From 3cb7c36aaf0f9e5a007b2a2604e6602e564493d9 Mon Sep 17 00:00:00 2001
From: andy <andy@sarsoo.xyz>
Date: Mon, 29 Mar 2021 18:34:04 +0100
Subject: [PATCH] added exp 3 code and graphing, tensorboard callbacks

---
 nncw.ipynb | 426 ++++++++++++++++++++++++++++++++++++++++++++++++-----
 1 file changed, 389 insertions(+), 37 deletions(-)

diff --git a/nncw.ipynb b/nncw.ipynb
index a4ed2f5..78a3405 100644
--- a/nncw.ipynb
+++ b/nncw.ipynb
@@ -22,6 +22,7 @@
     "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",
@@ -31,6 +32,8 @@
     "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",
@@ -468,14 +471,32 @@
    "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,)), \n",
-    "              tf.keras.layers.Dense(hidden_nodes, activation=activation(), kernel_initializer=weight_init()), \n",
-    "              tf.keras.layers.Dense(2, activation='softmax', kernel_initializer=weight_init())]\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(log_dir=os.path.normpath(os.path.join(path, prefix + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))), \n",
+    "                                          histogram_freq=1) "
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -706,7 +727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 16,
    "metadata": {
     "executionInfo": {
      "elapsed": 11274,
@@ -745,6 +766,7 @@
     "                        print_params=True,\n",
     "                        return_model=True,\n",
     "                        run_eagerly=False,\n",
+    "                        tboard=True,\n",
     "                        \n",
     "                        dtrain=data_train,\n",
     "                        dtest=data_test,\n",
@@ -763,6 +785,12 @@
     "                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",
@@ -773,13 +801,14 @@
     "                                             epochs=e, \n",
     "                                             verbose=verbose,\n",
     "                                             \n",
-    "                                             callbacks=callbacks,\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",
+    "                                                 ltest.to_numpy(),\n",
+    "                                                 callbacks=cb,\n",
     "                                                 batch_size=batch_size, \n",
     "                                                 verbose=verbose),\n",
     "                        \"optimizer\": model.optimizer.get_config(),\n",
@@ -808,7 +837,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 17,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1096,6 +1125,7 @@
     "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()"
@@ -1278,27 +1308,23 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 31,
+   "cell_type": "raw",
    "metadata": {
     "tags": [
      "exp1"
     ]
    },
-   "outputs": [],
    "source": [
     "pickle.dump(multi_param_results, open(\"result.p\", \"wb\"))"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 172,
+   "cell_type": "raw",
    "metadata": {
     "tags": [
      "exp1"
     ]
    },
-   "outputs": [],
    "source": [
     "exp1_testname = 'exp1-test5'\n",
     "multi_param_results = pickle.load(open(f\"results/{exp1_testname}.p\", \"rb\"))"
@@ -1621,6 +1647,8 @@
     "                           round_predictions=True,\n",
     "\n",
     "                           nmodels=num_models,\n",
+    "                           tboard=True,\n",
+    "                           exp='2',\n",
     "\n",
     "                           verbose=0,\n",
     "                           print_params=True,\n",
@@ -1648,6 +1676,12 @@
     "                    \"epochs\": list(),\n",
     "                    \"num_models\": m}\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",
@@ -1669,7 +1703,7 @@
     "                                epochs=e, \n",
     "                                verbose=verbose,\n",
     "\n",
-    "                                callbacks=callbacks,\n",
+    "                                callbacks=cb,\n",
     "                                validation_split=validation_split)\n",
     "            histories.append(history.history)\n",
     "            response[\"epochs\"].append(e)\n",
@@ -1749,7 +1783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 11,
    "metadata": {
     "tags": [
      "exp2"
@@ -1762,7 +1796,9 @@
      "text": [
       "Models: 1\n",
       "Models: 3\n",
-      "Models: 9\n"
+      "Models: 9\n",
+      "Models: 15\n",
+      "Models: 25\n"
      ]
     }
    ],
@@ -1774,7 +1810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 16,
    "metadata": {
     "tags": [
      "exp2"
@@ -1783,7 +1819,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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FzlHo/y2e95Y5/2/nFqn84vmlPHBeb3JzC3l/5RbufGMlD5zXm+9258a6KFWyM7ewxsZeRGWIH7WhHDW1DB3SG5b4flr4zQ6uf+lTHr94YFSPW6OGX6yujkFWL3yfdu9fzkcpoxhyeCH/7fkgSV1OCiSLEsmjMFRyKZoPsX3gccn5oucXFBayM/9btuQtZWv+UvYUeuPDN7bOtEgcQAsbSEPa4yBsMgue37FzF02bNi13m0LnKCwsmyAjTZ6F/utQUUw16K0kIhKxFo1S+NvEQSVqzFVlZoucc8NDrquLCZmCPA7/phUpVsB+l8LswoF8XNibjwp7s9G1jGgXCQYJZiSYYYHH3i1TwfPeegva3tsmIcGbd4m7yKu3gsMpKzicvBaskMTCdBrk9SE1vx8NC7uSYMkhj2cGu3/4gfT0ZuGPl+Afr8T6COIrWpbgbW9UvE3E+wyKYfaXW3lnxXec2ac1P+pZs1sJvvzyS3r06BHrMI6IyhA/akM5amoZPtr+Ku0a9mDt+mN4Z8UWbvpRV07svZcV21eEvHW2MspLyHWuyRpg9SezaGWN+KZ+X/rkLuKURtmcddBL9AVNO1HQaRQFnTJwnU4mIbV5ULIpTi7V63wA9h7ey8ebPmZ2zmw+2vgRW/Pm0ii5UaArz5PanlSmK0+vW7dh1RzP0bF43U4WZ+9ifLcUsrJ3Mmlkp2r5BRor8/Z+w6gBbSveMI6pDPGjNpSjJpahoLCA9M0DuH3enRzcejqndGvBtGU5vLxtOn8e+3BUj13nEnLROeOtpz1J7uGGrEs5QMv3r2Hj6L/StQkkfptF4tr3YPmzgEHrvtAlw5s6jIDkBlGLLS0ljdM6n8ZpnU8jrzCPZVuXBe53fj/7fZIsqURXnu3S2kUtlmhbvG4n1764jMcvHkhuzkrGj+kdmK/JSVlEYsM5x8H8g+zL28e+w/tK/N2ft5+9h/d6f/P2lpgPbOtvfyD/QPFOm09nEdC4bTMObpxIwYHOUS1DnUvIe75eAKc9Qc/hpzFv3jx6Dj+N1TzBnq8X0HXMfTDwUu9E6bbV8G2WNy1+Cj7+MyTWgw7DihN06/6QEJ2RTJITkhnWehjDWg/j1iG3svaHtV5y3jCHP3zyB/7wyR/o2qwrxxUeR7PtzejVohcJFpOeUKtkec6uQPKdlwNDO6fz+MUDWZ6zSwlZpI45VHCoTGIsnVBLLyu97f68/RV2aVwvsR6pyamkpaR5f5PTaNGgBZ2adKJRcqPAuk+zD9L1mGP4Lm85M76ZwWW9JjJgxPiofz/VuYQc6tamnsNPg6DboDCDVr28acS1kH8YNi0pTtCzH4BZv4H6TaDTyX6CHgPNj/OeW83MjG7NutGtWTeu7ns12w5sC3TlOXPTTN5/9/1AV55jO4xl6LFD474rz1C3DgztnK5kLFKD5Bfml6x9BtdCD+9nxQ8r+HTZp2WSbYkaat4+8grzyj1OoiXSKKURjZL9KaURjVMa06ZRmxKJNDU5tcQ2pR8nJyZHVK7LexcNRPQ3zm52Ni+teYnBowdz9ahBFT/5CNS5hFwlSSnQcaQ3jfl/kLsH1n9cnKC/fNvbrnG74tpzl9HQKLILxCqrZcOWjO82nvHdxvPfrP+S0DmBOTlzmL1hNq+tfY0GSQ0Y0XoEGe0zGN1+NOn1leREpFihK+RA3oGyNc6KmnoP7w9suz9vPwfzD5Z7HMNI3ZdaIjmmpqTSokGLskk0JXQiTU1OpX7i0R2nvvSogOd3Pz8wHzzuQXVTQq6K+o2h++neBLBnC6yb6yXnb2bB8ue95S17FSfojiOhXqNqD6VeQj1GdRzFuI5eV56ff/85czZ4Q0jOzpmNYfRv2T9w3rlLky7VHoOIHB3OOXILckM33wY9Dk6cobbdn7e/wmM1SGpQpvbZulHrsgnUT7JpyWklm4NT0vhk/idkjM6I/gtTzYJHBZy3Zl5gVMAV21coIce9xq2h30Xe5Bx8v7a49vzpc7DwcUhIgnZDixN024EQYfNJpBITEhnQcgADWg7gpsE3sW73OubmzGVOzhz+vOzPPLL0ETo17hRIzv2P6U9ilM6Bi0hJhwsOB5Jl6QuLQtVCN3y3gX++988yNdUCV1DucVISUgLJMjXZq52m10+nQ1qHMrXQcLXU1OTUaunmtyZd1xIsklEBo0EJubqZwTHdvGnY1VCQD5s/LU7Q8/4IWb+DlDTodGJxgj6mR7Wff+7cpDOdm3Rmcu/J7MzdyYcbva48M9dkMm3VtEBXnmPaj2Fkm5E0TG5YrccXqQ2KzpOWPu8ZqnYaKskWrT9ceLjc4yRaYokEWVBYQHpKOq1SW3m1zxC10FBNvSmJKUfplanFPn4M2g32WjaLrJ8PG5fAiTdE7bBKyNGWmATth3jT6Fvg8H5YvwC+nQPfzoX3b/e2a9SqODl3Hg1NqvfevfT66Zxz/Dmcc/w5HCo4xKIti8p05Tms9TDGtB9DRvsMWjaMzvlvkaOl0BVyMP9g6CQa4iKjcEm2ovOkQMiLiTo07lCilhrqYqPgJNsgqUGJ86ReHwOjovkSSTjtBsMrk2H8NG9+/fyS81GihHy0paRC11O8CWDfdsie559/zoLPM73lLboVJ+hOJ3lXdFeTeon1GNVuFKPajeLXw3/N6h2rmZMzh6ycLH678Lf8duFv6dW8lzeEZPsxdGvWTUNIylHjnPNugwm+Itdvyg3X1BucQHfs3UH+i/nsy9uH1+FrePUT65dJkK0atqJLcpeILzhKTU6tsU2zdUZhIRze502H9sKhfXBoT/nzzbrAs+cwLLERLEqCCc+UrDFHgRJyrDU6Bnr/1Jucg13ripu3P38ZFj8JlgBtBxUn6HZDIKletRw+wRLo3aI3vVv05voB17Np36ZAZyRPfPYEjy9/nDapbQLnnQe3GhzxrQNS9+QV5pV7pW6oi49CddSQ7/LLPU5SQlKZ5tum9ZvSLq0dewr20K1Tt8B9puEuOGqY3JDkBL2X41ZhIeTtD0qYe+Hw3grm/cRaYt7/WxFLgHpp3unEemneRbhN2lFv57cw/NaoJ2NQQo4vZpDexZsGXwGFBfDd58UJ+uPHvHPQSQ28N0eXDFL3NobCkyChen6ht23UloknTGTiCRPZc3gPH238KNCs/eKXL5KWnFbclWe7k2ic0rhajiuxVVBYwP78/ezM38lXu74K2YNR6b+BBBu0/FDBoXKPk2AJJRJlUW2zZYOWJWqhFdVM6yWG/0E6b948Rg1UU29MBJKolwgb7fkKvi2sOGEWTcE11MP7oIIWDiyhZAKtlwYpXiL1lvnzgfWNw88nNyh5HY/fTL2uy2V0XvYMHDdGNeQ6LSER2gzwppN+BXkHIWeRn6Dnwn/vZhAOVv3GO+9cVINu1rFaDt84pTFndDmDM7qcQV5BHku3LWXOBq9p+73s97yuPI8dxJj2YxjdbnSN7sqzpgrZXWCIzhfK1EJLJdcS3QXmhD5Ww6SGZRJkm0ZtSvR6VNEFR6XPk0occM67tqVSCTNEAg3URIuT6ECAJaUPaF4iDE6g9dK862bKJMy0EAk3aD65YVQ6Ywo+Z5yzPp/Oo39WfA45iklZCbkmSW5QnHQBDuxk1btP0qv+Ni9Jr3rdW96sc9AFYqOg4ZF3DJKcmMzw1sMZ3no4tw+9na92fRW4KOzBxQ/y4OIH6dqsKxntMhjbYSw9m/fUebUKhOsuMGQ/u6W6Cwxu6o2ku8DStcwW9VvQqXGnMkkz5+schvQdEqilFtVkU5NSdYtcPClKopVKmKXPmQatr6gmioWucTZuE7qG6ifcz9aso9/gkSUTcLSSaHXauKQ4+a6f5/0dP81broQsITVMZ0fLkVB0JeYPG7ya87dZ8MW/Yem/8AbI6Bc0QMbwIx4gw8zont6d7unduabfNWzdvzXQleczq57hHyv+wTENjmF0+9GMaT+GYa2HldvEWNOE6y5wyb4lbPlyS8iOGkJdyVtRd4FJllScGP1aZ5OUJoHuAsPdT1q6C8HKnPOf9908Tm538pG+RBKKc5B3IOKLirptWAtbngydQA/vgwp+iAWSaOkaZ1praBGihlom4QbNp6RWKYnu3jbP63Ohpgl1a1NRb41RpIRcmzTt4A2OMfBS71xOiQEynoSPH43KABmtUlsxofsEJnSfwIG8A8zfPJ85OXOYuX4mr371Kg2SGjCyzUgy2mcwqt0o0uunM23lNPoc06fEjfZLty6tlvFGw6mou8CKOmqIqLvA7V53gUVX7AYnzhYNWoROoKUuPCrapl5iPTXvxlogiR7hRUWH9nnbRpJE/YTYuCABElt582nHQouuFSTQ0udEG1bbtSVydCgh11YJCXBsb28aeZ03QMbGT7zkvG5uyQEyOo8qHiAjvcsRNSc1TG7IKR1P4ZSOp1BQWMDy7csDV23P2jCLBEug/zH9Ob7p8Ty98mkeGfMIULLv2NIq6i4wVOIMdaXv/rz9Fd4G0yCpQZkLjlo3al1ur0ZFf1ctW8W4k8fRIKmBmutjyTk4fKCcBBnhRUVFCbfCJIrfbFuqxpl2bAQXFZU+J5oaSKJLdB9ynaOEXFckpXg9g3U6Ebiz7AAZX/zb265Je29gjC5jvER9BANkJCYkBrqbu3nwzazbvS5wv/MrX72Cw/E/H/wP7ZPb892G7+jfsj9Pr3yax5Y9Vjzkmp9UK9tdYFpKGun10+nYuGPIvndD1VSPtLvATUmbSE1OrfLz6zTnvIsWj/ic6F5OPrQX5kSSREs32TbyOuhJPy7yi4rqpZVIoiJHQgm5ripvgIyvZ8Gn/gAZrXoHnX8ecUQDZBR15XlF7yvYmbuTeRvnMW3lNL7Z/Q2NUxqzK3dXoMn22NRjK+yQIbgmq+4CY8A5yM+tdMIM28RbwY8uwD+fWaqGmdrST6LefPbm7+ncvW/Ii4yKz4k2UhKVuKOELJ4yA2R8VVx7XvoMLPhrtQ6QkV4/nfZp7dl9eDdnNzub+bnzuWPYHVHvvL3OCyTR0Amz9aZl8PGnFSTQoAQcSRJNTi17FW7qMZDeOfKLior+RpBEc+bNo/MwNfVKzaOELGWZwTHdvWnYNf4AGcuKr+AuMUDGSUEDZHSP+PxzrMYbrZGcg/xDlbiIKMxFRUUJuDB8L1hdAdbgJ9FSTbqpLbwkGmlHC/WKaqK6XUokEkrIUrHEJGg/1JtCDpBxm7ddo2OLk3OX0d49imHEarzRo6YoiR5xRwv+8nKSaEByw7I1zIbNoWnHiM+Jfrx0BSdmnKokKhIDSshSeaEGyCg6//xtFnz+kre8RfegATJOLDFARqzGG61QUU20ChcVDdz5HSwrLF5fwX3GQOgk2iDdT6LhEmiYc6KJR/5xLkj6VslYJEaUkOXINToG+lzgTc7Bzm+DknMmLH4CLNEfIMPv4jNnsddJSXWMN5p/KKKOFiJq8o00iZZupm3QjP2N6tOofZejnkRFpHbQt4FULzNofpw3Dfkfb4CMLZ8V16CLBshIrAc4GHgZaYXdYdEamHM/DPs5LJ1WuftGC8of+B3wBuQo3UzboBk0bR8+YYa7sChMEl0zbx6tdN+oiFSRErJEV0KidzV224FlB8j44m345CkGBG8/98Hix0kNytYw6zfxRnKJtLeieo285KuaqIjEOX1LydEVPEDGKffC7Pu9GvOgy2Ho1SUTsMZdFpE6RHfGS+ysnw/LnmVdl8tgzbuQ+4PXH3eDZkrGIlLnKCFLbASPN9ppvDe02SuTveUiInWQErLERvB4o1ByvFERkTpI55AlNmI03qiISLxSDVlERCQOKCGLiIjEASVkERGROKCELCIiEgeUkEVEROKAErKIiEgcUEIWERGJA1FNyGb2EzNbY2ZrzezKEOt/ZmYrzGylmb1kZvWiGY+IiEi8ilpCNrMk4GFgLDAAuMXMmgetN+BPQIZzrre/+PxoxSMiIhLPollDHgqscs5tcs7tA94DTi21jQENzSwRSAW2RDEeERGRuGXOuejs2OwCvNrvdf78LYBzzk0N2uanwNNALjDLOXdxiP1cCVwJ0KZNm2HTp0+vthj37NlD48aNq21/saAyxI/aUA6VIX7UhnKoDGWNHj16kXNueKh1MevL2sySgauBPsAm4Hkzu8Q593zwds65p4CnAIYPH+5GjRpVbTHMmzeP6txfLKgM8aM2lENliB+1oRwqQ+VEs8l6M9A2aL6tv6xIfyDfObfBOVcAvA5oZAEREamTopmQFwO9zaytmTUCTgc+CFq/CehrZs38+XHAmijGIyIiEreilpCdc/nAzcAcYDnwJ+fcDjN718zaOOc2Aw8C881sBdAEeCJa8YiIiMSzqJ5Dds7NAGaUWnZG0OPHgcejGYOIiEhNoJ66RERE4oASsoiISBxQQhYREYkDSsgiIiJxQAlZREQkDighi4iIxAElZBERkTighCwiIhIHlJBFRETigBKyiIhIHFBCFhERiQNKyCIiInFACVlERCQOKCGLiIjEASVkERGROKCELCIiEgeUkEVEROKAErKIiEgcUEIWERGJA0rIIiIicUAJWUREJA4oIYuIiMQBJWQREZE4oIQsIiISB5SQRURE4oASsoiISBxQQhYREYkDSsgiIiJxQAlZREQkDighi4iIxAElZBERkTighCwiIhIHlJBFRETigBKyiIhIHFBCFhERiQNKyCIiInFACVlERCQOKCGLiIjEASVkERGROKCELCIiEgeUkEVEROKAErKIiEgcUEIWERGJA0rIIiIicSCqCdnMfmJma8xsrZldGWJ9czN7y8y+NLPVZnZcNOMRERGJV0nR2rGZJQEPA2OA3cBSM3vDObcjaLM/A5nOuRfNrCFg0YpHREQknkWzhjwUWOWc2+Sc2we8B5xatNLMmgCDnXMvAjjnDjjn9kcxHhERkbhlzrno7NjsAiDDOXedP38L4JxzU/35/sBfgfVATyALuMU5l19qP1cCVwK0adNm2PTp06stxj179tC4ceNq218sqAzxozaUQ2WIH7WhHCpDWaNHj17knBseal3UmqwjkIRXi74O+Bx4Frgc+EfwRs65p4CnAIYPH+5GjRpVbQHMmzeP6txfLKgM8aM2lENliB+1oRwqQ+VEs8l6M9A2aL6tv6zIJmCdc265c64QeAvoH8V4RERE4lY0E/JioLeZtTWzRsDpwAdFK51zW4BtZtbZX5QBfBHFeEREROJW1BKyfy74ZmAOsBz4k3Nuh5m9a2Zt/M1+BbxmZiuAxpRqrhYREakronoO2Tk3A5hRatkZQY+XAAOjGYOIiEhNUGEN2cx6Ho1ARERE6rJImqz/bmaLzew6M0uPekQiIiJ1UIUJ2Tk3GpgApAPzzOx1MzvX74lLREREqkFEF3U557LxusF8BO/e4VuBlWZ2WfRCExERqTsiOYf8IzN7HliIdy/xSc65kcAw4DdRjk9ERKROiKTZ+VLgn865OcELnXO7zewX0QlLRESkbokkId9HUA9b/qhMrZ1z3zjn3otaZCIiInVIJOeQXwaCB3zIA16KTjgiIiJ1UyQJOdE5l1c04z9OiV5IIiIidU8kCXm9mV1RNOMPh5gTvZBERETqnkgS8tXAj81ss5ltAk7BH59YREREqkeFF3U557YCFx6FWEREROqsChOymTUGrgV6AvWKljvnJkQxLhERkTolkibrF4B9eB2BPAM4YH00gxIREalrIknIbZxzfwFynXPvABcBo6MbloiISN0SSccgRfcgbzWzcXidhDSPXkgiIiJ1TyQJ+Xdm1gS4GXgMSANuimpUIiIidUy5CdnMEoCezrm3gM+BjKMRlIiISF1T7jlk51whcM5RikVERKTOiqTJepmZvQS8ChwoWuicezdqUYmIiNQxkSTkBsBB4MygZQ5QQhYREakmkfTUdfnRCERERKQui6Snrn/h1YhLcM5dEWJzERERqYJImqxfDXpcDzgX2BWVaEREROqoSJqs3ym16HUzWxileEREROqkSJqsGwbNJgD9gWbRCkhERKQuiqTJehXeOWTD60YzG2+MZBEREakmkTRZdz4agYiIiNRlFY72ZGa/NLOmQfPNzOz6qEYlIiJSx0Qy/OLlzrkfimacc7sA3ZssIiJSjSJJyAlmFmjaNrMUICV6IYmIiNQ9kVzUlQm853cQAl7t+KXohSQiIlL3RHJR1wNmtgw4xV/0sHPuveiGJSIiUrdEch/ycUBWURI2s4Zmdpxz7puoRyciIlJHRHIO+WW8+4+L5KEmaxERkWoVSUJOdM7lFc34j3VRl4iISDWKJCGvN7PAyE5mdiWwIXohiYiI1D2RJOSrgR+b2SYz2wRcAGhwCRERkWpUYUJ2zm0F/he4D/gS6Aq0iHJcIiIidUrYq6zNrAlwHnARcDzwJtDdOdfu6IQmIiJSd5R329M2YDFwq3NuAYCZ/fSoRCUiIlLHlNdkfTWwB3jGzB4ysyF4wzCKiIhINQubkJ1zzzjnzgSGAWuAB4BjzexPZnby0QpQRESkLojkoq5dzrl/OudOBToCa/Eu8KqQmf3EzNaY2Vr/dqlQ2ySY2SIze7VSkYuIiNQikdz2FOCc2+6c+z/n3NiKtvVHiHoYGAsMAG4xs+YhNv0fILsycYiIiNQ2lUrIlTQUWOWc2+Sc2we8B5wavIGZpeNdxf1kFOMQERGJe5EMv1hVbYBNQfObgLaltnkA+G0UYxAREakRzLnoXDhtZhcAGc656/z5WwDnnJvqzw8AbnPOXWRmGcB1zrkLQuznSuBKgDZt2gybPn16tcW4Z88eGjduXG37iwWVIX7UhnKoDPGjNpRDZShr9OjRi5xzw0OudM5FZQJGAm8EzT8KXBw0/wu8WnM28B2wH3iyvH0OGzbMVae5c+dW6/5iQWWIH7WhHCpD/KgN5VAZygIWujA5LprnkBcDvc2srZk1Ak4HPgj6IfB351xb51wnvPPI7znnro5iPCIiInEragnZOZcP3AzMAZYDf3LO7TCzd82sTbSOKyIiUhNF86IunHMzgBmllp0RYrssICuasYiIiMSzaDZZi4iISISUkEVEROKAErKIiEgcUEIWERGJA0rIIiIicUAJWUREJA4oIYuIiMQBJWQREZE4oIQsIiISB5SQRURE4oASsoiISBxQQhYREYkDSsgiIiJxQAlZREQkDighi4iIxAElZBERkTighCwiIhIHlJBFRETigBKyiIhIHFBCFhERiQNKyCIiInFACVlERCQOKCGLiIjEASVkERGROKCELCIiEgeUkEVEROKAErKIiEgcUEIWERGJA0rIIiIicUAJWUREJA4oIYuIiMQBJWQREZE4oIQsIiISB5SQRURE4oASsoiISBxQQhYREYkDSsgiIiJxQAlZREQkDighi4iIxAElZBERkTighCwiIhIHlJBFRETigBKyiIhIHFBCFhERiQNRTchm9hMzW2Nma83sylLrGprZe2b2pZmtMrProxmLiIhIPEuK1o7NLAl4GBgD7AaWmtkbzrkdQZs96Jyba2aNgCVm9p5z7utoxSQiIhKvollDHgqscs5tcs7tA94DTi1a6Zw74Jyb6z/eB6wBWkcxHhERkbhlzrno7NjsAiDDOXedP38L4JxzU0Ns2x6YB/R2zu0vte5K4EqANm3aDJs+fXq1xbhnzx4aN25cbfuLBZUhftSGcqgM8aM2lENlKGv06NGLnHPDQ62LWpN1pMysHpAJ3FI6GQM4554CngIYPny4GzVqVIn1+fn5bNy4kdzc3EofOy0tjQYNGlQp7nihMnjq169Pu3btSEqK3Vt63rx5lH5/1jQqQ/yoDeVQGSonmt9em4G2QfNtgcXBG5iZAc8C7zrnXq3KQTZu3EhaWhodO3bE213k9u7dS1paWlUOGzdUBnDOsXPnTjZu3EinTp2qLzARkaMomueQFwO9zaytf9HW6cAHpbb5PXDAOXd/VQ+Sm5tLenp6pZOx1B5mRnp6epVaSURE4kXUErJzLh+4GZgDLAf+5JzbYWbvmlkbM2sH3AYMNbPl/vTjqhxLyVj0HhCRmi6qJ9ycczOAGaWWnRE0q29RERER6lhPXU/O+4bF63aWWLZ43U6enPdNlfeZlJRE//79A9Ozzz57pGFWyvr16xk8eHDIdZ06dWLfvn2V2t/cuXMxM1atWlUd4YmISITqVELu374Z1764LJCUl27YzbUvLqN/+2ZV3mfTpk1Zvnx5YLrsssuqK9yYyMzMZOTIkWRmZkbtGAUFBVHbt4hITVWrEvLe3DyWZO8MOyUYXD/2eK5+bgn/7/UV/PLVVVw/9ngSjLDP2ZubV6VYWrRowZQpU+jTpw/jxo1j/37vjq5HH32U7t27069fP37xi18AsH37ds4//3wGDx7MiBEj+PTTTwGYPHky1113HcOGDaNr164sWLCAiy66iO7du3PHHXcEjnX48GEmTJjACSecwKRJk8jPzy8Tz3PPPceQIUPo168fN910U8iYCwoKePfdd3n66ad55ZVXil/XvXu59NJL6du3L/369ePDDz8E4Omnnw4smzJlCgAZGRmsXLkSgJUrV5KRkQHAvffey6RJkxg5ciQ33HADCxcuZMSIEQwcOJDTTz+d9evXhz3WXXfdxRNPPBGI59JLL+Xtt9+u/D9FRCSOxfw+5Oq05ru9XPB/CyLa9sXFGwC4+63ym2Zf/fkIBndKD7v+hx9+oH///oH5Rx55hDFjxrBjxw5OO+00pk6dymWXXcbrr7/OpZdeym9+8xtycnJITU1l9+7dANx4443ccccdDBkyhLVr13LJJZewaNEiwEtQixYt4oUXXuCss85i6dKltG7dmh49enDzzTcDXuJ7+umnGTx4MBdffDHPP/88kydPDsT0xRdf8NZbb7FgwQKSkpK47LLLeOeddzjzzDNLlGXOnDkMGjSI7t2706JFC5YvX07//v357W9/S4cOHXjuuecoLCxk7969rFixgkceeYQPP/yQpk2bsnNnyVMBoXz99ddkZWWRkpLCnj17+Oijj0hMTCQzM5P777+ff/zjHyGP1aZNGyZNmsQ111zDvn37mD9/Pv/6178qPJ6ISE1SqxJy92PTePXnI8rdZvWWPTz83684o3dr3lmxmZtP7U7P1uF7Yel+bPn3xxY1WZfWqFEjTjnlFAAGDRpEdnY2AEOHDuWSSy5h/PjxnHvuuQDMnDmzxDnbXbt2BR6fffbZAPTp04euXbvSsWNHAI4//nhycnJITk7m+OOPD5xHvuiii5gxY0aJhDxr1iwWLlwY2ObAgQMMGjSoTMyZmZlMmDABgAkTJpCZmUn//v2ZOXMmM2Z41+YlJCTQpEkT5syZw4UXXkjTpk0BSE8P/6OlyDnnnENKSkqgjJdeeinffPMN+fn5NG/ePPBalD5WkyZNSEpKYu3atcyfP59zzjknph2AiIhEQ636Vkurn1xubXbxup38ZfbXPHnpYIZ2TufU7s245c0vefzigQztXHFCqYx69eoFHicmJgbOm77zzjtkZWXx5ptv8sgjj/DJJ58AsGTJkpBJpmg/CQkJJfaZkJBAQUEBycnJJW75MbMytwAVFhZy1VVXcc8994SNNy8vj7feeosPPviA2267jYKCAurVq8fvf//7SpU7KSmJwsJCAA4dOlRiXcOGDQOP7777bs4880yuvvpqFi1axLXXXlvufidPnsyzzz7L/PnzefjhhysVk4hITVCrziFXZHnOrhLJd1CHJjx+8UCW5+yq4JnVo7CwkJycHMaNG8fUqVPZsGEDBQUFjBkzhr///e+B7T777LNK7Xft2rUsW7YM8Gq5J510Uon148aNIzMzkx07vIG2tm3bxpYtW0psM3PmTE488UQ2bNhAdnY2OTk5tGnThk8++YRTTjklEF9hYSG7d+9m7NixZGZmBprdi5qsO3bsGGgxeP3118PGvGfPHtq29Tpye+GFFwLLQx0LYPz48UyfPp1du3bRr1+/Sr0+IiI1QZ1KyFePOq5MTXho53SuHnVclfdZdA65aHrkkUfCbltQUMDEiRPp27cvgwcP5u677yYxMZG//OUvZGVl0a9fP0444QRefPHFSsXQu3dv/vCHP3DCCSeQkJDAxRdfXGJ9r169uPPOOxk3bhx9+/blzDPPLHPONzMzM9CEXuS8884jMzOTu+66i+zsbPr06cPAgQNZsWIFvXv35pe//CUnnngi/fv358EHHwTgpptu4qGHHmLQoEEcPnw4bMy33norv/rVrxg4cGCgGRsIeSyA1NRUhgwZwiWXXFKp10ZEpMZwztWYadiwYa60L774osyySO3Zs6fKz40XdaUMeXl5rlevXm7r1q1htzmS90J1mDt3bkyPXx1UhvhRG8qhMpQFLHRhclydqiFLzbR8+XK6du3KhAkTaNmyZazDERGJilp1UZfUTv3792fdunWxDkNEJKpUQxYREYkDSsgiIiJxQAlZREQkDighH6EWLVpUavt7772Xv/71rwCcccYZHDx4MOy2M2bMCHkbVXZ2dtgRniqSlZXFBRdcEHb95ZdfzpAhQ6q0bxERqbq6dVHXx49Bu8HQcWTxsvXzYeMSOPGGox7Ou+++W+76om4zj5bDhw8zZ84c0tLS+Pbbb+nSpUtUjlNQUEBiYmJU9i0iUlPVrRpyu8HwymQvCQOJGxd58+2qVtsMlpWVxY9+9CPOPfdcunXrVmJEpSeffJKuXbsycuRIvvzyy8DyovGKb7vtNp5++unA8iuuuII33niDadOmBUZR+vrrrxkyZAh9+/blscceC2z7u9/9LlDjhuIa+549exg7diwDBw4M9EddkQ8++IBRo0bxs5/9rMTwi4sWLWL48OH069cvMHpTqFGZStfcp0yZwrRp0wJlvf322xkwYACzZ8/mnnvuYciQIfTu3Zvbb7897LEKCwvp0aNHoMeuvXv30qVLl5AjWomI1GS1q4acuwe2rQ6/3hJg1C3w0kToeQ4NVr0JY+/0lm9YGPo5LXtC/fCDTwRbtmwZq1evplmzZvTq1Ysbb7yRpKQkHnroIZYsWUJiYiIDBgwo07Xl+PHjueuuu7jiiivIz89n1qxZ/O1vf+Oll14KbFM0ItT555/PbbfdVmEsDRo04K233iItLY3vvvuOH//4xxV2yZmZmcmFF15Iz549+elPf8odd9zB4cOHmThxIm+++Sa9e/cO9PAValSm4EExQmnfvn1gaMlBgwZx33334Zzj3HPP5eOPP2bIkCFljpWQkMCECRN4+eWXueqqq3j11Vc577zzNLiEiNQ6tetbbdtqePrHkW279F8YwLtTyt/uig+gw/CIdjly5EhatWoFeN1Zrl+/nh07djB27NjAqEihmqEHDx7Mt99+y65du1i8eDEnnngi9evXL7HNJ598wr///W8AJk6cyKxZs8qNxTnHrbfeGhjicM2aNeV2ZZmbm8vcuXN5+umnSUlJISkpiTVr1nDo0CE6depE7969geJRnUKNylRRQh4/fnzg8axZs/jjH/9Ibm4uW7duZfXq1aSlpYU81uTJk5k0aRJXXXUVzz33HI8++mi5xxERqYlqV0Ju2dNLoOX5bgXM+R30PAe36k1s7J1wbJ/y9xmhcCM8lR59KZRzzz2XN998k/nz55dIXEXC7SN4dCUoHmHphRdeYP/+/Xz66ackJSXRokWLchPyu+++y65du+jWrRvgNXmH6t+6POFiKVI02lNubi433ngjS5YsoXXr1txwww1ltg3WpUsXkpKSmD17Nrt376Zv374RxyQiUlPUrnPI9Rt7tdlwkyuEeX+Ei16Asx7l4DlPefOuMPxzImyuDmfo0KGBRLJv375ALbe0otGMZs6cyemnn15m/eDBg3nrrbcASgw+0b59+8DoSv/973/Zt28f4CXUVq1akZSUxNtvvx0Y6SmczMxMXnjhBbKzs8nOzmbJkiVkZmbSo0cPsrOzWblyJVA8qlOoUZlatmzJ5s2b2bt3L/v27eO///1vyGPl5uZiZjRv3pzdu3fz9ttvA4Q9Fni15EsuuYRLL7203HKIiNRUtSshV2TjEhg/LXCVdUG7Yd78xiVRO2SbNm245ZZbGDJkCKeeeiqDBg0Kud3gwYP5+uuvGTFiRJnmaoBHH32UBx54gH79+pWo6Z599tmsX7+ePn368M4779C8eXPAa9b+8MMPA8s7dOgQNsb9+/cze/ZsTj311MCyolrpV199xfPPP88VV1xBv379ArdMhRqVKSUlhVtvvZUBAwZw9tln06dP6JaHpk2bMmnSJHr27MlZZ50VuM0qJSUl5LEALrjgAnbt2lVmJCsRkVoj3KgT8ThptKey6koZ5syZ484999xyt9FoT0dOZYgftaEcKkNZlDPaU+06hyy10n333ce0adN48803Yx2KiEjU1K0ma6mR7rnnHtatW0e/fv1iHYqISNQoIYuIiMQBJWQREZE4oIQsIiISB5SQRURE4oAScjV45plnSElJqbDryHj16KOPltuLl4iIRF+dSsjTVk5j6dalJZYt3bqUaSunHdF+MzMzGTJkCG+88UbEzynqVjMeKCGLiMRenUrIfY7pw5S5UwJJefn3y5kydwp9jimnL+sK7Ny5k6+++oqHHnooMGTh1q1bGTNmDL179+aOO+4IDIk4bdo0zj//fDIyMhg/fjzbt2/n/PPPZ/DgwYwYMSIwElK45ZMnT+a6665j2LBhdO3alQULFjB58mS6d+/OHXfcEYjpueeeY8iQIfTr1y8wDGR2djb9+vVj0qRJnHDCCVx44YU453j88cfZvHkzI0eOPOrjL4uISLFa1THIvsP7WPvD2rDrEyyBa/pewy/n/JJTO57KB9kfcP2A60mwBD7d9mnI53Rt2pVGKY3C7vP111/nnHPOYeTIkaxdu5bvv/+e++67j3POOYcbb7yRp556qsT2n332GZ9++imNGzdm4sSJ3HHHHQwZMoS1a9dyySWXsGjRosBQi6WXgzce8KJFi3jhhRc466yzmDt3Ll27dqVHjx7cfPPNbN++nbfeeosFCxaQlJTEZZddxjvvvEOvXr344osvmD59OieccAJjxozho48+4tprr+WPf/wj8+fPp1Gj8OUUEZHoqlUJee0Pa7nsvcsi2vaVr14B4IFFD5S73bOnP8uAlgPCrs/MzOT+++/HzDjvvPN47bXXmD9/PnfddRcAF154Ibfffntg+x//+Mc0buwNWDFz5kxWrVoVWFd0DjrccigevrFPnz507dqVDh06kJKSwvHHH09OTg4ff/wxCxcuZPDgwQAcOHCAQYMG0atXL7p3707Pnt7oVQMGDCA7O5uTTz65gldKRESOhlqVkLs27cqzpz9b7jZrdq7hr8v/WqKG3D29e7n7DGfbtm189NFHXHjhhQAcPnyYHj164HVXGlrREIRFlixZQlJS2X9DuOVFQzwmJCSUGO4xISGBgoICCgsLueqqq7jnnntKPC87Ozvs8JAiIhJ7teoccqOURgxoOSDsVOgKeeLzJ/jzmD9z94i7eXD4gzzx+RMUusKwzymvufq1117j5z//eWDIws2bN5Odnc3AgQN55RWvBl70N5QxY8YEhjAErzm7vOWRGDduHJmZmYHhFrdt28aWLVvKfU5aWhp79+6N+BgiIlL9alVCrsiK7SuYOnoqg1p5QyD2b9GfqaOnsmL7iirtLzMzk3PPPbfEsrPOOovjjjuO1157jT59+rBq1apAE3Vpf/nLX8jKyqJfv36ccMIJgXGOwy2PRK9evbjzzjsZN24cffv25cwzzywxrnAoV111FWPGjNFFXSIisRRuGKh4nGrK8IsHDx50+fn5zjnnXn75ZffTn/40Ksdxru4MvxgJDb945FSG+FEbyqEylIWGXzy6srOz+dnPfkZBQQFNmjThX//6V6xDEhGROKeEHAU9evQI3DssIiISiTp1DllERCRe1YqEnJubW+6tRlK7OefIzc2NdRgiIkekxjdZt27dmo0bN5Kfn1/p5x48eJAGDRpEIaqjR2XwJCUl0bp162qKSETk6KvxCblJkyY0adKkSs+dN28eAwaE74WrJlAZRERqh6g2WZvZT8xsjZmtNbMrQ6wfamarzOxrM7s7mrGIiIjEs6glZDNLAh4GxgIDgFvMrHmpzR4HfgZ0B84ws6oPuyQiIlKDRbOGPBRY5Zzb5JzbB7wHnFq00szaAEnOuc+dcwXAS8BPohiPiIhI3IrmOeQ2wKag+U1A2wrWjy69E7+pu6i5e4+ZfVGNMR4DbK/G/cWCyhA/akM5VIb4URvKoTKU1Snciri/qMs59xTwVIUbVoGZLXTODY/Gvo8WlSF+1IZyqAzxozaUQ2WonGg2WW+mZI24rb8s0vUiIiJ1RjQT8mKgt5m1NbNGwOnAB0UrnXObgQIz62tmicBFwL+jGE8oUal5H2UqQ/yoDeVQGeJHbSiHylAJFs0erszsbGAqXuJ/yDn3pJm9C1zpnNtsZsOBfwL1geecc/dGLRgREZE4FtWELCIiIpGpFX1Zi4iI1HRKyCIiInGgTiZkM3vDzHaZ2auxjqWqzKy9mWWZ2Woz+9zMxsc6psoys6ZmtsTMlpvZSjO7KtYxVZWZNTSz9WY2NdaxVJWZZfvvpeVmNifW8VSFmXU2szn+52KFmaXGOqbKMLPu/utfNB00s3NjHVdVmNmv/K6RV5vZY2ZmsY6pssxsil+GlWZ2SdSPVxfPIZtZBpAGTHLOXRDbaKrGzFoDrZxzy83sWGAp0M05tz/GoUXMv7q+nnPugP/FuRIY7JzbEePQKs3MHgCOB3Kcc1NiHU9VmFk20NvvWa9GMrO5wK+dcx+aWTqwxzlX+aHg4oB/d0o20LEmfa4BzOwYYCHQC8gD5gFTnHMLYhpYJfhdOT8DjAQMmAOc5pz7IVrHrJM1ZOdcFrA31nEcCefcFufccv/xd8D3QHpMg6ok51yBc+6AP1sP701fE39FdwV64HUPKzFiZr2APOfchwDOuZ01NRn7zgZm1bRkHCQJ7w6aZH/aFttwKu0EYIFzLtc5dxD4DDgtmgeskwm5tjGzQUCicy4n1rFUlt9s/RmwEfijc+77WMdUBVOBO2IdRDVwwFwz+8TMJsY6mCroCuwzs3+b2TIz+3+xDugITQAyYx1EVTjntuN9Ljbgdfg00zn3TWyjqrSVQIb/HdUMyKBkZ1bVTgm5hvOb5Z4Fro51LFXhnPvBOdcP6AxcbGatYh1TZZjZOcBXzrmvYh1LNTjJOTcIr2b2/8ysb6wDqqQk4GTgf4ERwI/M7EexDalqzKwxXlPpu7GOpSr8BPYTvH6b2wIjzWxUTIOqJOfcauAxYDbwOl4TfEE0j6mEXIOZWT3gTeBB59z8GIdzRJxzW/GahE6OdSyVNBy4yD//OhW4qqaO7e2c2+T/3YKXCAbGNqJK2wQscc7lOOcO4ZWhf2xDqrJzgP8453JjHUgVnQJ87Z82OAi8g/dZqVGcc0845wY658bgnQtfG83jKSHXUP4Vi9OA2c6552IcTpWYWSszS/MfNwFGAWtiG1XlOOfucM61d851AqYA/3DO/SbGYVWamaUG/S8a4Y1jviq2UVXaJ0BLM2tmZgl476fqHB3uaKqxzdW+HLxacX3/4s0MathnG8DMWvp/u+MNKfxB+c84MnE/2lM0mNlMoB+QamYbgfE16eo/34nAhcDnQbdFXOqcWxG7kCqtI/Ck/+PCgL/UsPhrk1bAG/6dKYl4Pyw+iW1IleOcy/fPG8/Dez/9xzn3dozDqjT/x+lQ4KexjqWqnHML/W6SPwUKgVnAjNhGVSVv+f+P/cDl0b5IsE7e9iQiIhJv1GQtIiISB5SQRURE4oASsoiISBxQQhYREYkDSsgiIiJxQAlZJIrMzJnZ/UHzU81scjXtO+rdjJrZyf5IN4tKLe/kl+3WoGXD/GWTK7H/yRWNkOWPata70sGL1DBKyCLRtQ+YWNTpRrzwO2uIxETgXufcsBDr1uJ1s1lkAqD7yEWqSAlZJLoOAS/g9a9cQnDNz8x6m1mW//heM3vazD4ys3VmdpqZ/d0fV/b5Uvt43B+v9R2//2PM7Dgz+8C8saZnm1mnoOM9amZLgEtL7aeLv/5zM5thZulmdjlekn3IzP4vRNn2AN/7zzW83phmB+1zoJktNm9c4mfNrL6//Cdm9pUfx8lB24eMO2h9opk9b8VjHV9e8csvUnMoIYtE35+Bq4sSUoQ6AqOBS4BXgX/hjS3bxcwG+Ns0x+s6tRfwOXCTv/xvwDXOucHA/cAfg/ab55wb7JybVup4jwF/c871BT7GqxX/C693pRuccz8PE+creEl7ON6Y3ME9GT0DXO+c64PX09H/+q/BX4FxeANAdA/avry4weuXurNzrqe/z9fDxCRSI9XJrjNFjibn3HYzexu4ohJPe9c5V2BmK4C9zrnFAGa2Em8EnU/xat9FSWk68LDfD/XJwJt+N5iGlwyLvBLmeEOAs/zHz+ENBhCJGcD7eF1vvow/XqyZNQXqOeeKzj0/B9yCV4P+qmioUDN7GegQQdwA3wJtzOxx4C3n3H8ijFGkRlBCFjk6pgIzgfeCluVT3EpVr9T2h/y/hUGPi+ZDnf91/pQAbHXO9Q8Tx4Ewy6vUh65zbq+ZbQPOwBtcI5IB3EMdq6K4cc7tMrM+/rF+ZWanOuemVCFskbikJmuRo8CvEX5MyQED1lM8POD5VdhtPbxh+sAbaOQj59weYKuZnQWB866RXKG8JCi2iXgDNETqAeAO51xgrFjn3A/AITMbUmqfXwLdzKydmSUB4/3tK4zbzFoACc65l4F7qblDK4qEpIQscvT8AWgTNP8wcKuZLQVSqrC/HcCPzGwVMAB4xF9+MXC9mX2Gd9XzuAj2dYP/nM/xhi28L9IgnHNLnHOhzudOBh7395kG/N0f3/cGvNF/FgBfBW1fUdxtgbn++r9VJkaRmkCjPYmIiMQB1ZBFRETigBKyiIhIHFBCFhERiQNKyCIiInFACVlERCQOKCGLiIjEASVkERGROPD/AdSkFSh6fvsTAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 560x350 with 1 Axes>"
       ]
@@ -1840,7 +1876,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 24,
    "metadata": {
     "tags": [
      "exp2"
@@ -1851,17 +1887,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration 1/5\n",
-      "Iteration 2/5\n",
-      "Iteration 3/5\n",
-      "Iteration 4/5\n",
-      "Iteration 5/5\n"
+      "Iteration 1/2\n",
+      "Iteration 2/2\n"
      ]
     }
    ],
    "source": [
     "multi_ensem_results = list()\n",
-    "multi_ensem_iterations = 5\n",
+    "multi_ensem_iterations = 2\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=0.5, stratify=labels)\n",
@@ -1902,7 +1935,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 25,
    "metadata": {
     "tags": [
      "exp2"
@@ -1913,7 +1946,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5 Tests\n",
+      "2 Tests\n",
       "Models: [1, 3, 7, 11, 15]\n",
       "\n",
       "Loss: categorical_crossentropy\n",
@@ -1961,23 +1994,23 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 76,
+   "cell_type": "raw",
    "metadata": {
     "tags": [
      "exp2"
     ]
    },
-   "outputs": [],
    "source": [
     "pickle.dump(multi_ensem_results, open(\"result.p\", \"wb\"))"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 22,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "multi_ensem_results = pickle.load(open(\"results/exp2-test1.p\", \"rb\"))"
+    "multi_ensem_results = pickle.load(open(\"results/exp2-test3.p\", \"rb\"))"
    ]
   },
   {
@@ -2009,7 +2042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 26,
    "metadata": {
     "tags": [
      "exp2"
@@ -2020,7 +2053,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Models: 11, 74.3% Accurate\n"
+      "Models: 3, 77.1% Accurate\n"
      ]
     }
    ],
@@ -2045,7 +2078,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 29,
    "metadata": {
     "tags": [
      "exp2"
@@ -2054,7 +2087,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 560x350 with 1 Axes>"
       ]
@@ -2080,7 +2113,8 @@
     "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, 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",
@@ -2101,6 +2135,324 @@
     "\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": 7,
+   "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 | batch size | hidden nodes | epochs | models |\n",
+    "| --- | --- | --- | --- | --- | --- | --- | --- |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1/5\n",
+      "Iteration 2/5\n",
+      "Iteration 3/5\n",
+      "Iteration 4/5\n",
+      "Iteration 5/5\n"
+     ]
+    }
+   ],
+   "source": [
+    "multi_optim_results = list()\n",
+    "multi_optim_iterations = 5\n",
+    "\n",
+    "multi_optim_lr = 0.01\n",
+    "multi_optim_mom = 0.0\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=(1, 100),\n",
+    "                                                        hidden_nodes=16,\n",
+    "                                                        nmodels=[1, 3, 5, 7],\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": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5 Tests\n",
+      "Optimisers: ['SGD', 'Adam', 'RMSprop']\n",
+      "Models: [1, 3, 5, 7]\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] = [single_test[\"accuracy\"], \n",
+    "                                                                    np.mean([i[\"accuracy\"][-1] for i in single_test[\"history\"]]), \n",
+    "                                                                    single_test[\"individual_accuracy\"], \n",
+    "                                                                    single_test[\"agreement\"]]\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": "raw",
+   "metadata": {},
+   "source": [
+    "pickle.dump(multi_optim_results, open(\"result.p\", \"wb\"))"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "multi_optim_results = pickle.load(open(\"results/exp3-test1.p\", \"rb\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Best Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SGD: 1 Models, 65.4% Accurate\n",
+      "Adam: 7 Models, 96.6% Accurate\n",
+      "RMSprop: 1 Models, 96.6% 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": 40,
+   "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.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {