sandwiches.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detecting food in Hyperspectral Images\n",
    "\n",
    "George Miliarakis, Research Practice Report\n",
    "\n",
    "Hungry Robots Lab  \n",
    "Wageningen University & Research\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n",
    "from datetime import datetime\n",
    "from packaging import version\n",
    "import os, re\n",
    "# import numbers\n",
    "# import math\n",
    "from mpl_toolkits.axes_grid1 import ImageGrid\n",
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "import skimage\n",
    "import skimage.io as io\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import spectral\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.feature_extraction.image import extract_patches_2d, reconstruct_from_patches_2d\n",
    "from keras.ops.image import crop_images, pad_images, extract_patches\n",
    "from tensorflow.image import crop_to_bounding_box\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "import keras_cv\n",
    "import keras_tuner as kt\n",
    "from tensorflow import keras\n",
    "from keras import ops, saving\n",
    "from keras.layers import *\n",
    "from keras_cv.layers import *\n",
    "from keras.models import Model\n",
    "from keras.optimizers import SGD\n",
    "from keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau\n",
    "# from patchify import *\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression, Perceptron, SGDClassifier \n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from lazypredict.Supervised import LazyClassifier\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, roc_curve, roc_auc_score, RocCurveDisplay, classification_report, brier_score_loss, ConfusionMatrixDisplay,  jaccard_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn-v0_8-colorblind')\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams[\"figure.dpi\"] = 150\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"sans-serif\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "features = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(X, y, features, method = None):\n",
    "    '''Wrapper to preprocess X and y for model training/testing'''\n",
    "    y = skimage.color.rgb2gray(y)\n",
    "    # y = skimage.exposure.adjust_gamma(y, 1)\n",
    "    y[np.where(y>0.5)] = 1\n",
    "    y[np.where(y<0.5)] = 0\n",
    "    y = 1-y.T\n",
    "    # y = 1-y\n",
    "    # X = apply_PCA(X, features)\n",
    "    X = X.reshape(-1,3)\n",
    "    scaler = StandardScaler()\n",
    "    X = scaler.fit_transform(X)\n",
    "    if method == None:\n",
    "        # X, y = cubify(X,y)\n",
    "        # X = X.ravel()\n",
    "        y = y.ravel()\n",
    "    # # else: \n",
    "    # #     X = extract_patches_2d(image = X, patch_size=(32,32), max_patches=512)\n",
    "    # #     # X = np.reshape(X, (X.shape[0]*X.shape[1]*X.shape[2], X.shape[3]))\n",
    "    # #     y = extract_patches_2d(image = y, patch_size=(32,32), max_patches=512)\n",
    "    # #     # y = np.reshape(y, (y.shape[0]*y.shape[1]*y.shape[2],-1))\n",
    "    y = to_categorical(y, num_classes=2)\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Zero padding\n",
    "def pad0(X, margin=2):\n",
    "    '''Pad X with margin zeros'''\n",
    "    w, h, d = X.shape\n",
    "    padX = np.zeros((w + 2 * margin, h + 2* margin, d))\n",
    "    x_offset = margin\n",
    "    y_offset = margin\n",
    "    padX[x_offset:w + x_offset, y_offset:h + y_offset, :] = X\n",
    "    return padX\n",
    "\n",
    "## Image cubes creation\n",
    "def cubify(X, y, size=32, drop0labels = False):\n",
    "    '''Create cubes (patches) of X and y'''\n",
    "    margin = int((size - 1) / 2)\n",
    "    padX = pad0(X, margin=margin)\n",
    "    w, h, d = X.shape\n",
    "    # split patches\n",
    "    Xcubes = np.zeros((w * h, size, size, d))\n",
    "    ycubes = np.zeros((w * h))\n",
    "    index = 0\n",
    "    for r in range(margin, padX.shape[0] - margin):\n",
    "        for c in range(margin, padX.shape[1] - margin):\n",
    "            patch = padX[r - margin:r + margin + 1, c - margin:c + margin + 1]   \n",
    "            Xcubes[index, :, :, :] = patch\n",
    "            ycubes[index] = y[r-margin, c-margin]\n",
    "            index += 1\n",
    "    if drop0labels:\n",
    "        Xcubes = Xcubes[ycubes>0,:,:,:]\n",
    "        ycubes = ycubes[ycubes>0]\n",
    "        ycubes -= 1\n",
    "    return Xcubes, ycubes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(y, yhat):\n",
    "    if y.ndim == 2:\n",
    "        y = np.argmax(y, axis=1)\n",
    "    if yhat.ndim ==2:\n",
    "        yhat = np.argmax(yhat, axis=1)\n",
    "    report=classification_report(y,  yhat, digits=5, output_dict=True, zero_division = 0)\n",
    "    metrics = pd.DataFrame(report)\n",
    "    metrics.drop('support', axis=0, inplace=True)\n",
    "    metrics.loc[len(metrics.index)] = metrics.iloc[0,2]\n",
    "    metrics.drop(metrics.columns[0:4], axis=1, inplace=True)\n",
    "    metrics.rename(index={3:'accuracy'}, inplace=True)\n",
    "    metrics.loc[len(metrics.index)] = [roc_auc_score(y,yhat)]\n",
    "    metrics.rename(index={4:'AUC'}, inplace=True)\n",
    "    metrics.loc[len(metrics.index)] = [jaccard_score(y,  yhat)]\n",
    "    metrics.rename(index={5:'Jaccard score (IoU)'}, inplace=True)\n",
    "    metrics.loc[len(metrics.index)] = [brier_score_loss(y,yhat)]\n",
    "    metrics.rename(index={6:'Brier score'}, inplace=True)\n",
    "    metrics.sort_index(inplace=True)\n",
    "    return metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NN layer definition\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "@saving.register_keras_serializable(package=\"sase\")\n",
    "class BaseAttention(Layer):\n",
    "  def __init__(self, **kwargs):\n",
    "    super().__init__()\n",
    "    self.mha = MultiHeadAttention(num_heads=1, key_dim=64, **kwargs)\n",
    "    self.layernorm = LayerNormalization()\n",
    "    self.add = Add()\n",
    "\n",
    "  def get_config(self):\n",
    "    config = super(BaseAttention, self).get_config()\n",
    "    config.update({})\n",
    "    return config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "@saving.register_keras_serializable(package=\"sase\")\n",
    "class GlobalSelfAttention(BaseAttention):\n",
    "  def call(self, x):\n",
    "    attn_output = self.mha(\n",
    "        query=x,\n",
    "        value=x,\n",
    "        key=x)\n",
    "    x = self.add([x, attn_output])\n",
    "    x = self.layernorm(x)\n",
    "    return x\n",
    "\n",
    "  def get_config(self):\n",
    "    config = super(GlobalSelfAttention, self).get_config()\n",
    "    return config\n",
    "  \n",
    "  @classmethod\n",
    "  def from_config(cls, config):\n",
    "    return cls(**config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "@saving.register_keras_serializable(package=\"sase\")\n",
    "class FeedForward(Layer):\n",
    "  def __init__(self, d_model, dff, dropout_rate=0.2, **kwargs):\n",
    "    super().__init__(**kwargs)\n",
    "    self.d_model = d_model\n",
    "    self.dff = dff\n",
    "    self.dropout_rate = dropout_rate\n",
    "    self.seq = keras.models.Sequential([\n",
    "      Dense(dff, activation='relu'),\n",
    "      Dense(d_model),\n",
    "      Dropout(dropout_rate)\n",
    "    ])\n",
    "    self.add = Add()\n",
    "    self.layer_norm = LayerNormalization()\n",
    "\n",
    "  def call(self, x):\n",
    "    x = self.add([x, self.seq(x)])\n",
    "    x = self.layer_norm(x) \n",
    "    return x\n",
    "  \n",
    "  def get_config(self):\n",
    "    config = super(FeedForward, self).get_config()\n",
    "    config.update({\n",
    "        \"d_model\": self.d_model,\n",
    "        \"dff\": self.dff,\n",
    "        \"dropout_rate\": self.dropout_rate\n",
    "    })\n",
    "    return config\n",
    "  \n",
    "  @classmethod\n",
    "  def from_config(cls, config):\n",
    "    return cls(**config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "<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: \"FeedForwardModel\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"FeedForwardModel\"\u001b[0m\n"
      ]
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       "<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>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ Input (<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\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ FeedForward_Dense_1 │ (<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\">64</span>)     │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │ Input[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)             │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ FeedForward_Dense_2 │ (<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\">64</span>)     │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │ FeedForward_Dens… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)             │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ FeedForward_Dropout │ (<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\">64</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ FeedForward_Dens… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ FeedForward_Add     │ (<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\">64</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ Input[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],      │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)               │                   │            │ FeedForward_Drop… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ FeedForward_LayerN… │ (<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\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ FeedForward_Add[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalizatio…</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┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ Input (\u001b[38;5;33mInputLayer\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ FeedForward_Dense_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │      \u001b[38;5;34m4,160\u001b[0m │ Input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mDense\u001b[0m)             │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ FeedForward_Dense_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │      \u001b[38;5;34m4,160\u001b[0m │ FeedForward_Dens… │\n",
       "│ (\u001b[38;5;33mDense\u001b[0m)             │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ FeedForward_Dropout │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │          \u001b[38;5;34m0\u001b[0m │ FeedForward_Dens… │\n",
       "│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ FeedForward_Add     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │          \u001b[38;5;34m0\u001b[0m │ Input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],      │\n",
       "│ (\u001b[38;5;33mAdd\u001b[0m)               │                   │            │ FeedForward_Drop… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ FeedForward_LayerN… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m128\u001b[0m │ FeedForward_Add[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mLayerNormalizatio…\u001b[0m │                   │            │                   │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
      ]
     },
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       "<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\">8,448</span> (33.00 KB)\n",
       "</pre>\n"
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       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,448\u001b[0m (33.00 KB)\n"
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     "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\">8,448</span> (33.00 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,448\u001b[0m (33.00 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\"> 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"
    }
   ],
   "source": [
    "def feed_forward(x, d_model, dff, dropout_rate=0.2, name_prefix=\"\"):\n",
    "    # First Dense layer with ReLU activation\n",
    "    dense_1 = Dense(dff, activation='relu', name=f\"{name_prefix}Dense_1\")(x)\n",
    "    \n",
    "    # Second Dense layer (no activation)\n",
    "    dense_2 = Dense(d_model, name=f\"{name_prefix}Dense_2\")(dense_1)\n",
    "    \n",
    "    # Dropout layer\n",
    "    dropout = Dropout(dropout_rate, name=f\"{name_prefix}Dropout\")(dense_2)\n",
    "    \n",
    "    # Add layer (skip connection)\n",
    "    add = Add(name=f\"{name_prefix}Add\")([x, dropout])\n",
    "    \n",
    "    # Layer Normalization\n",
    "    norm = LayerNormalization(name=f\"{name_prefix}LayerNorm\")(add)\n",
    "    \n",
    "    return norm\n",
    "\n",
    "# Example usage:\n",
    "input_tensor = Input(shape=(1,64), name=\"Input\")\n",
    "output = feed_forward(input_tensor, d_model=64, dff=64, dropout_rate=0.2, name_prefix=\"FeedForward_\")\n",
    "\n",
    "model = Model(inputs=input_tensor, outputs=output, name=\"FeedForwardModel\")\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "@saving.register_keras_serializable(package=\"sase\")\n",
    "class SqueezeAndExcite1D(keras.Model):\n",
    "    def __init__(self, filters, bottleneck_filters=None, squeeze_activation=\"relu\", excite_activation=\"sigmoid\", **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.filters = filters\n",
    "\n",
    "        if bottleneck_filters and bottleneck_filters >= filters:\n",
    "            raise ValueError(\n",
    "                \"`bottleneck_filters` should be smaller than `filters`. Got \"\n",
    "                f\"`filters={filters}`, and \"\n",
    "                f\"`bottleneck_filters={bottleneck_filters}`.\"\n",
    "            )\n",
    "\n",
    "        if filters <= 0 or not isinstance(filters, int):\n",
    "            raise ValueError(\n",
    "                f\"`filters` should be a positive integer. Got {filters}\"\n",
    "            )\n",
    "\n",
    "        self.bottleneck_filters = bottleneck_filters or (filters // 4)\n",
    "        self.squeeze_activation = squeeze_activation\n",
    "        self.excite_activation = excite_activation\n",
    "        self.global_average_pool = GlobalAveragePooling1D(keepdims=True)\n",
    "        self.squeeze_conv = Conv1D(self.bottleneck_filters, 1, activation=self.squeeze_activation, name = 'squeeze')\n",
    "        self.excite_conv = Conv1D(self.filters, 1, activation=self.excite_activation, name = 'excite')\n",
    "\n",
    "    def call(self, inputs, training=None):\n",
    "        x = self.global_average_pool(inputs)  # x: (batch_size, 1, filters)\n",
    "        x = self.squeeze_conv(x)  # x: (batch_size, 1, bottleneck_filters)\n",
    "        x = self.excite_conv(x)  # x: (batch_size, 1, filters)\n",
    "        x = Multiply()([x, inputs])  # x: (batch_size, timesteps, filters)\n",
    "        return x\n",
    "\n",
    "    def get_config(self):\n",
    "        config = {\n",
    "            \"filters\": self.filters,\n",
    "            \"bottleneck_filters\": self.bottleneck_filters,\n",
    "            \"squeeze_activation\": self.squeeze_activation,\n",
    "            \"excite_activation\": self.excite_activation,\n",
    "            \"squeeze_conv\": self.squeeze_conv,\n",
    "            \"excite_conv\":self.excite_conv\n",
    "        }\n",
    "        base_config = super().get_config()\n",
    "        return dict(list(base_config.items()) + list(config.items()))\n",
    "\n",
    "    @classmethod\n",
    "    def from_config(cls, config):\n",
    "        if isinstance(config[\"squeeze_activation\"], dict):\n",
    "            config[\"squeeze_activation\"] = keras.activations.deserialize(config[\"squeeze_activation\"])\n",
    "        if isinstance(config[\"excite_activation\"], dict):\n",
    "            config[\"excite_activation\"] = keras.activations.deserialize(config[\"excite_activation\"])\n",
    "        if isinstance(config[\"squeeze_conv\"], dict):\n",
    "            config[\"squeeze_activation\"] = keras.activations.deserialize(config[\"squeeze_conv\"])\n",
    "        if isinstance(config[\"excite_conv\"], dict):\n",
    "            config[\"excite_activation\"] = keras.activations.deserialize(config[\"excite_conv\"])\n",
    "        return cls(**config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1D SASE\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model1D():\n",
    "    input_layer = Input(shape=(features,), name='input_layer')\n",
    "    x = Reshape((1, features))(input_layer)\n",
    "    c = Conv1D(filters=64, kernel_size=3, padding='same', activation='gelu')(x)\n",
    "    s = SqueezeAndExcite1D(64)(c)\n",
    "    g = GlobalSelfAttention()(s)\n",
    "    f = FeedForward(64, 64)(g)\n",
    "    s = Add()([c, f])\n",
    "    s = LayerNormalization()(s)\n",
    "    # FeedForward blocks\n",
    "    g = GlobalSelfAttention()(s)\n",
    "    f = FeedForward(64, 64)(g)\n",
    "    x = Flatten()(f)\n",
    "    # Dense layers\n",
    "    x = Dense(units=64, activation='gelu')(x)\n",
    "    x = Dropout(0.2)(x)\n",
    "    x = Dense(units=64, activation='gelu')(x)\n",
    "    x = Dropout(0.2)(x)\n",
    "    \n",
    "    output_layer = Dense(units=2, activation='sigmoid', name='output')(x)\n",
    "\n",
    "    model = Model(inputs=input_layer, outputs=output_layer)\n",
    "    \n",
    "    model.compile(\n",
    "        optimizer=SGD(learning_rate=0.0001, momentum=0.7758, weight_decay=0.00084),\n",
    "        loss='binary_crossentropy',\n",
    "        metrics=[keras.metrics.OneHotMeanIoU(num_classes=2, name='mIoU', sparse_y_pred=False), keras.metrics.F1Score(threshold=0.5), \"acc\", 'auc']\n",
    "    )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model1D()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "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_27\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_27\"\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>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</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\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ input_layer[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv1d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</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\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │ reshape_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ squeeze_and_excite… │ (<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\">64</span>)     │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,128</span> │ conv1d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SqueezeAndExcite1…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ global_self_attent… │ (<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\">64</span>)     │     <span style=\"color: #00af00; text-decoration-color: #00af00\">16,768</span> │ squeeze_and_exci… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalSelfAttenti…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ feed_forward_4      │ (<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\">64</span>)     │      <span style=\"color: #00af00; text-decoration-color: #00af00\">8,448</span> │ global_self_atte… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">FeedForward</span>)       │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</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\">64</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv1d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],   │\n",
       "│                     │                   │            │ feed_forward_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ layer_normalizatio… │ (<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\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ add_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LayerNormalizatio…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ global_self_attent… │ (<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\">64</span>)     │     <span style=\"color: #00af00; text-decoration-color: #00af00\">16,768</span> │ layer_normalizat… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalSelfAttenti…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ feed_forward_5      │ (<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\">64</span>)     │      <span style=\"color: #00af00; text-decoration-color: #00af00\">8,448</span> │ global_self_atte… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">FeedForward</span>)       │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ flatten_2 (<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\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ feed_forward_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_16 (<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\">64</span>)        │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │ flatten_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_16          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_17 (<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\">64</span>)        │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │ dropout_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_17          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ output (<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\">2</span>)         │        <span style=\"color: #00af00; text-decoration-color: #00af00\">130</span> │ dropout_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</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┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)         │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
       "│ (\u001b[38;5;33mInputLayer\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ reshape_2 (\u001b[38;5;33mReshape\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m3\u001b[0m)      │          \u001b[38;5;34m0\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv1d_2 (\u001b[38;5;33mConv1D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m640\u001b[0m │ reshape_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ squeeze_and_excite… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │      \u001b[38;5;34m2,128\u001b[0m │ conv1d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mSqueezeAndExcite1…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ global_self_attent… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │     \u001b[38;5;34m16,768\u001b[0m │ squeeze_and_exci… │\n",
       "│ (\u001b[38;5;33mGlobalSelfAttenti…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ feed_forward_4      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │      \u001b[38;5;34m8,448\u001b[0m │ global_self_atte… │\n",
       "│ (\u001b[38;5;33mFeedForward\u001b[0m)       │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_12 (\u001b[38;5;33mAdd\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │          \u001b[38;5;34m0\u001b[0m │ conv1d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],   │\n",
       "│                     │                   │            │ feed_forward_4[\u001b[38;5;34m0\u001b[0m… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ layer_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m128\u001b[0m │ add_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "│ (\u001b[38;5;33mLayerNormalizatio…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ global_self_attent… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │     \u001b[38;5;34m16,768\u001b[0m │ layer_normalizat… │\n",
       "│ (\u001b[38;5;33mGlobalSelfAttenti…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ feed_forward_5      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │      \u001b[38;5;34m8,448\u001b[0m │ global_self_atte… │\n",
       "│ (\u001b[38;5;33mFeedForward\u001b[0m)       │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m0\u001b[0m │ feed_forward_5[\u001b[38;5;34m0\u001b[0m… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_16 (\u001b[38;5;33mDense\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │      \u001b[38;5;34m4,160\u001b[0m │ flatten_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_16          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m0\u001b[0m │ dense_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_17 (\u001b[38;5;33mDense\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │      \u001b[38;5;34m4,160\u001b[0m │ dropout_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_17          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m0\u001b[0m │ dense_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ output (\u001b[38;5;33mDense\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)         │        \u001b[38;5;34m130\u001b[0m │ dropout_17[\u001b[38;5;34m0\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\">61,778</span> (241.32 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m61,778\u001b[0m (241.32 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\">61,778</span> (241.32 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m61,778\u001b[0m (241.32 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"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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prYB8cofjx49L2zW8p8/JySlXrpx47MaNG/WWOXTokFTtCxcuiBsVVyw8PFzvsc2bNxfL1KlTR749Pj5eOvzQoUN6j83Ozpbesi5cuFC+q4AX/Pjx49L25cuX6z02MzNTepipXbu2+mknNTX10f8YqoBebdq0kf/sO3bsqC5j6IY+LCxMPuJAqVKlrl+/Lh1l5Ib+008/VZxw1KhR6enp8g+NjIwUR+iUWFlZnT9/Xiqg94Ze8fAcGRnp5OSkKJOSkiLuvXXrluLNeZcuXZ4/fy4/w9atWxWHq58GzdO3b1/5afMcy3PIkCGKB8W7d+9KQ2yUKlVKGhEjXwmaV199VfEphfLXGWLoryu65wFxfF9R3759zTuJvHXAxx9/LN/1IhM0+Q0vxTGY64pzPH/5g7nOQuO5tsE8TykpKermQjt37lSXtPiAjJKGMWgAWKAxY8Y0btxYvd3Kykq6dVZMHyA3a9YsW1v9PUDlkzia0iH8Bbh161Zqaqq4rL6JFHXu3Hnu3LkzZ86cOXOm3nbpkyZNMtQB4bXXXhMXFK2gr1275uLi4uLi4uXl1alTJ73H2tjYSJ0jjEzYacYFDwgIEBdatmz50Ucf6T22VKlSX375pbh869Yt+YONyMHBofL/GKqAXrdu3ZKvSm+eTeHp6fn+++9LqyZO0fro0aMVK1bIt7i6uq5du1bRLNzd3X3t2rXyLTrVq3WFoUOHfvLJJ4qTqIeZvHbtmriwadOm58+fS9tLly69evVqe3t7eeEhQ4YMGTJEvmXXrl1mTN2t9uzZM/nq7du3jZfftm2b/L29IAhTp06VhmCYOHFiw4YNzaiG4htX/B4sgDySKCbJMp2Li4u0LHZ70UQxiueFEsyF4hbPi28wF4p5PNc2mBuXlJTk5+enGLuqbdu2/fr1Uxe2+ICMkoYEDQALZGQ2DWmW0IyMDOlWWM7GxqZ3796GDnd2dq5du7a4XBSjFZqhdu3aUqf3xYsXq+9cBUGwsrKaOXPm3Llz586dqzd1pRiMQE7qy52eni6/Yl26dImJiYmJibl8+bKhSSiSkpIMPWZIzLjgDx8+lMYiGThwoLW1wf+XtWzZUnqiCA4ONl4TE+Xk5CjmEjI034ohc+fOlY9TsHfvXr3fmlx4eLhiTNBPP/1U72Xv3Llzq1at5FsUffgV9E4k7OnpqdgiDVkqTiQs8fX1VbzjFSkGeHr8+PHff/9tpBomkmbSlbOxsenfv//MmTNHjhxZo0YNxd5Zs2ZJ9+vHjh376aefxOVq1arNmjXLvGoovnENsw9FJDk5WVrO7yOrRD7Tufp7eTGKVzwvlGAuFKt4XtyDuVCc47m2wdyIiIiItm3bnjlzRr6xVKlS0vBMChYfkFHSMEgwAEtjbW2taFcsJ3+pm5aWJrUnlzRv3tz4gI5NmjQRn/fEwRo1Z21t/cYbb4gzF6Smpvr5+fn4+IwcObJnz57yP9Y4IyXll0jvFVNLSkqKjY09e/ZsYGBgRkaG8cJmXHD5bWWLFi2Mn79p06bigBpSG5ACunfvnmLSjfw+wTo5OQUGBspHRAoICDDyXCQIQlRUlGKLkalG/Pz85DfxiYmJjx8/NjR0ZZMmTdQbjfwLUjzH2tjYfP/99+pid+/eVWwJDw/P88vKk3q6k/Lly//1119Sy7jk5OR33nlHPgNRampqYGDgunXrsrKyPvzwQ2n7/PnzjUwnbJziG09ISNDpdIqxEoo1+a8lz2ZKhsjHXjW7GU4BFa94XijBXChW8by4B3OhOMdzbYO5IT/++OPEiRMVkzdZW1sHBQVJE8MpWHxARklDggaApbG3t89XC2eFPB8kmjRpcuDAAUEQbt++nZ6erhidThOrVq26ceNGWFiYuHru3DlxKofatWu/8cYbPXr08PPz0zuQqsja2lrRhVsuz7uc58+fnzp16siRI6GhobGxsbGxsfJW03ky44LL784DAgKMfwXSU6L6LtM86rdzZtzTf/jhh6tWrZL+kPDw8LVr144fP95QecUNvbW1taFxoARBkN5Ryw9XDLUgsrGx0dvHx8i/oBs3bshXDx8+bHzOXYmhaYPzxdHRUbFl9erVUnZGEITKlStv3rzZ1dVV3gVD/OewbNmyK1euiFu8vb2lMUTMoHhhm5mZmZSUZMa795eWfPhSszMX8u4JWiVoil08L2AwF4pbPLeAYC4U23iubTBXe/bs2fvvv79lyxbFdgcHh3Xr1g0aNMjQgRYfkFHSkKABYGkMTdZoojznyHRzcxMXdDpdYmJiviag1RkeXq4gnJycDh8+PHHixJ9//ln+PvDWrVsbNmzYsGGDjY1Nt27dpk6d+vrrr6sPr1SpUunSpc343Ozs7G+//TYwMFBvZzFBEMQpWhWvwtSfbvxT1Bdc/tI1PDzcxNoqmpSbTd77Q2TGjJ6lSpVaunRpz549pS2zZs1ST5khUbzqrFq1qpGZodX3+tHR0Xpv6MuUKaP3qzf0Q01JSTH71rxQrr+ip0yFChXUF61y5cpjx45duHChtOX69evZ2dnz5s2TtowfP17RvP/x48eK88TFxYlPwrVq1VI0ClDf+icnJ1vS84D8Cd/sBI28BY15k1gXPGAWXTx/OYO5UNziuQUEc6F4xnPNg7nCnTt3+vTpc/nyZcV2V1fXHTt2qHtpyVl8QEZJQ4IGgKUpYLtWBwcH4wXkw5Tmt4tESkqKOXUygZOT05YtW+bNm7d9+/a9e/eeOnUqOztb2puTk7Nv3759+/bNmTNHPe6GeVcsMzOzU6dOp0+flm8sW7asi4tL/fr1mzVr1rZt227dutWtW9f4iH1mXPAnT55IW6pUqWJi/fP8IBNVr15dscW8u9UePXr06NFj//794mpSUpI0Cqaa4rFBfgXU1LkG85461Ozs7GxsbNT9jExh3lEKigSNu7u73mLSQ6AoKyvrxo0b8gfLsWPH5vlZ0vNVcHCw4uWt+htX/yqKNS8vL3t7e/GKxcbGZmZm5veZPzMz8+jRo+KylZWV3rEt8lTwgFl08fzlDOZCcYvnlhHMhWIYzzUP5nKXLl3q06dPQkKCYvuwYcNWrlxZsWJF44dbfEBGSUOCBgD+Jc+hIqXbU2tr6zzvGxTU7+sKV926dadOnTp16tRnz56dOHHi6NGjf/zxR2hoqPQCLTAwsEGDBkbe7JluxowZ0t183bp1J0+e3LVr1wYNGhgZ5VEvMy64/G385cuXjbTnLwrqj5MPhpovS5Ys+fPPP6WnrxUrVihm0JA0atRIvpqenp6UlGToNl39BKU43GylS5euW7eu/CubPn36iBEjTDlW3TvJDDVr1pSvGuqPoB6StnDHI1B84xUqVMhvKHjJ2dratmrV6q+//hIEITMzc926dUb6a+i1bds2aZ4gb29v87oOFTxgFl08t6RgLmgXzy0mmAvFLZ5rHswlv//+++DBgxUz9FWoUGHlypVvv/22KWew+ICMkoYEDQD8S56trKW7pWrVquX35vWFzf7o4ODQrVu3bt26ff3113FxcQsXLvzhhx/EXZs3by74Pf3jx4+l+RQaN2585MgRQy+sFHddamZccPnt6Y0bN17wPX3lypUdHBzkf9eDBw/MO5Wbm9uHH364fPlycTUzM1PvLEWCqkmIIAgRERGG+jhERkbKV+3t7QvxEjVq1Eh+T5+QkGDeTNXmUTTEMNT7RjG0hK2tbeGOgaL4xtWDRFiAwYMHiwkaQRAWLFgwevTofDWikX7VgiCYPVtWwQNm0cVziwnmgqbx3GKCuVAM47m2wVwUExMzZMgQxe/Kx8dn69atpje7KwkBGSUK02wDwL/ExsYa6oEvCEJ2dvbZs2fF5Xbt2knbpfkaUlNTs7Ky9B6bkJCgGJOvUBw6dGj9+vXr168/deqU3gJ16tRZuXLlsGHDxNULFy4U/EPDw8OlF7mffvqpobv5iIgI+ViteplxweU3kdKwr4bs2bNHvD6K1vsFobj/K8hL18DAQFPeRqpv6OUPwHIJCQm//vqrfEvDhg3zm0k0QvHyVjETqiQzM/Phv8m7aZitU6dO8pEFrl+/rne4UGniXpGrq6uRIR7MoPjGLfJ5YPjw4dLIO/Hx8Rs2bDD92L/++uvSpUvicqtWrfr06aMo8MICphnhpaQFc0HTeG5JwVwobvFc22AuCEJubu67776r+MGMHTs2JCQkX50iS0JARolCggYA/kWn0504ccLQ3m3btonTfAqC0KlTJ2m7k5OTuJCRkWHoLnPjxo2FVkuZvXv3jh49evTo0ePGjTNS7LXXXhMXnj9/XvDhLeVTbxiZbtOUKSHMuOAuLi7S7LA//vijkT/n8uXL/fr1E69PfHx8npUxUSHe01euXNnIUAWSRo0ayROCgiDs2bNHfXlzc3M///xzxWvb0aNHm109NcWwL1evXl20aJG62KBBg5xkXnnlFWmchdmzZw/5N+NDMMjZ2tpKT6eCIGRmZs6YMUNRJjExcf369fIt4jRPd+/efWCY+nl7//794q5+/fopdile2L7g1/4vRvny5T/44ANpddasWfK5fo1ITk5+7733pNW5c+eqy7ywgGlGeClpwVzQNJ5bUjAXils81zaYC4KwcuVKqZme9FmrV6/O74hXJSEgo0QhQQMASu+99540eoJcWlraggULxGV7e/sBAwZIu+RjXuh9uRcSEmJiO39D72wNadWqlbgQERGhmJhGTmp53qJFi4KPx+Hs7CwtyyfTlYuMjFyyZIkpZ8vvBbe1tZ05c6a4HBYWtmfPHkNnnjZtmnjHX65cub59+yr23rt37yKLVfYAAA1TSURBVJ//MT41iYLiGaaATwvvv/++h4dHnsWkPgginU7Xo0eP5cuXi/emubm5UVFRvXv33rRpk7yYq6ur/DG74N5++23F69/PP/980aJFN2/eFFefPXs2efLk3bt3y8t06tRJerF85MiR4H/LyMgwvQIjR46Ur65bt+6jjz6SnmHi4+O7desm/zatrKymTZsmCEKVKlUcDVNPXVyxYkVxl/ppQdG9pXnz5qbXvxiZPXu2NERIYmJi+/btV69ebfyQe/fu9evXT8p2+fr6du/eXV3sRQbM/IaXkhbMBU3juYUFc6FYxXPNg/nKlSsVW6Kjozt06NDeqKdPnyqOKiEBGSWIDgDyTz3vo2jx4sVF+rmhoaHSZw0fPly+a86cOeJ2R0dHI2dYu3atdIb79+9L2zt06CD/Qzp06JCeni4/8O7duz4+PlKByZMny/fGxsZKN8rlypU7ffq0tCs3N/fixYuKRuPe3t6KinXu3Fnc5eDgIK9YnuLj46WZxbt3756UlKQuc+zYMekt5RdffCFuNPGKffvtt+orJu9X4unpmZKSojjqjz/+UEyJMmnSJHmBAl7wzMxM6c6yfPnyu3fvVlTg8ePH8tEZvvrqK/Wf5u/vLxU4cOCAkYugoOhZYGdnl5aWpigj3eNKduzYYeiEhw4dEvQZN26cvNhbb72lt1jt2rUV80BLfvnlF/kZli1bJt/r4OCgtz7qd7n79u2T9p49e1bqBiLXqFGj1q1bq2tiZ2cXFhYmHa746gVBSExMNP3i63Q6eeM1UdmyZdu1a9esWTN1xd555x1Tzqkee+LUqVN6Syq+WRsbm/zWP7/Ul1oUHBxcpJ+r0+lOnDihGOh0+PDhx48ff/jwoaLkgwcPVq5cKe/fUbt27ZiYGL2nLeqAWZDwUhyDua44x/OXP5jrLDeeaxjMpV6Q+aUIPiUqIKOEYJBgAPgXcXLZkJCQKlWqdOzY8Y033qhUqdKpU6f27dsnvRVs2LDh9OnT5UfVqVPHz8/v4MGDgiCkpqa+/vrrPj4+Pj4+d+7cOXz48P379wVBKFOmzNChQxWdLyT169c/cuSIIAjPnj1r06aNl5dX1apVV61alWeFa9euPX36dPGN7oEDBzw9PSdOnOjm5taoUaPs7OyYmJjg4ODt27eLTQzq16//2WefFegCCYIgCNWrVx84cOAvv/wiCEJ4eLi7u/snn3zi6emZlpZ248aNX3/9VezNXrVq1YYNG548eVIQhB07dri7u9esWbN3797yU5l3wUuVKrVixQo/Pz9BEFJSUvr169e/f/9WrVp5eHgkJSWFhobu2LFDeqvm5+f3+eefF/yvlnh7e9evX18aoTYjI+PMmTPqrIHp3njjDX9/f8WLSrWFCxceO3ZMnUowNF5pz549DT0DFISPj8+0adPmz5+v2C7vKCGxtrZetmyZp6dnIVYgKCjIy8tL3hnh+fPn4s9MoVy5cl999VUhfrQgCMeOHZOvKobFsTDt2rX7448/+vTpI/VcCAoKCgoKEgShVq1anp6ederUefjw4e3bty9cuCAfmaJGjRqHDx92cXHRe9oXFjDNCC8lLZgLWsdzCwvmQrGK5xoGc/GfWMGVqICMkkLrDBGAYsmCW9AMGjTI+EyTzs7OsbGx6jM/efJE0atczsrKauvWrVKDXvVLV/XjZa1atUy8LBkZGS1btjRSZ5G9vf2ZM2fye8X0vnHV6XQPHz5UzHmsUK9evbCwsODgYPnGli1bFtYF1+l0u3btkgaMMKRNmzaG3qeZ/dJVp9MpHhICAwMVBfL1xlWn00VHR6u70ijeuOp0usjISOOXXdK3b1/FS2xdIbWg0el0mZmZX331VdmyZY3XwdnZ+ciRI4qTF7wFjU6nCwkJMTQrrcTDw+Pq1asmntD0FjSKPlZr1qzJb+Xzy9Af+MJe2IaGhhoZmkTN398/Pj7e+DmLNGAWMLwUu2CuK/7x/GUO5jqLjudaBXPjAy0ZoWhBUwIDMiweY9AAwL9YWVmtX79+1qxZ6vuV0qVLT506NSIiok6dOuoDK1SoIL5qtrVVNk7s0KHDmTNnhgwZkpOTY+hzfX19ly1blt+x8aSKnTp1atGiRQ4ODnoLWFlZDRs27Nq1a61btzbj/HpVqVLl7Nmz6vFTBUGoVKnSZ599Fh4e7unp2bdv365duxo5j9kXXBAEf3//8PBwvYNciDWcNWtWSEhIUbxPGzx4sHz1+PHjBTxh/fr1J02alGcxd3f3K1eu/Oc//1F0PJGrW7fuli1bdu3aJXWXKHSlSpWaPn36tWvXFNdBYmVlNXDgwLCwMENzx8oZ+VsMad++/aVLl3r37m1oQpNRo0adO3dOPVtKwclf2Nra2r755puF/hEvm6ZNm164cGHDhg2KaV8UrK2t27Vrt3v37l27duU5kcqLCZjmhZeSFsyFlyCeW1IwF4pVPNcqmMfGxppeSSNKYECGxbPSFcbw7wBKGmdn5zt37qi3L168OCAg4MXXp+A6duwYEhIiCMLgwYO3bdsmCEJycvK2bduuX7+emJjo4uLi7u7eoUMHQ5kCufT09PDw8IsXLyYmJjZs2LBx48biJDKmSE1NvXbtWkJCQoUKFZo0aZLnG0WFhISEY8eO3bx58+bNm3FxcVWqVKlXr169evVat25teh3y6+TJkxcuXIiMjMzKynJ2dvby8urdu7f8VjIrK+v06dNXrlwpW7ZsmzZtxHlVC/GCC4IQHR196dKlS5cuXbt2zcnJydnZuVmzZj179jTvGclEjRo1kgbyLFOmTGJiomKYhiKVlpZ2/PjxkJCQe/fuPXjwwM7OzsnJqW7dun5+fs2aNSuUoUNNdPfu3fDw8IiIiMjISAcHB09PT09PTw8PD2mkDEMCAgKWLl1apUqVgsycEh8fv3379ps3b965c6dq1ariR3t6epoy2a0Zrl+/Lk9SdOvW7cCBA0XxQXKGvs3g4OBBgwYV9acrREdH//7775GRkYmJiffv37e1ta1evXr16tU9PDz8/f0NzdBsRFEEzMIKLyUtmAsvQTwvgcFceGniubbB3AwEZFgmrZvwACiWtOriVHSklrqDBw/Wui4lggVccEXz8hUrVmhdo2KmS5cugiC0aNFC64rkw5QpU+Rf+q5du17Ahxq6haNFvSEWEF6Kl+J+wQnmBaRVMCcgwyLRxQkAAHOMGzdO3l7ghx9+0LAyxU50dPTRo0cFQRg2bJjWdTFVWlqafFxYLy8v9XS/AIodgnlBaBXMCciwVCRoAAAwR5kyZaZNmyatXrlyRbxJRZ4SEhLefPPNnJycWrVqffjhh1pXx1Tbtm179OiRtBoYGPgiu5IBKCIEc7NpGMwJyLBUJGgAADDThAkTmjRpIq3OnTtXw8oUF7Nnz3Z1dQ0PD69UqdKaNWvKlCmjdY1Mkp2dLZ+M1s/PT++IqgCKI4K5GTQM5gRkWDASNAAAmKl06dL//e9/pYmEjh07pp7QFApHjhxJS0vr1KlTWFhYz549ta6OqTZu3BgdHS0uOzg4rFmzRtv6AChEBHMzaBjMCciwYCRoAAAwX+vWrb/44gtpdcaMGRpWplgYMWLE0aNHjxw5kudMzC+PzMxM+Rv15cuXu7i4aFcdAIWPYJ5fWgVzAjIsm63WFQAAoHibPXt2WFiYOEvr48ePz54927p1a60r9fJ67733tK5Cvv32229ly5Z1c3MTBKFjx45jxozRukYACh/BPF+0CuYEZFg2K53h2cIAwBBnZ+c7d+6oty9evDggIODF16fgHjx4kJGRIQiCvb19lSpVtK6O5eOCAyYyNPJlcHDwoEGDXnBligXCywvGBUfJQUBGUaMFDQAIgiA4OTlpXYWShQsOoIgQXl4wLjgAFBbGoAEAAAAAANAYCRoAAAAAAACNkaABAAAAAADQGAkaAAAAAAAAjZGgAQAAAAAA0BgJGgAAAAAAAI2RoAEAAAAAANAYCRoAAAAAAACNkaABAAAAAADQGAkaAAAAAAAAjZGgAQAAAAAA0BgJGgAAAAAAAI2RoAEAAAAAANAYCRoAAAAAAACNkaABAAAAAADQGAkaAAAAAAAAjZGgAQAAAAAA0BgJGgAAAAAAAI2RoAEAAAAAANAYCRoAAAAAAACNkaABAAAAAADQGAkaAAAAAAAAjZGgAQAAAAAA0BgJGgAAAAAAAI2RoAEAAAAAANAYCRoAAAAAAACN2WpdAQAW5fjx41pXAQBKhN9///327dta1wIAABQaK51Op3UdABQ/zs7Od+7c0boWAAAAgMaCg4MHDRqkdS1gCejiBAAAAAAAoDESNAAAAAAAABojQQMAAAAAAKAxEjQAAAAAAAAaI0EDAAAAAACgMRI0AAAAAAAAGrPVugIAiqUaNWpoXQUAAABAe2XLltW6CrAQVjqdTus6AAAAAAAAlGh0cQIAAAAAANAYCRoAAAAAAACNkaABAAAAAADQGAkaAAAAAAAAjZGgAQAAAAAA0BgJGgAAAAAAAI2RoAEAAAAAANAYCRoAAAAAAACNkaABAAAAAADQGAkaAAAAAAAAjf0/4UbhMzgeCJEAAAAASUVORK5CYII=",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.utils.plot_model(model, 'Tex/figures/FF.png', True, False, show_layer_activations=True, expand_nested=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model training\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "rootdir = path = \"mats/sandwiches/\"\n",
    "regex = re.compile(r'[A-Z][0-9][\\w-]*(?=_corrected)')\n",
    "pngs = []\n",
    "mats = []\n",
    "for root, dirs, files in os.walk(rootdir):\n",
    "  for file in files:\n",
    "    if regex.match(file) and file[-1]=='g':\n",
    "      pngs.append(file)\n",
    "    else:\n",
    "      mats.append(file)\n",
    "mats.sort()\n",
    "pngs.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask3d = saving.load_model('mask.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat_3d = []\n",
    "yhat_sase = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain = mats[:3]\n",
    "ytrain = pngs[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 43ms/step - acc: 0.9223 - auc: 0.9634 - f1_score: 0.8682 - loss: 0.3521 - mIoU: 0.8206\n",
      "Epoch 2/2\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 38ms/step - acc: 0.9846 - auc: 0.9971 - f1_score: 0.9758 - loss: 0.1571 - mIoU: 0.9624\n",
      "Epoch 1/2\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 40ms/step - acc: 0.9792 - auc: 0.9956 - f1_score: 0.9731 - loss: 0.1280 - mIoU: 0.9527\n",
      "Epoch 2/2\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 40ms/step - acc: 0.9782 - auc: 0.9957 - f1_score: 0.9732 - loss: 0.1088 - mIoU: 0.9507\n",
      "Epoch 1/2\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 38ms/step - acc: 0.9828 - auc: 0.9972 - f1_score: 0.9721 - loss: 0.0825 - mIoU: 0.9500\n",
      "Epoch 2/2\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 40ms/step - acc: 0.9864 - auc: 0.9975 - f1_score: 0.9782 - loss: 0.0702 - mIoU: 0.9595\n"
     ]
    }
   ],
   "source": [
    "for mat, png in zip(Xtrain, ytrain):\n",
    "    X = sio.loadmat(path+mat)['cube'] #load HSI\n",
    "    y = skimage.io.imread(path+png) #load gt\n",
    "    X, y = preprocess(X, y, 3)\n",
    "    # checkpoint\n",
    "    callback = keras.callbacks.EarlyStopping(monitor='loss', patience=0)\n",
    "    model.fit(X,y, batch_size=256, epochs=2, callbacks=callback)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Testing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = [] #save results\n",
    "path='mats/sandwiches/' #set root directory\n",
    "for mat, png in zip(mats[3:], pngs[3:]):\n",
    "    X = sio.loadmat(path+mat)['cube'] #load HSI\n",
    "    y = skimage.io.imread(path+png) #load gt\n",
    "    im1 = [X.T[0], r\"spectral feature $\\displaystyle \\frac{R_{1300:1327 \\mathrm{nm}}}{R_{1485:1512 \\mathrm{nm}}}$\"+\"\\n\"]\n",
    "    im2 = [X.T[1], r\"spectral feature $\\displaystyle \\frac{R_{1300:1327 \\mathrm{nm}}-R_{1485\\:1512 \\mathrm{nm}}}{R_{1300:1327 \\mathrm{nm}}+R_{1485:1512 \\mathrm{nm}}}$\"+\"\\n\"]\n",
    "    \n",
    "    # preprocess ground truth image\n",
    "    y = skimage.color.rgb2gray(y)\n",
    "    y[np.where(y>0.5)] = 1\n",
    "    y[np.where(y<0.5)] = 0\n",
    "    y = 1-y\n",
    "    im3 = [1-y, \"PerClass Mira\"]\n",
    "    \n",
    "    # Perform KMeans clustering\n",
    "    X = sio.loadmat(path+mat)['cube']\n",
    "    m, c = spectral.kmeans(X, 2, 20)\n",
    "    im4 = [1-m.T, \"KMeans clusters\"]\n",
    "\n",
    "    # X = apply_PCA(X, features)\n",
    "    # X = X.reshape(539, 554, 3)\n",
    "    X, y = cubify(X, y.T, 25)\n",
    "    y = to_categorical(y, 2)\n",
    "    yhat = mask3d.predict(X, batch_size=256)\n",
    "    yhat_3d.append(yhat)\n",
    "    yhat = yhat[:, 1].reshape(257, 407).T\n",
    "    skimage.io.imsave(f'masks/sandwiches/3d/{mat[:-4]}.png', yhat.astype(np.uint8))\n",
    "    # Placeholder for 3D CNN result (you need to replace this with actual result)\n",
    "    im5 = [1-yhat, \"3D CNN\"]\n",
    "    \n",
    "    # Preprocess for SASE model and make prediction\n",
    "    X = sio.loadmat(path+mat)['cube'] #load HSI\n",
    "    y = skimage.io.imread(path+png) #load gt\n",
    "    X, y = preprocess(X, y, 3)\n",
    "    yhat = model.predict(X, batch_size=256) #detect food-bg\n",
    "    yhat_sase.append(yhat)\n",
    "    yhat = np.argmax(yhat, axis=1).reshape(257, 407).T\n",
    "    skimage.io.imsave(f'masks/sandwiches/sase/{mat[:-4]}.png', yhat.astype(np.uint8))\n",
    "    # print('\\nDisplaying performance metrics')\n",
    "    # metrics = evaluate(y, yhat) #get the classification performance metrics\n",
    "    im6 = [yhat, \"1D SASE\"]\n",
    "    \n",
    "    # List to hold all images and titles\n",
    "    images = [im1, im2, im3, im4, im5, im6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "ys = io.imread(path+pngs[0])\n",
    "ys = skimage.color.rgb2gray(ys)\n",
    "ys[np.where(ys>0.5)] = 1\n",
    "ys[np.where(ys<0.5)] = 0\n",
    "ys = 1-ys\n",
    "ys = np.array([])\n",
    "for png in pngs[1:]:\n",
    "    y = io.imread(path+png)\n",
    "    y = skimage.color.rgb2gray(y)\n",
    "    y[np.where(y>0.5)] = 1\n",
    "    y[np.where(y<0.5)] = 0\n",
    "    y = 1-y\n",
    "    ys = np.concatenate((ys, y.ravel()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 59ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 57ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 58ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 57ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 52ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 52ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 56ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 55ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 57ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 55ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 55ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 55ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 50ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 56ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 59ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 58ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 57ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 55ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 54ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 55ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 56ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 60ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 58ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 53ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 51ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 54ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 56ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 54ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 54ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 51ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 50ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 53ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 55ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 58ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 51ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 52ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 51ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 53ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 49ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 49ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 49ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 55ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 52ms/step\n",
      "\u001b[1m409/409\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n"
     ]
    }
   ],
   "source": [
    "path='mats/sandwiches/' #set root directory\n",
    "ys = yhat_3d = yhat_sase = np.array([])\n",
    "\n",
    "for mat, png in zip(mats[3:], pngs[3:]):\n",
    "    X = sio.loadmat(path+mat)['cube'] #load HSI\n",
    "    s = X.shape\n",
    "    y = skimage.io.imread(path+png) #load gt\n",
    "    # preprocess ground truth image\n",
    "    y = skimage.color.rgb2gray(y)\n",
    "    y[np.where(y>0.5)] = 1\n",
    "    y[np.where(y<0.5)] = 0\n",
    "    y = 1-y\n",
    "    ys = np.concatenate((ys, y.ravel()))\n",
    "    \n",
    "    X = X.reshape(-1,3)\n",
    "    X = scaler.fit_transform(X)\n",
    "    X = X.reshape(s[0],s[1],s[2])\n",
    "    X, y = cubify(X, y.T, 25)\n",
    "    y = to_categorical(y, 2)\n",
    "    yhat = mask3d.predict(X, batch_size=256)\n",
    "    yhat = np.argmax(yhat, axis=1).reshape(257, 407).T\n",
    "    yhat_3d = np.concatenate((yhat_3d,yhat.ravel()))\n",
    "    skimage.io.imsave(f'masks/sandwiches/3d/{mat[:-4]}.png', yhat.astype(np.uint8))\n",
    "    \n",
    "    # Preprocess for SASE model and make prediction\n",
    "    X = sio.loadmat(path+mat)['cube'] #load HSI\n",
    "    y = skimage.io.imread(path+png) #load gt\n",
    "    X, y = preprocess(X, y, 3)\n",
    "    yhat = model.predict(X, batch_size=256) #detect food-bg\n",
    "    yhat = np.argmax(yhat, axis=1).reshape(257, 407).T\n",
    "    yhat_sase = np.concatenate((yhat_sase,yhat.ravel()))\n",
    "    skimage.io.imsave(f'masks/sandwiches/sase/{mat[:-4]}.png', yhat.astype(np.uint8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>3D CNN</th>\n",
       "      <th>1D SASE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AUC</th>\n",
       "      <td>0.79</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brier score</th>\n",
       "      <td>0.31</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaccard score (IoU)</th>\n",
       "      <td>0.57</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.69</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1-score</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.69</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     3D CNN  1D SASE\n",
       "AUC                    0.79     0.97\n",
       "Brier score            0.31     0.02\n",
       "Jaccard score (IoU)    0.57     0.98\n",
       "accuracy               0.69     0.98\n",
       "f1-score               0.70     0.98\n",
       "precision              0.86     0.98\n",
       "recall                 0.69     0.98"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res  = pd.concat([evaluate(ys, yhat_3d), evaluate(ys, yhat_sase)], axis=1)\n",
    "res.columns = ['3D CNN', '1D SASE']\n",
    "res"
   ]
  }
 ],
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