From 5639d36097193879b61029eef539208ef0c36d48 Mon Sep 17 00:00:00 2001
From: dante <45801863+alexander-camuto@users.noreply.github.com>
Date: Mon, 1 Apr 2024 20:54:20 +0100
Subject: [PATCH] chore: verify aggr wasm unit test (#760)

---
 .github/workflows/pypi.yml                    |   14 +-
 .github/workflows/rust.yml                    |   12 +-
 .gitignore                                    |    3 +-
 Cargo.lock                                    |    4 +-
 Cargo.toml                                    |    8 +-
 examples/notebooks/keras_simple_demo.ipynb    |    1 +
 examples/notebooks/mnist_gan.ipynb            |   11 +-
 .../notebooks/mnist_gan_proof_splitting.ipynb |  725 +++-
 examples/notebooks/mnist_vae.ipynb            |    2 +
 examples/notebooks/random_forest.ipynb        |   33 +-
 .../notebooks/tictactoe_autoencoder.ipynb     |   16 +-
 .../tictactoe_binary_classification.ipynb     |    7 +-
 requirements.txt                              |   24 +-
 src/graph/mod.rs                              |    8 +-
 src/pfsys/evm/aggregation_kzg.rs              |   23 +
 src/wasm.rs                                   |   10 +-
 tests/py_integration_tests.rs                 |   49 +-
 tests/wasm.rs                                 |   21 +-
 tests/wasm/kzg1.srs                           |  Bin 0 -> 516 bytes
 tests/wasm/proof_aggr.json                    | 3075 +++++++++++++++++
 tests/wasm/vk_aggr.key                        |  Bin 0 -> 1287 bytes
 21 files changed, 3929 insertions(+), 117 deletions(-)
 create mode 100644 tests/wasm/kzg1.srs
 create mode 100644 tests/wasm/proof_aggr.json
 create mode 100644 tests/wasm/vk_aggr.key

diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml
index 737e6e826..a942044b2 100644
--- a/.github/workflows/pypi.yml
+++ b/.github/workflows/pypi.yml
@@ -25,7 +25,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.12
           architecture: x64
 
       - name: Set Cargo.toml version to match github tag
@@ -70,7 +70,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.12
           architecture: ${{ matrix.target }}
 
       - name: Set Cargo.toml version to match github tag
@@ -115,7 +115,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.12
           architecture: x64
 
       - name: Set Cargo.toml version to match github tag
@@ -176,7 +176,7 @@ jobs:
   #   - uses: actions/checkout@v4
   #   - uses: actions/setup-python@v4
   #     with:
-  #       python-version: 3.7
+  #       python-version: 3.12
 
   #   - name: Install cross-compilation tools for aarch64
   #     if: matrix.target == 'aarch64'
@@ -228,7 +228,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.12
           architecture: x64
 
       - name: Set Cargo.toml version to match github tag
@@ -263,7 +263,7 @@ jobs:
             apk add py3-pip
             pip3 install -U pip
             python3 -m venv .venv
-            source .venv/bin/activate            
+            source .venv/bin/activate
             pip3 install ezkl --no-index --find-links /io/dist/ --force-reinstall
             python3 -c "import ezkl"
 
@@ -287,7 +287,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.12
 
       - name: Set Cargo.toml version to match github tag
         shell: bash
diff --git a/.github/workflows/rust.yml b/.github/workflows/rust.yml
index 5d835f368..4024ff884 100644
--- a/.github/workflows/rust.yml
+++ b/.github/workflows/rust.yml
@@ -557,12 +557,14 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: "3.7"
+          python-version: "3.12"
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
+      - name: Install cmake
+        run: sudo apt-get install -y cmake
       - name: Install solc
         run: (hash svm 2>/dev/null || cargo install svm-rs) && svm install 0.8.20 && solc --version
       - name: Setup Virtual Env and Install python dependencies
@@ -581,7 +583,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: "3.7"
+          python-version: "3.12"
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-02-06
@@ -612,7 +614,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: "3.10"
+          python-version: "3.11"
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-02-06
@@ -630,6 +632,8 @@ jobs:
         run: python -m venv .env; source .env/bin/activate; pip install -r requirements.txt;
       - name: Build python ezkl
         run: source .env/bin/activate; unset CONDA_PREFIX; maturin develop --features python-bindings --release
+      - name: Tictactoe tutorials
+        run: source .env/bin/activate; cargo nextest run py_tests::tests::tictactoe_
       # - name: authenticate-kaggle-cli
       #   shell: bash
       #   env:
@@ -645,7 +649,5 @@ jobs:
         run: source .env/bin/activate; cargo nextest run py_tests::tests::voice_
       - name: NBEATS tutorial
         run: source .env/bin/activate; cargo nextest run py_tests::tests::nbeats_
-      - name: Tictactoe tutorials
-        run: source .env/bin/activate; cargo nextest run py_tests::tests::tictactoe_
       # - name: Postgres tutorials
       #   run: source .env/bin/activate; cargo nextest run py_tests::tests::postgres_ --test-threads 1
diff --git a/.gitignore b/.gitignore
index 052a7bf5a..9635e363c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -48,4 +48,5 @@ node_modules
 /dist
 timingData.json
 !tests/wasm/pk.key
-!tests/wasm/vk.key
\ No newline at end of file
+!tests/wasm/vk.key
+!tests/wasm/vk_aggr.key
\ No newline at end of file
diff --git a/Cargo.lock b/Cargo.lock
index 9fb9c6136..2c0cbe685 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -4601,9 +4601,9 @@ dependencies = [
 
 [[package]]
 name = "serde-wasm-bindgen"
-version = "0.4.5"
+version = "0.6.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e3b4c031cd0d9014307d82b8abf653c0290fbdaeb4c02d00c63cf52f728628bf"
+checksum = "8302e169f0eddcc139c70f139d19d6467353af16f9fce27e8c30158036a1e16b"
 dependencies = [
  "js-sys",
  "serde",
diff --git a/Cargo.toml b/Cargo.toml
index ec0738ecb..d4b0cf247 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -95,10 +95,10 @@ getrandom = { version = "0.2.8", features = ["js"] }
 instant = { version = "0.1", features = ["wasm-bindgen", "inaccurate"] }
 
 [target.'cfg(all(target_arch = "wasm32", target_os = "unknown"))'.dependencies]
-wasm-bindgen-rayon = { version = "1.0", optional = true }
-wasm-bindgen-test = "0.3.34"
-serde-wasm-bindgen = "0.4"
-wasm-bindgen = { version = "0.2.81", features = ["serde-serialize"] }
+wasm-bindgen-rayon = { version = "1.2.1", optional = true }
+wasm-bindgen-test = "0.3.42"
+serde-wasm-bindgen = "0.6.5"
+wasm-bindgen = { version = "0.2.92", features = ["serde-serialize"] }
 console_error_panic_hook = "0.1.7"
 wasm-bindgen-console-logger = "0.1.1"
 
diff --git a/examples/notebooks/keras_simple_demo.ipynb b/examples/notebooks/keras_simple_demo.ipynb
index e302e2626..356a9f4f6 100644
--- a/examples/notebooks/keras_simple_demo.ipynb
+++ b/examples/notebooks/keras_simple_demo.ipynb
@@ -67,6 +67,7 @@
                 "model.add(Dense(128, activation='relu'))\n",
                 "model.add(Dropout(0.5))\n",
                 "model.add(Dense(10, activation='softmax'))\n",
+                "model.output_names=['output']\n",
                 "\n",
                 "\n",
                 "# Train the model as you like here (skipped for brevity)\n",
diff --git a/examples/notebooks/mnist_gan.ipynb b/examples/notebooks/mnist_gan.ipynb
index 13d089b75..69deaa76f 100644
--- a/examples/notebooks/mnist_gan.ipynb
+++ b/examples/notebooks/mnist_gan.ipynb
@@ -38,7 +38,7 @@
                 "import logging\n",
                 "\n",
                 "import tensorflow as tf\n",
-                "from tensorflow.keras.optimizers.legacy import Adam\n",
+                "from tensorflow.keras.optimizers import Adam\n",
                 "from tensorflow.keras.layers import *\n",
                 "from tensorflow.keras.models import Model\n",
                 "from tensorflow.keras.datasets import mnist\n",
@@ -71,9 +71,11 @@
             },
             "outputs": [],
             "source": [
-                "opt = Adam()\n",
                 "ZDIM = 100\n",
                 "\n",
+                "opt = Adam()\n",
+                "\n",
+                "\n",
                 "# discriminator\n",
                 "# 0 if it's fake, 1 if it's real\n",
                 "x = in1 = Input((28,28))\n",
@@ -114,8 +116,11 @@
                 "\n",
                 "gm = Model(in1, x)\n",
                 "gm.compile('adam', 'mse')\n",
+                "gm.output_names=['output']\n",
                 "gm.summary()\n",
                 "\n",
+                "opt = Adam()\n",
+                "\n",
                 "# GAN\n",
                 "dm.trainable = False\n",
                 "x = dm(gm.output)\n",
@@ -415,7 +420,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.2"
         }
     },
     "nbformat": 4,
diff --git a/examples/notebooks/mnist_gan_proof_splitting.ipynb b/examples/notebooks/mnist_gan_proof_splitting.ipynb
index 217058673..5f150b45b 100644
--- a/examples/notebooks/mnist_gan_proof_splitting.ipynb
+++ b/examples/notebooks/mnist_gan_proof_splitting.ipynb
@@ -23,7 +23,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 1,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -50,7 +50,7 @@
                 "import logging\n",
                 "\n",
                 "import tensorflow as tf\n",
-                "from tensorflow.keras.optimizers.legacy import Adam\n",
+                "from tensorflow.keras.optimizers import Adam\n",
                 "from tensorflow.keras.layers import *\n",
                 "from tensorflow.keras.models import Model\n",
                 "from tensorflow.keras.datasets import mnist\n",
@@ -65,7 +65,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 2,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -77,9 +77,258 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 3,
             "metadata": {},
-            "outputs": [],
+            "outputs": [
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_1\"</span>\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1mModel: \"functional_1\"\u001b[0m\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+                            "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+                            "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+                            "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ reshape (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,664</span> │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ batch_normalization             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
+                            "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ elu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ELU</span>)                       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │       <span style=\"color: #00af00; text-decoration-color: #00af00\">204,928</span> │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ batch_normalization_1           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
+                            "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ elu_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ELU</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6272</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">802,944</span> │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ batch_normalization_2           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
+                            "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ elu_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ELU</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
+                            "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+                            "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+                            "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+                            "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ reshape (\u001b[38;5;33mReshape\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m1\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │         \u001b[38;5;34m1,664\u001b[0m │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ batch_normalization             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
+                            "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ elu (\u001b[38;5;33mELU\u001b[0m)                       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │       \u001b[38;5;34m204,928\u001b[0m │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ batch_normalization_1           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │           \u001b[38;5;34m512\u001b[0m │\n",
+                            "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ elu_1 (\u001b[38;5;33mELU\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6272\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │       \u001b[38;5;34m802,944\u001b[0m │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ batch_normalization_2           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │           \u001b[38;5;34m512\u001b[0m │\n",
+                            "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ elu_2 (\u001b[38;5;33mELU\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m129\u001b[0m │\n",
+                            "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,010,945</span> (3.86 MB)\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,010,945\u001b[0m (3.86 MB)\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,010,305</span> (3.85 MB)\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,010,305\u001b[0m (3.85 MB)\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> (2.50 KB)\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m640\u001b[0m (2.50 KB)\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_3\"</span>\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1mModel: \"functional_3\"\u001b[0m\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+                            "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+                            "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+                            "│ input_layer_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3136</span>)           │       <span style=\"color: #00af00; text-decoration-color: #00af00\">316,736</span> │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ batch_normalization_3           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3136</span>)           │        <span style=\"color: #00af00; text-decoration-color: #00af00\">12,544</span> │\n",
+                            "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ elu_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ELU</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3136</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ reshape_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ conv2d_transpose                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">204,928</span> │\n",
+                            "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)               │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ batch_normalization_4           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
+                            "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ elu_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ELU</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ conv2d_transpose_1              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)      │         <span style=\"color: #00af00; text-decoration-color: #00af00\">3,201</span> │\n",
+                            "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)               │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ activation (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ reshape_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+                            "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+                            "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+                            "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+                            "│ input_layer_1 (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3136\u001b[0m)           │       \u001b[38;5;34m316,736\u001b[0m │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ batch_normalization_3           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3136\u001b[0m)           │        \u001b[38;5;34m12,544\u001b[0m │\n",
+                            "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ elu_3 (\u001b[38;5;33mELU\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3136\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ reshape_1 (\u001b[38;5;33mReshape\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ conv2d_transpose                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │       \u001b[38;5;34m204,928\u001b[0m │\n",
+                            "│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)               │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ batch_normalization_4           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │           \u001b[38;5;34m512\u001b[0m │\n",
+                            "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ elu_4 (\u001b[38;5;33mELU\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ conv2d_transpose_1              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m1\u001b[0m)      │         \u001b[38;5;34m3,201\u001b[0m │\n",
+                            "│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)               │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ activation (\u001b[38;5;33mActivation\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m1\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ reshape_2 (\u001b[38;5;33mReshape\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n",
+                            "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">537,921</span> (2.05 MB)\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m537,921\u001b[0m (2.05 MB)\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">531,393</span> (2.03 MB)\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m531,393\u001b[0m (2.03 MB)\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,528</span> (25.50 KB)\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m6,528\u001b[0m (25.50 KB)\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
             "source": [
                 "opt = Adam()\n",
                 "ZDIM = 100\n",
@@ -126,6 +375,8 @@
                 "gm.compile('adam', 'mse')\n",
                 "gm.summary()\n",
                 "\n",
+                "opt = Adam()\n",
+                "\n",
                 "# GAN\n",
                 "dm.trainable = False\n",
                 "x = dm(gm.output)\n",
@@ -137,9 +388,28 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 4,
             "metadata": {},
-            "outputs": [],
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "   0: dloss:  0.8063   gloss:  0.6461\n",
+                        "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 1600x1600 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
             "source": [
                 "import numpy as np\n",
                 "from matplotlib.pyplot import figure, imshow, show\n",
@@ -177,9 +447,30 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 5,
             "metadata": {},
-            "outputs": [],
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "<matplotlib.legend.Legend at 0x34f625160>"
+                        ]
+                    },
+                    "execution_count": 5,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x800 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
             "source": [
                 "from matplotlib.pyplot import plot, legend\n",
                 "figure(figsize=(8,8))\n",
@@ -190,9 +481,46 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 6,
             "metadata": {},
-            "outputs": [],
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+                        "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
+                        "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
+                        "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
+                        "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
+                        "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
+                        "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
+                        "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
+                        "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
+                        "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "text/plain": [
+                            "<matplotlib.image.AxesImage at 0x34f32bd70>"
+                        ]
+                    },
+                    "execution_count": 6,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
             "source": [
                 "x = []\n",
                 "for i in range(10):\n",
@@ -209,9 +537,272 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 7,
             "metadata": {},
-            "outputs": [],
+            "outputs": [
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+                            "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+                            "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+                            "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3136</span>)           │       <span style=\"color: #00af00; text-decoration-color: #00af00\">316,736</span> │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ batch_normalization_3           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3136</span>)           │        <span style=\"color: #00af00; text-decoration-color: #00af00\">12,544</span> │\n",
+                            "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ elu_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ELU</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3136</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+                            "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+                            "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+                            "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+                            "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3136\u001b[0m)           │       \u001b[38;5;34m316,736\u001b[0m │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ batch_normalization_3           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3136\u001b[0m)           │        \u001b[38;5;34m12,544\u001b[0m │\n",
+                            "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ elu_3 (\u001b[38;5;33mELU\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3136\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
+                            "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">329,280</span> (1.26 MB)\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m329,280\u001b[0m (1.26 MB)\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">323,008</span> (1.23 MB)\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m323,008\u001b[0m (1.23 MB)\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,272</span> (24.50 KB)\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m6,272\u001b[0m (24.50 KB)\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+                            "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+                            "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+                            "│ reshape_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ conv2d_transpose                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">204,928</span> │\n",
+                            "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)               │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ batch_normalization_4           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
+                            "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ elu_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ELU</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+                            "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+                            "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+                            "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+                            "│ reshape_1 (\u001b[38;5;33mReshape\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ conv2d_transpose                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │       \u001b[38;5;34m204,928\u001b[0m │\n",
+                            "│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)               │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ batch_normalization_4           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │           \u001b[38;5;34m512\u001b[0m │\n",
+                            "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ elu_4 (\u001b[38;5;33mELU\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
+                            "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">205,440</span> (802.50 KB)\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m205,440\u001b[0m (802.50 KB)\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">205,184</span> (801.50 KB)\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m205,184\u001b[0m (801.50 KB)\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> (1.00 KB)\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m256\u001b[0m (1.00 KB)\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+                            "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+                            "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+                            "│ conv2d_transpose_1              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)      │         <span style=\"color: #00af00; text-decoration-color: #00af00\">3,201</span> │\n",
+                            "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)               │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ activation (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ reshape_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+                            "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+                            "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+                            "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+                            "│ conv2d_transpose_1              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m1\u001b[0m)      │         \u001b[38;5;34m3,201\u001b[0m │\n",
+                            "│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)               │                        │               │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ activation (\u001b[38;5;33mActivation\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m1\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+                            "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+                            "│ reshape_2 (\u001b[38;5;33mReshape\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n",
+                            "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,201</span> (12.50 KB)\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m3,201\u001b[0m (12.50 KB)\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,201</span> (12.50 KB)\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3,201\u001b[0m (12.50 KB)\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "transpose_input for input_0: shape must be rank 4, ignored\n"
+                    ]
+                }
+            ],
             "source": [
                 "\n",
                 "import numpy as np\n",
@@ -221,8 +812,19 @@
                 "\n",
                 "# split the model into 3 parts\n",
                 "gm2 = tf.keras.models.Sequential(gm.layers[0:4])\n",
-                "gm3 = tf.keras.models.Sequential(gm.layers[4:11])\n",
-                "gm4 = tf.keras.models.Sequential(gm.layers[11:])\n",
+                "# display gm2 \n",
+                "gm2.summary()\n",
+                "gm2.output_names=['output']\n",
+                "\n",
+                "gm3 = tf.keras.models.Sequential(gm.layers[4:8])\n",
+                "#  display gm3\n",
+                "gm3.summary() \n",
+                "gm3.output_names=['output']\n",
+                "\n",
+                "gm4 = tf.keras.models.Sequential(gm.layers[8:])\n",
+                "# display gm4\n",
+                "gm4.summary()\n",
+                "gm4.output_names=['output'] \n",
                 "\n",
                 "# After training, export to onnx (network.onnx) and create a data file (input.json)\n",
                 "x = 0.1*np.random.rand(1,*[1, ZDIM])\n",
@@ -271,9 +873,20 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 8,
             "metadata": {},
-            "outputs": [],
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "True"
+                        ]
+                    },
+                    "execution_count": 8,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
             "source": [
                 "import ezkl\n",
                 "\n",
@@ -300,9 +913,76 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 20,
+            "execution_count": 9,
             "metadata": {},
-            "outputs": [],
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "\n",
+                        "\n",
+                        " <------------- Numerical Fidelity Report (input_scale: 0, param_scale: 0, scale_input_multiplier: 10) ------------->\n",
+                        "\n",
+                        "+----------------+--------------+-------------+--------------+----------------+------------------+---------------+-----------------+--------------------+--------------------+------------------------+\n",
+                        "| mean_error     | median_error | max_error   | min_error    | mean_abs_error | median_abs_error | max_abs_error | min_abs_error   | mean_squared_error | mean_percent_error | mean_abs_percent_error |\n",
+                        "+----------------+--------------+-------------+--------------+----------------+------------------+---------------+-----------------+--------------------+--------------------+------------------------+\n",
+                        "| -0.00045216593 | 0.0071961936 | 0.059581105 | -0.051913798 | 0.011681631    | 0.0071961936     | 0.059581105   | 0.0000062934123 | 0.0002161761       | 1                  | 1                      |\n",
+                        "+----------------+--------------+-------------+--------------+----------------+------------------+---------------+-----------------+--------------------+--------------------+------------------------+\n",
+                        "\n",
+                        "\n"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Setting up split model 0\n",
+                        "Setting up split model 1\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "\n",
+                        "\n",
+                        " <------------- Numerical Fidelity Report (input_scale: 0, param_scale: 0, scale_input_multiplier: 10) ------------->\n",
+                        "\n",
+                        "+----------------+--------------+-------------+--------------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
+                        "| mean_error     | median_error | max_error   | min_error    | mean_abs_error | median_abs_error | max_abs_error | min_abs_error | mean_squared_error | mean_percent_error | mean_abs_percent_error |\n",
+                        "+----------------+--------------+-------------+--------------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
+                        "| -0.00008474619 | -0.002256453 | 0.003519658 | -0.003081262 | 0.0018818051   | 0.002256453      | 0.003519658   | 0.00017167516 | 0.000003900568     | 1                  | 1                      |\n",
+                        "+----------------+--------------+-------------+--------------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
+                        "\n",
+                        "\n"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Setting up split model 2\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "\n",
+                        "\n",
+                        " <------------- Numerical Fidelity Report (input_scale: 0, param_scale: 0, scale_input_multiplier: 10) ------------->\n",
+                        "\n",
+                        "+-------------+--------------+-------------+-------------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
+                        "| mean_error  | median_error | max_error   | min_error   | mean_abs_error | median_abs_error | max_abs_error | min_abs_error | mean_squared_error | mean_percent_error | mean_abs_percent_error |\n",
+                        "+-------------+--------------+-------------+-------------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
+                        "| -0.49951223 | -0.49951398  | -0.49951398 | -0.49951398 | 0.49951223     | 0.49951398       | 0.49951398    | 0.49951398    | 0.24951272         | -0.9980509         | 0.9980509              |\n",
+                        "+-------------+--------------+-------------+-------------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
+                        "\n",
+                        "\n"
+                    ]
+                }
+            ],
             "source": [
                 "# iterate over each submodel gen-settings, compile circuit and setup zkSNARK\n",
                 "\n",
@@ -370,6 +1050,9 @@
             "source": [
                 "# GENERATE A PROOF\n",
                 "\n",
+                "print(\"Proving split models\")\n",
+                "\n",
+                "\n",
                 "def prove_model(i): \n",
                 "    proof_path = os.path.join('proof_split_'+str(i)+'.json')\n",
                 "    witness_path = os.path.join('witness_split_'+str(i)+'.json')\n",
@@ -417,7 +1100,9 @@
                 "    assert res == True\n",
                 "    print(\"verified\")\n",
                 "\n",
+                "\n",
                 "for i in range(3):\n",
+                "    print(\"----- proving split \"+str(i))\n",
                 "    prove_model(i)"
             ]
         },
@@ -460,7 +1145,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.2"
         },
         "orig_nbformat": 4
     },
diff --git a/examples/notebooks/mnist_vae.ipynb b/examples/notebooks/mnist_vae.ipynb
index 69011e8fa..69ac6e5e9 100644
--- a/examples/notebooks/mnist_vae.ipynb
+++ b/examples/notebooks/mnist_vae.ipynb
@@ -349,6 +349,8 @@
                 "z_log_var = Dense(ZDIM)(x)\n",
                 "z = Lambda(lambda x: x[0] + K.exp(0.5 * x[1]) * K.random_normal(shape=K.shape(x[0])))([z_mu, z_log_var])\n",
                 "dec = get_decoder()\n",
+                "dec.output_names=['output']\n",
+                "\n",
                 "out = dec(z)\n",
                 "\n",
                 "mse_loss = mse(Reshape((28*28,))(in1), Reshape((28*28,))(out)) * 28 * 28\n",
diff --git a/examples/notebooks/random_forest.ipynb b/examples/notebooks/random_forest.ipynb
index 6c6d992bb..316f5a2a5 100644
--- a/examples/notebooks/random_forest.ipynb
+++ b/examples/notebooks/random_forest.ipynb
@@ -61,11 +61,10 @@
                 "from sklearn.datasets import load_iris\n",
                 "from sklearn.model_selection import train_test_split\n",
                 "from sklearn.ensemble import RandomForestClassifier as Rf\n",
-                "import sk2torch\n",
                 "import torch\n",
                 "import ezkl\n",
                 "import os\n",
-                "from torch import nn\n",
+                "from hummingbird.ml import convert\n",
                 "\n",
                 "\n",
                 "\n",
@@ -77,28 +76,12 @@
                 "clr.fit(X_train, y_train)\n",
                 "\n",
                 "\n",
-                "trees = []\n",
-                "for tree in clr.estimators_:\n",
-                "    trees.append(sk2torch.wrap(tree))\n",
                 "\n",
-                "\n",
-                "class RandomForest(nn.Module):\n",
-                "    def __init__(self, trees):\n",
-                "        super(RandomForest, self).__init__()\n",
-                "        self.trees = nn.ModuleList(trees)\n",
-                "\n",
-                "    def forward(self, x):\n",
-                "        out = self.trees[0](x)\n",
-                "        for tree in self.trees[1:]:\n",
-                "            out += tree(x)\n",
-                "        return out / len(self.trees)\n",
-                "\n",
-                "\n",
-                "torch_rf = RandomForest(trees)\n",
+                "torch_rf = convert(clr, 'torch')\n",
                 "# assert predictions from torch are = to sklearn \n",
                 "diffs = []\n",
                 "for i in range(len(X_test)):\n",
-                "    torch_pred = torch_rf(torch.tensor(X_test[i].reshape(1, -1)))\n",
+                "    torch_pred = torch_rf.predict(torch.tensor(X_test[i].reshape(1, -1)))\n",
                 "    sk_pred = clr.predict(X_test[i].reshape(1, -1))\n",
                 "    diffs.append(torch_pred[0].round() - sk_pred[0])\n",
                 "\n",
@@ -134,14 +117,12 @@
                 "\n",
                 "# export to onnx format\n",
                 "\n",
-                "torch_rf.eval()\n",
-                "\n",
                 "# Input to the model\n",
                 "shape = X_train.shape[1:]\n",
                 "x = torch.rand(1, *shape, requires_grad=False)\n",
-                "torch_out = torch_rf(x)\n",
+                "torch_out = torch_rf.predict(x)\n",
                 "# Export the model\n",
-                "torch.onnx.export(torch_rf,               # model being run\n",
+                "torch.onnx.export(torch_rf.model,               # model being run\n",
                 "                  # model input (or a tuple for multiple inputs)\n",
                 "                  x,\n",
                 "                  # where to save the model (can be a file or file-like object)\n",
@@ -158,7 +139,7 @@
                 "\n",
                 "data = dict(input_shapes=[shape],\n",
                 "            input_data=[d],\n",
-                "            output_data=[((o).detach().numpy()).reshape([-1]).tolist() for o in torch_out])\n",
+                "            output_data=[o.reshape([-1]).tolist() for o in torch_out])\n",
                 "\n",
                 "# Serialize data into file:\n",
                 "json.dump(data, open(\"input.json\", 'w'))\n"
@@ -321,7 +302,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.2"
         }
     },
     "nbformat": 4,
diff --git a/examples/notebooks/tictactoe_autoencoder.ipynb b/examples/notebooks/tictactoe_autoencoder.ipynb
index 8cdc43aa6..d8e4ce11c 100644
--- a/examples/notebooks/tictactoe_autoencoder.ipynb
+++ b/examples/notebooks/tictactoe_autoencoder.ipynb
@@ -13,7 +13,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 1,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -57,7 +57,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 2,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -119,7 +119,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 3,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -163,7 +163,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 4,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -217,7 +217,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 6,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -227,6 +227,10 @@
                 "        self.length = self.compute_length(self.file_good)\n",
                 "        self.data = self.load_data(self.file_good)\n",
                 "\n",
+                "    def __iter__(self):\n",
+                "        for i in range(len(self.data)):\n",
+                "            yield self.data[i]\n",
+                "\n",
                 "    def parse_json_object(self, line):\n",
                 "        try:\n",
                 "            return json.loads(line)\n",
@@ -749,7 +753,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.2"
         }
     },
     "nbformat": 4,
diff --git a/examples/notebooks/tictactoe_binary_classification.ipynb b/examples/notebooks/tictactoe_binary_classification.ipynb
index 9196eb8ef..c7369cf3e 100644
--- a/examples/notebooks/tictactoe_binary_classification.ipynb
+++ b/examples/notebooks/tictactoe_binary_classification.ipynb
@@ -209,6 +209,11 @@
                 "        self.length = self.compute_length(self.file_good, self.file_bad)\n",
                 "        self.data = self.load_data(self.file_good, self.file_bad)\n",
                 "\n",
+                "    def __iter__(self):\n",
+                "        for i in range(len(self.data)):\n",
+                "            yield self.data[i]\n",
+                "\n",
+                "\n",
                 "    def parse_json_object(self, line):\n",
                 "        try:\n",
                 "            return json.loads(line)\n",
@@ -637,7 +642,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.2"
         }
     },
     "nbformat": 4,
diff --git a/requirements.txt b/requirements.txt
index 6e1645055..26b647ac8 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,14 +1,14 @@
-attrs==22.2.0
-exceptiongroup==1.1.1
-importlib-metadata==6.1.0
+attrs==23.2.0
+exceptiongroup==1.2.0
+importlib-metadata==7.1.0
 iniconfig==2.0.0
-maturin==1.0.1
-packaging==23.0
-pluggy==1.0.0
-pytest==7.2.2
+maturin==1.5.0
+packaging==24.0
+pluggy==1.4.0
+pytest==8.1.1
 tomli==2.0.1
-typing-extensions==4.5.0
-zipp==3.15.0
-onnx==1.14.1
-onnxruntime==1.14.1
-numpy==1.21.6
\ No newline at end of file
+typing-extensions==4.10.0
+zipp==3.18.1
+onnx==1.15.0
+onnxruntime==1.17.1
+numpy==1.26.4
\ No newline at end of file
diff --git a/src/graph/mod.rs b/src/graph/mod.rs
index 15b932db8..8b6e1b615 100644
--- a/src/graph/mod.rs
+++ b/src/graph/mod.rs
@@ -39,7 +39,7 @@ use halo2_proofs::{
     plonk::{Circuit, ConstraintSystem, Error as PlonkError},
 };
 use halo2curves::bn256::{self, Fr as Fp, G1Affine};
-use halo2curves::ff::PrimeField;
+use halo2curves::ff::{Field, PrimeField};
 #[cfg(not(target_arch = "wasm32"))]
 use lazy_static::lazy_static;
 use log::{debug, error, trace, warn};
@@ -1451,7 +1451,8 @@ impl GraphCircuit {
 }
 
 #[derive(Clone, Debug, Default, Serialize, Deserialize)]
-struct CircuitSize {
+/// The configuration for the graph circuit
+pub struct CircuitSize {
     num_instances: usize,
     num_advice_columns: usize,
     num_fixed: usize,
@@ -1461,7 +1462,8 @@ struct CircuitSize {
 }
 
 impl CircuitSize {
-    pub fn from_cs(cs: &ConstraintSystem<Fp>, logrows: u32) -> Self {
+    ///
+    pub fn from_cs<F: Field>(cs: &ConstraintSystem<F>, logrows: u32) -> Self {
         CircuitSize {
             num_instances: cs.num_instance_columns(),
             num_advice_columns: cs.num_advice_columns(),
diff --git a/src/pfsys/evm/aggregation_kzg.rs b/src/pfsys/evm/aggregation_kzg.rs
index 6f9c79439..addd4b390 100644
--- a/src/pfsys/evm/aggregation_kzg.rs
+++ b/src/pfsys/evm/aggregation_kzg.rs
@@ -1,4 +1,8 @@
+#[cfg(not(target_arch = "wasm32"))]
+use crate::graph::CircuitSize;
 use crate::pfsys::{Snark, SnarkWitness};
+#[cfg(not(target_arch = "wasm32"))]
+use colored_json::ToColoredJson;
 use halo2_proofs::circuit::AssignedCell;
 use halo2_proofs::plonk::{self};
 use halo2_proofs::{
@@ -16,6 +20,8 @@ use halo2_wrong_ecc::{
 use halo2curves::bn256::{Bn256, Fq, Fr, G1Affine};
 use halo2curves::ff::PrimeField;
 use itertools::Itertools;
+#[cfg(not(target_arch = "wasm32"))]
+use log::debug;
 use log::trace;
 use rand::rngs::OsRng;
 use snark_verifier::loader::native::NativeLoader;
@@ -193,6 +199,23 @@ impl AggregationConfig {
         let main_gate_config = MainGate::<F>::configure(meta);
         let range_config =
             RangeChip::<F>::configure(meta, &main_gate_config, composition_bits, overflow_bits);
+
+        #[cfg(not(target_arch = "wasm32"))]
+        {
+            let circuit_size = CircuitSize::from_cs(meta, 23);
+
+            // not wasm
+
+            debug!(
+                "circuit size: \n {}",
+                circuit_size
+                    .as_json()
+                    .unwrap()
+                    .to_colored_json_auto()
+                    .unwrap()
+            );
+        }
+
         AggregationConfig {
             main_gate_config,
             range_config,
diff --git a/src/wasm.rs b/src/wasm.rs
index 8ccb29a59..ec3e9af8c 100644
--- a/src/wasm.rs
+++ b/src/wasm.rs
@@ -419,6 +419,8 @@ pub fn verifyAggr(
 
     let commit = Commitments::from_str(commitment).map_err(|e| JsError::new(&format!("{}", e)))?;
 
+    let orig_n = 1 << logrows;
+
     let mut reader = std::io::BufReader::new(&srs[..]);
     let result = match commit {
         Commitments::KZG => {
@@ -433,7 +435,7 @@ pub fn verifyAggr(
                     KZGSingleStrategy<_>,
                     _,
                     EvmTranscript<G1Affine, _, _, _>,
-                >(&proof, &params, &vk, strategy, logrows),
+                >(&proof, &params, &vk, strategy, orig_n),
 
                 TranscriptType::Poseidon => {
                     verify_proof_circuit::<
@@ -442,7 +444,7 @@ pub fn verifyAggr(
                         KZGSingleStrategy<_>,
                         _,
                         PoseidonTranscript<NativeLoader, _>,
-                    >(&proof, &params, &vk, strategy, logrows)
+                    >(&proof, &params, &vk, strategy, orig_n)
                 }
             }
         }
@@ -458,7 +460,7 @@ pub fn verifyAggr(
                     IPASingleStrategy<_>,
                     _,
                     EvmTranscript<G1Affine, _, _, _>,
-                >(&proof, &params, &vk, strategy, logrows),
+                >(&proof, &params, &vk, strategy, orig_n),
                 TranscriptType::Poseidon => {
                     verify_proof_circuit::<
                         VerifierIPA<_>,
@@ -466,7 +468,7 @@ pub fn verifyAggr(
                         IPASingleStrategy<_>,
                         _,
                         PoseidonTranscript<NativeLoader, _>,
-                    >(&proof, &params, &vk, strategy, logrows)
+                    >(&proof, &params, &vk, strategy, orig_n)
                 }
             }
         }
diff --git a/tests/py_integration_tests.rs b/tests/py_integration_tests.rs
index 4cd1b7ada..f1eca973e 100644
--- a/tests/py_integration_tests.rs
+++ b/tests/py_integration_tests.rs
@@ -56,33 +56,38 @@ mod py_tests {
             // source .env/bin/activate
             // pip install -r requirements.txt
             // maturin develop --release --features python-bindings
-
+            // first install tf2onnx as it has protobuf conflict with onnx
+            let status = Command::new("pip")
+                .args(["install", "tf2onnx==1.16.1"])
+                .status()
+                .expect("failed to execute process");
+            assert!(status.success());
             // now install torch, pandas, numpy, seaborn, jupyter
             let status = Command::new("pip")
                 .args([
                     "install",
-                    "torch-geometric==2.5.0",
-                    "torch==2.0.1",
-                    "torchvision==0.15.2",
-                    "pandas==2.0.3",
-                    "numpy==1.23",
-                    "seaborn==0.12.2",
-                    "jupyter==1.0.0",
-                    "onnx==1.14.0",
-                    "kaggle==1.5.15",
-                    "py-solc-x==1.1.1",
-                    "web3==6.5.0",
-                    "librosa==0.10.0.post2",
-                    "keras==2.12.0",
-                    "tensorflow==2.12.0",
-                    "tensorflow-datasets==4.9.3",
-                    "tf2onnx==1.14.0",
-                    "pytorch-lightning==2.0.6",
+                    "torch-geometric==2.5.2",
+                    "torch==2.2.2",
+                    "torchvision==0.17.2",
+                    "pandas==2.2.1",
+                    "numpy==1.26.4",
+                    "seaborn==0.13.2",
+                    "notebook==7.1.2",
+                    "nbconvert==7.16.3",
+                    "onnx==1.16.0",
+                    "kaggle==1.6.8",
+                    "py-solc-x==2.0.2",
+                    "web3==6.16.0",
+                    "librosa==0.10.1",
+                    "keras==3.1.1",
+                    "tensorflow==2.16.1",
+                    "tensorflow-datasets==4.9.4",
+                    "pytorch-lightning==2.2.1",
                     "sk2torch==1.2.0",
-                    "scikit-learn==1.3.1",
-                    "xgboost==1.7.6",
-                    "hummingbird-ml==0.4.9",
-                    "lightgbm==4.0.0",
+                    "scikit-learn==1.4.1.post1",
+                    "xgboost==2.0.3",
+                    "hummingbird-ml==0.4.11",
+                    "lightgbm==4.3.0",
                 ])
                 .status()
                 .expect("failed to execute process");
diff --git a/tests/wasm.rs b/tests/wasm.rs
index bc086cb7c..bca68ec66 100644
--- a/tests/wasm.rs
+++ b/tests/wasm.rs
@@ -11,7 +11,7 @@ mod wasm32 {
         bufferToVecOfFelt, compiledCircuitValidation, encodeVerifierCalldata, feltToBigEndian,
         feltToFloat, feltToInt, feltToLittleEndian, genPk, genVk, genWitness, inputValidation,
         pkValidation, poseidonHash, proofValidation, prove, settingsValidation, srsValidation,
-        u8_array_to_u128_le, verify, vkValidation, witnessValidation,
+        u8_array_to_u128_le, verify, verifyAggr, vkValidation, witnessValidation,
     };
     use halo2_solidity_verifier::encode_calldata;
     use halo2curves::bn256::{Fr, G1Affine};
@@ -27,10 +27,29 @@ mod wasm32 {
     pub const NETWORK: &[u8] = include_bytes!("../tests/wasm/network.onnx");
     pub const INPUT: &[u8] = include_bytes!("../tests/wasm/input.json");
     pub const PROOF: &[u8] = include_bytes!("../tests/wasm/proof.json");
+    pub const PROOF_AGGR: &[u8] = include_bytes!("../tests/wasm/proof_aggr.json");
     pub const SETTINGS: &[u8] = include_bytes!("../tests/wasm/settings.json");
     pub const PK: &[u8] = include_bytes!("../tests/wasm/pk.key");
     pub const VK: &[u8] = include_bytes!("../tests/wasm/vk.key");
+    pub const VK_AGGR: &[u8] = include_bytes!("../tests/wasm/vk_aggr.key");
     pub const SRS: &[u8] = include_bytes!("../tests/wasm/kzg");
+    pub const SRS1: &[u8] = include_bytes!("../tests/wasm/kzg1.srs");
+
+    #[wasm_bindgen_test]
+    async fn can_verify_aggr() {
+        let value = verifyAggr(
+            wasm_bindgen::Clamped(PROOF_AGGR.to_vec()),
+            wasm_bindgen::Clamped(VK_AGGR.to_vec()),
+            21,
+            wasm_bindgen::Clamped(SRS1.to_vec()),
+            "kzg",
+        )
+        .map_err(|_| "failed")
+        .unwrap();
+
+        // should not fail
+        assert!(value);
+    }
 
     #[wasm_bindgen_test]
     async fn verify_encode_verifier_calldata() {
diff --git a/tests/wasm/kzg1.srs b/tests/wasm/kzg1.srs
new file mode 100644
index 0000000000000000000000000000000000000000..21aee1789d50a27802d017df697d304ad76271d1
GIT binary patch
literal 516
zcmV+f0{i^|0001;4Uff*LtWE73&-^+>v#$*MtOK|qi$v|-v^q(cM1+V8y<@rhq|VF
z7LMyt*6|crjPmf_M%~O_z7I5m?-U#<g(JRIb{xo6m!doJjD*t;+B7A=yzK(ZhNCNN
zj!X)eiPWKesBVeVyyrqbce&xVC|boi)ZB{MeL#wT1$`C*)pLYqCuDDyPnUEOT9_UI
zW3Bf8s{Fj!$_AwDw}}idICbUj-A2@5A9=a^3b1wpNC?&!NkG1PvbeP1r2Z~aN(}ob
z&~>&XLb{q@X2?k{ZD0eKK~9aYU#u{FrTr^kMFd*hYn}tN*Xt>_B(<`91(l1ixl$Rw
zz1v<h`7H`2AiM(6wS$gw0eeX`8SC8N8iAKbCb;`MP?oPjI9C}!VN9rfbF{C-*aBEu
z-9uo8FQ8KVQ0WP4d+Iua@ctC`>1&9c`ki5To1XQT(trN`eH$ZU8lL|KLY}Vce*Gx!
zRv5=tTM1;lJbf}E`B&U_5H&NtHThM7Wc`!oprj2H8V8Lhz7eVWSSbX6#%`wj(q39}
z0wH}YMq1hKg0wGsilbwkG#==*gcyT9SmqefCTSEq2OE;DsZF9XT{0_UZwVDPuJrH%
z3_NcXqwrClnNB=n!p5U*2+`8X5|tnyO=mq@y1dvK?<c%>eO~YpnS0;@FfyhefcE0x
Gru`^wO!X!J

literal 0
HcmV?d00001

diff --git a/tests/wasm/proof_aggr.json b/tests/wasm/proof_aggr.json
new file mode 100644
index 000000000..2e2e33de9
--- /dev/null
+++ b/tests/wasm/proof_aggr.json
@@ -0,0 +1,3075 @@
+{
+    "protocol": null,
+    "instances": [
+        [
+            "24539dfcf7027422090000000000000000000000000000000000000000000000",
+            "7cd6ac1ca1c3edee0c0000000000000000000000000000000000000000000000",
+            "4c6e043353acde3a0f0000000000000000000000000000000000000000000000",
+            "404929425c2b0000000000000000000000000000000000000000000000000000",
+            "5471b1fbe62269e20b0000000000000000000000000000000000000000000000",
+            "e57b6212366d035f0f0000000000000000000000000000000000000000000000",
+            "cf931da0123fe572090000000000000000000000000000000000000000000000",
+            "a762b6c8b8030300000000000000000000000000000000000000000000000000",
+            "7f0fb12e8b3209ae010000000000000000000000000000000000000000000000",
+            "a9274fde97e91fa0040000000000000000000000000000000000000000000000",
+            "2efac2e0d08cc1ec040000000000000000000000000000000000000000000000",
+            "191a544565680200000000000000000000000000000000000000000000000000",
+            "2f3af080665eac6b0b0000000000000000000000000000000000000000000000",
+            "d0c24cfc0924d0b50d0000000000000000000000000000000000000000000000",
+            "5b13d4cbb2e3691f040000000000000000000000000000000000000000000000",
+            "98f9ae4c6f2c0100000000000000000000000000000000000000000000000000",
+            "3102000000000000000000000000000000000000000000000000000000000000",
+            "3005000000000000000000000000000000000000000000000000000000000000",
+            "0000000000000000000000000000000000000000000000000000000000000000",
+            "0000000000000000000000000000000000000000000000000000000000000000"
+        ]
+    ],
+    "proof": [
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+    "hex_proof": "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+    "transcript_type": "EVM",
+    "split": null,
+    "pretty_public_inputs": null,
+    "timestamp": 1711847814900,
+    "commitment": "KZG"
+}
\ No newline at end of file
diff --git a/tests/wasm/vk_aggr.key b/tests/wasm/vk_aggr.key
new file mode 100644
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literal 0
HcmV?d00001