diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..ebc852f
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+uTensor
\ No newline at end of file
diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..c7cadb4
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,3 @@
+{
+    "python.pythonPath": ".venv/bin/python"
+}
\ No newline at end of file
diff --git a/Pipfile b/Pipfile
index d01bf5d..0e71545 100644
--- a/Pipfile
+++ b/Pipfile
@@ -11,4 +11,4 @@ utensor-cgen = "*"
 jupyter = "*"
 
 [requires]
-python_version = "3.7"
+python_version = "3.6"
diff --git a/Pipfile.lock b/Pipfile.lock
index dfb3ab8..63016a9 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,11 +1,11 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "6c3bc297eeddfa47f377a263d05469425975412699f5ca22de2c0b28335c3c95"
+            "sha256": "c6bff961ec1007ad5943af12e7947b56aca301af6145443d049bafdca944151b"
         },
         "pipfile-spec": 6,
         "requires": {
-            "python_version": "3.7"
+            "python_version": "3.6"
         },
         "sources": [
             {
@@ -18,9 +18,10 @@
     "default": {
         "absl-py": {
             "hashes": [
-                "sha256:75e737d6ce7723d9ff9b7aa1ba3233c34be62ef18d5859e706b8fdc828989830"
+                "sha256:b20f504a7871a580be5268a18fbad48af4203df5d33dbc9272426cb806245a45",
+                "sha256:ea07d7d437798bffc14f39fccec3909d251a1e76e233205ded72b71c267e0178"
             ],
-            "version": "==0.9.0"
+            "version": "==0.10.0"
         },
         "appdirs": {
             "hashes": [
@@ -34,22 +35,45 @@
                 "sha256:5b26757dc6f79a3b7dc9fab95359328d5747fcb2409d331ea66d0272b90ab2a0",
                 "sha256:8b995ffe925347a2138d7ac0fe77155e4311a0ea6d6da4f5128fe4b3cbe5ed71"
             ],
-            "markers": "sys_platform == 'darwin'",
+            "markers": "sys_platform == 'darwin' and platform_system == 'Darwin'",
             "version": "==0.1.0"
         },
+        "argon2-cffi": {
+            "hashes": [
+                "sha256:05a8ac07c7026542377e38389638a8a1e9b78f1cd8439cd7493b39f08dd75fbf",
+                "sha256:0bf066bc049332489bb2d75f69216416329d9dc65deee127152caeb16e5ce7d5",
+                "sha256:18dee20e25e4be86680b178b35ccfc5d495ebd5792cd00781548d50880fee5c5",
+                "sha256:392c3c2ef91d12da510cfb6f9bae52512a4552573a9e27600bdb800e05905d2b",
+                "sha256:57358570592c46c420300ec94f2ff3b32cbccd10d38bdc12dc6979c4a8484fbc",
+                "sha256:6678bb047373f52bcff02db8afab0d2a77d83bde61cfecea7c5c62e2335cb203",
+                "sha256:6ea92c980586931a816d61e4faf6c192b4abce89aa767ff6581e6ddc985ed003",
+                "sha256:77e909cc756ef81d6abb60524d259d959bab384832f0c651ed7dcb6e5ccdbb78",
+                "sha256:7d455c802727710e9dfa69b74ccaab04568386ca17b0ad36350b622cd34606fe",
+                "sha256:9bee3212ba4f560af397b6d7146848c32a800652301843df06b9e8f68f0f7361",
+                "sha256:9dfd5197852530294ecb5795c97a823839258dfd5eb9420233c7cfedec2058f2",
+                "sha256:b160416adc0f012fb1f12588a5e6954889510f82f698e23ed4f4fa57f12a0647",
+                "sha256:ba7209b608945b889457f949cc04c8e762bed4fe3fec88ae9a6b7765ae82e496",
+                "sha256:cc0e028b209a5483b6846053d5fd7165f460a1f14774d79e632e75e7ae64b82b",
+                "sha256:d8029b2d3e4b4cea770e9e5a0104dd8fa185c1724a0f01528ae4826a6d25f97d",
+                "sha256:da7f0445b71db6d3a72462e04f36544b0de871289b0bc8a7cc87c0f5ec7079fa"
+            ],
+            "version": "==20.1.0"
+        },
         "astor": {
             "hashes": [
                 "sha256:070a54e890cefb5b3739d19f30f5a5ec840ffc9c50ffa7d23cc9fc1a38ebbfc5",
                 "sha256:6a6effda93f4e1ce9f618779b2dd1d9d84f1e32812c23a29b3fff6fd7f63fa5e"
             ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
             "version": "==0.8.1"
         },
         "attrs": {
             "hashes": [
-                "sha256:08a96c641c3a74e44eb59afb61a24f2cb9f4d7188748e76ba4bb5edfa3cb7d1c",
-                "sha256:f7b7ce16570fe9965acd6d30101a28f62fb4a7f9e926b3bbc9b61f8b04247e72"
+                "sha256:0ef97238856430dcf9228e07f316aefc17e8939fc8507e18c6501b761ef1a42a",
+                "sha256:2867b7b9f8326499ab5b0e2d12801fa5c98842d2cbd22b35112ae04bf85b4dff"
             ],
-            "version": "==19.3.0"
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
+            "version": "==20.1.0"
         },
         "backcall": {
             "hashes": [
@@ -71,21 +95,56 @@
                 "sha256:2bce3d8fab545a6528c8fa5d9f9ae8ebc85a56da365c7f85180bfe96a35ef22f",
                 "sha256:3c4c520fdb9db59ef139915a5db79f8b51bc2a7257ea0389f30c846883430a4b"
             ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
             "version": "==3.1.5"
         },
         "cachetools": {
             "hashes": [
-                "sha256:1d057645db16ca7fe1f3bd953558897603d6f0b9c51ed9d11eb4d071ec4e2aab",
-                "sha256:de5d88f87781602201cde465d3afe837546663b168e8b39df67411b0bf10cefc"
+                "sha256:513d4ff98dd27f85743a8dc0e92f55ddb1b49e060c2d5961512855cda2c01a98",
+                "sha256:bbaa39c3dede00175df2dc2b03d0cf18dd2d32a7de7beb68072d13043c9edb20"
             ],
-            "version": "==4.1.0"
+            "markers": "python_version ~= '3.5'",
+            "version": "==4.1.1"
         },
         "certifi": {
             "hashes": [
-                "sha256:5ad7e9a056d25ffa5082862e36f119f7f7cec6457fa07ee2f8c339814b80c9b1",
-                "sha256:9cd41137dc19af6a5e03b630eefe7d1f458d964d406342dd3edf625839b944cc"
-            ],
-            "version": "==2020.4.5.2"
+                "sha256:5930595817496dd21bb8dc35dad090f1c2cd0adfaf21204bf6732ca5d8ee34d3",
+                "sha256:8fc0819f1f30ba15bdb34cceffb9ef04d99f420f68eb75d901e9560b8749fc41"
+            ],
+            "version": "==2020.6.20"
+        },
+        "cffi": {
+            "hashes": [
+                "sha256:0da50dcbccd7cb7e6c741ab7912b2eff48e85af217d72b57f80ebc616257125e",
+                "sha256:12a453e03124069b6896107ee133ae3ab04c624bb10683e1ed1c1663df17c13c",
+                "sha256:15419020b0e812b40d96ec9d369b2bc8109cc3295eac6e013d3261343580cc7e",
+                "sha256:15a5f59a4808f82d8ec7364cbace851df591c2d43bc76bcbe5c4543a7ddd1bf1",
+                "sha256:23e44937d7695c27c66a54d793dd4b45889a81b35c0751ba91040fe825ec59c4",
+                "sha256:29c4688ace466a365b85a51dcc5e3c853c1d283f293dfcc12f7a77e498f160d2",
+                "sha256:57214fa5430399dffd54f4be37b56fe22cedb2b98862550d43cc085fb698dc2c",
+                "sha256:577791f948d34d569acb2d1add5831731c59d5a0c50a6d9f629ae1cefd9ca4a0",
+                "sha256:6539314d84c4d36f28d73adc1b45e9f4ee2a89cdc7e5d2b0a6dbacba31906798",
+                "sha256:65867d63f0fd1b500fa343d7798fa64e9e681b594e0a07dc934c13e76ee28fb1",
+                "sha256:672b539db20fef6b03d6f7a14b5825d57c98e4026401fce838849f8de73fe4d4",
+                "sha256:6843db0343e12e3f52cc58430ad559d850a53684f5b352540ca3f1bc56df0731",
+                "sha256:7057613efefd36cacabbdbcef010e0a9c20a88fc07eb3e616019ea1692fa5df4",
+                "sha256:76ada88d62eb24de7051c5157a1a78fd853cca9b91c0713c2e973e4196271d0c",
+                "sha256:837398c2ec00228679513802e3744d1e8e3cb1204aa6ad408b6aff081e99a487",
+                "sha256:8662aabfeab00cea149a3d1c2999b0731e70c6b5bac596d95d13f643e76d3d4e",
+                "sha256:95e9094162fa712f18b4f60896e34b621df99147c2cee216cfa8f022294e8e9f",
+                "sha256:99cc66b33c418cd579c0f03b77b94263c305c389cb0c6972dac420f24b3bf123",
+                "sha256:9b219511d8b64d3fa14261963933be34028ea0e57455baf6781fe399c2c3206c",
+                "sha256:ae8f34d50af2c2154035984b8b5fc5d9ed63f32fe615646ab435b05b132ca91b",
+                "sha256:b9aa9d8818c2e917fa2c105ad538e222a5bce59777133840b93134022a7ce650",
+                "sha256:bf44a9a0141a082e89c90e8d785b212a872db793a0080c20f6ae6e2a0ebf82ad",
+                "sha256:c0b48b98d79cf795b0916c57bebbc6d16bb43b9fc9b8c9f57f4cf05881904c75",
+                "sha256:da9d3c506f43e220336433dffe643fbfa40096d408cb9b7f2477892f369d5f82",
+                "sha256:e4082d832e36e7f9b2278bc774886ca8207346b99f278e54c9de4834f17232f7",
+                "sha256:e4b9b7af398c32e408c00eb4e0d33ced2f9121fd9fb978e6c1b57edd014a7d15",
+                "sha256:e613514a82539fc48291d01933951a13ae93b6b444a88782480be32245ed4afa",
+                "sha256:f5033952def24172e60493b68717792e3aebb387a8d186c43c020d9363ee7281"
+            ],
+            "version": "==1.14.2"
         },
         "chardet": {
             "hashes": [
@@ -99,6 +158,7 @@
                 "sha256:d2b5255c7c6349bc1bd1e59e08cd12acbbd63ce649f2588755783aa94dfb6b1a",
                 "sha256:dacca89f4bfadd5de3d7489b7c8a566eee0d3676333fbb50030263894c38c0dc"
             ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
             "version": "==7.1.2"
         },
         "colorama": {
@@ -106,6 +166,7 @@
                 "sha256:7d73d2a99753107a36ac6b455ee49046802e59d9d076ef8e47b61499fa29afff",
                 "sha256:e96da0d330793e2cb9485e9ddfd918d456036c7149416295932478192f4436a1"
             ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
             "version": "==0.4.3"
         },
         "cycler": {
@@ -127,6 +188,7 @@
                 "sha256:6687150770438374ab581bb7a1b327a847dd9c5749e396102de3fad4e8a3ef93",
                 "sha256:f684034d135af4c6cbb949b8a4d2ed61634515257a67299e5f940fbaa34377f5"
             ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
             "version": "==0.6.0"
         },
         "entrypoints": {
@@ -134,6 +196,7 @@
                 "sha256:589f874b313739ad35be6e0cd7efde2a4e9b6fea91edcc34e58ecbb8dbe56d19",
                 "sha256:c70dd71abe5a8c85e55e12c19bd91ccfeec11a6e99044204511f9ed547d48451"
             ],
+            "markers": "python_version >= '2.7'",
             "version": "==0.3"
         },
         "fasteners": {
@@ -154,6 +217,7 @@
             "hashes": [
                 "sha256:b1bead90b70cf6ec3f0710ae53a525360fa360d306a86583adc6bf83a4db537d"
             ],
+            "markers": "python_version >= '2.6' and python_version not in '3.0, 3.1, 3.2, 3.3'",
             "version": "==0.18.2"
         },
         "gast": {
@@ -164,10 +228,11 @@
         },
         "google-auth": {
             "hashes": [
-                "sha256:5e3f540b7b0b892000d542cea6b818b837c230e9a4db9337bb2973bcae0fc078",
-                "sha256:d6b390d3bb0969061ffec7e5766c45c1b39e13c302691e35029f1ad1ccd8ca3b"
+                "sha256:982e1f82cace752134660b4c0ff660761b32146a55abb3ad6d225529012af87c",
+                "sha256:f2498ad9cac3d2942d6c509ba18c4639656b366681881a1805f44f2a0c2d46f1"
             ],
-            "version": "==1.18.0"
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
+            "version": "==1.21.0"
         },
         "google-auth-oauthlib": {
             "hashes": [
@@ -186,46 +251,55 @@
         },
         "graphviz": {
             "hashes": [
-                "sha256:cb0e878f90378489f17aab140b68e64e44b79e4cb59a530c8863d84bf2e2e5f5",
-                "sha256:e104ba036c8aef84320ec80560e544cd3cad68c9f90394b4e2b87bc44ab09791"
+                "sha256:088562ef6e3dad5e8dde9389caf4a84f768e65dcaa08238cfcdf36d2b30ccf61",
+                "sha256:f5aad52a652c06825dcc5ee018d920fca26aef339386866094597fb3f2f222ce"
             ],
-            "version": "==0.14"
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
+            "version": "==0.14.1"
         },
         "grpcio": {
             "hashes": [
-                "sha256:10cdc8946a7c2284bbc8e16d346eaa2beeaae86ea598f345df86d4ef7dfedb84",
-                "sha256:23bc395a32c2465564cb242e48bdd2fdbe5a4aebf307649a800da1b971ee7f29",
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-                "sha256:9cfb4b71cc3c8757f137d47000f9d90d4bd818733f9ab4f78bd447e052a4cb9a",
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-                "sha256:a6dddb177b3cfa0cfe299fb9e07d6a3382cc79466bef48fe9c4326d5c5b1dcb8",
-                "sha256:a97ea91e31863c9a3879684b5fb3c6ab4b17c5431787548fc9f52b9483ea9c25",
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-                "sha256:b85f355fc24b68a6c52f2750e7141110d1fcd07dfdc9b282de0000550fe0511b",
-                "sha256:c3a0ef12ee86f6e72db50e01c3dba7735a76d8c30104b9b0f7fd9d65ceb9d93f",
-                "sha256:da0ca9b1089d00e39a8b83deec799a4e5c37ec1b44d804495424acde50531868",
-                "sha256:e90f3d11185c36593186e5ff1f581acc6ddfa4190f145b0366e579de1f52803b",
-                "sha256:ebf0ccb782027ef9e213e03b6d00bbd8dabd80959db7d468c0738e6d94b5204c",
-                "sha256:eede3039c3998e2cc0f6713f4ac70f235bd32967c9b958a17bf937aceebc12c3",
-                "sha256:ff7931241351521b8df01d7448800ce0d59364321d8d82c49b826d455678ff08"
-            ],
-            "version": "==1.29.0"
+                "sha256:013287f99c99b201aa8a5f6bc7918f616739b9be031db132d9e3b8453e95e151",
+                "sha256:0397616355760cd8282ed5ea34d51830ae4cb6613b7e5f66bed3be5d041b8b9a",
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+                "sha256:220c46b1fc9c9a6fcca4caac398f08f0ed43cdd63c45b7458983c4a1575ef6df",
+                "sha256:259240aab2603891553e17ad5b2655693df79e02a9b887ff605bdeb2fcd3dcc9",
+                "sha256:292635f05b6ce33f87116951d0b3d8d330bdfc5cac74f739370d60981e8c256c",
+                "sha256:344b50865914cc8e6d023457bffee9a640abb18f75d0f2bb519041961c748da9",
+                "sha256:3c2aa6d7a5e5bf73fdb1715eee777efe06dd39df03383f1cc095b2fdb34883e6",
+                "sha256:43d44548ad6ee738b941abd9f09e3b83a5c13f3e1410321023c3c148ba50e796",
+                "sha256:5043440c45c0a031f387e7f48527541c65d672005fb24cf18ef6857483557d39",
+                "sha256:58d7121f48cb94535a4cedcce32921d0d0a78563c7372a143dedeec196d1c637",
+                "sha256:5d7faa89992e015d245750ca9ac916c161bbf72777b2c60abc61da3fae41339e",
+                "sha256:5fb0923b16590bac338e92d98c7d8effb3cfad1d2e18c71bf86bde32c49cd6dd",
+                "sha256:63ee8e02d04272c3d103f44b4bce5d43ea757dd288673cea212d2f7da27967d2",
+                "sha256:64077e3a9a7cf2f59e6c76d503c8de1f18a76428f41a5b000dc53c48a0b772ff",
+                "sha256:739a72abffbd36083ff7adbb862cf1afc1e311c35834bed9c0361d8e68b063e1",
+                "sha256:75e383053dccb610590aa53eed5278db5c09bf498d3b5105ce6c776478f59352",
+                "sha256:7a11b1ebb3210f34913b8be6995936bf9ebc541a65ab69e75db5ce1fe5047e8f",
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@@ -263,10 +337,11 @@
         },
         "idna": {
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-            "version": "==2.9"
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@@ -276,11 +351,11 @@
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-            "version": "==1.6.1"
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         "intelhex": {
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@@ -291,18 +366,19 @@
         },
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-            "version": "==5.3.0"
+            "markers": "python_version >= '3.5'",
+            "version": "==5.3.4"
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@@ -320,16 +396,18 @@
         },
         "jedi": {
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-            "version": "==0.17.0"
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             "version": "==2.11.2"
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@@ -356,16 +434,18 @@
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-            "version": "==6.1.3"
+            "markers": "python_version >= '3.5'",
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@@ -373,6 +453,7 @@
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@@ -392,22 +473,26 @@
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@@ -419,42 +504,47 @@
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@@ -493,28 +583,41 @@
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@@ -526,10 +629,10 @@
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@@ -550,6 +653,7 @@
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@@ -557,46 +661,55 @@
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@@ -634,10 +747,11 @@
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@@ -659,6 +773,7 @@
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@@ -669,10 +784,11 @@
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@@ -691,30 +807,37 @@
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@@ -733,33 +856,34 @@
         },
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@@ -772,22 +896,53 @@
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@@ -795,6 +950,7 @@
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@@ -815,47 +971,49 @@
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@@ -869,12 +1027,14 @@
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+                "sha256:b4261601a71fd721a8bd6d7aa1cc1d6a8a93b4a9f5e96626f8e4d91e8beeaa6a",
+                "sha256:fa6c47b933f01060936d87ae9327fead68768b69c6c9ea2109c48be30f2d4dbc",
+                "sha256:7f71572defaecd16372f9006f33c2ec8c077c3cfa6f5911a9a90202beb513f3d"
             ],
             "version": "==1.3.0"
         },
@@ -883,7 +1043,7 @@
                 "sha256:109ea5a66744dd859bf16fe904b8d8b627adafb9408753161e766a92e7d681fa",
                 "sha256:6166864e23d6b5195a5cfed6cd9fed0fe774e226d8f854fcb23b7bbef0350233"
             ],
-            "markers": "python_version >= '3'",
+            "markers": "python_version >= '3.5'",
             "version": "==4.6"
         },
         "scipy": {
@@ -925,6 +1085,7 @@
                 "sha256:30639c035cdb23534cd4aa2dd52c3bf48f06e5f4a941509c8bafd8ce11080259",
                 "sha256:8b74bedcbbbaca38ff6d7491d76f2b06b3592611af620f8426e82dddb04a5ced"
             ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
             "version": "==1.15.0"
         },
         "soupsieve": {
@@ -932,12 +1093,14 @@
                 "sha256:1634eea42ab371d3d346309b93df7870a88610f0725d47528be902a0d95ecc55",
                 "sha256:a59dc181727e95d25f781f0eb4fd1825ff45590ec8ff49eadfd7f1a537cc0232"
             ],
+            "markers": "python_version >= '3.5'",
             "version": "==2.0.1"
         },
         "tensorboard": {
             "hashes": [
                 "sha256:3a36da59d4e13fb140d04636aaf544f1a38364a7e3609a15cdaf01a5d3073b37"
             ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
             "version": "==2.1.1"
         },
         "tensorflow": {
@@ -973,6 +1136,7 @@
                 "sha256:4804a774f802306a7d9af7322193c5390f1da0abb429e082a10ef1d46e6fb2c2",
                 "sha256:a43dcb3e353bc680dd0783b1d9c3fc28d529f190bc54ba9a229f72fe6e7a54d7"
             ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
             "version": "==0.8.3"
         },
         "testpath": {
@@ -991,29 +1155,26 @@
         },
         "torch": {
             "hashes": [
-                "sha256:0a83f41140222c7cc947aa29ed253f3e6fa490606d3d4acd02bfd9f338e3c707",
-                "sha256:5d909a55cd979fec2c9a7aa35012024b9cc106acbc496faf5de798b148406450",
-                "sha256:70046cf66eb40ead89df25b8dcc571c3007fc9849d4e1d254cc09b4b355374d4",
-                "sha256:a358cee1d35b86757bf915e320ba776d39c20e60db50779060842efc86f02edd",
-                "sha256:b84fd18fd8216b74a19828433c3beeb1f0d1d29f45dead3be9ed784ae6855966",
-                "sha256:bb2a3e6c9c9dbfda856bd1b1a55d88789a9488b569ffba9cd6d9aa536ef866ba",
-                "sha256:c42658f2982591dc4d0459645c9ab26e0ce18aa7ab0993c27c8bcb1c98931d11",
-                "sha256:ff1dbeaa017bae66036e8e7a698a5475ac5a0d7b0a690f0a04ac3b1133b1feb3"
+                "sha256:3838bd01af7dfb1f78573973f6842ce75b17e8e4f22be99c891dcb7c94bc13f5",
+                "sha256:4f9a4ad7947cef566afb0a323d99009fe8524f0b0f2ca1fb7ad5de0400381a5b",
+                "sha256:5357873e243bcfa804c32dc341f564e9a4c12addfc9baae4ee857fcc09a0a216",
+                "sha256:728facb972a5952323c6d790c2c5922b2b35c44b0bc7bdfa02f8639727671a0c",
+                "sha256:7669f4d923b5758e28b521ea749c795ed67ff24b45ba20296bc8cff706d08df8",
+                "sha256:87d65c01d1b70bb46070824f28bfd93c86d3c5c56b90cbbe836a3f2491d91c76"
             ],
-            "version": "==1.5.1"
+            "markers": "python_full_version >= '3.6.1'",
+            "version": "==1.6.0"
         },
         "torchvision": {
             "hashes": [
-                "sha256:3af59c61d20bb0bb63dbd50f324fcd975fc5b89fb78e6b5714ae613cd7b734cc",
-                "sha256:52be3710bf495fee9eb8f1cb4d310edd2da94b127b2ca0aa77075c1001bcbb91",
-                "sha256:80a8beaa3416035c9640d153b1f0a7c9b09aac8037a032bbc33fd4bc8657b091",
-                "sha256:9a544f241e1df7485c1fda64f06af4962ad4af1da6caeda352757ea800dd794d",
-                "sha256:b07e7c5a7274082a8bbe88975c817da10bffaf4020017347d185b5d4eacb891d",
-                "sha256:c122386a4604a6b611626d817981777bb06e747161a4954e4037c2e297fe1d86",
-                "sha256:c44819c3c903f2d6d269713a9aa81a9dcb0ab7716b4fc4dbdccf596e6fe894c9",
-                "sha256:eb6d7ef73ab8ed756d18c1c11ead3ba1be7b3e2fe5bf475e16a4426d7f3d6eec"
+                "sha256:0d1a5adfef4387659c7a0af3b72e16caa0c67224a422050ab65184d13ac9fb13",
+                "sha256:14c0bf60fa26aabaea64ef30b8e5d441ee78d1a5eed568c30806af19bbe6b638",
+                "sha256:8c8df7e1d1f3d4e088256be1e8c8d3eb90b016302baa4649742d47ae1531da37",
+                "sha256:a70d80bb8749c1e4a46fa56dc2fc857e98d14600841e02cc2fed766daf96c245",
+                "sha256:cfa2b367bc9acf20f18b151d0525970279719e81969c17214effe77245875354",
+                "sha256:f5686e0a0dd511ac33eb9d6279bd34edd9f282dcb7c8ad21e290882c6206504f"
             ],
-            "version": "==0.6.1"
+            "version": "==0.7.0"
         },
         "tornado": {
             "hashes": [
@@ -1027,6 +1188,7 @@
                 "sha256:c952975c8ba74f546ae6de2e226ab3cc3cc11ae47baf607459a6728585bb542a",
                 "sha256:c98232a3ac391f5faea6821b53db8db461157baa788f5d6222a193e9456e1740"
             ],
+            "markers": "python_version >= '3.5'",
             "version": "==6.0.4"
         },
         "traitlets": {
@@ -1038,18 +1200,19 @@
         },
         "typing-extensions": {
             "hashes": [
-                "sha256:6e95524d8a547a91e08f404ae485bbb71962de46967e1b71a0cb89af24e761c5",
-                "sha256:79ee589a3caca649a9bfd2a8de4709837400dfa00b6cc81962a1e6a1815969ae",
-                "sha256:f8d2bd89d25bc39dabe7d23df520442fa1d8969b82544370e03d88b5a591c392"
+                "sha256:7cb407020f00f7bfc3cb3e7881628838e69d8f3fcab2f64742a5e76b2f841918",
+                "sha256:99d4073b617d30288f569d3f13d2bd7548c3a7e4c8de87db09a9d29bb3a4a60c",
+                "sha256:dafc7639cde7f1b6e1acc0f457842a83e722ccca8eef5270af2d74792619a89f"
             ],
-            "version": "==3.7.4.2"
+            "version": "==3.7.4.3"
         },
         "urllib3": {
             "hashes": [
-                "sha256:3018294ebefce6572a474f0604c2021e33b3fd8006ecd11d62107a5d2a963527",
-                "sha256:88206b0eb87e6d677d424843ac5209e3fb9d0190d0ee169599165ec25e9d9115"
+                "sha256:91056c15fa70756691db97756772bb1eb9678fa585d9184f24534b100dc60f4a",
+                "sha256:e7983572181f5e1522d9c98453462384ee92a0be7fac5f1413a1e35c56cc0461"
             ],
-            "version": "==1.25.9"
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' and python_version < '4'",
+            "version": "==1.25.10"
         },
         "utensor-cgen": {
             "hashes": [
@@ -1060,10 +1223,10 @@
         },
         "wcwidth": {
             "hashes": [
-                "sha256:79375666b9954d4a1a10739315816324c3e73110af9d0e102d906fdb0aec009f",
-                "sha256:8c6b5b6ee1360b842645f336d9e5d68c55817c26d3050f46b235ef2bc650e48f"
+                "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784",
+                "sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83"
             ],
-            "version": "==0.2.4"
+            "version": "==0.2.5"
         },
         "webencodings": {
             "hashes": [
@@ -1077,15 +1240,16 @@
                 "sha256:2de2a5db0baeae7b2d2664949077c2ac63fbd16d98da0ff71837f7d1dea3fd43",
                 "sha256:6c80b1e5ad3665290ea39320b91e1be1e0d5f60652b964a3070216de83d2e47c"
             ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
             "version": "==1.0.1"
         },
         "wheel": {
             "hashes": [
-                "sha256:8788e9155fe14f54164c1b9eb0a319d98ef02c160725587ad60f14ddc57b6f96",
-                "sha256:df277cb51e61359aba502208d680f90c0493adec6f0e848af94948778aed386e"
+                "sha256:497add53525d16c173c2c1c733b8f655510e909ea78cc0e29d374243544b77a2",
+                "sha256:99a22d87add3f634ff917310a3d87e499f19e663413a52eb9232c447aa646c9f"
             ],
             "markers": "python_version >= '3'",
-            "version": "==0.34.2"
+            "version": "==0.35.1"
         },
         "widgetsnbextension": {
             "hashes": [
@@ -1105,6 +1269,7 @@
                 "sha256:aa36550ff0c0b7ef7fa639055d797116ee891440eac1a56f378e2d3179e0320b",
                 "sha256:c599e4d75c98f6798c509911d08a22e6c021d074469042177c8c86fb92eefd96"
             ],
+            "markers": "python_version >= '3.6'",
             "version": "==3.1.0"
         }
     },
diff --git a/README.md b/README.md
index 6b9d7cb..4cf10ab 100644
--- a/README.md
+++ b/README.md
@@ -125,11 +125,18 @@ tflm_keras_export(
 $ mbed deploy
 $ mbed compile -m auto -t GCC_ARM -f --sterm
 ```
-Expected output:
 
-```
+Expected output:
+```bash
 Simple MNIST end-to-end uTensor cli example (device)
-Predicted label: 7
+pred label: 8, expecting: 8
+pred label: 3, expecting: 3
+pred label: 5, expecting: 5
+pred label: 5, expecting: 5
+pred label: 1, expecting: 1
+pred label: 9, expecting: 9
+pred label: 3, expecting: 3
+pred label: 1, expecting: 1
 ```
 
 ## Join Us
diff --git a/gen_inputs_header.py b/gen_inputs_header.py
new file mode 100644
index 0000000..1947948
--- /dev/null
+++ b/gen_inputs_header.py
@@ -0,0 +1,48 @@
+import tensorflow as tf
+import numpy as np
+import argparse
+
+
+def main(num_samples=5, seed=None):
+    mnist = tf.keras.datasets.mnist
+    (_, _), (x_test, y_test) = mnist.load_data()
+    x_test = x_test / 255.0
+
+    # Add a channels dimension
+    x_test = x_test[..., np.newaxis]
+    total_N = y_test.shape[0]
+    num_samples = min(num_samples, total_N)
+    np.random.seed(seed)
+    idxs = np.random.choice(range(total_N), num_samples, replace=False)
+    x_selected = x_test[idxs].reshape(num_samples, -1)
+    y_selected = y_test[idxs]
+    with open("input_image.h", "w") as fid:
+        fid.write("// clang-format off\n")
+        fid.write(
+            "const float arr_input_image[{}][{}] = {{\n".format(
+                x_selected.shape[0], x_selected.shape[1]
+            )
+        )
+        for i in range(x_selected.shape[0]):
+            arr = x_selected[i]
+            fid.write("    {{ {}".format(", ".join(map(str, arr))))
+            fid.write("},\n")
+        fid.write("};\n")
+        fid.write("const int ref_labels[{}] = {{\n".format(y_selected.shape[0]))
+        fid.write("    " + ", ".join(map(str, y_selected)) + "\n")
+        fid.write("};\n\n")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--num-samples",
+        dest="num_samples",
+        default=5,
+        help="the number of inpute samples [default: %(default)s]",
+        type=int,
+        metavar="INTEGER",
+    )
+    parser.add_argument("--seed", default=None, help="the random seed", type=int)
+    args = vars(parser.parse_args())
+    main(**args)
diff --git a/input_image.h b/input_image.h
index 8d9face..e858a09 100644
--- a/input_image.h
+++ b/input_image.h
@@ -1,55 +1,15 @@
 // clang-format off
-const float arr_input_image[784] = {
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
-  0.0, 0.0, 0.0, 0.0, 0.32941176470588235, 0.7254901960784313, 0.6235294117647059, 0.592156862745098, 
-  0.23529411764705882, 0.1411764705882353, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8705882352941177, 0.996078431372549, 0.996078431372549, 
-  0.996078431372549, 0.996078431372549, 0.9450980392156862, 0.7764705882352941, 0.7764705882352941, 0.7764705882352941, 
-  0.7764705882352941, 0.7764705882352941, 0.7764705882352941, 0.7764705882352941, 0.7764705882352941, 
-  0.6666666666666666, 0.20392156862745098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2627450980392157, 
-  0.4470588235294118, 0.2823529411764706, 0.4470588235294118, 0.6392156862745098, 0.8901960784313725, 0.996078431372549, 
-  .8823529411764706, 0.996078431372549, 0.996078431372549, 0.996078431372549, 0.9803921568627451, 0.8980392156862745, 
-  0.996078431372549, 0.996078431372549, 0.5490196078431373, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.06666666666666667, 0.25882352941176473, 0.054901960784313725, 0.2627450980392157, 0.2627450980392157, 
-  0.2627450980392157, 0.23137254901960785, 0.08235294117647059, 0.9254901960784314, 0.996078431372549, 0.41568627450980394, 
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
-  0.3254901960784314, 0.9921568627450981, 0.8196078431372549, 0.07058823529411765, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.08627450980392157, 0.9137254901960784, 1.0, 
-  0.3254901960784314, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
-  0.0, 0.0, 0.0, 0.0, 0.5058823529411764, 0.996078431372549, 0.9333333333333333, 0.17254901960784313, 0.0, 0.0, 0.0, 0.0, 
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.23137254901960785, 
-  0.9764705882352941, 0.996078431372549, 0.24313725490196078, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5215686274509804, 0.996078431372549, 0.7333333333333333, 
-  0.0196078431372549, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
-  0.0, 0.0, 0.0, 0.03529411764705882, 0.803921568627451, 0.9725490196078431, 0.22745098039215686, 0.0, 0.0, 0.0, 0.0, 0.0, 
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.49411764705882355, 
-  0.996078431372549, 0.7137254901960784, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.29411764705882354, 0.984313725490196, 0.9411764705882353, 0.2235294117647059, 
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.07450980392156863, 
-  0.8666666666666667, 0.996078431372549, 0.6509803921568628, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.011764705882352941, 0.796078431372549, 0.996078431372549, 0.8588235294117647, 0.13725490196078433, 
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-  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 };
-const int ref_labels[1] = {
-    7
+const float arr_input_image[8][784] = {
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+    { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.047058823529411764, 0.7137254901960784, 0.2784313725490196, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.25882352941176473, 0.9686274509803922, 0.9372549019607843, 0.1803921568627451, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9058823529411765, 0.9882352941176471, 0.42745098039215684, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.26666666666666666, 0.9882352941176471, 0.9764705882352941, 0.34901960784313724, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.047058823529411764, 0.9882352941176471, 0.9882352941176471, 0.8392156862745098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.011764705882352941, 0.6862745098039216, 0.9882352941176471, 0.9882352941176471, 0.3411764705882353, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.20392156862745098, 0.9764705882352941, 0.9882352941176471, 0.6392156862745098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7333333333333333, 0.9882352941176471, 0.8627450980392157, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7333333333333333, 0.9882352941176471, 0.9607843137254902, 0.2235294117647059, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3607843137254902, 0.9882352941176471, 0.9921568627450981, 0.6745098039215687, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6235294117647059, 1.0, 0.9921568627450981, 0.41568627450980394, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.13333333333333333, 0.9921568627450981, 0.9882352941176471, 0.6, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9921568627450981, 0.9882352941176471, 0.6, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5411764705882353, 0.9882352941176471, 0.9725490196078431, 0.0392156862745098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.13333333333333333, 0.9882352941176471, 0.9882352941176471, 0.3254901960784314, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.13333333333333333, 0.9882352941176471, 0.9882352941176471, 0.4745098039215686, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.011764705882352941, 0.7137254901960784, 0.9882352941176471, 0.8666666666666667, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6901960784313725, 0.9882352941176471, 0.9019607843137255, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.29411764705882354, 0.9372549019607843, 0.9686274509803922, 0.2549019607843137, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6313725490196078, 0.7607843137254902, 0.19607843137254902, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0},
 };
-// clang-format on
\ No newline at end of file
+const int ref_labels[8] = {
+    8, 3, 5, 5, 1, 9, 3, 1
+};
+
diff --git a/main.cpp b/main.cpp
index f7e1ebc..728c104 100644
--- a/main.cpp
+++ b/main.cpp
@@ -23,24 +23,26 @@ int argmax(const Tensor &logits) {
   return max_index;
 }
 
-static My_model model;
+static MyModel model;
 
-int main(void) {
+int main(int argc, char **argv) {
+  printf("\n");
   printf("Simple MNIST end-to-end uTensor cli example (device)\n");
 
-  // create the input/output tensor
-  Tensor input_image = new RomTensor({1, 28, 28, 1}, flt, arr_input_image);
-  Tensor logits = new RamTensor({1, 10}, flt);
+  size_t num_samples = *(&ref_labels + 1) - ref_labels;
+  for (size_t i = 0; i < num_samples; ++i) {
+    // create the input/output tensor
+    Tensor input_image = new RomTensor({1, 28, 28, 1}, flt, arr_input_image[i]);
+    Tensor logits = new RamTensor({1, 10}, flt);
 
-  model.set_inputs({{My_model::input_0, input_image}})
-      .set_outputs({{My_model::output_0, logits}})
-      .eval();
-
-  int max_index = argmax(logits);
-  input_image.free();
-  logits.free();
-
-  printf("pred label: %d\r\n", max_index);
+    model.set_inputs({{MyModel::input_0, input_image}})
+        .set_outputs({{MyModel::output_0, logits}})
+        .eval();
+    int max_index = argmax(logits);
+    input_image.free();
+    logits.free();
 
+    printf("pred label: %d, expecting: %d\r\n", max_index, ref_labels[i]);
+  }
   return 0;
 }
diff --git a/mnist_conv.ipynb b/mnist_conv.ipynb
index 83a01b0..5460125 100644
--- a/mnist_conv.ipynb
+++ b/mnist_conv.ipynb
@@ -19,8 +19,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-06-30T04:08:49.781015Z",
-     "start_time": "2020-06-30T04:08:23.608609Z"
+     "end_time": "2020-08-27T12:41:36.312890Z",
+     "start_time": "2020-08-27T12:41:31.842215Z"
     }
    },
    "outputs": [],
@@ -33,8 +33,8 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-06-30T04:08:49.810814Z",
-     "start_time": "2020-06-30T04:08:49.783918Z"
+     "end_time": "2020-08-27T12:41:37.748636Z",
+     "start_time": "2020-08-27T12:41:37.713586Z"
     },
     "tags": []
    },
@@ -72,7 +72,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-27T12:41:42.661682Z",
+     "start_time": "2020-08-27T12:41:42.146260Z"
+    }
+   },
    "outputs": [],
    "source": [
     "mnist = tf.keras.datasets.mnist\n",
diff --git a/models/my_model/my_model.cpp b/models/my_model/my_model.cpp
index 5c8cacf..5ec2d6d 100644
--- a/models/my_model/my_model.cpp
+++ b/models/my_model/my_model.cpp
@@ -1,94 +1,126 @@
 /* Auto-generated by utensor cli */
-#include "uTensor.h"
 #include "models/my_model/my_model.hpp"
-#include "constants/my_model/params_my_model.hpp"
 
+#include "constants/my_model/params_my_model.hpp"
+#include "uTensor.h"
 
-My_model::My_model () :
-op_FullyConnectedOperator_000(TFLM::TfLiteFusedActivation::kTfLiteActRelu)
-, op_DepthwiseSeparableConvOperator_001({ 1, 1 }, VALID, 8, { 1, 1 }, TFLM::TfLiteFusedActivation::kTfLiteActRelu)
-, op_QuantizeOperator_002()
-, op_FullyConnectedOperator_003(TFLM::TfLiteFusedActivation::kTfLiteActNone)
-, op_ReshapeOperator_004({ 1, 288 })
-, op_MaxPoolOperator_005({ 2, 2 }, { 1, 2, 2, 1 }, VALID)
-, op_DequantizeOperator_006()
-{
+MyModel::MyModel()
+    : op_FullyConnectedOperator_000(
+          TFLM::TfLiteFusedActivation::kTfLiteActRelu),
+      op_DepthwiseSeparableConvOperator_001(
+          {1, 1}, VALID, 8, {1, 1},
+          TFLM::TfLiteFusedActivation::kTfLiteActRelu),
+      op_QuantizeOperator_002(),
+      op_FullyConnectedOperator_003(
+          TFLM::TfLiteFusedActivation::kTfLiteActNone),
+      op_ReshapeOperator_004({1, 288}),
+      op_MaxPoolOperator_005({2, 2}, {1, 2, 2, 1}, VALID),
+      op_DequantizeOperator_006() {
   Context::get_default_context()->set_ram_data_allocator(&ram_allocator);
   Context::get_default_context()->set_metadata_allocator(&metadata_allocator);
   // TODO: moving ROMTensor declarations here
 }
 
-void My_model::compute()
-{
+void MyModel::compute() {
   // update context in case there are multiple models being run
   Context::get_default_context()->set_ram_data_allocator(&ram_allocator);
   Context::get_default_context()->set_metadata_allocator(&metadata_allocator);
   // start rendering local snippets
-  Tensor t_input_1_int80 = new RamTensor({ 1, 28, 28, 1 }, i8);
-    int32_t t_input_1_int80_zp = -128;
-    float t_input_1_int80_scale = 0.003921569;
-    PerTensorQuantizationParams t_input_1_int80_quant_params(t_input_1_int80_zp, t_input_1_int80_scale);
-    t_input_1_int80->set_quantization_params(t_input_1_int80_quant_params);
-
+  Tensor t_input_1_int80 = new RamTensor({1, 28, 28, 1}, i8);
+  int32_t t_input_1_int80_zp = -128;
+  float t_input_1_int80_scale = 0.003921569;
+  PerTensorQuantizationParams t_input_1_int80_quant_params(
+      t_input_1_int80_zp, t_input_1_int80_scale);
+  t_input_1_int80->set_quantization_params(t_input_1_int80_quant_params);
 
   op_QuantizeOperator_002
-    .set_inputs({
-        { TflmSymQuantOps::QuantizeOperator<int8_t, float>::input, inputs[input_0].tensor() },
-    })
-    .set_outputs({
-        { TflmSymQuantOps::QuantizeOperator<int8_t, float>::output, t_input_1_int80}
-    })
-    .eval();
-
-  Tensor t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0 = new RamTensor({ 1, 14, 14, 1 }, i8);
-    int32_t t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0_zp = -128;
-    float t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0_scale = 0.003921569;
-    PerTensorQuantizationParams t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0_quant_params(t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0_zp, t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0_scale);
-    t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0->set_quantization_params(t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0_quant_params);
-
+      .set_inputs({
+          {TflmSymQuantOps::QuantizeOperator<int8_t, float>::input,
+           inputs[input_0].tensor()},
+      })
+      .set_outputs({{TflmSymQuantOps::QuantizeOperator<int8_t, float>::output,
+                     t_input_1_int80}})
+      .eval();
+
+  Tensor t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0 =
+      new RamTensor({1, 14, 14, 1}, i8);
+  int32_t t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0_zp = -128;
+  float t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0_scale =
+      0.003921569;
+  PerTensorQuantizationParams
+      t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0_quant_params(
+          t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0_zp,
+          t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0_scale);
+  t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0
+      ->set_quantization_params(
+          t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0_quant_params);
 
   op_MaxPoolOperator_005
-    .set_inputs({
-        { ReferenceOperators::MaxPoolOperator<int8_t>::in, t_input_1_int80 },
-    })
-    .set_outputs({
-        { ReferenceOperators::MaxPoolOperator<int8_t>::out, t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0}
-    })
-    .eval();
+      .set_inputs({
+          {ReferenceOperators::MaxPoolOperator<int8_t>::in, t_input_1_int80},
+      })
+      .set_outputs({{ReferenceOperators::MaxPoolOperator<int8_t>::out,
+                     t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0}})
+      .eval();
 
   t_input_1_int80.free();
 
-  Tensor t_StatefulPartitionedCallmy_modelconv2dConv2DReadVariableOp0 = new RomTensor({ 1, 3, 3, 8 }, i8, data_StatefulPartitionedCall_my_model_conv2d_Conv2D_ReadVariableOp_0);
-    int32_t arr_t_StatefulPartitionedCallmy_modelconv2dConv2DReadVariableOp0_zp[8] = { 0, 0, 0, 0, 0, 0, 0, 0 };
-    float arr_t_StatefulPartitionedCallmy_modelconv2dConv2DReadVariableOp0_scale[8] = { 0.0050831237, 0.005001122, 0.00810849, 0.009929325, 0.0103350375, 0.007988794, 0.00911208, 0.0147075765 };
-    PerChannelQuantizationParams t_StatefulPartitionedCallmy_modelconv2dConv2DReadVariableOp0_quant_params(arr_t_StatefulPartitionedCallmy_modelconv2dConv2DReadVariableOp0_zp, arr_t_StatefulPartitionedCallmy_modelconv2dConv2DReadVariableOp0_scale);
-    t_StatefulPartitionedCallmy_modelconv2dConv2DReadVariableOp0->set_quantization_params(t_StatefulPartitionedCallmy_modelconv2dConv2DReadVariableOp0_quant_params);
-
-
-  Tensor t_StatefulPartitionedCallmy_modelconv2dConv2D_bias0 = new RomTensor({ 8 }, i32, data_StatefulPartitionedCall_my_model_conv2d_Conv2D_bias_0);
-    int32_t arr_t_StatefulPartitionedCallmy_modelconv2dConv2D_bias0_zp[8] = { 0, 0, 0, 0, 0, 0, 0, 0 };
-    float arr_t_StatefulPartitionedCallmy_modelconv2dConv2D_bias0_scale[8] = { 1.993382e-05, 1.9612246e-05, 3.1798e-05, 3.8938535e-05, 4.052956e-05, 3.1328604e-05, 3.5733647e-05, 5.7676774e-05 };
-    PerChannelQuantizationParams t_StatefulPartitionedCallmy_modelconv2dConv2D_bias0_quant_params(arr_t_StatefulPartitionedCallmy_modelconv2dConv2D_bias0_zp, arr_t_StatefulPartitionedCallmy_modelconv2dConv2D_bias0_scale);
-    t_StatefulPartitionedCallmy_modelconv2dConv2D_bias0->set_quantization_params(t_StatefulPartitionedCallmy_modelconv2dConv2D_bias0_quant_params);
-
-
-  Tensor t_StatefulPartitionedCallmy_modelconv2dRelu0 = new RamTensor({ 1, 12, 12, 8 }, i8);
-    int32_t t_StatefulPartitionedCallmy_modelconv2dRelu0_zp = -128;
-    float t_StatefulPartitionedCallmy_modelconv2dRelu0_scale = 0.011555707;
-    PerTensorQuantizationParams t_StatefulPartitionedCallmy_modelconv2dRelu0_quant_params(t_StatefulPartitionedCallmy_modelconv2dRelu0_zp, t_StatefulPartitionedCallmy_modelconv2dRelu0_scale);
-    t_StatefulPartitionedCallmy_modelconv2dRelu0->set_quantization_params(t_StatefulPartitionedCallmy_modelconv2dRelu0_quant_params);
-
+  Tensor t_StatefulPartitionedCallmy_modelconv2dConv2DReadVariableOp0 =
+      new RomTensor(
+          {1, 3, 3, 8}, i8,
+          data_StatefulPartitionedCall_my_model_conv2d_Conv2D_ReadVariableOp_0);
+  int32_t
+      arr_t_StatefulPartitionedCallmy_modelconv2dConv2DReadVariableOp0_zp[8] = {
+          0, 0, 0, 0, 0, 0, 0, 0};
+  float arr_t_StatefulPartitionedCallmy_modelconv2dConv2DReadVariableOp0_scale
+      [8] = {0.0050831237, 0.005001122, 0.00810849, 0.009929325,
+             0.0103350375, 0.007988794, 0.00911208, 0.0147075765};
+  PerChannelQuantizationParams
+      t_StatefulPartitionedCallmy_modelconv2dConv2DReadVariableOp0_quant_params(
+          arr_t_StatefulPartitionedCallmy_modelconv2dConv2DReadVariableOp0_zp,
+          arr_t_StatefulPartitionedCallmy_modelconv2dConv2DReadVariableOp0_scale);
+  t_StatefulPartitionedCallmy_modelconv2dConv2DReadVariableOp0
+      ->set_quantization_params(
+          t_StatefulPartitionedCallmy_modelconv2dConv2DReadVariableOp0_quant_params);
+
+  Tensor t_StatefulPartitionedCallmy_modelconv2dConv2D_bias0 = new RomTensor(
+      {8}, i32, data_StatefulPartitionedCall_my_model_conv2d_Conv2D_bias_0);
+  int32_t arr_t_StatefulPartitionedCallmy_modelconv2dConv2D_bias0_zp[8] = {
+      0, 0, 0, 0, 0, 0, 0, 0};
+  float arr_t_StatefulPartitionedCallmy_modelconv2dConv2D_bias0_scale[8] = {
+      1.993382e-05, 1.9612246e-05, 3.1798e-05,    3.8938535e-05,
+      4.052956e-05, 3.1328604e-05, 3.5733647e-05, 5.7676774e-05};
+  PerChannelQuantizationParams
+      t_StatefulPartitionedCallmy_modelconv2dConv2D_bias0_quant_params(
+          arr_t_StatefulPartitionedCallmy_modelconv2dConv2D_bias0_zp,
+          arr_t_StatefulPartitionedCallmy_modelconv2dConv2D_bias0_scale);
+  t_StatefulPartitionedCallmy_modelconv2dConv2D_bias0->set_quantization_params(
+      t_StatefulPartitionedCallmy_modelconv2dConv2D_bias0_quant_params);
+
+  Tensor t_StatefulPartitionedCallmy_modelconv2dRelu0 =
+      new RamTensor({1, 12, 12, 8}, i8);
+  int32_t t_StatefulPartitionedCallmy_modelconv2dRelu0_zp = -128;
+  float t_StatefulPartitionedCallmy_modelconv2dRelu0_scale = 0.011555707;
+  PerTensorQuantizationParams
+      t_StatefulPartitionedCallmy_modelconv2dRelu0_quant_params(
+          t_StatefulPartitionedCallmy_modelconv2dRelu0_zp,
+          t_StatefulPartitionedCallmy_modelconv2dRelu0_scale);
+  t_StatefulPartitionedCallmy_modelconv2dRelu0->set_quantization_params(
+      t_StatefulPartitionedCallmy_modelconv2dRelu0_quant_params);
 
   op_DepthwiseSeparableConvOperator_001
-    .set_inputs({
-        { TflmSymQuantOps::DepthwiseSeparableConvOperator<int8_t>::in, t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0 },
-        { TflmSymQuantOps::DepthwiseSeparableConvOperator<int8_t>::filter, t_StatefulPartitionedCallmy_modelconv2dConv2DReadVariableOp0 },
-        { TflmSymQuantOps::DepthwiseSeparableConvOperator<int8_t>::bias, t_StatefulPartitionedCallmy_modelconv2dConv2D_bias0 },
-    })
-    .set_outputs({
-        { TflmSymQuantOps::DepthwiseSeparableConvOperator<int8_t>::out, t_StatefulPartitionedCallmy_modelconv2dRelu0}
-    })
-    .eval();
+      .set_inputs({
+          {TflmSymQuantOps::DepthwiseSeparableConvOperator<int8_t>::in,
+           t_StatefulPartitionedCallmy_modelmax_pooling2dMaxPool0},
+          {TflmSymQuantOps::DepthwiseSeparableConvOperator<int8_t>::filter,
+           t_StatefulPartitionedCallmy_modelconv2dConv2DReadVariableOp0},
+          {TflmSymQuantOps::DepthwiseSeparableConvOperator<int8_t>::bias,
+           t_StatefulPartitionedCallmy_modelconv2dConv2D_bias0},
+      })
+      .set_outputs(
+          {{TflmSymQuantOps::DepthwiseSeparableConvOperator<int8_t>::out,
+            t_StatefulPartitionedCallmy_modelconv2dRelu0}})
+      .eval();
 
   t_StatefulPartitionedCallmy_modelconv2dConv2D_bias0.free();
 
@@ -96,73 +128,111 @@ void My_model::compute()
 
   t_StatefulPartitionedCallmy_modelconv2dConv2DReadVariableOp0.free();
 
-  Tensor t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0 = new RamTensor({ 1, 6, 6, 8 }, i8);
-    int32_t t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0_zp = -128;
-    float t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0_scale = 0.011555707;
-    PerTensorQuantizationParams t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0_quant_params(t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0_zp, t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0_scale);
-    t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0->set_quantization_params(t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0_quant_params);
-
+  Tensor t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0 =
+      new RamTensor({1, 6, 6, 8}, i8);
+  int32_t t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0_zp = -128;
+  float t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0_scale =
+      0.011555707;
+  PerTensorQuantizationParams
+      t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0_quant_params(
+          t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0_zp,
+          t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0_scale);
+  t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0
+      ->set_quantization_params(
+          t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0_quant_params);
 
   op_MaxPoolOperator_005
-    .set_inputs({
-        { ReferenceOperators::MaxPoolOperator<int8_t>::in, t_StatefulPartitionedCallmy_modelconv2dRelu0 },
-    })
-    .set_outputs({
-        { ReferenceOperators::MaxPoolOperator<int8_t>::out, t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0}
-    })
-    .eval();
+      .set_inputs({
+          {ReferenceOperators::MaxPoolOperator<int8_t>::in,
+           t_StatefulPartitionedCallmy_modelconv2dRelu0},
+      })
+      .set_outputs({{ReferenceOperators::MaxPoolOperator<int8_t>::out,
+                     t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0}})
+      .eval();
 
   t_StatefulPartitionedCallmy_modelconv2dRelu0.free();
 
-  Tensor t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00 = new RamTensor({ 1, 288 }, i8);
-    int32_t t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00_zp = -128;
-    float t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00_scale = 0.011555707;
-    PerTensorQuantizationParams t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00_quant_params(t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00_zp, t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00_scale);
-    t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00->set_quantization_params(t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00_quant_params);
-
+  Tensor t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00 =
+      new RamTensor({1, 288}, i8);
+  int32_t
+      t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00_zp =
+          -128;
+  float
+      t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00_scale =
+          0.011555707;
+  PerTensorQuantizationParams
+      t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00_quant_params(
+          t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00_zp,
+          t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00_scale);
+  t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00
+      ->set_quantization_params(
+          t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00_quant_params);
 
   op_ReshapeOperator_004
-    .set_inputs({
-        { ReferenceOperators::ReshapeOperator<int8_t>::input, t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0 },
-    })
-    .set_outputs({
-        { ReferenceOperators::ReshapeOperator<int8_t>::output, t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00}
-    })
-    .eval();
+      .set_inputs({
+          {ReferenceOperators::ReshapeOperator<int8_t>::input,
+           t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0},
+      })
+      .set_outputs(
+          {{ReferenceOperators::ReshapeOperator<int8_t>::output,
+            t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00}})
+      .eval();
 
   t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool0.free();
 
-  Tensor t_StatefulPartitionedCallmy_modeldenseMatMulReadVariableOptranspose0 = new RomTensor({ 288, 16 }, i8, data_StatefulPartitionedCall_my_model_dense_MatMul_ReadVariableOp_transpose_0);
-    int32_t t_StatefulPartitionedCallmy_modeldenseMatMulReadVariableOptranspose0_zp = 0;
-    float t_StatefulPartitionedCallmy_modeldenseMatMulReadVariableOptranspose0_scale = 0.011830574;
-    PerTensorQuantizationParams t_StatefulPartitionedCallmy_modeldenseMatMulReadVariableOptranspose0_quant_params(t_StatefulPartitionedCallmy_modeldenseMatMulReadVariableOptranspose0_zp, t_StatefulPartitionedCallmy_modeldenseMatMulReadVariableOptranspose0_scale);
-    t_StatefulPartitionedCallmy_modeldenseMatMulReadVariableOptranspose0->set_quantization_params(t_StatefulPartitionedCallmy_modeldenseMatMulReadVariableOptranspose0_quant_params);
-
-
-  Tensor t_StatefulPartitionedCallmy_modeldenseMatMul_bias0 = new RomTensor({ 16 }, i32, data_StatefulPartitionedCall_my_model_dense_MatMul_bias_0);
-    int32_t t_StatefulPartitionedCallmy_modeldenseMatMul_bias0_zp = 0;
-    float t_StatefulPartitionedCallmy_modeldenseMatMul_bias0_scale = 0.00013671065;
-    PerTensorQuantizationParams t_StatefulPartitionedCallmy_modeldenseMatMul_bias0_quant_params(t_StatefulPartitionedCallmy_modeldenseMatMul_bias0_zp, t_StatefulPartitionedCallmy_modeldenseMatMul_bias0_scale);
-    t_StatefulPartitionedCallmy_modeldenseMatMul_bias0->set_quantization_params(t_StatefulPartitionedCallmy_modeldenseMatMul_bias0_quant_params);
-
-
-  Tensor t_StatefulPartitionedCallmy_modeldenseRelu0 = new RamTensor({ 1, 16 }, i8);
-    int32_t t_StatefulPartitionedCallmy_modeldenseRelu0_zp = -128;
-    float t_StatefulPartitionedCallmy_modeldenseRelu0_scale = 0.08426106;
-    PerTensorQuantizationParams t_StatefulPartitionedCallmy_modeldenseRelu0_quant_params(t_StatefulPartitionedCallmy_modeldenseRelu0_zp, t_StatefulPartitionedCallmy_modeldenseRelu0_scale);
-    t_StatefulPartitionedCallmy_modeldenseRelu0->set_quantization_params(t_StatefulPartitionedCallmy_modeldenseRelu0_quant_params);
-
+  Tensor t_StatefulPartitionedCallmy_modeldenseMatMulReadVariableOptranspose0 =
+      new RomTensor(
+          {288, 16}, i8,
+          data_StatefulPartitionedCall_my_model_dense_MatMul_ReadVariableOp_transpose_0);
+  int32_t
+      t_StatefulPartitionedCallmy_modeldenseMatMulReadVariableOptranspose0_zp =
+          0;
+  float
+      t_StatefulPartitionedCallmy_modeldenseMatMulReadVariableOptranspose0_scale =
+          0.011830574;
+  PerTensorQuantizationParams
+      t_StatefulPartitionedCallmy_modeldenseMatMulReadVariableOptranspose0_quant_params(
+          t_StatefulPartitionedCallmy_modeldenseMatMulReadVariableOptranspose0_zp,
+          t_StatefulPartitionedCallmy_modeldenseMatMulReadVariableOptranspose0_scale);
+  t_StatefulPartitionedCallmy_modeldenseMatMulReadVariableOptranspose0
+      ->set_quantization_params(
+          t_StatefulPartitionedCallmy_modeldenseMatMulReadVariableOptranspose0_quant_params);
+
+  Tensor t_StatefulPartitionedCallmy_modeldenseMatMul_bias0 = new RomTensor(
+      {16}, i32, data_StatefulPartitionedCall_my_model_dense_MatMul_bias_0);
+  int32_t t_StatefulPartitionedCallmy_modeldenseMatMul_bias0_zp = 0;
+  float t_StatefulPartitionedCallmy_modeldenseMatMul_bias0_scale =
+      0.00013671065;
+  PerTensorQuantizationParams
+      t_StatefulPartitionedCallmy_modeldenseMatMul_bias0_quant_params(
+          t_StatefulPartitionedCallmy_modeldenseMatMul_bias0_zp,
+          t_StatefulPartitionedCallmy_modeldenseMatMul_bias0_scale);
+  t_StatefulPartitionedCallmy_modeldenseMatMul_bias0->set_quantization_params(
+      t_StatefulPartitionedCallmy_modeldenseMatMul_bias0_quant_params);
+
+  Tensor t_StatefulPartitionedCallmy_modeldenseRelu0 =
+      new RamTensor({1, 16}, i8);
+  int32_t t_StatefulPartitionedCallmy_modeldenseRelu0_zp = -128;
+  float t_StatefulPartitionedCallmy_modeldenseRelu0_scale = 0.08426106;
+  PerTensorQuantizationParams
+      t_StatefulPartitionedCallmy_modeldenseRelu0_quant_params(
+          t_StatefulPartitionedCallmy_modeldenseRelu0_zp,
+          t_StatefulPartitionedCallmy_modeldenseRelu0_scale);
+  t_StatefulPartitionedCallmy_modeldenseRelu0->set_quantization_params(
+      t_StatefulPartitionedCallmy_modeldenseRelu0_quant_params);
 
   op_FullyConnectedOperator_000
-    .set_inputs({
-        { TflmSymQuantOps::FullyConnectedOperator<int8_t>::input, t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00 },
-        { TflmSymQuantOps::FullyConnectedOperator<int8_t>::filter, t_StatefulPartitionedCallmy_modeldenseMatMulReadVariableOptranspose0 },
-        { TflmSymQuantOps::FullyConnectedOperator<int8_t>::bias, t_StatefulPartitionedCallmy_modeldenseMatMul_bias0 },
-    })
-    .set_outputs({
-        { TflmSymQuantOps::FullyConnectedOperator<int8_t>::output, t_StatefulPartitionedCallmy_modeldenseRelu0}
-    })
-    .eval();
+      .set_inputs({
+          {TflmSymQuantOps::FullyConnectedOperator<int8_t>::input,
+           t_StatefulPartitionedCallmy_modelmax_pooling2d_1MaxPool_0_Reshape00},
+          {TflmSymQuantOps::FullyConnectedOperator<int8_t>::filter,
+           t_StatefulPartitionedCallmy_modeldenseMatMulReadVariableOptranspose0},
+          {TflmSymQuantOps::FullyConnectedOperator<int8_t>::bias,
+           t_StatefulPartitionedCallmy_modeldenseMatMul_bias0},
+      })
+      .set_outputs({{TflmSymQuantOps::FullyConnectedOperator<int8_t>::output,
+                     t_StatefulPartitionedCallmy_modeldenseRelu0}})
+      .eval();
 
   t_StatefulPartitionedCallmy_modeldenseMatMulReadVariableOptranspose0.free();
 
@@ -170,37 +240,55 @@ void My_model::compute()
 
   t_StatefulPartitionedCallmy_modeldenseMatMul_bias0.free();
 
-  Tensor t_StatefulPartitionedCallmy_modeldense_1MatMulReadVariableOptranspose0 = new RomTensor({ 16, 10 }, i8, data_StatefulPartitionedCall_my_model_dense_1_MatMul_ReadVariableOp_transpose_0);
-    int32_t t_StatefulPartitionedCallmy_modeldense_1MatMulReadVariableOptranspose0_zp = 0;
-    float t_StatefulPartitionedCallmy_modeldense_1MatMulReadVariableOptranspose0_scale = 0.009137652;
-    PerTensorQuantizationParams t_StatefulPartitionedCallmy_modeldense_1MatMulReadVariableOptranspose0_quant_params(t_StatefulPartitionedCallmy_modeldense_1MatMulReadVariableOptranspose0_zp, t_StatefulPartitionedCallmy_modeldense_1MatMulReadVariableOptranspose0_scale);
-    t_StatefulPartitionedCallmy_modeldense_1MatMulReadVariableOptranspose0->set_quantization_params(t_StatefulPartitionedCallmy_modeldense_1MatMulReadVariableOptranspose0_quant_params);
-
-
-  Tensor t_StatefulPartitionedCallmy_modeldense_1MatMul_bias0 = new RomTensor({ 10 }, i32, data_StatefulPartitionedCall_my_model_dense_1_MatMul_bias_0);
-    int32_t t_StatefulPartitionedCallmy_modeldense_1MatMul_bias0_zp = 0;
-    float t_StatefulPartitionedCallmy_modeldense_1MatMul_bias0_scale = 0.00076994824;
-    PerTensorQuantizationParams t_StatefulPartitionedCallmy_modeldense_1MatMul_bias0_quant_params(t_StatefulPartitionedCallmy_modeldense_1MatMul_bias0_zp, t_StatefulPartitionedCallmy_modeldense_1MatMul_bias0_scale);
-    t_StatefulPartitionedCallmy_modeldense_1MatMul_bias0->set_quantization_params(t_StatefulPartitionedCallmy_modeldense_1MatMul_bias0_quant_params);
-
-
-  Tensor t_Identity_int80 = new RamTensor({ 1, 10 }, i8);
-    int32_t t_Identity_int80_zp = 22;
-    float t_Identity_int80_scale = 0.1801216;
-    PerTensorQuantizationParams t_Identity_int80_quant_params(t_Identity_int80_zp, t_Identity_int80_scale);
-    t_Identity_int80->set_quantization_params(t_Identity_int80_quant_params);
-
+  Tensor t_StatefulPartitionedCallmy_modeldense_1MatMulReadVariableOptranspose0 =
+      new RomTensor(
+          {16, 10}, i8,
+          data_StatefulPartitionedCall_my_model_dense_1_MatMul_ReadVariableOp_transpose_0);
+  int32_t
+      t_StatefulPartitionedCallmy_modeldense_1MatMulReadVariableOptranspose0_zp =
+          0;
+  float
+      t_StatefulPartitionedCallmy_modeldense_1MatMulReadVariableOptranspose0_scale =
+          0.009137652;
+  PerTensorQuantizationParams
+      t_StatefulPartitionedCallmy_modeldense_1MatMulReadVariableOptranspose0_quant_params(
+          t_StatefulPartitionedCallmy_modeldense_1MatMulReadVariableOptranspose0_zp,
+          t_StatefulPartitionedCallmy_modeldense_1MatMulReadVariableOptranspose0_scale);
+  t_StatefulPartitionedCallmy_modeldense_1MatMulReadVariableOptranspose0
+      ->set_quantization_params(
+          t_StatefulPartitionedCallmy_modeldense_1MatMulReadVariableOptranspose0_quant_params);
+
+  Tensor t_StatefulPartitionedCallmy_modeldense_1MatMul_bias0 = new RomTensor(
+      {10}, i32, data_StatefulPartitionedCall_my_model_dense_1_MatMul_bias_0);
+  int32_t t_StatefulPartitionedCallmy_modeldense_1MatMul_bias0_zp = 0;
+  float t_StatefulPartitionedCallmy_modeldense_1MatMul_bias0_scale =
+      0.00076994824;
+  PerTensorQuantizationParams
+      t_StatefulPartitionedCallmy_modeldense_1MatMul_bias0_quant_params(
+          t_StatefulPartitionedCallmy_modeldense_1MatMul_bias0_zp,
+          t_StatefulPartitionedCallmy_modeldense_1MatMul_bias0_scale);
+  t_StatefulPartitionedCallmy_modeldense_1MatMul_bias0->set_quantization_params(
+      t_StatefulPartitionedCallmy_modeldense_1MatMul_bias0_quant_params);
+
+  Tensor t_Identity_int80 = new RamTensor({1, 10}, i8);
+  int32_t t_Identity_int80_zp = 22;
+  float t_Identity_int80_scale = 0.1801216;
+  PerTensorQuantizationParams t_Identity_int80_quant_params(
+      t_Identity_int80_zp, t_Identity_int80_scale);
+  t_Identity_int80->set_quantization_params(t_Identity_int80_quant_params);
 
   op_FullyConnectedOperator_003
-    .set_inputs({
-        { TflmSymQuantOps::FullyConnectedOperator<int8_t>::input, t_StatefulPartitionedCallmy_modeldenseRelu0 },
-        { TflmSymQuantOps::FullyConnectedOperator<int8_t>::filter, t_StatefulPartitionedCallmy_modeldense_1MatMulReadVariableOptranspose0 },
-        { TflmSymQuantOps::FullyConnectedOperator<int8_t>::bias, t_StatefulPartitionedCallmy_modeldense_1MatMul_bias0 },
-    })
-    .set_outputs({
-        { TflmSymQuantOps::FullyConnectedOperator<int8_t>::output, t_Identity_int80}
-    })
-    .eval();
+      .set_inputs({
+          {TflmSymQuantOps::FullyConnectedOperator<int8_t>::input,
+           t_StatefulPartitionedCallmy_modeldenseRelu0},
+          {TflmSymQuantOps::FullyConnectedOperator<int8_t>::filter,
+           t_StatefulPartitionedCallmy_modeldense_1MatMulReadVariableOptranspose0},
+          {TflmSymQuantOps::FullyConnectedOperator<int8_t>::bias,
+           t_StatefulPartitionedCallmy_modeldense_1MatMul_bias0},
+      })
+      .set_outputs({{TflmSymQuantOps::FullyConnectedOperator<int8_t>::output,
+                     t_Identity_int80}})
+      .eval();
 
   t_StatefulPartitionedCallmy_modeldense_1MatMul_bias0.free();
 
@@ -209,13 +297,13 @@ void My_model::compute()
   t_StatefulPartitionedCallmy_modeldense_1MatMulReadVariableOptranspose0.free();
 
   op_DequantizeOperator_006
-    .set_inputs({
-        { TflmSymQuantOps::DequantizeOperator<float, int8_t>::a, t_Identity_int80 },
-    })
-    .set_outputs({
-        { TflmSymQuantOps::DequantizeOperator<float, int8_t>::b, outputs[output_0].tensor()}
-    })
-    .eval();
+      .set_inputs({
+          {TflmSymQuantOps::DequantizeOperator<float, int8_t>::a,
+           t_Identity_int80},
+      })
+      .set_outputs({{TflmSymQuantOps::DequantizeOperator<float, int8_t>::b,
+                     outputs[output_0].tensor()}})
+      .eval();
 
   t_Identity_int80.free();
   // end of rendering local snippets
diff --git a/models/my_model/my_model.hpp b/models/my_model/my_model.hpp
index b7aa68d..d61a556 100644
--- a/models/my_model/my_model.hpp
+++ b/models/my_model/my_model.hpp
@@ -5,19 +5,21 @@
 
 using namespace uTensor;
 
-class My_model : public ModelInterface<1, 1> 
-{
+class MyModel : public ModelInterface<1, 1> {
  public:
   enum input_names : uint8_t { input_0 };
   enum output_names : uint8_t { output_0 };
-  My_model();
+  MyModel();
+
  protected:
   virtual void compute();
+
  private:
   // Operators
   TflmSymQuantOps::FullyConnectedOperator<int8_t> op_FullyConnectedOperator_000;
 
-  TflmSymQuantOps::DepthwiseSeparableConvOperator<int8_t> op_DepthwiseSeparableConvOperator_001;
+  TflmSymQuantOps::DepthwiseSeparableConvOperator<int8_t>
+      op_DepthwiseSeparableConvOperator_001;
 
   TflmSymQuantOps::QuantizeOperator<int8_t, float> op_QuantizeOperator_002;
 
@@ -34,4 +36,4 @@ class My_model : public ModelInterface<1, 1>
   localCircularArenaAllocator<960, uint16_t> metadata_allocator;
 };
 
-#endif // __MY_MODEL_INTERFACE_H
\ No newline at end of file
+#endif  // __MY_MODEL_INTERFACE_H
\ No newline at end of file
diff --git a/my_model.tflite b/my_model.tflite
new file mode 100644
index 0000000..26e75ca
Binary files /dev/null and b/my_model.tflite differ