diff --git a/Dockerfile b/Dockerfile
index f407289..ad66173 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -24,7 +24,7 @@ RUN cd /tmp && \
     echo "e1045ee415162f944b6aebfe560b8fee *Miniconda3-${MINICONDA_VERSION}-Linux-x86_64.sh" | md5sum -c - && \
     /bin/bash Miniconda3-${MINICONDA_VERSION}-Linux-x86_64.sh -f -b -p $CONDA_DIR && \
     rm Miniconda3-${MINICONDA_VERSION}-Linux-x86_64.sh && \
-    $CONDA_DIR/bin/conda config --prepend channels conda-forge/label/dev && \
+    $CONDA_DIR/bin/conda config --system --prepend channels conda-forge/label/dev && \
     $CONDA_DIR/bin/conda config --system --prepend channels conda-forge && \
     $CONDA_DIR/bin/conda config --system --set auto_update_conda false && \
     $CONDA_DIR/bin/conda config --system --set show_channel_urls true && \
diff --git a/Pipfile b/Pipfile
index 064e1d1..3d8c5c3 100644
--- a/Pipfile
+++ b/Pipfile
@@ -5,7 +5,9 @@ name = "pypi"
 
 [packages]
 black = "==18.9b0"
+chainer = "==6.0.0b1"
 comet-ml = "==1.0.42"
+cupy-cuda92 = "==6.0.0b1"
 cython = "==0.29.2"
 descartes = "==1.1.0"
 geopandas = {editable = true, ref = "0.4.0-26-g9e584cc", git = "https://github.com/geopandas/geopandas.git"}
@@ -13,11 +15,11 @@ gmt = {editable = true, ref = "0.1a3-131-g9772fa3", git = "https://github.com/we
 ipython = "==7.2.0"
 jupyterlab = "==0.35.4"
 jupytext = "==0.8.6"
-keras = "==2.2.4"
 livelossplot = "==0.2.3"
 matplotlib = "==3.0.2"
 netcdf4 = "==1.4.1"
 numpy = "==1.14.5"
+onnx_chainer = "==1.3.0a1"
 packaging = "==18.0"
 pandas = "==0.23.4"
 pyproj = "==1.9.6"
@@ -25,11 +27,9 @@ quilt = "==2.9.14"
 rasterio = "==1.0.13"
 requests = "==2.21.0"
 scikit-image = "==0.14.1"
-scikit-learn = "==0.20.2"
 shapely = "==1.7a1"
-tensorflow = "==1.10.1"
-tensorflow-gpu = "==1.10.1"
 toolz = "==0.9.0"
+tornado = "==5.1.1"
 tqdm = "==4.28.1"
 
 [dev-packages]
diff --git a/Pipfile.lock b/Pipfile.lock
index 4b0b2c9..913ed5c 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "3bfc490703949f2d8d6118fc724c7adc2e76ddde1d0f28ae9bde2aa15559846f"
+            "sha256": "4f82cf471c151a352d5f20bdcd0effb20258671be37d72def7312877cd106fba"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -16,12 +16,6 @@
         ]
     },
     "default": {
-        "absl-py": {
-            "hashes": [
-                "sha256:87519e3b91a3d573664c6e2ee33df582bb68dca6642ae3cf3a4361b1c0a4e9d6"
-            ],
-            "version": "==0.6.1"
-        },
         "affine": {
             "hashes": [
                 "sha256:e5970e2e53edd75fee60eb2550df365a1c3a58d78755e9e5164e345ac36df322",
@@ -36,13 +30,6 @@
             ],
             "version": "==1.4.3"
         },
-        "astor": {
-            "hashes": [
-                "sha256:95c30d87a6c2cf89aa628b87398466840f0ad8652f88eb173125a6df8533fb8d",
-                "sha256:fb503b9e2fdd05609fbf557b916b4a7824171203701660f0c55bbf5a7a68713e"
-            ],
-            "version": "==0.7.1"
-        },
         "attrs": {
             "hashes": [
                 "sha256:10cbf6e27dbce8c30807caf056c8eb50917e0eaafe86347671b57254006c3e69",
@@ -67,10 +54,10 @@
         },
         "bleach": {
             "hashes": [
-                "sha256:48d39675b80a75f6d1c3bdbffec791cf0bbbab665cf01e20da701c77de278718",
-                "sha256:73d26f018af5d5adcdabf5c1c974add4361a9c76af215fe32fdec8a6fc5fb9b9"
+                "sha256:213336e49e102af26d9cde77dd2d0397afabc5a6bf2fed985dc35b5d1e285a16",
+                "sha256:3fdf7f77adcf649c9911387df51254b813185e32b2c6619f690b593a617e19fa"
             ],
-            "version": "==3.0.2"
+            "version": "==3.1.0"
         },
         "certifi": {
             "hashes": [
@@ -110,6 +97,13 @@
             ],
             "version": "==1.0.3.4"
         },
+        "chainer": {
+            "hashes": [
+                "sha256:7d21fbd78d897ffb08f3c7bc9b4a2bfb720fc26b671454e664dd9ef36b10316c"
+            ],
+            "index": "pypi",
+            "version": "==6.0.0b1"
+        },
         "chardet": {
             "hashes": [
                 "sha256:84ab92ed1c4d4f16916e05906b6b75a6c0fb5db821cc65e70cbd64a3e2a5eaae",
@@ -161,11 +155,18 @@
             "index": "pypi",
             "version": "==1.0.42"
         },
-        "configobj": {
+        "cupy-cuda92": {
             "hashes": [
-                "sha256:a2f5650770e1c87fb335af19a9b7eb73fc05ccf22144eb68db7d00cd2bcb0902"
+                "sha256:02c3fdfcb757a923fbc01215ade695974c30ff08b2d8974fd1cecae0c55bba4c",
+                "sha256:14c74c2648aa9eccd2be36b0b8a56cace48f8f146a8ce43e7a98ec62ca7fd4b1",
+                "sha256:41f99af7f22d38cd047db2678a0c23a3789e7b9aa97d156f7e037a336938d1c9",
+                "sha256:6eb750762b24475b1421e1d9419ef9ac1be2a5c92827aa091972b78101467638",
+                "sha256:7e26f14660318f44bb8e8b75cd81c8d5bec9b96c1dda223f3cab1f563cbcadd9",
+                "sha256:87fba3d508057920d9cdbc6c7bf1922e195afdf97d58041075128287caad011e",
+                "sha256:e4e206200ee69b8274883308d3c635c19a474ef8dd5e9155aacd21fcc19a81ca"
             ],
-            "version": "==5.0.6"
+            "index": "pypi",
+            "version": "==6.0.0b1"
         },
         "cycler": {
             "hashes": [
@@ -213,10 +214,10 @@
                 "array"
             ],
             "hashes": [
-                "sha256:8a2c151d5862627c71fdc725760d710b7c037ec57730f453f392b896febfd0d5",
-                "sha256:a1fa4a3b2d7ce4dd0c68db4b68dadf2c283ff54d98bd72c556fc462000449ff7"
+                "sha256:21838b1144830ddf9d1f1acd59784bcdb944c315f0d000fff58d7b6a9a6c3317",
+                "sha256:e76088e8931b326c05a92d2658e07b94a6852b42c13a7560505a8b2354871454"
             ],
-            "version": "==1.0.0"
+            "version": "==1.1.0"
         },
         "decorator": {
             "hashes": [
@@ -243,17 +244,42 @@
         },
         "entrypoints": {
             "hashes": [
-                "sha256:10ad569bb245e7e2ba425285b9fa3e8178a0dc92fc53b1e1c553805e15a8825b",
-                "sha256:d2d587dde06f99545fb13a383d2cd336a8ff1f359c5839ce3a64c917d10c029f"
+                "sha256:589f874b313739ad35be6e0cd7efde2a4e9b6fea91edcc34e58ecbb8dbe56d19",
+                "sha256:c70dd71abe5a8c85e55e12c19bd91ccfeec11a6e99044204511f9ed547d48451"
             ],
-            "version": "==0.2.3"
+            "version": "==0.3"
         },
         "everett": {
             "hashes": [
-                "sha256:02a43a2b4194e6ed40757851b37f5acf3c086f37e7e8109a385a7198cfbbc51b",
-                "sha256:05b0d0bae138a3b1a7c365bdd991c0a5b06f20c2e01d6721896098ac93cf7ee9"
+                "sha256:35f69f6d8e45b2250a3d4b06b8e7f537d3cb296dae9a3ec4a4791258fe4de6eb",
+                "sha256:860011cc71520fe27c7b9e2539b72cc6df2e235705489ad47935b8da83c9b855"
+            ],
+            "version": "==1.0.1"
+        },
+        "fastrlock": {
+            "hashes": [
+                "sha256:0888072e2c6da8d72c774ecdcaa96395f354b2eb4d20409284aaffe9c1b83002",
+                "sha256:24dc4ade9d4fd410feb37748357b973be39703ca421faaf1851dcaaedf7f2045",
+                "sha256:2fcd6964340e1d0250f4e252febf495230fb948f8b8ed6e7010f816cd0d8fad8",
+                "sha256:3a8010de71a3ef18e31b282e08d2313c9a458e786d0d9773a2364605448e2691",
+                "sha256:5f4274edcc46e2b4464825a9e0ff7dd76c56245883d123413e6d54f1d862e08c",
+                "sha256:6a7e9cbc69a31502b00a436e5ee8255b5f9071656f99dca792170c523152df37",
+                "sha256:6abdbb35205792e2d2a8c441aaa41a613d43ee2d88b3af4fd9735ae7a5f7db6b",
+                "sha256:7aff59c126da8665648f9f5da70f4244aee46be1376b5ee07e0beaaf6ce9e721",
+                "sha256:905f6ae55e89b76f0e0353e8ad4440700ae3ebf89128c6b2ed406a4c2365827a",
+                "sha256:99408074357e1c5c6da68bfabe7c2fb61d1e7938da991ea0ba741fb3f20a5763",
+                "sha256:b96c177fc17be6f826ffac291ceb9b4326e048f930d99f27c76b13828fd12e78",
+                "sha256:e3c2348f215e0a1e5922e62df504f6f0b6d19a68abaffa6e9a92e2a7446155be",
+                "sha256:f89ae944157050242a9b1badbdcae449c8426cb0351cc567f8b9ef3298d6d7c6"
             ],
-            "version": "==0.9"
+            "version": "==0.4"
+        },
+        "filelock": {
+            "hashes": [
+                "sha256:b8d5ca5ca1c815e1574aee746650ea7301de63d87935b3463d26368b76e31633",
+                "sha256:d610c1bb404daf85976d7a82eb2ada120f04671007266b708606565dd03b5be6"
+            ],
+            "version": "==3.0.10"
         },
         "fiona": {
             "hashes": [
@@ -277,12 +303,6 @@
             ],
             "version": "==0.17.1"
         },
-        "gast": {
-            "hashes": [
-                "sha256:7068908321ecd2774f145193c4b34a11305bd104b4551b09273dfd1d6a374930"
-            ],
-            "version": "==0.2.0"
-        },
         "geopandas": {
             "editable": true,
             "git": "https://github.com/geopandas/geopandas.git",
@@ -293,76 +313,6 @@
             "git": "https://github.com/weiji14/gmt-python.git",
             "ref": "9772fa3d5825175a8760e57f1d6c39afeee20e4f"
         },
-        "grpcio": {
-            "hashes": [
-                "sha256:082bc981d6aabfdb26bfdeab63f5626df3d2c5ac3a9ae8533dfa5ce73432f4fe",
-                "sha256:0e8ff79b12b8b07198dd847974fc32a4ed8c0d52d5224fabb9d28bf4c2e3f4a9",
-                "sha256:11c8026a3d35e8b9ad6cda7bf4f5e51b9b82e7f29a590ad194f63957657fa808",
-                "sha256:145e82aec0a643d7569499b1aa0d5167c99d9d26a2b8c4e4b3f5cd51b99a8cdc",
-                "sha256:1a820ebf0c924cbfa299cb59e4bc9582a24abfec89d9a36c281d78fa941115ae",
-                "sha256:284bee4657c4dd7d48835128b31975e8b0ea3a2eeb084c5d46de215b31d1f8f5",
-                "sha256:2a8b6b569fd23f4d9f2c8201fd8995519dfbddc60ceeffa8bf5bea2a8e9cb72c",
-                "sha256:38b93080df498656aea1dbab632e32013c580c2d00bd8c30d0f1d2c9513b0469",
-                "sha256:4837ad8fdcf99df0e89214ba42001469cab807851f30481db41fd84fc9358ce7",
-                "sha256:5447336edd6fea8ab35eca34ff5289e369e22c375bc2ac8156a419fa467949ac",
-                "sha256:57705e31f76db45b51f3a98bcfd362c89d58e99f846337a25fed957b4d43ae4f",
-                "sha256:612e742c748df51c921a7eefd76195d76467e3cc00e084e089af5b111d8210b7",
-                "sha256:62c777f801aee22100d8ea5fa057020e37b65541a8000091879a8560b089da9d",
-                "sha256:8317d351ab1e80cf20676ef3d4929d3e760df10e6e5c289283c36c4c92ca61f7",
-                "sha256:8703efaf03396123426fdea08b369712df1248fa5fdfdbee3f87a410f52e9bac",
-                "sha256:8b72721e64becd4a3e9580f12dbdf618d41e80d3ae7585dc8a921dbf76c979bb",
-                "sha256:8bb7dbe20fe883ee22a6cb2c1317ea228b75a3ef60f3749584ee2634192e3452",
-                "sha256:9a7ed6160e6c14058b4676aac68a8bf268f171f4c371ff0a0c0ab81b90803f70",
-                "sha256:a46c34768f292fa0d97e929591e51ec20dc857321d83b198de1dad9c8183e8cb",
-                "sha256:a7f21a7b48fcd9f51029419b22a9bfea097973cca5d1529b8578f1d2919e6b23",
-                "sha256:adfee9c9099cae92c2a4948bc95cc2cc3185cdf59b371e056b8dd19ed434247e",
-                "sha256:b3bbeadc6b99e4a42bf23803f5e9b292f23f3e37cc7f75a9f5efbfa9b812abc1",
-                "sha256:b51d49d89758ea45841130c5c7be79c68612d8834bd600994b8a2672c59dc9b9",
-                "sha256:cbb95a586fdf3e795eba28b4acc75fdfdb59a14df62e747fe8bc4572ef37b647",
-                "sha256:cdea5595b30f027e6603887b71f343ca5b209da74b910fe04fc25e1dfe6df263",
-                "sha256:d64350156dc4b21914409e0c93ffeeb4ceba193716fb1ae570df699383c4cd63",
-                "sha256:e10bbef59706a90672b295c0f82dcb6329d829643b8dd7c3bd120f89a093d740",
-                "sha256:e68e6afbbae2cbfadaabd33ee40314963cd83500feff733c07edb172674a7f8b",
-                "sha256:f0c0e48c255a63fec78be2f240ff5a3bd4291b1f83976895f6ee0085362568d0",
-                "sha256:f7bb6617bae5e7333e66ec1e7aac1fe419b59e0e34a8717f97e1ce2791ab9d3a",
-                "sha256:fa6e14bce7ad5de2363abb644191489ddfffcdb2751337251f7ef962ab7e3293",
-                "sha256:fd6774bbb6c717f725b39394757445ead4f69c471118364933aadb81a4f16961"
-            ],
-            "version": "==1.17.1"
-        },
-        "h5py": {
-            "hashes": [
-                "sha256:05750b91640273c69989c657eaac34b091abdd75efc8c4824c82aaf898a2da0a",
-                "sha256:082a27208aa3a2286e7272e998e7e225b2a7d4b7821bd840aebf96d50977abbb",
-                "sha256:08e2e8297195f9e813e894b6c63f79372582787795bba2014a2db6a2de95f713",
-                "sha256:0dd2adeb2e9de5081eb8dcec88874e7fd35dae9a21557be3a55a3c7d491842a4",
-                "sha256:0f94de7a10562b991967a66bbe6dda9808e18088676834c0a4dcec3fdd3bcc6f",
-                "sha256:106e42e2e01e486a3d32eeb9ba0e3a7f65c12fa8998d63625fa41fb8bdc44cdb",
-                "sha256:1606c66015f04719c41a9863c156fc0e6b992150de21c067444bcb82e7d75579",
-                "sha256:1854c4beff9961e477e133143c5e5e355dac0b3ebf19c52cf7cc1b1ef757703c",
-                "sha256:1e9fb6f1746500ea91a00193ce2361803c70c6b13f10aae9a33ad7b5bd28e800",
-                "sha256:2cca17e80ddb151894333377675db90cd0279fa454776e0a4f74308376afd050",
-                "sha256:30e365e8408759db3778c361f1e4e0fe8e98a875185ae46c795a85e9bafb9cdf",
-                "sha256:3206bac900e16eda81687d787086f4ffd4f3854980d798e191a9868a6510c3ae",
-                "sha256:3c23d72058647cee19b30452acc7895621e2de0a0bd5b8a1e34204b9ea9ed43c",
-                "sha256:407b5f911a83daa285bbf1ef78a9909ee5957f257d3524b8606be37e8643c5f0",
-                "sha256:4162953714a9212d373ac953c10e3329f1e830d3c7473f2a2e4f25dd6241eef0",
-                "sha256:5fc7aba72a51b2c80605eba1c50dbf84224dcd206279d30a75c154e5652e1fe4",
-                "sha256:713ac19307e11de4d9833af0c4bd6778bde0a3d967cafd2f0f347223711c1e31",
-                "sha256:71b946d80ef3c3f12db157d7778b1fe74a517ca85e94809358b15580983c2ce2",
-                "sha256:8cc4aed71e20d87e0a6f02094d718a95252f11f8ed143bc112d22167f08d4040",
-                "sha256:9d41ca62daf36d6b6515ab8765e4c8c4388ee18e2a665701fef2b41563821002",
-                "sha256:a744e13b000f234cd5a5b2a1f95816b819027c57f385da54ad2b7da1adace2f3",
-                "sha256:b087ee01396c4b34e9dc41e3a6a0442158206d383c19c7d0396d52067b17c1cb",
-                "sha256:b0f03af381d33306ce67d18275b61acb4ca111ced645381387a02c8a5ee1b796",
-                "sha256:b9e4b8dfd587365bdd719ae178fa1b6c1231f81280b1375eef8626dfd8761bf3",
-                "sha256:c5dd4ec75985b99166c045909e10f0534704d102848b1d9f0992720e908928e7",
-                "sha256:d2b82f23cd862a9d05108fe99967e9edfa95c136f532a71cb3d28dc252771f50",
-                "sha256:e58a25764472af07b7e1c4b10b0179c8ea726446c7141076286e41891bf3a563",
-                "sha256:f3b49107fbfc77333fc2b1ef4d5de2abcd57e7ea3a1482455229494cf2da56ce"
-            ],
-            "version": "==2.9.0"
-        },
         "idna": {
             "hashes": [
                 "sha256:c357b3f628cf53ae2c4c05627ecc484553142ca23264e593d327bcde5e9c3407",
@@ -408,10 +358,10 @@
         },
         "jsonschema": {
             "hashes": [
-                "sha256:3ae8afd6f4ca6417f14bf43ef61341311598f14234cdb4174fe43d42b236a3c8",
-                "sha256:dfd8426040892c8d0ef6da574085f282569f189cb24b70091a66c21c12d6705e"
+                "sha256:3eae63135c4a2cd15ecfd1424494494be77bd8a27014c44c8c2343e61d908770",
+                "sha256:8ba4f6c03b9db02e51f4a21579b7b0364b7c174361998888fb5d18fab4ed73f1"
             ],
-            "version": "==3.0.0a3"
+            "version": "==3.0.0b1"
         },
         "jupyter-client": {
             "hashes": [
@@ -449,28 +399,6 @@
             "index": "pypi",
             "version": "==0.8.6"
         },
-        "keras": {
-            "hashes": [
-                "sha256:794d0c92c6c4122f1f0fcf3a7bc2f49054c6a54ddbef8d8ffafca62795d760b6",
-                "sha256:90b610a3dbbf6d257b20a079eba3fdf2eed2158f64066a7c6f7227023fd60bc9"
-            ],
-            "index": "pypi",
-            "version": "==2.2.4"
-        },
-        "keras-applications": {
-            "hashes": [
-                "sha256:721dda4fa4e043e5bbd6f52a2996885c4639a7130ae478059b3798d0706f5ae7",
-                "sha256:a03af60ddc9c5afdae4d5c9a8dd4ca857550e0b793733a5072e0725829b87017"
-            ],
-            "version": "==1.0.6"
-        },
-        "keras-preprocessing": {
-            "hashes": [
-                "sha256:90d04c1750bccceef88ac09475c291b4b5f6aa1eaf0603167061b1aa8b043c61",
-                "sha256:ef2e482c4336fcf7180244d06f4374939099daa3183816e82aee7755af35b754"
-            ],
-            "version": "==1.0.5"
-        },
         "kiwisolver": {
             "hashes": [
                 "sha256:0ee4ed8b3ae8f5f712b0aa9ebd2858b5b232f1b9a96b0943dceb34df2a223bc3",
@@ -511,13 +439,6 @@
             "index": "pypi",
             "version": "==0.2.3"
         },
-        "markdown": {
-            "hashes": [
-                "sha256:c00429bd503a47ec88d5e30a751e147dcb4c6889663cd3e2ba0afe858e009baa",
-                "sha256:d02e0f9b04c500cde6637c11ad7c72671f359b87b9fe924b2383649d8841db7c"
-            ],
-            "version": "==3.0.1"
-        },
         "markupsafe": {
             "hashes": [
                 "sha256:048ef924c1623740e70204aa7143ec592504045ae4429b59c30054cb31e3c432",
@@ -637,27 +558,30 @@
         },
         "netifaces": {
             "hashes": [
-                "sha256:0083ff8d89c559d0da0811c4930cf36e4945da0f03749e0f108678098d7d1607",
-                "sha256:179f2463469fe69c829c96c7b332c7fd3f01652311e36ae11e409e5b34eb9dad",
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-                "sha256:9cf8cb2de7524c34808e6111dfb9f89e3b7c568e6953b3e02b8397447a6d8303",
-                "sha256:a77263e046636a761a2c3eeb0a56b5f8fa64f865efec91a9be008a46412b4ddd",
-                "sha256:aea569ce1a5a75b010758097199f84d9a3a109a696473c635bcf82f8a43cc551",
-                "sha256:bd590fcb75421537d4149825e1e63cca225fd47dad861710c46bd1cb329d8cbd",
-                "sha256:e1037cfad0e99a23fb4829f40302f3696395358950ba9f0315363a0e1eb04af6",
-                "sha256:e6d52aee254f9cf6192b54c156c67d54dcf451bec6781580844af892e4bf36bb",
-                "sha256:e76d38d9cff51ecf9fd5b8d0adf63f7b8875e1ac8548ccb52264939e308b771e"
-            ],
-            "version": "==0.10.7"
+                "sha256:078986caf4d6a602a4257d3686afe4544ea74362b8928e9f4389b5cd262bc215",
+                "sha256:0c4304c6d5b33fbd9b20fdc369f3a2fef1a8bbacfb6fd05b9708db01333e9e7b",
+                "sha256:2dee9ffdd16292878336a58d04a20f0ffe95555465fee7c9bd23b3490ef2abf3",
+                "sha256:3095218b66d359092b82f07c5422293c2f6559cf8d36b96b379cc4cdc26eeffa",
+                "sha256:30ed89ab8aff715caf9a9d827aa69cd02ad9f6b1896fd3fb4beb998466ed9a3c",
+                "sha256:4921ed406386246b84465950d15a4f63480c1458b0979c272364054b29d73084",
+                "sha256:563a1a366ee0fb3d96caab79b7ac7abd2c0a0577b157cc5a40301373a0501f89",
+                "sha256:5b3167f923f67924b356c1338eb9ba275b2ba8d64c7c2c47cf5b5db49d574994",
+                "sha256:6d84e50ec28e5d766c9911dce945412dc5b1ce760757c224c71e1a9759fa80c2",
+                "sha256:755050799b5d5aedb1396046f270abfc4befca9ccba3074f3dbbb3cb34f13aae",
+                "sha256:75d3a4ec5035db7478520ac547f7c176e9fd438269e795819b67223c486e5cbe",
+                "sha256:7a25a8e28281504f0e23e181d7a9ed699c72f061ca6bdfcd96c423c2a89e75fc",
+                "sha256:7cc6fd1eca65be588f001005446a47981cbe0b2909f5be8feafef3bf351a4e24",
+                "sha256:86b8a140e891bb23c8b9cb1804f1475eb13eea3dbbebef01fcbbf10fbafbee42",
+                "sha256:ad10acab2ef691eb29a1cc52c3be5ad1423700e993cc035066049fa72999d0dc",
+                "sha256:b2ff3a0a4f991d2da5376efd3365064a43909877e9fabfa801df970771161d29",
+                "sha256:b47e8f9ff6846756be3dc3fb242ca8e86752cd35a08e06d54ffc2e2a2aca70ea",
+                "sha256:da298241d87bcf468aa0f0705ba14572ad296f24c4fda5055d6988701d6fd8e1",
+                "sha256:db881478f1170c6dd524175ba1c83b99d3a6f992a35eca756de0ddc4690a1940",
+                "sha256:f0427755c68571df37dc58835e53a4307884a48dec76f3c01e33eb0d4a3a81d7",
+                "sha256:f8885cc48c8c7ad51f36c175e462840f163cb4687eeb6c6d7dfaf7197308e36b",
+                "sha256:f911b7f0083d445c8d24cfa5b42ad4996e33250400492080f5018a28c026db2b"
+            ],
+            "version": "==0.10.9"
         },
         "networkx": {
             "hashes": [
@@ -712,6 +636,30 @@
             ],
             "version": "==7.352.0"
         },
+        "onnx": {
+            "hashes": [
+                "sha256:18256e0099bffa3da7422ff3dd6663e201a875338f31bafa0f4148c5b5c938dd",
+                "sha256:1e0159865d6ebe3f3e7b14d349e9f395b2bb67373cd8b577cd79127245006050",
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+                "sha256:4072a9234b0fa7ab6c0b9b78d9a5e1d61fb822fbc393da09fa4936c6ff0f690c",
+                "sha256:644c8c4173f1659715d773bf79ea6a870ef691beacd3bae3c0b85dfa00d4e2a9",
+                "sha256:65900d994c4a859e40f262c949a27331499773fd885a73ec8768548f6fd78d1b",
+                "sha256:6610f59ad9ddeded250a33071b5fff6632b127193adf6a00687b5f7ce4e4936d",
+                "sha256:6da872c38e4b414640670730371256c9ac1d270317d07f60d6826123cac32b74",
+                "sha256:9315d8fe8551883a96a815ac1cdcf8b029b2998e620c83b0f35754d5faf2c609",
+                "sha256:d1d1f7ea0cfc6890648343533377af5b31c4c797333976550a48bfde9b88672e",
+                "sha256:fa7d4fe66164303e94e2bf5169f751abdba9f9a565bbeb22f54b5497d1388ebe",
+                "sha256:fd1669a59e319079b50053c149f2a6a1493249619c7cfde77273f61dc71c6744"
+            ],
+            "version": "==1.3.0"
+        },
+        "onnx-chainer": {
+            "hashes": [
+                "sha256:3f73fa0a446621d95bc234236e65485908dba7bfd9f0557ed5b3595341ced268"
+            ],
+            "index": "pypi",
+            "version": "==1.3.0a1"
+        },
         "packaging": {
             "hashes": [
                 "sha256:0886227f54515e592aaa2e5a553332c73962917f2831f1b0f9b9f4380a4b9807",
@@ -783,38 +731,38 @@
         },
         "pillow": {
             "hashes": [
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-                "sha256:c5e2be6c263b64f6f7656e23e18a4a9980cffc671442795682e8c4e4f815dd9f",
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-                "sha256:dae06620d3978da346375ebf88b9e2dd7d151335ba668c995aea9ed07af7add4",
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-                "sha256:ff8cff01582fa1a7e533cb97f628531c4014af4b5f38e33cdcfe5eec29b6d888"
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-            "version": "==5.3.0"
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+                "sha256:b117287a5bdc81f1bac891187275ec7e829e961b8032c9e5ff38b70fd036c78f",
+                "sha256:ba04f57d1715ca5ff74bb7f8a818bf929a204b3b3c2c2826d1e1cc3b1c13398c",
+                "sha256:cd878195166723f30865e05d87cbaf9421614501a4bd48792c5ed28f90fd36ca",
+                "sha256:cee815cc62d136e96cf76771b9d3eb58e0777ec18ea50de5cfcede8a7c429aa8",
+                "sha256:d1722b7aa4b40cf93ac3c80d3edd48bf93b9208241d166a14ad8e7a20ee1d4f3",
+                "sha256:d7c1c06246b05529f9984435fc4fa5a545ea26606e7f450bdbe00c153f5aeaad",
+                "sha256:e9c8066249c040efdda84793a2a669076f92a301ceabe69202446abb4c5c5ef9",
+                "sha256:f227d7e574d050ff3996049e086e1f18c7bd2d067ef24131e50a1d3fe5831fbc",
+                "sha256:fc9a12aad714af36cf3ad0275a96a733526571e52710319855628f476dcb144e"
+            ],
+            "version": "==5.4.1"
         },
         "prometheus-client": {
             "hashes": [
@@ -861,20 +809,20 @@
         },
         "pyarrow": {
             "hashes": [
-                "sha256:08cf372e4b6147afc020c4b803e0141b1a64b149e3e0db606a87c9b727880ce8",
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-                "sha256:ff723618043421e05a302a1dd7169dfaa9a6a8ec87255be62407db9a205ed68e"
-            ],
-            "version": "==0.11.1"
+                "sha256:1179d450955caf1ed85ac83b38794dffbb4939a07f4a786c17525e6d67e48b4a",
+                "sha256:2f3c1b3929dfcc43b1705a97d090e576a4d24640ed7db03a0a86936315503812",
+                "sha256:33d17f90e51ddf27789ac0b57f42479df13ba06cf7b44673815bb4c15b268b55",
+                "sha256:5b151650b67ad36c91aaa74c4cdf9602b91f3e2b5ee133ddb3ffa51e0b42c84c",
+                "sha256:8ddd3aa357990e94a744ca023b42ba341e4b7d564260cba25c9baba11d9100b0",
+                "sha256:a9197d14e0f9f3aadcd759438145b605c00b6faab00a5f049213c02c160286c6",
+                "sha256:a9ca4c3841a9eeb1e5f65b897ce5cba7dd0484c23d4f83ecc8843fe6e83796bf",
+                "sha256:ad0a3b5dec11719a2b96ec2aa5e3dc37126de228ca7713ae1d0dbbc6d80681e9",
+                "sha256:b4352bf16d76a4dc9396fd664d1dcce40acaa10d0d1626e13c51a94532804b53",
+                "sha256:bf66738b559c10b4899b96b90cbcf8234bb9285c6d69e511ee4e1df936c81133",
+                "sha256:d6aca88ca466c8b08847386862f17a02855bdfb0f5145c93d75d97a8fc65c666",
+                "sha256:e4b879fb34706418ca70e4b86c05e2b0a082650cbd2444d5dfd069724bb90145"
+            ],
+            "version": "==0.12.0"
         },
         "pygments": {
             "hashes": [
@@ -885,13 +833,23 @@
         },
         "pyparsing": {
             "hashes": [
-                "sha256:40856e74d4987de5d01761a22d1621ae1c7f8774585acae358aa5c5936c6c90b",
-                "sha256:f353aab21fd474459d97b709e527b5571314ee5f067441dc9f88e33eecd96592"
+                "sha256:66c9268862641abcac4a96ba74506e594c884e3f57690a696d21ad8210ed667a",
+                "sha256:f6c5ef0d7480ad048c054c37632c67fca55299990fff127850181659eea33fc3"
             ],
-            "version": "==2.3.0"
+            "version": "==2.3.1"
         },
         "pyproj": {
             "hashes": [
+                "sha256:026074694f9e9a3110013802c5ceb2728070dbdde9f1038609f942845f4207d1",
+                "sha256:25e244b84da0b673e2969fdfe2d98f2f94c74a8baea1dd88928f2cf7c1410cba",
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+                "sha256:569c764b391e31d4b156acb09acde9afb0c1bf1a71ca6e829e4677220ca64a56",
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+                "sha256:5b1553d80b35c6582a79252fc2a4e5d82d95383fbbfc671650d6fe54e18bbb9c",
+                "sha256:629acc34d8c2ff6fa2875e6075555fcb17a033cd3e181613e8782110fcc2f6b1",
+                "sha256:a7fa5da448dcdbd787e70e21dcf6c71a14bc048db86027f2fc3fe005b9440b93",
+                "sha256:c6d7c3c11c9f8f043fb00658f2146c10e3e0e21b5459022ae5716994650d6a02",
                 "sha256:e0c02b1554b20c710d16d673817b2a89ff94738b0b537aead8ecb2edc4c4487b"
             ],
             "index": "pypi",
@@ -899,9 +857,9 @@
         },
         "pyrsistent": {
             "hashes": [
-                "sha256:59880cc33ac293515892b2969aa8f4ed2cec592cbd0be4c4e20f2410468bbc62"
+                "sha256:5a3827d57ad3e46820e5ee4ed5b9e0ee7bc4686df6634a7368bc1863a5c48a77"
             ],
-            "version": "==0.14.8"
+            "version": "==0.14.9"
         },
         "python-dateutil": {
             "hashes": [
@@ -912,10 +870,10 @@
         },
         "pytz": {
             "hashes": [
-                "sha256:31cb35c89bd7d333cd32c5f278fca91b523b0834369e757f4c5641ea252236ca",
-                "sha256:8e0f8568c118d3077b46be7d654cc8167fa916092e28320cde048e54bfc9f1e6"
+                "sha256:32b0891edff07e28efe91284ed9c31e123d84bea3fd98e1f72be2508f43ef8d9",
+                "sha256:d5f05e487007e29e03409f9398d074e158d920d36eb82eaf66fb1136b0c5374c"
             ],
-            "version": "==2018.7"
+            "version": "==2018.9"
         },
         "pywavelets": {
             "hashes": [
@@ -1053,40 +1011,6 @@
             "index": "pypi",
             "version": "==0.14.1"
         },
-        "scikit-learn": {
-            "hashes": [
-                "sha256:05d061606657af85365b5f71484e3362d924429edde17a90068960843ad597f5",
-                "sha256:071317afbb5c67fa493635376ddd724b414290255cbf6947c1155846956e93f7",
-                "sha256:0d03aaf19a25e59edac3099cda6879ba05129f0fa1e152e23b728ccd36104f57",
-                "sha256:1665ea0d4b75ef24f5f2a9d1527b7296eeabcbe3a1329791c954541e2ebde5a2",
-                "sha256:24eccb0ff31f84e88e00936c09197735ef1dcabd370aacb10e55dbc8ee464a78",
-                "sha256:27b48cabacce677a205e6bcda1f32bdc968fbf40cd2aa0a4f52852f6997fce51",
-                "sha256:2c51826b9daa87d7d356bebd39f8665f7c32e90e3b21cbe853d6c7f0d6b0d23b",
-                "sha256:3116299d392bd1d054655fa2a740e7854de87f1d573fa85503e64494e52ac795",
-                "sha256:3771861abe1fd1b2bbeaec7ba8cfca58fdedd75d790f099960e5332af9d1ff7a",
-                "sha256:473ba7d9a5eaec47909ee83d74b4a3be47a44505c5189d2cab67c0418cd030f1",
-                "sha256:621e2c91f9afde06e9295d128cb15cb6fc77dc00719393e9ec9d47119895b0d4",
-                "sha256:645865462c383e5faad473b93145a8aee97d839c9ad1fd7a17ae54ec8256d42b",
-                "sha256:80e2276d4869d302e84b7c03b5bac4a67f6cd331162e62ae775a3e5855441a60",
-                "sha256:84d2cfe0dee3c22b26364266d69850e0eb406d99714045929875032f91d3c918",
-                "sha256:87ea9ace7fe811638dfc39b850b60887509b8bfc93c4006d5552fa066d04ddc7",
-                "sha256:a4d1e535c75881f668010e6e53dfeb89dd50db85b05c5c45af1991c8b832d757",
-                "sha256:a4f14c4327d2e44567bfb3a0bee8c55470f820bc9a67af3faf200abd8ed79bf2",
-                "sha256:a7b3c24e193e8c6eaeac075b5d0bb0a7fea478aa2e4b991f6a7b030fc4fd410d",
-                "sha256:ab2919aca84f1ac6ef60a482148eec0944364ab1832e63f28679b16f9ef279c8",
-                "sha256:b0f79d5ff74f3c68a4198ad5b4dfa891326b5ce272dd064d11d572b25aae5b43",
-                "sha256:bc5bc7c7ee2572a1edcb51698a6caf11fae554194aaab9a38105d9ec419f29e6",
-                "sha256:bc5c750d548795def79576533f8f0f065915f17f48d6e443afce2a111f713747",
-                "sha256:c68969c30b3b2c1fe07c1376110928eade61da4fc29c24c9f1a89435a7d08abe",
-                "sha256:d3b4f791d2645fe936579d61f1ff9b5dcf0c8f50db7f0245ca8f16407d7a5a46",
-                "sha256:dac0cd9fdd8ac6dd6108a10558e2e0ca1b411b8ea0a3165641f9ab0b4322df4e",
-                "sha256:eb7ddbdf33eb822fdc916819b0ab7009d954eb43c3a78e7dd2ec5455e074922a",
-                "sha256:ed537844348402ed53420187b3a6948c576986d0b2811a987a49613b6a26f29e",
-                "sha256:fcca54733e692fe03b8584f7d4b9344f4b6e3a74f5b326c6e5f5e9d2504bdce7"
-            ],
-            "index": "pypi",
-            "version": "==0.20.2"
-        },
         "scipy": {
             "hashes": [
                 "sha256:02cb79ea38114dc480e9b08d6b87095728e8fb39b9a49b449ee443d678001611",
@@ -1159,46 +1083,6 @@
             ],
             "version": "==1.4.2"
         },
-        "tensorboard": {
-            "hashes": [
-                "sha256:64edbe66864e02719f85708ae01efe3448af964c042a502fd2046cc87a3b1f12",
-                "sha256:e4ea6ac2e47bf715b915f08a186e6205fa097318bd73f0b265d437b1d7834484"
-            ],
-            "version": "==1.10.0"
-        },
-        "tensorflow": {
-            "hashes": [
-                "sha256:002ed1550e2fdd82df5939c53737ed8871d21462c354604917dd9f12f44c65ed",
-                "sha256:316bcfda289c40f6ff9ff16ed747744d0b113b577e98e99c839a4da835011dbf",
-                "sha256:34dfc6b017edffc8dfef1b57146edf45a39160dd6f2819449d05251df0181f36",
-                "sha256:3cdebd17ef32ce867ab05b5b9da1b6dea8d54c3d050a03d26373d94ae09d010c",
-                "sha256:4e629651f1570771e525de0208a8b1df8209ca550ce82cf56539b106bceccab3",
-                "sha256:8f9596d3f8cf8eba1f595286d8c43d690add1060eda791f3f337599967700dc2",
-                "sha256:9483e7e4815960797e67e89c0ce968b1f6115ed4cd49961119d943c71da260ac",
-                "sha256:a6aeda09080852f762bdfbde4acbef6d6aa2e729febaee87fd55700e82060cf2",
-                "sha256:b82d124316ce8dea1f8ead72bbd92a83e0fb455b82b4a23d04d392972f820347",
-                "sha256:c94cdd829fbb76d885c95172128e9261bb2b930a75a721b2972ad465ed532aff"
-            ],
-            "index": "pypi",
-            "version": "==1.10.1"
-        },
-        "tensorflow-gpu": {
-            "hashes": [
-                "sha256:2872b24f8c86bd16c8667bed0346b68ba8472f6c4d47c5554c560faf5f5bc64a",
-                "sha256:5b1b4a34c8631b2c30d11c6eb83c9c7c5700c06c51d761e842da1a8f839f51a3",
-                "sha256:bcb1703fe27a1248c7768c2143a33b025872b007bb9699e6282cc98474c3b8c3",
-                "sha256:d7e1dcc722541be88c8fad4219314313d97af129b3e3567c5c20daecfb7f8593",
-                "sha256:e83febd2c21c9cd05d00f11276458ae69af11d3656483018aa9897143669d65d"
-            ],
-            "index": "pypi",
-            "version": "==1.10.1"
-        },
-        "termcolor": {
-            "hashes": [
-                "sha256:1d6d69ce66211143803fbc56652b41d73b4a400a2891d7bf7a1cdf4c02de613b"
-            ],
-            "version": "==1.1.0"
-        },
         "terminado": {
             "hashes": [
                 "sha256:55abf9ade563b8f9be1f34e4233c7b7bde726059947a593322e8a553cc4c067a",
@@ -1208,10 +1092,10 @@
         },
         "testfixtures": {
             "hashes": [
-                "sha256:1e0affc9b459f039ebf9ae6e8af4059ded4d293863d4af9ffcd83e3b5e8df9cc",
-                "sha256:b040b59e0089809c2f157d3463ea288a10d890661695581649f40ae967944829"
+                "sha256:969e967df5d8e12012b5c90986428919b1068c20841b0077b3e29e9a928605d3",
+                "sha256:b6c05222ce8d3c34a1353ff30c73da55f61ef58153229a5664ef7110ec340cdd"
             ],
-            "version": "==6.4.1"
+            "version": "==6.4.3"
         },
         "testpath": {
             "hashes": [
@@ -1244,6 +1128,7 @@
                 "sha256:d4b3e5329f572f055b587efc57d29bd051589fb5a43ec8898c77a47ec2fa2bbb",
                 "sha256:e5f2585afccbff22390cddac29849df463b252b711aa2ce7c5f3f342a5b3b444"
             ],
+            "index": "pypi",
             "version": "==5.1.1"
         },
         "tqdm": {
@@ -1261,6 +1146,22 @@
             ],
             "version": "==4.3.2"
         },
+        "typing": {
+            "hashes": [
+                "sha256:4027c5f6127a6267a435201981ba156de91ad0d1d98e9ddc2aa173453453492d",
+                "sha256:57dcf675a99b74d64dacf6fba08fb17cf7e3d5fdff53d4a30ea2a5e7e52543d4",
+                "sha256:a4c8473ce11a65999c8f59cb093e70686b6c84c98df58c1dae9b3b196089858a"
+            ],
+            "version": "==3.6.6"
+        },
+        "typing-extensions": {
+            "hashes": [
+                "sha256:07b2c978670896022a43c4b915df8958bec4a6b84add7f2c87b2b728bda3ba64",
+                "sha256:f3f0e67e1d42de47b5c67c32c9b26641642e9170fe7e292991793705cd5fef7c",
+                "sha256:fb2cd053238d33a8ec939190f30cfd736c00653a85a2919415cecf7dc3d9da71"
+            ],
+            "version": "==3.7.2"
+        },
         "urllib3": {
             "hashes": [
                 "sha256:61bf29cada3fc2fbefad4fdf059ea4bd1b4a86d2b6d15e1c7c0b582b9752fe39",
@@ -1289,21 +1190,6 @@
             ],
             "version": "==0.54.0"
         },
-        "werkzeug": {
-            "hashes": [
-                "sha256:c3fd7a7d41976d9f44db327260e263132466836cef6f91512889ed60ad26557c",
-                "sha256:d5da73735293558eb1651ee2fddc4d0dedcfa06538b8813a2e20011583c9e49b"
-            ],
-            "version": "==0.14.1"
-        },
-        "wheel": {
-            "hashes": [
-                "sha256:029703bf514e16c8271c3821806a1c171220cc5bdd325cbf4e7da1e056a01db6",
-                "sha256:1e53cdb3f808d5ccd0df57f964263752aa74ea7359526d3da6c02114ec1e1d44"
-            ],
-            "markers": "python_version >= '3'",
-            "version": "==0.32.3"
-        },
         "wurlitzer": {
             "hashes": [
                 "sha256:15a7cb8be359e8ee42093468a60bf462af332088ea62e767af64d83fcc332ac0",
@@ -1313,10 +1199,10 @@
         },
         "xarray": {
             "hashes": [
-                "sha256:0289fe73eb2b0a4bf3e0c670fc232690f7b00b374d4280de0f0faa9c3801b509",
-                "sha256:cb0503a614b5c95702c0468a136c2ce32f9e18c92c9c8d8031413339bb4016dd"
+                "sha256:431e43d8e14cd48dae44932e572fae8d209848ce31d3ff96c82037d0cc3970f3",
+                "sha256:af7147152629701f11e424caf8e4fbf5ea1dc2d03ed7a5ca31b83dd64387cfb2"
             ],
-            "version": "==0.11.1"
+            "version": "==0.11.2"
         },
         "xlrd": {
             "hashes": [
@@ -1428,10 +1314,10 @@
         },
         "jsonschema": {
             "hashes": [
-                "sha256:3ae8afd6f4ca6417f14bf43ef61341311598f14234cdb4174fe43d42b236a3c8",
-                "sha256:dfd8426040892c8d0ef6da574085f282569f189cb24b70091a66c21c12d6705e"
+                "sha256:3eae63135c4a2cd15ecfd1424494494be77bd8a27014c44c8c2343e61d908770",
+                "sha256:8ba4f6c03b9db02e51f4a21579b7b0364b7c174361998888fb5d18fab4ed73f1"
             ],
-            "version": "==3.0.0a3"
+            "version": "==3.0.0b1"
         },
         "jupyter-client": {
             "hashes": [
@@ -1507,10 +1393,10 @@
         },
         "pluggy": {
             "hashes": [
-                "sha256:447ba94990e8014ee25ec853339faf7b0fc8050cdc3289d4d71f7f410fb90095",
-                "sha256:bde19360a8ec4dfd8a20dcb811780a30998101f078fc7ded6162f0076f50508f"
+                "sha256:8ddc32f03971bfdf900a81961a48ccf2fb677cf7715108f85295c67405798616",
+                "sha256:980710797ff6a041e9a73a5787804f848996ecaa6f8a1b1e08224a5894f2074a"
             ],
-            "version": "==0.8.0"
+            "version": "==0.8.1"
         },
         "prompt-toolkit": {
             "hashes": [
@@ -1544,9 +1430,9 @@
         },
         "pyrsistent": {
             "hashes": [
-                "sha256:59880cc33ac293515892b2969aa8f4ed2cec592cbd0be4c4e20f2410468bbc62"
+                "sha256:5a3827d57ad3e46820e5ee4ed5b9e0ee7bc4686df6634a7368bc1863a5c48a77"
             ],
-            "version": "==0.14.8"
+            "version": "==0.14.9"
         },
         "pytest": {
             "hashes": [
@@ -1610,6 +1496,7 @@
                 "sha256:d4b3e5329f572f055b587efc57d29bd051589fb5a43ec8898c77a47ec2fa2bbb",
                 "sha256:e5f2585afccbff22390cddac29849df463b252b711aa2ce7c5f3f342a5b3b444"
             ],
+            "index": "pypi",
             "version": "==5.1.1"
         },
         "traitlets": {
diff --git a/deepbedmap.ipynb b/deepbedmap.ipynb
index 6c993f2..9da83f5 100644
--- a/deepbedmap.ipynb
+++ b/deepbedmap.ipynb
@@ -13,18 +13,11 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import math\n",
     "import os\n",
+    "import typing\n",
     "\n",
     "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n",
     "\n",
@@ -39,7 +32,7 @@
     "import skimage\n",
     "import xarray as xr\n",
     "\n",
-    "import keras\n",
+    "import chainer\n",
     "\n",
     "from features.environment import _load_ipynb_modules"
    ]
@@ -59,7 +52,7 @@
    },
    "outputs": [],
    "source": [
-    "def get_image_and_bounds(filepath: str):\n",
+    "def get_image_and_bounds(filepath: str) -> (np.ndarray, rasterio.coords.BoundingBox):\n",
     "    \"\"\"\n",
     "    Retrieve raster image in numpy array format and\n",
     "    geographic bounds as (xmin, ymin, xmax, ymax)\n",
@@ -68,8 +61,9 @@
     "        groundtruth = data.z.to_masked_array()\n",
     "        groundtruth = np.flipud(groundtruth)  # flip on y-axis...\n",
     "        groundtruth = np.expand_dims(\n",
-    "            np.expand_dims(groundtruth, axis=-1), axis=0\n",
+    "            np.expand_dims(groundtruth, axis=0), axis=0\n",
     "        )  # add extra dimensions (batch and channel)\n",
+    "        assert groundtruth.shape[0:2] == (1, 1)  # check that shape is like (1, 1, h, w)\n",
     "\n",
     "        xmin, xmax = float(data.x.min()), float(data.x.max())\n",
     "        ymin, ymax = float(data.y.min()), float(data.y.max())\n",
@@ -84,20 +78,11 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BoundingBox(left=-1593714.328, bottom=-164173.7848, right=-1575464.328, top=-97923.7848)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "test_file = \"2007tx\"  # \"istarxx\"\n",
     "test_filepath = f\"highres/{test_file}\"\n",
-    "groundtruth, window_bound = get_image_and_bounds(filepath=f\"{test_filepath}.nc\")\n",
-    "print(window_bound)"
+    "groundtruth, window_bound = get_image_and_bounds(filepath=f\"{test_filepath}.nc\")"
    ]
   },
   {
@@ -117,7 +102,7 @@
    "source": [
     "def get_deepbedmap_model_inputs(\n",
     "    window_bound: rasterio.coords.BoundingBox, padding=1000\n",
-    "):\n",
+    ") -> typing.Dict[str, np.ndarray]:\n",
     "    \"\"\"\n",
     "    Outputs one large tile for each of\n",
     "    BEDMAP2, REMA and MEASURES Ice Flow Velocity\n",
@@ -144,7 +129,11 @@
     "        padding=padding,\n",
     "    )\n",
     "\n",
-    "    return X_tile, W1_tile, W2_tile"
+    "    return (\n",
+    "        np.rollaxis(X_tile, axis=3, start=1),\n",
+    "        np.rollaxis(W1_tile, axis=3, start=1),\n",
+    "        np.rollaxis(W2_tile, axis=3, start=1),\n",
+    "    )"
    ]
   },
   {
@@ -163,10 +152,10 @@
     "    cm_norm: matplotlib.colors.Normalize = None,\n",
     "    title: str = None,\n",
     "):\n",
-    "    # Get x, y, z data\n",
+    "    # Get x, y, z data, assuming image in NCHW format\n",
     "    image = img[0, :, :, :]\n",
-    "    xx, yy = np.mgrid[0 : image.shape[0], 0 : image.shape[1]]\n",
-    "    zz = image[:, :, 0]\n",
+    "    xx, yy = np.mgrid[0 : image.shape[1], 0 : image.shape[2]]\n",
+    "    zz = image[0, :, :]\n",
     "\n",
     "    # Make the 3D plot\n",
     "    ax.view_init(elev=elev, azim=azim)\n",
@@ -223,11 +212,11 @@
    ],
    "source": [
     "fig, axarr = plt.subplots(nrows=1, ncols=3, squeeze=False, figsize=(16, 12))\n",
-    "axarr[0, 0].imshow(X_tile[0, :, :, 0], cmap=\"BrBG\")\n",
+    "axarr[0, 0].imshow(X_tile[0, 0, :, :], cmap=\"BrBG\")\n",
     "axarr[0, 0].set_title(\"BEDMAP2\\n(1000m resolution)\")\n",
-    "axarr[0, 1].imshow(W1_tile[0, :, :, 0], cmap=\"BrBG\")\n",
+    "axarr[0, 1].imshow(W1_tile[0, 0, :, :], cmap=\"BrBG\")\n",
     "axarr[0, 1].set_title(\"Reference Elevation Model of Antarctica\\n(100m resolution)\")\n",
-    "axarr[0, 2].imshow(W2_tile[0, :, :, 0], cmap=\"BrBG\")\n",
+    "axarr[0, 2].imshow(W2_tile[0, 0, :, :], cmap=\"BrBG\")\n",
     "axarr[0, 2].set_title(\"MEaSUREs Ice Velocity\\n(450m, resampled to 500m)\")\n",
     "plt.show()"
    ]
@@ -295,29 +284,19 @@
    },
    "outputs": [],
    "source": [
-    "def load_trained_model(model_inputs: tuple):\n",
+    "def load_trained_model(\n",
+    "    filepath: str = \"model/weights/srgan_generator_model_weights.npz\"\n",
+    "):\n",
     "    \"\"\"\n",
-    "    Creates a custom DeepBedMap neural network model\n",
-    "    according to the shapes of the raster image inputs.\n",
-    "\n",
-    "    Also loads trained parameter weights into the model.\n",
+    "    Builds the Generator component of the DeepBedMap neural network.\n",
+    "    Also loads trained parameter weights into the model from a .npz file.\n",
     "    \"\"\"\n",
     "    srgan_train = _load_ipynb_modules(\"srgan_train.ipynb\")\n",
     "\n",
-    "    X_tile, W1_tile, W2_tile = model_inputs\n",
-    "\n",
-    "    network = srgan_train.generator_network(\n",
-    "        input1_shape=X_tile.shape[1:],\n",
-    "        input2_shape=W1_tile.shape[1:],\n",
-    "        input3_shape=W2_tile.shape[1:],\n",
-    "    )\n",
-    "\n",
-    "    model = keras.models.Model(\n",
-    "        inputs=network.inputs, outputs=network.outputs, name=\"generator_model\"\n",
-    "    )\n",
+    "    model = srgan_train.GeneratorModel()\n",
     "\n",
     "    # Load trained neural network weights into model\n",
-    "    model.load_weights(filepath=\"model/weights/srgan_generator_model_weights.hdf5\")\n",
+    "    chainer.serializers.load_npz(file=filepath, obj=model)\n",
     "\n",
     "    return model"
    ]
@@ -334,17 +313,10 @@
    "execution_count": 10,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 1s 646ms/step\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "(1, 264, 72, 1)"
+       "(1, 1, 264, 72)"
       ]
      },
      "execution_count": 10,
@@ -353,8 +325,8 @@
     }
    ],
    "source": [
-    "model = load_trained_model(model_inputs=(X_tile, W1_tile, W2_tile))\n",
-    "Y_hat = model.predict(x=[X_tile, W1_tile, W2_tile], verbose=1)\n",
+    "model = load_trained_model()\n",
+    "Y_hat = model.forward(inputs={\"x\": X_tile, \"w1\": W1_tile, \"w2\": W2_tile}).array\n",
     "Y_hat.shape"
    ]
   },
@@ -372,7 +344,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1152x864 with 3 Axes>"
       ]
@@ -385,11 +357,11 @@
    ],
    "source": [
     "fig, axarr = plt.subplots(nrows=1, ncols=3, squeeze=False, figsize=(16, 12))\n",
-    "axarr[0, 0].imshow(X_tile[0, :, :, 0], cmap=\"BrBG\")\n",
+    "axarr[0, 0].imshow(X_tile[0, 0, :, :], cmap=\"BrBG\")\n",
     "axarr[0, 0].set_title(\"BEDMAP2\")\n",
-    "axarr[0, 1].imshow(Y_hat[0, :, :, 0], cmap=\"BrBG\")\n",
+    "axarr[0, 1].imshow(Y_hat[0, 0, :, :], cmap=\"BrBG\")\n",
     "axarr[0, 1].set_title(\"Super Resolution Generative Adversarial Network prediction\")\n",
-    "axarr[0, 2].imshow(groundtruth[0, :, :, 0], cmap=\"BrBG\")\n",
+    "axarr[0, 2].imshow(groundtruth[0, 0, :, :], cmap=\"BrBG\")\n",
     "axarr[0, 2].set_title(\"Groundtruth grids\")\n",
     "plt.show()"
    ]
@@ -401,7 +373,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x720 with 2 Axes>"
       ]
@@ -455,14 +427,16 @@
    "source": [
     "def save_array_to_grid(window_bound: tuple, array: np.ndarray, outfilepath: str):\n",
     "    \"\"\"\n",
-    "    Saves a numpy array to geotiff and netcdf format\n",
+    "    Saves a numpy array to geotiff and netcdf format.\n",
+    "    Appends \".tif\" and \".nc\" file extension to the outfilepath\n",
+    "    for geotiff and netcdf outputs respectively.\n",
     "    \"\"\"\n",
     "\n",
     "    assert array.ndim == 4\n",
-    "    assert array.shape[3] == 1  # check that there is only one channel\n",
+    "    assert array.shape[1] == 1  # check that there is only one channel\n",
     "\n",
     "    transform = rasterio.transform.from_bounds(\n",
-    "        *window_bound, height=array.shape[1], width=array.shape[2]\n",
+    "        *window_bound, height=array.shape[2], width=array.shape[3]\n",
     "    )\n",
     "\n",
     "    # Save array as a GeoTiff first\n",
@@ -470,14 +444,14 @@
     "        f\"{outfilepath}.tif\",\n",
     "        mode=\"w\",\n",
     "        driver=\"GTiff\",\n",
-    "        height=array.shape[1],\n",
-    "        width=array.shape[2],\n",
+    "        height=array.shape[2],\n",
+    "        width=array.shape[3],\n",
     "        count=1,\n",
     "        crs=\"EPSG:3031\",\n",
     "        transform=transform,\n",
     "        dtype=array.dtype,\n",
     "    ) as new_geotiff:\n",
-    "        new_geotiff.write(array[0, :, :, 0], 1)\n",
+    "        new_geotiff.write(array[0, 0, :, :], 1)\n",
     "\n",
     "    # Convert deepbedmap3 and cubicbedmap2 from geotiff to netcdf format\n",
     "    xr.open_rasterio(f\"{outfilepath}.tif\").to_netcdf(f\"{outfilepath}.nc\")"
@@ -503,17 +477,17 @@
    "source": [
     "# Save Bicubic Resampled BEDMAP2 to GeoTiff and NetCDF format\n",
     "cubicbedmap2 = skimage.transform.rescale(\n",
-    "    image=X_tile[0].astype(np.int32),\n",
-    "    scale=4,\n",
-    "    order=3,\n",
+    "    image=X_tile[0, 0, :, :].astype(np.int32),\n",
+    "    scale=4,  # 4x upscaling\n",
+    "    order=3,  # cubic interpolation\n",
     "    mode=\"reflect\",\n",
     "    anti_aliasing=True,\n",
-    "    multichannel=True,\n",
+    "    multichannel=False,\n",
     "    preserve_range=True,\n",
     ")\n",
     "save_array_to_grid(\n",
     "    window_bound=window_bound,\n",
-    "    array=np.expand_dims(cubicbedmap2, axis=0),\n",
+    "    array=np.expand_dims(np.expand_dims(cubicbedmap2, axis=0), axis=0),\n",
     "    outfilepath=\"model/cubicbedmap\",\n",
     ")"
    ]
@@ -567,10 +541,10 @@
       "\n",
       "==> track_deepbedmap3.xyzi <==\n",
       "# x\ty\tz\n",
-      "-1593496.33\t-104797.8003\t-1074.669904\t-1182.94189157\n",
-      "-1593491.331\t-104797.7531\t-1074.68\t-1182.71039494\n",
-      "-1593486.331\t-104797.7058\t-1074.683558\t-1182.481519\n",
-      "-1593481.331\t-104797.6599\t-1074.695031\t-1182.25533966\n",
+      "-1593496.33\t-104797.8003\t-1074.669904\t-1243.02064091\n",
+      "-1593491.331\t-104797.7531\t-1074.68\t-1242.88752689\n",
+      "-1593486.331\t-104797.7058\t-1074.683558\t-1242.75766879\n",
+      "-1593481.331\t-104797.6599\t-1074.695031\t-1242.63093238\n",
       "\n",
       "==> track_groundtruth.xyzi <==\n",
       "# x\ty\tz\n",
@@ -783,56 +757,56 @@
        "      <td>-1.582823e+06</td>\n",
        "      <td>-127943.948452</td>\n",
        "      <td>-1255.901352</td>\n",
-       "      <td>-1264.744392</td>\n",
-       "      <td>-8.843040</td>\n",
+       "      <td>-1321.530331</td>\n",
+       "      <td>-65.628979</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>4.306205e+03</td>\n",
        "      <td>29434.912966</td>\n",
        "      <td>73.216368</td>\n",
-       "      <td>37.147125</td>\n",
-       "      <td>41.640410</td>\n",
+       "      <td>106.018069</td>\n",
+       "      <td>59.667879</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>-1.593587e+06</td>\n",
        "      <td>-164048.233300</td>\n",
        "      <td>-1390.940804</td>\n",
-       "      <td>-1344.369508</td>\n",
-       "      <td>-238.431369</td>\n",
+       "      <td>-1498.067524</td>\n",
+       "      <td>-274.114707</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>-1.585696e+06</td>\n",
        "      <td>-160901.037700</td>\n",
        "      <td>-1327.500988</td>\n",
-       "      <td>-1298.081222</td>\n",
-       "      <td>-42.027530</td>\n",
+       "      <td>-1417.397437</td>\n",
+       "      <td>-119.243754</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>-1.582073e+06</td>\n",
        "      <td>-104396.422700</td>\n",
        "      <td>-1250.925200</td>\n",
-       "      <td>-1255.112397</td>\n",
-       "      <td>-4.533575</td>\n",
+       "      <td>-1300.770379</td>\n",
+       "      <td>-59.098591</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>-1.579456e+06</td>\n",
        "      <td>-101515.335350</td>\n",
        "      <td>-1195.214216</td>\n",
-       "      <td>-1236.538209</td>\n",
-       "      <td>25.026685</td>\n",
+       "      <td>-1219.812088</td>\n",
+       "      <td>-18.455616</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>-1.575591e+06</td>\n",
        "      <td>-98049.505510</td>\n",
        "      <td>-962.574500</td>\n",
-       "      <td>-1175.481684</td>\n",
-       "      <td>112.055620</td>\n",
+       "      <td>-1154.733987</td>\n",
+       "      <td>95.064976</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -841,13 +815,13 @@
       "text/plain": [
        "                  x              y             z  z_interpolated         error\n",
        "count  4.009500e+04   40095.000000  40095.000000    40095.000000  40095.000000\n",
-       "mean  -1.582823e+06 -127943.948452  -1255.901352    -1264.744392     -8.843040\n",
-       "std    4.306205e+03   29434.912966     73.216368       37.147125     41.640410\n",
-       "min   -1.593587e+06 -164048.233300  -1390.940804    -1344.369508   -238.431369\n",
-       "25%   -1.585696e+06 -160901.037700  -1327.500988    -1298.081222    -42.027530\n",
-       "50%   -1.582073e+06 -104396.422700  -1250.925200    -1255.112397     -4.533575\n",
-       "75%   -1.579456e+06 -101515.335350  -1195.214216    -1236.538209     25.026685\n",
-       "max   -1.575591e+06  -98049.505510   -962.574500    -1175.481684    112.055620"
+       "mean  -1.582823e+06 -127943.948452  -1255.901352    -1321.530331    -65.628979\n",
+       "std    4.306205e+03   29434.912966     73.216368      106.018069     59.667879\n",
+       "min   -1.593587e+06 -164048.233300  -1390.940804    -1498.067524   -274.114707\n",
+       "25%   -1.585696e+06 -160901.037700  -1327.500988    -1417.397437   -119.243754\n",
+       "50%   -1.582073e+06 -104396.422700  -1250.925200    -1300.770379    -59.098591\n",
+       "75%   -1.579456e+06 -101515.335350  -1195.214216    -1219.812088    -18.455616\n",
+       "max   -1.575591e+06  -98049.505510   -962.574500    -1154.733987     95.064976"
       ]
      },
      "execution_count": 20,
@@ -993,12 +967,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Difference      : -19.99\n"
+      "Difference      : 26.14\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1152x648 with 1 Axes>"
       ]
@@ -1058,9 +1032,9 @@
      "output_type": "stream",
      "text": [
       "Groundtruth RMSE: 7.318264583382579\n",
-      "DeepBedMap3 RMSE: 42.56853102930003\n",
+      "DeepBedMap3 RMSE: 88.69796997562734\n",
       "CubicBedMap RMSE: 62.557033278894885\n",
-      "Difference      : -19.988502249594852\n"
+      "Difference      : 26.14093669673246\n"
      ]
     }
    ],
@@ -1087,9 +1061,9 @@
    }
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "deepbedmap",
    "language": "python",
-   "name": "python3"
+   "name": "deepbedmap"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/deepbedmap.py b/deepbedmap.py
index 9b3d1cb..f89cf03 100644
--- a/deepbedmap.py
+++ b/deepbedmap.py
@@ -22,6 +22,7 @@
 # %%
 import math
 import os
+import typing
 
 os.environ["CUDA_VISIBLE_DEVICES"] = ""
 
@@ -36,7 +37,7 @@
 import skimage
 import xarray as xr
 
-import keras
+import chainer
 
 from features.environment import _load_ipynb_modules
 
@@ -44,7 +45,7 @@
 # ## Get bounding box of area we want to predict on
 
 # %%
-def get_image_and_bounds(filepath: str):
+def get_image_and_bounds(filepath: str) -> (np.ndarray, rasterio.coords.BoundingBox):
     """
     Retrieve raster image in numpy array format and
     geographic bounds as (xmin, ymin, xmax, ymax)
@@ -53,8 +54,9 @@ def get_image_and_bounds(filepath: str):
         groundtruth = data.z.to_masked_array()
         groundtruth = np.flipud(groundtruth)  # flip on y-axis...
         groundtruth = np.expand_dims(
-            np.expand_dims(groundtruth, axis=-1), axis=0
+            np.expand_dims(groundtruth, axis=0), axis=0
         )  # add extra dimensions (batch and channel)
+        assert groundtruth.shape[0:2] == (1, 1)  # check that shape is like (1, 1, h, w)
 
         xmin, xmax = float(data.x.min()), float(data.x.max())
         ymin, ymax = float(data.y.min()), float(data.y.max())
@@ -69,7 +71,6 @@ def get_image_and_bounds(filepath: str):
 test_file = "2007tx"  # "istarxx"
 test_filepath = f"highres/{test_file}"
 groundtruth, window_bound = get_image_and_bounds(filepath=f"{test_filepath}.nc")
-print(window_bound)
 
 # %% [markdown]
 # ## Get neural network input datasets for our area of interest
@@ -77,7 +78,7 @@ def get_image_and_bounds(filepath: str):
 # %%
 def get_deepbedmap_model_inputs(
     window_bound: rasterio.coords.BoundingBox, padding=1000
-):
+) -> typing.Dict[str, np.ndarray]:
     """
     Outputs one large tile for each of
     BEDMAP2, REMA and MEASURES Ice Flow Velocity
@@ -104,7 +105,11 @@ def get_deepbedmap_model_inputs(
         padding=padding,
     )
 
-    return X_tile, W1_tile, W2_tile
+    return (
+        np.rollaxis(X_tile, axis=3, start=1),
+        np.rollaxis(W1_tile, axis=3, start=1),
+        np.rollaxis(W2_tile, axis=3, start=1),
+    )
 
 
 # %%
@@ -116,10 +121,10 @@ def plot_3d_view(
     cm_norm: matplotlib.colors.Normalize = None,
     title: str = None,
 ):
-    # Get x, y, z data
+    # Get x, y, z data, assuming image in NCHW format
     image = img[0, :, :, :]
-    xx, yy = np.mgrid[0 : image.shape[0], 0 : image.shape[1]]
-    zz = image[:, :, 0]
+    xx, yy = np.mgrid[0 : image.shape[1], 0 : image.shape[2]]
+    zz = image[0, :, :]
 
     # Make the 3D plot
     ax.view_init(elev=elev, azim=azim)
@@ -142,11 +147,11 @@ def plot_3d_view(
 
 # %%
 fig, axarr = plt.subplots(nrows=1, ncols=3, squeeze=False, figsize=(16, 12))
-axarr[0, 0].imshow(X_tile[0, :, :, 0], cmap="BrBG")
+axarr[0, 0].imshow(X_tile[0, 0, :, :], cmap="BrBG")
 axarr[0, 0].set_title("BEDMAP2\n(1000m resolution)")
-axarr[0, 1].imshow(W1_tile[0, :, :, 0], cmap="BrBG")
+axarr[0, 1].imshow(W1_tile[0, 0, :, :], cmap="BrBG")
 axarr[0, 1].set_title("Reference Elevation Model of Antarctica\n(100m resolution)")
-axarr[0, 2].imshow(W2_tile[0, :, :, 0], cmap="BrBG")
+axarr[0, 2].imshow(W2_tile[0, 0, :, :], cmap="BrBG")
 axarr[0, 2].set_title("MEaSUREs Ice Velocity\n(450m, resampled to 500m)")
 plt.show()
 
@@ -183,29 +188,19 @@ def plot_3d_view(
 # That way we can predict directly on an arbitrarily sized window.
 
 # %%
-def load_trained_model(model_inputs: tuple):
+def load_trained_model(
+    filepath: str = "model/weights/srgan_generator_model_weights.npz"
+):
     """
-    Creates a custom DeepBedMap neural network model
-    according to the shapes of the raster image inputs.
-
-    Also loads trained parameter weights into the model.
+    Builds the Generator component of the DeepBedMap neural network.
+    Also loads trained parameter weights into the model from a .npz file.
     """
     srgan_train = _load_ipynb_modules("srgan_train.ipynb")
 
-    X_tile, W1_tile, W2_tile = model_inputs
-
-    network = srgan_train.generator_network(
-        input1_shape=X_tile.shape[1:],
-        input2_shape=W1_tile.shape[1:],
-        input3_shape=W2_tile.shape[1:],
-    )
-
-    model = keras.models.Model(
-        inputs=network.inputs, outputs=network.outputs, name="generator_model"
-    )
+    model = srgan_train.GeneratorModel()
 
     # Load trained neural network weights into model
-    model.load_weights(filepath="model/weights/srgan_generator_model_weights.hdf5")
+    chainer.serializers.load_npz(file=filepath, obj=model)
 
     return model
 
@@ -214,8 +209,8 @@ def load_trained_model(model_inputs: tuple):
 # ## Make prediction
 
 # %%
-model = load_trained_model(model_inputs=(X_tile, W1_tile, W2_tile))
-Y_hat = model.predict(x=[X_tile, W1_tile, W2_tile], verbose=1)
+model = load_trained_model()
+Y_hat = model.forward(inputs={"x": X_tile, "w1": W1_tile, "w2": W2_tile}).array
 Y_hat.shape
 
 # %% [markdown]
@@ -223,11 +218,11 @@ def load_trained_model(model_inputs: tuple):
 
 # %%
 fig, axarr = plt.subplots(nrows=1, ncols=3, squeeze=False, figsize=(16, 12))
-axarr[0, 0].imshow(X_tile[0, :, :, 0], cmap="BrBG")
+axarr[0, 0].imshow(X_tile[0, 0, :, :], cmap="BrBG")
 axarr[0, 0].set_title("BEDMAP2")
-axarr[0, 1].imshow(Y_hat[0, :, :, 0], cmap="BrBG")
+axarr[0, 1].imshow(Y_hat[0, 0, :, :], cmap="BrBG")
 axarr[0, 1].set_title("Super Resolution Generative Adversarial Network prediction")
-axarr[0, 2].imshow(groundtruth[0, :, :, 0], cmap="BrBG")
+axarr[0, 2].imshow(groundtruth[0, 0, :, :], cmap="BrBG")
 axarr[0, 2].set_title("Groundtruth grids")
 plt.show()
 
@@ -262,14 +257,16 @@ def load_trained_model(model_inputs: tuple):
 # %%
 def save_array_to_grid(window_bound: tuple, array: np.ndarray, outfilepath: str):
     """
-    Saves a numpy array to geotiff and netcdf format
+    Saves a numpy array to geotiff and netcdf format.
+    Appends ".tif" and ".nc" file extension to the outfilepath
+    for geotiff and netcdf outputs respectively.
     """
 
     assert array.ndim == 4
-    assert array.shape[3] == 1  # check that there is only one channel
+    assert array.shape[1] == 1  # check that there is only one channel
 
     transform = rasterio.transform.from_bounds(
-        *window_bound, height=array.shape[1], width=array.shape[2]
+        *window_bound, height=array.shape[2], width=array.shape[3]
     )
 
     # Save array as a GeoTiff first
@@ -277,14 +274,14 @@ def save_array_to_grid(window_bound: tuple, array: np.ndarray, outfilepath: str)
         f"{outfilepath}.tif",
         mode="w",
         driver="GTiff",
-        height=array.shape[1],
-        width=array.shape[2],
+        height=array.shape[2],
+        width=array.shape[3],
         count=1,
         crs="EPSG:3031",
         transform=transform,
         dtype=array.dtype,
     ) as new_geotiff:
-        new_geotiff.write(array[0, :, :, 0], 1)
+        new_geotiff.write(array[0, 0, :, :], 1)
 
     # Convert deepbedmap3 and cubicbedmap2 from geotiff to netcdf format
     xr.open_rasterio(f"{outfilepath}.tif").to_netcdf(f"{outfilepath}.nc")
@@ -299,17 +296,17 @@ def save_array_to_grid(window_bound: tuple, array: np.ndarray, outfilepath: str)
 # %%
 # Save Bicubic Resampled BEDMAP2 to GeoTiff and NetCDF format
 cubicbedmap2 = skimage.transform.rescale(
-    image=X_tile[0].astype(np.int32),
-    scale=4,
-    order=3,
+    image=X_tile[0, 0, :, :].astype(np.int32),
+    scale=4,  # 4x upscaling
+    order=3,  # cubic interpolation
     mode="reflect",
     anti_aliasing=True,
-    multichannel=True,
+    multichannel=False,
     preserve_range=True,
 )
 save_array_to_grid(
     window_bound=window_bound,
-    array=np.expand_dims(cubicbedmap2, axis=0),
+    array=np.expand_dims(np.expand_dims(cubicbedmap2, axis=0), axis=0),
     outfilepath="model/cubicbedmap",
 )
 
diff --git a/environment.yml b/environment.yml
index edfe329..fb029ac 100644
--- a/environment.yml
+++ b/environment.yml
@@ -4,9 +4,9 @@ channels:
   - conda-forge/label/dev
   - nodefaults
 dependencies:
-  - defaults::cudnn=7.1.2[md5=4a402b88bb77e6ab2dcf3bfe6522f9cf]
-  - hcc::cuda_driver=390.46[md5=8fb0b6c39a9bf6128b1191db53ed903e]
-  - defaults::cudatoolkit=9.0[md5=5d0febed868b80a18e74077d5d0f17bc]
+  - defaults::cudnn=7.2.1[md5=6a84069dcf4aca8ba9493d3cb320090e]
+  - hcc::cuda_driver=410.73[md5=941787b750b372f4a240287634589d24]
+  - defaults::cudatoolkit=9.2[md5=f81c96e01ccb9028800101b35e71b844]
   - gmt=6.0.0a17[md5=bea1e9a2cc29280f8ba173123f115496]
   - pip=18.1[md5=d68c7e5109ba0bf4b1cfe60f0f47870a]
   - conda-forge::python=3.6.6[md5=fe9f54422cdaf8779147b6a02cab2dd1]
diff --git a/features/environment.py b/features/environment.py
index 828dbcd..acf7b58 100644
--- a/features/environment.py
+++ b/features/environment.py
@@ -32,10 +32,15 @@ def _load_ipynb_modules(ipynb_path: str):
     source, meta = pyexporter.from_notebook_node(nb=nb)
     assert isinstance(source, str)
 
-    # parse the .py string to pick out only 'import' and 'def function's
+    # parse the .py string to pick out only 'class', 'import' and 'def function's
     parsed_code = ast.parse(source=source)
     for node in parsed_code.body[:]:
-        if node.__class__ not in [ast.FunctionDef, ast.Import, ast.ImportFrom]:
+        if node.__class__ not in [
+            ast.ClassDef,
+            ast.FunctionDef,
+            ast.Import,
+            ast.ImportFrom,
+        ]:
             parsed_code.body.remove(node)
     assert len(parsed_code.body) > 0
 
@@ -108,7 +113,7 @@ def _download_deepbedmap_model_weights_from_comet():
 
     # Download the neural network weight file (hdf5 format) to the right place!
     r = requests.get(url=asset_url, headers=authHeader)
-    open(file="model/weights/srgan_generator_model_weights.hdf5", mode="wb").write(
+    open(file="model/weights/srgan_generator_model_weights.npz", mode="wb").write(
         r.content
     )
 
diff --git a/features/steps/test_deepbedmap.py b/features/steps/test_deepbedmap.py
index 8a38aa1..1cbf123 100644
--- a/features/steps/test_deepbedmap.py
+++ b/features/steps/test_deepbedmap.py
@@ -29,22 +29,20 @@ def get_model_input_raster_images(context):
 
 @when("pass those images into our trained neural network model")
 def predict_using_trained_neural_network(context):
-    model = context.deepbedmap.load_trained_model(
-        model_inputs=(context.X_tile, context.W1_tile, context.W2_tile)
-    )
-    context.Y_hat = model.predict(
-        x=[context.X_tile, context.W1_tile, context.W2_tile], verbose=0
-    )
+    model = context.deepbedmap.load_trained_model()
+    context.Y_hat = model.forward(
+        inputs={"x": context.X_tile, "w1": context.W1_tile, "w2": context.W2_tile}
+    ).array
 
 
 @then("a four times upsampled super resolution bed elevation map is returned")
 def step_impl(context):
-    # Ensure input (X_tile) and output (Y_hat) shape is like (1, height, width, 1)
+    # Ensure input (X_tile) and output (Y_hat) shape is like (1, 1, height, width)
     assert context.X_tile.ndim == 4
     assert context.Y_hat.ndim == 4
 
     # Check that High Resolution output shape (DeepBedMap) divided by
     # Low Resolution input shape (BEDMAP2) minus 2 pixel (1km) padding
     # is exactly equal to 4
-    assert context.Y_hat.shape[1] / (context.X_tile.shape[1] - 2) == 4.0
     assert context.Y_hat.shape[2] / (context.X_tile.shape[2] - 2) == 4.0
+    assert context.Y_hat.shape[3] / (context.X_tile.shape[3] - 2) == 4.0
diff --git a/model/README.md b/model/README.md
index 312d0b2..2905b78 100644
--- a/model/README.md
+++ b/model/README.md
@@ -8,6 +8,6 @@ This folder contains the files which are directly related to the training of the
   - \*_data.npy  (\*hidden in git, preprocessed raster tiles from data_prep.ipynb)
 
 - weights/
-  - [srgan_generator_model_architecture.json](weights/srgan_generator_model_architecture.json)  (Keras model architecture of Generator Network in JSON)
-  - srgan_generator_model_weights.hdf5  (\*hidden in git but available at https://www.comet.ml/weiji14/deepbedmap under experiment assets, trained neural network weights)
-  - srgan_generator_model.hdf5  (\*hidden in git, contains both neural network model architecture and weights)
+  - [srgan_generator_model_architecture.onnx.txt](weights/srgan_generator_model_architecture.onnx.txt)  (Chainer model architecture of Generator Network in ONNX text format)
+  - srgan_generator_model_architecture.onnx  (\*hidden in git, Chainer model architecture of Generator Network in ONNX binary format)
+  - srgan_generator_model_weights.npz  (\*hidden in git but available at https://www.comet.ml/weiji14/deepbedmap under experiment assets, trained neural network weights)
diff --git a/model/weights/srgan_generator_model_architecture.json b/model/weights/srgan_generator_model_architecture.json
deleted file mode 100644
index ebd79b7..0000000
--- a/model/weights/srgan_generator_model_architecture.json
+++ /dev/null
@@ -1,4646 +0,0 @@
-{
-  "class_name": "Model",
-  "config": {
-    "name": "generator_model",
-    "layers": [
-      {
-        "name": "input_1",
-        "class_name": "InputLayer",
-        "config": {
-          "batch_input_shape": [
-            null,
-            10,
-            10,
-            1
-          ],
-          "dtype": "float32",
-          "sparse": false,
-          "name": "input_1"
-        },
-        "inbound_nodes": []
-      },
-      {
-        "name": "input_2",
-        "class_name": "InputLayer",
-        "config": {
-          "batch_input_shape": [
-            null,
-            100,
-            100,
-            1
-          ],
-          "dtype": "float32",
-          "sparse": false,
-          "name": "input_2"
-        },
-        "inbound_nodes": []
-      },
-      {
-        "name": "input_3",
-        "class_name": "InputLayer",
-        "config": {
-          "batch_input_shape": [
-            null,
-            20,
-            20,
-            1
-          ],
-          "dtype": "float32",
-          "sparse": false,
-          "name": "input_3"
-        },
-        "inbound_nodes": []
-      },
-      {
-        "name": "conv2d_1",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_1",
-          "trainable": true,
-          "filters": 32,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "valid",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "input_1",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_2",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_2",
-          "trainable": true,
-          "filters": 32,
-          "kernel_size": [
-            30,
-            30
-          ],
-          "strides": [
-            10,
-            10
-          ],
-          "padding": "valid",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "input_2",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_3",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_3",
-          "trainable": true,
-          "filters": 32,
-          "kernel_size": [
-            6,
-            6
-          ],
-          "strides": [
-            2,
-            2
-          ],
-          "padding": "valid",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "input_3",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "concatenate_1",
-        "class_name": "Concatenate",
-        "config": {
-          "name": "concatenate_1",
-          "trainable": true,
-          "axis": -1
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_1",
-              0,
-              0,
-              {}
-            ],
-            [
-              "conv2d_2",
-              0,
-              0,
-              {}
-            ],
-            [
-              "conv2d_3",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_4",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_4",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "concatenate_1",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "p_re_lu_1",
-        "class_name": "PReLU",
-        "config": {
-          "name": "p_re_lu_1",
-          "trainable": true,
-          "alpha_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "alpha_regularizer": null,
-          "alpha_constraint": null,
-          "shared_axes": [
-            1,
-            2
-          ]
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_4",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_5",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_5",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "p_re_lu_1",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "batch_normalization_1",
-        "class_name": "BatchNormalization",
-        "config": {
-          "name": "batch_normalization_1",
-          "trainable": true,
-          "axis": -1,
-          "momentum": 0.99,
-          "epsilon": 0.001,
-          "center": true,
-          "scale": true,
-          "beta_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "gamma_initializer": {
-            "class_name": "Ones",
-            "config": {}
-          },
-          "moving_mean_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "moving_variance_initializer": {
-            "class_name": "Ones",
-            "config": {}
-          },
-          "beta_regularizer": null,
-          "gamma_regularizer": null,
-          "beta_constraint": null,
-          "gamma_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "conv2d_5",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "p_re_lu_2",
-        "class_name": "PReLU",
-        "config": {
-          "name": "p_re_lu_2",
-          "trainable": true,
-          "alpha_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "alpha_regularizer": null,
-          "alpha_constraint": null,
-          "shared_axes": [
-            1,
-            2
-          ]
-        },
-        "inbound_nodes": [
-          [
-            [
-              "batch_normalization_1",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "conv2d_6",
-        "class_name": "Conv2D",
-        "config": {
-          "name": "conv2d_6",
-          "trainable": true,
-          "filters": 64,
-          "kernel_size": [
-            3,
-            3
-          ],
-          "strides": [
-            1,
-            1
-          ],
-          "padding": "same",
-          "data_format": "channels_last",
-          "dilation_rate": [
-            1,
-            1
-          ],
-          "activation": "linear",
-          "use_bias": true,
-          "kernel_initializer": {
-            "class_name": "VarianceScaling",
-            "config": {
-              "scale": 1.0,
-              "mode": "fan_avg",
-              "distribution": "uniform",
-              "seed": null
-            }
-          },
-          "bias_initializer": {
-            "class_name": "Zeros",
-            "config": {}
-          },
-          "kernel_regularizer": null,
-          "bias_regularizer": null,
-          "activity_regularizer": null,
-          "kernel_constraint": null,
-          "bias_constraint": null
-        },
-        "inbound_nodes": [
-          [
-            [
-              "p_re_lu_2",
-              0,
-              0,
-              {}
-            ]
-          ]
-        ]
-      },
-      {
-        "name": "batch_normalization_2",
-        "class_name": "BatchNormalization",
-        "config": {
-          "name": "batch_normalization_2",
-          "trainable": true,
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diff --git a/model/weights/srgan_generator_model_architecture.onnx.txt b/model/weights/srgan_generator_model_architecture.onnx.txt
new file mode 100644
index 0000000..82b3885
--- /dev/null
+++ b/model/weights/srgan_generator_model_architecture.onnx.txt
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+      ints: 1
+      type: INTS
+    }
+    attribute {
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+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
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+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_12"
+    output: "LeakyRelu_8"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "Add_0"
+    input: "LeakyRelu_5"
+    input: "LeakyRelu_6"
+    input: "LeakyRelu_7"
+    input: "LeakyRelu_8"
+    output: "Concat_8"
+    op_type: "Concat"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
+    input: "Concat_8"
+    input: "Input_30"
+    input: "Input_31"
+    output: "Conv_13"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+      type: INT
+    }
+    attribute {
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+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+    }
+  }
+  node {
+    input: "Conv_13"
+    input: "Input_32"
+    output: "Mul_1"
+    op_type: "Mul"
+  }
+  node {
+    input: "Mul_1"
+    input: "Add_0"
+    output: "Add_1"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_1"
+    input: "Input_33"
+    input: "Input_34"
+    output: "Conv_14"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+    }
+    attribute {
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+    }
+    attribute {
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+    }
+  }
+  node {
+    input: "Conv_14"
+    output: "LeakyRelu_9"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "Add_1"
+    input: "LeakyRelu_9"
+    output: "Concat_9"
+    op_type: "Concat"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
+    input: "Concat_9"
+    input: "Input_35"
+    input: "Input_36"
+    output: "Conv_15"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+    }
+    attribute {
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+    }
+    attribute {
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+    }
+  }
+  node {
+    input: "Conv_15"
+    output: "LeakyRelu_10"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "Add_1"
+    input: "LeakyRelu_9"
+    input: "LeakyRelu_10"
+    output: "Concat_10"
+    op_type: "Concat"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
+    input: "Concat_10"
+    input: "Input_37"
+    input: "Input_38"
+    output: "Conv_16"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 3
+      ints: 3
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+    }
+    attribute {
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+    }
+    attribute {
+      name: "strides"
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+    }
+  }
+  node {
+    input: "Conv_16"
+    output: "LeakyRelu_11"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "Add_1"
+    input: "LeakyRelu_9"
+    input: "LeakyRelu_10"
+    input: "LeakyRelu_11"
+    output: "Concat_11"
+    op_type: "Concat"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
+    input: "Concat_11"
+    input: "Input_39"
+    input: "Input_40"
+    output: "Conv_17"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 3
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+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
+      name: "strides"
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+    }
+  }
+  node {
+    input: "Conv_17"
+    output: "LeakyRelu_12"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "Add_1"
+    input: "LeakyRelu_9"
+    input: "LeakyRelu_10"
+    input: "LeakyRelu_11"
+    input: "LeakyRelu_12"
+    output: "Concat_12"
+    op_type: "Concat"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
+    input: "Concat_12"
+    input: "Input_41"
+    input: "Input_42"
+    output: "Conv_18"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
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+    }
+  }
+  node {
+    input: "Conv_18"
+    input: "Input_43"
+    output: "Mul_2"
+    op_type: "Mul"
+  }
+  node {
+    input: "Mul_2"
+    input: "Add_1"
+    output: "Add_2"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_2"
+    input: "Input_44"
+    output: "Mul_3"
+    op_type: "Mul"
+  }
+  node {
+    input: "Mul_3"
+    input: "LeakyRelu_0"
+    output: "Add_3"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_3"
+    input: "Input_45"
+    input: "Input_46"
+    output: "Conv_19"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 3
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+      type: INTS
+    }
+    attribute {
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+      ints: 1
+      ints: 1
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+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_19"
+    output: "LeakyRelu_13"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "Add_3"
+    input: "LeakyRelu_13"
+    output: "Concat_13"
+    op_type: "Concat"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
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+    input: "Input_47"
+    input: "Input_48"
+    output: "Conv_20"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+      i: 1
+      type: INT
+    }
+    attribute {
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+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
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+    }
+  }
+  node {
+    input: "Conv_20"
+    output: "LeakyRelu_14"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "Add_3"
+    input: "LeakyRelu_13"
+    input: "LeakyRelu_14"
+    output: "Concat_14"
+    op_type: "Concat"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
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+    input: "Input_49"
+    input: "Input_50"
+    output: "Conv_21"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+      i: 1
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+    }
+    attribute {
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+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_21"
+    output: "LeakyRelu_15"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "Add_3"
+    input: "LeakyRelu_13"
+    input: "LeakyRelu_14"
+    input: "LeakyRelu_15"
+    output: "Concat_15"
+    op_type: "Concat"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
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+    input: "Input_51"
+    input: "Input_52"
+    output: "Conv_22"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_22"
+    output: "LeakyRelu_16"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "Add_3"
+    input: "LeakyRelu_13"
+    input: "LeakyRelu_14"
+    input: "LeakyRelu_15"
+    input: "LeakyRelu_16"
+    output: "Concat_16"
+    op_type: "Concat"
+    attribute {
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+      i: 1
+      type: INT
+    }
+  }
+  node {
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+    input: "Input_53"
+    input: "Input_54"
+    output: "Conv_23"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_23"
+    input: "Input_55"
+    output: "Mul_4"
+    op_type: "Mul"
+  }
+  node {
+    input: "Mul_4"
+    input: "Add_3"
+    output: "Add_4"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_4"
+    input: "Input_56"
+    input: "Input_57"
+    output: "Conv_24"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+      i: 1
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+    }
+    attribute {
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+      ints: 3
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+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_24"
+    output: "LeakyRelu_17"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "Add_4"
+    input: "LeakyRelu_17"
+    output: "Concat_17"
+    op_type: "Concat"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
+    input: "Concat_17"
+    input: "Input_58"
+    input: "Input_59"
+    output: "Conv_25"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+      i: 1
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+    }
+    attribute {
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+      ints: 3
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+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_25"
+    output: "LeakyRelu_18"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "Add_4"
+    input: "LeakyRelu_17"
+    input: "LeakyRelu_18"
+    output: "Concat_18"
+    op_type: "Concat"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
+    input: "Concat_18"
+    input: "Input_60"
+    input: "Input_61"
+    output: "Conv_26"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+      i: 1
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+    }
+    attribute {
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+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_26"
+    output: "LeakyRelu_19"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
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+    input: "LeakyRelu_17"
+    input: "LeakyRelu_18"
+    input: "LeakyRelu_19"
+    output: "Concat_19"
+    op_type: "Concat"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
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+    input: "Input_62"
+    input: "Input_63"
+    output: "Conv_27"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  node {
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+    output: "LeakyRelu_20"
+    op_type: "LeakyRelu"
+    attribute {
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+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
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+    input: "LeakyRelu_17"
+    input: "LeakyRelu_18"
+    input: "LeakyRelu_19"
+    input: "LeakyRelu_20"
+    output: "Concat_20"
+    op_type: "Concat"
+    attribute {
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+      i: 1
+      type: INT
+    }
+  }
+  node {
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+    input: "Input_64"
+    input: "Input_65"
+    output: "Conv_28"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+      i: 1
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+    }
+    attribute {
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+      ints: 3
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+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 1
+      ints: 1
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+    }
+  }
+  node {
+    input: "Conv_28"
+    input: "Input_66"
+    output: "Mul_5"
+    op_type: "Mul"
+  }
+  node {
+    input: "Mul_5"
+    input: "Add_4"
+    output: "Add_5"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_5"
+    input: "Input_67"
+    input: "Input_68"
+    output: "Conv_29"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+      type: INT
+    }
+    attribute {
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+      ints: 3
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+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 1
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+    }
+  }
+  node {
+    input: "Conv_29"
+    output: "LeakyRelu_21"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "Add_5"
+    input: "LeakyRelu_21"
+    output: "Concat_21"
+    op_type: "Concat"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
+    input: "Concat_21"
+    input: "Input_69"
+    input: "Input_70"
+    output: "Conv_30"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+    }
+    attribute {
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+    }
+    attribute {
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+    }
+  }
+  node {
+    input: "Conv_30"
+    output: "LeakyRelu_22"
+    op_type: "LeakyRelu"
+    attribute {
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+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "Add_5"
+    input: "LeakyRelu_21"
+    input: "LeakyRelu_22"
+    output: "Concat_22"
+    op_type: "Concat"
+    attribute {
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+      type: INT
+    }
+  }
+  node {
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+    input: "Input_71"
+    input: "Input_72"
+    output: "Conv_31"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
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+    }
+    attribute {
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+    }
+    attribute {
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+    }
+  }
+  node {
+    input: "Conv_31"
+    output: "LeakyRelu_23"
+    op_type: "LeakyRelu"
+    attribute {
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+      f: 0.20000000298023224
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+    }
+  }
+  node {
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+    input: "LeakyRelu_21"
+    input: "LeakyRelu_22"
+    input: "LeakyRelu_23"
+    output: "Concat_23"
+    op_type: "Concat"
+    attribute {
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+      i: 1
+      type: INT
+    }
+  }
+  node {
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+    input: "Input_73"
+    input: "Input_74"
+    output: "Conv_32"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
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+    }
+    attribute {
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+    }
+    attribute {
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+    }
+  }
+  node {
+    input: "Conv_32"
+    output: "LeakyRelu_24"
+    op_type: "LeakyRelu"
+    attribute {
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+      f: 0.20000000298023224
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+    }
+  }
+  node {
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+    input: "LeakyRelu_21"
+    input: "LeakyRelu_22"
+    input: "LeakyRelu_23"
+    input: "LeakyRelu_24"
+    output: "Concat_24"
+    op_type: "Concat"
+    attribute {
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+      type: INT
+    }
+  }
+  node {
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+    input: "Input_75"
+    input: "Input_76"
+    output: "Conv_33"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+    }
+    attribute {
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+    }
+    attribute {
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+    }
+  }
+  node {
+    input: "Conv_33"
+    input: "Input_77"
+    output: "Mul_6"
+    op_type: "Mul"
+  }
+  node {
+    input: "Mul_6"
+    input: "Add_5"
+    output: "Add_6"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_6"
+    input: "Input_78"
+    output: "Mul_7"
+    op_type: "Mul"
+  }
+  node {
+    input: "Mul_7"
+    input: "Add_3"
+    output: "Add_7"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_7"
+    input: "Input_79"
+    input: "Input_80"
+    output: "Conv_34"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
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+      type: INTS
+    }
+    attribute {
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+    }
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+    }
+    attribute {
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+    }
+    attribute {
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+    }
+  }
+  node {
+    input: "Conv_34"
+    output: "LeakyRelu_25"
+    op_type: "LeakyRelu"
+    attribute {
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+      f: 0.20000000298023224
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+    }
+  }
+  node {
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+    input: "LeakyRelu_25"
+    output: "Concat_25"
+    op_type: "Concat"
+    attribute {
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+      type: INT
+    }
+  }
+  node {
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+    input: "Input_81"
+    input: "Input_82"
+    output: "Conv_35"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
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+    }
+    attribute {
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+    }
+    attribute {
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+    }
+  }
+  node {
+    input: "Conv_35"
+    output: "LeakyRelu_26"
+    op_type: "LeakyRelu"
+    attribute {
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+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
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+    input: "LeakyRelu_25"
+    input: "LeakyRelu_26"
+    output: "Concat_26"
+    op_type: "Concat"
+    attribute {
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+      type: INT
+    }
+  }
+  node {
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+    input: "Input_83"
+    input: "Input_84"
+    output: "Conv_36"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
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+      ints: 3
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+    }
+    attribute {
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+    }
+    attribute {
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+    }
+  }
+  node {
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+    output: "LeakyRelu_27"
+    op_type: "LeakyRelu"
+    attribute {
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+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
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+    input: "LeakyRelu_25"
+    input: "LeakyRelu_26"
+    input: "LeakyRelu_27"
+    output: "Concat_27"
+    op_type: "Concat"
+    attribute {
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+      i: 1
+      type: INT
+    }
+  }
+  node {
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+    input: "Input_85"
+    input: "Input_86"
+    output: "Conv_37"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
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+      ints: 3
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+    }
+    attribute {
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+    }
+    attribute {
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+    }
+  }
+  node {
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+    output: "LeakyRelu_28"
+    op_type: "LeakyRelu"
+    attribute {
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+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
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+    input: "LeakyRelu_25"
+    input: "LeakyRelu_26"
+    input: "LeakyRelu_27"
+    input: "LeakyRelu_28"
+    output: "Concat_28"
+    op_type: "Concat"
+    attribute {
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+      i: 1
+      type: INT
+    }
+  }
+  node {
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+    input: "Input_87"
+    input: "Input_88"
+    output: "Conv_38"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 3
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+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+    }
+  }
+  node {
+    input: "Conv_38"
+    input: "Input_89"
+    output: "Mul_8"
+    op_type: "Mul"
+  }
+  node {
+    input: "Mul_8"
+    input: "Add_7"
+    output: "Add_8"
+    op_type: "Add"
+  }
+  node {
+    input: "Add_8"
+    input: "Input_90"
+    input: "Input_91"
+    output: "Conv_39"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
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+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 1
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+    }
+  }
+  node {
+    input: "Conv_39"
+    output: "LeakyRelu_29"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "Add_8"
+    input: "LeakyRelu_29"
+    output: "Concat_29"
+    op_type: "Concat"
+    attribute {
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+      i: 1
+      type: INT
+    }
+  }
+  node {
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+    input: "Input_92"
+    input: "Input_93"
+    output: "Conv_40"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 3
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+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 1
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+    }
+  }
+  node {
+    input: "Conv_40"
+    output: "LeakyRelu_30"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
+    input: "Add_8"
+    input: "LeakyRelu_29"
+    input: "LeakyRelu_30"
+    output: "Concat_30"
+    op_type: "Concat"
+    attribute {
+      name: "axis"
+      i: 1
+      type: INT
+    }
+  }
+  node {
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+    input: "Input_94"
+    input: "Input_95"
+    output: "Conv_41"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 3
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+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 1
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+    }
+  }
+  node {
+    input: "Conv_41"
+    output: "LeakyRelu_31"
+    op_type: "LeakyRelu"
+    attribute {
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+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
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+    input: "LeakyRelu_29"
+    input: "LeakyRelu_30"
+    input: "LeakyRelu_31"
+    output: "Concat_31"
+    op_type: "Concat"
+    attribute {
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+      i: 1
+      type: INT
+    }
+  }
+  node {
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+    input: "Input_96"
+    input: "Input_97"
+    output: "Conv_42"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
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+      ints: 3
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+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
+      name: "strides"
+      ints: 1
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+      type: INTS
+    }
+  }
+  node {
+    input: "Conv_42"
+    output: "LeakyRelu_32"
+    op_type: "LeakyRelu"
+    attribute {
+      name: "alpha"
+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
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+    input: "LeakyRelu_29"
+    input: "LeakyRelu_30"
+    input: "LeakyRelu_31"
+    input: "LeakyRelu_32"
+    output: "Concat_32"
+    op_type: "Concat"
+    attribute {
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+      i: 1
+      type: INT
+    }
+  }
+  node {
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+    input: "Input_98"
+    input: "Input_99"
+    output: "Conv_43"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 3
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+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 1
+      ints: 1
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+    }
+  }
+  node {
+    input: "Conv_43"
+    input: "Input_100"
+    output: "Mul_9"
+    op_type: "Mul"
+  }
+  node {
+    input: "Mul_9"
+    input: "Add_8"
+    output: "Add_9"
+    op_type: "Add"
+  }
+  node {
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+    input: "Input_101"
+    input: "Input_102"
+    output: "Conv_44"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 3
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+    }
+    attribute {
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+    }
+    attribute {
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+      ints: 1
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+    }
+  }
+  node {
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+    output: "LeakyRelu_33"
+    op_type: "LeakyRelu"
+    attribute {
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+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
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+    output: "Concat_33"
+    op_type: "Concat"
+    attribute {
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+      i: 1
+      type: INT
+    }
+  }
+  node {
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+    input: "Input_103"
+    input: "Input_104"
+    output: "Conv_45"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
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+    }
+    attribute {
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+    }
+    attribute {
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+    }
+  }
+  node {
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+    output: "LeakyRelu_34"
+    op_type: "LeakyRelu"
+    attribute {
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+      f: 0.20000000298023224
+      type: FLOAT
+    }
+  }
+  node {
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+    input: "LeakyRelu_33"
+    input: "LeakyRelu_34"
+    output: "Concat_34"
+    op_type: "Concat"
+    attribute {
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+      i: 1
+      type: INT
+    }
+  }
+  node {
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+    input: "Input_106"
+    output: "Conv_46"
+    op_type: "Conv"
+    attribute {
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+      ints: 1
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+    }
+    attribute {
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+    }
+    attribute {
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+    }
+    attribute {
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+    }
+  }
+  node {
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+    output: "LeakyRelu_35"
+    op_type: "LeakyRelu"
+    attribute {
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+  }
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+    input: "LeakyRelu_34"
+    input: "LeakyRelu_35"
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+    op_type: "Concat"
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+    }
+  }
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+    input: "Input_108"
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+    }
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+    }
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+    }
+    attribute {
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+    }
+  }
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+    input: "LeakyRelu_35"
+    input: "LeakyRelu_36"
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+    op_type: "Concat"
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+    }
+  }
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+    input: "Input_110"
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+    attribute {
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+    attribute {
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+    }
+    attribute {
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+  }
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+    input: "Input_111"
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+  }
+  node {
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+    input: "Add_9"
+    output: "Add_10"
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+  }
+  node {
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+  }
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+    op_type: "Add"
+  }
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+    }
+    attribute {
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+  }
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+    op_type: "Concat"
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+  }
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+    input: "Input_116"
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+    }
+    attribute {
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+  }
+  node {
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+    op_type: "LeakyRelu"
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+    op_type: "Concat"
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+    }
+  }
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+    input: "Input_118"
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+    attribute {
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+    }
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+    }
+    attribute {
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+  }
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+    }
+  }
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+    attribute {
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+    attribute {
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+    }
+    attribute {
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+    }
+  }
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+    input: "LeakyRelu_39"
+    input: "LeakyRelu_40"
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+    }
+  }
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+    }
+    attribute {
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+    }
+  }
+  node {
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+    input: "Input_123"
+    output: "Mul_12"
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+  }
+  node {
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+    input: "Add_11"
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+    op_type: "Add"
+  }
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+    input: "Input_125"
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+    }
+    attribute {
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+    }
+  }
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+    op_type: "Concat"
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+    }
+  }
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+    attribute {
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+    }
+    attribute {
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+  }
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+    op_type: "Concat"
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+    }
+  }
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+    }
+    attribute {
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+    }
+  }
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+    op_type: "LeakyRelu"
+    attribute {
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+    input: "LeakyRelu_42"
+    input: "LeakyRelu_43"
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+    op_type: "Concat"
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+    }
+  }
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+    }
+    attribute {
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+    }
+  }
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+    op_type: "LeakyRelu"
+    attribute {
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+  }
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+    input: "LeakyRelu_42"
+    input: "LeakyRelu_43"
+    input: "LeakyRelu_44"
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+    }
+  }
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+    }
+    attribute {
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+    }
+  }
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+    output: "Mul_13"
+    op_type: "Mul"
+  }
+  node {
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+    input: "Add_12"
+    output: "Add_13"
+    op_type: "Add"
+  }
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+    }
+    attribute {
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+    }
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+    attribute {
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+    attribute {
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+    }
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+    op_type: "Concat"
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+    }
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+    }
+    attribute {
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+    }
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+    }
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+    attribute {
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+          dim {
+            dim_value: 192
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_54"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_56"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
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+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_57"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_58"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
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+          }
+          dim {
+            dim_value: 96
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_59"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_60"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
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+          dim {
+            dim_value: 128
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_61"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_62"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 160
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_63"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_64"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
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+          dim {
+            dim_value: 192
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_65"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_67"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_68"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_69"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 96
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_70"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_71"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 128
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_72"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_73"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 160
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_74"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_75"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 192
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_76"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 64
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_146"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 8
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_145"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 8
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_134"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 8
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_123"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 8
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_112"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 8
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_111"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 8
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_100"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 8
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_89"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 8
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_78"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 8
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_77"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 8
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_66"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 8
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_55"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 8
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_44"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 8
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_43"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 8
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_32"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 8
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_21"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 8
+          }
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_6"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 10
+          }
+          dim {
+            dim_value: 10
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_3"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 100
+          }
+          dim {
+            dim_value: 100
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "Input_0"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 20
+          }
+          dim {
+            dim_value: 20
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "Conv_68"
+    type {
+      tensor_type {
+        elem_type: FLOAT
+        shape {
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 32
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  domain: ""
+  version: 8
+}
+
diff --git a/srgan_train.ipynb b/srgan_train.ipynb
index 1fd9011..81e4847 100644
--- a/srgan_train.ipynb
+++ b/srgan_train.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Super-Resolution Generative Adversarial Network training\n",
+    "# **Super-Resolution Generative Adversarial Network training**\n",
     "\n",
     "Here in this jupyter notebook, we will train a super-resolution generative adversarial network (SRGAN), to create a high-resolution Antarctic bed Digital Elevation Model(DEM) from a low-resolution DEM.\n",
     "In addition to that, we use additional correlated inputs that can also tell us something about the bed topography.\n",
@@ -16,7 +16,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 0. Setup libraries"
+    "# 0. Setup libraries"
    ]
   },
   {
@@ -24,32 +24,25 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Python       : 3.6.6 | packaged by conda-forge | (default, Oct 11 2018, 14:33:06) \n",
-      "Numpy        : 1.14.5\n",
-      "Keras        : 2.2.4\n",
-      "Tensorflow   : 1.10.1\n"
+      "Platform: Linux-4.15.0-43-generic-x86_64-with-debian-stretch-sid\n",
+      "Chainer: 6.0.0b1\n",
+      "NumPy: 1.14.5\n",
+      "CuPy:\n",
+      "  CuPy Version          : 6.0.0b1\n",
+      "  CUDA Root             : /usr/local/cuda\n",
+      "  CUDA Build Version    : 9020\n",
+      "  CUDA Driver Version   : 10000\n",
+      "  CUDA Runtime Version  : 9020\n",
+      "  cuDNN Build Version   : 7301\n",
+      "  cuDNN Version         : 7201\n",
+      "  NCCL Build Version    : 2307\n",
+      "iDeep: Not Available\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'/device:GPU:0'"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -67,33 +60,19 @@
     "import pandas as pd\n",
     "import quilt\n",
     "import skimage.transform\n",
-    "import sklearn.model_selection\n",
     "import tqdm\n",
     "\n",
-    "import keras\n",
-    "from keras import backend as K\n",
-    "from keras.layers import (\n",
-    "    Add,\n",
-    "    BatchNormalization,\n",
-    "    Concatenate,\n",
-    "    Conv2D,\n",
-    "    Conv2DTranspose,\n",
-    "    Dense,\n",
-    "    Flatten,\n",
-    "    Input,\n",
-    "    Lambda,\n",
-    ")\n",
-    "from keras.layers.advanced_activations import LeakyReLU, PReLU\n",
-    "from keras.models import Model\n",
+    "import chainer\n",
+    "import chainer.functions as F\n",
+    "import chainer.links as L\n",
+    "import cupy\n",
     "import livelossplot\n",
+    "import onnx_chainer\n",
     "\n",
     "from features.environment import _load_ipynb_modules\n",
     "\n",
     "print(\"Python       :\", sys.version.split(\"\\n\")[0])\n",
-    "print(\"Numpy        :\", np.__version__)\n",
-    "print(\"Keras        :\", keras.__version__)\n",
-    "print(\"Tensorflow   :\", K.tf.__version__)\n",
-    "K.tf.test.gpu_device_name()"
+    "chainer.print_runtime_info()"
    ]
   },
   {
@@ -105,7 +84,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/weiji14/deepbedmap/865e6812c7a84718bca3b3182817f825\n",
+      "COMET INFO: old comet version (1.0.42) detected. current: 1.0.44 please update your comet lib with command: `pip install --no-cache-dir --upgrade comet_ml`\n",
+      "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/weiji14/deepbedmap/d64dd9dd8dc54b3397a36d26337080c3\n",
       "\n"
      ]
     }
@@ -115,17 +95,19 @@
     "seed = 42\n",
     "random.seed = seed\n",
     "np.random.seed(seed=seed)\n",
-    "K.tf.set_random_seed(seed=seed)\n",
+    "# cupy.random.seed(seed=seed)\n",
     "\n",
     "# Start tracking experiment using Comet.ML\n",
-    "experiment = comet_ml.Experiment(workspace=\"weiji14\", project_name=\"deepbedmap\")"
+    "experiment = comet_ml.Experiment(\n",
+    "    workspace=\"weiji14\", project_name=\"deepbedmap\", disabled=False\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 1. Load data"
+    "# 1. Load data"
    ]
   },
   {
@@ -146,7 +128,7 @@
     "hash = \"1ccc9dc7f6344e1ec27b7aa972f2739d192d3e5adef8a64528b86bc799e2df60\"\n",
     "quilt.install(package=\"weiji14/deepbedmap/model/train\", hash=hash, force=True)\n",
     "pkg = quilt.load(pkginfo=\"weiji14/deepbedmap/model/train\", hash=hash)\n",
-    "experiment.log_parameter(\"dataset_hash\", hash)"
+    "experiment.log_parameter(name=\"dataset_hash\", value=hash)"
    ]
   },
   {
@@ -178,72 +160,141 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Split dataset into training (train) and development (dev) sets"
+    "## 1.1 Convert arrays for Chainer\n",
+    "- From Numpy (CPU) to CuPy (GPU) format\n",
+    "- From NHWC format to NCHW format, where N=number of tiles, H=height, W=width, C=channels"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using GPU\n"
+     ]
+    }
+   ],
    "source": [
-    "def train_dev_split(dataset: np.ndarray, test_size=0.05, random_state=42):\n",
-    "    \"\"\"\n",
-    "    Split our dataset up into training and development sets.\n",
-    "    Used for cross validation purposes to check for overfitting.\n",
-    "\n",
-    "    >>> dataset = np.ones(shape=(100, 4, 4, 1))\n",
-    "    >>> train, dev = train_dev_split(dataset=dataset, test_size=0.05, random_state=42)\n",
-    "    >>> train.shape\n",
-    "    (95, 4, 4, 1)\n",
-    "    >>> dev.shape\n",
-    "    (5, 4, 4, 1)\n",
-    "    \"\"\"\n",
-    "    return sklearn.model_selection.train_test_split(\n",
-    "        dataset,\n",
-    "        test_size=test_size,\n",
-    "        train_size=1 - test_size,\n",
-    "        random_state=random_state,\n",
-    "        shuffle=True,\n",
-    "    )"
+    "# Detect if there is a CUDA GPU first\n",
+    "try:\n",
+    "    cupy.cuda.get_device_id()\n",
+    "    xp = cupy\n",
+    "    print(\"Using GPU\")\n",
+    "    experiment.log_parameter(name=\"use_gpu\", value=True)\n",
+    "\n",
+    "    W1_data = chainer.backend.cuda.to_gpu(array=W1_data)\n",
+    "    W2_data = chainer.backend.cuda.to_gpu(array=W2_data)\n",
+    "    X_data = chainer.backend.cuda.to_gpu(array=X_data)\n",
+    "    Y_data = chainer.backend.cuda.to_gpu(array=Y_data)\n",
+    "except:  # CUDARuntimeError\n",
+    "    xp = np\n",
+    "    print(\"Using CPU only\")\n",
+    "    experiment.log_parameter(name=\"use_gpu\", value=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2480, 1, 100, 100) (2480, 1, 20, 20) (2480, 1, 10, 10) (2480, 1, 32, 32)\n"
+     ]
+    }
+   ],
+   "source": [
+    "W1_data = xp.rollaxis(a=W1_data, axis=3, start=1)\n",
+    "W2_data = xp.rollaxis(a=W2_data, axis=3, start=1)\n",
+    "X_data = xp.rollaxis(a=X_data, axis=3, start=1)\n",
+    "Y_data = xp.rollaxis(a=Y_data, axis=3, start=1)\n",
+    "print(W1_data.shape, W2_data.shape, X_data.shape, Y_data.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.2 Split dataset into training (train) and development (dev) sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training dataset: 2356 tiles, Development dataset: 124 tiles\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = chainer.datasets.DictDataset(X=X_data, W1=W1_data, W2=W2_data, Y=Y_data)\n",
+    "train_set, dev_set = chainer.datasets.split_dataset_random(\n",
+    "    dataset=dataset, first_size=int(len(X_data) * 0.95), seed=seed\n",
+    ")\n",
+    "experiment.log_parameters(\n",
+    "    dic={\"train_set_samples\": len(train_set), \"dev_set_samples\": len(dev_set)}\n",
+    ")\n",
+    "print(\n",
+    "    f\"Training dataset: {len(train_set)} tiles, Development dataset: {len(dev_set)} tiles\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "W1_train, W1_dev = train_dev_split(dataset=W1_data)\n",
-    "W2_train, W2_dev = train_dev_split(dataset=W2_data)\n",
-    "X_train, X_dev = train_dev_split(dataset=X_data)\n",
-    "Y_train, Y_dev = train_dev_split(dataset=Y_data)"
+    "batch_size = 32\n",
+    "experiment.log_parameter(name=\"batch_size\", value=batch_size)\n",
+    "train_iter = chainer.iterators.SerialIterator(\n",
+    "    dataset=train_set, batch_size=batch_size, repeat=True, shuffle=True\n",
+    ")\n",
+    "dev_iter = chainer.iterators.SerialIterator(\n",
+    "    dataset=dev_set, batch_size=batch_size, repeat=True, shuffle=False\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2. Architect model\n",
+    "# 2. Architect model\n",
     "\n",
-    "Super Resolution Generative Adversarial Network model based on [Ledig et al. 2017](https://arxiv.org/abs/1609.04802v5).\n",
-    "Keras implementation below takes some hints from https://github.com/eriklindernoren/Keras-GAN/blob/master/srgan/srgan.py"
+    "Enhanced Super Resolution Generative Adversarial Network (ESRGAN) model based on [Wang et al. 2018](https://arxiv.org/abs/1809.00219v2).\n",
+    "Refer to original Pytorch implementation at https://github.com/xinntao/ESRGAN.\n",
+    "See also previous (non-enhanced) SRGAN model architecture by [Ledig et al. 2017](https://arxiv.org/abs/1609.04802)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Generator Network Architecture\n",
+    "## 2.1 Generator Network Architecture\n",
     "\n",
-    "![SRGAN architecture - Generator Network](https://arxiv-sanity-sanity-production.s3.amazonaws.com/render-output/399644/images/used/jpg/generator.jpg)\n",
-    "![3-in-1 Generator Network](https://yuml.me/01862e1a.png)\n",
+    "![ESRGAN architecture - Generator Network composed of many Dense Convolutional Blocks](https://github.com/xinntao/ESRGAN/raw/master/figures/architecture.jpg)\n",
+    "\n",
+    "3 main components: 1) Input Block, 2) Residual Blocks, 3) Upsampling Blocks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1.1 Input block, specially customized for DeepBedMap to take in 3 different inputs\n",
     "\n",
-    "Details of the first convolutional layer:\n",
+    "Details of the first convolutional layer for each input:\n",
     "\n",
     "- Input tiles are 8000m by 8000m.\n",
     "- Convolution filter kernels are 3000m by 3000m.\n",
@@ -255,142 +306,393 @@
     "- Convolution filter kernels are 30pixels by 30pixels\n",
     "- Strides are 10pixels by 10pixels\n",
     "\n",
-    "Note that first convolutional layer uses '**valid**' padding, see https://keras.io/layers/convolutional/ for more information.\n",
+    "Note that these first convolutional layers uses '**valid**' padding, see https://keras.io/layers/convolutional/ for more information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "class DeepbedmapInputBlock(chainer.Chain):\n",
+    "    \"\"\"\n",
+    "    Custom input block for DeepBedMap.\n",
+    "\n",
+    "    Each filter kernel is 3km by 3km in size, with a 1km stride and no padding.\n",
+    "    So for a 1km resolution image, (i.e. 1km pixel size):\n",
+    "    kernel size is (3, 3), stride is (1, 1), and pad is (0, 0)\n",
+    "\n",
+    "      (?,1,10,10) --Conv2D-- (?,32,8,8) \\\n",
+    "    (?,1,100,100) --Conv2D-- (?,32,8,8) --Concat-- (?,96,8,8)\n",
+    "      (?,1,20,20) --Conv2D-- (?,32,8,8) /\n",
+    "\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, out_channels=32):\n",
+    "        super().__init__()\n",
+    "        init_weights = chainer.initializers.HeNormal(scale=0.1, fan_option=\"fan_in\")\n",
+    "\n",
+    "        with self.init_scope():\n",
+    "            self.conv_on_X = L.Convolution2D(\n",
+    "                in_channels=1,\n",
+    "                out_channels=out_channels,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=(0, 0),  # 'valid' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "            self.conv_on_W1 = L.Convolution2D(\n",
+    "                in_channels=1,\n",
+    "                out_channels=out_channels,\n",
+    "                ksize=(30, 30),\n",
+    "                stride=(10, 10),\n",
+    "                pad=(0, 0),  # 'valid' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "            self.conv_on_W2 = L.Convolution2D(\n",
+    "                in_channels=1,\n",
+    "                out_channels=out_channels,\n",
+    "                ksize=(6, 6),\n",
+    "                stride=(2, 2),\n",
+    "                pad=(0, 0),  # 'valid' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "\n",
+    "    def forward(self, x, w1, w2):\n",
+    "        \"\"\"\n",
+    "        Forward computation, i.e. evaluate based on inputs X, W1 and W2\n",
+    "        \"\"\"\n",
+    "        x_ = self.conv_on_X(x)\n",
+    "        w1_ = self.conv_on_W1(w1)\n",
+    "        w2_ = self.conv_on_W2(w2)\n",
+    "\n",
+    "        a = F.concat(xs=(x_, w1_, w2_))\n",
+    "        return a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1.2 Residual Block\n",
+    "\n",
+    "![The Residual in Residual Dense Block in detail](https://raw.githubusercontent.com/xinntao/ESRGAN/master/figures/RRDB.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "class ResidualDenseBlock(chainer.Chain):\n",
+    "    \"\"\"\n",
+    "    Residual Dense Block made up of 5 Convolutional2D-LeakyReLU layers.\n",
+    "    Final output has a residual scaling factor.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, in_out_channels: int = 64, inter_channels: int = 32):\n",
+    "        super().__init__()\n",
+    "        init_weights = chainer.initializers.HeNormal(scale=0.1, fan_option=\"fan_in\")\n",
+    "\n",
+    "        with self.init_scope():\n",
+    "            self.conv_layer1 = L.Convolution2D(\n",
+    "                in_channels=in_out_channels,\n",
+    "                out_channels=inter_channels,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=1,  # 'same' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "            self.conv_layer2 = L.Convolution2D(\n",
+    "                in_channels=None,\n",
+    "                out_channels=inter_channels,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=1,  # 'same' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "            self.conv_layer3 = L.Convolution2D(\n",
+    "                in_channels=None,\n",
+    "                out_channels=inter_channels,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=1,  # 'same' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "            self.conv_layer4 = L.Convolution2D(\n",
+    "                in_channels=None,\n",
+    "                out_channels=inter_channels,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=1,  # 'same' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "            self.conv_layer5 = L.Convolution2D(\n",
+    "                in_channels=None,\n",
+    "                out_channels=in_out_channels,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=1,  # 'same' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "\n",
+    "    def forward(self, x, residual_scaling: float = 0.2):\n",
+    "        \"\"\"\n",
+    "        Forward computation, i.e. evaluate based on input x\n",
+    "        \"\"\"\n",
+    "\n",
+    "        a0 = x\n",
+    "\n",
+    "        a1 = self.conv_layer1(a0)\n",
+    "        a1 = F.leaky_relu(x=a1, slope=0.2)\n",
+    "        a1_cat = F.concat(xs=(a0, a1), axis=1)\n",
+    "\n",
+    "        a2 = self.conv_layer2(a1_cat)\n",
+    "        a2 = F.leaky_relu(x=a2, slope=0.2)\n",
+    "        a2_cat = F.concat(xs=(a0, a1, a2), axis=1)\n",
+    "\n",
+    "        a3 = self.conv_layer3(a2_cat)\n",
+    "        a3 = F.leaky_relu(x=a3, slope=0.2)\n",
+    "        a3_cat = F.concat(xs=(a0, a1, a2, a3), axis=1)\n",
+    "\n",
+    "        a4 = self.conv_layer4(a3_cat)\n",
+    "        a4 = F.leaky_relu(x=a4, slope=0.2)\n",
+    "        a4_cat = F.concat(xs=(a0, a1, a2, a3, a4), axis=1)\n",
+    "\n",
+    "        a5 = self.conv_layer5(a4_cat)\n",
+    "\n",
+    "        # Final concatenation, with residual scaling of 0.2\n",
+    "        a6 = F.add(a5 * residual_scaling, a0)\n",
+    "\n",
+    "        return a6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "class ResInResDenseBlock(chainer.Chain):\n",
+    "    \"\"\"\n",
+    "    Residual in Residual Dense block made of 3 Residual Dense Blocks\n",
+    "\n",
+    "       ------------  ----------  ------------\n",
+    "      |            ||          ||            |\n",
+    "    -----DenseBlock--DenseBlock--DenseBlock-(+)--\n",
+    "      |                                      |\n",
+    "       --------------------------------------\n",
+    "\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, denseblock_class=ResidualDenseBlock, out_channels: int = 64):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        with self.init_scope():\n",
+    "            self.residual_dense_block1 = denseblock_class()\n",
+    "            self.residual_dense_block2 = denseblock_class()\n",
+    "            self.residual_dense_block3 = denseblock_class()\n",
+    "\n",
+    "    def forward(self, x, residual_scaling: float = 0.2):\n",
+    "        \"\"\"\n",
+    "        Forward computation, i.e. evaluate based on input x\n",
+    "        \"\"\"\n",
+    "        a1 = self.residual_dense_block1(x)\n",
+    "        a2 = self.residual_dense_block2(a1)\n",
+    "        a3 = self.residual_dense_block3(a2)\n",
+    "\n",
+    "        # Final concatenation, with residual scaling of 0.2\n",
+    "        a4 = F.add(a3 * residual_scaling, x)\n",
+    "\n",
+    "        return a4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1.3 Build the Generator Network, with upsampling layers!\n",
+    "\n",
+    "![3 inputs feeding into the Generator Network, producing a high resolution prediction output](https://yuml.me/dffffcb0.png)\n",
     "\n",
     "<!--[W2_input(MEASURES)|20x20x1]-k6n32s2>[W2_inter|8x8x32],[W2_inter]->[Concat|8x8x96]\n",
     "[X_input(BEDMAP2)|10x10x1]-k3n32s1>[X_inter|8x8x32],[X_inter]->[Concat|8x8x96]\n",
     "[W1_input(REMA)|100x100x1]-k30n32s10>[W1_inter|8x8x32],[W1_inter]->[Concat|8x8x96]\n",
-    "[Concat|8x8x96]->[Generator-Network],[Generator-Network]->[Y_hat(High-Resolution_DEM)|32x32x1]-->"
+    "[Concat|8x8x96]->[Generator-Network|Many-Residual-Blocks],[Generator-Network]->[Y_hat(High-Resolution_DEM)|32x32x1]-->"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {
     "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
-    "def generator_network(\n",
-    "    input1_shape: typing.Tuple[int, int, int] = (10, 10, 1),\n",
-    "    input2_shape: typing.Tuple[int, int, int] = (100, 100, 1),\n",
-    "    input3_shape: typing.Tuple[int, int, int] = (20, 20, 1),\n",
-    "    num_residual_blocks: int = 16,\n",
-    "    scaling: int = 4,\n",
-    "    output_channels: int = 1,\n",
-    ") -> keras.engine.network.Network:\n",
+    "class GeneratorModel(chainer.Chain):\n",
     "    \"\"\"\n",
     "    The generator network which is a deconvolutional neural network.\n",
     "    Converts a low resolution input into a super resolution output.\n",
     "\n",
+    "    Glues the input block with several residual blocks and upsampling layers\n",
+    "\n",
     "    Parameters:\n",
     "      input_shape -- shape of input tensor in tuple format (height, width, channels)\n",
-    "      num_residual_blocks -- how many Conv-BatchNorm-PReLU-Conv-BatchNorm blocks to use\n",
+    "      num_residual_blocks -- how many Conv-LeakyReLU-Conv blocks to use\n",
     "      scaling -- even numbered integer to increase resolution (e.g. 0, 2, 4, 6, 8)\n",
-    "      output_channels -- integer representing number of output channels/filters/kernels\n",
+    "      out_channels -- integer representing number of output channels/filters/kernels\n",
     "\n",
     "    Example:\n",
     "      An input_shape of (8,8,1) passing through 16 residual blocks with a scaling of 4\n",
     "      and output_channels 1 will result in an image of shape (32,32,1)\n",
     "\n",
-    "    >>> generator_network().input_shape\n",
-    "    [(None, 10, 10, 1), (None, 100, 100, 1), (None, 20, 20, 1)]\n",
-    "    >>> generator_network().output_shape\n",
-    "    (None, 32, 32, 1)\n",
-    "    >>> generator_network().count_params()\n",
-    "    1614593\n",
+    "    >>> generator_model = GeneratorModel()\n",
+    "    >>> y_pred = generator_model.forward(\n",
+    "    ...     inputs={\n",
+    "    ...         \"x\": np.random.rand(1, 1, 10, 10).astype(\"float32\"),\n",
+    "    ...         \"w1\": np.random.rand(1, 1, 100, 100).astype(\"float32\"),\n",
+    "    ...         \"w2\": np.random.rand(1, 1, 20, 20).astype(\"float32\"),\n",
+    "    ...     }\n",
+    "    ... )\n",
+    "    >>> y_pred.shape\n",
+    "    (1, 1, 32, 32)\n",
+    "    >>> generator_model.count_params()\n",
+    "    3333249\n",
     "    \"\"\"\n",
     "\n",
-    "    assert num_residual_blocks >= 1  # ensure that we have 1 or more residual blocks\n",
-    "    assert scaling % 2 == 0  # ensure scaling factor is even, i.e. 0, 2, 4, 8, etc\n",
-    "    assert scaling >= 0  # ensure that scaling factor is zero or a positive number\n",
-    "    assert output_channels >= 1  # ensure that we have 1 or more output channels\n",
-    "\n",
-    "    ## Input images\n",
-    "    inp1 = Input(shape=input1_shape)  # low resolution image\n",
-    "    assert inp1.shape.ndims == 4  # has to be shape like (?,10,10,1) for 10x10 grid\n",
-    "    inp2 = Input(shape=input2_shape)  # other image (e.g. REMA)\n",
-    "    assert inp2.shape.ndims == 4  # has to be shape like (?,100,100,1) for 100x100 grid\n",
-    "    inp3 = Input(shape=input3_shape)  # other image (MEASURES Ice Flow)\n",
-    "    assert inp3.shape.ndims == 4  # has to be shape like (?,20,20,1) for 20x20 grid\n",
-    "\n",
-    "    # 0 part\n",
-    "    # Resize inputs to right scale using convolution (hardcoded kernel_size and strides)\n",
-    "    inp1r = Conv2D(filters=32, kernel_size=(3, 3), strides=(1, 1), padding=\"valid\")(\n",
-    "        inp1\n",
-    "    )\n",
-    "    inp2r = Conv2D(filters=32, kernel_size=(30, 30), strides=(10, 10), padding=\"valid\")(\n",
-    "        inp2\n",
-    "    )\n",
-    "    inp3r = Conv2D(filters=32, kernel_size=(6, 6), strides=(2, 2), padding=\"valid\")(\n",
-    "        inp3\n",
-    "    )\n",
-    "\n",
-    "    # Concatenate all inputs\n",
-    "    # SEE https://distill.pub/2016/deconv-checkerboard/\n",
-    "    X = Concatenate()([inp1r, inp2r, inp3r])  # Concatenate all the inputs together\n",
-    "\n",
-    "    # 1st part\n",
-    "    # Pre-residual k3n64s1 (originally k9n64s1)\n",
-    "    X0 = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding=\"same\")(X)\n",
-    "    X0 = PReLU(shared_axes=[1, 2])(X0)\n",
-    "\n",
-    "    # 2nd part\n",
-    "    # Residual blocks k3n64s1\n",
-    "    def residual_block(input_tensor):\n",
-    "        x = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding=\"same\")(\n",
-    "            input_tensor\n",
-    "        )\n",
-    "        x = BatchNormalization()(x)\n",
-    "        x = PReLU(shared_axes=[1, 2])(x)\n",
-    "        x = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding=\"same\")(x)\n",
-    "        x = BatchNormalization()(x)\n",
-    "        return Add()([x, input_tensor])\n",
-    "\n",
-    "    X = residual_block(X0)\n",
-    "    for _ in range(num_residual_blocks - 1):\n",
-    "        X = residual_block(X)\n",
-    "\n",
-    "    # 3rd part\n",
-    "    # Post-residual blocks k3n64s1\n",
-    "    X = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding=\"same\")(X)\n",
-    "    X = BatchNormalization()(X)\n",
-    "    X = Add()([X, X0])\n",
-    "\n",
-    "    # 4th part\n",
-    "    # Upsampling (if 4; run twice, if 8; run thrice, etc.) k3n256s1\n",
-    "    for p, _ in enumerate(range(scaling // 2), start=1):\n",
-    "        X = Conv2D(filters=256, kernel_size=(3, 3), strides=(1, 1), padding=\"same\")(X)\n",
-    "        pixelshuffleup = lambda images: K.tf.depth_to_space(input=images, block_size=2)\n",
-    "        X = Lambda(function=pixelshuffleup, name=f\"pixelshuffleup_{p}\")(X)\n",
-    "        X = PReLU(shared_axes=[1, 2])(X)\n",
-    "\n",
-    "    # 5th part\n",
-    "    # Generate high resolution output k9n1s1 (originally k9n3s1 for RGB image)\n",
-    "    outp = Conv2D(\n",
-    "        filters=output_channels,\n",
-    "        kernel_size=(9, 9),\n",
-    "        strides=(1, 1),\n",
-    "        padding=\"same\",\n",
-    "        name=\"generator_output\",\n",
-    "    )(X)\n",
-    "\n",
-    "    # Create neural network with input low-res images and output prediction\n",
-    "    network = keras.engine.network.Network(\n",
-    "        inputs=[inp1, inp2, inp3], outputs=[outp], name=\"generator_network\"\n",
-    "    )\n",
-    "\n",
-    "    return network"
+    "    def __init__(\n",
+    "        self,\n",
+    "        inblock_class=DeepbedmapInputBlock,\n",
+    "        resblock_class=ResInResDenseBlock,\n",
+    "        num_residual_blocks: int = 4,\n",
+    "        out_channels: int = 1,\n",
+    "    ):\n",
+    "        super().__init__()\n",
+    "        self.num_residual_blocks = num_residual_blocks\n",
+    "        init_weights = chainer.initializers.HeNormal(scale=0.1, fan_option=\"fan_in\")\n",
+    "\n",
+    "        with self.init_scope():\n",
+    "\n",
+    "            # Initial Input and Residual Blocks\n",
+    "            self.input_block = inblock_class()\n",
+    "            self.pre_residual_conv_layer = L.Convolution2D(\n",
+    "                in_channels=None,\n",
+    "                out_channels=64,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=1,  # 'same' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "            self.residual_network = resblock_class().repeat(\n",
+    "                n_repeat=num_residual_blocks\n",
+    "            )\n",
+    "            self.post_residual_conv_layer = L.Convolution2D(\n",
+    "                in_channels=None,\n",
+    "                out_channels=64,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=1,  # 'same' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "\n",
+    "            # Upsampling Layers\n",
+    "            self.pre_upsample_conv_layer_1 = L.Convolution2D(\n",
+    "                in_channels=None,\n",
+    "                out_channels=256,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=1,  # 'same' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "            self.pre_upsample_conv_layer_2 = L.Convolution2D(\n",
+    "                in_channels=None,\n",
+    "                out_channels=256,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=1,  # 'same' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "\n",
+    "            # Final post-upsamle layers\n",
+    "            self.final_conv_layer1 = L.Convolution2D(\n",
+    "                in_channels=None,\n",
+    "                out_channels=64,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=1,  # 'same' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "            self.final_conv_layer2 = L.Convolution2D(\n",
+    "                in_channels=None,\n",
+    "                out_channels=out_channels,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=1,  # 'same' padding\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "\n",
+    "    def forward(self, inputs: dict):\n",
+    "        \"\"\"\n",
+    "        Forward computation, i.e. evaluate based on inputs\n",
+    "\n",
+    "        Input dictionary needs to have keys \"x\", \"w1\", \"w2\"\n",
+    "        \"\"\"\n",
+    "        # 0 part\n",
+    "        # Resize inputs o right scale using convolution (hardcoded kernel_size and strides)\n",
+    "        # Also concatenate all inputs\n",
+    "        a0 = self.input_block(x=inputs[\"x\"], w1=inputs[\"w1\"], w2=inputs[\"w2\"])\n",
+    "\n",
+    "        # 1st part\n",
+    "        # Pre-residual k3n64s1\n",
+    "        a1 = self.pre_residual_conv_layer(a0)\n",
+    "        a1 = F.leaky_relu(x=a1, slope=0.2)\n",
+    "\n",
+    "        # 2nd part\n",
+    "        # Residual blocks k3n64s1\n",
+    "        a2 = self.residual_network(a1)\n",
+    "\n",
+    "        # 3rd part\n",
+    "        # Post-residual blocks k3n64s1\n",
+    "        a3 = self.post_residual_conv_layer(a2)\n",
+    "        a3 = F.add(a1, a3)\n",
+    "\n",
+    "        # 4th part\n",
+    "        # Upsampling (if 4; run twice, if 8; run thrice, etc.) k3n256s1\n",
+    "        a4_1 = self.pre_upsample_conv_layer_1(a3)\n",
+    "        a4_1 = F.depth2space(X=a4_1, r=2)\n",
+    "        a4_1 = F.leaky_relu(x=a4_1, slope=0.2)\n",
+    "        a4_2 = self.pre_upsample_conv_layer_2(a4_1)\n",
+    "        a4_2 = F.depth2space(X=a4_2, r=2)\n",
+    "        a4_2 = F.leaky_relu(x=a4_2, slope=0.2)\n",
+    "\n",
+    "        # 5th part\n",
+    "        # Generate high resolution output k3n64s1 and k3n1s1\n",
+    "        a5_1 = self.final_conv_layer1(a4_2)\n",
+    "        a5_1 = F.leaky_relu(x=a5_1, slope=0.2)\n",
+    "        a5_2 = self.final_conv_layer2(a5_1)\n",
+    "\n",
+    "        return a5_2"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Discriminator Network Architecture\n",
+    "## 2.2 Discriminator Network Architecture\n",
     "\n",
     "Discriminator component is based on Deep Convolutional Generative Adversarial Networks by [Radford et al., 2015](https://arxiv.org/abs/1511.06434).\n",
-    "Keras implementation below takes some hints from https://github.com/erilyth/DCGANs/blob/master/DCGAN-CIFAR10/dcgan.py and https://github.com/yashk2810/DCGAN-Keras/blob/master/DCGAN.ipynb\n",
+    "\n",
+    "Note that figure below shows the 2017 (non-enhanced) SRGAN discriminator neural network architecture.\n",
+    "The 2018 ESRGAN version is basically the same architecture, as only the loss function was changed.\n",
+    "Note that the BatchNormalization layers **are still preserved** within the Convolutional blocks (see relevant line in original Pytorch implementation [here](https://github.com/xinntao/BasicSR/blob/902b4ae1f4beec7359de6e62ed0aebfc335d8dfd/codes/models/modules/architecture.py#L88)).\n",
     "\n",
     "![SRGAN architecture - Discriminator Network](https://arxiv-sanity-sanity-production.s3.amazonaws.com/render-output/399644/images/used/jpg/discriminator.jpg)\n",
     "\n",
@@ -399,87 +701,135 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "metadata": {
     "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
-    "def discriminator_network(\n",
-    "    input_shape: typing.Tuple[int, int, int] = (32, 32, 1)\n",
-    ") -> keras.engine.network.Network:\n",
+    "class DiscriminatorModel(chainer.Chain):\n",
     "    \"\"\"\n",
     "    The discriminator network which is a convolutional neural network.\n",
     "    Takes ONE high resolution input image and predicts whether it is\n",
     "    real or fake on a scale of 0 to 1, where 0 is fake and 1 is real.\n",
     "\n",
-    "    >>> discriminator_network().input_shape\n",
-    "    (None, 32, 32, 1)\n",
-    "    >>> discriminator_network().output_shape\n",
-    "    (None, 1)\n",
-    "    >>> discriminator_network().count_params()\n",
-    "    6828033\n",
-    "    \"\"\"\n",
-    "\n",
-    "    ## Input images\n",
-    "    inp = Input(shape=input_shape)  # high resolution/groundtruth image to discriminate\n",
-    "    assert inp.shape.ndims == 4  # needs to be shape like (?,32,32,1) for 8x8 grid\n",
+    "    Consists of several Conv2D-BatchNorm-LeakyReLU blocks, followed by\n",
+    "    a fully connected linear layer with LeakyReLU activation and a final\n",
+    "    fully connected linear layer with Sigmoid activation.\n",
     "\n",
-    "    # 1st part\n",
-    "    # Convolutonal Block without Batch Normalization k3n64s1\n",
-    "    X = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding=\"same\")(inp)\n",
-    "    X = LeakyReLU(alpha=0.2)(X)\n",
-    "\n",
-    "    # 2nd part\n",
-    "    # Convolutional Blocks with Batch Normalization k3n{64*f}s{1or2}\n",
-    "    for f, s in zip([1, 1, 2, 2, 4, 4, 8, 8], [1, 2, 1, 2, 1, 2, 1, 2]):\n",
-    "        X = Conv2D(filters=64 * f, kernel_size=(3, 3), strides=(s, s), padding=\"same\")(\n",
-    "            X\n",
-    "        )\n",
-    "        X = BatchNormalization()(X)\n",
-    "        X = LeakyReLU(alpha=0.2)(X)\n",
-    "\n",
-    "    # 3rd part\n",
-    "    # Flatten, Dense (Fully Connected) Layers and Output\n",
-    "    X = Flatten()(X)\n",
-    "    X = Dense(units=1024)(X)  # ??!! Flatten?\n",
-    "    X = LeakyReLU(alpha=0.2)(X)\n",
-    "    outp = Dense(units=1, activation=\"sigmoid\", name=\"discriminator_output\")(X)\n",
-    "\n",
-    "    # Create neural network with input highres/groundtruth images, output validity 0/1\n",
-    "    network = keras.engine.network.Network(\n",
-    "        inputs=[inp], outputs=[outp], name=\"discriminator_network\"\n",
-    "    )\n",
+    "    >>> discriminator_model = DiscriminatorModel()\n",
+    "    >>> y_pred = discriminator_model.forward(\n",
+    "    ...     inputs={\n",
+    "    ...         \"x\": np.random.rand(2, 1, 32, 32).astype(\"float32\"),\n",
+    "    ...     }\n",
+    "    ... )\n",
+    "    >>> y_pred.shape\n",
+    "    (2, 1)\n",
+    "    >>> discriminator_model.count_params()\n",
+    "    6824193\n",
+    "    \"\"\"\n",
     "\n",
-    "    return network"
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        init_weights = chainer.initializers.GlorotUniform(scale=1.0)\n",
+    "\n",
+    "        with self.init_scope():\n",
+    "\n",
+    "            self.conv_layer0 = L.Convolution2D(\n",
+    "                in_channels=None,\n",
+    "                out_channels=64,\n",
+    "                ksize=(3, 3),\n",
+    "                stride=(1, 1),\n",
+    "                pad=1,  # 'same' padding\n",
+    "                nobias=False,  # default, have bias\n",
+    "                initialW=init_weights,\n",
+    "            )\n",
+    "            self.conv_layer1 = L.Convolution2D(None, 64, 3, 1, 1, False, init_weights)\n",
+    "            self.conv_layer2 = L.Convolution2D(None, 64, 3, 2, 1, False, init_weights)\n",
+    "            self.conv_layer3 = L.Convolution2D(None, 128, 3, 1, 1, False, init_weights)\n",
+    "            self.conv_layer4 = L.Convolution2D(None, 128, 3, 2, 1, False, init_weights)\n",
+    "            self.conv_layer5 = L.Convolution2D(None, 256, 3, 1, 1, False, init_weights)\n",
+    "            self.conv_layer6 = L.Convolution2D(None, 256, 3, 2, 1, False, init_weights)\n",
+    "            self.conv_layer7 = L.Convolution2D(None, 512, 3, 1, 1, False, init_weights)\n",
+    "            self.conv_layer8 = L.Convolution2D(None, 512, 3, 2, 1, False, init_weights)\n",
+    "\n",
+    "            self.batch_norm1 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)\n",
+    "            self.batch_norm2 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)\n",
+    "            self.batch_norm3 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)\n",
+    "            self.batch_norm4 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)\n",
+    "            self.batch_norm5 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)\n",
+    "            self.batch_norm6 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)\n",
+    "            self.batch_norm7 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)\n",
+    "            self.batch_norm8 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)\n",
+    "\n",
+    "            self.linear_1 = L.Linear(in_size=None, out_size=1024, initialW=init_weights)\n",
+    "            self.linear_2 = L.Linear(in_size=None, out_size=1, initialW=init_weights)\n",
+    "\n",
+    "    def forward(self, inputs: dict):\n",
+    "        \"\"\"\n",
+    "        Forward computation, i.e. evaluate based on inputs\n",
+    "\n",
+    "        Input dictionary needs to have keys \"x\"\n",
+    "        \"\"\"\n",
+    "\n",
+    "        # 1st part\n",
+    "        # Convolutonal Block without Batch Normalization k3n64s1\n",
+    "        a0 = self.conv_layer0(x=inputs[\"x\"])\n",
+    "        a0 = F.leaky_relu(x=a0, slope=0.2)\n",
+    "\n",
+    "        # 2nd part\n",
+    "        # Convolutional Blocks with Batch Normalization k3n{64*f}s{1or2}\n",
+    "        a1 = self.conv_layer1(x=a0)\n",
+    "        a1 = self.batch_norm1(x=a1)\n",
+    "        a1 = F.leaky_relu(x=a1, slope=0.2)\n",
+    "        a2 = self.conv_layer2(x=a1)\n",
+    "        a2 = self.batch_norm2(x=a2)\n",
+    "        a2 = F.leaky_relu(x=a2, slope=0.2)\n",
+    "        a3 = self.conv_layer3(x=a2)\n",
+    "        a3 = self.batch_norm3(x=a3)\n",
+    "        a3 = F.leaky_relu(x=a3, slope=0.2)\n",
+    "        a4 = self.conv_layer4(x=a3)\n",
+    "        a4 = self.batch_norm4(x=a4)\n",
+    "        a4 = F.leaky_relu(x=a4, slope=0.2)\n",
+    "        a5 = self.conv_layer5(x=a4)\n",
+    "        a5 = self.batch_norm5(x=a5)\n",
+    "        a5 = F.leaky_relu(x=a5, slope=0.2)\n",
+    "        a6 = self.conv_layer6(x=a5)\n",
+    "        a6 = self.batch_norm6(x=a6)\n",
+    "        a6 = F.leaky_relu(x=a6, slope=0.2)\n",
+    "        a7 = self.conv_layer7(x=a6)\n",
+    "        a7 = self.batch_norm7(x=a7)\n",
+    "        a7 = F.leaky_relu(x=a7, slope=0.2)\n",
+    "        a8 = self.conv_layer8(x=a7)\n",
+    "        a8 = self.batch_norm8(x=a8)\n",
+    "        a8 = F.leaky_relu(x=a8, slope=0.2)\n",
+    "\n",
+    "        # 3rd part\n",
+    "        # Flatten, Dense (Fully Connected) Layers and Output\n",
+    "        a9 = F.reshape(x=a8, shape=(len(a8), -1))  # flatten while keeping batch_size\n",
+    "        a9 = self.linear_1(x=a9)\n",
+    "        a9 = F.leaky_relu(x=a9, slope=0.2)\n",
+    "        a10 = self.linear_2(x=a9)\n",
+    "        # a10 = F.sigmoid(x=a10)  # no sigmoid activation, as it is in the loss function\n",
+    "\n",
+    "        return a10"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Combine Generator and Discriminator Networks\n",
+    "## 2.3 Define Loss function and Metrics for the Generator and Discriminator Networks\n",
     "\n",
-    "Here we combine the Generator and Discriminator neural network models together, and define the Perceptual Loss function where:\n",
+    "Now we define the Perceptual Loss function for our Generator and Discriminator neural network models, where:\n",
     "\n",
     "$$Perceptual Loss = Content Loss + Adversarial Loss$$\n",
     "\n",
-    "The original SRGAN paper by [Ledig et al. 2017](https://arxiv.org/abs/1609.04802v5) calculates *Content Loss* based on the ReLU activation layers of the pre-trained 19 layer VGG network.\n",
-    "The implementation below is less advanced, simply using a pixel-wise [Mean Squared Error (MSE) loss](https://keras.io/losses/#mean_squared_error) as the *Content Loss*.\n",
-    "Specifically, the *Content Loss* is calculated as the MSE difference between the output of the generator model (i.e. the predicted Super Resolution Image) and that of the groundtruth image (i.e. the true High Resolution Image).\n",
-    "\n",
-    "The *Adversarial Loss* or *Generative Loss* (confusing I know) is the same as in the original SRGAN paper.\n",
-    "It is defined based on the probabilities of the discriminator believing that the reconstructed Super Resolution Image is a natural High Resolution Image.\n",
-    "The implementation below uses the [Binary CrossEntropy loss](https://keras.io/losses/#binary_crossentropy).\n",
-    "Specifically, this *Adversarial Loss* is calculated between the output of the discriminator model (a value between 0 and 1) and that of the groundtruth label (a boolean value of either 0 or 1).\n",
-    "\n",
-    "Source code for the implementations of these loss functions in Keras can be found at https://github.com/keras-team/keras/blob/master/keras/losses.py.\n",
-    "\n",
-    "![Perceptual Loss in a Super Resolution Generative Adversarial Network](https://yuml.me/69dc9a87.png)\n",
+    "![Perceptual Loss in an Enhanced Super Resolution Generative Adversarial Network](https://yuml.me/db58d683.png)\n",
     "\n",
     "<!--\n",
     "[LowRes-Inputs]-Generator>[SuperResolution_DEM]\n",
-    "[SuperResolution_DEM]-.->[note:Content-Loss|MeanSquaredError{bg:yellow}]\n",
+    "[SuperResolution_DEM]-.->[note:Content-Loss|MeanAbsoluteError{bg:yellow}]\n",
     "[HighRes-Groundtruth_DEM]-.->[note:Content-Loss]\n",
     "[SuperResolution_DEM]-Discriminator>[False_or_True_Prediction]\n",
     "[HighRes-Groundtruth_DEM]-Discriminator>[False_or_True_Prediction]\n",
@@ -491,164 +841,281 @@
     "-->"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Content Loss\n",
+    "\n",
+    "The original SRGAN paper by [Ledig et al. 2017](https://arxiv.org/abs/1609.04802v5) calculates *Content Loss* based on the ReLU activation layers of the pre-trained 19 layer VGG network.\n",
+    "The implementation below is less advanced, simply using an L1 loss, i.e., a pixel-wise [Mean Absolute Error (MAE) loss](https://keras.io/losses/#mean_absolute_error) as the *Content Loss*.\n",
+    "Specifically, the *Content Loss* is calculated as the MAE difference between the output of the generator model (i.e. the predicted Super Resolution Image) and that of the groundtruth image (i.e. the true High Resolution Image).\n",
+    "\n",
+    "$$ e_i = ||G(x_{i}) - y_i||_{1} $$\n",
+    "\n",
+    "$$ Loss_{Content} = Mean Absolute Error = \\dfrac{1}{n} \\sum\\limits_{i=1}^n e_i $$\n",
+    "\n",
+    "where $G(x_{i})$ is the Generator Network's predicted value, and $y_i$ is the groundtruth value, respectively at pixel $i$.\n",
+    "$e_i$ thus represents the absolute error (L1 loss) (denoted by $||\\dots||_{1}$) between the predicted and groundtruth value.\n",
+    "We then sum all the pixel-wise errors $e_i,\\dots,e_n$ and divide by the number of pixels $n$ to get the Arithmetic Mean $\\dfrac{1}{n} \\sum\\limits_{i=1}^n$ of our error which is our *Content Loss*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adversarial Loss\n",
+    "\n",
+    "The *Adversarial Loss* or *Generative Loss* (confusing I know) is the same as in the original SRGAN paper.\n",
+    "It is defined based on the probabilities of the discriminator believing that the reconstructed Super Resolution Image is a natural High Resolution Image.\n",
+    "The implementation below uses the [Binary CrossEntropy loss](https://keras.io/losses/#binary_crossentropy).\n",
+    "Specifically, this *Adversarial Loss* is calculated between the output of the discriminator model (a value between 0 and 1) and that of the groundtruth label (a boolean value of either 0 or 1).\n",
+    "\n",
+    "$$ Loss_{Adversarial} = Binary Cross Entropy Loss = -\\dfrac{1}{n} \\sum\\limits_{i=1}^n ( y_i ln(\\sigma(x_i)) + (1-y_i) ln(1 - \\sigma(x_i) ) $$\n",
+    "\n",
+    "where $\\sigma$ is the [Sigmoid](https://en.wikipedia.org/wiki/Sigmoid_function) activation function, $\\sigma = \\dfrac{1}{1+e^{-x}} = \\dfrac{e^x}{e^x+1}$, $y_i$ is the groundtruth label (1 for real, 0 for fake) and $x_i$ is the prediction (before sigmoid activation is applied), all respectively at pixel $i$.\n",
+    "\n",
+    "$\\sigma(x)$ is basically the sigmoid activated output from a Standard Discriminator neural network, which some people also denote as $D(.)$.\n",
+    "Technically, some people also write $D(x) = \\sigma(C(x))$, where $C(x)$ is the raw, non-transformed output from the Discriminator neural network (i.e. no sigmoid activation applied) on the input data $x$.\n",
+    "For simplicity, we now denote $C(x)$ simply as $x$ in the following equations, i.e. using $\\sigma(x)$ to replace $\\sigma(C(x))$.\n",
+    "\n",
+    "Again, the [Binary Cross Entropy Loss](https://en.wikipedia.org/wiki/Cross_entropy#Cross-entropy_error_function_and_logistic_regression) calculated on one pixel is defined as follows:\n",
+    "\n",
+    "$$ -( y ln(\\sigma(x)) + (1-y) ln(1 - \\sigma(x) )$$\n",
+    "\n",
+    "With the full expansion as such:\n",
+    "\n",
+    "$$ -\\bigg[ y ln\\big(\\dfrac{e^x}{e^x+1}\\big) + (1-y) ln\\big(1 - \\dfrac{e^x}{e^x+1}\\big) \\bigg] $$\n",
+    "\n",
+    "The above equation is mathematically equivalent to the one below, and can be derived using [Logarithm rules](https://en.wikipedia.org/wiki/Logarithm#Product,_quotient,_power,_and_root) such as the Power Rule and Product Rule, and using the fact that $ln(e)=1$ and $ln(1)=0$:\n",
+    "\n",
+    "$$ -[ xy - ln(1+e^x) ] $$\n",
+    "\n",
+    "However, this reformed equation is numerically unstable (see discussion [here](https://www.reddit.com/r/MachineLearning/comments/4euzmk/logsumexp_for_logistic_regression/)), and is good for values of $x<0$.\n",
+    "For values of $x>=0$, there is an alternative representation which we can derive:\n",
+    "\n",
+    "$$ -[ xy - ln(1+e^x) - x + x ] $$\n",
+    "$$ -[ x(y-1) - ln(1 + e^x) + ln(e^x) ] $$\n",
+    "$$ -\\bigg[ x(y-1) - ln\\big(\\dfrac{e^x}{1+e^x}\\big) \\bigg] $$\n",
+    "$$ -\\bigg[ x(y-1) - ln\\big(\\dfrac{1}{1+e^{-x}}\\big) \\bigg] $$\n",
+    "$$ - [ x(y-1) - ln(1) + ln(1+e^{-x}) ] $$\n",
+    "$$ - [ x(y-1) + ln(1+e^{-x}) $$\n",
+    "\n",
+    "In order to have a numerically stable function that works for both $x<0$ and $x>=0$, we can write it like so as in Caffe's implementation:\n",
+    "\n",
+    "$$ -[ x(y - 1_{x>=0} - ln(1+e^{x-2x\\cdot1_{x>=0}}) ] $$\n",
+    "\n",
+    "Alternatively, Chainer does it like so:\n",
+    "\n",
+    "$$ -[ x(y - 1_{x>=0} - ln(1+e^{-|x|}) ] $$\n",
+    "\n",
+    "Or in Python code (the Chainer implemention from [here](https://github.com/chainer/chainer/blob/v6.0.0b1/chainer/functions/loss/sigmoid_cross_entropy.py#L41-L44)), bearing in mind that the natural logarithm $ln$ is `np.log` in Numpy:\n",
+    "\n",
+    "```python\n",
+    "    sigmoidbinarycrossentropyloss = -(x * (y - (x >= 0)) - np.log1p(np.exp(-np.abs(x))))\n",
+    "```\n",
+    "\n",
+    "See also how [Pytorch](https://pytorch.org/docs/stable/nn.html?highlight=bcewithlogitsloss#torch.nn.BCEWithLogitsLoss) and [Tensorflow](https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits) implements this in a numerically stable manner."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {
     "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
-    "def compile_srgan_model(\n",
-    "    g_network: keras.engine.network.Network,\n",
-    "    d_network: keras.engine.network.Network,\n",
-    "    metrics: typing.Dict[str, str] = None,\n",
-    ") -> typing.Dict[str, keras.engine.training.Model]:\n",
+    "def calculate_generator_loss(\n",
+    "    y_pred: chainer.variable.Variable,\n",
+    "    y_true: cupy.ndarray,\n",
+    "    fake_labels: cupy.ndarray,\n",
+    "    real_labels: cupy.ndarray,\n",
+    "    fake_minus_real_target: cupy.ndarray,\n",
+    "    real_minus_fake_target: cupy.ndarray,\n",
+    "    content_loss_weighting: float = 5e-3,\n",
+    "    adversarial_loss_weighting: float = 1e-2,\n",
+    ") -> chainer.variable.Variable:\n",
     "    \"\"\"\n",
-    "    Creates a Super Resolution Generative Adversarial Network (SRGAN)\n",
-    "    by joining a generator network with a discriminator network.\n",
-    "\n",
-    "    Returns a dictionary containing:\n",
-    "    1) generator model (trainable, not compiled)\n",
-    "    2) discriminator model (trainable, compiled)\n",
-    "    3) srgan model (trainable generator, untrainable discriminator, compiled)\n",
-    "\n",
-    "    The SRGAN model will be compiled with an optimizer (e.g. Adam)\n",
-    "    and have separate loss functions and metrics for its\n",
-    "    generator and discriminator component.\n",
-    "\n",
-    "    >>> metrics = {\"generator_network\": 'mse', \"discriminator_network\": 'accuracy'}\n",
-    "    >>> models = compile_srgan_model(\n",
-    "    ...     g_network=generator_network(),\n",
-    "    ...     d_network=discriminator_network(),\n",
-    "    ...     metrics=metrics,\n",
+    "    This function calculates the weighted sum between\n",
+    "    \"Content Loss\" and \"Adversarial Loss\".\n",
+    "    which forms the basis for training the Generator Network.\n",
+    "\n",
+    "    >>> calculate_generator_loss(\n",
+    "    ...     y_pred=chainer.variable.Variable(data=np.ones(shape=(2, 1, 3, 3))),\n",
+    "    ...     y_true=np.full(shape=(2, 1, 3, 3), fill_value=10.0),\n",
+    "    ...     fake_labels=np.array([[-1.2], [0.5]]),\n",
+    "    ...     real_labels=np.array([[0.5], [-0.8]]),\n",
+    "    ...     fake_minus_real_target=np.array([[1], [1]]).astype(np.int32),\n",
+    "    ...     real_minus_fake_target=np.array([[0], [0]]).astype(np.int32),\n",
     "    ... )\n",
-    "    >>> models['discriminator_model'].trainable\n",
-    "    True\n",
-    "    >>> models['srgan_model'].get_layer(name='generator_network').trainable\n",
-    "    True\n",
-    "    >>> models['srgan_model'].get_layer(name='discriminator_network').trainable\n",
-    "    False\n",
-    "    >>> models['srgan_model'].count_params()\n",
-    "    8442626\n",
+    "    variable(0.06234614)\n",
     "    \"\"\"\n",
-    "\n",
-    "    # Check that our neural networks are named properly\n",
-    "    assert g_network.name == \"generator_network\"\n",
-    "    assert d_network.name == \"discriminator_network\"\n",
-    "    assert g_network.trainable == True  # check that generator is trainable\n",
-    "    assert d_network.trainable == True  # check that discriminator is trainable\n",
-    "\n",
-    "    ## Both trainable\n",
-    "    # Create keras models (trainable) out of the networks (graph only)\n",
-    "    g_model = Model(\n",
-    "        inputs=g_network.inputs, outputs=g_network.outputs, name=\"generator_model\"\n",
-    "    )\n",
-    "    d_model = Model(\n",
-    "        inputs=d_network.inputs, outputs=d_network.outputs, name=\"discriminator_model\"\n",
-    "    )\n",
-    "    d_model.compile(\n",
-    "        optimizer=keras.optimizers.Adam(lr=0.001),\n",
-    "        loss={\"discriminator_output\": keras.losses.binary_crossentropy},\n",
+    "    # Content Loss (L1, Mean Absolute Error) between 2D images\n",
+    "    content_loss = F.mean_absolute_error(x0=y_pred, x1=y_true)\n",
+    "\n",
+    "    # Adversarial Loss between 1D labels\n",
+    "    adversarial_loss = calculate_discriminator_loss(\n",
+    "        real_labels_pred=real_labels,\n",
+    "        fake_labels_pred=fake_labels,\n",
+    "        real_minus_fake_target=real_minus_fake_target,  # Zeros (0) instead of ones (1)\n",
+    "        fake_minus_real_target=fake_minus_real_target,  # Ones (1) instead of zeros (0)\n",
     "    )\n",
     "\n",
-    "    ## One trainable (generator), one untrainable (discriminator)\n",
-    "    # Connect Generator Network to Discriminator Network\n",
-    "    g_out = g_network(inputs=g_network.inputs)  # g_in --(g_network)--> g_out\n",
-    "    d_out = d_network(inputs=g_out)  # g_out --(d_network)--> d_out\n",
-    "\n",
-    "    # Create and Compile the Super Resolution Generative Adversarial Network Model!\n",
-    "    model = Model(inputs=g_network.inputs, outputs=[g_out, d_out])\n",
-    "    model.get_layer(\n",
-    "        name=\"discriminator_network\"\n",
-    "    ).trainable = False  # combined model should not train discriminator\n",
-    "    model.compile(\n",
-    "        optimizer=keras.optimizers.Adam(lr=0.001),\n",
-    "        loss={\n",
-    "            \"generator_network\": keras.losses.mean_squared_error,\n",
-    "            \"discriminator_network\": keras.losses.binary_crossentropy,\n",
-    "        },\n",
-    "        metrics=metrics,\n",
-    "    )\n",
+    "    # Get generator loss\n",
+    "    weighted_content_loss = content_loss_weighting * content_loss\n",
+    "    weighted_adversarial_loss = adversarial_loss_weighting * adversarial_loss\n",
+    "    g_loss = weighted_content_loss + weighted_adversarial_loss\n",
     "\n",
-    "    return {\n",
-    "        \"generator_model\": g_model,\n",
-    "        \"discriminator_model\": d_model,\n",
-    "        \"srgan_model\": model,\n",
-    "    }"
+    "    return g_loss"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {
     "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
-    "def psnr(y_true: np.ndarray, y_pred: np.ndarray) -> np.ndarray:\n",
+    "def psnr(\n",
+    "    y_true: cupy.ndarray, y_pred: cupy.ndarray, data_range=2 ** 32\n",
+    ") -> cupy.ndarray:\n",
     "    \"\"\"\n",
-    "    Peak Signal-Noise Ratio (PSNR) metric.\n",
+    "    Peak Signal-Noise Ratio (PSNR) metric, calculated batchwise.\n",
     "    See https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Definition\n",
     "\n",
-    "    >>> y_true, y_pred = np.ones(shape=(3, 3)), np.full(shape=(3, 3), fill_value=2)\n",
-    "    >>> K.eval(psnr(y_true=y_true, y_pred=y_pred))\n",
-    "    array([221.80709678, 221.80709678, 221.80709678])\n",
+    "    Can take in either numpy (CPU) or cupy (GPU) arrays as input.\n",
+    "    Implementation is same as skimage.measure.compare_psnr with data_range=2**32\n",
+    "\n",
+    "    >>> psnr(\n",
+    "    ...     y_true=np.ones(shape=(2, 1, 3, 3)),\n",
+    "    ...     y_pred=np.full(shape=(2, 1, 3, 3), fill_value=2),\n",
+    "    ... )\n",
+    "    192.65919722494797\n",
     "    \"\"\"\n",
+    "    xp = chainer.backend.get_array_module(y_true)\n",
+    "\n",
+    "    # Calculate Mean Squred Error along predetermined axes\n",
+    "    mse = xp.mean(xp.square(xp.subtract(y_pred, y_true)), axis=None)\n",
     "\n",
-    "    mse = (\n",
-    "        K.mean(K.square(K.np.subtract(y_pred, y_true)), axis=-1) + K.epsilon()\n",
-    "    )  # add epsilon to prevent zero division\n",
-    "    return K.np.multiply(\n",
-    "        20, K.log(2 ** 16 / K.sqrt(mse))\n",
-    "    )  # setting MAX_I as 2^16, i.e. max for int16"
+    "    # Calculate Peak Signal-Noise Ratio, setting MAX_I as 2^32, i.e. max for int32\n",
+    "    return xp.multiply(20, xp.log10(data_range / xp.sqrt(mse)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "def calculate_discriminator_loss(\n",
+    "    real_labels_pred: chainer.variable.Variable,\n",
+    "    fake_labels_pred: chainer.variable.Variable,\n",
+    "    real_minus_fake_target: cupy.ndarray,\n",
+    "    fake_minus_real_target: cupy.ndarray,\n",
+    ") -> chainer.variable.Variable:\n",
+    "    \"\"\"\n",
+    "    This function purely calculates the \"Adversarial Loss\"\n",
+    "    in a Relativistic Average Generative Adversarial Network (RaGAN).\n",
+    "\n",
+    "    It forms the basis for training the Discriminator Network,\n",
+    "    but it is also used as part of the Generator Network's loss function.\n",
+    "\n",
+    "    See paper by Jolicoeur-Martineau, 2018 at https://arxiv.org/abs/1807.00734\n",
+    "    for the mathematical details of the RaGAN loss function.\n",
+    "\n",
+    "    Original Sigmoid_Cross_Entropy formula:\n",
+    "    -(y * np.log(sigmoid(x)) + (1 - y) * np.log(1 - sigmoid(x)))\n",
+    "\n",
+    "    Numerically stable formula:\n",
+    "    -(x * (y - (x >= 0)) - np.log1p(np.exp(-np.abs(x))))\n",
+    "\n",
+    "    where y = the target difference between real and fake labels (i.e. 1 - 0 = 1)\n",
+    "          x = the calculated difference between real_labels_pred and fake_labels_pred\n",
+    "\n",
+    "    >>> calculate_discriminator_loss(\n",
+    "    ...     real_labels_pred=chainer.variable.Variable(data=np.array([[1.1], [-0.5]])),\n",
+    "    ...     fake_labels_pred=chainer.variable.Variable(data=np.array([[-0.3], [1.0]])),\n",
+    "    ...     real_minus_fake_target=np.array([[1], [1]]),\n",
+    "    ...     fake_minus_real_target=np.array([[0], [0]]),\n",
+    "    ... )\n",
+    "    variable(1.56670504)\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Calculate arithmetic mean of real/fake predicted labels\n",
+    "    real_labels_pred_avg = F.mean(real_labels_pred)\n",
+    "    fake_labels_pred_avg = F.mean(fake_labels_pred)\n",
+    "\n",
+    "    # Binary Cross-Entropy Loss with Sigmoid\n",
+    "    real_versus_fake_loss = F.sigmoid_cross_entropy(\n",
+    "        x=(real_labels_pred - fake_labels_pred_avg), t=real_minus_fake_target\n",
+    "    )  # let predicted labels from real images be more realistic than those from fake\n",
+    "    fake_versus_real_loss = F.sigmoid_cross_entropy(\n",
+    "        x=(fake_labels_pred - real_labels_pred_avg), t=fake_minus_real_target\n",
+    "    )  # let predicted labels from fake images be less realistic than those from real\n",
+    "\n",
+    "    # Relativistic average Standard GAN's Discriminator Loss\n",
+    "    d_loss = real_versus_fake_loss + fake_versus_real_loss\n",
+    "\n",
+    "    return d_loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "__________________________________________________________________________________________________\n",
-      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
-      "==================================================================================================\n",
-      "input_1 (InputLayer)            (None, 10, 10, 1)    0                                            \n",
-      "__________________________________________________________________________________________________\n",
-      "input_2 (InputLayer)            (None, 100, 100, 1)  0                                            \n",
-      "__________________________________________________________________________________________________\n",
-      "input_3 (InputLayer)            (None, 20, 20, 1)    0                                            \n",
-      "__________________________________________________________________________________________________\n",
-      "generator_network (Network)     (None, 32, 32, 1)    1614593     input_1[0][0]                    \n",
-      "                                                                 input_2[0][0]                    \n",
-      "                                                                 input_3[0][0]                    \n",
-      "__________________________________________________________________________________________________\n",
-      "discriminator_network (Network) (None, 1)            6828033     generator_network[1][0]          \n",
-      "==================================================================================================\n",
-      "Total params: 8,442,626\n",
-      "Trainable params: 1,610,369\n",
-      "Non-trainable params: 6,832,257\n",
-      "__________________________________________________________________________________________________\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "K.clear_session()  # Reset Keras/Tensorflow graph\n",
-    "metrics = {\"generator_network\": psnr, \"discriminator_network\": \"accuracy\"}\n",
-    "models = compile_srgan_model(\n",
-    "    g_network=generator_network(), d_network=discriminator_network(), metrics=metrics\n",
+    "# Build the models\n",
+    "generator_model = GeneratorModel()\n",
+    "discriminator_model = DiscriminatorModel()\n",
+    "experiment.log_parameter(\n",
+    "    name=\"num_residual_blocks\", value=generator_model.num_residual_blocks\n",
     ")\n",
-    "models[\"srgan_model\"].summary()"
+    "\n",
+    "# Transfer models to GPU if available\n",
+    "if xp == cupy:  # Check if CuPy was loaded, i.e. GPU is available\n",
+    "    generator_model.to_gpu(device=0)\n",
+    "    discriminator_model.to_gpu(device=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup optimizer, using Adam\n",
+    "generator_optimizer = chainer.optimizers.Adam(alpha=6e-4, eps=1e-8).setup(\n",
+    "    link=generator_model\n",
+    ")\n",
+    "experiment.log_parameters(\n",
+    "    dic={\n",
+    "        \"generator_optimizer\": \"adam\",\n",
+    "        \"generator_lr\": generator_optimizer.alpha,  # learning rate\n",
+    "        \"generator_epsilon\": generator_optimizer.eps,\n",
+    "    }\n",
+    ")\n",
+    "discriminator_optimizer = chainer.optimizers.Adam(alpha=6e-4, eps=1e-8).setup(\n",
+    "    link=discriminator_model\n",
+    ")\n",
+    "experiment.log_parameters(\n",
+    "    dic={\n",
+    "        \"discriminator_optimizer\": \"adam\",\n",
+    "        \"discriminator_lr\": discriminator_optimizer.alpha,  # learning rate\n",
+    "        \"discriminator_adam_epsilon\": discriminator_optimizer.eps,\n",
+    "    }\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 3. Train model\n",
+    "# 3. Train model\n",
     "\n",
     "[Gherkin](https://en.wikipedia.org/wiki/Gherkin_(language))/Plain English statement at what the Super-Resolution Generative Adversarial Network below does\n",
     "\n",
@@ -662,30 +1129,32 @@
     "      Scenario: Train discriminator to beat generator\n",
     "        Given fake generated images from a generator\n",
     "          And real groundtruth images\n",
-    "         When the two sets of images are fed into the discriminator\n",
-    "         Then the discriminator should know the fakes from the real images\n",
+    "         When the two sets of images are fed into the discriminator for comparison\n",
+    "         Then the discriminator should learn to know the fakes from the real images\n",
     "\n",
     "      Scenario: Train generator to fool discriminator\n",
-    "        Given what we think the discriminator believes is real\n",
-    "         When our inputs are fed into the super resolution model\n",
-    "         Then the generator should create a more authentic looking image\n",
+    "        Given fake generated images from a generator\n",
+    "          And what we think the discriminator believes is real\n",
+    "         When we compare the fake images to the real ones\n",
+    "         Then the generator should learn to create a more authentic looking image\n",
     "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 19,
    "metadata": {
     "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
-    "def train_discriminator(\n",
-    "    models: typing.Dict[str, keras.engine.training.Model],\n",
-    "    generator_inputs: typing.List[np.ndarray],\n",
-    "    groundtruth_images: np.ndarray,\n",
-    "    verbose: int = 1,\n",
-    ") -> (typing.Dict[str, keras.engine.training.Model], list):\n",
+    "def train_eval_discriminator(\n",
+    "    input_arrays: typing.Dict[str, cupy.ndarray],\n",
+    "    g_model,\n",
+    "    d_model,\n",
+    "    d_optimizer=None,\n",
+    "    train: bool = True,\n",
+    ") -> (float, float):\n",
     "    \"\"\"\n",
     "    Trains the Discriminator within a Super Resolution Generative Adversarial Network.\n",
     "    Discriminator is trainable, Generator is not trained (only produces predictions).\n",
@@ -695,137 +1164,176 @@
     "    - Fake images combined with real groundtruth images\n",
     "    - Discriminator trained with these images and their Fake(0)/Real(1) labels\n",
     "\n",
-    "    >>> generator_inputs = [\n",
-    "    ...     np.random.RandomState(seed=42).rand(32, s, s, 1) for s in [10, 100, 20]\n",
-    "    ... ]\n",
-    "    >>> groundtruth_images = np.random.RandomState(seed=42).rand(32,32,32,1)\n",
-    "    >>> models = compile_srgan_model(\n",
-    "    ...     g_network=generator_network(), d_network=discriminator_network()\n",
+    "    >>> train_arrays = {\n",
+    "    ...     \"X\": np.random.RandomState(seed=42).rand(2, 1, 10, 10).astype(np.float32),\n",
+    "    ...     \"W1\": np.random.RandomState(seed=42).rand(2, 1, 100, 100).astype(np.float32),\n",
+    "    ...     \"W2\": np.random.RandomState(seed=42).rand(2, 1, 20, 20).astype(np.float32),\n",
+    "    ...     \"Y\": np.random.RandomState(seed=42).rand(2, 1, 32, 32).astype(np.float32),\n",
+    "    ... }\n",
+    "    >>> discriminator_model = DiscriminatorModel()\n",
+    "    >>> discriminator_optimizer = chainer.optimizers.Adam(alpha=0.001, eps=1e-7).setup(\n",
+    "    ...     link=discriminator_model\n",
     "    ... )\n",
-    "\n",
-    "    >>> d_weight0 = K.eval(models['discriminator_model'].weights[0][0,0,0,0])\n",
-    "    >>> _, _ = train_discriminator(\n",
-    "    ...     models=models,\n",
-    "    ...     generator_inputs=generator_inputs,\n",
-    "    ...     groundtruth_images=groundtruth_images,\n",
-    "    ...     verbose=0,\n",
+    "    >>> generator_model = GeneratorModel()\n",
+    "\n",
+    "    >>> d_weight0 = [d for d in discriminator_model.params()][-3][0].array\n",
+    "    >>> d_train_loss, d_train_accu = train_eval_discriminator(\n",
+    "    ...     input_arrays=train_arrays,\n",
+    "    ...     g_model=generator_model,\n",
+    "    ...     d_model=discriminator_model,\n",
+    "    ...     d_optimizer=discriminator_optimizer,\n",
     "    ... )\n",
-    "    >>> d_weight1 = K.eval(models['discriminator_model'].weights[0][0,0,0,0])\n",
-    "\n",
+    "    >>> d_weight1 = [d for d in discriminator_model.params()][-3][0].array\n",
     "    >>> d_weight0 != d_weight1  #check that training has occurred (i.e. weights changed)\n",
     "    True\n",
     "    \"\"\"\n",
-    "\n",
-    "    # hardcoded check that we are passing in 3 numpy arrays as input\n",
-    "    assert len(generator_inputs) == 3\n",
-    "    # check that X_data and W1_data have same length (batch size)\n",
-    "    assert generator_inputs[0].shape[0] == generator_inputs[1].shape[0]\n",
-    "    # check that X_data and W2_data have same length (batch size)\n",
-    "    assert generator_inputs[0].shape[0] == generator_inputs[2].shape[0]\n",
-    "\n",
     "    # @pytest.fixture\n",
-    "    g_model = models[\"generator_model\"]\n",
-    "    d_model = models[\"discriminator_model\"]\n",
+    "    if train == True:\n",
+    "        assert d_optimizer is not None  # Optimizer required for neural network training\n",
+    "    xp = chainer.backend.get_array_module(input_arrays[\"Y\"])\n",
     "\n",
     "    # @given(\"fake generated images from a generator\")\n",
-    "    fake_images = g_model.predict(x=generator_inputs, batch_size=32)\n",
-    "    fake_labels = np.zeros(shape=len(generator_inputs[0]))\n",
+    "    generator_inputs = {\n",
+    "        \"x\": input_arrays[\"X\"],\n",
+    "        \"w1\": input_arrays[\"W1\"],\n",
+    "        \"w2\": input_arrays[\"W2\"],\n",
+    "    }\n",
+    "    fake_images = g_model.forward(inputs=generator_inputs).array\n",
+    "    fake_labels = xp.zeros(shape=(len(fake_images), 1)).astype(xp.int32)\n",
     "\n",
     "    # @given(\"real groundtruth images\")\n",
-    "    real_images = groundtruth_images  # groundtruth images i.e. Y_data\n",
-    "    real_labels = np.ones(shape=len(groundtruth_images))\n",
+    "    real_images = input_arrays[\"Y\"]\n",
+    "    real_labels = xp.ones(shape=(len(real_images), 1)).astype(xp.int32)\n",
+    "\n",
+    "    # @when(\"the two sets of images are fed into the discriminator for comparison\")\n",
+    "    real_labels_pred = d_model.forward(inputs={\"x\": real_images})\n",
+    "    fake_labels_pred = d_model.forward(inputs={\"x\": fake_images})\n",
+    "    real_minus_fake_target = xp.ones(shape=(len(real_images), 1)).astype(xp.int32)\n",
+    "    fake_minus_real_target = xp.zeros(shape=(len(real_images), 1)).astype(xp.int32)\n",
+    "    d_loss = calculate_discriminator_loss(\n",
+    "        real_labels_pred=real_labels_pred,  # real image should get close to 1\n",
+    "        fake_labels_pred=fake_labels_pred,  # fake image should get close to 0\n",
+    "        real_minus_fake_target=real_minus_fake_target,  # where 1 (real) - 0 (fake) = 1 (target)\n",
+    "        fake_minus_real_target=fake_minus_real_target,  # where 0 (fake) - 1 (real) = 0 (target)?\n",
+    "    )\n",
     "\n",
-    "    # @when(\"the two sets of images are fed into the discriminator\")\n",
-    "    images = np.concatenate([fake_images, real_images])\n",
-    "    labels = np.concatenate([fake_labels, real_labels])\n",
-    "    assert d_model.trainable == True\n",
-    "    d_metrics = d_model.fit(\n",
-    "        x=images, y=labels, epochs=1, batch_size=32, shuffle=True, verbose=verbose\n",
-    "    ).history\n",
+    "    predicted_labels = xp.concatenate([real_labels_pred.array, fake_labels_pred.array])\n",
+    "    groundtruth_labels = xp.concatenate([real_labels, fake_labels])\n",
+    "    d_accu = F.binary_accuracy(y=predicted_labels, t=groundtruth_labels)\n",
     "\n",
-    "    # @then(\"the discriminator should know the fakes from the real images\")\n",
-    "    # assert d_weight0 != d_weight1  # check that training occurred i.e. weights changed\n",
+    "    # @then(\"the discriminator should learn to know the fakes from the real images\")\n",
+    "    if train == True:\n",
+    "        d_model.cleargrads()  # clear/zero all gradients\n",
+    "        d_loss.backward()  # renew gradients\n",
+    "        d_optimizer.update()  # backpropagate the loss using optimizer\n",
     "\n",
-    "    return models, d_metrics[\"loss\"][0]"
+    "    return float(d_loss.array), float(d_accu.array)  # return discriminator metrics"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 20,
    "metadata": {
     "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": [
-    "def train_generator(\n",
-    "    models: typing.Dict[str, keras.engine.training.Model],\n",
-    "    generator_inputs: typing.List[np.ndarray],\n",
-    "    groundtruth_images: np.ndarray,\n",
-    "    verbose: int = 1,\n",
-    ") -> (typing.Dict[str, keras.engine.training.Model], list):\n",
+    "def train_eval_generator(\n",
+    "    input_arrays: typing.Dict[str, cupy.ndarray],\n",
+    "    g_model,\n",
+    "    d_model,\n",
+    "    g_optimizer=None,\n",
+    "    train: bool = True,\n",
+    ") -> (float, float):\n",
     "    \"\"\"\n",
-    "    Trains the Generator within a Super Resolution Generative Adversarial Network.\n",
+    "    Evaluates and/or trains the Generator for one minibatch\n",
+    "    within a Super Resolution Generative Adversarial Network.\n",
     "    Discriminator is not trainable, Generator is trained.\n",
     "\n",
+    "    If train is set to False, only forward pass is run, i.e. evaluation/prediction only\n",
+    "    If train is set to True, forward and backward pass are run, i.e. train with backprop\n",
+    "\n",
     "    Steps:\n",
-    "    - Labels of the SRGAN output are set to Real(1)\n",
-    "    - Generator is trained to match these Real(1) labels\n",
-    "\n",
-    "    >>> generator_inputs = [\n",
-    "    ...     np.random.RandomState(seed=42).rand(32, s, s, 1) for s in [10, 100, 20]\n",
-    "    ... ]\n",
-    "    >>> groundtruth_images = np.random.RandomState(seed=42).rand(32,32,32,1)\n",
-    "    >>> models = compile_srgan_model(\n",
-    "    ...     g_network=generator_network(), d_network=discriminator_network()\n",
+    "    - Generator produces fake images\n",
+    "    - Fake images compared with real groundtruth images\n",
+    "    - Generator is trained to be more like real image\n",
+    "\n",
+    "    >>> train_arrays = {\n",
+    "    ...     \"X\": np.random.RandomState(seed=42).rand(2, 1, 10, 10).astype(np.float32),\n",
+    "    ...     \"W1\": np.random.RandomState(seed=42).rand(2, 1, 100, 100).astype(np.float32),\n",
+    "    ...     \"W2\": np.random.RandomState(seed=42).rand(2, 1, 20, 20).astype(np.float32),\n",
+    "    ...     \"Y\": np.random.RandomState(seed=42).rand(2, 1, 32, 32).astype(np.float32),\n",
+    "    ... }\n",
+    "    >>> generator_model = GeneratorModel()\n",
+    "    >>> generator_optimizer = chainer.optimizers.Adam(alpha=0.001, eps=1e-7).setup(\n",
+    "    ...     link=generator_model\n",
     "    ... )\n",
-    "\n",
-    "    >>> g_weight0 = K.eval(models['generator_model'].weights[0][0,0,0,0])\n",
-    "    >>> _, _ = train_generator(\n",
-    "    ...     models=models,\n",
-    "    ...     generator_inputs=generator_inputs,\n",
-    "    ...     groundtruth_images=groundtruth_images,\n",
-    "    ...     verbose=0,\n",
+    "    >>> discriminator_model = DiscriminatorModel()\n",
+    "\n",
+    "    >>> g_weight0 = [g for g in generator_model.params()][8][0, 0, 0, 0].array\n",
+    "    >>> _ = train_eval_generator(\n",
+    "    ...     input_arrays=train_arrays,\n",
+    "    ...     g_model=generator_model,\n",
+    "    ...     d_model=discriminator_model,\n",
+    "    ...     g_optimizer=generator_optimizer,\n",
     "    ... )\n",
-    "    >>> g_weight1 = K.eval(models['generator_model'].weights[0][0,0,0,0])\n",
-    "\n",
+    "    >>> g_weight1 = [g for g in generator_model.params()][8][0, 0, 0, 0].array\n",
     "    >>> g_weight0 != g_weight1  #check that training has occurred (i.e. weights changed)\n",
     "    True\n",
     "    \"\"\"\n",
     "\n",
     "    # @pytest.fixture\n",
-    "    srgan_model = models[\"srgan_model\"]\n",
+    "    if train == True:\n",
+    "        assert g_optimizer is not None  # Optimizer required for neural network training\n",
+    "    xp = chainer.backend.get_array_module(input_arrays[\"Y\"])\n",
+    "\n",
+    "    # @given(\"fake generated images from a generator\")\n",
+    "    generator_inputs = {\n",
+    "        \"x\": input_arrays[\"X\"],\n",
+    "        \"w1\": input_arrays[\"W1\"],\n",
+    "        \"w2\": input_arrays[\"W2\"],\n",
+    "    }\n",
+    "    fake_images = g_model.forward(inputs=generator_inputs)\n",
+    "    fake_labels = d_model.forward(inputs={\"x\": fake_images}).array.astype(xp.float32)\n",
     "\n",
     "    # @given(\"what we think the discriminator believes is real\")\n",
-    "    true_labels = np.ones(shape=len(generator_inputs[0]))\n",
-    "\n",
-    "    # @when(\"our inputs are fed into the super resolution model\")\n",
-    "    assert srgan_model.get_layer(name=\"discriminator_network\").trainable == False\n",
-    "    g_metrics = srgan_model.fit(\n",
-    "        x=generator_inputs,\n",
-    "        y={\n",
-    "            \"generator_network\": groundtruth_images,\n",
-    "            \"discriminator_network\": true_labels,\n",
-    "        },\n",
-    "        batch_size=32,\n",
-    "        verbose=verbose,\n",
-    "    ).history\n",
-    "\n",
-    "    # @then(\"the generator should create a more authentic looking image\")\n",
-    "    # assert g_weight0 != g_weight1  # check that training occurred i.e. weights changed\n",
-    "\n",
-    "    return models, [m[0] for m in g_metrics.values()]"
+    "    real_images = input_arrays[\"Y\"]\n",
+    "    real_labels = xp.ones(shape=(len(real_images), 1)).astype(xp.float32)\n",
+    "\n",
+    "    # @when(\"we compare the fake images to the real ones\")\n",
+    "    fake_minus_real_target = xp.ones(shape=(len(real_images), 1)).astype(xp.int32)\n",
+    "    real_minus_fake_target = xp.zeros(shape=(len(real_images), 1)).astype(xp.int32)\n",
+    "    g_loss = calculate_generator_loss(\n",
+    "        # content loss inputs, 2D images\n",
+    "        y_pred=fake_images,\n",
+    "        y_true=real_images,\n",
+    "        # adversarial loss inputs, 1D labels\n",
+    "        fake_labels=fake_labels,  # fake label 'should' get close to 1\n",
+    "        real_labels=real_labels,  # real label 'should' get close to 0\n",
+    "        fake_minus_real_target=fake_minus_real_target,  # where 1 (fake) - 0 (real) = 1 (target)\n",
+    "        real_minus_fake_target=real_minus_fake_target,  # where 0 (real) - 1 (fake) = 0 (target)?\n",
+    "    )\n",
+    "    g_psnr = psnr(y_pred=fake_images.array, y_true=real_images)\n",
+    "\n",
+    "    # @then(\"the generator should learn to create a more authentic looking image\")\n",
+    "    if train == True:\n",
+    "        g_model.cleargrads()  # clear/zero all gradients\n",
+    "        g_loss.backward()  # renew gradients\n",
+    "        g_optimizer.update()  # backpropagate the loss using optimizer\n",
+    "\n",
+    "    return float(g_loss.array), float(g_psnr)  # return generator loss and metric values"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
-       "<Figure size 1152x648 with 6 Axes>"
+       "<Figure size 1512x648 with 4 Axes>"
       ]
      },
      "metadata": {
@@ -837,100 +1345,137 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 25%|██▌       | 25/100 [05:17<14:40, 11.74s/it, discriminator_network_loss_actual=0.00657, loss=5.13e+3, generator_network_loss=5.13e+3, discriminator_network_loss=4.59, generator_network_psnr=157, discriminator_network_acc=0.0951, val_discriminator_network_loss_actual=0.0154, val_loss=4.21e+3, val_generator_network_loss=4.21e+3, val_discriminator_network_loss=3.93, val_generator_network_psnr=159, val_discriminator_network_acc=0.234]"
+      "100%|██████████| 50/50 [13:01<00:00, 14.93s/epoch, discriminator_loss=1.26e-5, discriminator_accu=0.583, generator_loss=0.562, generator_psnr=157, val_discriminator_loss=3.33e-7, val_discriminator_accu=0.586, val_generator_loss=0.598, val_generator_psnr=156]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/1\n",
-      "4712/4712 [==============================] - 4s 819us/step - loss: 0.0131\n",
-      "124/124 [==============================] - 0s 237us/step\n",
-      "Epoch 1/1\n",
-      "2048/2356 [=========================>....] - ETA: 0s - loss: 4988.3061 - generator_network_loss: 4981.8080 - discriminator_network_loss: 6.4981 - generator_network_psnr: 158.1775 - discriminator_network_acc: 0.0361"
+      "\n"
      ]
     }
    ],
    "source": [
-    "epochs = 100\n",
-    "with tqdm.trange(epochs) as t:\n",
-    "    metric_names = [\"discriminator_network_loss_actual\"] + models[\n",
-    "        \"srgan_model\"\n",
-    "    ].metrics_names\n",
-    "    columns = metric_names + [f\"val_{metric_name}\" for metric_name in metric_names]\n",
-    "    dataframe = pd.DataFrame(index=np.arange(0, epochs), columns=columns)\n",
-    "    for i in t:\n",
-    "        ## Part 1 - Train Discriminator\n",
-    "        _, d_train_loss = train_discriminator(\n",
-    "            models=models,\n",
-    "            generator_inputs=[X_train, W1_train, W2_train],\n",
-    "            groundtruth_images=Y_train,\n",
+    "epochs = 50\n",
+    "experiment.log_parameter(name=\"num_epochs\", value=epochs)\n",
+    "\n",
+    "metric_names = [\n",
+    "    \"discriminator_loss\",\n",
+    "    \"discriminator_accu\",\n",
+    "    \"generator_loss\",\n",
+    "    \"generator_psnr\",\n",
+    "]\n",
+    "columns = metric_names + [f\"val_{metric_name}\" for metric_name in metric_names]\n",
+    "dataframe = pd.DataFrame(index=np.arange(epochs), columns=columns)\n",
+    "progressbar = tqdm.tqdm(unit=\"epoch\", total=epochs, position=0)\n",
+    "\n",
+    "train_iter.reset()\n",
+    "dev_iter.reset()\n",
+    "\n",
+    "for i in range(epochs):\n",
+    "    metrics_dict = {mn: [] for mn in columns}  # reset metrics dictionary\n",
+    "\n",
+    "    ## Part 1 - Training on training dataset\n",
+    "    while i == train_iter.epoch:  # while we are in epoch i, run minibatch training\n",
+    "        train_batch = train_iter.next()\n",
+    "        train_arrays = chainer.dataset.concat_examples(batch=train_batch)\n",
+    "        ## 1.1 - Train Discriminator\n",
+    "        d_train_loss, d_train_accu = train_eval_discriminator(\n",
+    "            input_arrays=train_arrays,\n",
+    "            g_model=generator_model,\n",
+    "            d_model=discriminator_model,\n",
+    "            d_optimizer=discriminator_optimizer,\n",
     "        )\n",
-    "        d_dev_loss = models[\"discriminator_model\"].evaluate(\n",
-    "            x=models[\"generator_model\"].predict(\n",
-    "                x=[X_dev, W1_dev, W2_dev], batch_size=32\n",
-    "            ),\n",
-    "            y=np.zeros(shape=len(X_dev)),\n",
+    "        metrics_dict[\"discriminator_loss\"].append(d_train_loss)\n",
+    "        metrics_dict[\"discriminator_accu\"].append(d_train_accu)\n",
+    "\n",
+    "        ## 1.2 - Train Generator\n",
+    "        g_train_loss, g_train_psnr = train_eval_generator(\n",
+    "            input_arrays=train_arrays,\n",
+    "            g_model=generator_model,\n",
+    "            d_model=discriminator_model,\n",
+    "            g_optimizer=generator_optimizer,\n",
     "        )\n",
-    "\n",
-    "        ## Part 2 - Train Generator\n",
-    "        _, g_train_metrics = train_generator(\n",
-    "            models=models,\n",
-    "            generator_inputs=[X_train, W1_train, W2_train],\n",
-    "            groundtruth_images=Y_train,\n",
+    "        metrics_dict[\"generator_loss\"].append(g_train_loss)\n",
+    "        metrics_dict[\"generator_psnr\"].append(g_train_psnr)\n",
+    "\n",
+    "    ## Part 2 - Evaluation on development dataset\n",
+    "    while i == dev_iter.epoch:  # while we are in epoch i, evaluate on each minibatch\n",
+    "        dev_batch = dev_iter.next()\n",
+    "        dev_arrays = chainer.dataset.concat_examples(batch=dev_batch)\n",
+    "        ## 2.1 - Evaluate Discriminator\n",
+    "        d_train_loss, d_train_accu = train_eval_discriminator(\n",
+    "            input_arrays=dev_arrays,\n",
+    "            g_model=generator_model,\n",
+    "            d_model=discriminator_model,\n",
+    "            train=False,\n",
     "        )\n",
-    "        g_dev_metrics = models[\"srgan_model\"].evaluate(\n",
-    "            x=[X_dev, W1_dev, W2_dev],\n",
-    "            y={\n",
-    "                \"generator_network\": Y_dev,\n",
-    "                \"discriminator_network\": np.ones(shape=len(X_dev)),\n",
-    "            },\n",
-    "        )\n",
-    "\n",
-    "        ## Plot loss and metric information using pandas and livelossplot\n",
-    "        dataframe.loc[i] = (\n",
-    "            [d_train_loss] + g_train_metrics + [d_dev_loss] + g_dev_metrics\n",
+    "        metrics_dict[\"val_discriminator_loss\"].append(d_train_loss)\n",
+    "        metrics_dict[\"val_discriminator_accu\"].append(d_train_accu)\n",
+    "\n",
+    "        ## 2.2 - Evaluate Generator\n",
+    "        g_dev_loss, g_dev_psnr = train_eval_generator(\n",
+    "            input_arrays=dev_arrays,\n",
+    "            g_model=generator_model,\n",
+    "            d_model=discriminator_model,\n",
+    "            train=False,\n",
     "        )\n",
-    "        livelossplot.draw_plot(\n",
-    "            logs=dataframe.to_dict(orient=\"records\"),\n",
-    "            metrics=metric_names,\n",
-    "            max_cols=3,\n",
-    "            figsize=(16, 9),\n",
-    "            max_epoch=epochs,\n",
-    "        )\n",
-    "        t.set_postfix(ordered_dict=dataframe.loc[i].to_dict())\n",
-    "        experiment.log_metrics(dic=dataframe.loc[i].to_dict(), step=i)"
+    "        metrics_dict[\"val_generator_loss\"].append(g_dev_loss)\n",
+    "        metrics_dict[\"val_generator_psnr\"].append(g_dev_psnr)\n",
+    "\n",
+    "    ## Part 3 - Plot loss and metric information using livelossplot\n",
+    "    dataframe.loc[i] = [np.mean(metrics_dict[metric]) for metric in dataframe.keys()]\n",
+    "    livelossplot.draw_plot(\n",
+    "        logs=dataframe.to_dict(orient=\"records\"),\n",
+    "        metrics=metric_names,\n",
+    "        max_cols=4,\n",
+    "        figsize=(21, 9),\n",
+    "        max_epoch=epochs,\n",
+    "    )\n",
+    "    progressbar.set_postfix(ordered_dict=dataframe.loc[i].to_dict())\n",
+    "    experiment.log_metrics(dic=dataframe.loc[i].to_dict(), step=i)\n",
+    "    progressbar.update(n=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = models[\"generator_model\"]"
+    "model = generator_model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
     "os.makedirs(name=\"model/weights\", exist_ok=True)\n",
-    "# generator model's parameter weights and architecture\n",
-    "model.save(filepath=\"model/weights/srgan_generator_model.hdf5\")\n",
-    "# just the model weights\n",
-    "model.save_weights(filepath=\"model/weights/srgan_generator_model_weights.hdf5\")\n",
-    "# just the model architecture\n",
-    "with open(\"model/weights/srgan_generator_model_architecture.json\", \"w\") as json_file:\n",
-    "    json_file.write(model.to_json(indent=2))\n",
+    "# Save generator model's parameter weights in Numpy Zipped format\n",
+    "chainer.serializers.save_npz(\n",
+    "    file=\"model/weights/srgan_generator_model_weights.npz\", obj=model\n",
+    ")\n",
+    "# Save generator model's architecture in ONNX format\n",
+    "dummy_inputs = {\n",
+    "    \"x\": np.random.rand(32, 1, 10, 10).astype(\"float32\"),\n",
+    "    \"w1\": np.random.rand(32, 1, 100, 100).astype(\"float32\"),\n",
+    "    \"w2\": np.random.rand(32, 1, 20, 20).astype(\"float32\"),\n",
+    "}\n",
+    "_ = onnx_chainer.export(\n",
+    "    model=model,\n",
+    "    args={\"inputs\": dummy_inputs},\n",
+    "    filename=\"model/weights/srgan_generator_model_architecture.onnx\",\n",
+    "    export_params=False,\n",
+    "    save_text=True,\n",
+    ")\n",
     "\n",
     "# Upload model weights file to Comet.ML and finish Comet.ML experiment\n",
     "experiment.log_asset(\n",
-    "    file_path=\"model/weights/srgan_generator_model_weights.hdf5\",\n",
-    "    file_name=\"srgan_generator_model_weights\",\n",
+    "    file_path=\"model/weights/srgan_generator_model_weights.npz\",\n",
+    "    file_name=\"srgan_generator_model_weights.npz\",\n",
     ")"
    ]
   },
@@ -938,19 +1483,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 4. Evaluate model"
+    "# 4. Evaluate model"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Evaluation on independent test set"
+    "## Evaluation on independent test set"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 24,
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -971,8 +1516,8 @@
     "    )\n",
     "\n",
     "    # Run input datasets through trained neural network model\n",
-    "    model = deepbedmap.load_trained_model(model_inputs=(X_tile, W1_tile, W2_tile))\n",
-    "    Y_hat = model.predict(x=[X_tile, W1_tile, W2_tile], verbose=1)\n",
+    "    model = deepbedmap.load_trained_model()\n",
+    "    Y_hat = model.forward(inputs={\"x\": X_tile, \"w1\": W1_tile, \"w2\": W2_tile}).array\n",
     "\n",
     "    # Save infered deepbedmap to grid file(s)\n",
     "    deepbedmap.save_array_to_grid(\n",
@@ -999,7 +1544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1009,8 +1554,7 @@
       "Tiling: lowres/bedmap2_bed.tif\n",
       "Tiling: misc/REMA_100m_dem.tif\n",
       "Tiling: misc/MEaSUREs_IceFlowSpeed_450m.tif\n",
-      "1/1 [==============================] - 1s 1s/step\n",
-      "Experiment yielded Root Mean Square Error of 42.57 on test set\n"
+      "Experiment yielded Root Mean Square Error of 88.70 on test set\n"
      ]
     }
    ],
@@ -1022,7 +1566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1030,7 +1574,7 @@
      "output_type": "stream",
      "text": [
       "COMET INFO: Uploading stats to Comet before program termination (may take several seconds)\n",
-      "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/weiji14/deepbedmap/865e6812c7a84718bca3b3182817f825\n",
+      "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/weiji14/deepbedmap/d64dd9dd8dc54b3397a36d26337080c3\n",
       "\n"
      ]
     }
diff --git a/srgan_train.py b/srgan_train.py
index 2800986..cbbc717 100644
--- a/srgan_train.py
+++ b/srgan_train.py
@@ -14,7 +14,7 @@
 # ---
 
 # %% [markdown]
-# # Super-Resolution Generative Adversarial Network training
+# # **Super-Resolution Generative Adversarial Network training**
 #
 # Here in this jupyter notebook, we will train a super-resolution generative adversarial network (SRGAN), to create a high-resolution Antarctic bed Digital Elevation Model(DEM) from a low-resolution DEM.
 # In addition to that, we use additional correlated inputs that can also tell us something about the bed topography.
@@ -22,7 +22,7 @@
 # <img src="https://yuml.me/diagram/scruffy;dir:LR/class/[BEDMAP2 (1000m)]->[Generator model],[REMA (100m)]->[Generator model],[MEASURES Ice Flow Velocity (450m)]->[Generator model],[Generator model]->[High res bed DEM (250m)],[High res bed DEM (250m)]->[Discriminator model],[Groundtruth Image (250m)]->[Discriminator model],[Discriminator model]->[True/False]" alt="3 input SRGAN model"/>
 
 # %% [markdown]
-# ## 0. Setup libraries
+# # 0. Setup libraries
 
 # %%
 import os
@@ -39,52 +39,40 @@
 import pandas as pd
 import quilt
 import skimage.transform
-import sklearn.model_selection
 import tqdm
 
-import keras
-from keras import backend as K
-from keras.layers import (
-    Add,
-    BatchNormalization,
-    Concatenate,
-    Conv2D,
-    Conv2DTranspose,
-    Dense,
-    Flatten,
-    Input,
-    Lambda,
-)
-from keras.layers.advanced_activations import LeakyReLU, PReLU
-from keras.models import Model
+import chainer
+import chainer.functions as F
+import chainer.links as L
+import cupy
 import livelossplot
+import onnx_chainer
 
 from features.environment import _load_ipynb_modules
 
 print("Python       :", sys.version.split("\n")[0])
-print("Numpy        :", np.__version__)
-print("Keras        :", keras.__version__)
-print("Tensorflow   :", K.tf.__version__)
-K.tf.test.gpu_device_name()
+chainer.print_runtime_info()
 
 # %%
 # Set seed values
 seed = 42
 random.seed = seed
 np.random.seed(seed=seed)
-K.tf.set_random_seed(seed=seed)
+# cupy.random.seed(seed=seed)
 
 # Start tracking experiment using Comet.ML
-experiment = comet_ml.Experiment(workspace="weiji14", project_name="deepbedmap")
+experiment = comet_ml.Experiment(
+    workspace="weiji14", project_name="deepbedmap", disabled=False
+)
 
 # %% [markdown]
-# ## 1. Load data
+# # 1. Load data
 
 # %%
 hash = "1ccc9dc7f6344e1ec27b7aa972f2739d192d3e5adef8a64528b86bc799e2df60"
 quilt.install(package="weiji14/deepbedmap/model/train", hash=hash, force=True)
 pkg = quilt.load(pkginfo="weiji14/deepbedmap/model/train", hash=hash)
-experiment.log_parameter("dataset_hash", hash)
+experiment.log_parameter(name="dataset_hash", value=hash)
 
 # %%
 W1_data = pkg.W1_data()  # miscellaneous data REMA
@@ -98,50 +86,77 @@
 print(W1_data.shape, W2_data.shape, X_data.shape, Y_data.shape)
 
 # %% [markdown]
-# ### Split dataset into training (train) and development (dev) sets
+# ## 1.1 Convert arrays for Chainer
+# - From Numpy (CPU) to CuPy (GPU) format
+# - From NHWC format to NCHW format, where N=number of tiles, H=height, W=width, C=channels
 
 # %%
-def train_dev_split(dataset: np.ndarray, test_size=0.05, random_state=42):
-    """
-    Split our dataset up into training and development sets.
-    Used for cross validation purposes to check for overfitting.
-
-    >>> dataset = np.ones(shape=(100, 4, 4, 1))
-    >>> train, dev = train_dev_split(dataset=dataset, test_size=0.05, random_state=42)
-    >>> train.shape
-    (95, 4, 4, 1)
-    >>> dev.shape
-    (5, 4, 4, 1)
-    """
-    return sklearn.model_selection.train_test_split(
-        dataset,
-        test_size=test_size,
-        train_size=1 - test_size,
-        random_state=random_state,
-        shuffle=True,
-    )
+# Detect if there is a CUDA GPU first
+try:
+    cupy.cuda.get_device_id()
+    xp = cupy
+    print("Using GPU")
+    experiment.log_parameter(name="use_gpu", value=True)
+
+    W1_data = chainer.backend.cuda.to_gpu(array=W1_data)
+    W2_data = chainer.backend.cuda.to_gpu(array=W2_data)
+    X_data = chainer.backend.cuda.to_gpu(array=X_data)
+    Y_data = chainer.backend.cuda.to_gpu(array=Y_data)
+except:  # CUDARuntimeError
+    xp = np
+    print("Using CPU only")
+    experiment.log_parameter(name="use_gpu", value=False)
 
+# %%
+W1_data = xp.rollaxis(a=W1_data, axis=3, start=1)
+W2_data = xp.rollaxis(a=W2_data, axis=3, start=1)
+X_data = xp.rollaxis(a=X_data, axis=3, start=1)
+Y_data = xp.rollaxis(a=Y_data, axis=3, start=1)
+print(W1_data.shape, W2_data.shape, X_data.shape, Y_data.shape)
+
+# %% [markdown]
+# ## 1.2 Split dataset into training (train) and development (dev) sets
 
 # %%
-W1_train, W1_dev = train_dev_split(dataset=W1_data)
-W2_train, W2_dev = train_dev_split(dataset=W2_data)
-X_train, X_dev = train_dev_split(dataset=X_data)
-Y_train, Y_dev = train_dev_split(dataset=Y_data)
+dataset = chainer.datasets.DictDataset(X=X_data, W1=W1_data, W2=W2_data, Y=Y_data)
+train_set, dev_set = chainer.datasets.split_dataset_random(
+    dataset=dataset, first_size=int(len(X_data) * 0.95), seed=seed
+)
+experiment.log_parameters(
+    dic={"train_set_samples": len(train_set), "dev_set_samples": len(dev_set)}
+)
+print(
+    f"Training dataset: {len(train_set)} tiles, Development dataset: {len(dev_set)} tiles"
+)
 
+# %%
+batch_size = 32
+experiment.log_parameter(name="batch_size", value=batch_size)
+train_iter = chainer.iterators.SerialIterator(
+    dataset=train_set, batch_size=batch_size, repeat=True, shuffle=True
+)
+dev_iter = chainer.iterators.SerialIterator(
+    dataset=dev_set, batch_size=batch_size, repeat=True, shuffle=False
+)
 
 # %% [markdown]
-# ## 2. Architect model
+# # 2. Architect model
 #
-# Super Resolution Generative Adversarial Network model based on [Ledig et al. 2017](https://arxiv.org/abs/1609.04802v5).
-# Keras implementation below takes some hints from https://github.com/eriklindernoren/Keras-GAN/blob/master/srgan/srgan.py
+# Enhanced Super Resolution Generative Adversarial Network (ESRGAN) model based on [Wang et al. 2018](https://arxiv.org/abs/1809.00219v2).
+# Refer to original Pytorch implementation at https://github.com/xinntao/ESRGAN.
+# See also previous (non-enhanced) SRGAN model architecture by [Ledig et al. 2017](https://arxiv.org/abs/1609.04802).
 
 # %% [markdown]
-# ### Generator Network Architecture
+# ## 2.1 Generator Network Architecture
 #
-# ![SRGAN architecture - Generator Network](https://arxiv-sanity-sanity-production.s3.amazonaws.com/render-output/399644/images/used/jpg/generator.jpg)
-# ![3-in-1 Generator Network](https://yuml.me/01862e1a.png)
+# ![ESRGAN architecture - Generator Network composed of many Dense Convolutional Blocks](https://github.com/xinntao/ESRGAN/raw/master/figures/architecture.jpg)
 #
-# Details of the first convolutional layer:
+# 3 main components: 1) Input Block, 2) Residual Blocks, 3) Upsampling Blocks
+
+# %% [markdown]
+# ### 2.1.1 Input block, specially customized for DeepBedMap to take in 3 different inputs
+#
+# Details of the first convolutional layer for each input:
 #
 # - Input tiles are 8000m by 8000m.
 # - Convolution filter kernels are 3000m by 3000m.
@@ -153,209 +168,479 @@ def train_dev_split(dataset: np.ndarray, test_size=0.05, random_state=42):
 # - Convolution filter kernels are 30pixels by 30pixels
 # - Strides are 10pixels by 10pixels
 #
-# Note that first convolutional layer uses '**valid**' padding, see https://keras.io/layers/convolutional/ for more information.
+# Note that these first convolutional layers uses '**valid**' padding, see https://keras.io/layers/convolutional/ for more information.
+
+# %%
+class DeepbedmapInputBlock(chainer.Chain):
+    """
+    Custom input block for DeepBedMap.
+
+    Each filter kernel is 3km by 3km in size, with a 1km stride and no padding.
+    So for a 1km resolution image, (i.e. 1km pixel size):
+    kernel size is (3, 3), stride is (1, 1), and pad is (0, 0)
+
+      (?,1,10,10) --Conv2D-- (?,32,8,8) \
+    (?,1,100,100) --Conv2D-- (?,32,8,8) --Concat-- (?,96,8,8)
+      (?,1,20,20) --Conv2D-- (?,32,8,8) /
+
+    """
+
+    def __init__(self, out_channels=32):
+        super().__init__()
+        init_weights = chainer.initializers.HeNormal(scale=0.1, fan_option="fan_in")
+
+        with self.init_scope():
+            self.conv_on_X = L.Convolution2D(
+                in_channels=1,
+                out_channels=out_channels,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=(0, 0),  # 'valid' padding
+                initialW=init_weights,
+            )
+            self.conv_on_W1 = L.Convolution2D(
+                in_channels=1,
+                out_channels=out_channels,
+                ksize=(30, 30),
+                stride=(10, 10),
+                pad=(0, 0),  # 'valid' padding
+                initialW=init_weights,
+            )
+            self.conv_on_W2 = L.Convolution2D(
+                in_channels=1,
+                out_channels=out_channels,
+                ksize=(6, 6),
+                stride=(2, 2),
+                pad=(0, 0),  # 'valid' padding
+                initialW=init_weights,
+            )
+
+    def forward(self, x, w1, w2):
+        """
+        Forward computation, i.e. evaluate based on inputs X, W1 and W2
+        """
+        x_ = self.conv_on_X(x)
+        w1_ = self.conv_on_W1(w1)
+        w2_ = self.conv_on_W2(w2)
+
+        a = F.concat(xs=(x_, w1_, w2_))
+        return a
+
+
+# %% [markdown]
+# ### 2.1.2 Residual Block
+#
+# ![The Residual in Residual Dense Block in detail](https://raw.githubusercontent.com/xinntao/ESRGAN/master/figures/RRDB.png)
+
+# %%
+class ResidualDenseBlock(chainer.Chain):
+    """
+    Residual Dense Block made up of 5 Convolutional2D-LeakyReLU layers.
+    Final output has a residual scaling factor.
+    """
+
+    def __init__(self, in_out_channels: int = 64, inter_channels: int = 32):
+        super().__init__()
+        init_weights = chainer.initializers.HeNormal(scale=0.1, fan_option="fan_in")
+
+        with self.init_scope():
+            self.conv_layer1 = L.Convolution2D(
+                in_channels=in_out_channels,
+                out_channels=inter_channels,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=1,  # 'same' padding
+                initialW=init_weights,
+            )
+            self.conv_layer2 = L.Convolution2D(
+                in_channels=None,
+                out_channels=inter_channels,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=1,  # 'same' padding
+                initialW=init_weights,
+            )
+            self.conv_layer3 = L.Convolution2D(
+                in_channels=None,
+                out_channels=inter_channels,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=1,  # 'same' padding
+                initialW=init_weights,
+            )
+            self.conv_layer4 = L.Convolution2D(
+                in_channels=None,
+                out_channels=inter_channels,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=1,  # 'same' padding
+                initialW=init_weights,
+            )
+            self.conv_layer5 = L.Convolution2D(
+                in_channels=None,
+                out_channels=in_out_channels,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=1,  # 'same' padding
+                initialW=init_weights,
+            )
+
+    def forward(self, x, residual_scaling: float = 0.2):
+        """
+        Forward computation, i.e. evaluate based on input x
+        """
+
+        a0 = x
+
+        a1 = self.conv_layer1(a0)
+        a1 = F.leaky_relu(x=a1, slope=0.2)
+        a1_cat = F.concat(xs=(a0, a1), axis=1)
+
+        a2 = self.conv_layer2(a1_cat)
+        a2 = F.leaky_relu(x=a2, slope=0.2)
+        a2_cat = F.concat(xs=(a0, a1, a2), axis=1)
+
+        a3 = self.conv_layer3(a2_cat)
+        a3 = F.leaky_relu(x=a3, slope=0.2)
+        a3_cat = F.concat(xs=(a0, a1, a2, a3), axis=1)
+
+        a4 = self.conv_layer4(a3_cat)
+        a4 = F.leaky_relu(x=a4, slope=0.2)
+        a4_cat = F.concat(xs=(a0, a1, a2, a3, a4), axis=1)
+
+        a5 = self.conv_layer5(a4_cat)
+
+        # Final concatenation, with residual scaling of 0.2
+        a6 = F.add(a5 * residual_scaling, a0)
+
+        return a6
+
+
+# %%
+class ResInResDenseBlock(chainer.Chain):
+    """
+    Residual in Residual Dense block made of 3 Residual Dense Blocks
+
+       ------------  ----------  ------------
+      |            ||          ||            |
+    -----DenseBlock--DenseBlock--DenseBlock-(+)--
+      |                                      |
+       --------------------------------------
+
+    """
+
+    def __init__(self, denseblock_class=ResidualDenseBlock, out_channels: int = 64):
+        super().__init__()
+
+        with self.init_scope():
+            self.residual_dense_block1 = denseblock_class()
+            self.residual_dense_block2 = denseblock_class()
+            self.residual_dense_block3 = denseblock_class()
+
+    def forward(self, x, residual_scaling: float = 0.2):
+        """
+        Forward computation, i.e. evaluate based on input x
+        """
+        a1 = self.residual_dense_block1(x)
+        a2 = self.residual_dense_block2(a1)
+        a3 = self.residual_dense_block3(a2)
+
+        # Final concatenation, with residual scaling of 0.2
+        a4 = F.add(a3 * residual_scaling, x)
+
+        return a4
+
+
+# %% [markdown]
+# ### 2.1.3 Build the Generator Network, with upsampling layers!
+#
+# ![3 inputs feeding into the Generator Network, producing a high resolution prediction output](https://yuml.me/dffffcb0.png)
 #
 # <!--[W2_input(MEASURES)|20x20x1]-k6n32s2>[W2_inter|8x8x32],[W2_inter]->[Concat|8x8x96]
 # [X_input(BEDMAP2)|10x10x1]-k3n32s1>[X_inter|8x8x32],[X_inter]->[Concat|8x8x96]
 # [W1_input(REMA)|100x100x1]-k30n32s10>[W1_inter|8x8x32],[W1_inter]->[Concat|8x8x96]
-# [Concat|8x8x96]->[Generator-Network],[Generator-Network]->[Y_hat(High-Resolution_DEM)|32x32x1]-->
+# [Concat|8x8x96]->[Generator-Network|Many-Residual-Blocks],[Generator-Network]->[Y_hat(High-Resolution_DEM)|32x32x1]-->
 
 # %%
-def generator_network(
-    input1_shape: typing.Tuple[int, int, int] = (10, 10, 1),
-    input2_shape: typing.Tuple[int, int, int] = (100, 100, 1),
-    input3_shape: typing.Tuple[int, int, int] = (20, 20, 1),
-    num_residual_blocks: int = 16,
-    scaling: int = 4,
-    output_channels: int = 1,
-) -> keras.engine.network.Network:
+class GeneratorModel(chainer.Chain):
     """
     The generator network which is a deconvolutional neural network.
     Converts a low resolution input into a super resolution output.
 
+    Glues the input block with several residual blocks and upsampling layers
+
     Parameters:
       input_shape -- shape of input tensor in tuple format (height, width, channels)
-      num_residual_blocks -- how many Conv-BatchNorm-PReLU-Conv-BatchNorm blocks to use
+      num_residual_blocks -- how many Conv-LeakyReLU-Conv blocks to use
       scaling -- even numbered integer to increase resolution (e.g. 0, 2, 4, 6, 8)
-      output_channels -- integer representing number of output channels/filters/kernels
+      out_channels -- integer representing number of output channels/filters/kernels
 
     Example:
       An input_shape of (8,8,1) passing through 16 residual blocks with a scaling of 4
       and output_channels 1 will result in an image of shape (32,32,1)
 
-    >>> generator_network().input_shape
-    [(None, 10, 10, 1), (None, 100, 100, 1), (None, 20, 20, 1)]
-    >>> generator_network().output_shape
-    (None, 32, 32, 1)
-    >>> generator_network().count_params()
-    1614593
+    >>> generator_model = GeneratorModel()
+    >>> y_pred = generator_model.forward(
+    ...     inputs={
+    ...         "x": np.random.rand(1, 1, 10, 10).astype("float32"),
+    ...         "w1": np.random.rand(1, 1, 100, 100).astype("float32"),
+    ...         "w2": np.random.rand(1, 1, 20, 20).astype("float32"),
+    ...     }
+    ... )
+    >>> y_pred.shape
+    (1, 1, 32, 32)
+    >>> generator_model.count_params()
+    3333249
     """
 
-    assert num_residual_blocks >= 1  # ensure that we have 1 or more residual blocks
-    assert scaling % 2 == 0  # ensure scaling factor is even, i.e. 0, 2, 4, 8, etc
-    assert scaling >= 0  # ensure that scaling factor is zero or a positive number
-    assert output_channels >= 1  # ensure that we have 1 or more output channels
-
-    ## Input images
-    inp1 = Input(shape=input1_shape)  # low resolution image
-    assert inp1.shape.ndims == 4  # has to be shape like (?,10,10,1) for 10x10 grid
-    inp2 = Input(shape=input2_shape)  # other image (e.g. REMA)
-    assert inp2.shape.ndims == 4  # has to be shape like (?,100,100,1) for 100x100 grid
-    inp3 = Input(shape=input3_shape)  # other image (MEASURES Ice Flow)
-    assert inp3.shape.ndims == 4  # has to be shape like (?,20,20,1) for 20x20 grid
-
-    # 0 part
-    # Resize inputs to right scale using convolution (hardcoded kernel_size and strides)
-    inp1r = Conv2D(filters=32, kernel_size=(3, 3), strides=(1, 1), padding="valid")(
-        inp1
-    )
-    inp2r = Conv2D(filters=32, kernel_size=(30, 30), strides=(10, 10), padding="valid")(
-        inp2
-    )
-    inp3r = Conv2D(filters=32, kernel_size=(6, 6), strides=(2, 2), padding="valid")(
-        inp3
-    )
-
-    # Concatenate all inputs
-    # SEE https://distill.pub/2016/deconv-checkerboard/
-    X = Concatenate()([inp1r, inp2r, inp3r])  # Concatenate all the inputs together
-
-    # 1st part
-    # Pre-residual k3n64s1 (originally k9n64s1)
-    X0 = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding="same")(X)
-    X0 = PReLU(shared_axes=[1, 2])(X0)
-
-    # 2nd part
-    # Residual blocks k3n64s1
-    def residual_block(input_tensor):
-        x = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding="same")(
-            input_tensor
-        )
-        x = BatchNormalization()(x)
-        x = PReLU(shared_axes=[1, 2])(x)
-        x = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding="same")(x)
-        x = BatchNormalization()(x)
-        return Add()([x, input_tensor])
-
-    X = residual_block(X0)
-    for _ in range(num_residual_blocks - 1):
-        X = residual_block(X)
-
-    # 3rd part
-    # Post-residual blocks k3n64s1
-    X = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding="same")(X)
-    X = BatchNormalization()(X)
-    X = Add()([X, X0])
-
-    # 4th part
-    # Upsampling (if 4; run twice, if 8; run thrice, etc.) k3n256s1
-    for p, _ in enumerate(range(scaling // 2), start=1):
-        X = Conv2D(filters=256, kernel_size=(3, 3), strides=(1, 1), padding="same")(X)
-        pixelshuffleup = lambda images: K.tf.depth_to_space(input=images, block_size=2)
-        X = Lambda(function=pixelshuffleup, name=f"pixelshuffleup_{p}")(X)
-        X = PReLU(shared_axes=[1, 2])(X)
-
-    # 5th part
-    # Generate high resolution output k9n1s1 (originally k9n3s1 for RGB image)
-    outp = Conv2D(
-        filters=output_channels,
-        kernel_size=(9, 9),
-        strides=(1, 1),
-        padding="same",
-        name="generator_output",
-    )(X)
-
-    # Create neural network with input low-res images and output prediction
-    network = keras.engine.network.Network(
-        inputs=[inp1, inp2, inp3], outputs=[outp], name="generator_network"
-    )
-
-    return network
+    def __init__(
+        self,
+        inblock_class=DeepbedmapInputBlock,
+        resblock_class=ResInResDenseBlock,
+        num_residual_blocks: int = 4,
+        out_channels: int = 1,
+    ):
+        super().__init__()
+        self.num_residual_blocks = num_residual_blocks
+        init_weights = chainer.initializers.HeNormal(scale=0.1, fan_option="fan_in")
+
+        with self.init_scope():
+
+            # Initial Input and Residual Blocks
+            self.input_block = inblock_class()
+            self.pre_residual_conv_layer = L.Convolution2D(
+                in_channels=None,
+                out_channels=64,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=1,  # 'same' padding
+                initialW=init_weights,
+            )
+            self.residual_network = resblock_class().repeat(
+                n_repeat=num_residual_blocks
+            )
+            self.post_residual_conv_layer = L.Convolution2D(
+                in_channels=None,
+                out_channels=64,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=1,  # 'same' padding
+                initialW=init_weights,
+            )
+
+            # Upsampling Layers
+            self.pre_upsample_conv_layer_1 = L.Convolution2D(
+                in_channels=None,
+                out_channels=256,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=1,  # 'same' padding
+                initialW=init_weights,
+            )
+            self.pre_upsample_conv_layer_2 = L.Convolution2D(
+                in_channels=None,
+                out_channels=256,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=1,  # 'same' padding
+                initialW=init_weights,
+            )
+
+            # Final post-upsamle layers
+            self.final_conv_layer1 = L.Convolution2D(
+                in_channels=None,
+                out_channels=64,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=1,  # 'same' padding
+                initialW=init_weights,
+            )
+            self.final_conv_layer2 = L.Convolution2D(
+                in_channels=None,
+                out_channels=out_channels,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=1,  # 'same' padding
+                initialW=init_weights,
+            )
+
+    def forward(self, inputs: dict):
+        """
+        Forward computation, i.e. evaluate based on inputs
+
+        Input dictionary needs to have keys "x", "w1", "w2"
+        """
+        # 0 part
+        # Resize inputs o right scale using convolution (hardcoded kernel_size and strides)
+        # Also concatenate all inputs
+        a0 = self.input_block(x=inputs["x"], w1=inputs["w1"], w2=inputs["w2"])
+
+        # 1st part
+        # Pre-residual k3n64s1
+        a1 = self.pre_residual_conv_layer(a0)
+        a1 = F.leaky_relu(x=a1, slope=0.2)
+
+        # 2nd part
+        # Residual blocks k3n64s1
+        a2 = self.residual_network(a1)
+
+        # 3rd part
+        # Post-residual blocks k3n64s1
+        a3 = self.post_residual_conv_layer(a2)
+        a3 = F.add(a1, a3)
+
+        # 4th part
+        # Upsampling (if 4; run twice, if 8; run thrice, etc.) k3n256s1
+        a4_1 = self.pre_upsample_conv_layer_1(a3)
+        a4_1 = F.depth2space(X=a4_1, r=2)
+        a4_1 = F.leaky_relu(x=a4_1, slope=0.2)
+        a4_2 = self.pre_upsample_conv_layer_2(a4_1)
+        a4_2 = F.depth2space(X=a4_2, r=2)
+        a4_2 = F.leaky_relu(x=a4_2, slope=0.2)
+
+        # 5th part
+        # Generate high resolution output k3n64s1 and k3n1s1
+        a5_1 = self.final_conv_layer1(a4_2)
+        a5_1 = F.leaky_relu(x=a5_1, slope=0.2)
+        a5_2 = self.final_conv_layer2(a5_1)
+
+        return a5_2
 
 
 # %% [markdown]
-# ### Discriminator Network Architecture
+# ## 2.2 Discriminator Network Architecture
 #
 # Discriminator component is based on Deep Convolutional Generative Adversarial Networks by [Radford et al., 2015](https://arxiv.org/abs/1511.06434).
-# Keras implementation below takes some hints from https://github.com/erilyth/DCGANs/blob/master/DCGAN-CIFAR10/dcgan.py and https://github.com/yashk2810/DCGAN-Keras/blob/master/DCGAN.ipynb
+#
+# Note that figure below shows the 2017 (non-enhanced) SRGAN discriminator neural network architecture.
+# The 2018 ESRGAN version is basically the same architecture, as only the loss function was changed.
+# Note that the BatchNormalization layers **are still preserved** within the Convolutional blocks (see relevant line in original Pytorch implementation [here](https://github.com/xinntao/BasicSR/blob/902b4ae1f4beec7359de6e62ed0aebfc335d8dfd/codes/models/modules/architecture.py#L88)).
 #
 # ![SRGAN architecture - Discriminator Network](https://arxiv-sanity-sanity-production.s3.amazonaws.com/render-output/399644/images/used/jpg/discriminator.jpg)
 #
 # ![Discriminator Network](https://yuml.me/diagram/scruffy/class/[High-Resolution_DEM|32x32x1]->[Discriminator-Network],[Discriminator-Network]->[False/True|0/1])
 
 # %%
-def discriminator_network(
-    input_shape: typing.Tuple[int, int, int] = (32, 32, 1)
-) -> keras.engine.network.Network:
+class DiscriminatorModel(chainer.Chain):
     """
     The discriminator network which is a convolutional neural network.
     Takes ONE high resolution input image and predicts whether it is
     real or fake on a scale of 0 to 1, where 0 is fake and 1 is real.
 
-    >>> discriminator_network().input_shape
-    (None, 32, 32, 1)
-    >>> discriminator_network().output_shape
-    (None, 1)
-    >>> discriminator_network().count_params()
-    6828033
-    """
-
-    ## Input images
-    inp = Input(shape=input_shape)  # high resolution/groundtruth image to discriminate
-    assert inp.shape.ndims == 4  # needs to be shape like (?,32,32,1) for 8x8 grid
-
-    # 1st part
-    # Convolutonal Block without Batch Normalization k3n64s1
-    X = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding="same")(inp)
-    X = LeakyReLU(alpha=0.2)(X)
+    Consists of several Conv2D-BatchNorm-LeakyReLU blocks, followed by
+    a fully connected linear layer with LeakyReLU activation and a final
+    fully connected linear layer with Sigmoid activation.
 
-    # 2nd part
-    # Convolutional Blocks with Batch Normalization k3n{64*f}s{1or2}
-    for f, s in zip([1, 1, 2, 2, 4, 4, 8, 8], [1, 2, 1, 2, 1, 2, 1, 2]):
-        X = Conv2D(filters=64 * f, kernel_size=(3, 3), strides=(s, s), padding="same")(
-            X
-        )
-        X = BatchNormalization()(X)
-        X = LeakyReLU(alpha=0.2)(X)
-
-    # 3rd part
-    # Flatten, Dense (Fully Connected) Layers and Output
-    X = Flatten()(X)
-    X = Dense(units=1024)(X)  # ??!! Flatten?
-    X = LeakyReLU(alpha=0.2)(X)
-    outp = Dense(units=1, activation="sigmoid", name="discriminator_output")(X)
-
-    # Create neural network with input highres/groundtruth images, output validity 0/1
-    network = keras.engine.network.Network(
-        inputs=[inp], outputs=[outp], name="discriminator_network"
-    )
+    >>> discriminator_model = DiscriminatorModel()
+    >>> y_pred = discriminator_model.forward(
+    ...     inputs={
+    ...         "x": np.random.rand(2, 1, 32, 32).astype("float32"),
+    ...     }
+    ... )
+    >>> y_pred.shape
+    (2, 1)
+    >>> discriminator_model.count_params()
+    6824193
+    """
 
-    return network
+    def __init__(self):
+        super().__init__()
+        init_weights = chainer.initializers.GlorotUniform(scale=1.0)
+
+        with self.init_scope():
+
+            self.conv_layer0 = L.Convolution2D(
+                in_channels=None,
+                out_channels=64,
+                ksize=(3, 3),
+                stride=(1, 1),
+                pad=1,  # 'same' padding
+                nobias=False,  # default, have bias
+                initialW=init_weights,
+            )
+            self.conv_layer1 = L.Convolution2D(None, 64, 3, 1, 1, False, init_weights)
+            self.conv_layer2 = L.Convolution2D(None, 64, 3, 2, 1, False, init_weights)
+            self.conv_layer3 = L.Convolution2D(None, 128, 3, 1, 1, False, init_weights)
+            self.conv_layer4 = L.Convolution2D(None, 128, 3, 2, 1, False, init_weights)
+            self.conv_layer5 = L.Convolution2D(None, 256, 3, 1, 1, False, init_weights)
+            self.conv_layer6 = L.Convolution2D(None, 256, 3, 2, 1, False, init_weights)
+            self.conv_layer7 = L.Convolution2D(None, 512, 3, 1, 1, False, init_weights)
+            self.conv_layer8 = L.Convolution2D(None, 512, 3, 2, 1, False, init_weights)
+
+            self.batch_norm1 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)
+            self.batch_norm2 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)
+            self.batch_norm3 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)
+            self.batch_norm4 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)
+            self.batch_norm5 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)
+            self.batch_norm6 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)
+            self.batch_norm7 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)
+            self.batch_norm8 = L.BatchNormalization(axis=(0, 2, 3), eps=0.001)
+
+            self.linear_1 = L.Linear(in_size=None, out_size=1024, initialW=init_weights)
+            self.linear_2 = L.Linear(in_size=None, out_size=1, initialW=init_weights)
+
+    def forward(self, inputs: dict):
+        """
+        Forward computation, i.e. evaluate based on inputs
+
+        Input dictionary needs to have keys "x"
+        """
+
+        # 1st part
+        # Convolutonal Block without Batch Normalization k3n64s1
+        a0 = self.conv_layer0(x=inputs["x"])
+        a0 = F.leaky_relu(x=a0, slope=0.2)
+
+        # 2nd part
+        # Convolutional Blocks with Batch Normalization k3n{64*f}s{1or2}
+        a1 = self.conv_layer1(x=a0)
+        a1 = self.batch_norm1(x=a1)
+        a1 = F.leaky_relu(x=a1, slope=0.2)
+        a2 = self.conv_layer2(x=a1)
+        a2 = self.batch_norm2(x=a2)
+        a2 = F.leaky_relu(x=a2, slope=0.2)
+        a3 = self.conv_layer3(x=a2)
+        a3 = self.batch_norm3(x=a3)
+        a3 = F.leaky_relu(x=a3, slope=0.2)
+        a4 = self.conv_layer4(x=a3)
+        a4 = self.batch_norm4(x=a4)
+        a4 = F.leaky_relu(x=a4, slope=0.2)
+        a5 = self.conv_layer5(x=a4)
+        a5 = self.batch_norm5(x=a5)
+        a5 = F.leaky_relu(x=a5, slope=0.2)
+        a6 = self.conv_layer6(x=a5)
+        a6 = self.batch_norm6(x=a6)
+        a6 = F.leaky_relu(x=a6, slope=0.2)
+        a7 = self.conv_layer7(x=a6)
+        a7 = self.batch_norm7(x=a7)
+        a7 = F.leaky_relu(x=a7, slope=0.2)
+        a8 = self.conv_layer8(x=a7)
+        a8 = self.batch_norm8(x=a8)
+        a8 = F.leaky_relu(x=a8, slope=0.2)
+
+        # 3rd part
+        # Flatten, Dense (Fully Connected) Layers and Output
+        a9 = F.reshape(x=a8, shape=(len(a8), -1))  # flatten while keeping batch_size
+        a9 = self.linear_1(x=a9)
+        a9 = F.leaky_relu(x=a9, slope=0.2)
+        a10 = self.linear_2(x=a9)
+        # a10 = F.sigmoid(x=a10)  # no sigmoid activation, as it is in the loss function
+
+        return a10
 
 
 # %% [markdown]
-# ### Combine Generator and Discriminator Networks
+# ## 2.3 Define Loss function and Metrics for the Generator and Discriminator Networks
 #
-# Here we combine the Generator and Discriminator neural network models together, and define the Perceptual Loss function where:
+# Now we define the Perceptual Loss function for our Generator and Discriminator neural network models, where:
 #
 # $$Perceptual Loss = Content Loss + Adversarial Loss$$
 #
-# The original SRGAN paper by [Ledig et al. 2017](https://arxiv.org/abs/1609.04802v5) calculates *Content Loss* based on the ReLU activation layers of the pre-trained 19 layer VGG network.
-# The implementation below is less advanced, simply using a pixel-wise [Mean Squared Error (MSE) loss](https://keras.io/losses/#mean_squared_error) as the *Content Loss*.
-# Specifically, the *Content Loss* is calculated as the MSE difference between the output of the generator model (i.e. the predicted Super Resolution Image) and that of the groundtruth image (i.e. the true High Resolution Image).
-#
-# The *Adversarial Loss* or *Generative Loss* (confusing I know) is the same as in the original SRGAN paper.
-# It is defined based on the probabilities of the discriminator believing that the reconstructed Super Resolution Image is a natural High Resolution Image.
-# The implementation below uses the [Binary CrossEntropy loss](https://keras.io/losses/#binary_crossentropy).
-# Specifically, this *Adversarial Loss* is calculated between the output of the discriminator model (a value between 0 and 1) and that of the groundtruth label (a boolean value of either 0 or 1).
-#
-# Source code for the implementations of these loss functions in Keras can be found at https://github.com/keras-team/keras/blob/master/keras/losses.py.
-#
-# ![Perceptual Loss in a Super Resolution Generative Adversarial Network](https://yuml.me/69dc9a87.png)
+# ![Perceptual Loss in an Enhanced Super Resolution Generative Adversarial Network](https://yuml.me/db58d683.png)
 #
 # <!--
 # [LowRes-Inputs]-Generator>[SuperResolution_DEM]
-# [SuperResolution_DEM]-.->[note:Content-Loss|MeanSquaredError{bg:yellow}]
+# [SuperResolution_DEM]-.->[note:Content-Loss|MeanAbsoluteError{bg:yellow}]
 # [HighRes-Groundtruth_DEM]-.->[note:Content-Loss]
 # [SuperResolution_DEM]-Discriminator>[False_or_True_Prediction]
 # [HighRes-Groundtruth_DEM]-Discriminator>[False_or_True_Prediction]
@@ -366,115 +651,237 @@ def discriminator_network(
 # [note:Adversarial-Loss]-.->[note:Perceptual-Loss{bg:gold}]
 # -->
 
+# %% [markdown]
+# ### Content Loss
+#
+# The original SRGAN paper by [Ledig et al. 2017](https://arxiv.org/abs/1609.04802v5) calculates *Content Loss* based on the ReLU activation layers of the pre-trained 19 layer VGG network.
+# The implementation below is less advanced, simply using an L1 loss, i.e., a pixel-wise [Mean Absolute Error (MAE) loss](https://keras.io/losses/#mean_absolute_error) as the *Content Loss*.
+# Specifically, the *Content Loss* is calculated as the MAE difference between the output of the generator model (i.e. the predicted Super Resolution Image) and that of the groundtruth image (i.e. the true High Resolution Image).
+#
+# $$ e_i = ||G(x_{i}) - y_i||_{1} $$
+#
+# $$ Loss_{Content} = Mean Absolute Error = \dfrac{1}{n} \sum\limits_{i=1}^n e_i $$
+#
+# where $G(x_{i})$ is the Generator Network's predicted value, and $y_i$ is the groundtruth value, respectively at pixel $i$.
+# $e_i$ thus represents the absolute error (L1 loss) (denoted by $||\dots||_{1}$) between the predicted and groundtruth value.
+# We then sum all the pixel-wise errors $e_i,\dots,e_n$ and divide by the number of pixels $n$ to get the Arithmetic Mean $\dfrac{1}{n} \sum\limits_{i=1}^n$ of our error which is our *Content Loss*.
+
+# %% [markdown]
+# ### Adversarial Loss
+#
+# The *Adversarial Loss* or *Generative Loss* (confusing I know) is the same as in the original SRGAN paper.
+# It is defined based on the probabilities of the discriminator believing that the reconstructed Super Resolution Image is a natural High Resolution Image.
+# The implementation below uses the [Binary CrossEntropy loss](https://keras.io/losses/#binary_crossentropy).
+# Specifically, this *Adversarial Loss* is calculated between the output of the discriminator model (a value between 0 and 1) and that of the groundtruth label (a boolean value of either 0 or 1).
+#
+# $$ Loss_{Adversarial} = Binary Cross Entropy Loss = -\dfrac{1}{n} \sum\limits_{i=1}^n ( y_i ln(\sigma(x_i)) + (1-y_i) ln(1 - \sigma(x_i) ) $$
+#
+# where $\sigma$ is the [Sigmoid](https://en.wikipedia.org/wiki/Sigmoid_function) activation function, $\sigma = \dfrac{1}{1+e^{-x}} = \dfrac{e^x}{e^x+1}$, $y_i$ is the groundtruth label (1 for real, 0 for fake) and $x_i$ is the prediction (before sigmoid activation is applied), all respectively at pixel $i$.
+#
+# $\sigma(x)$ is basically the sigmoid activated output from a Standard Discriminator neural network, which some people also denote as $D(.)$.
+# Technically, some people also write $D(x) = \sigma(C(x))$, where $C(x)$ is the raw, non-transformed output from the Discriminator neural network (i.e. no sigmoid activation applied) on the input data $x$.
+# For simplicity, we now denote $C(x)$ simply as $x$ in the following equations, i.e. using $\sigma(x)$ to replace $\sigma(C(x))$.
+#
+# Again, the [Binary Cross Entropy Loss](https://en.wikipedia.org/wiki/Cross_entropy#Cross-entropy_error_function_and_logistic_regression) calculated on one pixel is defined as follows:
+#
+# $$ -( y ln(\sigma(x)) + (1-y) ln(1 - \sigma(x) )$$
+#
+# With the full expansion as such:
+#
+# $$ -\bigg[ y ln\big(\dfrac{e^x}{e^x+1}\big) + (1-y) ln\big(1 - \dfrac{e^x}{e^x+1}\big) \bigg] $$
+#
+# The above equation is mathematically equivalent to the one below, and can be derived using [Logarithm rules](https://en.wikipedia.org/wiki/Logarithm#Product,_quotient,_power,_and_root) such as the Power Rule and Product Rule, and using the fact that $ln(e)=1$ and $ln(1)=0$:
+#
+# $$ -[ xy - ln(1+e^x) ] $$
+#
+# However, this reformed equation is numerically unstable (see discussion [here](https://www.reddit.com/r/MachineLearning/comments/4euzmk/logsumexp_for_logistic_regression/)), and is good for values of $x<0$.
+# For values of $x>=0$, there is an alternative representation which we can derive:
+#
+# $$ -[ xy - ln(1+e^x) - x + x ] $$
+# $$ -[ x(y-1) - ln(1 + e^x) + ln(e^x) ] $$
+# $$ -\bigg[ x(y-1) - ln\big(\dfrac{e^x}{1+e^x}\big) \bigg] $$
+# $$ -\bigg[ x(y-1) - ln\big(\dfrac{1}{1+e^{-x}}\big) \bigg] $$
+# $$ - [ x(y-1) - ln(1) + ln(1+e^{-x}) ] $$
+# $$ - [ x(y-1) + ln(1+e^{-x}) $$
+#
+# In order to have a numerically stable function that works for both $x<0$ and $x>=0$, we can write it like so as in Caffe's implementation:
+#
+# $$ -[ x(y - 1_{x>=0} - ln(1+e^{x-2x\cdot1_{x>=0}}) ] $$
+#
+# Alternatively, Chainer does it like so:
+#
+# $$ -[ x(y - 1_{x>=0} - ln(1+e^{-|x|}) ] $$
+#
+# Or in Python code (the Chainer implemention from [here](https://github.com/chainer/chainer/blob/v6.0.0b1/chainer/functions/loss/sigmoid_cross_entropy.py#L41-L44)), bearing in mind that the natural logarithm $ln$ is `np.log` in Numpy:
+#
+# ```python
+#     sigmoidbinarycrossentropyloss = -(x * (y - (x >= 0)) - np.log1p(np.exp(-np.abs(x))))
+# ```
+#
+# See also how [Pytorch](https://pytorch.org/docs/stable/nn.html?highlight=bcewithlogitsloss#torch.nn.BCEWithLogitsLoss) and [Tensorflow](https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits) implements this in a numerically stable manner.
+
 # %%
-def compile_srgan_model(
-    g_network: keras.engine.network.Network,
-    d_network: keras.engine.network.Network,
-    metrics: typing.Dict[str, str] = None,
-) -> typing.Dict[str, keras.engine.training.Model]:
+def calculate_generator_loss(
+    y_pred: chainer.variable.Variable,
+    y_true: cupy.ndarray,
+    fake_labels: cupy.ndarray,
+    real_labels: cupy.ndarray,
+    fake_minus_real_target: cupy.ndarray,
+    real_minus_fake_target: cupy.ndarray,
+    content_loss_weighting: float = 5e-3,
+    adversarial_loss_weighting: float = 1e-2,
+) -> chainer.variable.Variable:
     """
-    Creates a Super Resolution Generative Adversarial Network (SRGAN)
-    by joining a generator network with a discriminator network.
-
-    Returns a dictionary containing:
-    1) generator model (trainable, not compiled)
-    2) discriminator model (trainable, compiled)
-    3) srgan model (trainable generator, untrainable discriminator, compiled)
-
-    The SRGAN model will be compiled with an optimizer (e.g. Adam)
-    and have separate loss functions and metrics for its
-    generator and discriminator component.
-
-    >>> metrics = {"generator_network": 'mse', "discriminator_network": 'accuracy'}
-    >>> models = compile_srgan_model(
-    ...     g_network=generator_network(),
-    ...     d_network=discriminator_network(),
-    ...     metrics=metrics,
+    This function calculates the weighted sum between
+    "Content Loss" and "Adversarial Loss".
+    which forms the basis for training the Generator Network.
+
+    >>> calculate_generator_loss(
+    ...     y_pred=chainer.variable.Variable(data=np.ones(shape=(2, 1, 3, 3))),
+    ...     y_true=np.full(shape=(2, 1, 3, 3), fill_value=10.0),
+    ...     fake_labels=np.array([[-1.2], [0.5]]),
+    ...     real_labels=np.array([[0.5], [-0.8]]),
+    ...     fake_minus_real_target=np.array([[1], [1]]).astype(np.int32),
+    ...     real_minus_fake_target=np.array([[0], [0]]).astype(np.int32),
     ... )
-    >>> models['discriminator_model'].trainable
-    True
-    >>> models['srgan_model'].get_layer(name='generator_network').trainable
-    True
-    >>> models['srgan_model'].get_layer(name='discriminator_network').trainable
-    False
-    >>> models['srgan_model'].count_params()
-    8442626
+    variable(0.06234614)
     """
-
-    # Check that our neural networks are named properly
-    assert g_network.name == "generator_network"
-    assert d_network.name == "discriminator_network"
-    assert g_network.trainable == True  # check that generator is trainable
-    assert d_network.trainable == True  # check that discriminator is trainable
-
-    ## Both trainable
-    # Create keras models (trainable) out of the networks (graph only)
-    g_model = Model(
-        inputs=g_network.inputs, outputs=g_network.outputs, name="generator_model"
-    )
-    d_model = Model(
-        inputs=d_network.inputs, outputs=d_network.outputs, name="discriminator_model"
-    )
-    d_model.compile(
-        optimizer=keras.optimizers.Adam(lr=0.001),
-        loss={"discriminator_output": keras.losses.binary_crossentropy},
+    # Content Loss (L1, Mean Absolute Error) between 2D images
+    content_loss = F.mean_absolute_error(x0=y_pred, x1=y_true)
+
+    # Adversarial Loss between 1D labels
+    adversarial_loss = calculate_discriminator_loss(
+        real_labels_pred=real_labels,
+        fake_labels_pred=fake_labels,
+        real_minus_fake_target=real_minus_fake_target,  # Zeros (0) instead of ones (1)
+        fake_minus_real_target=fake_minus_real_target,  # Ones (1) instead of zeros (0)
     )
 
-    ## One trainable (generator), one untrainable (discriminator)
-    # Connect Generator Network to Discriminator Network
-    g_out = g_network(inputs=g_network.inputs)  # g_in --(g_network)--> g_out
-    d_out = d_network(inputs=g_out)  # g_out --(d_network)--> d_out
-
-    # Create and Compile the Super Resolution Generative Adversarial Network Model!
-    model = Model(inputs=g_network.inputs, outputs=[g_out, d_out])
-    model.get_layer(
-        name="discriminator_network"
-    ).trainable = False  # combined model should not train discriminator
-    model.compile(
-        optimizer=keras.optimizers.Adam(lr=0.001),
-        loss={
-            "generator_network": keras.losses.mean_squared_error,
-            "discriminator_network": keras.losses.binary_crossentropy,
-        },
-        metrics=metrics,
-    )
+    # Get generator loss
+    weighted_content_loss = content_loss_weighting * content_loss
+    weighted_adversarial_loss = adversarial_loss_weighting * adversarial_loss
+    g_loss = weighted_content_loss + weighted_adversarial_loss
 
-    return {
-        "generator_model": g_model,
-        "discriminator_model": d_model,
-        "srgan_model": model,
-    }
+    return g_loss
 
 
 # %%
-def psnr(y_true: np.ndarray, y_pred: np.ndarray) -> np.ndarray:
+def psnr(
+    y_true: cupy.ndarray, y_pred: cupy.ndarray, data_range=2 ** 32
+) -> cupy.ndarray:
     """
-    Peak Signal-Noise Ratio (PSNR) metric.
+    Peak Signal-Noise Ratio (PSNR) metric, calculated batchwise.
     See https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Definition
 
-    >>> y_true, y_pred = np.ones(shape=(3, 3)), np.full(shape=(3, 3), fill_value=2)
-    >>> K.eval(psnr(y_true=y_true, y_pred=y_pred))
-    array([221.80709678, 221.80709678, 221.80709678])
+    Can take in either numpy (CPU) or cupy (GPU) arrays as input.
+    Implementation is same as skimage.measure.compare_psnr with data_range=2**32
+
+    >>> psnr(
+    ...     y_true=np.ones(shape=(2, 1, 3, 3)),
+    ...     y_pred=np.full(shape=(2, 1, 3, 3), fill_value=2),
+    ... )
+    192.65919722494797
     """
+    xp = chainer.backend.get_array_module(y_true)
 
-    mse = (
-        K.mean(K.square(K.np.subtract(y_pred, y_true)), axis=-1) + K.epsilon()
-    )  # add epsilon to prevent zero division
-    return K.np.multiply(
-        20, K.log(2 ** 16 / K.sqrt(mse))
-    )  # setting MAX_I as 2^16, i.e. max for int16
+    # Calculate Mean Squred Error along predetermined axes
+    mse = xp.mean(xp.square(xp.subtract(y_pred, y_true)), axis=None)
+
+    # Calculate Peak Signal-Noise Ratio, setting MAX_I as 2^32, i.e. max for int32
+    return xp.multiply(20, xp.log10(data_range / xp.sqrt(mse)))
 
 
 # %%
-K.clear_session()  # Reset Keras/Tensorflow graph
-metrics = {"generator_network": psnr, "discriminator_network": "accuracy"}
-models = compile_srgan_model(
-    g_network=generator_network(), d_network=discriminator_network(), metrics=metrics
+def calculate_discriminator_loss(
+    real_labels_pred: chainer.variable.Variable,
+    fake_labels_pred: chainer.variable.Variable,
+    real_minus_fake_target: cupy.ndarray,
+    fake_minus_real_target: cupy.ndarray,
+) -> chainer.variable.Variable:
+    """
+    This function purely calculates the "Adversarial Loss"
+    in a Relativistic Average Generative Adversarial Network (RaGAN).
+
+    It forms the basis for training the Discriminator Network,
+    but it is also used as part of the Generator Network's loss function.
+
+    See paper by Jolicoeur-Martineau, 2018 at https://arxiv.org/abs/1807.00734
+    for the mathematical details of the RaGAN loss function.
+
+    Original Sigmoid_Cross_Entropy formula:
+    -(y * np.log(sigmoid(x)) + (1 - y) * np.log(1 - sigmoid(x)))
+
+    Numerically stable formula:
+    -(x * (y - (x >= 0)) - np.log1p(np.exp(-np.abs(x))))
+
+    where y = the target difference between real and fake labels (i.e. 1 - 0 = 1)
+          x = the calculated difference between real_labels_pred and fake_labels_pred
+
+    >>> calculate_discriminator_loss(
+    ...     real_labels_pred=chainer.variable.Variable(data=np.array([[1.1], [-0.5]])),
+    ...     fake_labels_pred=chainer.variable.Variable(data=np.array([[-0.3], [1.0]])),
+    ...     real_minus_fake_target=np.array([[1], [1]]),
+    ...     fake_minus_real_target=np.array([[0], [0]]),
+    ... )
+    variable(1.56670504)
+    """
+
+    # Calculate arithmetic mean of real/fake predicted labels
+    real_labels_pred_avg = F.mean(real_labels_pred)
+    fake_labels_pred_avg = F.mean(fake_labels_pred)
+
+    # Binary Cross-Entropy Loss with Sigmoid
+    real_versus_fake_loss = F.sigmoid_cross_entropy(
+        x=(real_labels_pred - fake_labels_pred_avg), t=real_minus_fake_target
+    )  # let predicted labels from real images be more realistic than those from fake
+    fake_versus_real_loss = F.sigmoid_cross_entropy(
+        x=(fake_labels_pred - real_labels_pred_avg), t=fake_minus_real_target
+    )  # let predicted labels from fake images be less realistic than those from real
+
+    # Relativistic average Standard GAN's Discriminator Loss
+    d_loss = real_versus_fake_loss + fake_versus_real_loss
+
+    return d_loss
+
+
+# %%
+# Build the models
+generator_model = GeneratorModel()
+discriminator_model = DiscriminatorModel()
+experiment.log_parameter(
+    name="num_residual_blocks", value=generator_model.num_residual_blocks
+)
+
+# Transfer models to GPU if available
+if xp == cupy:  # Check if CuPy was loaded, i.e. GPU is available
+    generator_model.to_gpu(device=0)
+    discriminator_model.to_gpu(device=0)
+
+# %%
+# Setup optimizer, using Adam
+generator_optimizer = chainer.optimizers.Adam(alpha=6e-4, eps=1e-8).setup(
+    link=generator_model
+)
+experiment.log_parameters(
+    dic={
+        "generator_optimizer": "adam",
+        "generator_lr": generator_optimizer.alpha,  # learning rate
+        "generator_epsilon": generator_optimizer.eps,
+    }
+)
+discriminator_optimizer = chainer.optimizers.Adam(alpha=6e-4, eps=1e-8).setup(
+    link=discriminator_model
+)
+experiment.log_parameters(
+    dic={
+        "discriminator_optimizer": "adam",
+        "discriminator_lr": discriminator_optimizer.alpha,  # learning rate
+        "discriminator_adam_epsilon": discriminator_optimizer.eps,
+    }
 )
-models["srgan_model"].summary()
 
 # %% [markdown]
-# ## 3. Train model
+# # 3. Train model
 #
 # [Gherkin](https://en.wikipedia.org/wiki/Gherkin_(language))/Plain English statement at what the Super-Resolution Generative Adversarial Network below does
 #
@@ -488,22 +895,24 @@ def psnr(y_true: np.ndarray, y_pred: np.ndarray) -> np.ndarray:
 #       Scenario: Train discriminator to beat generator
 #         Given fake generated images from a generator
 #           And real groundtruth images
-#          When the two sets of images are fed into the discriminator
-#          Then the discriminator should know the fakes from the real images
+#          When the two sets of images are fed into the discriminator for comparison
+#          Then the discriminator should learn to know the fakes from the real images
 #
 #       Scenario: Train generator to fool discriminator
-#         Given what we think the discriminator believes is real
-#          When our inputs are fed into the super resolution model
-#          Then the generator should create a more authentic looking image
+#         Given fake generated images from a generator
+#           And what we think the discriminator believes is real
+#          When we compare the fake images to the real ones
+#          Then the generator should learn to create a more authentic looking image
 # ```
 
 # %%
-def train_discriminator(
-    models: typing.Dict[str, keras.engine.training.Model],
-    generator_inputs: typing.List[np.ndarray],
-    groundtruth_images: np.ndarray,
-    verbose: int = 1,
-) -> (typing.Dict[str, keras.engine.training.Model], list):
+def train_eval_discriminator(
+    input_arrays: typing.Dict[str, cupy.ndarray],
+    g_model,
+    d_model,
+    d_optimizer=None,
+    train: bool = True,
+) -> (float, float):
     """
     Trains the Discriminator within a Super Resolution Generative Adversarial Network.
     Discriminator is trainable, Generator is not trained (only produces predictions).
@@ -513,194 +922,274 @@ def train_discriminator(
     - Fake images combined with real groundtruth images
     - Discriminator trained with these images and their Fake(0)/Real(1) labels
 
-    >>> generator_inputs = [
-    ...     np.random.RandomState(seed=42).rand(32, s, s, 1) for s in [10, 100, 20]
-    ... ]
-    >>> groundtruth_images = np.random.RandomState(seed=42).rand(32,32,32,1)
-    >>> models = compile_srgan_model(
-    ...     g_network=generator_network(), d_network=discriminator_network()
+    >>> train_arrays = {
+    ...     "X": np.random.RandomState(seed=42).rand(2, 1, 10, 10).astype(np.float32),
+    ...     "W1": np.random.RandomState(seed=42).rand(2, 1, 100, 100).astype(np.float32),
+    ...     "W2": np.random.RandomState(seed=42).rand(2, 1, 20, 20).astype(np.float32),
+    ...     "Y": np.random.RandomState(seed=42).rand(2, 1, 32, 32).astype(np.float32),
+    ... }
+    >>> discriminator_model = DiscriminatorModel()
+    >>> discriminator_optimizer = chainer.optimizers.Adam(alpha=0.001, eps=1e-7).setup(
+    ...     link=discriminator_model
     ... )
-
-    >>> d_weight0 = K.eval(models['discriminator_model'].weights[0][0,0,0,0])
-    >>> _, _ = train_discriminator(
-    ...     models=models,
-    ...     generator_inputs=generator_inputs,
-    ...     groundtruth_images=groundtruth_images,
-    ...     verbose=0,
+    >>> generator_model = GeneratorModel()
+
+    >>> d_weight0 = [d for d in discriminator_model.params()][-3][0].array
+    >>> d_train_loss, d_train_accu = train_eval_discriminator(
+    ...     input_arrays=train_arrays,
+    ...     g_model=generator_model,
+    ...     d_model=discriminator_model,
+    ...     d_optimizer=discriminator_optimizer,
     ... )
-    >>> d_weight1 = K.eval(models['discriminator_model'].weights[0][0,0,0,0])
-
+    >>> d_weight1 = [d for d in discriminator_model.params()][-3][0].array
     >>> d_weight0 != d_weight1  #check that training has occurred (i.e. weights changed)
     True
     """
-
-    # hardcoded check that we are passing in 3 numpy arrays as input
-    assert len(generator_inputs) == 3
-    # check that X_data and W1_data have same length (batch size)
-    assert generator_inputs[0].shape[0] == generator_inputs[1].shape[0]
-    # check that X_data and W2_data have same length (batch size)
-    assert generator_inputs[0].shape[0] == generator_inputs[2].shape[0]
-
     # @pytest.fixture
-    g_model = models["generator_model"]
-    d_model = models["discriminator_model"]
+    if train == True:
+        assert d_optimizer is not None  # Optimizer required for neural network training
+    xp = chainer.backend.get_array_module(input_arrays["Y"])
 
     # @given("fake generated images from a generator")
-    fake_images = g_model.predict(x=generator_inputs, batch_size=32)
-    fake_labels = np.zeros(shape=len(generator_inputs[0]))
+    generator_inputs = {
+        "x": input_arrays["X"],
+        "w1": input_arrays["W1"],
+        "w2": input_arrays["W2"],
+    }
+    fake_images = g_model.forward(inputs=generator_inputs).array
+    fake_labels = xp.zeros(shape=(len(fake_images), 1)).astype(xp.int32)
 
     # @given("real groundtruth images")
-    real_images = groundtruth_images  # groundtruth images i.e. Y_data
-    real_labels = np.ones(shape=len(groundtruth_images))
+    real_images = input_arrays["Y"]
+    real_labels = xp.ones(shape=(len(real_images), 1)).astype(xp.int32)
+
+    # @when("the two sets of images are fed into the discriminator for comparison")
+    real_labels_pred = d_model.forward(inputs={"x": real_images})
+    fake_labels_pred = d_model.forward(inputs={"x": fake_images})
+    real_minus_fake_target = xp.ones(shape=(len(real_images), 1)).astype(xp.int32)
+    fake_minus_real_target = xp.zeros(shape=(len(real_images), 1)).astype(xp.int32)
+    d_loss = calculate_discriminator_loss(
+        real_labels_pred=real_labels_pred,  # real image should get close to 1
+        fake_labels_pred=fake_labels_pred,  # fake image should get close to 0
+        real_minus_fake_target=real_minus_fake_target,  # where 1 (real) - 0 (fake) = 1 (target)
+        fake_minus_real_target=fake_minus_real_target,  # where 0 (fake) - 1 (real) = 0 (target)?
+    )
 
-    # @when("the two sets of images are fed into the discriminator")
-    images = np.concatenate([fake_images, real_images])
-    labels = np.concatenate([fake_labels, real_labels])
-    assert d_model.trainable == True
-    d_metrics = d_model.fit(
-        x=images, y=labels, epochs=1, batch_size=32, shuffle=True, verbose=verbose
-    ).history
+    predicted_labels = xp.concatenate([real_labels_pred.array, fake_labels_pred.array])
+    groundtruth_labels = xp.concatenate([real_labels, fake_labels])
+    d_accu = F.binary_accuracy(y=predicted_labels, t=groundtruth_labels)
 
-    # @then("the discriminator should know the fakes from the real images")
-    # assert d_weight0 != d_weight1  # check that training occurred i.e. weights changed
+    # @then("the discriminator should learn to know the fakes from the real images")
+    if train == True:
+        d_model.cleargrads()  # clear/zero all gradients
+        d_loss.backward()  # renew gradients
+        d_optimizer.update()  # backpropagate the loss using optimizer
 
-    return models, d_metrics["loss"][0]
+    return float(d_loss.array), float(d_accu.array)  # return discriminator metrics
 
 
 # %%
-def train_generator(
-    models: typing.Dict[str, keras.engine.training.Model],
-    generator_inputs: typing.List[np.ndarray],
-    groundtruth_images: np.ndarray,
-    verbose: int = 1,
-) -> (typing.Dict[str, keras.engine.training.Model], list):
+def train_eval_generator(
+    input_arrays: typing.Dict[str, cupy.ndarray],
+    g_model,
+    d_model,
+    g_optimizer=None,
+    train: bool = True,
+) -> (float, float):
     """
-    Trains the Generator within a Super Resolution Generative Adversarial Network.
+    Evaluates and/or trains the Generator for one minibatch
+    within a Super Resolution Generative Adversarial Network.
     Discriminator is not trainable, Generator is trained.
 
+    If train is set to False, only forward pass is run, i.e. evaluation/prediction only
+    If train is set to True, forward and backward pass are run, i.e. train with backprop
+
     Steps:
-    - Labels of the SRGAN output are set to Real(1)
-    - Generator is trained to match these Real(1) labels
-
-    >>> generator_inputs = [
-    ...     np.random.RandomState(seed=42).rand(32, s, s, 1) for s in [10, 100, 20]
-    ... ]
-    >>> groundtruth_images = np.random.RandomState(seed=42).rand(32,32,32,1)
-    >>> models = compile_srgan_model(
-    ...     g_network=generator_network(), d_network=discriminator_network()
+    - Generator produces fake images
+    - Fake images compared with real groundtruth images
+    - Generator is trained to be more like real image
+
+    >>> train_arrays = {
+    ...     "X": np.random.RandomState(seed=42).rand(2, 1, 10, 10).astype(np.float32),
+    ...     "W1": np.random.RandomState(seed=42).rand(2, 1, 100, 100).astype(np.float32),
+    ...     "W2": np.random.RandomState(seed=42).rand(2, 1, 20, 20).astype(np.float32),
+    ...     "Y": np.random.RandomState(seed=42).rand(2, 1, 32, 32).astype(np.float32),
+    ... }
+    >>> generator_model = GeneratorModel()
+    >>> generator_optimizer = chainer.optimizers.Adam(alpha=0.001, eps=1e-7).setup(
+    ...     link=generator_model
     ... )
-
-    >>> g_weight0 = K.eval(models['generator_model'].weights[0][0,0,0,0])
-    >>> _, _ = train_generator(
-    ...     models=models,
-    ...     generator_inputs=generator_inputs,
-    ...     groundtruth_images=groundtruth_images,
-    ...     verbose=0,
+    >>> discriminator_model = DiscriminatorModel()
+
+    >>> g_weight0 = [g for g in generator_model.params()][8][0, 0, 0, 0].array
+    >>> _ = train_eval_generator(
+    ...     input_arrays=train_arrays,
+    ...     g_model=generator_model,
+    ...     d_model=discriminator_model,
+    ...     g_optimizer=generator_optimizer,
     ... )
-    >>> g_weight1 = K.eval(models['generator_model'].weights[0][0,0,0,0])
-
+    >>> g_weight1 = [g for g in generator_model.params()][8][0, 0, 0, 0].array
     >>> g_weight0 != g_weight1  #check that training has occurred (i.e. weights changed)
     True
     """
 
     # @pytest.fixture
-    srgan_model = models["srgan_model"]
+    if train == True:
+        assert g_optimizer is not None  # Optimizer required for neural network training
+    xp = chainer.backend.get_array_module(input_arrays["Y"])
 
-    # @given("what we think the discriminator believes is real")
-    true_labels = np.ones(shape=len(generator_inputs[0]))
+    # @given("fake generated images from a generator")
+    generator_inputs = {
+        "x": input_arrays["X"],
+        "w1": input_arrays["W1"],
+        "w2": input_arrays["W2"],
+    }
+    fake_images = g_model.forward(inputs=generator_inputs)
+    fake_labels = d_model.forward(inputs={"x": fake_images}).array.astype(xp.float32)
 
-    # @when("our inputs are fed into the super resolution model")
-    assert srgan_model.get_layer(name="discriminator_network").trainable == False
-    g_metrics = srgan_model.fit(
-        x=generator_inputs,
-        y={
-            "generator_network": groundtruth_images,
-            "discriminator_network": true_labels,
-        },
-        batch_size=32,
-        verbose=verbose,
-    ).history
+    # @given("what we think the discriminator believes is real")
+    real_images = input_arrays["Y"]
+    real_labels = xp.ones(shape=(len(real_images), 1)).astype(xp.float32)
+
+    # @when("we compare the fake images to the real ones")
+    fake_minus_real_target = xp.ones(shape=(len(real_images), 1)).astype(xp.int32)
+    real_minus_fake_target = xp.zeros(shape=(len(real_images), 1)).astype(xp.int32)
+    g_loss = calculate_generator_loss(
+        # content loss inputs, 2D images
+        y_pred=fake_images,
+        y_true=real_images,
+        # adversarial loss inputs, 1D labels
+        fake_labels=fake_labels,  # fake label 'should' get close to 1
+        real_labels=real_labels,  # real label 'should' get close to 0
+        fake_minus_real_target=fake_minus_real_target,  # where 1 (fake) - 0 (real) = 1 (target)
+        real_minus_fake_target=real_minus_fake_target,  # where 0 (real) - 1 (fake) = 0 (target)?
+    )
+    g_psnr = psnr(y_pred=fake_images.array, y_true=real_images)
 
-    # @then("the generator should create a more authentic looking image")
-    # assert g_weight0 != g_weight1  # check that training occurred i.e. weights changed
+    # @then("the generator should learn to create a more authentic looking image")
+    if train == True:
+        g_model.cleargrads()  # clear/zero all gradients
+        g_loss.backward()  # renew gradients
+        g_optimizer.update()  # backpropagate the loss using optimizer
 
-    return models, [m[0] for m in g_metrics.values()]
+    return float(g_loss.array), float(g_psnr)  # return generator loss and metric values
 
 
 # %%
-epochs = 100
-with tqdm.trange(epochs) as t:
-    metric_names = ["discriminator_network_loss_actual"] + models[
-        "srgan_model"
-    ].metrics_names
-    columns = metric_names + [f"val_{metric_name}" for metric_name in metric_names]
-    dataframe = pd.DataFrame(index=np.arange(0, epochs), columns=columns)
-    for i in t:
-        ## Part 1 - Train Discriminator
-        _, d_train_loss = train_discriminator(
-            models=models,
-            generator_inputs=[X_train, W1_train, W2_train],
-            groundtruth_images=Y_train,
-        )
-        d_dev_loss = models["discriminator_model"].evaluate(
-            x=models["generator_model"].predict(
-                x=[X_dev, W1_dev, W2_dev], batch_size=32
-            ),
-            y=np.zeros(shape=len(X_dev)),
-        )
-
-        ## Part 2 - Train Generator
-        _, g_train_metrics = train_generator(
-            models=models,
-            generator_inputs=[X_train, W1_train, W2_train],
-            groundtruth_images=Y_train,
+epochs = 50
+experiment.log_parameter(name="num_epochs", value=epochs)
+
+metric_names = [
+    "discriminator_loss",
+    "discriminator_accu",
+    "generator_loss",
+    "generator_psnr",
+]
+columns = metric_names + [f"val_{metric_name}" for metric_name in metric_names]
+dataframe = pd.DataFrame(index=np.arange(epochs), columns=columns)
+progressbar = tqdm.tqdm(unit="epoch", total=epochs, position=0)
+
+train_iter.reset()
+dev_iter.reset()
+
+for i in range(epochs):
+    metrics_dict = {mn: [] for mn in columns}  # reset metrics dictionary
+
+    ## Part 1 - Training on training dataset
+    while i == train_iter.epoch:  # while we are in epoch i, run minibatch training
+        train_batch = train_iter.next()
+        train_arrays = chainer.dataset.concat_examples(batch=train_batch)
+        ## 1.1 - Train Discriminator
+        d_train_loss, d_train_accu = train_eval_discriminator(
+            input_arrays=train_arrays,
+            g_model=generator_model,
+            d_model=discriminator_model,
+            d_optimizer=discriminator_optimizer,
         )
-        g_dev_metrics = models["srgan_model"].evaluate(
-            x=[X_dev, W1_dev, W2_dev],
-            y={
-                "generator_network": Y_dev,
-                "discriminator_network": np.ones(shape=len(X_dev)),
-            },
+        metrics_dict["discriminator_loss"].append(d_train_loss)
+        metrics_dict["discriminator_accu"].append(d_train_accu)
+
+        ## 1.2 - Train Generator
+        g_train_loss, g_train_psnr = train_eval_generator(
+            input_arrays=train_arrays,
+            g_model=generator_model,
+            d_model=discriminator_model,
+            g_optimizer=generator_optimizer,
         )
-
-        ## Plot loss and metric information using pandas and livelossplot
-        dataframe.loc[i] = (
-            [d_train_loss] + g_train_metrics + [d_dev_loss] + g_dev_metrics
+        metrics_dict["generator_loss"].append(g_train_loss)
+        metrics_dict["generator_psnr"].append(g_train_psnr)
+
+    ## Part 2 - Evaluation on development dataset
+    while i == dev_iter.epoch:  # while we are in epoch i, evaluate on each minibatch
+        dev_batch = dev_iter.next()
+        dev_arrays = chainer.dataset.concat_examples(batch=dev_batch)
+        ## 2.1 - Evaluate Discriminator
+        d_train_loss, d_train_accu = train_eval_discriminator(
+            input_arrays=dev_arrays,
+            g_model=generator_model,
+            d_model=discriminator_model,
+            train=False,
         )
-        livelossplot.draw_plot(
-            logs=dataframe.to_dict(orient="records"),
-            metrics=metric_names,
-            max_cols=3,
-            figsize=(16, 9),
-            max_epoch=epochs,
+        metrics_dict["val_discriminator_loss"].append(d_train_loss)
+        metrics_dict["val_discriminator_accu"].append(d_train_accu)
+
+        ## 2.2 - Evaluate Generator
+        g_dev_loss, g_dev_psnr = train_eval_generator(
+            input_arrays=dev_arrays,
+            g_model=generator_model,
+            d_model=discriminator_model,
+            train=False,
         )
-        t.set_postfix(ordered_dict=dataframe.loc[i].to_dict())
-        experiment.log_metrics(dic=dataframe.loc[i].to_dict(), step=i)
+        metrics_dict["val_generator_loss"].append(g_dev_loss)
+        metrics_dict["val_generator_psnr"].append(g_dev_psnr)
+
+    ## Part 3 - Plot loss and metric information using livelossplot
+    dataframe.loc[i] = [np.mean(metrics_dict[metric]) for metric in dataframe.keys()]
+    livelossplot.draw_plot(
+        logs=dataframe.to_dict(orient="records"),
+        metrics=metric_names,
+        max_cols=4,
+        figsize=(21, 9),
+        max_epoch=epochs,
+    )
+    progressbar.set_postfix(ordered_dict=dataframe.loc[i].to_dict())
+    experiment.log_metrics(dic=dataframe.loc[i].to_dict(), step=i)
+    progressbar.update(n=1)
 
 # %%
-model = models["generator_model"]
+model = generator_model
 
 # %%
 os.makedirs(name="model/weights", exist_ok=True)
-# generator model's parameter weights and architecture
-model.save(filepath="model/weights/srgan_generator_model.hdf5")
-# just the model weights
-model.save_weights(filepath="model/weights/srgan_generator_model_weights.hdf5")
-# just the model architecture
-with open("model/weights/srgan_generator_model_architecture.json", "w") as json_file:
-    json_file.write(model.to_json(indent=2))
+# Save generator model's parameter weights in Numpy Zipped format
+chainer.serializers.save_npz(
+    file="model/weights/srgan_generator_model_weights.npz", obj=model
+)
+# Save generator model's architecture in ONNX format
+dummy_inputs = {
+    "x": np.random.rand(32, 1, 10, 10).astype("float32"),
+    "w1": np.random.rand(32, 1, 100, 100).astype("float32"),
+    "w2": np.random.rand(32, 1, 20, 20).astype("float32"),
+}
+_ = onnx_chainer.export(
+    model=model,
+    args={"inputs": dummy_inputs},
+    filename="model/weights/srgan_generator_model_architecture.onnx",
+    export_params=False,
+    save_text=True,
+)
 
 # Upload model weights file to Comet.ML and finish Comet.ML experiment
 experiment.log_asset(
-    file_path="model/weights/srgan_generator_model_weights.hdf5",
-    file_name="srgan_generator_model_weights",
+    file_path="model/weights/srgan_generator_model_weights.npz",
+    file_name="srgan_generator_model_weights.npz",
 )
 
 # %% [markdown]
-# ## 4. Evaluate model
+# # 4. Evaluate model
 
 # %% [markdown]
-# ### Evaluation on independent test set
+# ## Evaluation on independent test set
 
 # %%
 def get_deepbedmap_test_result(test_filepath: str = "highres/2007tx"):
@@ -718,8 +1207,8 @@ def get_deepbedmap_test_result(test_filepath: str = "highres/2007tx"):
     )
 
     # Run input datasets through trained neural network model
-    model = deepbedmap.load_trained_model(model_inputs=(X_tile, W1_tile, W2_tile))
-    Y_hat = model.predict(x=[X_tile, W1_tile, W2_tile], verbose=1)
+    model = deepbedmap.load_trained_model()
+    Y_hat = model.forward(inputs={"x": X_tile, "w1": W1_tile, "w2": W2_tile}).array
 
     # Save infered deepbedmap to grid file(s)
     deepbedmap.save_array_to_grid(
diff --git a/test_ipynb.ipynb b/test_ipynb.ipynb
index 6f87d88..c1c87f3 100644
--- a/test_ipynb.ipynb
+++ b/test_ipynb.ipynb
@@ -252,137 +252,124 @@
    "execution_count": 3,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Trying:\n",
-      "    metrics = {\"generator_network\": 'mse', \"discriminator_network\": 'accuracy'}\n",
+      "    discriminator_model = DiscriminatorModel()\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    models = compile_srgan_model(\n",
-      "        g_network=generator_network(),\n",
-      "        d_network=discriminator_network(),\n",
-      "        metrics=metrics,\n",
+      "    y_pred = discriminator_model.forward(\n",
+      "        inputs={\n",
+      "            \"x\": np.random.rand(2, 1, 32, 32).astype(\"float32\"),\n",
+      "        }\n",
       "    )\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    models['discriminator_model'].trainable\n",
-      "Expecting:\n",
-      "    True\n",
-      "ok\n",
-      "Trying:\n",
-      "    models['srgan_model'].get_layer(name='generator_network').trainable\n",
+      "    y_pred.shape\n",
       "Expecting:\n",
-      "    True\n",
+      "    (2, 1)\n",
       "ok\n",
       "Trying:\n",
-      "    models['srgan_model'].get_layer(name='discriminator_network').trainable\n",
+      "    discriminator_model.count_params()\n",
       "Expecting:\n",
-      "    False\n",
+      "    6824193\n",
       "ok\n",
       "Trying:\n",
-      "    models['srgan_model'].count_params()\n",
-      "Expecting:\n",
-      "    8442626\n",
-      "ok\n",
-      "Trying:\n",
-      "    discriminator_network().input_shape\n",
-      "Expecting:\n",
-      "    (None, 32, 32, 1)\n",
+      "    generator_model = GeneratorModel()\n",
+      "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    discriminator_network().output_shape\n",
-      "Expecting:\n",
-      "    (None, 1)\n",
+      "    y_pred = generator_model.forward(\n",
+      "        inputs={\n",
+      "            \"x\": np.random.rand(1, 1, 10, 10).astype(\"float32\"),\n",
+      "            \"w1\": np.random.rand(1, 1, 100, 100).astype(\"float32\"),\n",
+      "            \"w2\": np.random.rand(1, 1, 20, 20).astype(\"float32\"),\n",
+      "        }\n",
+      "    )\n",
+      "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    discriminator_network().count_params()\n",
+      "    y_pred.shape\n",
       "Expecting:\n",
-      "    6828033\n",
+      "    (1, 1, 32, 32)\n",
       "ok\n",
       "Trying:\n",
-      "    generator_network().input_shape\n",
+      "    generator_model.count_params()\n",
       "Expecting:\n",
-      "    [(None, 10, 10, 1), (None, 100, 100, 1), (None, 20, 20, 1)]\n",
+      "    3333249\n",
       "ok\n",
       "Trying:\n",
-      "    generator_network().output_shape\n",
+      "    calculate_discriminator_loss(\n",
+      "        real_labels_pred=chainer.variable.Variable(data=np.array([[1.1], [-0.5]])),\n",
+      "        fake_labels_pred=chainer.variable.Variable(data=np.array([[-0.3], [1.0]])),\n",
+      "        real_minus_fake_target=np.array([[1], [1]]),\n",
+      "        fake_minus_real_target=np.array([[0], [0]]),\n",
+      "    )\n",
       "Expecting:\n",
-      "    (None, 32, 32, 1)\n",
+      "    variable(1.56670504)\n",
       "ok\n",
       "Trying:\n",
-      "    generator_network().count_params()\n",
+      "    calculate_generator_loss(\n",
+      "        y_pred=chainer.variable.Variable(data=np.ones(shape=(2, 1, 3, 3))),\n",
+      "        y_true=np.full(shape=(2, 1, 3, 3), fill_value=10.0),\n",
+      "        fake_labels=np.array([[-1.2], [0.5]]),\n",
+      "        real_labels=np.array([[0.5], [-0.8]]),\n",
+      "        fake_minus_real_target=np.array([[1], [1]]).astype(np.int32),\n",
+      "        real_minus_fake_target=np.array([[0], [0]]).astype(np.int32),\n",
+      "    )\n",
       "Expecting:\n",
-      "    1614593\n",
+      "    variable(0.06234614)\n",
       "ok\n",
       "Trying:\n",
-      "    y_true, y_pred = np.ones(shape=(3, 3)), np.full(shape=(3, 3), fill_value=2)\n",
-      "Expecting nothing\n",
-      "ok\n",
-      "Trying:\n",
-      "    K.eval(psnr(y_true=y_true, y_pred=y_pred))\n",
+      "    psnr(\n",
+      "        y_true=np.ones(shape=(2, 1, 3, 3)),\n",
+      "        y_pred=np.full(shape=(2, 1, 3, 3), fill_value=2),\n",
+      "    )\n",
       "Expecting:\n",
-      "    array([221.80709678, 221.80709678, 221.80709678])\n",
+      "    192.65919722494797\n",
       "ok\n",
       "Trying:\n",
-      "    dataset = np.ones(shape=(100, 4, 4, 1))\n",
+      "    train_arrays = {\n",
+      "        \"X\": np.random.RandomState(seed=42).rand(2, 1, 10, 10).astype(np.float32),\n",
+      "        \"W1\": np.random.RandomState(seed=42).rand(2, 1, 100, 100).astype(np.float32),\n",
+      "        \"W2\": np.random.RandomState(seed=42).rand(2, 1, 20, 20).astype(np.float32),\n",
+      "        \"Y\": np.random.RandomState(seed=42).rand(2, 1, 32, 32).astype(np.float32),\n",
+      "    }\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    train, dev = train_dev_split(dataset=dataset, test_size=0.05, random_state=42)\n",
+      "    discriminator_model = DiscriminatorModel()\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    train.shape\n",
-      "Expecting:\n",
-      "    (95, 4, 4, 1)\n",
-      "ok\n",
-      "Trying:\n",
-      "    dev.shape\n",
-      "Expecting:\n",
-      "    (5, 4, 4, 1)\n",
-      "ok\n",
-      "Trying:\n",
-      "    generator_inputs = [\n",
-      "        np.random.RandomState(seed=42).rand(32, s, s, 1) for s in [10, 100, 20]\n",
-      "    ]\n",
-      "Expecting nothing\n",
-      "ok\n",
-      "Trying:\n",
-      "    groundtruth_images = np.random.RandomState(seed=42).rand(32,32,32,1)\n",
+      "    discriminator_optimizer = chainer.optimizers.Adam(alpha=0.001, eps=1e-7).setup(\n",
+      "        link=discriminator_model\n",
+      "    )\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    models = compile_srgan_model(\n",
-      "        g_network=generator_network(), d_network=discriminator_network()\n",
-      "    )\n",
+      "    generator_model = GeneratorModel()\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    d_weight0 = K.eval(models['discriminator_model'].weights[0][0,0,0,0])\n",
+      "    d_weight0 = [d for d in discriminator_model.params()][-3][0].array\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    _, _ = train_discriminator(\n",
-      "        models=models,\n",
-      "        generator_inputs=generator_inputs,\n",
-      "        groundtruth_images=groundtruth_images,\n",
-      "        verbose=0,\n",
+      "    d_train_loss, d_train_accu = train_eval_discriminator(\n",
+      "        input_arrays=train_arrays,\n",
+      "        g_model=generator_model,\n",
+      "        d_model=discriminator_model,\n",
+      "        d_optimizer=discriminator_optimizer,\n",
       "    )\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    d_weight1 = K.eval(models['discriminator_model'].weights[0][0,0,0,0])\n",
+      "    d_weight1 = [d for d in discriminator_model.params()][-3][0].array\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
@@ -391,36 +378,43 @@
       "    True\n",
       "ok\n",
       "Trying:\n",
-      "    generator_inputs = [\n",
-      "        np.random.RandomState(seed=42).rand(32, s, s, 1) for s in [10, 100, 20]\n",
-      "    ]\n",
+      "    train_arrays = {\n",
+      "        \"X\": np.random.RandomState(seed=42).rand(2, 1, 10, 10).astype(np.float32),\n",
+      "        \"W1\": np.random.RandomState(seed=42).rand(2, 1, 100, 100).astype(np.float32),\n",
+      "        \"W2\": np.random.RandomState(seed=42).rand(2, 1, 20, 20).astype(np.float32),\n",
+      "        \"Y\": np.random.RandomState(seed=42).rand(2, 1, 32, 32).astype(np.float32),\n",
+      "    }\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    groundtruth_images = np.random.RandomState(seed=42).rand(32,32,32,1)\n",
+      "    generator_model = GeneratorModel()\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    models = compile_srgan_model(\n",
-      "        g_network=generator_network(), d_network=discriminator_network()\n",
+      "    generator_optimizer = chainer.optimizers.Adam(alpha=0.001, eps=1e-7).setup(\n",
+      "        link=generator_model\n",
       "    )\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    g_weight0 = K.eval(models['generator_model'].weights[0][0,0,0,0])\n",
+      "    discriminator_model = DiscriminatorModel()\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    _, _ = train_generator(\n",
-      "        models=models,\n",
-      "        generator_inputs=generator_inputs,\n",
-      "        groundtruth_images=groundtruth_images,\n",
-      "        verbose=0,\n",
+      "    g_weight0 = [g for g in generator_model.params()][8][0, 0, 0, 0].array\n",
+      "Expecting nothing\n",
+      "ok\n",
+      "Trying:\n",
+      "    _ = train_eval_generator(\n",
+      "        input_arrays=train_arrays,\n",
+      "        g_model=generator_model,\n",
+      "        d_model=discriminator_model,\n",
+      "        g_optimizer=generator_optimizer,\n",
       "    )\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
-      "    g_weight1 = K.eval(models['generator_model'].weights[0][0,0,0,0])\n",
+      "    g_weight1 = [g for g in generator_model.params()][8][0, 0, 0, 0].array\n",
       "Expecting nothing\n",
       "ok\n",
       "Trying:\n",
@@ -428,19 +422,32 @@
       "Expecting:\n",
       "    True\n",
       "ok\n",
-      "2 items had no tests:\n",
+      "15 items had no tests:\n",
       "    srgan_train\n",
+      "    srgan_train.DeepbedmapInputBlock\n",
+      "    srgan_train.DeepbedmapInputBlock.__init__\n",
+      "    srgan_train.DeepbedmapInputBlock.forward\n",
+      "    srgan_train.DiscriminatorModel.__init__\n",
+      "    srgan_train.DiscriminatorModel.forward\n",
+      "    srgan_train.GeneratorModel.__init__\n",
+      "    srgan_train.GeneratorModel.forward\n",
+      "    srgan_train.ResInResDenseBlock\n",
+      "    srgan_train.ResInResDenseBlock.__init__\n",
+      "    srgan_train.ResInResDenseBlock.forward\n",
+      "    srgan_train.ResidualDenseBlock\n",
+      "    srgan_train.ResidualDenseBlock.__init__\n",
+      "    srgan_train.ResidualDenseBlock.forward\n",
       "    srgan_train.get_deepbedmap_test_result\n",
       "7 items passed all tests:\n",
-      "   6 tests in srgan_train.compile_srgan_model\n",
-      "   3 tests in srgan_train.discriminator_network\n",
-      "   3 tests in srgan_train.generator_network\n",
-      "   2 tests in srgan_train.psnr\n",
-      "   4 tests in srgan_train.train_dev_split\n",
-      "   7 tests in srgan_train.train_discriminator\n",
-      "   7 tests in srgan_train.train_generator\n",
-      "32 tests in 9 items.\n",
-      "32 passed and 0 failed.\n",
+      "   4 tests in srgan_train.DiscriminatorModel\n",
+      "   4 tests in srgan_train.GeneratorModel\n",
+      "   1 tests in srgan_train.calculate_discriminator_loss\n",
+      "   1 tests in srgan_train.calculate_generator_loss\n",
+      "   1 tests in srgan_train.psnr\n",
+      "   8 tests in srgan_train.train_eval_discriminator\n",
+      "   8 tests in srgan_train.train_eval_generator\n",
+      "27 tests in 22 items.\n",
+      "27 passed and 0 failed.\n",
       "Test passed.\n"
      ]
     }
@@ -521,7 +528,7 @@
       "    Given some view of Antarctica -1593714.328,-164173.7848,-1575464.328,-97923.7848        # features/steps/test_deepbedmap.py:6\n",
       "    When we gather low and high resolution images related to that view                      # features/steps/test_deepbedmap.py:14\n",
       "    And pass those images into our trained neural network model                             # features/steps/test_deepbedmap.py:30\n",
-      "    Then a four times upsampled super resolution bed elevation map is returned              # features/steps/test_deepbedmap.py:40\n",
+      "    Then a four times upsampled super resolution bed elevation map is returned              # features/steps/test_deepbedmap.py:38\n",
       "\n"
      ]
     }