
+    tag: "explicit"
+    arg: "1"
+    arg: "4"
+    arg: "4"
+    arg: "1"
+  }
+  quant {
+    quantized_dimension: 0
+  }
+}
+operation {
+  type: "TransposeConv"
+  input: "conv2d_transpose/input_sizes"
+  input: "Const_transposed"
+  input: "Hole"
+  output: "conv2d_transpose"
+  transpose_conv_options {
+    padding: VALID
+    stride_w: 1
+    stride_h: 1
+  }
+}
+operation {
+  type: "Mul"
+  input: "conv2d_transpose"
+  input: "FusedBatchNormV3_mul_0_param"
+  output: "FusedBatchNormV3_mul_0"
+  mul_options {
+    activation: NONE
+  }
+}
+operation {
+  type: "Add"
+  input: "FusedBatchNormV3_mul_0"
+  input: "FusedBatchNormV3_add_param"
+  output: "FusedBatchNormV3"
+  add_options {
+    activation: NONE
+  }
+}
+input: "Hole"
+output: "FusedBatchNormV3"
diff --git a/res/TensorFlowLiteRecipes/Net_TConv_BN_001/test.rule b/res/TensorFlowLiteRecipes/Net_TConv_BN_001/test.rule
new file mode 100644
index 000000000..0988ecf28
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/Net_TConv_BN_001/test.rule
@@ -0,0 +1,7 @@
+# To check if BatchNorm op(mul + add) is fused to Transposed Convolution op
+
+RULE    "VERIFY_FILE_FORMAT"      $(verify_file_format) '=' 1
+
+RULE    "TCONV_EXIST"             $(op_count TRANSPOSE_CONV) '=' 1
+RULE    "NO_MUL"                  $(op_count MUL) '=' 0
+RULE    "NO_ADD"                  $(op_count ADD) '=' 0
diff --git a/res/TensorFlowLiteRecipes/PRelu_001/test.recipe b/res/TensorFlowLiteRecipes/PRelu_001/test.recipe
new file mode 100644
index 000000000..c18acdbbc
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/PRelu_001/test.recipe
@@ -0,0 +1,27 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+}
+operand {
+  name: "alpha"
+  type: FLOAT32
+  shape { dim: 1 dim: 1 dim: 3 }
+  filler {
+    tag: "explicit"
+    arg: "0.1" arg: "0.3" arg: "0.5"
+  }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 dim: 4 dim: 3 }
+}
+operation {
+  type: "PRelu"
+  input: "ifm"
+  input: "alpha"
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/PRelu_001/test.reverse b/res/TensorFlowLiteRecipes/PRelu_001/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/PRelu_001/test.reverse
diff --git a/res/TensorFlowLiteRecipes/UnidirectionalSequenceLSTM_000/test.recipe b/res/TensorFlowLiteRecipes/UnidirectionalSequenceLSTM_000/test.recipe
new file mode 100644
index 000000000..773d44343
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/UnidirectionalSequenceLSTM_000/test.recipe
@@ -0,0 +1,185 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 3 dim: 1 dim: 2 }
+}
+operand {
+  name: "input_to_input_weights"
+  type: FLOAT32
+  shape { dim: 4 dim: 2 }
+}
+operand {
+  name: "input_to_forget_weights"
+  type: FLOAT32
+  shape { dim: 4 dim: 2 }
+}
+operand {
+  name: "input_to_cell_weights"
+  type: FLOAT32
+  shape { dim: 4 dim: 2 }
+}
+operand {
+  name: "input_to_output_weights"
+  type: FLOAT32
+  shape { dim: 4 dim: 2 }
+}
+operand {
+  name: "recurrent_to_input_weights"
+  type: FLOAT32
+  shape { dim: 4 dim: 4 }
+}
+operand {
+  name: "recurrent_to_forget_weights"
+  type: FLOAT32
+  shape { dim: 4 dim: 4 }
+}
+operand {
+  name: "recurrent_to_cell_weights"
+  type: FLOAT32
+  shape { dim: 4 dim: 4 }
+}
+operand {
+  name: "recurrent_to_output_weights"
+  type: FLOAT32
+  shape { dim: 4 dim: 4 }
+}
+operand {
+  name: "cell_to_input_weights"
+  type: FLOAT32
+  shape { dim: 4 }
+}
+operand {
+  name: "cell_to_forget_weights"
+  type: FLOAT32
+  shape { dim: 4 }
+}
+operand {
+  name: "cell_to_output_weights"
+  type: FLOAT32
+  shape { dim: 4 }
+}
+operand {
+  name: "input_gate_bias"
+  type: FLOAT32
+  shape { dim: 4 }
+}
+operand {
+  name: "forget_gate_bias"
+  type: FLOAT32
+  shape { dim: 4 }
+}
+operand {
+  name: "cell_gate_bias"
+  type: FLOAT32
+  shape { dim: 4 }
+}
+operand {
+  name: "output_gate_bias"
+  type: FLOAT32
+  shape { dim: 4 }
+}
+operand {
+  name: "projection_weight"
+  type: FLOAT32
+  shape { dim: 4 dim: 4 }
+}
+operand {
+  name: "projection_bias"
+  type: FLOAT32
+  shape { dim: 4 }
+}
+operand {
+  name: "activation_state"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 }
+}
+operand {
+  name: "cell_state"
+  type: FLOAT32
+  shape { dim: 1 dim: 4 }
+}
+operand {
+  name: "input_layer_norm_coefficients"
+  type: FLOAT32
+  shape { dim: 4 }
+}
+operand {
+  name: "forget_layer_norm_coefficients"
+  type: FLOAT32
+  shape { dim: 4 }
+}
+operand {
+  name: "cell_layer_norm_coefficients"
+  type: FLOAT32
+  shape { dim: 4 }
+}
+operand {
+  name: "output_layer_norm_coefficients"
+  type: FLOAT32
+  shape { dim: 4 }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 3 dim: 1 dim: 4 }
+}
+operation {
+  type: "UnidirectionalSequenceLSTM"
+  unidirectional_sequence_lstm_options {
+    activation: NONE
+    cell_clip: 0.0
+    proj_clip: 0.0
+    time_major: false
+    asymmetric_quantize_inputs: false
+  }
+  input: "ifm"
+  input: "input_to_input_weights"
+  input: "input_to_forget_weights"
+  input: "input_to_cell_weights"
+  input: "input_to_output_weights"
+  input: "recurrent_to_input_weights"
+  input: "recurrent_to_forget_weights"
+  input: "recurrent_to_cell_weights"
+  input: "recurrent_to_output_weights"
+  input: "cell_to_input_weights"
+  input: "cell_to_forget_weights"
+  input: "cell_to_output_weights"
+  input: "input_gate_bias"
+  input: "forget_gate_bias"
+  input: "cell_gate_bias"
+  input: "output_gate_bias"
+  input: "projection_weight"
+  input: "projection_bias"
+  input: "activation_state"
+  input: "cell_state"
+  input: "input_layer_norm_coefficients"
+  input: "forget_layer_norm_coefficients"
+  input: "cell_layer_norm_coefficients"
+  input: "output_layer_norm_coefficients"
+  output: "ofm"
+}
+input: "ifm"
+input: "input_to_input_weights"
+input: "input_to_forget_weights"
+input: "input_to_cell_weights"
+input: "input_to_output_weights"
+input: "recurrent_to_input_weights"
+input: "recurrent_to_forget_weights"
+input: "recurrent_to_cell_weights"
+input: "recurrent_to_output_weights"
+input: "cell_to_input_weights"
+input: "cell_to_forget_weights"
+input: "cell_to_output_weights"
+input: "input_gate_bias"
+input: "forget_gate_bias"
+input: "cell_gate_bias"
+input: "output_gate_bias"
+input: "projection_weight"
+input: "projection_bias"
+input: "activation_state"
+input: "cell_state"
+input: "input_layer_norm_coefficients"
+input: "forget_layer_norm_coefficients"
+input: "cell_layer_norm_coefficients"
+input: "output_layer_norm_coefficients"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/UnidirectionalSequenceLSTM_000/test.reverse b/res/TensorFlowLiteRecipes/UnidirectionalSequenceLSTM_000/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/UnidirectionalSequenceLSTM_000/test.reverse
diff --git a/res/TensorFlowLiteRecipes/UnidirectionalSequenceLSTM_001/test.recipe b/res/TensorFlowLiteRecipes/UnidirectionalSequenceLSTM_001/test.recipe
new file mode 100644
index 000000000..5938cc115
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/UnidirectionalSequenceLSTM_001/test.recipe
@@ -0,0 +1,323 @@
+operand {
+  name: "ifm"
+  type: FLOAT32
+  shape { dim: 1 dim: 28 dim: 28 }
+}
+operand {
+  name: "input_to_input_weights"
+  type: FLOAT32
+  shape { dim: 20 dim: 28 }
+  filler {
+    tag: "explicit"
+    arg: "0.1687648445367813" arg: "0.04799923673272133" arg: "0.195631742477417" arg: "0.10485544055700302" arg: "0.018675213679671288" arg: "0.13739116489887238" arg: "0.0898093432188034" arg: "-0.28823068737983704" arg: "-0.02585843950510025" arg: "0.05994327738881111" arg: "0.07523486018180847" arg: "0.0797467827796936" arg: "0.3736445903778076" arg: "0.6627118587493896" arg: "0.3780449628829956" arg: "0.36176905035972595" arg: "-0.2041059285402298" arg: "0.1464163213968277" arg: "0.4136067032814026" arg: "0.1049080342054367" arg: "0.11873452365398407" arg: "-0.05727154389023781" arg: "-0.04963447153568268" arg: "-0.332282155752182" arg: "0.07995595782995224" arg: "-0.20255199074745178" arg: "-0.05633578822016716" arg: "0.11420387774705887"
+    arg: "-0.029032165184617043" arg: "0.0007604139973409474" arg: "-0.31187641620635986" arg: "0.1813918948173523" arg: "-0.31930115818977356" arg: "0.05086275562644005" arg: "-0.029340023174881935" arg: "0.039944298565387726" arg: "0.002410847693681717" arg: "-0.32042407989501953" arg: "0.22277581691741943" arg: "0.44808948040008545" arg: "0.439309686422348" arg: "0.3975866138935089" arg: "0.30034393072128296" arg: "0.1280607134103775" arg: "-0.06160789355635643" arg: "-0.09105906635522842" arg: "-0.23636692762374878" arg: "-0.3308735489845276" arg: "-0.572564423084259" arg: "-0.6935749053955078" arg: "-0.5299585461616516" arg: "-0.27702340483665466" arg: "-0.7308681011199951" arg: "-0.6580930352210999" arg: "-0.4219287633895874" arg: "0.20912277698516846"
+    arg: "0.060638219118118286" arg: "-0.12734581530094147" arg: "0.14102879166603088" arg: "-0.2901698052883148" arg: "-0.24771066009998322" arg: "-0.3001070022583008" arg: "-0.22395247220993042" arg: "-0.4311608672142029" arg: "-0.17570453882217407" arg: "0.08624797314405441" arg: "0.018354324623942375" arg: "0.5221205949783325" arg: "0.14471562206745148" arg: "0.03486153483390808" arg: "0.16570599377155304" arg: "0.16822484135627747" arg: "0.7482292056083679" arg: "0.7351004481315613" arg: "0.27890756726264954" arg: "0.5524212121963501" arg: "0.23095451295375824" arg: "0.4314809739589691" arg: "0.3363182246685028" arg: "0.6496651768684387" arg: "0.5523284077644348" arg: "0.4055400490760803" arg: "0.4644913971424103" arg: "0.09175155311822891"
+    arg: "0.21605326235294342" arg: "0.08236871659755707" arg: "-0.17103230953216553" arg: "-0.18498405814170837" arg: "-0.5800216794013977" arg: "-0.487117737531662" arg: "-0.7577510476112366" arg: "-0.4389793574810028" arg: "-0.6259695291519165" arg: "-0.6731855273246765" arg: "-0.5440476536750793" arg: "-0.4302021861076355" arg: "-0.28402388095855713" arg: "-0.22358544170856476" arg: "-0.30889278650283813" arg: "0.12872418761253357" arg: "0.03388393670320511" arg: "0.034014951437711716" arg: "0.2464621216058731" arg: "-0.08322205394506454" arg: "0.20342972874641418" arg: "-0.14718593657016754" arg: "0.2358621209859848" arg: "0.2880614697933197" arg: "0.08535374701023102" arg: "-0.09865032881498337" arg: "-0.44678133726119995" arg: "-0.11892740428447723"
+    arg: "0.1754414290189743" arg: "0.24821344017982483" arg: "0.10287455469369888" arg: "0.2701801657676697" arg: "0.41744735836982727" arg: "0.19831198453903198" arg: "0.46229296922683716" arg: "0.16203390061855316" arg: "0.12368197739124298" arg: "-0.27287790179252625" arg: "0.014193423092365265" arg: "0.11754778772592545" arg: "-0.05268847197294235" arg: "0.017977338284254074" arg: "0.381906121969223" arg: "0.17318789660930634" arg: "0.1804313063621521" arg: "-0.021003693342208862" arg: "0.5185583829879761" arg: "0.5880140662193298" arg: "0.4118947982788086" arg: "0.0662691667675972" arg: "0.08239153027534485" arg: "0.04901377111673355" arg: "0.12195281684398651" arg: "0.46632856130599976" arg: "0.18819667398929596" arg: "0.18338985741138458"
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+    arg: "0.08968205004930496" arg: "0.3198257088661194" arg: "0.07224604487419128" arg: "0.5600743889808655" arg: "0.024834752082824707" arg: "-0.02439100854098797" arg: "-0.1513833850622177" arg: "0.13906888663768768" arg: "0.06407716870307922" arg: "0.5332576036453247" arg: "0.24956916272640228" arg: "-0.044385701417922974" arg: "-0.4433465301990509" arg: "-0.19094131886959076" arg: "0.4768398106098175" arg: "0.21503591537475586" arg: "-0.218861386179924" arg: "-0.4321509003639221" arg: "-0.24130387604236603" arg: "-0.07977084070444107"
+    arg: "0.002716424874961376" arg: "-0.1713045984506607" arg: "-0.12604790925979614" arg: "-0.03560760244727135" arg: "-0.5757992267608643" arg: "-0.1557251513004303" arg: "-0.05827505886554718" arg: "0.3337538540363312" arg: "-0.4115898907184601" arg: "0.5126633048057556" arg: "0.14806263148784637" arg: "0.40081098675727844" arg: "0.8833869695663452" arg: "-0.19723086059093475" arg: "0.09533816576004028" arg: "0.03869156911969185" arg: "-0.2973725199699402" arg: "0.022853707894682884" arg: "-0.0228166151791811" arg: "-0.4052131772041321"
+    arg: "0.12930545210838318" arg: "0.01575206033885479" arg: "-0.21314911544322968" arg: "0.5510196685791016" arg: "0.06540991365909576" arg: "-0.07084762305021286" arg: "0.3234975337982178" arg: "0.19345852732658386" arg: "0.16359369456768036" arg: "-0.02992691472172737" arg: "0.07857825607061386" arg: "0.3506908714771271" arg: "0.16494658589363098" arg: "0.07570064812898636" arg: "0.32486459612846375" arg: "-0.14951008558273315" arg: "-0.022363830357789993" arg: "-0.42179420590400696" arg: "-0.24661937355995178" arg: "-0.08302409946918488"
+    arg: "0.2494393140077591" arg: "-0.12944501638412476" arg: "-0.010796070098876953" arg: "0.15976394712924957" arg: "-0.01106332242488861" arg: "0.25831347703933716" arg: "0.18664048612117767" arg: "-0.03495928645133972" arg: "0.01873226836323738" arg: "0.02704462595283985" arg: "0.1773315966129303" arg: "0.09905895590782166" arg: "0.137725368142128" arg: "-0.4195314347743988" arg: "0.20205777883529663" arg: "0.25744083523750305" arg: "-0.4343162178993225" arg: "0.08337675034999847" arg: "-0.24768808484077454" arg: "0.05348324030637741"
+    arg: "-0.07421242445707321" arg: "0.08401253819465637" arg: "0.24182510375976562" arg: "-0.19996227324008942" arg: "-0.26596978306770325" arg: "-0.10460428893566132" arg: "-0.09030365198850632" arg: "0.3622499406337738" arg: "0.32519716024398804" arg: "0.3067288398742676" arg: "-0.0695832222700119" arg: "-0.10316962748765945" arg: "-0.09733156114816666" arg: "0.4681766629219055" arg: "0.3733525574207306" arg: "-0.013295430690050125" arg: "-0.11883660405874252" arg: "-0.10412082821130753" arg: "0.05678151175379753" arg: "-0.11783196032047272"
+    arg: "0.048583026975393295" arg: "-0.9528340101242065" arg: "0.10752814263105392" arg: "0.273784339427948" arg: "0.23048622906208038" arg: "-0.2551514804363251" arg: "-0.21344983577728271" arg: "0.2589189112186432" arg: "-0.1326867789030075" arg: "-0.14273332059383392" arg: "0.11125936359167099" arg: "0.10763772577047348" arg: "-0.3638816177845001" arg: "0.6586386561393738" arg: "0.6191070675849915" arg: "0.2745305895805359" arg: "-0.21111124753952026" arg: "0.23943224549293518" arg: "-0.5838378667831421" arg: "-0.7447165250778198"
+    arg: "0.27415889501571655" arg: "-0.10696078091859818" arg: "-0.1905016303062439" arg: "0.17716637253761292" arg: "-0.17008160054683685" arg: "-0.38646024465560913" arg: "0.17075011134147644" arg: "-0.0971580222249031" arg: "0.36582818627357483" arg: "0.3553922176361084" arg: "0.3533395528793335" arg: "0.46518567204475403" arg: "0.12306690216064453" arg: "0.3765827715396881" arg: "0.27485108375549316" arg: "0.026894697919487953" arg: "-0.13947726786136627" arg: "-0.4675980508327484" arg: "0.000053708172345068306" arg: "-0.1514354646205902"
+    arg: "0.034218866378068924" arg: "-0.3962448537349701" arg: "-0.08128349483013153" arg: "0.10788826644420624" arg: "-0.3110845983028412" arg: "0.25610488653182983" arg: "-0.5693814754486084" arg: "0.6281890273094177" arg: "0.0010718648554757237" arg: "-0.21038493514060974" arg: "0.18425892293453217" arg: "-0.35341814160346985" arg: "-0.2984526455402374" arg: "0.29100173711776733" arg: "0.4346262514591217" arg: "-0.02309197559952736" arg: "0.06577077507972717" arg: "-0.24334858357906342" arg: "-0.34281492233276367" arg: "-0.4032599627971649"
+    arg: "0.34936246275901794" arg: "0.3322518467903137" arg: "-0.2656654119491577" arg: "0.22830642759799957" arg: "-0.11201204359531403" arg: "-0.1707642823457718" arg: "0.007749658077955246" arg: "0.43952593207359314" arg: "0.14750634133815765" arg: "0.42360368371009827" arg: "0.1105399876832962" arg: "-0.06718066334724426" arg: "-0.175845667719841" arg: "0.023229194805026054" arg: "0.35441142320632935" arg: "0.35180309414863586" arg: "-0.561530351638794" arg: "-0.1788090020418167" arg: "0.05351807549595833" arg: "-0.3240300118923187"
+    arg: "0.2829385995864868" arg: "0.09240324050188065" arg: "0.10970980674028397" arg: "1.01627779006958" arg: "-0.3717207908630371" arg: "-0.2776918113231659" arg: "0.6677582263946533" arg: "-0.2235853224992752" arg: "-0.06214175000786781" arg: "0.23073340952396393" arg: "0.3371483087539673" arg: "-0.029265087097883224" arg: "0.25156235694885254" arg: "0.43319517374038696" arg: "0.035503044724464417" arg: "0.12156634777784348" arg: "-0.24198615550994873" arg: "-0.42002007365226746" arg: "-0.11373946070671082" arg: "-0.28098201751708984"
+  }
+}
+operand {
+  name: "input_gate_bias"
+  type: FLOAT32
+  shape { dim: 20 }
+  filler {
+    tag: "explicit"
+    arg: "0.39238446950912476" arg: "-0.040046464651823044" arg: "0.13657712936401367" arg: "0.35934528708457947" arg: "0.321681946516037" arg: "0.0616583526134491" arg: "0.11477429419755936" arg: "0.20044274628162384" arg: "0.011154969222843647" arg: "0.24244074523448944" arg: "0.27598848938941956" arg: "0.4028998911380768" arg: "0.21931242942810059" arg: "0.3108941316604614" arg: "0.1841004192829132" arg: "0.14638805389404297" arg: "0.46200960874557495" arg: "0.24594353139400482" arg: "0.07526364177465439" arg: "-0.22416549921035767"
+  }
+}
+operand {
+  name: "forget_gate_bias"
+  type: FLOAT32
+  shape { dim: 20 }
+  filler {
+    tag: "explicit"
+    arg: "1.2047474384307861" arg: "1.2191035747528076" arg: "0.871356725692749" arg: "1.0395587682724" arg: "1.150162935256958" arg: "1.0623992681503296" arg: "1.0699368715286255" arg: "1.0769526958465576" arg: "1.1270850896835327" arg: "1.151424527168274" arg: "1.1118133068084717" arg: "1.150691032409668" arg: "0.9700227975845337" arg: "1.0458472967147827" arg: "1.0566719770431519" arg: "1.036710262298584" arg: "1.1118052005767822" arg: "0.9024409651756287" arg: "0.968490481376648" arg: "1.0276471376419067"
+  }
+}
+operand {
+  name: "cell_gate_bias"
+  type: FLOAT32
+  shape { dim: 20 }
+  filler {
+    tag: "explicit"
+    arg: "0.027094807475805283" arg: "0.08994408696889877" arg: "0.048134010285139084" arg: "-0.24551978707313538" arg: "0.016918446868658066" arg: "0.0765792727470398" arg: "-0.0031757261604070663" arg: "0.1118675172328949" arg: "-0.0806640088558197" arg: "0.003836719784885645" arg: "-0.02241756208240986" arg: "0.1585727483034134" arg: "0.07568418234586716" arg: "-0.008664635010063648" arg: "-0.0036717928014695644" arg: "-0.036391645669937134" arg: "-0.012257440015673637" arg: "0.05013420805335045" arg: "-0.014501656405627728" arg: "0.22225865721702576"
+  }
+}
+operand {
+  name: "output_gate_bias"
+  type: FLOAT32
+  shape { dim: 20 }
+  filler {
+    tag: "explicit"
+    arg: "0.2127157747745514" arg: "0.3538936972618103" arg: "0.283548504114151" arg: "1.0181398391723633" arg: "0.40145981311798096" arg: "0.27438417077064514" arg: "0.2998640537261963" arg: "0.5031589865684509" arg: "0.0011858611833304167" arg: "0.5359497666358948" arg: "0.5380197763442993" arg: "0.7726592421531677" arg: "0.27104392647743225" arg: "0.4670105576515198" arg: "0.47913044691085815" arg: "0.4600663185119629" arg: "0.3923473060131073" arg: "-0.03211608901619911" arg: "0.6604049205780029" arg: "0.2065485268831253"
+  }
+}
+operand {
+  name: "activation_state"
+  type: FLOAT32
+  shape { dim: 1 dim: 20 }
+  filler {
+    tag: "explicit"
+  }
+}
+operand {
+  name: "cell_state"
+  type: FLOAT32
+  shape { dim: 1 dim: 20 }
+  filler {
+    tag: "explicit"
+  }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 1 dim: 28 dim: 20 }
+}
+operation {
+  type: "UnidirectionalSequenceLSTM"
+  unidirectional_sequence_lstm_options {
+    activation: TANH
+    cell_clip: 10.0
+    proj_clip: 0.0
+    time_major: false
+    asymmetric_quantize_inputs: false
+  }
+  input: "ifm"
+  input: "input_to_input_weights"
+  input: "input_to_forget_weights"
+  input: "input_to_cell_weights"
+  input: "input_to_output_weights"
+  input: "recurrent_to_input_weights"
+  input: "recurrent_to_forget_weights"
+  input: "recurrent_to_cell_weights"
+  input: "recurrent_to_output_weights"
+  input: ""
+  input: ""
+  input: ""
+  input: "input_gate_bias"
+  input: "forget_gate_bias"
+  input: "cell_gate_bias"
+  input: "output_gate_bias"
+  input: ""
+  input: ""
+  input: "activation_state"
+  input: "cell_state"
+  input: ""
+  input: ""
+  input: ""
+  input: ""
+  output: "ofm"
+}
+input: "ifm"
+output: "ofm"
diff --git a/res/TensorFlowLiteRecipes/UnidirectionalSequenceLSTM_001/test.reverse b/res/TensorFlowLiteRecipes/UnidirectionalSequenceLSTM_001/test.reverse
new file mode 100644
index 000000000..e69de29bb
--- /dev/null
+++ b/res/TensorFlowLiteRecipes/UnidirectionalSequenceLSTM_001/test.reverse
diff --git a/res/TensorFlowLiteRecipes/Unique_000/test.recipe b/res/TensorFlowLiteRecipes/Unique_000/test.recipe
index 3110b5ed9..887380c48 100644
--- a/res/TensorFlowLiteRecipes/Unique_000/test.recipe
+++ b/res/TensorFlowLiteRecipes/Unique_000/test.recipe
@@ -6,7 +6,7 @@ operand {
 operand {
   name: "ofm"
   type: FLOAT32
-  shape { dim: 0 }
+  shape { }
 }
 operand {
   name: "ofm_idx"
diff --git a/res/TensorFlowLiteRecipes/Unique_001/test.recipe b/res/TensorFlowLiteRecipes/Unique_001/test.recipe
index d654f79b9..9beb51690 100644
--- a/res/TensorFlowLiteRecipes/Unique_001/test.recipe
+++ b/res/TensorFlowLiteRecipes/Unique_001/test.recipe
@@ -6,7 +6,7 @@ operand {
 operand {
   name: "ofm"
   type: FLOAT32
-  shape { dim: 0 }
+  shape { }
 }
 operand {
   name: "ofm_idx"
diff --git a/res/TensorFlowLiteRecipes/Unique_002/test.recipe b/res/TensorFlowLiteRecipes/Unique_002/test.recipe
index d9f2393b8..67b947ff8 100644
--- a/res/TensorFlowLiteRecipes/Unique_002/test.recipe
+++ b/res/TensorFlowLiteRecipes/Unique_002/test.recipe
@@ -6,7 +6,7 @@ operand {
 operand {
   name: "ofm"
   type: INT32
-  shape { dim: 0 }
+  shape { }
 }
 operand {
   name: "ofm_idx"
diff --git a/res/TensorFlowLiteRecipes/Unique_003/test.recipe b/res/TensorFlowLiteRecipes/Unique_003/test.recipe
index de9e87af9..375db66e8 100644
--- a/res/TensorFlowLiteRecipes/Unique_003/test.recipe
+++ b/res/TensorFlowLiteRecipes/Unique_003/test.recipe
@@ -6,7 +6,7 @@ operand {
 operand {
   name: "ofm"
   type: INT32
-  shape { dim: 0 }
+  shape { }
 }
 operand {
   name: "ofm_idx"
diff --git a/res/TensorFlowLiteRecipes/Unique_U8_000/test.recipe b/res/TensorFlowLiteRecipes/Unique_U8_000/test.recipe
index 3906d2c5e..d3985e401 100644
--- a/res/TensorFlowLiteRecipes/Unique_U8_000/test.recipe
+++ b/res/TensorFlowLiteRecipes/Unique_U8_000/test.recipe
@@ -7,7 +7,7 @@ operand {
 operand {
   name: "ofm"
   type: UINT8
-  shape { dim: 0 }
+  shape { }
 }
 operand {
   name: "ofm_idx"
diff --git a/res/TensorFlowLiteRecipes/Unique_U8_001/test.recipe b/res/TensorFlowLiteRecipes/Unique_U8_001/test.recipe
index 2bac10ae7..b08dd85cc 100644
--- a/res/TensorFlowLiteRecipes/Unique_U8_001/test.recipe
+++ b/res/TensorFlowLiteRecipes/Unique_U8_001/test.recipe
@@ -7,7 +7,7 @@ operand {
 operand {
   name: "ofm"
   type: UINT8
-  shape { dim: 0 }
+  shape { }
 }
 operand {
   name: "ofm_idx"
diff --git a/res/TensorFlowPythonExamples/examples/atrous_conv2d/__init__.py b/res/TensorFlowPythonExamples/examples/atrous_conv2d/__init__.py
new file mode 100644
index 000000000..90756b0b0
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/atrous_conv2d/__init__.py
@@ -0,0 +1,8 @@
+import tensorflow as tf
+import numpy as np
+
+in_ = tf.compat.v1.placeholder(tf.float32, shape=(1, 32, 32, 3), name="Hole")
+
+filters = np.random.uniform(low=-1., high=1, size=[5, 5, 3, 32]).astype(np.float32)
+
+op_ = tf.compat.v1.nn.atrous_conv2d(in_, filters, 2, "VALID")
diff --git a/res/TensorFlowPythonExamples/examples/flatten/__init__.py b/res/TensorFlowPythonExamples/examples/flatten/__init__.py
new file mode 100644
index 000000000..bb6dbaa2b
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/flatten/__init__.py
@@ -0,0 +1,5 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(3, 3), name="Hole")
+
+op_ = tf.compat.v1.layers.flatten(in_)
diff --git a/res/TensorFlowPythonExamples/examples/instance_norm/__init__.py b/res/TensorFlowPythonExamples/examples/instance_norm/__init__.py
new file mode 100644
index 000000000..b44942c39
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/instance_norm/__init__.py
@@ -0,0 +1,22 @@
+import tensorflow as tf
+
+sess = tf.Session()
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=(3, 3), name="Hole")
+norm_ = tf.contrib.layers.instance_norm(in_)
+
+# we need to save checkpoint to freeze dropped model
+init = tf.initialize_all_variables()
+sess.run(init)
+
+saver = tf.train.Saver()
+saver.save(sess, './ckpt/instance_norm.ckpt')
+
+# use below command to freeze this model after running tfpem.py
+'''
+freeze_graph --input_graph instance_norm.pbtxt \
+--input_binary=false \
+--input_checkpoint=./ckpt/instance_norm.ckpt \
+--output_node_names=InstanceNorm/instancenorm/add_1 \
+--output_graph instance_norm_fr.pbtxt
+'''
diff --git a/res/TensorFlowPythonExamples/examples/unidirectional_sequence_LSTM/__init__.py b/res/TensorFlowPythonExamples/examples/unidirectional_sequence_LSTM/__init__.py
new file mode 100644
index 000000000..eaeb32ac3
--- /dev/null
+++ b/res/TensorFlowPythonExamples/examples/unidirectional_sequence_LSTM/__init__.py
@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+in_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=[28, 28, 3], name="Hole")
+op_ = tf.compat.v1.keras.layers.LSTM(1, time_major=False, return_sequences=True)(in_)