shallow-training/nncw.ipynb

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     "timestamp": 1615991459232,
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    "id": "TGIxH9Tmt5zp"
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras.optimizers as tf_optim\n",
    "tf.get_logger().setLevel('ERROR')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "import random\n",
    "import pickle\n",
    "import json\n",
    "import math\n",
    "import datetime\n",
    "import os\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "fig_dpi = 70"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fksHv5rXACEX"
   },
   "source": [
    "# Neural Network Training\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l4zqVWyRAM0Z"
   },
   "source": [
    "## Load Dataset\n",
    "\n",
    "Read CSVs dumped from MatLab and parse into Pandas DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 331
    },
    "executionInfo": {
     "elapsed": 2441,
     "status": "ok",
     "timestamp": 1615991459234,
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      "displayName": "Andy Pack",
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     "user_tz": 0
    },
    "id": "Hj5l_tdZuYh7",
    "outputId": "fbfa9406-f662-4ebc-8ba2-67950714627c"
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clump thickness</th>\n",
       "      <th>Uniformity of cell size</th>\n",
       "      <th>Uniformity of cell shape</th>\n",
       "      <th>Marginal adhesion</th>\n",
       "      <th>Single epithelial cell size</th>\n",
       "      <th>Bare nuclei</th>\n",
       "      <th>Bland chomatin</th>\n",
       "      <th>Normal nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.441774</td>\n",
       "      <td>0.313448</td>\n",
       "      <td>0.320744</td>\n",
       "      <td>0.280687</td>\n",
       "      <td>0.321602</td>\n",
       "      <td>0.354363</td>\n",
       "      <td>0.343777</td>\n",
       "      <td>0.286695</td>\n",
       "      <td>0.158941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.281574</td>\n",
       "      <td>0.305146</td>\n",
       "      <td>0.297191</td>\n",
       "      <td>0.285538</td>\n",
       "      <td>0.221430</td>\n",
       "      <td>0.360186</td>\n",
       "      <td>0.243836</td>\n",
       "      <td>0.305363</td>\n",
       "      <td>0.171508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Clump thickness  Uniformity of cell size  Uniformity of cell shape  \\\n",
       "count       699.000000               699.000000                699.000000   \n",
       "mean          0.441774                 0.313448                  0.320744   \n",
       "std           0.281574                 0.305146                  0.297191   \n",
       "min           0.100000                 0.100000                  0.100000   \n",
       "25%           0.200000                 0.100000                  0.100000   \n",
       "50%           0.400000                 0.100000                  0.100000   \n",
       "75%           0.600000                 0.500000                  0.500000   \n",
       "max           1.000000                 1.000000                  1.000000   \n",
       "\n",
       "       Marginal adhesion  Single epithelial cell size  Bare nuclei  \\\n",
       "count         699.000000                   699.000000   699.000000   \n",
       "mean            0.280687                     0.321602     0.354363   \n",
       "std             0.285538                     0.221430     0.360186   \n",
       "min             0.100000                     0.100000     0.100000   \n",
       "25%             0.100000                     0.200000     0.100000   \n",
       "50%             0.100000                     0.200000     0.100000   \n",
       "75%             0.400000                     0.400000     0.500000   \n",
       "max             1.000000                     1.000000     1.000000   \n",
       "\n",
       "       Bland chomatin  Normal nucleoli     Mitoses  \n",
       "count      699.000000       699.000000  699.000000  \n",
       "mean         0.343777         0.286695    0.158941  \n",
       "std          0.243836         0.305363    0.171508  \n",
       "min          0.100000         0.100000    0.100000  \n",
       "25%          0.200000         0.100000    0.100000  \n",
       "50%          0.300000         0.100000    0.100000  \n",
       "75%          0.500000         0.400000    0.100000  \n",
       "max          1.000000         1.000000    1.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('features.csv', header=None).T\n",
    "data.columns = ['Clump thickness', 'Uniformity of cell size', 'Uniformity of cell shape', 'Marginal adhesion', 'Single epithelial cell size', 'Bare nuclei', 'Bland chomatin', 'Normal nucleoli', 'Mitoses']\n",
    "labels = pd.read_csv('targets.csv', header=None).T\n",
    "labels.columns = ['Benign', 'Malignant']\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "executionInfo": {
     "elapsed": 2436,
     "status": "ok",
     "timestamp": 1615991459236,
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      "displayName": "Andy Pack",
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     },
     "user_tz": 0
    },
    "id": "qc1Mku6h041u",
    "outputId": "94e38c34-0419-4a02-ac8c-17bbc83f777b"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Benign</th>\n",
       "      <th>Malignant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   Benign  Malignant\n",
       "0       1          0\n",
       "1       1          0\n",
       "2       1          0\n",
       "3       0          1\n",
       "4       1          0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h9QsJjWEMbLu"
   },
   "source": [
    "### Explore Dataset\n",
    "\n",
    "The classes are uneven in their occurences, stratify when splitting later on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2430,
     "status": "ok",
     "timestamp": 1615991459237,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "rjjiSYAZMa4k",
    "outputId": "ae0c3772-00be-4f2b-80d2-9cd62a6b6e08"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Benign       458\n",
       "Malignant    241\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.astype(bool).sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E9lVYI14AUMf"
   },
   "source": [
    "## Split Dataset\n",
    "\n",
    "Using a 50/50 split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 2604,
     "status": "ok",
     "timestamp": 1615991459418,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "L83Ae5l9wM35"
   },
   "outputs": [],
   "source": [
    "data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=0.5, stratify=labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qf2U199fNjmJ"
   },
   "source": [
    "## Generate & Retrieve Model\n",
    "\n",
    "Get a shallow model with a single hidden layer of varying nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 2598,
     "status": "ok",
     "timestamp": 1615991459419,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "SgoQ-NjWB0T5"
   },
   "outputs": [],
   "source": [
    "def get_model(hidden_nodes=9, activation=lambda: 'sigmoid', weight_init=lambda: 'glorot_uniform'):\n",
    "    layers = [tf.keras.layers.InputLayer(input_shape=(9,), name='Input'), \n",
    "              tf.keras.layers.Dense(hidden_nodes, activation=activation(), kernel_initializer=weight_init(), name='Hidden'), \n",
    "              tf.keras.layers.Dense(2, activation='softmax', kernel_initializer=weight_init(), name='Output')]\n",
    "\n",
    "    model = tf.keras.models.Sequential(layers)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get a Keras Tensorboard callback for dumping data for later analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tensorboard_callback(path='tensorboard-logs', prefix=''):\n",
    "    return tf.keras.callbacks.TensorBoard(\n",
    "        log_dir=os.path.normpath(os.path.join(path, prefix + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))), histogram_freq=1\n",
    "    ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QT5B9PTUN3pj"
   },
   "source": [
    "# Example Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mQGAUtIPAd6e"
   },
   "source": [
    "## Define Model\n",
    "\n",
    "Variable number of hidden nodes. All using 9D outputs except the last layer which is 2D for binary classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7889,
     "status": "ok",
     "timestamp": 1615991464716,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "fYA34P0Vu_pX",
    "outputId": "aded18b8-aa7f-4362-a614-837c8a0f526f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_2 (Dense)              (None, 9)                 90        \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 2)                 20        \n",
      "=================================================================\n",
      "Total params: 110\n",
      "Trainable params: 110\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = get_model(9)\n",
    "model.compile('sgd', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KZSwFe-AAs1y"
   },
   "source": [
    "## Train Model\n",
    "\n",
    "Example 10 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 11304,
     "status": "ok",
     "timestamp": 1615991468137,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "s8U9Atu3zelS",
    "outputId": "8439e8d2-7a5d-495f-a192-a34f01e95bfa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "11/11 [==============================] - 1s 2ms/step - loss: 0.6257 - accuracy: 0.6607\n",
      "Epoch 2/5\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.6226 - accuracy: 0.6651\n",
      "Epoch 3/5\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.6326 - accuracy: 0.6424\n",
      "Epoch 4/5\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.6158 - accuracy: 0.6696\n",
      "Epoch 5/5\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.6228 - accuracy: 0.6534\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2cd249f3400>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(data_train.to_numpy(), labels_train.to_numpy(), epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 11294,
     "status": "ok",
     "timestamp": 1615991468137,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "VnUEJdXovzi-",
    "outputId": "02075086-352c-4a23-fac5-ad54d11e0e35"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loss', 'accuracy']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metrics_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 11285,
     "status": "ok",
     "timestamp": 1615991468138,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "r0vxP3Ah42ib",
    "outputId": "061113ba-52db-4fbe-c7f9-b5d3d85438ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=float32, numpy=0.6561605>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metrics[1].result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z7bn8pKTAynt",
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "# Experiment 1\n",
    "\n",
    "The below function runs an iteration of layer/epoch investigations.\n",
    "Returns the amount of layers/epochs used as well as the results and the model.\n",
    "\n",
    "Using cancer dataset (as in E2) and 'trainscg' or an optimiser of your choice, vary nodes and epochs (that is using early stopping for epochs) over suitable range, to find optimal choice in terms of classification test error rate of node/epochs for 50/50% random train/test split (no validation set). It is suggested that you initially try epochs = [ 1 2 4 8 16 32 64], nodes = [2 8 32], so there would be 21 node/epoch combinations. \n",
    "\n",
    "(Hint1: from the 'advanced script' in E2, nodes can be changed to xx, with hiddenLayerSize = xx; and epochs changed to xx by addingnet. trainParam.epochs = xx; placed afternet = patternnet(hiddenLayerSize, trainFcn); --see 'trainscg' help documentation for changing epochs). \n",
    "\n",
    "Repeat each of the 21 node/epoch combinations at least thirty times, with different 50/50 split and take average and report classification error rate and standard deviation (std). Graph classification train and test error rate and std as node-epoch changes, that is plot error rate vs epochs for different number of nodes. Report the optimal value for test error rate and associated node/epoch values. \n",
    "\n",
    "(Hint2: as epochs increases you can expect the test error rate to reach a minimum and then start increasing, you may need to set the stopping criteria to achieve the desired number of epochs - Hint 3: to find classification error rates for train and test set, you need to check the code from E2, to determine how you may obtain the train and test set patterns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "executionInfo": {
     "elapsed": 11274,
     "status": "ok",
     "timestamp": 1615991468138,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "mYWhCSW4A57V",
    "tags": [
     "exp1",
     "exp-func"
    ]
   },
   "outputs": [],
   "source": [
    "# hidden_nodes = [2, 8, 16, 24, 32]\n",
    "# epochs = [1, 2, 4, 8, 16, 32, 64, 100, 150, 200]\n",
    "hidden_nodes = [2, 8, 16]\n",
    "epochs = [1, 2, 4, 8]\n",
    "\n",
    "def evaluate_parameters(hidden_nodes=hidden_nodes, \n",
    "                        epochs=epochs, \n",
    "                        batch_size=128,\n",
    "                        optimizer=lambda: 'sgd',\n",
    "                        loss=lambda: 'categorical_crossentropy',\n",
    "                        metrics=['accuracy'],\n",
    "                        callbacks=None,\n",
    "                        validation_split=None,\n",
    "\n",
    "                        verbose=0,\n",
    "                        print_params=True,\n",
    "                        return_model=True,\n",
    "                        run_eagerly=False,\n",
    "                        tboard=True,\n",
    "                        \n",
    "                        dtrain=data_train,\n",
    "                        dtest=data_test,\n",
    "                        ltrain=labels_train,\n",
    "                        ltest=labels_test):\n",
    "    for idx1, hn in enumerate(hidden_nodes):\n",
    "        for idx2, e in enumerate(epochs):\n",
    "            if print_params:\n",
    "                print(f\"Nodes: {hn}, Epochs: {e}\")\n",
    "\n",
    "            model = get_model(hn)\n",
    "            model.compile(\n",
    "                optimizer=optimizer(),\n",
    "                loss=loss(),\n",
    "                metrics=metrics,\n",
    "                run_eagerly=run_eagerly\n",
    "                )\n",
    "            \n",
    "            if tboard:\n",
    "                if callbacks is not None:\n",
    "                    cb = [i() for i in callbacks] + [tensorboard_callback(prefix=f'exp1-{hn}-{e}-')]\n",
    "                else:\n",
    "                    cb = [tensorboard_callback(prefix=f'exp1-{hn}-{e}-')]\n",
    "            \n",
    "            response = {\"nodes\": hn, \n",
    "                        \"epochs\": e,\n",
    "                        ##############\n",
    "                        ##   TRAIN\n",
    "                        ##############\n",
    "                        \"history\": model.fit(dtrain.to_numpy(), \n",
    "                                             ltrain.to_numpy(), \n",
    "                                             epochs=e, \n",
    "                                             verbose=verbose,\n",
    "                                             \n",
    "                                             callbacks=cb,\n",
    "                                             validation_split=validation_split).history,\n",
    "                        ##############\n",
    "                        ##   TEST\n",
    "                        ##############\n",
    "                        \"results\": model.evaluate(dtest.to_numpy(), \n",
    "                                                 ltest.to_numpy(),\n",
    "                                                 callbacks=cb,\n",
    "                                                 batch_size=batch_size, \n",
    "                                                 verbose=verbose),\n",
    "                        \"optimizer\": model.optimizer.get_config(),\n",
    "                        \"loss\": model.loss,\n",
    "                        \"model_config\": json.loads(model.to_json())\n",
    "                       }\n",
    "\n",
    "            if return_model:\n",
    "                response[\"model\"] = model\n",
    "\n",
    "            yield response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r-63V9qb-i4w",
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "## Single Iteration\n",
    "Run a single iteration of epoch/layer investigations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 313592,
     "status": "ok",
     "timestamp": 1615991770468,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "ZmGFkE9y8E4H",
    "outputId": "243fb136-bc07-4438-afb7-f2d21758168d",
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nodes: 2, Epochs: 1\n",
      "Nodes: 2, Epochs: 2\n",
      "Nodes: 2, Epochs: 4\n",
      "Nodes: 2, Epochs: 8\n",
      "Nodes: 8, Epochs: 1\n",
      "Nodes: 8, Epochs: 2\n",
      "Nodes: 8, Epochs: 4\n",
      "Nodes: 8, Epochs: 8\n",
      "Nodes: 16, Epochs: 1\n",
      "Nodes: 16, Epochs: 2\n",
      "Nodes: 16, Epochs: 4\n",
      "Nodes: 16, Epochs: 8\n"
     ]
    }
   ],
   "source": [
    "# es = tf.keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', patience = 5)\n",
    "single_results = list(evaluate_parameters(return_model=False, validation_split=0.2\n",
    "                                          , optimizer = lambda: tf.keras.optimizers.SGD(learning_rate=0.5, momentum=0.5)\n",
    "#                                           , callbacks=[es]\n",
    "                                         ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mdWK3-M6SK8_",
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "### Train/Test Curves\n",
    "\n",
    "For a single test from the set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 517
    },
    "executionInfo": {
     "elapsed": 314527,
     "status": "ok",
     "timestamp": 1615991771417,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "F9Xre1o6SesD",
    "outputId": "d6b817aa-58cd-4510-807f-e5e6bcf62f18",
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nodes: 2, Epochs: 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1050x490 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "single_result = random.choice([i for i in single_results if i[\"epochs\"] > 1])\n",
    "single_history = single_result[\"history\"]\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15,7))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "################\n",
    "##    LOSS\n",
    "################\n",
    "ax = axes[0]\n",
    "ax.set_title(\"Training vs Validation Loss\")\n",
    "ax.plot(single_history['loss'], label=\"train\", lw=2)\n",
    "ax.plot(single_history['val_loss'], label=\"validation\", lw=2, c=(1,0,0))\n",
    "ax.set_xlabel(\"Epochs\")\n",
    "ax.grid()\n",
    "ax.legend()\n",
    "\n",
    "################\n",
    "##  ACCURACY\n",
    "################\n",
    "ax = axes[1]\n",
    "ax.set_title(\"Training vs Validation Accuracy\")\n",
    "ax.plot(single_history['accuracy'], label=\"train\", lw=2)\n",
    "ax.plot(single_history['val_accuracy'], label=\"validation\", lw=2, c=(1,0,0))\n",
    "ax.set_xlabel(\"Epochs\")\n",
    "# ax.set_ylim(0, 1)\n",
    "ax.grid()\n",
    "ax.legend()\n",
    "\n",
    "print(f\"Nodes: {single_result['nodes']}, Epochs: {single_result['epochs']}\")\n",
    "# plt.tight_layout()\n",
    "# plt.savefig('fig.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0IQ7HfJCSDud",
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "### Accuracy Surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 705
    },
    "executionInfo": {
     "elapsed": 315450,
     "status": "ok",
     "timestamp": 1615991772345,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "X3MWHLxJElbc",
    "outputId": "134671d0-bfd3-4ee6-aa02-1a2a5b23f3ca",
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x350 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, Y = np.meshgrid(epochs, hidden_nodes)\n",
    "\n",
    "shaped_result = np.reshape([r[\"results\"][1] for r in single_results], \n",
    "                           (len(hidden_nodes), len(epochs)))\n",
    "\n",
    "fig = plt.figure(figsize=(8, 5))\n",
    "fig.set_dpi(fig_dpi)\n",
    "ax = plt.axes(projection='3d')\n",
    "\n",
    "surf = ax.plot_surface(X, Y, shaped_result, cmap='viridis')\n",
    "ax.set_title('Model test accuracy over different training periods with different numbers of nodes')\n",
    "ax.set_xlabel('Epochs')\n",
    "ax.set_ylabel('Hidden Nodes')\n",
    "ax.set_zlabel('Accuracy')\n",
    "ax.view_init(30, -110)\n",
    "# ax.set_zlim([0, 1])\n",
    "fig.colorbar(surf, shrink=0.3, aspect=6)\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('fig.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C793_RHvSGai",
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "### Error Rate Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 668
    },
    "executionInfo": {
     "elapsed": 316211,
     "status": "ok",
     "timestamp": 1615991773109,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "tpClZMptrq-q",
    "outputId": "f9fe93f9-7b67-4772-83e4-9e3567fd9318",
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UajQuxbpMuA0rr/+ppsLa1K+UUko1I3rGr5RSSjUjQXlIj785nU7TtWtXv80vLy+P+Hhf+wdpekK9fhD6dQz1+kHo11Hr1/T5s47ff//9PmNMcu0lT1xIJP6uXbuyePFiv80vMzOT0aNH+21+jU2o1w9Cv46hXj8I/Tpq/Zo+f9ZRRLb5ZUY+0KZ+pZRSqhnRxK+UUko1I5r4lVJKqWYkJK7xK6WUajglJSXs3LmTwsJCr2USEhLYuHFjA0bV8Opbx5SUFFq08OURIoER0MQvIhOBJ7BaFh4xxrxaZXwmVn/zEcA7xpi/2sOnAaOxOmwB+LUxZksgY1VKKeWbnTt3kpCQQJcuXRDx/OC53NxcEhISGjiyhlWfOhYWFrJz587QTPz2U9+exHqYSzawXEQ+qvLs5YnGmBy77EIRmWGM+cEed4MxZmag4lNKKVU/hYWFNSZ95V1UVBQlJSVBjSGQ1/iHAOuMMbuMMXlYj4Id517AfpIRWGf8Efj2bHullFJBpkm/fhrDegtYl732M5fHGmOut9/fDhhjzONVyn0LpAMvGGPutIdNA4ZhPVZwFnCvMaa0ynRTsB5lSvv27Ye+/fbbfos9JyeHxMREv82vsQn1+kHo1zHU6wehX8emXL+EhAS6d+9eY5nS0lIcDkcDRRQc9a3j5s2byc3NrTRszJgx3xtjhvkrthoZYwLyAi4CnnN7fztwm5eyCVjPL06z36cAAkQD7wLX1bSsoUOHGn9asGCBX+fX2IR6/YwJ/TqGev2MCf06NuX6bdiwodYyOTk5AVn2jh07zJgxY8zJJ59s0tPTzXvvveex3FVXXWVSU1NNcXGxMcaYNWvWmDFjxtRpWW3atKlxfH3r6Gn9AYtNgPJx1Vcgm/p3Ax3c3newh1VjjMkFvgbOtt/vsddFITAdGBzAOCu5esYTPLr5o4ZanFJKqToIDw/n6aefZv369cyZM4ebbrqJo0ePeixbUlKCP1uDQ0Ug7+pfAqSJSAesm/vGA38rH2k/NzjSGLNfRKKAs4Cn7HEpxpg9IhIGnAesC2CclezM3cE2s5RjxaVERYR2M5VSSp2I3MJiNu3N9TguPz+f2Njies23V3ICCdERHselpKSQkpICQHJyMm3btuXQoUPExcVVK3vTTTfx2GOPcfnll1caXlBQwB/+8AdWrVpFdHQ0L7/8MgMGDGD//v1ceumlZGVlMXHixIrypaWl3HHHHWRmZlJUVMQdd9zBpEmTWLduHddeey1lZWUAzJkzh6SkpHrVuSEFLPEbY0pE5FZgHtZNhI8aYw6KyCysa/MRwH9FJNIe/545fhf/myLS1h6+GHg2UHFWdVrXYbz+41w+Wb+aS/oPbKjFKqVUk7Npby4X/fM7v8/3g6uHMyi1da3lli9fTmlpKZ06dfI4vlevXvTq1YtPPvmk0j0Jzz//PAkJCaxevZrFixdz1VVXsWrVKh544AEmTpzILbfcwosvvlhR/rXXXiMlJYWlS5dSUFDAsGHDOPvss3n99de55pprmDp1KgUFBU3mnoaA/o7fGPMp8GmVYRPc3g7yMt1pgYyrJpekjeH1Hx/mo/XfaOJXSqka9EpO4IOrh3scZ53xx9Z7vrU5dOgQV155Ja+88kqN5e6++26uueYaXnvttYphCxcu5I477gBg2LBhFBQUkJ2dzcKFC7nnnnsAmDRpEvfddx9gncmvXbuWN954A4Ds7Gx+/vlnhgwZwmOPPcbBgwe55JJLOOmkk+pV34amPfdV0TGhPZFlrVh36AeKS8uIcGivxkop5UlCdITXM/Pc3IiAdeBz7NgxLrjgAu666y5GjBhRY9mMjAxatWrF119/7dO8Pf3crqysjJdeeokxY8ZUGt67d2/GjBnDjBkzOPPMM3n//ffJyMjwvSJBolnNg9TIbpRG/sx3Ww7WXlgppVSDMcYwefJkTjvtNK644gqfprnnnnt4/PHjvyQfOXIkb731FgBLliwhNjaWFi1aMHLkSN59912AivEA48aN44UXXqC01PpV+dq1ayktLWXr1q1069aNm2++mXHjxrF+/Xp/VTOgNPF7kJHYg7DIQ3y4em2wQ1FKKeVm0aJFvPvuu3z88ccMGDCAAQMGsGbNmhqnGT16NJ07d654f91113HkyBH69evH9ddfz+uvvw7A//7v//Lpp5+SlpbG9u3bK8pPnTqV1NRUBg4cSFpaGjfffDPGGD788EPS0tIYMGAAu3fv5sILLwxMpf1Mm/o96BFj3QQyf/sSSkrHEK7N/Uop1SiMHDmy4i76mkybNq3S+0WLFlX8HxMTU3G93l27du2YO3duxfu///3vADgcDh555BEeeeSRSuVvvfVW7r///jpE3zhoRvPAGeEkPrwlBY7NLNl6KNjhKKWUUn6jid8DEWFo+0FExm3jszV7gh2OUkop5Tea+L0Y5HRB5F4+37CF0jJ9dpBSSqnQoInfC5fTBUB22Y8s3abN/UoppUKDJn4verbqSVxEPDGJ25mtzf1KKaVChCZ+LxxhDgYmDSCx5S/MXruXMm3uV0qpRuGpp56ib9++9OnThxtuuKH8Ka+VjB07lpEjR1a8nzlzJpMnT/Z5Gdu2bWPQII+dyzZ5mvhr4HK6yDPbyTqaw/Idh4MdjlJKNXv79+/nueeeY/ny5axZs4bly5ezePFij2W3b9/ON99808ARNn6a+GswyDmIMsqIT9zJLG3uV0qpRqGkpITCwkKKi4spLi72+kS8W2+9lYcffrja8AMHDnDuuefSr18/xo4dy7Zt2wDYvHkzgwcPpl+/fjz77PFnwx09epTJkyczePBgXC4XX375JQCZmZmkp6fTv3//JtU6oB341KBvm75EOaJI7riPz9fu5b5z+hAWVr0fZ6WUapYKcyDLcze1jvx8qOdDekjqA9GJHke1a9eO2267jc6dOxMeHs7VV19Nt27dPJY944wzeOutt1i5cmWl4ffffz+jRo1ixowZvPvuu9xwww18+umn3HTTTdx999386le/4s4776wo/9BDDzFx4kSmTZvGgQMHGDlyJBs2bOC5557jySef5MwzzyQ7O7t+dQ0CTfw1iHBE0K9dPw7mbWVPdiE//HIEV5dWwQ5LKaUah6z18K+zPI6qZ8q3/P4L6DzM46jDhw8zc+ZMtm3bRkxMDOPHjyczM5PRo0d7LH/33Xfz8MMPc+WVV1YMW7hwIbNmzQLgkksu4cYbbwRg6dKlzJgxA7Cezlf+YJ85c+Ywc+ZMHnzwQcBqAdi3bx/Dhg3jrrvuYsOGDVx88cW0aNHiRGrdYDTx18LldPGvrH8RG1XG7DV7NPErpVS5pD5WkvbgRB7LS1Ifr6O++uorunfvTuvW1lMBzznnHBYvXuw18V9wwQX85S9/4ccff/Q6z/In8nl6Mh9YT+ebMWMGXbp0qTT8lltu4YILLmDmzJkMGzaMb7/9lg4dOtRYtcZAr/HXIiMpg6KyIjJ65DF77V6Pd48qpVSzFJ1onZl7eJV2GOx1XK0vL838AJ06deLbb7+lsLCQ0tJS5s+fT69evbyWFxFuv/12nnrqqYph7k/n++CDDxgyZAgAgwYN4pNPPgGqP53P/Zp/+aWDn3/+mf79+/PnP/+ZPn36sHXr1rqvwyDQxF+L/u36Ey7hJDv3sOtIAat3Np3rOEopFWqGDRvGhAkTGDhwIP369aNbt26cd955NU5z2WWX4XA4Kt7ff//9zJ8/n379+vH888/zzDPPAPD000/z0EMP0b9/f4qKiirK33fffWRnZ9OvXz/69OlT8Yjf559/nr59+9KvXz/at2/P8OHDA1Bj/9Om/lrERsTSp00fsss2ER1xMrPW7qF/p5bBDksppZqthx56iIceeqjGMvPnz6/4Pzw8vOLOfYC2bdsyc+bMatN0796dpUuXVhseFxfHq6++Wm34E088QUJCgu+BNxJ6xu+DDGcGqw+sZGzPNsxas0eb+5VSSjVZmvh94HK6yC/Jp3/3An45VMC63TnBDkkppZSqF038PhiYNBBBCI/bSlR4mHbmo5RSqsnSxO+DFlEt6N6qO2sP/MCYnu20uV8ppVSTpYnfR64kFyuyVjA+LZltB/PZsCc32CEppZRSdaaJ30euZBdHjh3hpA5HiXSEMXutNvcrpZRqejTx+8iV5AJg05FVjO7Zls+0uV8ppYLiwgsvpFWrVlx00UWVhh88eJDzzz+f3r1706dPH7Zs2VJt2smTJ9O1a1dKSkoAWLt2LWPHjq3T8tu2bVvv2BsDTfw+ahfbjs4JnVm+bznj01L4ef9RfsrKC3ZYSinV7Nx4441Mnz7d4/BLL72UjRs3smzZMpKTkz1OX1JSwttvvx3oMBstTfx14HK6WLZvGaf3TiLCIXy2Wpv7lVKqoY0dO7ZaxznZ2dksW7aMyy67DIDY2Fji4uI8Tn/TTTfx2GOPVWu1LSgo4IorrqBfv34MGTKkomve/fv3c9ppp5GWlsZdd91VUb60tJRbb72VwYMH079/f958800A1qxZQ0ZGBgMGDGDAgAFkZWX5q+p+EdCe+0RkIvAE1gHGI8aYV6uMzwRaABHAO8aYv9rDuwHvAi2Br4BrTCNoV3c5XXy0+SPyyrI4pXtbZq/dw81n9gx2WEopFRR5RXn8dOQnj+Py8/OJLajfQ3p6tOxBfGR8nabZunUrbdu2ZdKkSaxfv56xY8fy2GOPER5ePc316tWLXr168cknn9C9e/eK4c8//zwJCQmsXr2axYsXc9VVV7Fq1SoeeOABJk6cyC233MKLL75YUX769OmkpKSwdOlSCgoKGDZsGGeffTYvv/wy11xzDVOnTqWgoKBSd8GNQcASv4iEA08CpwLZwHIR+cgYc9Ct2ERjTI5ddqGIzDDG/AA8AtxvjJkpIh8A5wDV+1dsYBnODACW71vOhPQM7vhgNZuzcume1PS6bFRKqRP105GfuHL2lbUXrKPp46czMGlgnaYpKSlhyZIlPPfcc/Tr148rr7yS119/nalTp3osf/fdd3PNNdfw2muvVQxbuHAhd9xxB2A9E6CgoIDs7GwWLlzIPffcA1iP673vvvsAmDt3Lhs3buSNN94ArFaHn3/+meHDh/PXv/6VgwcPcskll3DSSSfVeR0EUiDP+IcA64wxuwBEZDYwDqi4sGKMKe8CL8J+GbGeizgCuNge9wZwLo0g8XeM70hSbBIrslZw84Dx3BMmzF6zlz+drolfKdX89GjZg+njq19rhxN7LG+Plj3qPE2HDh3o2rUrAwYMAOD888+v1F9/VRkZGbRq1Yqvv/7ap/l7emRvWVkZL730EmPGjKk0fPDgwQwZMoQZM2Zw5pln8v7775ORkeFzXQItkIm/PbDL7f0uoNqDikXkWyAdeMEYs1JE2gKH3Jr2vU03BZgC0L59ezIzM/0WeE5Ojtf5dZbOLNy6kNNLFtO7dRjvLd5M/4imda2/pvqFilCvY6jXD0K/jk25fgkJCeTmWn2ZdI/p7rFMaWRpvZu4zTFD7rGa+0rJz8+npKSkIo74+HjatGnDmjVrSE1NZc6cOfTq1atifLni4mLy8/PJzc3lpptuYsqUKXTt2pXc3FwGDx7MtGnTSE9PZ9myZURHRxMWFsaQIUP497//zXXXXce//vUvjDHk5uYyduxYnn32Wfr374/D4WD9+vX06tWLHTt2kJqaypQpU1i3bh3Lly+nR4/jBzMFBQXB/eyNMQF5ARcBz7m9vx24zUvZBOALIA1oC6x1GzcYmFnTsoYOHWr8acGCBV7HvbPhHZM2Lc3sz99v3vp+u+ly50yzJSvXr8sPtJrqFypCvY6hXj9jQr+OTbl+GzZsqLVMTk5OwJZ/+umnm7Zt25qYmBjToUMH8+233xpjjFm6dKkZOHCgSUtLM5dffrkpLCysNu1VV11lZsyYUfF+xIgRZsyYMcYYY/Lz882kSZNMenq6GTx4sFmxYoUxxpisrCxz6qmnmr59+5q77rrLtGnTxhhjzOHDh80dd9xh0tPTTd++fc0ZZ5xhiouLzcMPP2z69Olj+vfvb8477zyTl5dXKQZP6w9YbAKUj6u+AnnGv5vKZ+odgCWeChpjckXka+BsrJsBW4uI2Cujgz2vRsHltH7Pv3zfcsb1Gcu9H69l9tq9XHeq56NepZRS/vXVV195HD5o0CBWrFhR47TTpk2r9H7RokUV/8fExFRcr3fXrl075s6dW/H+73//OwAOh4NHHnmERx55pFL5u+++m7vvvrvGOIIpkD/nWwKkiUgHEYkHxmOd1QMgIi1EpJ39fxRwFrCx/MgH64Y+gEnAjADGWScntTyJllEtWb5vOW3ioxh2UmvtxU8ppVSTEbDEb4wpAW4F5gErgSeMMQdFZJaItMf6qd5sEVkNLAcWGGPKb+C7E3hARLYAh4HPAhVnXYVJGAOTBrJ833IAxqelsHZXDjsO5gc5MqWUUqp2Af0dvzHmU+DTKsMmuL0d5GW6nwBXAEM7IS6ni/m/zCf7WDZn9U3mvk/WMmvtHq4e0y3YoSmlVIMwxni8013VzAS/Sxrtua8+BjkHYTCszFpJu4QohqS2ZvYabe5XSjUP0dHRHDp0qFEksabm2LFjHjsVakjBXXoT1at1L2LDY1m+bzljOo3hnH4p/OWTdew8nE/HVvX73apSSjUVHTt2ZOfOnezfv99rmYKCAmJiYhowqoZX3zqmpKQEIBrfaeKvh/CwcAYkDWB5lnWd/6y+yfzvp+v4fO1epoxqXD00KaWUv4WHh5OamlpjmczMTAYOrFvve01NU62jNvXXk8vpYv2B9eQX5+NMjGZQl1Z8ps39SimlGjlN/PWUkZRBiSlh9YHVgHV3/w87jrD7SEGQI1NKKaW808RfT+nt0okIi2DFPquziPHp1nOfP1+7N5hhKaWUUjXSxF9PUY4o0tumV/yeP6VFDBmdW2pnPkoppRo1TfwnwOV0sWr/KopLiwGYkJ7Csu2H2ZdTGOTIlFJKKc808Z8Al9PFsdJjrDu4DoCz05IxRpv7lVJKNV6a+E/AgKQBhElYRXN/x1ax9O/Ukll6d79SSqlGShP/CYiLiKN3694ViR9gQloyS7YdYn/usSBGppRSSnmmif8EuZwuVmatpLSsFLB+1mcMfL5Om/uVUko1Ppr4T5DL6SK3OJefjvwEQOc2saR1SNS++5VSSjVKmvhPUEZSBkDl5v70FBb/fJCDedrcr5RSqnHRxH+CWkW3oluLbpUS//i0FMoMzFm/L4iRKaWUUtVp4vcDl9PF8n3LKx5R2bVtHCenJOrd/UoppRodTfx+kOHM4FDhIbblbKsYNiEtmW+3HOTw0aLgBaaUUkpVoYnfD1xOF0BFv/0AE/qlUFpm+FKb+5VSSjUimvj9IDkumQ7xHSpd5+/WLp5ezgRmad/9SimlGhFN/H5Sfp3f3fj0ZBZtPkB2fnGQolJKKaUq08TvJy6ni91Hd7Mn7/gZ/oT0FIpLDV9u0OZ+pZRSjYMmfj8pv86/POv4WX+PpHi6tYvTznyUUko1Gpr4/aRzQmfaRLep1NwvIpyTnsI3Px0gp1Cb+5VSSgWfJn4/ERFcTlelO/sBxqenUFRaxtwNWUGKTCmllDpOE78fuZwufs7+mYMFByuG9U5OoGvbOD7T5n6llFKNgCZ+Pyq/zv9D1g8Vw0SE8WnJLPhxP3nHSoIVmlJKKQVo4ver7i27kxCZUO1nfRPSUygqKWPuRm3uV0opFVya+P3IEeYgIymjWuLv2z6Rzq1j9e5+pZRSQRfQxC8iE0Vkk4j8JCJTqoyLFZHZIrJRRNaJyJ/cxt0vIjtFZKX9GhXIOP0pw5nBpsObyCvKqxgmIoxPT2bepizyi7S5XymlVPAELPGLSDjwJHAaMBC4XUTaVCn2f8aY3sBQ4DoR6V5l3AD79U2g4vQ3l9NFmSlj5f6VlYZPSEuhsLiMeRv3BycwpZRSisCe8Q8B1hljdhlj8oDZwLjykcaYfGPMAvv/PGATkBLAeBpEn9Z9iAmPqdbc369jCzq0jNG++5VSSgWVlD9D3u8zFrkIGGuMud5+fztgjDGPeyjbCcgE0owxR0XkfuBKIA9YBNxuHxy4TzMFmALQvn37oW+//bbfYs/JySExMbHe0z+z5xlKTAm3tr+10vC3NxQyd0cxz50RT5RDTjTMejvR+jUFoV7HUK8fhH4dtX5Nnz/rOGbMmO+NMcP8MrPaGGMC8gIuAp5ze387cJuHclHAt8BFbsOcgMN+PQE8VtOyhg4davxpwYIFJzT9Cz+8YAZOH2gKigsqDV++/ZDpcudMM3vNnhOa/4k60fo1BaFex1CvnzGhX0etX9PnzzoCi02A8nHVVyCb+ncDHdzed7CHVRARAaYDs4wxH5QPN8bsM8aUGmNKgX8BgwMYp9+5nC6Ky4pZc2BNpeEDOrYkpUU0s/TufqWUUkESyMS/BEgTkQ4iEg+MB76oUubvQL4x5kH3gSLifq3/fGBdAOP0u/R26YSHhVe7zh8WJoxPS+HrDfsoLC4NUnRKKaWas4AlfmNMCXArMA9YCTxhjDkoIrNEpL2IdATuBIa4/WzvLHvyR0VkjYisBjKA+wIVZyDEhMfQt03faokfYEJ6MkeLSvnmpwNBiEwppVRzFx7ImRtjPgU+rTJsgttbj3e4GWOuCGRcDcHldPH2xrcpLismIiyiYnhG51YkJUQxe80ezuzjDGKESimlmiPtuS9AXE4XBSUFbDy4sdJwq7k/mS/X7+NYiTb3K6WUalia+ANkQNIABPHS3J9C7rESFm3W5n6llFINSxN/gCRGJtKrdS+WZ1VP/INSW9M2PopZa/YGITKllFLNmSb+AHI5XazYt4IyU1ZpuCNMODvNyZx1eykqKfMytVJKKeV/mvgDKCMpg5yiHDYf2Vxt3IS0FHIKS/h2izb3K6WUajia+AMow5kBwIp9K6qNG9K1NW3iIpmtzf1KKaUakCb+AGob05bUxFSPN/iFO8IY1zeZL9bvpbhUm/uVUko1DE38AeZyuli+b3n5MwgqmZCezJH8Yr7/+VAQIlNKKdUcaeIPMJfTxf6C/ezM3Vlt3LCT2tAyNoLPtO9+pZRSDUQTf4C5nC4Alu1bVm1chCOMcX2su/tLtLlfKaVUA9DEH2Dt49uTHJfs8To/WJ35HDxaxJJt2tyvlFIq8DTxNwCX08WKrOp39gOM6NaWxOhwvbtfKaVUg9DE3wBcThe/5P7CvqP7qo2LDA/jzD7JfL5uL6Vl1W8AVEoppfxJE38DcCVZ1/m9nfVPSE9mf+4xlmlzv1JKqQDTxN8AurboSquoVl6v84/s0ZaEqHBmr9XmfqWUUoGlib8BiEjF7/k9iQp3cEYfJ7PX7qFMm/uVUkoFkCb+BpLhzGDzkc0cKTzicfz4tGT25Rzjh18ON2xgSimlmhVN/A2k/Pf8P2T94HH86J7tiIt08Nlqbe5XSikVOJr4G0ivVr2Ii4jz2twfHeHg9JO1uV8ppVRgaeJvII4wBwOSBnhN/GDd3b8nu5BVO480XGBKKaWaFU38DWiQcxAbDm0gvzjf4/gxPZOIiXDo3f1KKaUCRhN/VVvm0m7v/IDM2uV0UWpKWbl/pcfxMZEOTuudxGer93h8mp9SSil1ompN/CLSUkQeFZG37fe9ROTiwIcWJMtep9eGp2HzV36fdd82fYkMi6yluT+FXUcKWLMr2+/LV0oppXw54/83sAHoa7/fCtwXsIiC7cKXyE3oAe9eCbs897RXX5GOSPq168eKfd7nO7ZXO6IjwpilffcrpZQKAF8Sf3tjzOtAKYAxpggI3WfIRsayrv990KIDvHkxHNzi19m7nC5W719NUWmRx/FxUeGM7ZnE7LXa3K+UUsr/fEn8R0WkJWAARCQDyA1kUMFWEpEIl/8XHBHwxq8gL8tv885wZlBUVsTaA2u9lhmfnsz2g/ms35Pjt+UqpZRS4Fvivw34GOgqIl8D7wA3BjKoRqFlZyv55x+GNy+CY/451hnQbgAOcXh9YA/A6Sc7iQwPY9aaPX5ZplJKKVWu1sRvjFkGnAGMBG4C+hpjfLr4LSITRWSTiPwkIlOqjIsVkdkislFE1onIn9zGtRWRefZ0H4pIdJ1q5S/OvvDbtyBrI7x7OZR4bp6vi9iIWPq06cOyfcu8lomPCmdMz3bMWrNXm/uVUkr5lS939S8xxpQYY9YZY9YYY4pFZIkP04UDTwKnAQOB20WkTZVi/2eM6Q0MBa4Tke728LuA/xpjegA/A1MIltSR8OtX4OcF8Mm1UHbitzdkJGWwMmslpWWlXstMSE9m64GjbNoX0ldVlFJKNTCvid8+6+4LxIvIySLSx34NAxJ9mPcQYJ0xZpcxJg+YDYwrH2mMyTfGLLD/zwM2ASn26POA/9j/vwGcW9eK+VWf82HCY7DmffjyxH/Q4HK6OFp8lE2HN3ktc/rJTiIdene/Ukop/wqvYdw5wGSgE/CC2/Bc4B4f5t0e2OX2fhfQwVNBEekE9APKLyG0MMZk1zZdgxoyFXL3wjePQ0IyjPhT7dN4keHMAGD5vuX0adPHY5nE6AhG9WjLrDV7uOXMnvVellJKKeXOa+I3xvwb+LeIXGCM+ThQAYhIFPAucLsx5mgdppuCfQmgffv2ZGZm+i2mnJwcz/MLH03PlFUkz7mXjTsPk5U8tt7LaB/Rni/WfUGXA128ljkpspivswp5+7N5dEhw1HtZVXmtXwgJ9TqGev0g9Ouo9Wv6mmodazrjLzdDRH4L9AGiygcaY+6oZbrdVD5T7wBUujdARASYDswyxnzgNipbRMrP+jvY86rEGPMq8CrAsGHDzOjRo32oim8yMzPxOr+Rp8C7k+i98Rl6Z5wC3U+v3zIWZzJn2xxGjRqFtRqq659fzOtrvyQrqiO/Hd2jXsvxuOya6hciQr2OoV4/CP06av2avqZaR1977hsEXAFsB0YAcT5MtwRIE5EOIhIPjAe+qFLm70C+MebBKsNn2ssDuByY4cPyGoYjHC56HdoPhPeuhN0/1Gs2g5yDOHzsMFuzt3ot0yI2glO6t2X2Wv1Zn1JKKf/wJfH3NcbcCuQYY57n+F36NTLGlAC3AvOAlcATxpiDIjJLRNqLSEfgTmCIiKy0X2fZk/8duFhENgPdsc/sG43IWPjtu5CQYvXud+jnOs+i/Dp/TT/rA+vu/o17c9myP69eoSqllFLufEn8xfbfbBHpCcQCTl9mboz51BjT0xjT3Rjzsj1sgjFmtzFmpzFGjDF9jTED7NcXdpn9xpgx9nQXGGMK6lO5gIprA1d8COKA/9S9d7+k2CQ6JXSqsSMfgHF9knGECbO1Mx+llFJ+4Evif83usvc+rJ/kbQAeC2RQTUZF734HrTP/Ovbu53K6WLZ3WY2d9LSKi2REtzb6sz6llFJ+4UvPfS8ZY44YY+YbY7oZY1KwrsErgOQ0+M1bkLXeuuZfh979MpIy2Je/j91Hq927WMn4tBTW78lh2wGff/SglFJKeVRj4heR4SJysYgk2+/7icibwHcNEl1T0XUU/OoV2DIPPrnO5979BjkHAdT4mF6AcX2dhAnM0pv8lFJKnaCaeu57DJgG/AqYJSKPAF8DywHtUaaqvhfA+EdhzXvw1V98mqRjQkeSYpJYvm95jeXaxkcx7KQ2zNbmfqWUUieotp77BhhjCkSkNfALkG6Mqfst7M3F0D9A3l745gmIT4YR19dYXETIcGbUmvgBxqencN/Ha/nlUD6dWsf6K2KllFLNTE1N/YXld9MbYw4BP2rS98Fp98GASTDnz7Dmg1qLu5wutuVs40DBgRrLndXXiQj6m36llFInpKbE31VE3rNf7wOpbu/fa6gAmxwROPcZ6DEOProatsytsbjL6QJqv86flBDN4NTWene/UkqpE1JT4r8AeN5+PVfl/fOBDqxJc0TAxdMgpT+8ewXsXum1aLeW3UiMTPSpuf+c9BRW/nKEXUcaX7cGSimlmgavid8Ys6CmV0MG2SRFxsFl71lP8nvzIq+9+4VJmM/X+c9OSwbQznyUUkrVmy8d+Kj6imsDl38IEgZv/Bry9nssNsg5iB8P/0hOUU6Ns3MmRjOoSytmr9XmfqWUUvWjiT/QWnWxevc7egDeuhiOVe9zPyMpA4NhZdbKWmc3Pj2F5dsPsze7MADBKqWUCnW1deATJiL/aKhgQlZyOvzmTdi3Dt67olrvfr3b9CYmPKbWB/YAjC9v7te7+5VSStVDjYnfGFMGZIiIo4HiCV1dR8OFL1m9+316faXe/SLCIhjQbkCtd/YDtG8Zw8DOLbUzH6WUUvXiS1P/T8B8EblNRK4tfwU6sJCU9isY/wisfhe+vr/SqAxnBusOrKOgpPY79iekpbB0+yGycrS5XymlVN34kvi3Al8BcUA7t5eqj6F/hJE3w6Jn4LsXKga7nC5KTAmr96+udRZnpyVjDHyxTs/6lVJK1U1NXfYCYIx5AEBEEuz3dXv2rKru9P+F3H3wxd0QnwTpF5HeNp2IsAhW7FvB0JShNU7eqXUs/Tu24LM1e7hieGrDxKyUUiok1HrGbz+RbyWwFFgmIitEpF/AIwtlInDes9D9TKt3v5/nEx0eTXrbdJ9+zw/W3f1Lth5if+6xAAerlFIqlPjS1P8y8EdjTG9jTC/ganuYOhGOCLjk39Yd/+9cDntWkeHMYNX+VRSXFtc6+fi0ZMoMzFmvzf1KKaV850vijzHGfF/+xhizBIgJXEjNSGQcTHrfau5/4yJcsR0oLC1k/aH1tU7apU0cfdsn6t39Siml6sSXxL9KRJ4TkWH261mg9jvQlG/i2sIVH4IIA+Y8SBhhPjf3T0hP4bufD3LoaFHthZVSSil8S/x/wLqz/077tR2YGsigmp1WqTDpA+LzDtCrTFi+5/taJwGrub+0zDBH7+5XSinlo9p67hNghTHmCWPMhfbrCWOM/oDc31L6wW/exJWXzQ+7F1NaXPsqPqldPL2TE5ilffcrpZTyUW099xmspv6+DRRP83bSGAYN+B9yKWPzJ1PBmFonmZCewrebD3AkX5v7lVJK1c6Xpv6+wA8islZElojIUhFZEujAmquBg61OEZftmAtf3V9r+QnpyZSUGb5cvy/AkSmllAoFNXbgYzf1Xwxo034DaR3dmpNanMSK6A5MWvQ0JCTDsGu8lu+elEBPZzyz1uzh4kGdGi5QpZRSTZIvTf3vGWO2V301UHzNksvpYjmFmH6/gc/vhrX/rbH8+LQUFm4+QHZB7b//V0op1bz50tSfKSJnBzwSVSHDmcHBwoNsP/V26Haa3bvfAq/lJ6SnUFxq+HqDNvcrpZSqmS+J/7fALBHJE5EsEdkvIlmBDqw5G+QcBMCKA6vhkungTIN3JsEez90n9HTGc1K7OGZpZz5KKaVqUWviN8a0NcaEGWPijTFJxph2xpgkX2YuIhNFZJOI/CQiUzyMf15E9onIsirDp4nIzyKy0n51871KTV9yXDId4jtYHflExdu9+7WDNy+Cw9uqlRcRzklPIfOn/eQWanO/Ukop77wmfhG53O3/MVXG3VjbjEUkHHgSOA0YCNwuIm2qFHsLmOBlFjcYYwbYry21LS/UZCRlHO/BL64tXP6h9fO+//wKjh6oVn58WgpFJWXM3aiNMUoppbyr6Yz/Frf/n6oy7iof5j0EWGeM2WWMyQNmA+PcCxhjFgEHfQm0uXE5XezK28Xeo3bzfeuucPkHkJcFb10CRUcrlT85JYHUNrHMWrMnCNEqpZRqKmr6OZ94+d/Te0/aA7vc3u8COvgYF8DjIvIQMAu41xhTWikA69LBFID27duTmZlZh1nXLCcnx6/zq4+y4jIA3lzwJoPjB1cMb9nnDtJWPcCRl85lXfq9mLDjH2FaixK+2LCPOXMXEB3u/SNqDPULtFCvY6jXD0K/jlq/pq+p1rGmxG+8/O/pvb/dDewFooB/Yz0K+PlKARjzKvAqwLBhw8zo0aP9tvDMzEz8Ob/6MMbw3HvPcbTVUUYPd49lNHRrT+v//g+jDr8PF7wAYiX51t2ymfmPhRxr04Nx/dt7nXdjqF+ghXodQ71+EPp11Po1fU21jjU19fcrv4sfSLf/L3+f5sO8d1P5DL+DPaxWxpg9xlIITAcG1zZNqBERXE4XK/atqD4y/SI46++w6i34+q8Vg/u2T6RT6xhmr9XmfqWUUp55TfzGmHC3u/gj7P/L30f6MO8lQJqIdBCReGA88IUvQYlIiv03DDgPWOfLdKHG5XSxJXsLhwoPVR85/FoYcQMsfBK+fwmwDhYmpKUwb+N+8otKGjhapZRSTYEvv+OvF2NMCXArMA9YCTxhjDkoIrNEpD1YP9sDvsNqXdgpIhfbk78pIquB1YADeDZQcTZmLqcLgB/2/eC5wBkPQL9LYfadsPZDAManp1BQXMqCTfsbKkyllFJNSI199Z8oY8ynwKdVhk1w+3+yl+lOC2RcTUWPlj1IiEhgedZyTu9yevUCYWFw/vPWz/s++iPEtaV/6ig6tIzhszV7GJ+e0vBBK6WUatQCdsavTpwjzMFA58Djv+f3WCjC7t2vL7wzCdm3lvFpyczdmEVhcan36ZRSSjVLmvgbuYykDDYe2kheUZ73QlHxcNn7ENsG3vg156eWkF9UyoIftblfKaVUZZr4GzmX00WZKWPV/lU1F4xvB1d8CKaMtHm/o1dCEbO1Mx+llFJVaOJv5Pq26Uu0I7rm5v5yrU+CSR8guXt5LfIxFm3YwbESbe5XSil1nCb+Ri7CEUG/dv18S/wA7QfApf+hfcGPPFL2JAs36lm/Ukqp4zTxNwEup4s1B9ZwrPSYbxN0Ow3Of4HTHCuJ/+o26+E+SimlFJr4mwSX00VxWTFr9q/xeZqw/pfweYc/MfTIbEq/+mvtEyillGoWNPE3Af3a9SNcwn1v7re1PP1mXio5B8eiJ+H7lwMUnVJKqaZEE38TEBMeQ5+2fViR5aHf/hoMTm3Nq1FXsaLFmTD7Dlj3cWACVEop1WRo4m8iXE4XP2T9QEmZ733wO8KEcWntmZr9O8pOGgsfToWt3wQuSKWUUo2eJv4mwpXkoqCkgI2HNtZpugnpKRwshO9cT0FSH3jnMuLytgYoSqWUUo2dJv4mYkDSAASp83X+oV1b0zoukpmbcmGS1btf2sr7Ycf3gQlUKaVUo6aJv4loEdWCnq161jnxhzvCOKuvky/W7aMkpi1c8SEl4XHw+tkw9yEoLQ5QxEoppRojTfxNSIYzgxVZKygzZXWabnxaCoeOFvH91kPQ+iR+GPwUDJ4CmY/Cv86Cg1sCFLFSSqnGRhN/E+Jyusg+ls2WI3VL1MO7taFFTASz7L77yxxRMOExmPQBHPkF/jkSlk/Tjn6UUqoZ0MTfhLicLgBW7Kvbz/oiHGGM6+Pki3V7KS1zS+49zoRrv4OTToUZN8I7k+DoAX+GrJRSqpHRxN+EtI1pS5fELnW+zg8woV8KB/KKWLL1UOURcW3hN2/Cuc/Cz/PgheHw05d+ilgppVRjo4m/iXE5XSzftxxTx2b5U7q1JSE6nNlrPTy0RwRcV8HVC6FlJ3jzIvjsNijK91PUSimlGgtN/E2My+kiqyCLnXk76zRdZHgYZ/ZxMnvtXsq8HTS06Qa//wLG3AnLXoOXx8KeVScetFJKqUZDE38Tk5GUAVC/5v60FPbnHuOnw6XeCzki4NR7rAOA0mPwyumw8Ckoq2EapZRSTYYm/iamQ3wHnLHOeiX+UT3bEh8VztK9PnT722mI1fTf71L46n7497lwZEfdA1ZKKdWoaOJvYkQEl9NV5zv7AaLCHZxxchJL95ZQXOpDXwBRCXDB83DJdMhaDy+OhNXv1yNqpZRSjYUm/ibI5XSxI3cHWflZdZ72VxkdOVxoOPvpTOZv8nH6PufDNd9Bhwz4cAp88D9QcKTOy1ZKKRV8mviboPr+nh9gdM923DU0hghHGJNfX8rvXl/Clv15tU+YmAKXfwhn/x9smAEvnqJP+lNKqSZIE38TdFKLk2gV1ape1/kB+rQJ57MbRvHQhWms2pnNWU9l8tcZ68nOr6Xf/rAwGHYN/GE+xLS0rvt/+RcoOVavOJRSSjU8TfxNkIgwMGkgy7Pql/gBHGHCpKFdmHfbWCaPSGX6d9sY+/g8/rN4OyW1Xf939oGpc2HE9bDoGXj1dMiq2+OClVJKBYcm/ibK5XTx0+GfyD6WfULzaRETwb0T+zDn5tFkdG7FfR+v5ZxnF7Jocy1d94ZHwbgH4cpP4ehBeHkMfP+y9vevlFKNXEATv4hMFJFNIvKTiEzxMP55EdknIsuqDO8mIstEZLOI/FNEJJBxNkWuZOs6/w9ZP/hlfie1i+e1yYOZ/vshlBnDpFe/Z+r0ZWw7cLSWCcfAtd9Cr/Ew+3ar17/cvX6JSSmllP8FLPGLSDjwJHAaMBC4XUTaVCn2FjDBw+SPAPcbY7oDbYFzAhVnU9WrVS/iIuLqfZ3fm9E92zHrxlE8cF5flmw9xJlPLeDvszaQW1jD9f+YVnDR63Dhy/DLEnhxBGyY6de4lFJK+Ucgz/iHAOuMMbuMMXnAbGCcewFjzCLgoPsw++x+BPCZPegN4NwAxtkkhYeFM6DdAL8nfrCe5nfViFTm3zaWy4Z05tWFWzn18fm8s2RH5af7uROB/pdanf607QnvToJP/wTHfPjFgFJKqQYTyMTfHtjl9n4X0MGH6doAh8zxp9D4Ol2z43K62HBwA/nFgXmYTqu4SB44P43ZN47i5JRE7vpwDec9t5Dvfz5Yw0RdYPJncPpfYOVb8M+RsHOZ9/JKKaUaVHiwA6gv+56BKQDt27cnMzPTb/POycnx6/wCxVHooMSU8MbcN+gd09vn6epTvyndDYMSY3hrQy6XvryYIcnhXNo7inax3o4dBxOf8Si91z9BzGtnsj31N+zocgmEOeq03PpqKp9hfYV6/SD066j1a/qabB2NMQF5YTXXf+T2/mngMg/lUoFlbu8F2A2I/f4C4OWaljV06FDjTwsWLPDr/ALlWMkxkzE9w/xjxT/qNN2J1O9Ycal5JXOLSfvfz02PP88yj32+0eQVFtcwQZ4xM2425n8TjXnldGMObqn3suuiqXyG9RXq9TMm9Ouo9Wv6/FlHYLEJUD6u+gpkU/8SIE1EOohIPDAe+KK2icpXAMdv6JsEzAhYlE1YpCOS9HbprMiqew9+9V5meBhTRp3E/NvGcpGrIy/M38ypj8/nv8t3Uubp+n9kHEx8Ei57Dw5vg3+OghX/0Z/9KaVUkAQs8RtjSoBbgXnASuAJY8xBEZklIu0BRGQa8B3QT0R2isjF9uR3Ag+IyBbgMMdv9FNVuJwuVu9fTVFpUYMut018FA9fmM5nN4yiW7t4bn1/FRe++C3Ltx/2PEHPs6z+/lNHwqfXw3tXQP6hBo1ZKaVUgH/Hb4z51BjT0xjT3Rjzsj1sgjFmt/3/ZGNMijEm0hjT0Rjzvj38J2OMyxjTzRjzB2OMD4+Sa55cSS6OlR5j3cF1QVn+ySmJvDV1KP+83MXho0X8+sVvufGdH9h9pKB64fh28Nt3YOJT8NNX8MJw2Px1wwetlFLNmPbc18T1T+qPQxwB+Vmfr0SEs9OSmXPzaO48uzdfrd/HaU/M5+mvfqSgqLRqYRj0e7j6G+vBP2/8CmbfCcUeDhSUUkr5nSb+Ji4uIo6TW58c1MRfLjrCwTVjuzHv9rGc1789z3z9E6c/MZ9PVu4qv3HzuLY94H++hFG3wZKX4eVTYe+a4ASulFLNiCb+EJDhzGBl1kpKy0prL9wAkhKiefSi/nx63Ujat4zhxndWctE/v2PVL0cqF3REwOn3weRZUHwUXjkNFj0LZXplRymlAkUTfwhwOV3kFefx4+Efgx1KJekdW/D+1cP5x28Hsje7kPOfX8St760iK6ewcsEuw60e/9J+DV/eB9PPg+ydwQlaKaVCnCb+EJCRlAHQKJr7qxIRzu3fnq9vHcMtZ/Zk1po9jH18Ps/P20xhsVsLRXQLuPCfVp//e9dY/f2v/W/wAldKqRCliT8EtIxuSfeW3Rtl4i8XHeHghtN7MPe2MZzVN5nHvtjEGU8uYPaaPZWv/6f9Cq75FlIGwAe/hw//AIUn9uhhpZRSx2niDxEup4sVWSuq30TXyKS0iOGpSwfw4bUjaBsfxTVvruA3Ly9m3W635N6iA1zxMYx7CNZ9BC+OhO3fBi1mpZQKJZr4Q4TL6eJQ4SG25mwNdig+yejcig+vGcFTl/Zn28GjTPzHQu7+cDUH8o5ZBcLCYMT1MHUeRMXD6xPgqwegpGE7KlJKqVCjiT9ENObr/N6EhQkXDuzIvNvG8qdTu/Phil2c+th8Xs7cQlGJfWd/cpqV/IddCwufhNfOhP2N6yZGpZRqSjTxhwhnnJOO8R1Zsa/h+u33l9jIcG4Z14uvbx3DmF7teHjWRsY9tYAv1++zLl1ERMPZD8MVH0HePnhpNCx9Vfv7V0qpetDEH0JcTleTOuOvqmOrWJ67LIP3/jic+Ohwpk5fxhWvLWHT3lyrQLfTrBv/epwJn90Kb10KeVnBDVoppZoYTfwhxOV0sefoHnbn7Q52KCdkSNfWfHrdSB79dT827s1l/DOZ3PfxWg4fLYLY1nDJdDj/Bdi+yOrvf9PsYIeslFJNhib+EOJyuoCmdZ3fm7Aw4ZLBnZh32xj+MLob7y79hTGPzeNfC7dSXGZg4CSr05823eDt38CMm6DoaLDDVkqpRk8TfwjplNCJdjHtQiLxl0uIjuCu8b358pbRDO/Whr/OXM/ZT2cyb1MWtO5qdfd76p9hxXT45yjYFTp1V0qpQNDEH0JEhAxnRkgl/nJd2sTx0hWDeGvqUCIcYfzu9aVMfn0Jmw8Wwpg7rAf+YOC1cZD5GJjG8dwCpZRqbDTxhxiX08W2nG0cLDgY7FACYkS3tnx2wygeujCN1TuzOfvpTP46Yz3ZrfvBH7+BAZNg7oP0X3E3HN4W7HCVUqrR0cQfYsqv86/Iano/6/OVI0yYNLQL824by+QRqUz/bhtjH5/Hf344SMk5T8Nv3iImf5fV49/Kt/Rnf0op5UYTf4jp3rI7iZGJIdncX1WLmAjundiHOTePJqNzK+77eC3nPLuQReFDWT7kOeg8DD6+Bt6fDPmHgh2uUko1Cpr4Q0yYhJGRFJrX+b05qV08r00ezPTfD6HMGCa9+j2PrY1m21nTYMLj8OPn1tP+tswLdqhKKRV0mvhDkMvpYtOhTeQW5QY7lAY1umc7Zt04igfO68umQyWc+XQmfz8wkqOT50J8EvznAvjiz1BcGOxQlVIqaDTxh6AMZwYGww9ZPwQ7lAYX4QjjqhGpPDomnsuGdObVhVsZM2037/V/nbJTbobvnodXToN964IdqlJKBYUm/hB0cpuTiQmPaVbN/VUlRAoPnJ/G7BtHcXJKInd8vJFzN5zO+rPehmM58PJY6yCgrCzYoSqlVIPSxB+CIsIi6N+uf5N8YI+/9XQmMP33Q3jtqkHkF5Uy4ZMybm3zPEe7T4Qv7oE3LoScpt3FsVJK1YUm/hCV4cxg7cG1FJQUBDuUoBMRTj/ZyRc3jebec05mzs+FDFx3CTN7/A2za4XV3/+6j4MdplJKNQhN/CFqkHMQJWUlrNm/JtihNBqR4WFMGXUS828by0WujtywthvnlT5KVlwPeP8q+PhaKMwJdphKKRVQmvhDVHrbdMLDwlme1Xyv83vTJj6Khy9M57MbRhGf1JVhu25kWuzvKVv9HvxzJOxYHOwQlVIqYDTxh6jo8GjS2qQ16xv8anNySiJvTR3KC5cP5l+cx8SCB9ibD+b18TD3QSgtDnaISinld5r4Q5jL6WL1/tUUl2kC80ZEODstmTk3j+bcs85mQuGDvFFqPein7NVxcGBzsENUSim/CmjiF5GJIrJJRH4SkSkexg8RkXUisllE/uI2fJqI/CwiK+1Xt0DGGapcThcFJQVsOLgh2KE0etERDq4Z243Pbx/Hmn73cFXRnRza8zMlL47ELHtd+/tXSoWMgCV+EQkHngROAwYCt4tImyrFngd+C/QCJohIutu4G4wxA+zXlkDFGcoGJA1AEG3ur4OkhGgevag/t117HXe0e5Gvi/oiM28i+/WLIG9/sMNTSqkTFsgz/iHAOmPMLmNMHjAbGFc+UkTaA+HGmNXGmFLgHWBiAONpdhIiE+jdurcm/npI79iC164dT9Gvp/P38OsI3/4NuU8N5sjKmcEOTSmlToiYADVhishFwFhjzPX2+9sBY4x53H4/CLjfGDPRfn8xMMYYc72ITAOGAceAWcC99sGB+/ynAFMA2rdvP/Ttt9/2W+w5OTkkJib6bX7B9MHBD1ict5hHOz9KmFjHeaFUP2/8WceiUsOSTduZsPsZBoRtZlH8WRwbMIXwyGi/zN/nOMqK2F+yn33F+zhw9ABxMXGEEYZDHDhwWH/FUfdh9vAwCcOBAxFp0Hp5E+rbqdav6fNnHceMGfO9MWaYX2ZWi/CGWEg93A3sBaKAfwNXY10WqGCMeRV4FWDYsGFm9OjRflt4ZmYm/pxfMB3bfox58+eRkp5Cr9a9gNCqnzf+ruMZp8Kewxfy+Zv3csb+6exctJadpz7LKaNO92uiLC4rZnfebrbnbGdb9ja252y3/s/Zxr78fZULH/XbYitxiIPwsPCKvxUvCa/03iEOIsIiKr2vrXx4WDgRYRE4whwey5S/d4Q52Jy3mbTOaUSIW3kv05SPL4/HUyyN5YCmXKjvh6FeP2i6dQxk4t8NdHB73wFYUsv43QDGmD32sEIRmQ5cHMA4Q1pGUgYAy/ctr0j8qn5SWiWQcv0zbFp2Li1mXcfQry/h7aVX0v/Sv9C3Y2uf52OMISs/qyKhlyf37Tnb2Zm7kxJTUlHWGeskNTGVMR3H0CWxC6ktUumS2IWNKzYybPgwisuKKSkrodSUUlJWcvxlSiq9Ly0rpcSUUFxWbP3voUz5sPLxxWXFld57m8Z92UeLj1Z6X/5/bXF6teBEPrHK3A8G3P+PCIuo8aClLgc05Qct3g5oKsaHhfPj0R8xvxifDoIqDrDcDnIa6wGNavwCmfiXAGki0gHIBsYDfysfaYzZLSKlItIPWAf8BpgKICIpxpg9IhIGnGePV/XQJqYNXVt0ZUXWCi47+bJghxMSeg06g7K+S9nx9k1ctuN1lrz8HY/0+Rv/M3EMbeOjKsplH8tmW842duTsqJbg3btSToxMJLVFKv3a9ePcbudaCT4xlU4JnYiNiPUYw/aw7bSIahHwujYEY0zFAYH7gcKi7xYxaMig0DygKTf3xNdffQ9oAtKqE+YgQqzyG/I3ELknUg9oGqGAJX5jTImI3ArMw7qJ8FFjzEERmQVMMcbsBq4H3gaigf8YY8r7l31TRNra0y0Gng1UnM1BRlIGC3YuwBijO5OfhMUkkvr7f5G98iyiv7iD3jumcPUrw5BOyUTGHGJH7naOHDtSUT7aEU3nxM50SezCqA6j6JLYpSLBt4xuGbR6NAYiUpEM3LUKb0WH+A5epmr8vB3QlL//7vvvGOgaGNQDmsKywsAc0JSbU//1V/Ugo1rLSh0Oak64VcftgMa9VSevNK/+FQyigF7jN8Z8CnxaZdgEt/8XA309THdaIONqblxOF//96b/syN1Bl8QuwQ6nySkpK2F33u5KZ+3l/+89uheSrZt7HGY9LQ//SP6h7gzpfDqjUk+uaJpPik2quLlSNQ/eDmjKJUUk0a1l0+uipLYDmvKDhO+Xfk//jP7BOaApCfABjW1q0tQArOHAa6w39yk/GuQcBMCKfSs08XthjGF/wf7jST37eIKvet09KTaJ1MTUijP31MRUuiR0osOG2Ti+vJ8D5HHjtiHM2JnKfRP7kByXEMSaKeVftR3QlNsVtYu+baqd1zVavh7QuB8o7Fq7K9hh14sm/mYgJT6FlLgUlu1bxoU9Lgx2OEGVfSy72lm7p+vuCZEJdE3sSnrbdCaeNJEuLawE3zmhs9fr7gy/DrqdRrv/TuGtfQ/zn11rOf+ZX3Hx0O7cfGZPWsdFNlAtlVJ15esBjbtsR3YAIwocTfzNhMvpajYd+RSWFLKraBdfbv+y2s/iDh87XFHO23X3LoldaBnVsn73QySdjEydC3Mf5Ipv/8FZrdbzu6V/YOzKXdx0Rk+uGN6FCIc2+SvVlJWWGfKLSigubZpdeWvibyZcThczf55pXZMOAeXX3T2dve85av8adJd1g1CH+A50SezCOSedYzXL22fvAbvuHh4F4/6G9DgT50dX81n0vbzbcgp3zyzize+3c+/EPpzaK8n/y1VKVWKM4VhJGUePlZBfVMrRohKOHisl3/5bUFz5fX5RCUeLSsm3y5dPk3/M/ltUytFjJRwrKQPgxoxoTg9yHetDE38z4XK6AOs6fzzxQY7GNzVed8/bWelmnKSYJLq06MIpHU4hNTGVvF/ymDB8Ah3jOxLhiAhOBbqOhmsWITNv4Tfrnuesk1ZzXd4Ufvf6Usb2ase95/She1LT+CyUCrTi0jI72VZPxtWG28n5aFEpBV6Sc/k0ZT6elEdHhBEXGU5slIO4yHBiIq2/7eKjiGsTTmykg7go66/1Cif6yNbArpQA0cTfTKQmptI6ujXL9y1nDGOCHU4lOUU5bM+ufs19W862el93zzyYSdcWXRu6KtXFtIKL/gW9xtPqs1t5M+xmMoffx82rIzn76UyuGN6Fm07vSYvYIB2cKFVHZWWGwpLSWs6US1i75RjLCjdZwyvKVS9bnrSLSst8Wr4jTIhzS8JxUeHERDiIjw4nKTGqUvKOjXIQG+EgNiq88nAPSdwRVvdLe5mZ2+s8TWOgib+ZEBFcThcrslYwplXDJ/7CkkJ+yf3F4011hwoPVZSLckTRObEzqYmpnNLhlON3zZ/IdfdgE4F+l0CnochHVzPmh5tZnD6Jp8J/zyvfbefjH3Zxy7he/HZwJ8L1+r/yo6KSsmpnyBV/q5wdu581W2fRlcu6n337KnbrVmIjw4mLspJrXKSDmEgHKYnRlZJzeTKuWtZK2NZfK4E7iHSENc3vgUZEE38z4nK6+HL7l+QlBqbTiZKyEvbk7alI7O4Jfu/RvRisNrcwCau47j6h64RKndk445yh+3v3Vl1g8kxY9AyR8x7izhbfcvlvn+Evy2O57+O1vPHddv5ybh9O6d422JGqBlZaZigoPp5oqybjiiTt3qRdKUlXSeL23xIf27kjHWGVE7CdcFvGRtC+ZYzHZBxTNTm7Je3YSAdLFy9i7JjG1bqoLJr4m5Hyfvu3FG6p9zyMMRwoOOCxM5tfcn+p8bp7l8QudE7sTKf4TsG77h5sYQ4YdQt0OxX+O5UOH17Ia6NvJ3PYZP4260cmvfo9Z/Zx8ucJJ5PaNi7Y0aoqym8W83amXHE92sP15qPHSigoPv7+YHYeZQu+5GhRCYXFvjVzi1Ctqbr8rLl1XGT1M2U7GVckafczarckHYhfmoTpWXmjpYm/GenZqifxEfFsLtxca9mcopzKfcy7XYPPL8mvKJcQkUBqi1T6tunLhK4TKiX4uAhNXF61Hwh/zIQv74MF/8foDl8x64qXeOuncJ788kfOfGoBvz+lK9ef1p2E6GZ6kHSCSkrLyC8urXaGXHFHdw1nyhXN4x7OsH29WSwqPKxaco6LDKdtfBSxkQ6yIwrpntqhWjKOiaj83j3RR4VrM3e9lJVCcT4UF1p/SwqhuMB6lRQc/7+4wB5XtaznaVs4LwL06XyqEXOEORiYNJDN+63E7+t198iwyIrr7iPaj6j0lLhWUa30i6i+ImPhnCegxzj45DoiXh7NVeP/j/NuvZSnv/6JVxdu5b8rdnLbuF5cPKhTvW4+agqMMRQWl1VLzmv2l5C/do91bbnY8zXqmn6SVVTi+81iVZNzbKSD+KhwkhKiarj2fPzmMPcbzXy9Wcx6pGsff6zCpqmstIbkW/9kXHl6e3xZcd3jc0RBRMzxV7jb/5FxENuGsrCmeVCuib+ZyXBmsHDXQs764Cz2HN1T63X3LoldSI5LDt3r7o1Bz7Pgmu/g0z/Bp3+iVe8veODcZ5k0rAt/m7meuz5cw38Wb+cvE/sw9KQ2QQ21qKTM7UzZSrR1uSnM413gxaUYb2fRS1dUehsT4ajURF2eaJPtm8UqEriXJm1PTeR6s5gbPybjtKzdsCXGczIuKYDSorrH50MyJiIWIqKtv+HRbmWjPYzz8j48BsJq/87Lzcysx0oOPk38zczZqWfz9Yav6dW+V0WzfJcWXegY35FIh3YpGzTx7eC3b8PyafDFPfDicHpe8ALTf386czdm8eBnG7j05cVMSE/m7vEn1zq7sjJjnSV7+X3z8evNNf8kq2oS97WnskhHmP076MrXm1vERJDSIqb6WXSVu77josLZuHYVo4YNOf676ggHYSHa6lEjv58ZexpXcILJuHryDCsrhog2djL2kKjrnIyjrXtk1AnTxN/MdEzoyDXJ1zB6RNO7LhXyRGDQ7yB1FHw4Fd74NTLkj5x+5gOMumk007/bxjNf/8RXGxYwoG0Yb25fdrx3sWOVz6gLin37yVX5zWIVSdotCbeKi3E7U67+86qKJF0pcVvzigw/8Raigh2OxnuDY32Tsdu4Xru2w56XKzdJBzgZW39jILZ1wJPx6sxMRo/W75nGSBO/Uo1N2+7wP3NgwSPwzROwdQGRv3qFKaP6ceHADjz11Y/MX7uToohjxNl3c3dqFeP2W2dvP7M6npzLz6KjI0KombvWZFzb9WAv14cDlIzjigyEt6khGXtorvaYqD0kbj0zVjXQxK9UY+SIgNPuhe5nwId/gFdOg9Pvo83wP/HgBelktj7M6NGnBDvK2pUn43rcnNVt22Y48kGAk3Fkzck0tlUNZ70nloxX6BmxChJN/Eo1Zp2HwdULYfad8OVf4Kcv4cJ/ntg8TyAZN8yZsZWM25Y54GiLwCTj8rJ6ZqyaIU38SjV20Ylw4YvQcxzMuAleGEGXlAlQ9p2PybjKuECdGfty1lvrtePjyfh7PSNWKiA08SvVVPS9EDoNhU+uo8uWt+GXSLfk6iGZBiAZK6WaPk38SjUlie3hio/IXDCP0WNODXY0SqkmSHtlUaopEj0DV0rVjyZ+pZRSqhnRxK+UUko1I5r4lVJKqWZEE79SSinVjGjiV0oppZoRTfxKKaVUM6KJXymllGpGNPErpZRSzYgmfqWUUqoZEWNMsGM4YSKyF9jmx1m2A/b7cX6NTajXD0K/jqFePwj9Omr9mj5/1jHVGJPsp3nVKCQSv7+JyGJjzLBgxxEooV4/CP06hnr9IPTrqPVr+ppqHbWpXymllGpGNPF79mqwAwiwUK8fhH4dQ71+EPp11Po1fU2yjtrUr5RSSjUjesavlFJKNSOa+JVSSqlmRBO/Ukop1Yxo4ncjIh+JyGER+SDYsQSCiHQSkfkisl5EVovIxcGOyZ9EpKWILBORlSKyVkSmBjumQBGRWBHZLiKPBzsWfxORbfb2uVJE5gU7Hn8Tka4iMs/eD9eISFywY/InEellf3blrwIRuSDYcfmTiNwsIuvsz/BZEZFgx1QXenOfGxEZCyQAVxljLgpuNP4nIimA0xizUkSSgeVAT2PM0SCH5hci4gCijDH59pfpWmCQMeZgkEPzOxF5COgO/GKMuS3Y8fiTiGwD0owxecGOJRBEZAFwrzHmGxFpDeQYY0qCHVcgiEg8VudqXULoe6YdsBjoCxQDmcBtxpjvghpYHegZvxtjzHwgN9hxBIoxZo8xZqX9/17gANA6qEH5kTGm1BiTb7+NAsR+hRQR6QH0BmYHOxZVNyLSFyg2xnwDYIw5FKpJ33Ye8HWoJH034UA0EGG/soIbTt1o4m+mRMQFOIwxvwQ7Fn+ym/tXATuBx4wxB4IdUwA8Dtwd7CACyAALRGSpiEwKdjB+1gPIE5EZIrJCRO4JdkABdgnwbrCD8CdjzH6sfXAHsBv4yhizJbhR1Y0m/mbIbl6cDvwh2LH4mzHmiDGmP9AVuExEnMGOyZ9E5HzgR2PMj8GOJYBGGmNcWGeL94hIv2AH5EfhwCjgWmA4cKaInBnckAJDRBKBEcCsYMfiTyLSCpgIpAIdgBEiMjqoQdWRJv5mRkSigI+B/zPGfBvkcALGGLMPWIX1JRtKhgG/sa+DPw5MFZG/BDck/zLG7LL/7sFKGhnBjcivdgHLjDG/GGOOYdVvQHBDCpjzgTnGmMJgB+JnZwCb7cs0BcBnWPtlk6GJvxmx7zydBsw1xvwnyOH4nYg4RSTB/r8FMBrYFNyo/MsYc7cxppMxJhW4DXjFGPPXIIflNyIS5/YZxgOnAeuCG5VfLQWSRKSViIRhbaMbghxToIRcM7/tF6yz/Gj7huKxNLHvmfBgB9CYiMhXQH8gTkR2Ahc3pTs1fXAKcCmw2u3nNVcYY9YELyS/6gK8bB/gCPCPEKpbc+EEPrJ/HeXAOrBZGtyQ/McYU2Jf18/E2kbnGGNmBjksv7MPvIcAvw52LP5mjFksIrOAH4Ay4Gvg0+BGVTf6cz6llFKqGdGmfqWUUqoZ0cSvlFJKNSOa+JVSSqlmRBO/Ukop1Yxo4ldKKaWaEU38SjUDIlJS5YlpV/px3veLyPX+mp9SKrD0d/xKNQ9HjDEDgh2EUir49IxfqWZMRA6IyPP2s8U/s/tXR0QyRGSJ/bz46SISbQ8fKiKLRWSViMx3m9UAEckUkZ9F5Dd22fYissguuzrE+txXqsnSxK9U89CySlP/qfbwNlhdOPcFVgO32MP/DfzJGJMOHAWuFZFI4E1giv0gpF+5zb8bcDpwJvCgPey3wHy7bAawOYD1U0r5SJv6lWoevDX1HwM+tP9/G3hSRFoCUcaY7+3h/wFuB74Cthlj1oL1LHm3+cw0xhQDW+zpweqXfrqIlAAfaPfJSjUOesavlCpn7Fd9HKs2M2MysZ4PsRt4W0TOO4HYlFJ+oolfqeYtCuvxqWA9wGmhMeYIcExEBtvDJ2E9VGYjkCoiaQAi0rqmGYtIF2CvMeYlrFYDvcavVCOgTf1KNQ8tRWSl2/t/G2OeAg4CZ4rIQ8A2rOvyAJOBF+2b+lYCLxpjikTkcuBfIhJlT3taDcscC9wuIsXAEbd5K6WCSJ/Op1QzJiIHjDFtgx2HUqrhaFO/Ukop1YzoGb9SSinVjOgZv1JKKdWMaOJXSimlmhFN/EoppVQzoolfKaWUakY08SullFLNyP8DHvQcFamvrOUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 560x350 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 5))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "for layer in hidden_nodes:\n",
    "    plt.plot(epochs, \n",
    "             1 - np.array([i[\"results\"][1] \n",
    "                  for i in single_results \n",
    "                  if i[\"nodes\"] == layer]), \n",
    "             label=f'{layer} Nodes')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.title(\"Test error rates for a single iteration of different epochs and hidden node training\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Error Rate\")\n",
    "plt.ylim(0)\n",
    "\n",
    "# plt.savefig('fig.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7mJaKjlCxEkt",
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "## Multiple Iterations\n",
    "\n",
    "Run multiple iterations of the epoch/layer investigations and average\n",
    "\n",
    "### CSV Results\n",
    "\n",
    "| test | learning rate | momentum | batch size | hidden nodes | epochs |\n",
    "| --- | --- | --- | --- | --- | --- |\n",
    "|1|0.01|0|128|2, 8, 12, 16, 24, 32, 64, 128, 256|1, 2, 4, 8, 16, 32, 64, 100, 150, 200|\n",
    "|2|0.5|0.1|128|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
    "|3|0.2|0.05|128|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
    "|4|0.08|0.04|128|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
    "|5|0.08|0|128|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
    "|6|0.06|0|128|1, 2, 3, 4, 5, 6, 7, 8|1, 2, 4, 8, 16, 32, 64, 100|\n",
    "|7|0.06|0|35|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
    "\n",
    "### Pickle Results\n",
    "\n",
    "| test | learning rate | momentum | batch size | hidden nodes | epochs |\n",
    "| --- | --- | --- | --- | --- | --- |\n",
    "|1|0.01|0|128|2, 8, 12, 16, 24, 32, 64, 128, 256|1, 2, 4, 8, 16, 32, 64, 100, 150, 200|\n",
    "|2|0.5|0.1|128|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
    "|3|1|0.3|20|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
    "|4|0.6|0.1|20|2, 8, 16, 24, 32|1, 2, 4, 8, 16, 32, 64, 100, 150, 200|\n",
    "|5|0.05|0.01|20|2, 8, 16, 24, 32|1, 2, 4, 8, 16, 32, 64, 100, 150, 200|\n",
    "|6|1.5|0.5|20|2, 8, 16, 24, 32|1, 2, 4, 8, 16, 32, 64, 100, 150, 200|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "-lsKo4BCP3yw",
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1/30\n",
      "Iteration 2/30\n",
      "Iteration 3/30\n",
      "Iteration 4/30\n",
      "Iteration 5/30\n",
      "Iteration 6/30\n",
      "Iteration 7/30\n",
      "Iteration 8/30\n",
      "Iteration 9/30\n",
      "Iteration 10/30\n",
      "Iteration 11/30\n",
      "Iteration 12/30\n",
      "Iteration 13/30\n",
      "Iteration 14/30\n",
      "Iteration 15/30\n",
      "Iteration 16/30\n",
      "Iteration 17/30\n",
      "Iteration 18/30\n",
      "Iteration 19/30\n",
      "Iteration 20/30\n",
      "Iteration 21/30\n",
      "Iteration 22/30\n",
      "Iteration 23/30\n",
      "Iteration 24/30\n",
      "Iteration 25/30\n",
      "Iteration 26/30\n",
      "Iteration 27/30\n",
      "Iteration 28/30\n",
      "Iteration 29/30\n",
      "Iteration 30/30\n"
     ]
    }
   ],
   "source": [
    "multi_param_results = list()\n",
    "multi_iterations = 30\n",
    "for i in range(multi_iterations):\n",
    "    print(f\"Iteration {i+1}/{multi_iterations}\")\n",
    "    data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=0.5, stratify=labels)\n",
    "    multi_param_results.append(list(evaluate_parameters(dtrain=data_train, \n",
    "                                                  dtest=data_test, \n",
    "                                                  ltrain=labels_train, \n",
    "                                                  ltest=labels_test,\n",
    "                                                  optimizer=lambda: tf.keras.optimizers.SGD(learning_rate=1.5, momentum=0.5),\n",
    "                                                  return_model=False,\n",
    "                                                  print_params=False,\n",
    "                                                  batch_size=20)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "### Accuracy Tensor\n",
    "\n",
    "Create a tensor for holding the accuracy results\n",
    "\n",
    "(Iterations x [Test/Train] x Number of nodes x Number of epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30 Tests\n",
      "Nodes: [2, 8, 16, 24, 32]\n",
      "Epochs: [1, 2, 4, 8, 16, 32, 64, 100, 150, 200]\n",
      "\n",
      "Loss: categorical_crossentropy\n",
      "LR: 0.6\n",
      "Momentum: 0.1\n"
     ]
    }
   ],
   "source": [
    "multi_param_epochs = sorted(list({i[\"epochs\"] for i in multi_param_results[0]}))\n",
    "multi_param_nodes = sorted(list({i[\"nodes\"] for i in multi_param_results[0]}))\n",
    "multi_param_iter = len(multi_param_results)\n",
    "\n",
    "accuracy_tensor = np.zeros((multi_param_iter, 2, len(multi_param_nodes), len(multi_param_epochs)))\n",
    "for iter_idx, iteration in enumerate(multi_param_results):\n",
    "    for single_test in iteration:\n",
    "        accuracy_tensor[iter_idx, :,\n",
    "                        multi_param_nodes.index(single_test['nodes']), \n",
    "                        multi_param_epochs.index(single_test['epochs'])] = [single_test[\"results\"][1], \n",
    "                                                                            single_test[\"history\"][\"accuracy\"][-1]]\n",
    "        \n",
    "mean_param_accuracy = np.mean(accuracy_tensor, axis=0)\n",
    "std_param_accuracy = np.std(accuracy_tensor, axis=0)\n",
    "\n",
    "print(f'{multi_param_iter} Tests')\n",
    "print(f'Nodes: {multi_param_nodes}')\n",
    "print(f'Epochs: {multi_param_epochs}')\n",
    "print()\n",
    "print(f'Loss: {multi_param_results[0][0][\"loss\"]}')\n",
    "print(f'LR: {multi_param_results[0][0][\"optimizer\"][\"learning_rate\"]:.3}')\n",
    "print(f'Momentum: {multi_param_results[0][0][\"optimizer\"][\"momentum\"]:.3}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "#### Export/Import Test Sets\n",
    "\n",
    "Export mean and standard deviations for retrieval and visualisation "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "pickle.dump(multi_param_results, open(\"result.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "outputs": [],
   "source": [
    "exp1_testname = 'exp1-test4'\n",
    "multi_param_results = pickle.load(open(f\"results/{exp1_testname}.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "np.savetxt(\"exp1-mean.csv\", mean_param_accuracy, delimiter=',')\n",
    "np.savetxt(\"exp1-std.csv\", std_param_accuracy, delimiter=',')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "mean_param_accuracy = np.loadtxt(\"results/test1-exp1-mean.csv\", delimiter=',')\n",
    "std_param_accuracy = np.loadtxt(\"results/test1-exp1-std.csv\", delimiter=',')\n",
    "# multi_iterations = 30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "### Best Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nodes: 32, Epochs: 32, 1e+02% Accurate\n"
     ]
    }
   ],
   "source": [
    "best_param_accuracy_idx = np.unravel_index(np.argmax(mean_param_accuracy[0, :, :]), mean_param_accuracy.shape)\n",
    "best_param_accuracy = mean_param_accuracy[best_param_accuracy_idx]\n",
    "best_param_accuracy_nodes = multi_param_nodes[best_param_accuracy_idx[1]]\n",
    "best_param_accuracy_epochs = multi_param_epochs[best_param_accuracy_idx[2]]\n",
    "\n",
    "print(f'Nodes: {best_param_accuracy_nodes}, Epochs: {best_param_accuracy_epochs}, {best_param_accuracy * 100:.1}% Accurate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "### Test Accuracy Surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "executionInfo": {
     "elapsed": 2653358,
     "status": "aborted",
     "timestamp": 1615994110345,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "ZGJVhz6iJU-7",
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 420x280 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, Y = np.meshgrid(multi_param_epochs, multi_param_nodes)\n",
    "\n",
    "# fig = plt.figure(figsize=(10, 5))\n",
    "fig = plt.figure()\n",
    "fig.set_dpi(fig_dpi)\n",
    "ax = plt.axes(projection='3d')\n",
    "\n",
    "surf = ax.plot_surface(X, Y, mean_param_accuracy[0, :, :], cmap='coolwarm')\n",
    "ax.set_title(f'Average Accuracy')\n",
    "ax.set_xlabel('Epochs')\n",
    "ax.set_ylabel('Hidden Nodes')\n",
    "ax.set_zlabel('Accuracy')\n",
    "ax.view_init(30, -110)\n",
    "# ax.set_zlim([0, 1])\n",
    "fig.colorbar(surf, shrink=0.3, aspect=6)\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig(f'graphs/{exp1_testname}-acc-surf.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "### Test Error Rate Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "executionInfo": {
     "elapsed": 2653349,
     "status": "aborted",
     "timestamp": 1615994110347,
     "user": {
      "displayName": "Andy Pack",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
      "userId": "16615063155528027547"
     },
     "user_tz": 0
    },
    "id": "Jrn3hKQAlGcc",
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 420x280 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig = plt.figure(figsize=(7, 5))\n",
    "fig = plt.figure()\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "for idx, layer in enumerate(mean_param_accuracy[0, :, :]):\n",
    "#     plt.errorbar(epochs, 1- layer, yerr=std_param_accuracy[idx], label=f'{hidden_nodes[idx]} Nodes')\n",
    "    plt.plot(multi_param_epochs, 1 - layer, '-', label=f'{multi_param_nodes[idx]} Nodes', lw=2)\n",
    "\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.title(f\"Test error rates for different epochs and hidden nodes\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Error Rate\")\n",
    "plt.ylim(0)\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig(f'graphs/{exp1_testname}-error-rate-curves.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "source": [
    "### Test/Train Error Over Nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x560 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(math.ceil(len(multi_param_nodes) / 2), 2, figsize=(8, 8*math.ceil(len(multi_param_nodes) / 2)/3))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "for idx, (nodes, ax) in enumerate(zip(multi_param_nodes, axes.flatten())):\n",
    "    ax.set_title(f'Error Rates For {nodes} Nodes')\n",
    "#     ax.errorbar(multi_param_epochs, 1 - mean_param_accuracy[0, idx, :], fmt='x', ls='-', yerr=std_param_accuracy[0, idx, :], markersize=4, lw=1, label='Test', capsize=4, c=(0, 0, 1), ecolor=(0, 0, 1, 0.5))\n",
    "#     ax.errorbar(multi_param_epochs, 1 - mean_param_accuracy[1, idx, :], fmt='x', ls='-', yerr=std_param_accuracy[1, idx, :], markersize=4, lw=1, label='Train', capsize=4, c=(1, 0, 0), ecolor=(1, 0, 0, 0.5))\n",
    "    ax.plot(multi_param_epochs, 1 - mean_param_accuracy[0, idx, :], 'x', ls='-', lw=1, label='Test', c=(0, 0, 1))\n",
    "    ax.plot(multi_param_epochs, 1 - mean_param_accuracy[1, idx, :], 'x', ls='-', lw=1, label='Train', c=(1, 0, 0))\n",
    "    ax.set_ylim(0, np.round(np.max(1 - mean_param_accuracy + std_param_accuracy) + 0.05, 1))\n",
    "    ax.legend()\n",
    "    ax.grid()\n",
    "\n",
    "fig.tight_layout()\n",
    "# fig.savefig(f'graphs/{exp1_testname}-test-train-error-rate.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "tags": [
     "exp1"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x560 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(math.ceil(len(multi_param_nodes) / 2), 2, figsize=(8, 8*math.ceil(len(multi_param_nodes) / 2)/3))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "for idx, (nodes, ax) in enumerate(zip(multi_param_nodes, axes.flatten())):\n",
    "    ax.set_title(f'Error Rate Std Dev. For {nodes} Nodes')\n",
    "    ax.plot(multi_param_epochs, std_param_accuracy[0, idx, :], 'x', ls='-', lw=1, label='Test', c=(0, 0, 1))\n",
    "    ax.plot(multi_param_epochs, std_param_accuracy[1, idx, :], 'x', ls='-', lw=1, label='Train', c=(1, 0, 0))\n",
    "    ax.set_ylim(0, np.round(np.max(std_param_accuracy) + 0.05, 1))\n",
    "    ax.legend()\n",
    "    ax.grid()\n",
    "\n",
    "fig.tight_layout()\n",
    "# fig.savefig(f'graphs/{exp1_testname}-test-train-error-rate-std.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eUPJuxUtVUc3",
    "tags": [
     "exp2"
    ]
   },
   "source": [
    "# Experiment 2\n",
    "\n",
    "For cancer dataset, choose an appropriate value of node and epochs, based on Exp 1) and use ensemble of individual (base) classifiers with random starting weights and Majority Vote to see if performance improves - repeat the majority vote ensemble at least thirty times with different 50/50 split and average and graph (Each classifier in the ensemble sees the same training patterns). Repeat for a different odd number (prevents tied vote) of individual classifiers between 3 and 25, and comment on the result of individualclassifier accuracy vs ensemble accuracy as number of base classifiers varies. Consider changing the number of nodes/epochs (both less complex and more complex) to see if you obtain better performance, and comment on the result with respect to why the optimal node/epoch combination may be different for an ensemble compared with the base classifier, as in Exp 1). \n",
    "\n",
    "(Hint4: to implement majority vote you need to determine the predicted class labels -probably easier to implement yourself rather than use the ensemble matlab functions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "exp2",
     "exp-func"
    ]
   },
   "outputs": [],
   "source": [
    "num_models=[1, 3, 9, 15, 25]\n",
    "\n",
    "def evaluate_ensemble_vote(hidden_nodes=16, \n",
    "                           epochs=50, \n",
    "                           batch_size=128,\n",
    "                           optimizer=lambda: 'sgd',\n",
    "                           weight_init=lambda: 'glorot_uniform',\n",
    "                           loss=lambda: 'categorical_crossentropy',\n",
    "                           metrics=['accuracy'],\n",
    "                           callbacks=None,\n",
    "                           validation_split=None,\n",
    "                           round_predictions=True,\n",
    "\n",
    "                           nmodels=num_models,\n",
    "                           tboard=True,\n",
    "                           exp='2',\n",
    "\n",
    "                           verbose=0,\n",
    "                           print_params=True,\n",
    "                           return_model=True,\n",
    "\n",
    "                           dtrain=data_train,\n",
    "                           dtest=data_test,\n",
    "                           ltrain=labels_train,\n",
    "                           ltest=labels_test):\n",
    "    for m in nmodels:\n",
    "        if print_params:\n",
    "            print(f\"Models: {m}\")\n",
    "            \n",
    "        response = {\"epochs\": list(),\n",
    "                    \"num_models\": m}\n",
    "            \n",
    "        ###################\n",
    "        ##   GET MODELS\n",
    "        ###################\n",
    "        if isinstance(hidden_nodes, tuple): # for range of hidden nodes, calculate value per model\n",
    "            if m == 1:\n",
    "                models = [get_model(int(np.mean(hidden_nodes)), weight_init=weight_init)]\n",
    "                response[\"nodes\"] = [int(np.mean(hidden_nodes))]\n",
    "                \n",
    "            else:\n",
    "                models = [get_model(int(i), weight_init=weight_init) \n",
    "                          for i in np.linspace(*hidden_nodes, num=m)]\n",
    "                response[\"nodes\"] = [int(i) for i in np.linspace(*hidden_nodes, num=m)]\n",
    "                \n",
    "        else: # not a range of epochs, just set to given value\n",
    "            models = [get_model(hidden_nodes, weight_init=weight_init) for _ in range(m)]\n",
    "            response[\"nodes\"] = hidden_nodes\n",
    "\n",
    "        for model in models:        \n",
    "            model.compile(\n",
    "                optimizer=optimizer(),\n",
    "                loss=loss(),\n",
    "                metrics=metrics\n",
    "                )        \n",
    "        \n",
    "        if tboard:\n",
    "            if callbacks is not None:\n",
    "                cb = [i() for i in callbacks] + [tensorboard_callback(prefix=f'exp{exp}-{m}-')]\n",
    "            else:\n",
    "                cb = [tensorboard_callback(prefix=f'exp{exp}-{m}-')]\n",
    "        \n",
    "        ###################\n",
    "        ##  TRAIN MODELS\n",
    "        ###################\n",
    "        histories = list()\n",
    "        for idx, model in enumerate(models):\n",
    "            if isinstance(epochs, tuple): # for range of epochs, calculate value per model\n",
    "                if m == 1:\n",
    "                    e = np.mean(epochs) # average, not lower bound if single model\n",
    "                else:\n",
    "                    e = np.linspace(*epochs, num=m)[idx]\n",
    "                e = int(e)\n",
    "            else: # not a range of epochs, just set to given value\n",
    "                e = epochs\n",
    "                \n",
    "#             print(m, e) # debug\n",
    "            \n",
    "            history = model.fit(dtrain.to_numpy(), \n",
    "                                ltrain.to_numpy(), \n",
    "                                epochs=e, \n",
    "                                verbose=verbose,\n",
    "\n",
    "                                callbacks=cb,\n",
    "                                validation_split=validation_split)\n",
    "            histories.append(history.history)\n",
    "            response[\"epochs\"].append(e)\n",
    "\n",
    "        ########################\n",
    "        ##  FEEDFORWARD TEST\n",
    "        ########################\n",
    "        # TEST DATA PREDICTIONS\n",
    "        response[\"predictions\"] = [model(dtest.to_numpy()) for model in models]\n",
    "        # TEST LABEL TENSOR\n",
    "        ltest_tensor = tf.constant(ltest.to_numpy())\n",
    "\n",
    "        ########################\n",
    "        ##  ENSEMBLE ACCURACY\n",
    "        ########################\n",
    "        ensem_sum_rounded = sum(tf.math.round(pred) for pred in response[\"predictions\"])\n",
    "        ensem_sum = sum(response[\"predictions\"])\n",
    "        # round predictions to onehot vectors and sum over all ensemble models\n",
    "        # take argmax for ensemble predicted class\n",
    "        \n",
    "        correct = 0 # number of correct ensemble predictions\n",
    "        correct_num_models = 0 # when correctly predicted ensembley, proportion of models correctly classifying\n",
    "        individual_accuracy = 0 # proportion of models correctly classifying\n",
    "        \n",
    "         # pc = predicted class, pcr = rounded predicted class, gt = ground truth\n",
    "        for pc, pcr, gt in zip(ensem_sum, ensem_sum_rounded, ltest_tensor):\n",
    "            gt_argmax = tf.math.argmax(gt)\n",
    "            \n",
    "            if round_predictions:\n",
    "                pred_val = pcr\n",
    "            else:\n",
    "                pred_val = pc\n",
    "            \n",
    "            correct_models = pcr[gt_argmax] / m # use rounded value so will divide nicely\n",
    "            individual_accuracy += correct_models\n",
    "            \n",
    "            if tf.math.argmax(pred_val) == gt_argmax: # ENSEMBLE EVALUATE HERE\n",
    "                correct += 1\n",
    "                correct_num_models += correct_models\n",
    "                \n",
    "#                 print(pc.numpy(), pcr.numpy(), gt.numpy(), (pcr[gt_argmax] / m).numpy(), True) # debug\n",
    "#             else:\n",
    "#                 print(pc.numpy(), pcr.numpy(), gt.numpy(), (pcr[gt_argmax] / m).numpy(), False)\n",
    "                \n",
    "        ########################\n",
    "        ##      RESULTS\n",
    "        ########################\n",
    "        response.update({\n",
    "            \"history\": histories,\n",
    "            \"optimizer\": model.optimizer.get_config(),\n",
    "            \"model_config\": json.loads(model.to_json()),\n",
    "            \"loss\": model.loss,\n",
    "            \"round_predictions\": round_predictions,\n",
    "            \n",
    "            \"accuracy\": correct / len(ltest), # average number of correct ensemble predictions\n",
    "            \"agreement\": correct_num_models / correct, # when correctly predicted ensembley, average proportion of models correctly classifying\n",
    "            \"individual_accuracy\": individual_accuracy / len(ltest) # average proportion of individual models correctly classifying\n",
    "        })\n",
    "\n",
    "        if return_model:\n",
    "            response[\"models\"] = models\n",
    "\n",
    "        yield response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "source": [
    "## Single Iteration\n",
    "Run a single iteration of ensemble model investigations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models: 1\n",
      "13 [50]\n",
      "Models: 3\n",
      "[1, 13, 25] [50, 50, 50]\n",
      "Models: 9\n",
      "[1, 4, 7, 10, 13, 16, 19, 22, 25] [50, 50, 50, 50, 50, 50, 50, 50, 50]\n",
      "Models: 15\n",
      "[1, 2, 4, 6, 7, 9, 11, 13, 14, 16, 18, 19, 21, 23, 25] [50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50]\n",
      "Models: 25\n",
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25] [50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50]\n"
     ]
    }
   ],
   "source": [
    "single_ensem_results = list()\n",
    "# for test in evaluate_ensemble_vote(epochs=(5, 300), optimizer=lambda: tf.keras.optimizers.SGD(learning_rate=0.02)):\n",
    "for test in evaluate_ensemble_vote(hidden_nodes=(1, 25), optimizer=lambda: tf.keras.optimizers.SGD(learning_rate=0.02)):\n",
    "    single_ensem_results.append(test)\n",
    "    print(test[\"nodes\"], test[\"epochs\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "outputs": [
    {
     "data": {
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Ul5dz1lln8fe//50ZM2ZE6vTxxx/zxBNP8NZbb5Gdnc33v/99nn76aU466aQmX/tPfvIT7r77boYMGcJTTz3FFVdcwaOPPtrgNZSUlHDqqadyxhlncPLJe3/BQgWyiEgHd3CfIh676PCYsl27dtG1a1cAlq6v4KZ/f8qJI/vyzJL1/OKbwxjRt7jZdTalsSHrwsJCvvEN77xQ48ePj/QuJ06cyDnnnMP06dOZNm0a4A0Xf/RR/Y+Dbdu2Re6feuqpAJSVlXHQQQcxePBgAA488EBWr15N9+7dOfDAAyPbkc8880zmzJkTE8gvvfQSb7/9dmSZXbt2MX78+CZfV1VVFa+99lqkjsFgMHIccqLXkEoKZBGRDq4oP6dBb7ayMoeioiLmr9jKbS9/xj3fK2fi0FKmje0fs0051fLy8iL3s7KyCAaDADz99NPMnTuX2bNnc/PNN/Puu+8CsGDBgoRnswqvJxAIxKwzEAhE1hl9aJGZNTjUKBQKccEFF3DNNdckXf9QKETv3r0jPzaiTwzS2GtIFW1DFhHZhy1avS0mfCcOLeWOs8axaPW2Zh6ZOqFQiNWrV3Pssccyc+ZMvvzyS4LBIEcffTR//vOfI8t98MEHLVrv8uXLee+99wB45JFH+PrXvx4z/9hjj+WRRx5hy5YtAGzatIn169c3uc7i4mJ69+7Nk0961zsKBoMsWbKk0ddQVFREZWVli+rdGAWyiMg+7MIpBzToCU8cWsqFUw7Yq/WGtyGHbzfffHOjywaDQc4++2xGjRpFeXk5v/3tb8nKyuK2225j7ty5jB49muHDh/PQQw+1qA4jR47kj3/8I8OHDycQCHDWWWfFzD/00EO5+uqrOfbYYxk1ahQnnXQSW7dubWRt9R566CFuu+02Ro8ezaRJk3jppZcafQ1nnnkm//mf/5mSnbrMObdXK2hLkyZNcm+//Tagi2+nktoyNdSOqaF2TM4nn3zCIYcc0uh8ncs6NfamHRO9R2b2jnNuUqLl1UMWERHJAApkERGRDKBAFhERyQAKZBERkQygQBYREckACmQREWmxnj17tmj5a6+9lttvvx2AE088kd27dze67Jw5cxIeRtXUFZ6aM3fuXM4444yYsjfffDNy2FZhYSGHHHIIY8aM4ZJLLkl6vddff32r6pOIztQlIrIve+NWGFAOgyfXl616E9YsgK8lHzyp9MwzzzQ5P3zazHSbPHly5IxcRx11FLfffjsjR45s0Tquv/56rrzyypTURz1kEZF92YByeHSGF8Lg/X10hleeAnPnzuWb3/wm06ZNY9iwYTFXVLrnnns46KCDmDx5Mp988kmkPHy94l/+8pf89a9/jZSfd955PP7448yaNYvLL78cgM8++4wJEyYwatQobr311siy0T1uqO+xV1RUcMwxxzBu3DjGjBkTubxiS0RfISp8damqqiqmTp1KWVkZZWVlPP/881x99dWRE6RcdNFFLX6eeOohi4h0dNUVsGlpTFHWrl3QtStYAKZcAf84G0acBkufgKN/5ZV/+Xbj6+w1AvKbvgBF2HvvvcfSpUvp3r07hx56KJdeeinZ2dlcf/31LFiwgKysLMaOHdvg1JbTp0/nN7/5Deeddx51dXW89NJL3HnnnfzjH/+ILBO+ItTpp5/OL3/5y2br0qVLF5544gmKiorYsGEDxx9/fItOyRl/hajvfve7PP3001RXV9OjRw+ee+45nHNUVlZy/PHHc/fdd6fsutAKZBGRjm7TUvjr8TFFXRMtt/Bv3t9nLm9+nec9D4MSnlCqgcmTJ9O7d2/AO53lqlWr2LJlC8cccwzdunUDEg9Dl5eX88UXX7Bt2zbmz5/P1772NfLz82OWeffddyPnlT777LN56aWXmqyLc44rr7yS119/naysLJYtW0ZtbW1SrwMaXiGqqqqKSZMmccIJJ3DppZdy5ZVX8q1vfYvDDz+8mTW1nAJZRKSj6zXCC9Ao0ZdfZMNieOW/Y3vIfcqaX2eSGrvCU/zVlxKZNm0as2fP5s0332T69OkN5je2juzsbEKhUGS6pqYGgAcffJCdO3fy/vvvk52dTc+ePVsUyPFXiIo+deaiRYt46qmn+MUvfsHZZ5/NxRdfnPR6k6FtyCIiHV1+sdebjboF+0/w7rsQzLsBznwQTrnF+zvvBq887jExtySHqxszceJEXn75ZXbs2EFVVVWklxtv+vTpPPzww7z44ouccMIJDeaXl5fzxBNPAMRcfGLw4MGRoeJ///vfVFVVAd425N69e5Odnc1TTz0VudJTsuKvELV582bWr1/PunXrKCgo4Nxzz+XSSy+NPHf0D5C9pUAWEdmXrVkA02fV72U9eLI3vWZBWp+2X79+XHHFFUyYMIHjjjuO8ePHJ1yuvLyczz77jMMPP7zBcDXALbfcwnXXXcfo0aNjerqnn346q1atoqysjKeffpoePXoA3rD2a6+9FikfNGhQi+odf4WoM844g61bt7J48WImTJjAmDFjuOWWWyI7r5177rmUlZWlZKcuXe1J1JYponZMDbVjcnS1p7ahqz2JiIh0MgpkERGRDKBAFhERyQAKZBERkQygQBYREckACmQREZEMoEAWEZEWy87OZuzYsYwYMYLx48fzl7/8JTLvrrvu4pFHHmnH2rWNVF56EXTqTBGRfdqsJbMo26+M8b3rT8yxcONCFm9ezIyRM1q93m7duvH+++8D8OWXXzJt2jScc1x44YUpOUlGY4LBIFlZWWlbf0uk8tKLoB6yiMg+rWy/Mi5/9XIWblwIeGF8+auXU7ZfM+eyboFBgwZx4403cueddwKxl0a85ZZbOPjggxk9ejQ//vGPAZg9ezYTJ05k7NixnHTSSWzfvh2ATz/9lPLyckaPHs0vfvGLyAUerr32Ws4991wmT57MJZdcwueff87xxx9PeXk5xxxzDCtXrgRotPyoo47isssuY/z48YwaNYr33nuPk046iQMPPDDmEo5//OMfI5d6nDlzJgCvvfZawstLpvrSi6AesohIh1dVW8Xy7ctjynbt2kXX3V0JWIAfjfoRP3/l5xw3+DheWPUCF4+5mIAFeH/T+42u86BuB1GYW5h0HcaNG8eyZcsalP/+979n9erVFBQUsGPHDgCOPPJITjvtNMyMW2+9lTvuuIOrr76aSy+9lF//+tdMmzaNX//61zHr+eyzz5g7dy65ubmRyx4OGTKEl19+mSuuuIJHH32Un/zkJwnLAQoLC1m4cCHXXXcd3/nOd3j33XcBGD58OBdffDEvvPACa9asYf78+YRCIb75zW8ydepUIPHlJa+77rqUXnoRFMgiIh3e8u3L+f6z3292uUc/9cLpuneua3bZ+0+4n7G9xiZdh8ZOwzxx4kTOOeccpk+fzrRp0wBviHv69Ols3LiR3bt3c9hhhwGwcOFCTjvtNAC+853v8Nxzz0XWc9ppp5Gbm0tVVRWvvfZaZF3OOQoKChotDwtf/rGsrIzy8vLIZSGLiorYtm0bL7zwAk8//TSvvfYa4J0y89NPPyU/Pz/h5SVbeo7sZCiQRUQ6uIO6HcT9J9wfUxZ9+cVlW5dx+6LbY3rIB5ce3Ow6W2LRokUJz6399NNPM3fuXGbPns3NN9/Mu+++yyWXXMLVV1/Ncccdx1NPPcWsWbOaXX/4tYRCIXr37t2gZxq+ylNjPdbwJSIDgUDM5SIDgQDBYJBQKMQ111zDueeeG/O4Z555ptHLS6aaAllEpIMrzC1s0JsNXxRh4caF3P3h3fzp6D8xvvd4Ttr/JC5/9XJmHjkzZkevvbF69Wouv/zyBtcHDoVCrF69mmOPPZavf/3rDBo0iGAwSEVFBf3798c5x/331/+QGDduHE8++SSnnnpqZKg5XnFxMb179+bJJ5/klFNOIRgM8vHHHzNy5MhGy5Nx3HHHcd111zF9+nS6du3KypUr6d69e5OPCYdzqnYy005dIiL7sMWbF8eE7/je45l55EwWb168V+sN79A0YsQIpk2bxkUXXcQPf/jDmGWCwSBnn302o0aNory8nN/+9rdkZWVxzTXXcMoppzBhwgQGDhwYWf7mm2/m97//PaNHj2br1q0UFye+JvNDDz3EbbfdxujRoykrK+Oll15qsjwZU6dO5Vvf+haTJk1i5MiRnHPOOVRXVzf5mFReehF0+UVBbZkqasfUUDsmZ1+8/OKuXbvo0qULZsYNN9zAxo0bI3s7t5e2vPyihqxFRCQjzJ8/n0svvZRgMMiAAQNihrM7AwWyiIhkhKOOOiqlhxF1NGndhmxmJ5vZMjNbbmbnJ5j/XTNbbGZLzOwfZpaXaD0iItJQR9rk2Nm05r1JWyCbWTZwE3AMMBa4wsx6RM034EbgKOdceDe409NVHxGRfUl+fj5bt25VKGcg5xxbt24lPz+/RY9L55D1ROAj59xaADN7FjgOeDhqGQO6mtl2oABYn8b6iIjsMwYMGMCaNWvYvHlzwvm7d++mS5cubVyrfU9r2zE/P58BAwa06DHpDOR+wNqo6bVA//CEc86Z2cXAEqAaeMk5Nzd+Jf5Q9/kA/fr1Y968eYB3EHj4vuwdtWVqqB1TQ+2YGhUVFY0eNiTJ25t2/PLLL1u0fLvt1GVmOcCFQBleWP/dzM5xzv09ejnn3L3AveAd9hQ+HEKHRqSO2jI11I6poXZMDbVjarRlO6Zzp651RPWI/fvroqbHAHXOuS+dc0HgX8DkNNZHREQkY6UzkOcDI82sv5kVAicAz0fNXwuMMrPwucmOBRpeKkRERKQTSFsgO+fqgMuAV4BFwI3OuS1m9oyZ9XPOrQP+B3jTzBYDJcDd6aqPiIhIJkvrNmTn3BxgTlzZiVH37wDuSGcdREREOgJdXEJERCQDKJBFREQygAJZREQkAyiQRUREMoACWUREJAMokEVERDKAAllERCQDKJBFREQygAJZREQkAyiQRUREMoACWUREJAMokEVERDKAAllERCQDKJBFREQygAJZREQkAyiQRUREMoACWUREJAMokEVERDKAAllERCQDKJBFREQygAJZREQkAyiQRUREMoACWUREJAMokEVERDKAAllERCQDKJBFREQygAJZREQkAyiQRUREMoACWUREJAMokEVERDKAAllERCQDKJBFREQygAJZREQkAyiQRUREMoACWUREJAMokEVERDKAAllERCQDKJBFREQygAJZREQkAyiQRUREMoACWUREJAMokEVERDKAAllERCQDKJBFREQyQFoD2cxONrNlZrbczM5PML+HmT1hZp+Y2VIzOyCd9REREclU2elasZllAzcBRwM7gIVm9rhzbkvUYn8CHnHOPWRmXQFLV31EREQyWTp7yBOBj5xza51zVcCzwHHhmWZWApQ75x4CcM7tcs7tTGN9REREMlbaeshAP2Bt1PRaoH/U9FDgKzN7EBgBzAWucM7VRa/EH+o+H6Bfv37MmzcPgIqKish92Ttqy9RQO6aG2jE11I6p0ZbtmM5ATua5JwIXAx8C9wM/AP4SvZBz7l7gXoBJkya5KVOmADBv3jzC92XvqC1TQ+2YGmrH1FA7pkZbtmM6h6zXEdsj7u+Xha0FVjjnFjnnQsATwJg01kdERCRjpTOQ5wMjzay/mRUCJwDPh2c659YDm8xsqF90FPBxGusjIiKSsZoNZDPrZmbXm9nD/vTBZja9ucf524IvA14BFgE3Oue2mNkzZtbPX+z/Af9nZouBYuKGq0VERDqLZLYh3wfMBqb60yuAR/1bk5xzc4A5cWUnRt1fAIxLsq4iIiL7rGSGrPs55/4GBAGcc7VAKK21EhER6WSSCeSdZtYNcABmNg6oTGelREREOptkhqwvxxuyHmpmLwEDgTPTWSkREZHOptlAds4tMLNvAAfj9ag/cc7tSXvNREREOpFk9rKe75yrc8595Jxb7JzbY2bz26JyIiIinUWjPWQz6wn0BgrNbDj1F34o9m8iIiKSIk0NWZ8EzMDbZnxnVHkl8Ks01klERKTTaTSQnXP3AfeZ2TTn3Oy2q5KIiEjnk8xe1k+a2XfxrsiUFy50zl2ZtlqJiIh0Mskch3wfUA58D1gFTAYK0lkpERGRziaZQD7UOXcZUOGcuwM4Bhib3mqJiIh0LskEcviY4x1mNgzoirf3tYiIiKRIMtuQ/9c/deZvgGfxAvl36ayUiIhIZ5PMmbru9u/OBQ4AMLMBaayTiIhIp9PkkLWZHW5m082sjz89ysweBN5qk9qJiIh0Eo0GspndAMwCTgeeMbM/Ai8BC4FhbVI7ERGRTqK5M3WNcc7tNrNSYDVQ5pz7om2qJiIi0nk0NWRd7ZzbDeCc2wp8qjAWERFJj6Z6yEPN7J/+fQOGRE3jnPt2WmsmIiLSiTQVyNPipm9PYz1EREQ6taYuLvFqW1ZERESkM0vmTF0iIiKSZgpkERGRDNDciUECZnZbW1VGRESks2oykJ1zIWCcmWW1UX1EREQ6pWQuLrEcmGtmTwC7woXOuTvTVisREZFOJplAXuHfCvybiIiIpFgyV3v6HYCZFfnTlemulIiISGfT7F7W/hWeFgHvAgvM7D0zG5X2momIiHQiyQxZ3wP8yDn3DoCZTfTLJqWzYiIiIp1JMschdwmHMYBzbj7QJX1VEhER6XyS6SF/YGa3A3/3p88CPkxflURERDqfZHrIF+LtZf1L/7YKuCCdlRIREelsmuwhm5kB7znnRgA3tk2VREREOp/mztTl8IasD22j+oiIiHRKyWxDPhR438w+xTtTl+Fl9cS01kxERKQTSWbIejpQ3TbVERER6ZyaDGTnnDOzfzrnRrdVhURERDqjZPaynmdmU9NeExERkU4smW3I3wV+ama7iN2G3CutNRMREelEkrm4RM+2qIiIiEhn1uiQtZmdE3X/yLh5P09npURERDqbprYh/yLq/s1x885NQ11EREQ6raYC2Rq5n2g68QrMTjazZWa23MzOb2SZgJm9Y2aPJbNOERGRfVFT25BdI/cTTTdgZtnATcDRwA5goZk97pzbErfoD4GVQFaztRUREdlHNdVDHmVmm8xsM1Dm3w9Pj0xi3ROBj5xza51zVcCzwHHRC5hZKXAm3vWVRUREOq1Ge8jOuWQOiWpKP2Bt1PRaoH/cMtcB/7mXzyMiItLh7W3otpqZjQW6O+fmmtlRTSx3PnA+QL9+/Zg3bx4AFRUVkfuyd9SWqaF2TA21Y2qoHVOjLdsxnYG8jtgecX9gftT0JOAIM1sJ5ANFZnaPc+7C6JU45+4F7gWYNGmSmzJlCgDz5s0jfF/2jtoyNdSOqaF2TA21Y2q0ZTsmc+rM1poPjDSz/mZWCJwAPB+e6Zz7s3Ouv3NuCN525Gfjw1hERKSzSFsgO+fqgMuAV4BFwI3OuS1m9oyZ9UvX84qIiHREad2G7JybA8yJKzsxwXJzgbnprIuIiEgmS+eQtYiIiCRJgSwiIpIBFMgiIiIZQIEsIiKSARTIIiIiGUCBLCIikgEUyCIiIhlAgSwiIpIBFMgiIiIZQIEsIiKSARTIIiIiGUCBLCIikgEUyCIiIhlAgSwiIpIBFMgiIiIZQIEsIiKSARTIIiIiGUCBLCIikgEUyCIiIhlAgSwiIpIBFMgiIiIZQIEsIiKSARTIIiIiGUCBLCIikgEUyCIiIhlAgSwiIpIBFMgiIiIZQIEsIiKSARTIIiIiGUCBLCIikgEUyCIiIhlAgSwiIpIBFMgiIiIZQIEsIiKSARTIIiIiGUCBLCIikgEUyCIiIhlAgSwiIpIBFMjt4Y1bYdWbsWWr3vTKRUSkU1Igt4cB5fDojPpQXvWmNz2gvD1rJSIi7Si7vSvQKfUvh7Hfg/tPhdIDYMdqOOJy6DYYnAOz9q6hiIi0MQVyW9q9HRbOgnfuhsp1UNgbNn8CFoCXfufdupRCnzLoOwr6jPLu9zgIsvRWiYjsy/Qt3xa2fwlv3wXv3Qe1VTDsBPj6pfDajXDsb+Gdu+CY33rBvOFD2LAYFt4HNRXe47PzodeI2KDuNQLyCtv1ZYmISOookNNp7Xvw1u3w0WwIZMOY78Kkn8Kur7xtxtNnweDJMOjw+umxZ3uPdQ62r4L1fkBvWAyfveiFOgAGPQ70Qjo6qAt7tccrFRGRvZTWQDazk4Eb8XYe+6Nz7t6oeV2B/wOGAkHgLufcbemsT5sIhWD5C/DmbbDqdejaA6ZcARPOh8L9vGXeeK4+jMH7O30WrFlQX2YG3Yd4txGn1q9/5xbYuDg2qJfOBhfy5hf2rh/q7lMGfUdD96EQ0P57IiKZLG2BbGbZwE3A0cAOYKGZPe6c2xK12P845141s0JggZk965z7LF11Sqs91fDhP+CtO+CrT72dtU6+GUZ/F3K6xC77tUsaPn7w5PowbkpBD9j/KO8Wee7dsGmpF87hoH7nLtizy5ufUwB9RsYGda8RkJPf2lcrIiIpls4e8kTgI+fcWgAzexY4DngYwDm3C3jVv19lZsuAvkDHCuSdW+Dde+Hdv8DOzTBoMpz5Oxg2te16pTldoP947xYWCsLWL+q3Sa//0OtJv/sXb75lwX4HQ58y+u8qhC+cF9RdS9umziIiEsOcc+lZsdkZwFHOuYv96SsA55ybmWDZgcA8YKRzbmfcvPOB8wH69et32MMPPwxARUUFxcXFaal7MrrsWkv/1U/Qe/1LBEJ1bO41mbWDvkVl8bB2q1Mycmq2UVj1OYWVKyio+oLCyi/ountdZH513n5UFe3PzsKhVBXtT1Xh/tTk99KhWElo78/kvkLtmBpqx9RIdTseeeSR7zjnJiWa1+47dZlZHvAIcEV8GAP4253vBZg0aZKbMmUKAPPmzSN8v804B1++7e2o9cnTkNMVJpwHk35Mr+5D6Ki7U73x8nN87cDusOFD8jd8SP6GxfRc/TgEa7wF8ktih7v7jPJ611k57VvxDNMun8l9kNoxNdSOqdGW7ZjOQF4H9I+a7g/Mj17AzAy4H3jGOfdYGuuyd4J18MmT8ObtsHYBFPbxDlcq/wF06d7etdtrweyuMOgw7xYp3ANfLfd3HPvQuy16CKq3e/OzcmG/Q2KPl+49EvL1i1xEpDXSGcjzgZFm1h9vp64TgP+MW+YPwC7n3H+lsR6tV1MFix70dtTavsrbEWran2HkGZCd2+rV3jPvc8YM7M7EofXba+ev2Mqi1du4cMoBqaj53svKgd4jvNvo73hlzsGONfV7d2/4EFbMg/f/Xv+47kMbntikqK+GvEVEmpG2QHbO1ZnZZcAreIc9Xe+c22Jmz+BtEw4AvwSWmtki/2G/dM49n646Ja1yg3c2rQV/9XqE+x/t7TF9wDEpCZYxA7vz04fe446zxjFxaCnzV2yNTGc0M+g20LsdcmJ9+e5tsGFJbFAvewZCdd78rj1jD8PqU+YdQx3Iap/XIZnnjVu9c7lHH2mw6k3vUMBERyWI7IPSug3ZOTcHmBNXFvVNTmZ1mzYu9bYPf/hPwEHZdDj8p16AJMk5R2VNHZsra/iqsobNVdF/a72/VTWEQo5v3/0WpQU57KwJ8p0JA3HOUVVTR2Feu2/ab5ku3WHoEd4trK7GOy1o9PHSC/4GtZXe/Owu0PvQ2KDuNQJyu7bPa5D21X88/PNcOHEmuTUh+OxlmH2Rd3y+SCfRwb75997bf7+G4gMPZ8SkqV6Bc6x6eiYlHz1At92rIK8EDv8JTPwRlPT3F3HsrA0mCFjv7+Zw0PrTtXWhBs9bkJvFfkV57FeUR/9uXRgzsBvLN1by1hdb6d41h/vfWsX9b63CDIb2LKCsfwll/UsY2b+EQ/sVU5TfwXagys7zQrbv6PqyUAi2r4w9XvrT52Hh37z5FvDPPhZ3YpOCnu3yEqQF6mqgegdUV0DNjvr71Tu8U8DG3E+wXE2Fd3KbR7/PJIA38M5u989zoUs3b8fC/G7+/W6Jy/JL6u/nFWkziXQ4nS6Qiw88nF7P/YiPQ3dwQF4FtS//gcE7v2RnTg8WHHIFb5ecxPqqbDY/sZ6vqlZGera79wQbrCs/J+CFbGEevYvyGNmvmJ6FXuiG/+5XmEfPoly65sY2dXiY+orjD2bWmyuZ9YMJ5OdksWTtDhb7tzkfrCN8VNrQngWM7F9CWf9iRvpBXdzRQjoQgNL9vduI0+rLqzZ7Zx+LDuqP/lV/9rGivvV7d4eDWmcfS51QsD40owMzJky3x4Vp3HLhPfIbk9PVC8y8Ym/Hv/wS7zSv+SX103nFsOoN+OhxOGgq9B/rXZCleof3/Lu3Q8Vab3r3dtjT4KCMepblr7uk6RBvLNi1OUXaQacL5BGTpvJJ8FaGPX8OWeYwl8XNdadze/W3CC7KIjd7gx+iXpge3KcoZjr6b0FuFtaKX+HR24wnDi1lwpDSyPT5R+wfWa6qpo6P/HAOB/VTH9aH9JAeXf2Q9m6H9i+hpEsHC2nwTilaeIy3jT6sdpd/9rGoE5u8dQfU7fbm5xb5Zx+LCupew72eeWfinHemtvjeZyRAE/VM48I0vBmhMYHsqDD1A7SoL+w3PDZMY+77y+X5f5M5RG7VmzDvBlbs/32Grn8evv7zps9eV1frv46owK7e7u3TEF9WvQO2fO6X7fB6503JK/ZDuqRh7zsS3N0SB/te7PApnVunC2SAQ752Gh++Np5R1Qt4dr8fcPiJv+JUv1dbnJ/dqpBtiUWrt0XCGGDi0FLuOGsci1Zvi9nzujAvm8P278Fh+/eIlFXV1PHx+goWr6kP6WcWryfkh/TguJAe2a+Ekq4dMKRzu3o7+Qwory8LBb0v1fBhWBsWw5J/wfx7vPmBbO9QrOjjpfuMzOxD04J7Eg/fNuil7kgQuv50eOe5xuQVx4ZkfgmUDGwiQON6rTld0j/8u+rNyAVWVq+qY+iR3429AEsi2bn+j7n9Wv584VGBSIhvTxDsUWUV67wfiOHeuWs4YhaR07WREE+id94WbS0Zq1MG8tK3n6Nf9We8NfSnTF7xIJs2fZMD9p/aZs+f6NCmiUNLY8K4MYV52UwY4vWqw3bV1rF0XUVkqHvJ2h08GxXSg0q7+j3o4khQd+vaAX/FB7Jgv2HerewMr8w5b6/4DYthwwfe39XvwAcP1z+uZJB/GFZUUJcM8C4Asjd79oZC3uU0/ZAs3v4RfLo7qtfWTM+0pqL+fOONyc5vGKbdh0YFZlSAxi+XV+xtS+0Iw69rFtSH76p5iS+4kkqBLO+HWmt+rDnnve/NhXikbKt3GttwWVPD+1m5TW8bbyrY84oV5h1cpwvkpW8/R6/nfsSmqXdz+KSpLH17Cr2e+xFLubt+R68OpmtuNuVDSimPC+lwT3rx2govpJfUh/SA7l0iO42xtY6ynbV0L+iAIW0GxX2927Dj6surK2DjR7FBvfwFCNZ68/O7QbdBMPcPMOkncNBxsOZdmHc9jJvhHYbT2JBvtT/kWVNZv50bGAPwXnTdAnEh2Q0Kenk7rjXomcYtFx4C7ixD8HtzwZW2Zub90MkrAga2/PHhTQwt6Z2Hh91rq5qoV6A+rPNLKKsGNgxJIti7a7t5huh0gVzx2VswtT58R0yaylLu9so7aCAn0jU3m/GDSxk/uD6kd9cGWbreC+fwcPcLSzcSDDluePff9O/mhXTZgJLIsHdpRwxp8MJs8OHeLSy4BzYviz1e2gxem+ndwt78k/c3p6Bhj7Oob6MBuuiTLxgz6cj6ebkF6rFIQzldvFtRn5Y/NrgnKsx3QPW2xoN9w0rYtgLW+fOrK4Amrl0Q+Ux3S36IPVzWWX44plmnC+RJ5/yuQdmISVP3qTBuTJfcLMYP7s74wfXDdNV7gjz87Dyy9xvqD3lX8OLHG6nzu9L9u3VhpD/UHQ7pHoUd9J8vK8ffEWwk8F2vzDn49zVeCE+80Osth8M3q2X/HhUb5nk7lomkS1aOdxhgEocCLo4/B3MoVL+vQlO98/D8yvWw6eP6sqb2Vcju0kRPPFFZVLDndNUPV1+nC2SJlZ+TxQHdsphy+JBIWfWeIJ9sqPS2R6/xetIvfbwpEtJ9S/JjdxzrX8J+RR00pL98y7uO9bG/9c7Odui3oHRoe9eq0+kQp5Pt6AKB+u3mLd107hzU7kxuiH33dm9P920r6+fXVTdRr5zmjytvrCy3KD2HP7bTmeMUyNJAfk4WYwZ2Y8zAbpGy6j1BloVD2h/unrtsE3uCXkj3KY4K6QHesdK9ivLb6RUkKWrPXgZPhkGHN79nr6RF9OlkgY5zOtnOwgzyCr1byYCWP35PdWzvu9ne+Sf1Zc1tN88rTv6EMdGHrOWXND4KNqC8/rsAYr8r0kiBLEnJz8li9MBujI4K6Zq66JD2tk2/+ml9SPcuzosZ6i7rX0Kv4gwK6eg9eyH9e/YK4O1wuKWqli07a9m6s4avqmrZurOWw4aW8r3/fYfeXWBz9XyOO7Q3767cyicbKijMy/Zu+dkU5+dE7hfmZZOXHUj7oYqyl3LyIadPK7eb1yUI7u2NB/u2lbHb2Zvabp5b1HiIH3QcPPRtDikZDe98Ct++L+3fCwpkabW87CxGDejGqAHdImW1dSE+3VgZcwjWna98Tm3Q2xu5V1FcSA8ooXd7hXRH2rM3g1XvCbJ1Zy1bqmr5amcNW6tq2bKzhi1+mTevfjrRWe/MoHvXXArysvmyspbi/Gxe+WQTT36wLnJkQGNysiwqoHMoysumKL8+sAvzsymKBLoX5kX5/jKR+Tnk5yjYM1JWNhT08G4tFQr5R0hsT653XrnBOwd/uCxUR69Nr8OUK9vke0GBLCmVmx2InNrT320qEtJLokL6z69+Hjnn937+aUcjQT2ghD7F+fpybCe1dSEvRHfWRAL1Kz9QE4VtVU3inX1KuuTQozCXHgW59C3pwqH9S+hZkEuPwjxKC3L9eXn0KMyle9dcFq7axk8feo/pw3KZu96446xxTBjSnV21Qapq6qisrqOqpo6q6jqqavZEpuP/VlXvoaJ6D2u37/am/XnBZpI9K2CRnng4rL1gz4kpiwn5/GyK4uZ3beUZ/CQNAgGv19ulW8sfu/INeHQGX/Y4gkHv3QcHHK0esnR80SF9pl+2Jxgb0ovXVnDXvC8iId2zMDfSiw7/7VuikG6NumCIrbtqY8I13KPdEtV7Dc+rrE4csEX52fTwA7VnYR6H9CnygtUP1fq/uXQvyCUnK/mdbaK3GVevXsL0o0fGnF62IC+b3sWtbwPnHNV7QlTW7PED3Qv2ykjA11FZvSdmOjx/Y0VNVLDviWySaUzAoCCvPrC9sM6J66lHB78/Lz/2MQW52QQC+ry3i1VvwmM/gG/fx8pVdQwafH6b7F+iQJZ2kZMV4NB+JRzar4TvTPDK9gRDLN9Y5R0nvc4L6nvmfUGNH9I9CuJCekAJ/TphSAdDju27vDBNFK71017Qbt+1J+F6CnKzKPWDtGdhLgfsV0CPwjw/dHMpLYi+n0tedvpOHBF9Otl5qxs/nWxrmRldcrPokptFr6K9W1f1nmBUTz2qZ+6HfWW4LGb+HrZsqYkpq0lwVbh4hQkCvL6nnhPTU6/fxu7N+2p3iB2791CYl02Wgr1l2vrMcT4FsmSMnKwAI/oVM6JfMd/2z4BUFwzx2eaqmHN33/v6F1Tv8b7MSiMhXczIfl5QD+jepUOFdCjk2LF7T0ygfpUoXP0e7tZdtZELjETLzwnE9FIHlnanZ2FUuPrl4dDNz8mcMzPtzelk21p+Thb5OVn03Mvj8WvqguysCfohHtVzjxmG39OgJ79m2+6YskTb5CNeeQGArrlZMUPtRXE7xkVvT68P/pyYIfyCvOwWjXp0aO20f4kCWTJadlaAQ/oUc0ifYqaX14f055t3xhyC9dfXV0a+mLp3zYkMkYf37k4U0uk69tU5R0V1XX24xu/YFB28VbVs21WbcPtmblbAC1E/UMNnTutR2DBcexQ2vMSnZLa87CzysrP2+mx4e4Ihdsb01L2gfnfRYgYMPSiyvT12G3wd6yuqqdq0J1K2s7aJYPfl5wS8HecSBHl9Tz12eD68g12RX16Yl01udicJ9hbSf7B0ONlZAQ7uU8TBfYo4Y7x3TGQw5Pjc70mHg3rWG/Uh3a1rTqQHHQ7p0QO6xWynbOzYV+ccO2uDbKmqaRiuVd6hO1t21rJq405qXn+RrTtrE25nzA5YJFx7FuZycJ8iJkf1XEvjArYwL/1XHpOOLycrQLeuuQ0uGJO1KYcphw1Kej3BkIsJ9Ogd56KH2sND8OHpzVU1rPhqZ/1ytXUJR3Ci5WYH4raxZ8cEfYOeu9+rj5+frkPe2utENQpk2SdkBYxhvYsY1ruI/4gK6S82V3nbo9d4x0k/8NbKSE+gpEsOA0u7MONv8znioJ68/tlXHH1wL/4x/0vueOUztviH8Hy1szays1m0gBGznbVfQYBDhvb1wjVuJ6ceBXkUd1HASubKChglXXL2+prqoZBjZ23iHeeipyur/VD3p7ftqmX11l31Q/U1dS0/5C3BNvX4Q96KGxwS1/CQt/Y6UY0CWfZZWQHjoN5FHNS7iG+N9cqCIceKr3ZG7d29gz3BEM9/tBGANz/fEumtDi4tYOzA7v6wsR+8UYfrlHTJidkLdt68eUyZcmh7vFSRjBEImN+bzYGS1q/HOdfsIW/hoK9I8SFvRfnZDOiWz/f+9x36F0BFcCF3nj0+7fs0KJClU8kKGAf2KuTAXoVMG9s/8sv3O+UDeWTB6sjwtYi0LzOjIC87JYe81dSFqKhu/JC3+uH4PTHzS7rk8MWOGn5+7OA2+V5QIEunFT0MNXFoKVOG7RczLSIdn5lF9oxvySFv4e+H6cNyeWj+l3ztwJ5p/17Qrm7SaUUf+wqxx76KSOcV/WP9lAPzuOOscfz0ofeYv2JrWp9XPWTptDrSsa8i0nbSfaKaxqiHLCLtbtaSWSzcuDCmbOHGhcxaMqt9KiSd2oVTDmgQvBOHlqb92twKZOm0FAKZo2y/Mi5/9fLI+7Fw40Iuf/VyyvYra+eaibQdDVm3g1lLZlG2Xxnje4+PlC3cuJDFmxczY+SM9qtYJxMOgZlHzmR87/GREJh55Mw2rYdzjjpXR8iFCIaCBF2QYCgYU9Zgvr9M+H5dKMn5cfcTLR8zv5FlYu43MT/kQtS5uubX4YLU1tUy47kZZJNNcEWQ0vxSrn3zWvKy8sjLzvP++rfcrFzys/Jj/uZl5ZGfXX8//pablRuZH/OYrHyyAzpGXNqfArkdpDsInHM4HCEXwjlHiFDkfrg8el5FsILNuzY3+piQC+FwkfshQg3uhx8TKfcfE/PXxU43qFOCxzf2mMjzJiiPX29Tr6e8Vzk/fvHHDOs+jGVblzGp7ySe/PxJHl/+eKPBkuh+KBRiR+UObplzC6FQbPA1Fqzh+yHX/EUG0i1gAbIsi+xAduR+lmWRFchK/r7/NyeQQ34gP2Z+wAJkW3aj6wgEvPkfbP6A+RvmM7bXWIZ1H0ZNsIaaYA21wVqqg9XUBGuoqK2gpq4mMi96fl0o8ZWqmmNYfZgH6n8ANBb8Tf1AiMwPNFwu/jF5WXlkBTLnnOLSvhTI7WBcr3F8b/j3OP+F8ynMKaSitoIe+T24+vWrmw+wJEOqxb5M/etsL4YRMO/MOwEC9fctQIAAGJH7ZoZhfLD5A4pyili+fTkrKlY0CKhwmETfz8nOIdv8AAtkkVedR5/ufRoNtuhlo+8nCrakl41+rubmx9craplM6B0u3LiQxz97nFO7n8qblW/y83E/jxlFSkYwFIwEdHRgx9zqaqgJ+SFeVx0J8waPqYsN+4raitjl/PWEl2vV/x2QHciODfPoW3aCsE90S/AD4tPdn1K8qTjhD4i8rDxyAjkZ8b5LPQVyG1u2dRkzF8zk7fVv0yO/B1uqt3Boj0MZ3mN4JCAC5odIOFiiAyZqOlwWMx13v7HHhIMKYPmnyxl+yPCEARZZRxLB1qJ6R9Ut0eOTqXei8pZ+wYRHJy4YdQEPfvwg1339uhaHQNi8efOYcsSUVj22s4seJdq5bCenH3x6zChSsrICWXQNdKVrTtc01rYh5xx1obpILz465MOhn+jHQXh+g8fE/UCorK1s8AMhqVGBZxufZVhMbz56SL8lQ/7J/kCIfkymjwq012ZFBXIb2bxrM7e9fxuzP5tNzy49OW/kecz5fA4/H/dzHvz4Qa6YcEWrg2BvzVs/jykHdL4giQ6B8b3HM7bX2FaFgOy9xZsXR9p93rJ5jO89nplHzmTx5sUd4r0wM3KycsjJyqGIvbzgcgsFQ0FqQ7UNAvvtBW8zYtSIVv1ACM+vrK3kq+BXjS63N6MCTQ7lJznk39QPiEQjA8mOCkRvVgTabP8SBXKa7dqzi/uW3sfflvwN5xw/Gv0jxuw3hl+/8WsFQTuLDgGgw4XAviRRr2N87/F6H5KQFciiS6ALXbK7xJSvz1uf1vYLjwrUBGsS9+wb6dGH5ze5ucD/MdDYD4g9oT2tqnN4VKBBiCfo0Q/rNoyL/n0RfbP7UrG+ghuPujHtn0cFcpqEXIgnP3+SW9+/lc27NnPqAafys7E/o3dBb2YtmaUgyAAKAZHWix4VKKSwTZ87PCrQ2H4ArfmBEJ5fWVsZCf68rDxW1KzgolEXtcn3ggI5Deavn8/MBTP5eOvHTOwzkduPuZ3hPYZH5isIRERaL3pUoCRvLy4p1YTwMPWp3U/lseWPManfJPWQO5IVO1Zw08KbmLt6LkOKh3Dr0bdy1MCjtCejiEgHkqqdDFtKgZwC26u38+cP/sw/l/2TwtxCrpp4FdMPnk5OYO8u9C0iIm2vvXYyVCDvhdpgLQ9/8jB3f3g31XXVnDPiHC4YdQHFuXtx8U4REWlX7bVZUYHcCs45/r3q39y88GbWVK3huMHHcen4SxlYNLC9qyYiIh2UArmFPtz8ITMXzOT9Te8zquco/vuI/2Zsr7HtXS0REengFMhJWle1jlveu4VnVzxLv4J+XD/leqYOmaodtkREJCUUyM2oqq3i3sX38sDSB8jJyuHScZdyzohzyMvKa++qiYjIPkSB3Ii6UB3/Wv4v7lh0BztqdnDGsDP48egf06NLj/aumoiI7IMUyHGcc7y29jVuWnATn+/4nCP6H8Fl5ZdxQLcD2rtqIiKyD1MgR1m2dRk3LriRt9a/xUHdD+Lub97N5H6T27taIiLSCaQ1kM3sZOBGIAD80Tl3b9z8icDfgDzgfufc79NZH0h8Wa2Xv3yZexffy0dbPqI0v5TfTf4dpx1wWsZfIkxERPYdaQtkM8sGbgKOBnYAC83scefclqjF7gC+C3wEvOHPX5yuOkHsZbVG9BjBH975A49/9ji5gVwuKLuA80ae1+bXUhUREUlnD3ki8JFzbi2AmT0LHAc87E/3A7Kdcx/60/8ATgbSGsjje4/nhik3cPFLF+Nw7Nyzk6/1+xrXTr6WPgV90vnUIiIijUpnIPcD1kZNrwX6NzP/yPiVmNn5wPn+ZIWZfezf3w/Y3NrK5ZTm9Mvulj2wbnvd6iVbl6y7m7tbu6p9wV61pUSoHVND7ZgaasfUSHU7DmlsRsbv1OVvd743vtzM3nbOTWqHKu1z1JapoXZMDbVjaqgdU6Mt2zGQxnWvI7ZH3N8vS3a+iIhIp5HOQJ4PjDSz/mZWCJwAPB+e6ZxbBwTNbJSZZQFnAk+2YP0Nes3SamrL1FA7pobaMTXUjqnRZu1ozrn0rdzsVGAmXvBf75y7x8yeAc53zq0zs0nA/wL5wAPOuWvTVhkREZEMltZAFhERkeSkc8haREREkqRAFhERyQAdMpDN7GQzW2Zmy/3jlKUVzGylmX1oZovM7JX2rk9HYmaPm9k2M3ssqmyimX1kZp+Z2W/bs34dRSPtONfMPvE/l4vMrEt71rEjMLOBfrst9f+np/vlB5jZAv8zeZfpAu5NaqIdZ5nZF1GfybRcbajDbUP2T8m5lKhTcgKT407JKUkws5XASOdcVXvXpaMxs6OAIuBc59wZftm7wA/xTwULXJDuU8F2dI2041zgYufckvarWcdiZn2B3s65RWbWB+97cRhwHzDLOfeU/6NnlnPuqfasayZroh3vAB5Ld9t1xB5y5JScfpCET8kp0macc3OByvB09KlgnXNBIHwqWGlCfDtK6zjn1jvnFvn3NwBfAaXAZOBpf7G/A6e0SwU7iCbasU10xEBu7pSckjwHvGpm75rZ2e1dmQ5On8vUesjM3jezX7R3RToaMxsPZAG7ga2ufhhUn8kWCLejc261XzTTzD4wsz/4585IuYw/daak1dedc2v9YZoXzWxx+GIfIu3obP9zWQLMMbNlzrmnm32UYGalwP3ABe1dl44sQTteBWzAu1TwfcBFeMPYKdURe8g65WaKhK/E5ZxbDzwDjGvfGnVo+lymSNTncgfwT2BC+9aoYzCzPGA28D/OuTeBLUBp1I5c+kwmIUE7hoeynXOuGi+o0/KZ7IiB3OQpOSU5ZlZgZkX+/ULgGLydkaQVUnAqWMHbadPMevr3c/H+v/W5bIYfurOAl51zDwD4Q9VvAyf5i52NPpNNStSOfnlf/28AOJU0fSY73F7WkPiUnO1cpQ7HzPYHHvcns4C/OOf+1I5V6lDM7EVgNFAAbAWm422T16lgWyBBO34HuBXIwftcPglc5TriF1UbMrOvA/OA6E1O3wOq8XYw7Aa8BFzknAu1eQU7iCba8U9AT7zMeRv4qXOuJuXPr8+5iIhI++uIQ9YiIiL7HAWyiIhIBlAgi4iIZAAFsoiISAZQIIuIiGQABbJIGpmZM7P/ipqeaWYzUrTur1Kxnmae4wgzW2Jm78SVD/Ff25VRZYf5ZTNasP4ZZjazmWXmmtnIFldepINRIIukVxVwdvgkLJmiBefiPRu41jl3WIJ5y/FOkhD2bUBXtxJpJQWySHrVAA8CP4mfEd3zM7OR/mUHMbNrzeyvZva6ma0ws6lm9mf/Gq1/j1vHHf41mJ82s2K/7AAze96/Du7LZjYk6vluMbMFeCc7iF7P/v78D81sjpmVmtkP8EL2ejO7K8FrqwC+8h9rwFHAy1HrHGdm881ssZndb2b5fvnJZvapX48jopZPWO+o+Vlm9ne/HRb79RPZZyiQRdLvT8CF4UBK0mDgSOAc4DHgb8ChwP5mNtZfpgfeKf4OxTuzUPjKSHcCP3LOlQP/BdwQtd49zrly59ysuOe7FbjTOTcK71rO1zrn/gbMAS5xzl3USD0fxQvtSXjXjq2Lmncf8DPnXBmwE/iJ3wa3A8cChwMHRy3fVL0BxgBDnXMj/HX+q5E6iXRIutqTSJo55zab2VPAeS142DPOuaCZLQYqnXPzAcxsCTAEeB+v9x0OpYeBm/zzkh8BzPavKWB4YRj2aCPPN4H6a+U+QP01dJszB3gO6I13IYipfj27AXnOufC25weAK/B60J+GL2lnZv8EBiVRb4AvgH5mdgfwhHPuhSTrKNIhKJBF2sZM4EXg2aiyOupHqfLilg+fJzcUdT88nWj7r/NvAWCjc25MI/XY1Uh5q86h65yrNLNNwInA5fiB3NzDEpQ1V2+cc9vMrMx/rv9nZsc55y5vRbVFMpKGrEXagN8jfAP4j6jiVXjDsACnt2K1ecBp/v3vAK875yqAjWZ2CkS2uyazh/KCqLqdjXeC/WRdh3cBiGC4wDm3Hagxs/Bl6sLr/AQYZmYDzCwb76IcJFNv/ypQAefcP4FrqW87kX2CAlmk7fwR6Bc1fRNwpZktBHJbsb4twDfN7CNgLHCzX34W8DMz+wBvr+djk1jXJf5jPgSmAL9LthLOuQXOuUTbc2cAd/jrLAL+7F9P9hK8Kw+9BXwatXxz9e4PvOrPv7MldRTpCHS1JxERkQygHrKIiEgGUCCLiIhkAAWyiIhIBlAgi4iIZAAFsoiISAZQIIuIiGQABbKIiEgG+P8BJDG7+d3zJgkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 560x350 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 5))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "ensem_x = [i[\"num_models\"] for i in single_ensem_results]\n",
    "\n",
    "plt.plot(ensem_x, 1 - np.array([i[\"accuracy\"] for i in single_ensem_results]), 'x-', label='Ensemble Test')\n",
    "plt.plot(ensem_x, 1 - np.array([i[\"individual_accuracy\"] for i in single_ensem_results]), 'x-', label='Individual Test')\n",
    "plt.plot(ensem_x, 1 - np.array([i[\"agreement\"] for i in single_ensem_results]), 'x-', label='Disagreement')\n",
    "\n",
    "plt.title(\"Test Error Rates for Horizontal Model Ensembles\")\n",
    "plt.ylim(0)\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.ylabel(\"Error Rate\")\n",
    "plt.xlabel(\"Number of Models\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "source": [
    "## Multiple Iterations\n",
    "Run multiple iterations of the ensemble model investigations and average\n",
    "\n",
    "### CSV Results\n",
    "\n",
    "| test | learning rate | momentum | batch size | hidden nodes | epochs | models |\n",
    "| --- | --- | --- | --- | --- | --- | --- |\n",
    "|1|0.06|0|128|16|50|1, 3, 9, 15, 25|\n",
    "|2|0.06|0|35|16|1 - 100|1, 3, 9, 15, 25|\n",
    "\n",
    "### Pickle Results\n",
    "\n",
    "| test | learning rate | momentum | batch size | hidden nodes | epochs | models |\n",
    "| --- | --- | --- | --- | --- | --- | --- |\n",
    "|3|0.06|0.05|35|16|1 - 300|1, 3, 9, 15, 25|\n",
    "|4|0.06|0.05|35|1 - 50|50|1, 3, 9, 15, 25|\n",
    "|5|0.06|0.05|35|1 - 300|50|1, 3, 9, 15, 25|\n",
    "|6|0.001|0.01|35|1 - 400|50|1, 3, 9, 15, 25|\n",
    "|7|0.01|0.01|35|1 - 400|30 - 150|1, 3, 9, 15, 25|\n",
    "|8|0.03|0.01|35|1 - 400|5 - 100|1, 3, 9, 15, 25|\n",
    "|9|0.1|0.01|35|1 - 400|20|1, 3, 9, 15, 25|\n",
    "|10|0.15|0.01|35|1 - 400|20|1, 3, 9, 15, 25, 35, 45|\n",
    "|11|0.15|0.01|35|1 - 400|10|1, 3, 9, 15, 25, 35, 45|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size=35\n",
    "test_size=0.5\n",
    "epochs=10\n",
    "lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(0.1,\n",
    "                                                             decay_steps=100000,\n",
    "                                                             decay_rate=0.96)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1/30\n",
      "Iteration 2/30\n",
      "Iteration 3/30\n",
      "Iteration 4/30\n",
      "Iteration 5/30\n",
      "Iteration 6/30\n",
      "Iteration 7/30\n",
      "Iteration 8/30\n",
      "Iteration 9/30\n",
      "Iteration 10/30\n",
      "Iteration 11/30\n",
      "Iteration 12/30\n",
      "Iteration 13/30\n",
      "Iteration 14/30\n",
      "Iteration 15/30\n",
      "Iteration 16/30\n",
      "Iteration 17/30\n",
      "Iteration 18/30\n",
      "Iteration 19/30\n",
      "Iteration 20/30\n",
      "Iteration 21/30\n",
      "Iteration 22/30\n",
      "Iteration 23/30\n",
      "Iteration 24/30\n",
      "Iteration 25/30\n",
      "Iteration 26/30\n",
      "Iteration 27/30\n",
      "Iteration 28/30\n",
      "Iteration 29/30\n",
      "Iteration 30/30\n"
     ]
    }
   ],
   "source": [
    "multi_ensem_results = list()\n",
    "multi_ensem_iterations = 30\n",
    "for i in range(multi_ensem_iterations):\n",
    "    print(f\"Iteration {i+1}/{multi_ensem_iterations}\")\n",
    "    data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=test_size, stratify=labels)\n",
    "    multi_ensem_results.append(list(evaluate_ensemble_vote(epochs=10,\n",
    "                                                           hidden_nodes=(1, 400),\n",
    "                                                           nmodels=[1, 3, 9, 15, 25, 35, 45],\n",
    "                                                           optimizer=lambda: tf.keras.optimizers.SGD(learning_rate=0.15, momentum=0.01),\n",
    "                                                           weight_init=lambda: 'random_uniform',\n",
    "                                                           batch_size=batch_size,\n",
    "                                                           dtrain=data_train, \n",
    "                                                           dtest=data_test, \n",
    "                                                           ltrain=labels_train, \n",
    "                                                           ltest=labels_test,\n",
    "                                                           return_model=False,\n",
    "                                                           print_params=False)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "699"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "source": [
    "### Accuracy Tensor\n",
    "\n",
    "Create a tensor for holding the accuracy results\n",
    "\n",
    "(Iterations x Param x Number of models)\n",
    "\n",
    "#### Params\n",
    "0. Test Accuracy\n",
    "1. Train Accuracy\n",
    "2. Individual Accuracy\n",
    "3. Agreement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_tensor_data(test):\n",
    "    return [test[\"accuracy\"], \n",
    "            np.mean([i[\"accuracy\"][-1] for i in test[\"history\"]]), # avg train acc\n",
    "            test[\"individual_accuracy\"], \n",
    "            test[\"agreement\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30 Tests\n",
      "Models: [1, 3, 9, 15, 25, 35, 45]\n",
      "\n",
      "Loss: categorical_crossentropy\n",
      "LR: 0.15\n",
      "Momentum: 0.01\n"
     ]
    }
   ],
   "source": [
    "multi_ensem_models = sorted(list({i[\"num_models\"] for i in multi_ensem_results[0]}))\n",
    "multi_ensem_iter = len(multi_ensem_results)\n",
    "\n",
    "accuracy_ensem_tensor = np.zeros((multi_ensem_iter, 4, len(multi_ensem_models)))\n",
    "for iter_idx, iteration in enumerate(multi_ensem_results):\n",
    "    for single_test in iteration:\n",
    "        \n",
    "        ensem_models_idx = multi_ensem_models.index(single_test['num_models'])\n",
    "        accuracy_ensem_tensor[iter_idx, :, ensem_models_idx] = test_tensor_data(single_test)\n",
    "        \n",
    "mean_ensem_accuracy = np.mean(accuracy_ensem_tensor, axis=0)\n",
    "std_ensem_accuracy = np.std(accuracy_ensem_tensor, axis=0)\n",
    "\n",
    "print(f'{multi_ensem_iter} Tests')\n",
    "print(f'Models: {multi_ensem_models}')\n",
    "print()\n",
    "print(f'Loss: {multi_ensem_results[0][0][\"loss\"]}')\n",
    "print(f'LR: {multi_ensem_results[0][0][\"optimizer\"][\"learning_rate\"]:.3}')\n",
    "print(f'Momentum: {multi_ensem_results[0][0][\"optimizer\"][\"momentum\"]:.3}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "source": [
    "#### Export/Import Test Sets\n",
    "\n",
    "Export mean and standard deviations for retrieval and visualisation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "outputs": [],
   "source": [
    "pickle.dump(multi_ensem_results, open(\"results/exp2-test11.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp2_testname = 'exp2-test6'\n",
    "multi_ensem_results = pickle.load(open(f\"results/{exp2_testname}.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "np.savetxt(\"exp2-mean.csv\", mean_ensem_accuracy, delimiter=',')\n",
    "np.savetxt(\"exp2-std.csv\", std_ensem_accuracy, delimiter=',')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "mean_ensem_accuracy = np.loadtxt(\"results/test1-exp2-mean.csv\", delimiter=',')\n",
    "std_ensem_accuracy = np.loadtxt(\"results/test1-exp2-std.csv\", delimiter=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "source": [
    "### Best Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models: 1, 71.8% Accurate\n"
     ]
    }
   ],
   "source": [
    "best_ensem_accuracy_idx = np.unravel_index(np.argmax(mean_ensem_accuracy[0, :]), mean_ensem_accuracy.shape)\n",
    "best_ensem_accuracy = mean_ensem_accuracy[best_ensem_accuracy_idx]\n",
    "best_ensem_accuracy_models = multi_ensem_models[best_ensem_accuracy_idx[1]]\n",
    "\n",
    "print(f'Models: {best_ensem_accuracy_models}, {best_ensem_accuracy * 100:.3}% Accurate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "source": [
    "### Test/Train Error Over Model Numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": [
     "exp2"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x350 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 5))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "# plt.plot(multi_ensem_models, 1 - mean_ensem_accuracy[0, :], 'x-', label='Ensemble Test')\n",
    "# plt.plot(multi_ensem_models, 1 - mean_ensem_accuracy[2, :], 'x-', label='Individual Test')\n",
    "# plt.plot(multi_ensem_models, 1 - mean_ensem_accuracy[1, :], 'x-', label='Individual Train')\n",
    "# plt.plot(multi_ensem_models, 1 - mean_ensem_accuracy[3, :], 'x-', label='Agreement')\n",
    "\n",
    "plt.errorbar(multi_ensem_models, 1 - mean_ensem_accuracy[0, :], yerr=std_ensem_accuracy[0, :], capsize=4, label='Ensemble Test')\n",
    "plt.errorbar(multi_ensem_models, 1 - mean_ensem_accuracy[2, :], yerr=std_ensem_accuracy[2, :], capsize=4, label='Individual Test')\n",
    "plt.errorbar(multi_ensem_models, 1 - mean_ensem_accuracy[1, :], yerr=std_ensem_accuracy[1, :], capsize=4, label='Individual Train')\n",
    "plt.errorbar(multi_ensem_models, 1 - mean_ensem_accuracy[3, :], yerr=std_ensem_accuracy[3, :], capsize=4, label='Disagreement')\n",
    "\n",
    "plt.title(f\"Error Rate for Horizontal Ensemble Models\")\n",
    "# plt.ylim(0, 1)\n",
    "plt.ylim(0, np.max(1 - mean_ensem_accuracy + std_ensem_accuracy) + 0.05)\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.xlabel(\"Number of Models\")\n",
    "plt.ylabel(\"Error Rate\")\n",
    "# plt.savefig(f'graphs/{exp2_testname}-error-rate-curves.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FSZq1mNiVZq_",
    "tags": [
     "ex3"
    ]
   },
   "source": [
    "# Experiment 3\n",
    "\n",
    "Repeat Exp 2) for cancer dataset with two different optimisers of your choice e.g. 'trainlm' and 'trainrp'. Comment and discuss the result and decide which is more appropriate training algorithm for the problem. In your discussion, include in your description a detailed account of how the training algorithms (optimisations) work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_optimisers(optimizers=[(lambda: 'sgd', 'sgd'), \n",
    "                                    (lambda: 'adam', 'adam'), \n",
    "                                    (lambda: 'rmsprop', 'rmsprop')],\n",
    "                        weight_init=lambda: 'glorot_uniform',\n",
    "                        print_params=True,\n",
    "                        **kwargs\n",
    "                       ):\n",
    "    for o in optimizers:\n",
    "        \n",
    "        if print_params:\n",
    "            print(f'Optimiser: {o[1]}')\n",
    "            \n",
    "        yield list(evaluate_ensemble_vote(optimizer=o[0],\n",
    "                                          weight_init=weight_init,\n",
    "                                          exp=f'3-{o[1]}',\n",
    "                                          print_params=print_params,\n",
    "                                          **kwargs\n",
    "                                         ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single Iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimiser: sgd\n",
      "Models: 1\n",
      "Models: 3\n",
      "Models: 5\n",
      "Optimiser: adam\n",
      "Models: 1\n",
      "Models: 3\n",
      "Models: 5\n",
      "Optimiser: rmsprop\n",
      "Models: 1\n",
      "Models: 3\n",
      "Models: 5\n"
     ]
    }
   ],
   "source": [
    "single_optim_results = list()\n",
    "for test in evaluate_optimisers(epochs=(5, 300), nmodels=[1, 3, 5]):\n",
    "    single_optim_results.append(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Iterations\n",
    "\n",
    "### Pickle Results\n",
    "\n",
    "| test | optim1 | optim2 | optim3 | lr | momentum | epsilon | batch size | hidden nodes | epochs | models |\n",
    "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n",
    "| 1 | SGD | Adam | RMSprop | 0.1 | 0.0 | 1e7 | 35 | 16 | 1 - 100 | 1, 3, 9, 15, 25 |\n",
    "| 2 | SGD | Adam | RMSprop | 0.05 | 0.01 | 1e7 | 35 | 16 | 1 - 100 | 1, 3, 9, 15, 25 |\n",
    "| 3 | SGD | Adam | RMSprop | 0.1 | 0.01 | 1e7 | 35 | 1 - 400 | 20 | 1, 3, 9, 15, 25, 35, 45 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1/30\n",
      "Iteration 2/30\n",
      "Iteration 3/30\n",
      "Iteration 4/30\n",
      "Iteration 5/30\n",
      "Iteration 6/30\n",
      "Iteration 7/30\n",
      "Iteration 8/30\n",
      "Iteration 9/30\n",
      "Iteration 10/30\n",
      "Iteration 11/30\n",
      "Iteration 12/30\n",
      "Iteration 13/30\n",
      "Iteration 14/30\n",
      "Iteration 15/30\n",
      "Iteration 16/30\n",
      "Iteration 17/30\n",
      "Iteration 18/30\n",
      "Iteration 19/30\n",
      "Iteration 20/30\n",
      "Iteration 21/30\n",
      "Iteration 22/30\n",
      "Iteration 23/30\n",
      "Iteration 24/30\n",
      "Iteration 25/30\n",
      "Iteration 26/30\n",
      "Iteration 27/30\n",
      "Iteration 28/30\n",
      "Iteration 29/30\n",
      "Iteration 30/30\n"
     ]
    }
   ],
   "source": [
    "multi_optim_results = list()\n",
    "multi_optim_iterations = 30\n",
    "\n",
    "multi_optim_lr = 0.1\n",
    "multi_optim_mom = 0.01\n",
    "multi_optim_eps = 1e-07\n",
    "multi_optims = [(lambda: tf_optim.SGD(learning_rate=multi_optim_lr, \n",
    "                                      momentum=multi_optim_mom), 'sgd'), \n",
    "                (lambda: tf_optim.Adam(learning_rate=multi_optim_lr, \n",
    "                                       epsilon=multi_optim_eps), 'adam'), \n",
    "                (lambda: tf_optim.RMSprop(learning_rate=multi_optim_lr, \n",
    "                                          momentum=multi_optim_mom, \n",
    "                                          epsilon=multi_optim_eps), 'rmsprop')]\n",
    "\n",
    "for i in range(multi_optim_iterations):\n",
    "    print(f\"Iteration {i+1}/{multi_optim_iterations}\")\n",
    "    data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=0.5, stratify=labels)\n",
    "    multi_optim_results.append(list(evaluate_optimisers(epochs=20,\n",
    "                                                        hidden_nodes=(1, 400),\n",
    "                                                        nmodels=[1, 3, 9, 15, 25, 35, 45],\n",
    "                                                        optimizers=multi_optims,\n",
    "                                                        weight_init=lambda: 'random_uniform',\n",
    "                                                        batch_size=35,\n",
    "                                                        dtrain=data_train, \n",
    "                                                        dtest=data_test, \n",
    "                                                        ltrain=labels_train, \n",
    "                                                        ltest=labels_test,\n",
    "                                                        return_model=False,\n",
    "                                                        print_params=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy Tensor\n",
    "\n",
    "Create a tensor for holding the accuracy results\n",
    "\n",
    "(Iterations x Param x Number of models)\n",
    "\n",
    "#### Params\n",
    "0. Test Accuracy\n",
    "1. Train Accuracy\n",
    "2. Individual Accuracy\n",
    "3. Agreement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30 Tests\n",
      "Optimisers: ['SGD', 'Adam', 'RMSprop']\n",
      "Models: [1, 3, 9, 15, 25, 35, 45]\n",
      "\n",
      "Loss: categorical_crossentropy\n"
     ]
    }
   ],
   "source": [
    "multi_optim_results_dict = dict() # indexed by optimiser name\n",
    "multi_optim_iter = len(multi_optim_results) # number of iterations (30)\n",
    "\n",
    "#####################################\n",
    "## INDIVIDUAL RESULTS TO DICTIONARY\n",
    "#####################################\n",
    "for iter_idx, iteration in enumerate(multi_optim_results): # of 30 iterations\n",
    "    for model_idx, model_test in enumerate(iteration): # of 3 optimisers\n",
    "        for single_optim_test in model_test: # single tests for each optimisers\n",
    "            \n",
    "            single_optim_name = single_optim_test[\"optimizer\"][\"name\"]\n",
    "            if single_optim_name not in multi_optim_results_dict:\n",
    "                multi_optim_results_dict[single_optim_name] = list(list() for _ in range(multi_optim_iter))\n",
    "\n",
    "            multi_optim_results_dict[single_optim_name][iter_idx].append(single_optim_test)\n",
    "\n",
    "# list of numbers of models used in test\n",
    "multi_optim_models = sorted(list({i[\"num_models\"] for i in multi_optim_results[0][0]}))\n",
    "\n",
    "##################################\n",
    "## DICTIONARY TO RESULTS TENSORS\n",
    "##################################\n",
    "optim_tensors = dict()\n",
    "for optim, optim_results in multi_optim_results_dict.items():\n",
    "    \n",
    "    accuracy_optim_tensor = np.zeros((multi_optim_iter, 4, len(multi_optim_models)))\n",
    "    for iter_idx, iteration in enumerate(optim_results):\n",
    "        for single_test in iteration:\n",
    "\n",
    "            optim_models_idx = multi_optim_models.index(single_test['num_models'])\n",
    "            accuracy_optim_tensor[iter_idx, :, optim_models_idx] = test_tensor_data(single_test)\n",
    "            \n",
    "    optim_tensors[optim] = {\n",
    "        \"accuracy\": accuracy_optim_tensor,\n",
    "        \"mean\": np.mean(accuracy_optim_tensor, axis=0),\n",
    "        \"std\": np.std(accuracy_optim_tensor, axis=0)\n",
    "    }\n",
    "\n",
    "print(f'{multi_optim_iter} Tests')\n",
    "print(f'Optimisers: {list(multi_optim_results_dict.keys())}')\n",
    "print(f'Models: {multi_optim_models}')\n",
    "print()\n",
    "print(f'Loss: {multi_optim_results[0][0][0][\"loss\"]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Export/Import Test Sets\n",
    "\n",
    "Export mean and standard deviations for retrieval and visualisation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(multi_optim_results, open(\"results/exp3-test3.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp3_testname = 'exp3-test3'\n",
    "multi_optim_results = pickle.load(open(f\"results/{exp3_testname}.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGD: 35 Models, 90.4% Accurate\n",
      "Adam: 15 Models, 96.9% Accurate\n",
      "RMSprop: 35 Models, 96.8% Accurate\n"
     ]
    }
   ],
   "source": [
    "for optim, optim_results in optim_tensors.items():\n",
    "    best_optim_accuracy_idx = np.unravel_index(np.argmax(optim_results[\"mean\"][0, :]), optim_results[\"mean\"].shape)\n",
    "    best_optim_accuracy = optim_results[\"mean\"][best_optim_accuracy_idx]\n",
    "    best_optim_accuracy_models = multi_optim_models[best_optim_accuracy_idx[1]]\n",
    "\n",
    "    print(f'{optim}: {best_optim_accuracy_models} Models, {best_optim_accuracy * 100:.3}% Accurate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimiser Error Rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1680x350 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(24, 5))\n",
    "fig.set_dpi(fig_dpi)\n",
    "\n",
    "for idx, ((optimiser_name, tensors_dict), ax) in enumerate(zip(optim_tensors.items(), axes.flatten())):\n",
    "    ax.plot(multi_optim_models, 1 - tensors_dict[\"mean\"][0, :], 'x-', label='Ensemble Test')\n",
    "    ax.plot(multi_optim_models, 1 - tensors_dict[\"mean\"][2, :], 'x-', label='Individual Test')\n",
    "    ax.plot(multi_optim_models, 1 - tensors_dict[\"mean\"][1, :], 'x-', label='Individual Train')\n",
    "    ax.plot(multi_optim_models, 1 - tensors_dict[\"mean\"][3, :], 'x-', label='Disagreement')\n",
    "\n",
    "#     ax.errorbar(multi_optim_models, 1 - tensors_dict[\"mean\"][0, :], yerr=tensors_dict[\"std\"][0, :], capsize=4, label='Ensemble Test')\n",
    "#     ax.errorbar(multi_optim_models, 1 - tensors_dict[\"mean\"][2, :], yerr=tensors_dict[\"std\"][2, :], capsize=4, label='Individual Test')\n",
    "#     ax.errorbar(multi_optim_models, 1 - tensors_dict[\"mean\"][1, :], yerr=tensors_dict[\"std\"][1, :], capsize=4, label='Individual Train')\n",
    "#     ax.errorbar(multi_optim_models, 1 - tensors_dict[\"mean\"][3, :], yerr=tensors_dict[\"std\"][3, :], capsize=4, label='Disagreement')\n",
    "\n",
    "    ax.set_title(f\"{optimiser_name} Error Rate for Ensemble Models\")\n",
    "#     ax.set_ylim(0, 1)\n",
    "    ax.set_ylim(0, np.max([np.max(1 - i[\"mean\"] + i[\"std\"]) for i in optim_tensors.values()]) + 0.03)\n",
    "    ax.grid()\n",
    "    ax.legend()\n",
    "    ax.set_xlabel(\"Number of Models\")\n",
    "    ax.set_ylabel(\"Error Rate\")\n",
    "\n",
    "# plt.savefig(f'graphs/{exp3_testname}-error-rate-curves.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "authorship_tag": "ABX9TyNAMGLKzaoWaq1wvQ+w0w8h",
   "collapsed_sections": [],
   "name": "nncw.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "toc-autonumbering": false,
  "toc-showcode": false,
  "toc-showmarkdowntxt": false,
  "toc-showtags": false
 },
 "nbformat": 4,
 "nbformat_minor": 4
}