diff --git a/1-first-project/Abgabe-Copy1.ipynb b/1-first-project/Abgabe-Copy1.ipynb
new file mode 100644
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+++ b/1-first-project/Abgabe-Copy1.ipynb
@@ -0,0 +1,760 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a5b326e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "glob_path = '/opt/iui-datarelease2-sose2021/*/split_letters_csv/*'\n",
+    "\n",
+    "pickle_file = 'data.pickle'\n",
+    "\n",
+    "checkpoint_path = \"training_copy/cp.ckpt\"\n",
+    "checkpoint_dir = os.path.dirname(checkpoint_path)\n",
+    "\n",
+    "# divisor for neuron count step downs (hard to describe), e.g. dense_step = 3: layer1=900, layer2 = 300, layer3 = 100, layer4 = 33...\n",
+    "dense_steps = 2\n",
+    "# amount of dense/dropout layers\n",
+    "layer_count = 3\n",
+    "# how much to drop\n",
+    "drop_count = 0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e834add0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from glob import glob\n",
+    "import pandas as pd\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "def dl_from_blob(filename) -> list:\n",
+    "    all_data = []\n",
+    "    \n",
+    "    for path in tqdm(glob(filename)):\n",
+    "        path = path\n",
+    "        df = pd.read_csv(path, ';')\n",
+    "        u = path.split('/')[3]\n",
+    "        l = ''.join(filter(lambda x: x.isalpha(), path.split('/')[5]))[0] \n",
+    "        d = {\n",
+    "            'file': path,\n",
+    "            'data': df,\n",
+    "            'user': u,\n",
+    "            'label': l\n",
+    "        }\n",
+    "        all_data.append(d)\n",
+    "    return all_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ec9e9e4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def save_pickle(f, structure):\n",
+    "    _p = open(f, 'wb')\n",
+    "    pickle.dump(structure, _p)\n",
+    "    _p.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "44338438",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "def load_pickles(f) -> list:\n",
+    "    _p = open(pickle_file, 'rb')\n",
+    "    _d = pickle.load(_p)\n",
+    "    _p.close()\n",
+    "    \n",
+    "    return _d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2627c3c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading data...\n",
+      "data.pickle found...\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "def load_data() -> list:\n",
+    "    if os.path.isfile(pickle_file):\n",
+    "        print(f'{pickle_file} found...')\n",
+    "        return load_pickles(pickle_file)\n",
+    "    print(f'Didn\\'t find {pickle_file}...')\n",
+    "    all_data = dl_from_blob(glob_path)\n",
+    "    print(f'Creating {pickle_file}...')\n",
+    "    save_pickle(pickle_file, all_data)\n",
+    "    return all_data\n",
+    "\n",
+    "print(\"Loading data...\")\n",
+    "data = load_data()\n",
+    "# plot_pd(data[0]['data'], False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "71e6d157",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def plot_pd(data, force=True):\n",
+    "    fig, axs = plt.subplots(5, 3, figsize=(3*3, 3*5))\n",
+    "    axs[0][0].plot(data['Acc1 X'])\n",
+    "    axs[0][1].plot(data['Acc1 Y'])\n",
+    "    axs[0][2].plot(data['Acc1 Z'])\n",
+    "    axs[1][0].plot(data['Acc2 X'])\n",
+    "    axs[1][1].plot(data['Acc2 Y'])\n",
+    "    axs[1][2].plot(data['Acc2 Z'])\n",
+    "    axs[2][0].plot(data['Gyro X'])\n",
+    "    axs[2][1].plot(data['Gyro Y'])\n",
+    "    axs[2][2].plot(data['Gyro Z'])\n",
+    "    axs[3][0].plot(data['Mag X'])\n",
+    "    axs[3][1].plot(data['Mag Y'])\n",
+    "    axs[3][2].plot(data['Mag Z'])\n",
+    "    axs[4][0].plot(data['Time'])\n",
+    "\n",
+    "    if force:\n",
+    "        for a in axs:\n",
+    "            for b in a:\n",
+    "                b.plot(data['Force'])\n",
+    "    else:\n",
+    "        axs[4][1].plot(data['Force'])\n",
+    "\n",
+    "def plot_np(data, force=True):\n",
+    "    fig, axs = plt.subplots(5, 3, figsize=(3*3, 3*5))\n",
+    "    axs[0][0].plot(data[0])\n",
+    "    axs[0][1].plot(data[1])\n",
+    "    axs[0][2].plot(data[2])\n",
+    "    axs[1][0].plot(data[3])\n",
+    "    axs[1][1].plot(data[4])\n",
+    "    axs[1][2].plot(data[5])\n",
+    "    axs[2][0].plot(data[6])\n",
+    "    axs[2][1].plot(data[7])\n",
+    "    axs[2][2].plot(data[8])\n",
+    "    axs[3][0].plot(data[9])\n",
+    "    axs[3][1].plot(data[10])\n",
+    "    axs[3][2].plot(data[11])\n",
+    "    axs[4][0].plot(data[13])\n",
+    "\n",
+    "    if force:\n",
+    "        for a in axs:\n",
+    "            for b in a:\n",
+    "                b.plot(data[12])\n",
+    "    else:\n",
+    "        axs[4][1].plot(data[12])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "19c4c56e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mill_drop(entry):\n",
+    "    #drop millis on single\n",
+    "    data_wo_mill = entry['data'].drop(labels='Millis', axis=1, inplace=False)\n",
+    "    drop_entry = entry\n",
+    "    drop_entry['data'] = data_wo_mill.reset_index(drop=True)\n",
+    "    \n",
+    "    return drop_entry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ea509043",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def cut_force(drop_entry):\n",
+    "    # force trans\n",
+    "    shorten_entry = drop_entry\n",
+    "    shorten_data = shorten_entry['data']\n",
+    "    sf_entry = shorten_data['Force']\n",
+    "    leeway = 10\n",
+    "    \n",
+    "    try:\n",
+    "        thresh = 70\n",
+    "        temps_over_T = np.where(sf_entry > thresh)[0]\n",
+    "        shorten_data = shorten_data[max(temps_over_T.min()-leeway,0):min(len(sf_entry)-1,temps_over_T.max()+leeway)]\n",
+    "    except:\n",
+    "        thresold = 0.05\n",
+    "        thresh = sf_entry.max()*thresold\n",
+    "        temps_over_T = np.where(sf_entry > thresh)[0]\n",
+    "        shorten_data = shorten_data[max(temps_over_T.min()-leeway,0):min(len(sf_entry)-1,temps_over_T.max()+leeway)]\n",
+    "    \n",
+    "    shorten_entry['data'] = shorten_data.reset_index(drop=True)\n",
+    "    return shorten_entry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7025983c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def norm_force(shorten_entry, flist):\n",
+    "    fnorm_entry = shorten_entry\n",
+    "    u = fnorm_entry['user']\n",
+    "    d = fnorm_entry['data']\n",
+    "    \n",
+    "    \n",
+    "    d['Force'] = ((d['Force'] - flist[u].mean())/flist[u].std())\n",
+    "    \n",
+    "    fnorm_entry['data'] = fnorm_entry['data'].reset_index(drop=True)\n",
+    "    return fnorm_entry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "64860f57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def time_trans(fnorm_entry):\n",
+    "    #timetrans\n",
+    "    time_entry = fnorm_entry\n",
+    "    \n",
+    "    time_entry['data']['Time'] = fnorm_entry['data']['Time']-fnorm_entry['data']['Time'][0]\n",
+    "    \n",
+    "    time_entry['data'] = time_entry['data'].reset_index(drop=True)\n",
+    "\n",
+    "    return time_entry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0bb308a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def norm(time_entry):\n",
+    "    # normalize\n",
+    "    norm_entry = time_entry\n",
+    "    \n",
+    "    norm_entry['data']['Acc1 X'] = norm_entry['data']['Acc1 X'] / 32768\n",
+    "    norm_entry['data']['Acc1 Y'] = norm_entry['data']['Acc1 Y'] / 32768\n",
+    "    norm_entry['data']['Acc1 Z'] = norm_entry['data']['Acc1 Z'] / 32768\n",
+    "    norm_entry['data']['Acc2 X'] = norm_entry['data']['Acc2 X'] / 8192\n",
+    "    norm_entry['data']['Acc2 Y'] = norm_entry['data']['Acc2 Y'] / 8192\n",
+    "    norm_entry['data']['Acc2 Z'] = norm_entry['data']['Acc2 Z'] / 8192\n",
+    "    norm_entry['data']['Gyro X'] = norm_entry['data']['Gyro X'] / 32768\n",
+    "    norm_entry['data']['Gyro Y'] = norm_entry['data']['Gyro Y'] / 32768\n",
+    "    norm_entry['data']['Gyro Z'] = norm_entry['data']['Gyro Z'] / 32768\n",
+    "    norm_entry['data']['Mag X']  = norm_entry['data']['Mag X']  / 8192\n",
+    "    norm_entry['data']['Mag Y']  = norm_entry['data']['Mag Y']  / 8192\n",
+    "    norm_entry['data']['Mag Z']  = norm_entry['data']['Mag Z']  / 8192\n",
+    "    \n",
+    "    norm_entry['data'] = norm_entry['data'].reset_index(drop=True)\n",
+    "    \n",
+    "    return norm_entry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1171c8ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preprocessing...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 26179/26179 [01:29<00:00, 292.14it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def preproc(d):\n",
+    "    flist = {}  \n",
+    "    d_res = []\n",
+    "    for e in data:\n",
+    "        if e['user'] not in flist:\n",
+    "            flist[e['user']] = e['data']['Force']\n",
+    "        else:\n",
+    "            flist[e['user']] = flist[e['user']].append(e['data']['Force'])\n",
+    "    \n",
+    "    for e in tqdm(data):\n",
+    "        d_res.append(preproc_entry(e, flist))\n",
+    "    return d_res\n",
+    "    \n",
+    "def preproc_entry(entry, flist):\n",
+    "    drop_entry = mill_drop(entry)\n",
+    "#     plot_pd(drop_entry['data'])\n",
+    "#     \n",
+    "    shorten_entry = cut_force(drop_entry)\n",
+    "#     plot_pd(shorten_entry['data'])\n",
+    "#     \n",
+    "    fnorm_entry = norm_force(shorten_entry, flist)\n",
+    "#     plot_pd(fnorm_entry['data'])\n",
+    "#     \n",
+    "    time_entry = time_trans(shorten_entry)\n",
+    "#     plot_pd(time_entry['data'])\n",
+    "#     \n",
+    "    norm_entry = norm(time_entry)\n",
+    "#     plot_pd(norm_entry['data'], False)\n",
+    "    return norm_entry\n",
+    "\n",
+    "print(\"Preprocessing...\")\n",
+    "pdata = preproc(data)\n",
+    "# plot_pd(pdata[0]['data'], False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2d576b5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Truncating...\n"
+     ]
+    }
+   ],
+   "source": [
+    "def throw(pdata):\n",
+    "    llist = pd.Series([len(x['data']) for x in pdata])\n",
+    "    threshold = int(llist.quantile(threshold_p))\n",
+    "    longdex = np.where(llist <= threshold)[0]\n",
+    "    return np.array(pdata)[longdex]\n",
+    "\n",
+    "llist = pd.Series([len(x['data']) for x in pdata])\n",
+    "threshold_p = 0.75\n",
+    "threshold = int(llist.quantile(threshold_p))\n",
+    "\n",
+    "print(\"Truncating...\")\n",
+    "tpdata = throw(pdata)\n",
+    "# plot_pd(tpdata[0]['data'], False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b3eb709e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  9%|▉         | 1785/19640 [00:00<00:01, 17844.40it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Padding...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 19640/19640 [00:01<00:00, 18711.98it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
+    "# ltpdata = []\n",
+    "def elong(tpdata):\n",
+    "    for x in tqdm(tpdata):\n",
+    "        y = x['data'].to_numpy().T\n",
+    "        x['data'] = pad_sequences(y, dtype=float, padding='post', maxlen=threshold)\n",
+    "    return tpdata\n",
+    "\n",
+    "print(\"Padding...\")\n",
+    "ltpdata = elong(tpdata)\n",
+    "# plot_np(ltpdata[0]['data'], False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "73a2a874",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.regularizers import l2\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Flatten, BatchNormalization, Dropout\n",
+    "from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "\n",
+    "def build_model(shape, classes):\n",
+    "    model = Sequential()\n",
+    "    \n",
+    "    ncount = shape[0]*shape[1]\n",
+    "    \n",
+    "    model.add(Flatten(input_shape=shape, name='flatten'))\n",
+    "    \n",
+    "    model.add(Dropout(drop_count, name=f'dropout_{drop_count*100}'))\n",
+    "    model.add(BatchNormalization(name='batchNorm'))\n",
+    "    \n",
+    "    for i in range(1,layer_count+1):\n",
+    "        neurons = int(ncount/pow(dense_steps,i))\n",
+    "        if neurons <= classes:\n",
+    "            break\n",
+    "        model.add(Dropout(drop_count*i, name=f'HiddenDropout_{drop_count*i*100:.0f}'))\n",
+    "        model.add(Dense(neurons, activation='relu', \n",
+    "                        kernel_regularizer=l2(0.001), name=f'Hidden_{i}')\n",
+    "                 )\n",
+    "    \n",
+    "    model.add(Dense(classes, activation='softmax', name='Output'))\n",
+    "    \n",
+    "    model.compile(\n",
+    "        optimizer=Adam(),\n",
+    "        loss=\"categorical_crossentropy\", \n",
+    "        metrics=[\"acc\"],\n",
+    "    )\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "8ae93baa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "checkpoint_file = './goat.weights'\n",
+    "\n",
+    "def train(X_train, y_train, X_test, y_test):\n",
+    "    model = build_model(X_train[0].shape, 52)\n",
+    "    \n",
+    "    model.summary()\n",
+    "    \n",
+    "    # Create a callback that saves the model's weights\n",
+    "    model_checkpoint = ModelCheckpoint(filepath=checkpoint_path, monitor='loss', \n",
+    "\t\t\tsave_best_only=True)\n",
+    "    \n",
+    "    history = model.fit(X_train, y_train, \n",
+    "                  epochs=30,\n",
+    "                  batch_size=256,\n",
+    "                  shuffle=True,\n",
+    "                  validation_data=(X_test, y_test),\n",
+    "                verbose=2,\n",
+    "            callbacks=[model_checkpoint]\n",
+    "             )\n",
+    "    \n",
+    "    \n",
+    "    model.load_weights(checkpoint_path)\n",
+    "    print(\"Evaluate on test data\")\n",
+    "    return model, history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "9668ef09",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'  # this is required\n",
+    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'         # set to '0' for GPU0, '1' for GPU1 or '2' for GPU2. Check \"gpustat\" in a terminal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "0bd41ed5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder, LabelBinarizer\n",
+    "\n",
+    "X = np.array([x['data'] for x in ltpdata])\n",
+    "y = np.array([x['label'] for x in ltpdata])\n",
+    "\n",
+    "lb = LabelBinarizer()\n",
+    "y_tran = lb.fit_transform(y)\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y_tran, test_size=0.2, random_state=177013)\n",
+    "\n",
+    "X_train=X_train.reshape(X_train.shape[0],X_train.shape[1],X_train.shape[2])\n",
+    "X_test=X_test.reshape(X_test.shape[0],X_test.shape[1],X_test.shape[2])\n",
+    "\n",
+    "train_shape = X_train[0].shape\n",
+    "classes = y_train[0].shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "2c25c41b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "flatten (Flatten)            (None, 1050)              0         \n",
+      "_________________________________________________________________\n",
+      "dropout_10.0 (Dropout)       (None, 1050)              0         \n",
+      "_________________________________________________________________\n",
+      "batchNorm (BatchNormalizatio (None, 1050)              4200      \n",
+      "_________________________________________________________________\n",
+      "HiddenDropout_10 (Dropout)   (None, 1050)              0         \n",
+      "_________________________________________________________________\n",
+      "Hidden_1 (Dense)             (None, 525)               551775    \n",
+      "_________________________________________________________________\n",
+      "HiddenDropout_20 (Dropout)   (None, 525)               0         \n",
+      "_________________________________________________________________\n",
+      "Hidden_2 (Dense)             (None, 262)               137812    \n",
+      "_________________________________________________________________\n",
+      "HiddenDropout_30 (Dropout)   (None, 262)               0         \n",
+      "_________________________________________________________________\n",
+      "Hidden_3 (Dense)             (None, 131)               34453     \n",
+      "_________________________________________________________________\n",
+      "Output (Dense)               (None, 52)                6864      \n",
+      "=================================================================\n",
+      "Total params: 735,104\n",
+      "Trainable params: 733,004\n",
+      "Non-trainable params: 2,100\n",
+      "_________________________________________________________________\n",
+      "Epoch 1/30\n",
+      "62/62 - 2s - loss: 4.8396 - acc: 0.0671 - val_loss: 4.7298 - val_acc: 0.0710\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 2/30\n",
+      "62/62 - 0s - loss: 4.0757 - acc: 0.1609 - val_loss: 4.2353 - val_acc: 0.1031\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 3/30\n",
+      "62/62 - 0s - loss: 3.5292 - acc: 0.2483 - val_loss: 3.9189 - val_acc: 0.1349\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 4/30\n",
+      "62/62 - 0s - loss: 3.1635 - acc: 0.3097 - val_loss: 3.5697 - val_acc: 0.2070\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 5/30\n",
+      "62/62 - 0s - loss: 2.8876 - acc: 0.3607 - val_loss: 3.3103 - val_acc: 0.2487\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 6/30\n",
+      "62/62 - 0s - loss: 2.6724 - acc: 0.4022 - val_loss: 3.0531 - val_acc: 0.3032\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 7/30\n",
+      "62/62 - 0s - loss: 2.5206 - acc: 0.4299 - val_loss: 2.8832 - val_acc: 0.3450\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 8/30\n",
+      "62/62 - 0s - loss: 2.3844 - acc: 0.4576 - val_loss: 2.5853 - val_acc: 0.4234\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 9/30\n",
+      "62/62 - 0s - loss: 2.2780 - acc: 0.4808 - val_loss: 2.3759 - val_acc: 0.4672\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 10/30\n",
+      "62/62 - 0s - loss: 2.2042 - acc: 0.4960 - val_loss: 2.2155 - val_acc: 0.5005\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 11/30\n",
+      "62/62 - 0s - loss: 2.1139 - acc: 0.5190 - val_loss: 2.0585 - val_acc: 0.5425\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 12/30\n",
+      "62/62 - 0s - loss: 2.0391 - acc: 0.5350 - val_loss: 1.9542 - val_acc: 0.5687\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 13/30\n",
+      "62/62 - 0s - loss: 1.9897 - acc: 0.5411 - val_loss: 1.9089 - val_acc: 0.5759\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 14/30\n",
+      "62/62 - 0s - loss: 1.9307 - acc: 0.5551 - val_loss: 1.8783 - val_acc: 0.5832\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 15/30\n",
+      "62/62 - 0s - loss: 1.8869 - acc: 0.5673 - val_loss: 1.8283 - val_acc: 0.5942\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 16/30\n",
+      "62/62 - 0s - loss: 1.8516 - acc: 0.5720 - val_loss: 1.7902 - val_acc: 0.5965\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 17/30\n",
+      "62/62 - 0s - loss: 1.8156 - acc: 0.5860 - val_loss: 1.7896 - val_acc: 0.5894\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 18/30\n",
+      "62/62 - 0s - loss: 1.7877 - acc: 0.5929 - val_loss: 1.7737 - val_acc: 0.6006\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 19/30\n",
+      "62/62 - 0s - loss: 1.7562 - acc: 0.5984 - val_loss: 1.7304 - val_acc: 0.6212\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 20/30\n",
+      "62/62 - 0s - loss: 1.7235 - acc: 0.6043 - val_loss: 1.7242 - val_acc: 0.6156\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 21/30\n",
+      "62/62 - 0s - loss: 1.6954 - acc: 0.6149 - val_loss: 1.7041 - val_acc: 0.6263\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 22/30\n",
+      "62/62 - 0s - loss: 1.6949 - acc: 0.6165 - val_loss: 1.7357 - val_acc: 0.6227\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 23/30\n",
+      "62/62 - 0s - loss: 1.6688 - acc: 0.6208 - val_loss: 1.6868 - val_acc: 0.6354\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 24/30\n",
+      "62/62 - 0s - loss: 1.6374 - acc: 0.6268 - val_loss: 1.6755 - val_acc: 0.6275\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 25/30\n",
+      "62/62 - 0s - loss: 1.6202 - acc: 0.6383 - val_loss: 1.6566 - val_acc: 0.6393\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 26/30\n",
+      "62/62 - 0s - loss: 1.5944 - acc: 0.6424 - val_loss: 1.6365 - val_acc: 0.6441\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 27/30\n",
+      "62/62 - 0s - loss: 1.5963 - acc: 0.6435 - val_loss: 1.6578 - val_acc: 0.6334\n",
+      "Epoch 28/30\n",
+      "62/62 - 0s - loss: 1.5958 - acc: 0.6412 - val_loss: 1.6364 - val_acc: 0.6357\n",
+      "Epoch 29/30\n",
+      "62/62 - 0s - loss: 1.5655 - acc: 0.6501 - val_loss: 1.6174 - val_acc: 0.6510\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Epoch 30/30\n",
+      "62/62 - 0s - loss: 1.5553 - acc: 0.6498 - val_loss: 1.6273 - val_acc: 0.6410\n",
+      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
+      "Evaluate on test data\n",
+      "CPU times: user 40.6 s, sys: 3.77 s, total: 44.3 s\n",
+      "Wall time: 36.3 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "if 'model' not in locals():\n",
+    "    tf.keras.backend.clear_session()\n",
+    "    model, history = train(np.array(X_train), np.array(y_train), np.array(X_test), np.array(y_test))\n",
+    "else:\n",
+    "    print(\"Loaded weights...\")\n",
+    "    model.load_weights(checkpoint_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "adb16aa9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(14, 75)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test[0].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f26ada5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_keras_history(history, name='', acc='acc'):\n",
+    "    \"\"\"Plots keras history.\"\"\"\n",
+    "    import matplotlib.pyplot as plt\n",
+    "\n",
+    "    training_acc = history.history[acc]\n",
+    "    validation_acc = history.history['val_' + acc]\n",
+    "    loss = history.history['loss']\n",
+    "    val_loss = history.history['val_loss']\n",
+    "\n",
+    "    epochs = range(len(training_acc))\n",
+    "\n",
+    "    plt.ylim(0, 1)\n",
+    "    plt.plot(epochs, training_acc, 'tab:blue', label='Training acc')\n",
+    "    plt.plot(epochs, validation_acc, 'tab:orange', label='Validation acc')\n",
+    "    plt.title('Training and validation accuracy  ' + name)\n",
+    "    plt.legend()\n",
+    "\n",
+    "    plt.figure()\n",
+    "\n",
+    "    plt.plot(epochs, loss, 'tab:green', label='Training loss')\n",
+    "    plt.plot(epochs, val_loss, 'tab:red', label='Validation loss')\n",
+    "    plt.title('Training and validation loss  ' + name)\n",
+    "    plt.legend()\n",
+    "    plt.show()\n",
+    "    plt.close()\n",
+    "plot_keras_history(history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d32900e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/1-first-project/Abgabe-wip.ipynb b/1-first-project/Abgabe-wip.ipynb
deleted file mode 100644
index 06dd79f..0000000
--- a/1-first-project/Abgabe-wip.ipynb
+++ /dev/null
@@ -1,1321 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "617fbc6e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "glob_path = '/opt/iui-datarelease2-sose2021/*/split_letters_csv/*'\n",
-    "\n",
-    "pickle_file = 'data.pickle'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "4ad43419",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from glob import glob\n",
-    "import pandas as pd\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "def dl_from_blob(filename) -> list:\n",
-    "    all_data = []\n",
-    "    \n",
-    "    for path in tqdm(glob(filename)):\n",
-    "        path = path\n",
-    "        df = pd.read_csv(path, ';')\n",
-    "        u = path.split('/')[3]\n",
-    "        l = ''.join(filter(lambda x: x.isalpha(), path.split('/')[5]))[0] \n",
-    "        d = {\n",
-    "            'file': path,\n",
-    "            'data': df,\n",
-    "            'user': u,\n",
-    "            'label': l\n",
-    "        }\n",
-    "        all_data.append(d)\n",
-    "    return all_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "a30c00f3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def save_pickle(f, structure):\n",
-    "    _p = open(f, 'wb')\n",
-    "    pickle.dump(structure, _p)\n",
-    "    _p.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "7b942135",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pickle\n",
-    "\n",
-    "def load_pickles(f) -> list:\n",
-    "    _p = open(pickle_file, 'rb')\n",
-    "    _d = pickle.load(_p)\n",
-    "    _p.close()\n",
-    "    \n",
-    "    return _d"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "365d7f88",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading data...\n",
-      "data.pickle found...\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "def load_data() -> list:\n",
-    "    if os.path.isfile(pickle_file):\n",
-    "        print(f'{pickle_file} found...')\n",
-    "        return load_pickles(pickle_file)\n",
-    "    print(f'Didn\\'t find {pickle_file}...')\n",
-    "    all_data = dl_from_blob(glob_path)\n",
-    "    print(f'Creating {pickle_file}...')\n",
-    "    save_pickle(pickle_file, all_data)\n",
-    "    return all_data\n",
-    "\n",
-    "print(\"Loading data...\")\n",
-    "data = load_data()\n",
-    "# plot_pd(data[0]['data'], False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "986ef149",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "def plot_pd(data, force=True):\n",
-    "    fig, axs = plt.subplots(5, 3, figsize=(3*3, 3*5))\n",
-    "    axs[0][0].plot(data['Acc1 X'])\n",
-    "    axs[0][1].plot(data['Acc1 Y'])\n",
-    "    axs[0][2].plot(data['Acc1 Z'])\n",
-    "    axs[1][0].plot(data['Acc2 X'])\n",
-    "    axs[1][1].plot(data['Acc2 Y'])\n",
-    "    axs[1][2].plot(data['Acc2 Z'])\n",
-    "    axs[2][0].plot(data['Gyro X'])\n",
-    "    axs[2][1].plot(data['Gyro Y'])\n",
-    "    axs[2][2].plot(data['Gyro Z'])\n",
-    "    axs[3][0].plot(data['Mag X'])\n",
-    "    axs[3][1].plot(data['Mag Y'])\n",
-    "    axs[3][2].plot(data['Mag Z'])\n",
-    "    axs[4][0].plot(data['Time'])\n",
-    "\n",
-    "    if force:\n",
-    "        for a in axs:\n",
-    "            for b in a:\n",
-    "                b.plot(data['Force'])\n",
-    "    else:\n",
-    "        axs[4][1].plot(data['Force'])\n",
-    "\n",
-    "def plot_np(data, force=True):\n",
-    "    fig, axs = plt.subplots(5, 3, figsize=(3*3, 3*5))\n",
-    "    axs[0][0].plot(data[0])\n",
-    "    axs[0][1].plot(data[1])\n",
-    "    axs[0][2].plot(data[2])\n",
-    "    axs[1][0].plot(data[3])\n",
-    "    axs[1][1].plot(data[4])\n",
-    "    axs[1][2].plot(data[5])\n",
-    "    axs[2][0].plot(data[6])\n",
-    "    axs[2][1].plot(data[7])\n",
-    "    axs[2][2].plot(data[8])\n",
-    "    axs[3][0].plot(data[9])\n",
-    "    axs[3][1].plot(data[10])\n",
-    "    axs[3][2].plot(data[11])\n",
-    "    axs[4][0].plot(data[13])\n",
-    "\n",
-    "    if force:\n",
-    "        for a in axs:\n",
-    "            for b in a:\n",
-    "                b.plot(data[12])\n",
-    "    else:\n",
-    "        axs[4][1].plot(data[12])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "56812df6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def mill_drop(entry):\n",
-    "    #drop millis on single\n",
-    "    data_wo_mill = entry['data'].drop(labels='Millis', axis=1, inplace=False)\n",
-    "    drop_entry = entry\n",
-    "    drop_entry['data'] = data_wo_mill.reset_index(drop=True)\n",
-    "    \n",
-    "    return drop_entry"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "9a3298c0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "def cut_force(drop_entry):\n",
-    "    # force trans\n",
-    "    shorten_entry = drop_entry\n",
-    "    shorten_data = shorten_entry['data']\n",
-    "    sf_entry = shorten_data['Force']\n",
-    "    leeway = 10\n",
-    "    \n",
-    "    try:\n",
-    "        thresh = 70\n",
-    "        temps_over_T = np.where(sf_entry > thresh)[0]\n",
-    "        shorten_data = shorten_data[max(temps_over_T.min()-leeway,0):min(len(sf_entry)-1,temps_over_T.max()+leeway)]\n",
-    "    except:\n",
-    "        thresold = 0.05\n",
-    "        thresh = sf_entry.max()*thresold\n",
-    "        temps_over_T = np.where(sf_entry > thresh)[0]\n",
-    "        shorten_data = shorten_data[max(temps_over_T.min()-leeway,0):min(len(sf_entry)-1,temps_over_T.max()+leeway)]\n",
-    "    \n",
-    "    shorten_entry['data'] = shorten_data.reset_index(drop=True)\n",
-    "    return shorten_entry"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "6ad1c481",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def norm_force(shorten_entry, flist):\n",
-    "    fnorm_entry = shorten_entry\n",
-    "    u = fnorm_entry['user']\n",
-    "    d = fnorm_entry['data']\n",
-    "    \n",
-    "    \n",
-    "    d['Force'] = ((d['Force'] - flist[u].mean())/flist[u].std())\n",
-    "    \n",
-    "    fnorm_entry['data'] = fnorm_entry['data'].reset_index(drop=True)\n",
-    "    return fnorm_entry"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "8a53c07c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def time_trans(fnorm_entry):\n",
-    "    #timetrans\n",
-    "    time_entry = fnorm_entry\n",
-    "    \n",
-    "    time_entry['data']['Time'] = fnorm_entry['data']['Time']-fnorm_entry['data']['Time'][0]\n",
-    "    \n",
-    "    time_entry['data'] = time_entry['data'].reset_index(drop=True)\n",
-    "\n",
-    "    return time_entry"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "571664a9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def norm(time_entry):\n",
-    "    # normalize\n",
-    "    norm_entry = time_entry\n",
-    "    \n",
-    "    norm_entry['data']['Acc1 X'] = norm_entry['data']['Acc1 X'] / 32768\n",
-    "    norm_entry['data']['Acc1 Y'] = norm_entry['data']['Acc1 Y'] / 32768\n",
-    "    norm_entry['data']['Acc1 Z'] = norm_entry['data']['Acc1 Z'] / 32768\n",
-    "    norm_entry['data']['Acc2 X'] = norm_entry['data']['Acc2 X'] / 8192\n",
-    "    norm_entry['data']['Acc2 Y'] = norm_entry['data']['Acc2 Y'] / 8192\n",
-    "    norm_entry['data']['Acc2 Z'] = norm_entry['data']['Acc2 Z'] / 8192\n",
-    "    norm_entry['data']['Gyro X'] = norm_entry['data']['Gyro X'] / 32768\n",
-    "    norm_entry['data']['Gyro Y'] = norm_entry['data']['Gyro Y'] / 32768\n",
-    "    norm_entry['data']['Gyro Z'] = norm_entry['data']['Gyro Z'] / 32768\n",
-    "    norm_entry['data']['Mag X']  = norm_entry['data']['Mag X']  / 8192\n",
-    "    norm_entry['data']['Mag Y']  = norm_entry['data']['Mag Y']  / 8192\n",
-    "    norm_entry['data']['Mag Z']  = norm_entry['data']['Mag Z']  / 8192\n",
-    "#     norm_entry['data']['Mag Z']  = norm_entry['data']['Mag Z']  / 4096\n",
-    "    \n",
-    "    norm_entry['data'] = norm_entry['data'].reset_index(drop=True)\n",
-    "    \n",
-    "    return norm_entry"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "b70412b3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def preproc(d):\n",
-    "    flist = {}  \n",
-    "    d_res = []\n",
-    "    for e in data:\n",
-    "        if e['user'] not in flist:\n",
-    "            flist[e['user']] = e['data']['Force']\n",
-    "        else:\n",
-    "            flist[e['user']] = flist[e['user']].append(e['data']['Force'])\n",
-    "    \n",
-    "    for e in tqdm(data):\n",
-    "        d_res.append(preproc_entry(e, flist))\n",
-    "    return d_res\n",
-    "    \n",
-    "def preproc_entry(entry, flist):\n",
-    "    drop_entry = mill_drop(entry)\n",
-    "#     plot_pd(drop_entry['data'])\n",
-    "#     \n",
-    "    shorten_entry = cut_force(drop_entry)\n",
-    "#     plot_pd(shorten_entry['data'])\n",
-    "#     \n",
-    "    fnorm_entry = norm_force(shorten_entry, flist)\n",
-    "#     plot_pd(fnorm_entry['data'])\n",
-    "#     \n",
-    "    time_entry = time_trans(shorten_entry)\n",
-    "#     plot_pd(time_entry['data'])\n",
-    "#     \n",
-    "    norm_entry = norm(time_entry)\n",
-    "#     plot_pd(norm_entry['data'], False)\n",
-    "    return norm_entry\n",
-    "\n",
-    "print(\"Preprocessing...\")\n",
-    "pdata = preproc(data)\n",
-    "# plot_pd(pdata[0]['data'], False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "32b81b5b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def throw(pdata):\n",
-    "    llist = pd.Series([len(x['data']) for x in pdata])\n",
-    "    threshold = int(llist.quantile(threshold_p))\n",
-    "    longdex = np.where(llist <= threshold)[0]\n",
-    "    return np.array(pdata)[longdex]\n",
-    "\n",
-    "llist = pd.Series([len(x['data']) for x in pdata])\n",
-    "threshold_p = 0.75\n",
-    "threshold = int(llist.quantile(threshold_p))\n",
-    "\n",
-    "print(\"Truncating...\")\n",
-    "tpdata = throw(pdata)\n",
-    "# plot_pd(tpdata[0]['data'], False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "dda26384",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
-    "# ltpdata = []\n",
-    "def elong(tpdata):\n",
-    "    for x in tqdm(tpdata):\n",
-    "        y = x['data'].to_numpy().T\n",
-    "        x['data'] = pad_sequences(y, dtype=float, padding='post', maxlen=threshold)\n",
-    "    return tpdata\n",
-    "\n",
-    "print(\"Padding...\")\n",
-    "ltpdata = elong(tpdata)\n",
-    "# plot_np(ltpdata[0]['data'], False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "6d717d0d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense, Flatten, BatchNormalization, Dropout, Conv2D, MaxPooling2D\n",
-    "\n",
-    "\n",
-    "def build_model():\n",
-    "    model = Sequential()\n",
-    "    ncount = train_shape[0]*train_shape[1]\n",
-    "    \n",
-    "#     model.add(Conv2D(64, (5, 5), input_shape=train_shape, activation='relu', padding='same'))\n",
-    "#     model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "    \n",
-    "#     model.add(Conv2D(64, (5, 5), activation='relu', padding='same'))\n",
-    "#     model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "    \n",
-    "    model.add(Flatten(input_shape=train_shape))\n",
-    "    \n",
-    "    model.add(BatchNormalization())\n",
-    "    \n",
-    "    model.add(Dropout(0.1))\n",
-    "    \n",
-    "    for i in range(2,5):\n",
-    "        model.add(Dense(int(ncount/i), activation='relu'))\n",
-    "        model.add(Dropout(0.1))\n",
-    "    \n",
-    "    model.add(Dense(classes, activation='softmax'))\n",
-    "\n",
-    "    model.compile(\n",
-    "        optimizer=tf.keras.optimizers.Adam(0.001),\n",
-    "        loss=\"categorical_crossentropy\", \n",
-    "        metrics=[\"acc\"],\n",
-    "    )\n",
-    "\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "815c345d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "checkpoint_file = './goat.weights'\n",
-    "\n",
-    "\n",
-    "def train(X_train, y_train, X_test, y_test):\n",
-    "    model = build_model()\n",
-    "    \n",
-    "    model.summary()\n",
-    "    \n",
-    "    model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
-    "        filepath = checkpoint_file,\n",
-    "        save_weights_only=True,\n",
-    "        monitor='val_acc',\n",
-    "        mode='max',\n",
-    "        save_best_only=True\n",
-    "    )\n",
-    "    \n",
-    "    model.fit(X_train, y_train, \n",
-    "              epochs=30,\n",
-    "              batch_size=256,\n",
-    "              shuffle=True,\n",
-    "              validation_data=(X_test, y_test),\n",
-    "              verbose=1,\n",
-    "              callbacks=[model_checkpoint_callback]\n",
-    "             )\n",
-    "    \n",
-    "    print(\"Evaluate on test data\")\n",
-    "    results = model.evaluate(X_test, y_test, batch_size=128, verbose=0)\n",
-    "    print(\"test loss, test acc:\", results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "8b1d12b1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'  # this is required\n",
-    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'         # set to '0' for GPU0, '1' for GPU1 or '2' for GPU2. Check \"gpustat\" in a terminal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "8ba0b7cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import LabelEncoder, LabelBinarizer\n",
-    "\n",
-    "X = np.array([x['data'] for x in ltpdata])\n",
-    "y = np.array([x['label'] for x in ltpdata])\n",
-    "\n",
-    "lb = LabelBinarizer()\n",
-    "y_tran = lb.fit_transform(y)\n",
-    "\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y_tran, test_size=0.2, random_state=177013)\n",
-    "\n",
-    "X_train=X_train.reshape(X_train.shape[0],X_train.shape[1],X_train.shape[2],1)\n",
-    "X_test=X_test.reshape(X_test.shape[0],X_test.shape[1],X_test.shape[2],1)\n",
-    "\n",
-    "train_shape = X_train[0].shape\n",
-    "classes = y_train[0].shape[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "e0d634da",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training...\n",
-      "Model: \"sequential\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d (Conv2D)              (None, 14, 75, 64)        1664      \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d (MaxPooling2D) (None, 7, 37, 64)         0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_1 (Conv2D)            (None, 7, 37, 64)         102464    \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 3, 18, 64)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten (Flatten)            (None, 3456)              0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization (BatchNo (None, 3456)              13824     \n",
-      "_________________________________________________________________\n",
-      "dropout (Dropout)            (None, 3456)              0         \n",
-      "_________________________________________________________________\n",
-      "dense (Dense)                (None, 525)               1814925   \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 525)               0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 350)               184100    \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 350)               0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 262)               91962     \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 262)               0         \n",
-      "_________________________________________________________________\n",
-      "dense_3 (Dense)              (None, 52)                13676     \n",
-      "=================================================================\n",
-      "Total params: 2,222,615\n",
-      "Trainable params: 2,215,703\n",
-      "Non-trainable params: 6,912\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/30\n",
-      "62/62 [==============================] - 4s 14ms/step - loss: 3.4102 - acc: 0.1025 - val_loss: 3.8896 - val_acc: 0.0438\n",
-      "Epoch 2/30\n",
-      "62/62 [==============================] - 1s 10ms/step - loss: 2.7475 - acc: 0.2131 - val_loss: 3.7641 - val_acc: 0.0461\n",
-      "Epoch 3/30\n",
-      "62/62 [==============================] - 1s 9ms/step - loss: 2.2471 - acc: 0.3301 - val_loss: 3.5046 - val_acc: 0.1347\n",
-      "Epoch 4/30\n",
-      "62/62 [==============================] - 1s 9ms/step - loss: 1.8689 - acc: 0.4238 - val_loss: 3.2023 - val_acc: 0.2352\n",
-      "Epoch 5/30\n",
-      "62/62 [==============================] - 1s 9ms/step - loss: 1.6068 - acc: 0.4923 - val_loss: 3.8644 - val_acc: 0.0558\n",
-      "Epoch 6/30\n",
-      "62/62 [==============================] - 1s 9ms/step - loss: 1.3984 - acc: 0.5521 - val_loss: 2.1733 - val_acc: 0.4010\n",
-      "Epoch 7/30\n",
-      "62/62 [==============================] - 1s 10ms/step - loss: 1.2403 - acc: 0.5896 - val_loss: 1.9064 - val_acc: 0.4376\n",
-      "Epoch 8/30\n",
-      "62/62 [==============================] - 1s 9ms/step - loss: 1.1112 - acc: 0.6230 - val_loss: 1.8146 - val_acc: 0.4743\n",
-      "Epoch 9/30\n",
-      "62/62 [==============================] - 1s 9ms/step - loss: 1.0028 - acc: 0.6547 - val_loss: 2.0000 - val_acc: 0.4236\n",
-      "Epoch 10/30\n",
-      "62/62 [==============================] - 1s 9ms/step - loss: 0.9017 - acc: 0.6884 - val_loss: 2.1602 - val_acc: 0.5038\n",
-      "Epoch 11/30\n",
-      "62/62 [==============================] - 1s 9ms/step - loss: 0.8505 - acc: 0.7011 - val_loss: 2.1521 - val_acc: 0.5624\n",
-      "Epoch 12/30\n",
-      "62/62 [==============================] - 1s 10ms/step - loss: 0.7718 - acc: 0.7303 - val_loss: 2.2699 - val_acc: 0.5736\n",
-      "Epoch 13/30\n",
-      "62/62 [==============================] - 1s 10ms/step - loss: 0.7100 - acc: 0.7485 - val_loss: 1.8627 - val_acc: 0.5550\n",
-      "Epoch 14/30\n",
-      "62/62 [==============================] - 1s 9ms/step - loss: 0.6737 - acc: 0.7576 - val_loss: 1.9876 - val_acc: 0.5636\n",
-      "Epoch 15/30\n",
-      "62/62 [==============================] - 1s 9ms/step - loss: 0.6239 - acc: 0.7723 - val_loss: 2.1203 - val_acc: 0.5540\n",
-      "Epoch 16/30\n",
-      "62/62 [==============================] - 1s 10ms/step - loss: 0.5808 - acc: 0.7914 - val_loss: 8.2953 - val_acc: 0.3977\n",
-      "Epoch 17/30\n",
-      "62/62 [==============================] - 1s 10ms/step - loss: 0.5578 - acc: 0.7981 - val_loss: 2.3404 - val_acc: 0.5339\n",
-      "Epoch 18/30\n",
-      "62/62 [==============================] - 1s 10ms/step - loss: 0.5090 - acc: 0.8137 - val_loss: 1.7944 - val_acc: 0.5937\n",
-      "Epoch 19/30\n",
-      "62/62 [==============================] - 1s 10ms/step - loss: 0.4675 - acc: 0.8289 - val_loss: 2.0554 - val_acc: 0.5866\n",
-      "Epoch 20/30\n",
-      "62/62 [==============================] - 1s 10ms/step - loss: 0.4484 - acc: 0.8343 - val_loss: 1.8284 - val_acc: 0.5832\n",
-      "Epoch 21/30\n",
-      "62/62 [==============================] - 1s 9ms/step - loss: 0.4210 - acc: 0.8490 - val_loss: 2.1521 - val_acc: 0.6219\n",
-      "Epoch 22/30\n",
-      "62/62 [==============================] - 1s 9ms/step - loss: 0.4036 - acc: 0.8523 - val_loss: 1.9749 - val_acc: 0.6477\n",
-      "Epoch 23/30\n",
-      "62/62 [==============================] - 1s 10ms/step - loss: 0.3790 - acc: 0.8581 - val_loss: 2.8492 - val_acc: 0.6143\n",
-      "Epoch 24/30\n",
-      "62/62 [==============================] - 1s 10ms/step - loss: 0.3494 - acc: 0.8700 - val_loss: 10.8548 - val_acc: 0.5950\n",
-      "Epoch 25/30\n",
-      "62/62 [==============================] - 1s 9ms/step - loss: 0.3566 - acc: 0.8714 - val_loss: 1.9813 - val_acc: 0.6278\n",
-      "Epoch 26/30\n",
-      "62/62 [==============================] - 1s 9ms/step - loss: 0.3400 - acc: 0.8782 - val_loss: 4.9607 - val_acc: 0.3447\n",
-      "Epoch 27/30\n",
-      "62/62 [==============================] - 1s 9ms/step - loss: 0.3165 - acc: 0.8824 - val_loss: 2.1550 - val_acc: 0.6049\n",
-      "Epoch 28/30\n",
-      "62/62 [==============================] - 1s 9ms/step - loss: 0.3147 - acc: 0.8845 - val_loss: 3.1088 - val_acc: 0.4463\n",
-      "Epoch 29/30\n",
-      "62/62 [==============================] - 1s 9ms/step - loss: 0.2944 - acc: 0.8921 - val_loss: 3.7178 - val_acc: 0.5479\n",
-      "Epoch 30/30\n",
-      "62/62 [==============================] - 1s 9ms/step - loss: 0.2819 - acc: 0.8980 - val_loss: 3.5398 - val_acc: 0.5876\n",
-      "Evaluate on test data\n",
-      "test loss, test acc: [3.5338056087493896, 0.5878309607505798]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Training...\")\n",
-    "train(X_train, y_train, X_test, y_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "9ad292f7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((14, 75, 1), 52)"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_shape, classes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "6681106e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(14, 75, 1)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train[0].shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "ea5ff2c7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "d = data[0]['data'].reshape(1,data[0]['data'].shape[0], data[0]['data'].shape[1], X_train[0].shape[2])\n",
-    "pd.DataFrame(d.reshape(d.shape[1], d.shape[2]).T)\n",
-    "dd = d.reshape(d.shape[1], d.shape[2])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "9c346a61",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 648x1080 with 15 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def pplot(dd):\n",
-    "    x = dd.shape[0]\n",
-    "    fix = int(x/3)+1\n",
-    "    fiy = 3\n",
-    "    fig, axs = plt.subplots(fix, fiy, figsize=(3*fiy, 3*fix))\n",
-    "    \n",
-    "    for i in range(x):\n",
-    "        axs[int(i/3)][i%3].plot(dd[i])\n",
-    "        \n",
-    "pplot(dd)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "c6ba9fb3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 648x1080 with 15 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "conv = Conv2D(1, (3,3), input_shape=train_shape, activation='relu', padding='same')\n",
-    "c = conv(d)\n",
-    "cc = pd.DataFrame(c.numpy().reshape(d.shape[1], d.shape[2]))\n",
-    "\n",
-    "pplot(cc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "2631a70f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 648x648 with 9 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pol = MaxPooling2D(pool_size=(2, 2))\n",
-    "p = pol(c)\n",
-    "pp = pd.DataFrame(p.numpy().reshape(p.shape[1], p.shape[2]))\n",
-    "pplot(pp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "706a384c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>9</th>\n",
-       "      <th>10</th>\n",
-       "      <th>11</th>\n",
-       "      <th>12</th>\n",
-       "      <th>13</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.001282</td>\n",
-       "      <td>0.412811</td>\n",
-       "      <td>-0.256378</td>\n",
-       "      <td>0.000854</td>\n",
-       "      <td>-0.409180</td>\n",
-       "      <td>0.262451</td>\n",
-       "      <td>0.006683</td>\n",
-       "      <td>0.005463</td>\n",
-       "      <td>0.008514</td>\n",
-       "      <td>0.017822</td>\n",
-       "      <td>0.096680</td>\n",
-       "      <td>-0.016479</td>\n",
-       "      <td>-0.561079</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-0.016144</td>\n",
-       "      <td>0.409729</td>\n",
-       "      <td>-0.254547</td>\n",
-       "      <td>0.009766</td>\n",
-       "      <td>-0.405640</td>\n",
-       "      <td>0.272217</td>\n",
-       "      <td>0.005798</td>\n",
-       "      <td>0.004425</td>\n",
-       "      <td>0.008240</td>\n",
-       "      <td>0.017822</td>\n",
-       "      <td>0.096680</td>\n",
-       "      <td>-0.016602</td>\n",
-       "      <td>-0.561079</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-0.006683</td>\n",
-       "      <td>0.408264</td>\n",
-       "      <td>-0.252441</td>\n",
-       "      <td>0.011963</td>\n",
-       "      <td>-0.401001</td>\n",
-       "      <td>0.274780</td>\n",
-       "      <td>0.004425</td>\n",
-       "      <td>0.002502</td>\n",
-       "      <td>0.006561</td>\n",
-       "      <td>0.018555</td>\n",
-       "      <td>0.096558</td>\n",
-       "      <td>-0.016357</td>\n",
-       "      <td>-0.561079</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-0.011963</td>\n",
-       "      <td>0.407440</td>\n",
-       "      <td>-0.253662</td>\n",
-       "      <td>-0.002930</td>\n",
-       "      <td>-0.400635</td>\n",
-       "      <td>0.282837</td>\n",
-       "      <td>0.002716</td>\n",
-       "      <td>0.001312</td>\n",
-       "      <td>0.005707</td>\n",
-       "      <td>0.018433</td>\n",
-       "      <td>0.096680</td>\n",
-       "      <td>-0.015991</td>\n",
-       "      <td>-0.561079</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-0.011292</td>\n",
-       "      <td>0.401672</td>\n",
-       "      <td>-0.246674</td>\n",
-       "      <td>-0.006226</td>\n",
-       "      <td>-0.399658</td>\n",
-       "      <td>0.289795</td>\n",
-       "      <td>0.000549</td>\n",
-       "      <td>0.001709</td>\n",
-       "      <td>0.005615</td>\n",
-       "      <td>0.018311</td>\n",
-       "      <td>0.096680</td>\n",
-       "      <td>-0.016357</td>\n",
-       "      <td>-0.561079</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>70</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>71</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>72</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
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-       "\n",
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-       "\n",
-       "[75 rows x 14 columns]"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(c.numpy().reshape(c.shape[1], c.shape[2]).T)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "73042b1b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "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": "3.8.5"
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-}
diff --git a/1-first-project/Abgabe.ipynb b/1-first-project/Abgabe.ipynb
index 8f86b5d..9c62eea 100644
--- a/1-first-project/Abgabe.ipynb
+++ b/1-first-project/Abgabe.ipynb
@@ -2,8 +2,8 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "dab4afe8",
+   "execution_count": null,
+   "id": "5046da60",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,6 +13,7 @@
     "\n",
     "pickle_file = 'data.pickle'\n",
     "\n",
+    "create_new = True\n",
     "checkpoint_path = \"training_1/cp.ckpt\"\n",
     "checkpoint_dir = os.path.dirname(checkpoint_path)\n",
     "\n",
@@ -26,8 +27,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "fb114dda",
+   "execution_count": null,
+   "id": "52925985",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,8 +56,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "6b23cd45",
+   "execution_count": null,
+   "id": "3dc191e7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,8 +69,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "e3cdaf0c",
+   "execution_count": null,
+   "id": "858d807e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -85,19 +86,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "c1b8c553",
+   "execution_count": null,
+   "id": "0885776a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading data...\n",
-      "data.pickle found...\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "def load_data() -> list:\n",
@@ -117,8 +109,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "7d8249b6",
+   "execution_count": null,
+   "id": "4ec7f36a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -173,8 +165,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "6e7b9412",
+   "execution_count": null,
+   "id": "631fedbc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -189,8 +181,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "52de5447",
+   "execution_count": null,
+   "id": "c86572fb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -219,8 +211,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "e962f5cf",
+   "execution_count": null,
+   "id": "875a8179",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -238,8 +230,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "56056f60",
+   "execution_count": null,
+   "id": "084feef0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -256,8 +248,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "2a353441",
+   "execution_count": null,
+   "id": "ab674796",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -285,25 +277,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "ee11dd88",
+   "execution_count": null,
+   "id": "cb1866fc",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Preprocessing...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 26179/26179 [01:30<00:00, 290.54it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def preproc(d):\n",
     "    flist = {}  \n",
@@ -342,18 +319,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "d32af185",
+   "execution_count": null,
+   "id": "12ec5a1a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Truncating...\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def throw(pdata):\n",
     "    llist = pd.Series([len(x['data']) for x in pdata])\n",
@@ -372,32 +341,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "317f32d3",
+   "execution_count": null,
+   "id": "44aaa509",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  9%|▉         | 1822/19640 [00:00<00:00, 18211.98it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Padding...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 19640/19640 [00:01<00:00, 18646.81it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
     "# ltpdata = []\n",
@@ -414,8 +361,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "39e3d5e1",
+   "execution_count": null,
+   "id": "594a3d76",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -458,8 +405,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "13ba96af",
+   "execution_count": null,
+   "id": "e63d8c6a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -475,8 +422,8 @@
     "\t\t\tsave_best_only=True)\n",
     "    \n",
     "    history = model.fit(X_train, y_train, \n",
-    "                  epochs=30,\n",
-    "                  batch_size=256,\n",
+    "                  epochs=100,\n",
+    "                  batch_size=32,\n",
     "                  shuffle=True,\n",
     "                  validation_data=(X_test, y_test),\n",
     "                verbose=2,\n",
@@ -491,8 +438,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "519579cf",
+   "execution_count": null,
+   "id": "8ba620e1",
    "metadata": {
     "tags": []
    },
@@ -504,8 +451,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "3bf061f4",
+   "execution_count": null,
+   "id": "4449465c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -529,213 +476,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "a8dffb69",
+   "execution_count": null,
+   "id": "f36d7904",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "flatten (Flatten)            (None, 1050)              0         \n",
-      "_________________________________________________________________\n",
-      "dropout_10.0 (Dropout)       (None, 1050)              0         \n",
-      "_________________________________________________________________\n",
-      "batchNorm (BatchNormalizatio (None, 1050)              4200      \n",
-      "_________________________________________________________________\n",
-      "HiddenDropout_10 (Dropout)   (None, 1050)              0         \n",
-      "_________________________________________________________________\n",
-      "Hidden_1 (Dense)             (None, 525)               551775    \n",
-      "_________________________________________________________________\n",
-      "HiddenDropout_20 (Dropout)   (None, 525)               0         \n",
-      "_________________________________________________________________\n",
-      "Hidden_2 (Dense)             (None, 262)               137812    \n",
-      "_________________________________________________________________\n",
-      "HiddenDropout_30 (Dropout)   (None, 262)               0         \n",
-      "_________________________________________________________________\n",
-      "Hidden_3 (Dense)             (None, 131)               34453     \n",
-      "_________________________________________________________________\n",
-      "Output (Dense)               (None, 52)                6864      \n",
-      "=================================================================\n",
-      "Total params: 735,104\n",
-      "Trainable params: 733,004\n",
-      "Non-trainable params: 2,100\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/30\n",
-      "62/62 - 2s - loss: 4.8415 - acc: 0.0671 - val_loss: 4.7052 - val_acc: 0.0723\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 2/30\n",
-      "62/62 - 0s - loss: 4.0441 - acc: 0.1667 - val_loss: 4.2215 - val_acc: 0.1181\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 3/30\n",
-      "62/62 - 0s - loss: 3.4989 - acc: 0.2526 - val_loss: 3.8433 - val_acc: 0.1752\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 4/30\n",
-      "62/62 - 0s - loss: 3.1289 - acc: 0.3177 - val_loss: 3.5399 - val_acc: 0.2210\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 5/30\n",
-      "62/62 - 0s - loss: 2.8521 - acc: 0.3696 - val_loss: 3.3127 - val_acc: 0.2620\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 6/30\n",
-      "62/62 - 0s - loss: 2.6560 - acc: 0.4068 - val_loss: 3.0547 - val_acc: 0.3236\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 7/30\n",
-      "62/62 - 0s - loss: 2.5031 - acc: 0.4395 - val_loss: 2.7836 - val_acc: 0.3933\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 8/30\n",
-      "62/62 - 0s - loss: 2.3725 - acc: 0.4638 - val_loss: 2.5959 - val_acc: 0.4104\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 9/30\n",
-      "62/62 - 0s - loss: 2.2528 - acc: 0.4946 - val_loss: 2.3562 - val_acc: 0.4705\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 10/30\n",
-      "62/62 - 0s - loss: 2.1670 - acc: 0.5070 - val_loss: 2.1431 - val_acc: 0.5323\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 11/30\n",
-      "62/62 - 0s - loss: 2.0921 - acc: 0.5232 - val_loss: 2.0376 - val_acc: 0.5624\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 12/30\n",
-      "62/62 - 0s - loss: 2.0189 - acc: 0.5411 - val_loss: 1.9824 - val_acc: 0.5644\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 13/30\n",
-      "62/62 - 0s - loss: 1.9840 - acc: 0.5495 - val_loss: 1.8983 - val_acc: 0.5802\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 14/30\n",
-      "62/62 - 0s - loss: 1.9388 - acc: 0.5557 - val_loss: 1.8335 - val_acc: 0.6003\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 15/30\n",
-      "62/62 - 0s - loss: 1.8664 - acc: 0.5737 - val_loss: 1.8275 - val_acc: 0.5909\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 16/30\n",
-      "62/62 - 0s - loss: 1.8345 - acc: 0.5861 - val_loss: 1.7849 - val_acc: 0.5942\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 17/30\n",
-      "62/62 - 0s - loss: 1.8048 - acc: 0.5876 - val_loss: 1.7853 - val_acc: 0.5950\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 18/30\n",
-      "62/62 - 0s - loss: 1.7855 - acc: 0.5971 - val_loss: 1.7390 - val_acc: 0.6227\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 19/30\n",
-      "62/62 - 0s - loss: 1.7337 - acc: 0.6060 - val_loss: 1.7086 - val_acc: 0.6225\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 20/30\n",
-      "62/62 - 0s - loss: 1.7177 - acc: 0.6057 - val_loss: 1.6970 - val_acc: 0.6303\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 21/30\n",
-      "62/62 - 0s - loss: 1.6984 - acc: 0.6166 - val_loss: 1.7008 - val_acc: 0.6151\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 22/30\n",
-      "62/62 - 0s - loss: 1.6698 - acc: 0.6223 - val_loss: 1.6824 - val_acc: 0.6171\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 23/30\n",
-      "62/62 - 0s - loss: 1.6533 - acc: 0.6265 - val_loss: 1.6574 - val_acc: 0.6329\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 24/30\n",
-      "62/62 - 0s - loss: 1.6341 - acc: 0.6373 - val_loss: 1.6485 - val_acc: 0.6283\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 25/30\n",
-      "62/62 - 0s - loss: 1.6232 - acc: 0.6366 - val_loss: 1.6606 - val_acc: 0.6334\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 26/30\n",
-      "62/62 - 0s - loss: 1.6098 - acc: 0.6337 - val_loss: 1.6851 - val_acc: 0.6225\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 27/30\n",
-      "62/62 - 0s - loss: 1.5843 - acc: 0.6459 - val_loss: 1.6324 - val_acc: 0.6377\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 28/30\n",
-      "62/62 - 0s - loss: 1.5674 - acc: 0.6538 - val_loss: 1.6377 - val_acc: 0.6286\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 29/30\n",
-      "62/62 - 0s - loss: 1.5684 - acc: 0.6464 - val_loss: 1.6092 - val_acc: 0.6459\n",
-      "Epoch 30/30\n",
-      "62/62 - 0s - loss: 1.5520 - acc: 0.6559 - val_loss: 1.6159 - val_acc: 0.6433\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Evaluate on test data\n",
-      "CPU times: user 40.5 s, sys: 4.35 s, total: 44.9 s\n",
-      "Wall time: 37.2 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
-    "if 'model' not in locals():\n",
+    "if not os.path.isdir(checkpoint_dir) or create_new:\n",
     "    tf.keras.backend.clear_session()\n",
-    "    model, history = train(np.array(X_train), np.array(y_train), np.array(X_test), np.array(y_test))\n",
+    "    model, history = train(np.array(X), np.array(y_tran), np.array(X_test), np.array(y_test))\n",
     "else:\n",
     "    print(\"Loaded weights...\")\n",
+    "    model = build_model(X_train[0].shape, 52)\n",
     "    model.load_weights(checkpoint_path)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "9b57245b",
+   "cell_type": "markdown",
+   "id": "45d3110f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
    "source": [
-    "def plot_keras_history(history, name='', acc='acc'):\n",
-    "    \"\"\"Plots keras history.\"\"\"\n",
-    "    import matplotlib.pyplot as plt\n",
-    "\n",
-    "    training_acc = history.history[acc]\n",
-    "    validation_acc = history.history['val_' + acc]\n",
-    "    loss = history.history['loss']\n",
-    "    val_loss = history.history['val_loss']\n",
-    "\n",
-    "    epochs = range(len(training_acc))\n",
-    "\n",
-    "    plt.ylim(0, 1)\n",
-    "    plt.plot(epochs, training_acc, 'tab:blue', label='Training acc')\n",
-    "    plt.plot(epochs, validation_acc, 'tab:orange', label='Validation acc')\n",
-    "    plt.title('Training and validation accuracy  ' + name)\n",
-    "    plt.legend()\n",
-    "\n",
-    "    plt.figure()\n",
-    "\n",
-    "    plt.plot(epochs, loss, 'tab:green', label='Training loss')\n",
-    "    plt.plot(epochs, val_loss, 'tab:red', label='Validation loss')\n",
-    "    plt.title('Training and validation loss  ' + name)\n",
-    "    plt.legend()\n",
-    "    plt.show()\n",
-    "    plt.close()\n",
-    "plot_keras_history(history)"
+    "# Evaluation"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4960be86",
+   "id": "e05ce2ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ptest = [lb.classes_[e] for e in np.argmax(model.predict(X_test), axis=-1)]\n",
+    "ltest = lb.inverse_transform(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8b2f0353",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "import seaborn as sn\n",
+    "\n",
+    "from sklearn.metrics import classification_report\n",
+    "\n",
+    "set_digits = sorted(list(set(ltest)))\n",
+    "\n",
+    "test_cm = confusion_matrix(ltest, ptest, labels=list(set_digits), normalize='true')\n",
+    "\n",
+    "df_cm = pd.DataFrame(test_cm, index=set_digits, columns=set_digits)\n",
+    "plt.figure(figsize = (20,14))\n",
+    "sn_plot = sn.heatmap(df_cm, cmap=\"Greys\")\n",
+    "plt.ylabel(\"True Label\")\n",
+    "plt.xlabel(\"Predicted Label\")\n",
+    "plt.show()\n",
+    "\n",
+    "print(classification_report(ltest, ptest, zero_division=0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5121b883",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/1-first-project/checkpoint b/1-first-project/checkpoint
deleted file mode 100644
index a3ed6e4..0000000
--- a/1-first-project/checkpoint
+++ /dev/null
@@ -1,2 +0,0 @@
-model_checkpoint_path: "goat.weights"
-all_model_checkpoint_paths: "goat.weights"
diff --git a/1-first-project/goat.weights.data-00000-of-00001 b/1-first-project/goat.weights.data-00000-of-00001
deleted file mode 100644
index e67af86..0000000
Binary files a/1-first-project/goat.weights.data-00000-of-00001 and /dev/null differ
diff --git a/1-first-project/goat.weights.index b/1-first-project/goat.weights.index
deleted file mode 100644
index c3e091e..0000000
Binary files a/1-first-project/goat.weights.index and /dev/null differ
diff --git a/1-first-project/training_1/cp.ckpt/saved_model.pb b/1-first-project/training_1/cp.ckpt/saved_model.pb
index 2894984..c3b9eab 100644
Binary files a/1-first-project/training_1/cp.ckpt/saved_model.pb and b/1-first-project/training_1/cp.ckpt/saved_model.pb differ
diff --git a/1-first-project/training_1/cp.ckpt/variables/variables.data-00000-of-00001 b/1-first-project/training_1/cp.ckpt/variables/variables.data-00000-of-00001
index 9bfae42..1240828 100644
Binary files a/1-first-project/training_1/cp.ckpt/variables/variables.data-00000-of-00001 and b/1-first-project/training_1/cp.ckpt/variables/variables.data-00000-of-00001 differ
diff --git a/1-first-project/training_1/cp.ckpt/variables/variables.index b/1-first-project/training_1/cp.ckpt/variables/variables.index
index 61cbba3..0a5fab5 100644
Binary files a/1-first-project/training_1/cp.ckpt/variables/variables.index and b/1-first-project/training_1/cp.ckpt/variables/variables.index differ
diff --git a/1-first-project/training__bak/cp.ckpt/keras_metadata.pb b/1-first-project/training__bak/cp.ckpt/keras_metadata.pb
new file mode 100644
index 0000000..6376063
--- /dev/null
+++ b/1-first-project/training__bak/cp.ckpt/keras_metadata.pb
@@ -0,0 +1,15 @@
+
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+�root.layer-1"_tf_keras_layer*�{"name": "dropout_10.0", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout_10.0", "trainable": true, "dtype": "float32", "rate": 0.1, "noise_shape": null, "seed": null}, "shared_object_id": 2}2


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+�	root.layer_with_weights-3"_tf_keras_layer*�{"name": "Hidden_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "Hidden_3", "trainable": true, "dtype": "float32", "units": 131, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 19}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 20}, "kernel_regularizer": {"class_name": "L2", "config": {"l2": 0.0010000000474974513}, "shared_object_id": 21}, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 22, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 262}}, "shared_object_id": 32}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 262]}}2


+�
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+�
�
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+�
�
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diff --git a/1-first-project/training__bak/cp.ckpt/saved_model.pb b/1-first-project/training__bak/cp.ckpt/saved_model.pb
new file mode 100644
index 0000000..56f208a
Binary files /dev/null and b/1-first-project/training__bak/cp.ckpt/saved_model.pb differ
diff --git a/1-first-project/training__bak/cp.ckpt/variables/variables.data-00000-of-00001 b/1-first-project/training__bak/cp.ckpt/variables/variables.data-00000-of-00001
new file mode 100644
index 0000000..8eb2265
Binary files /dev/null and b/1-first-project/training__bak/cp.ckpt/variables/variables.data-00000-of-00001 differ
diff --git a/1-first-project/training__bak/cp.ckpt/variables/variables.index b/1-first-project/training__bak/cp.ckpt/variables/variables.index
new file mode 100644
index 0000000..bfa5b67
Binary files /dev/null and b/1-first-project/training__bak/cp.ckpt/variables/variables.index differ
diff --git a/2-second-project/tdt/DataViz.ipynb b/2-second-project/tdt/DataViz.ipynb
index dd4c05e..4a93451 100644
--- a/2-second-project/tdt/DataViz.ipynb
+++ b/2-second-project/tdt/DataViz.ipynb
@@ -2,7 +2,40 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b91d212e",
+   "id": "eafd6e6c",
+   "metadata": {},
+   "source": [
+    "# Change Scenario here.\n",
+    "\n",
+    "|     | GameType | HeightNorm | ArmNorm |\n",
+    "|:---:|:--------:|:----------:|:-------:|\n",
+    "| SYY |  Sorting |      ✅     |    ✅    |\n",
+    "| SYN |  Sorting |      ✅     |    ❌    |\n",
+    "| SNY |  Sorting |      ❌     |    ✅    |\n",
+    "| SNN |  Sorting |      ❌     |    ❌    |\n",
+    "| JYY |   Jenga  |      ✅     |    ✅    |\n",
+    "| JYN |   Jenga  |      ✅     |    ❌    |\n",
+    "| JNY |   Jenga  |      ❌     |    ✅    |\n",
+    "| JNN |   Jenga  |      ❌     |    ❌    |\n",
+    "\n",
+    "Weights for the corresponding scenario are loaded automatically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "89c6a73c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Possibilities: 'SYY', 'SYN', 'SNY', 'SNN', \n",
+    "#                'JYY', 'JYN', 'JNY', 'JNN'\n",
+    "cenario = 'SNY'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c1dc34e",
    "metadata": {},
    "source": [
     "## Constants"
@@ -10,8 +43,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "1bf63c18",
+   "execution_count": 2,
+   "id": "6921bc6b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,8 +56,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "61e91687",
+   "execution_count": 3,
+   "id": "9b20b30b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,15 +67,12 @@
     "\n",
     "pickle_file = '../data.pickle'\n",
     "\n",
-    "checkpoint_path = \"training_1/cp.ckpt\"\n",
-    "checkpoint_dir = os.path.dirname(checkpoint_path)\n",
-    "\n",
     "pd.set_option('display.float_format', lambda x: '%.2f' % x)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "85e6ab7c",
+   "id": "047b3321",
    "metadata": {},
    "source": [
     "# Config"
@@ -50,14 +80,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "3f97f28e",
+   "execution_count": 4,
+   "id": "0c2275bf",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Possibilities: 'SYY', 'SYN', 'SNY', 'SNN', \n",
-    "#                'JYY', 'JYN', 'JNY', 'JNN'\n",
-    "cenario = 'SYN'\n",
+    "create_new = False\n",
+    "checkpoint_path = f\"training_{cenario}/cp.ckpt\"\n",
+    "checkpoint_dir = os.path.dirname(checkpoint_path)\n",
     "\n",
     "win_sz = 5\n",
     "stride_sz = 1\n",
@@ -74,7 +104,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fddb1e58",
+   "id": "10d070f3",
    "metadata": {},
    "source": [
     "# Helper Functions"
@@ -82,8 +112,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "8d1865b9",
+   "execution_count": 5,
+   "id": "46f13510",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,7 +131,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09693b32",
+   "id": "8aa25439",
    "metadata": {},
    "source": [
     "# Loading Data"
@@ -109,8 +139,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "ccbd870e",
+   "execution_count": 6,
+   "id": "94f77686",
    "metadata": {
     "tags": []
    },
@@ -154,8 +184,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "f0f4201c",
+   "execution_count": 7,
+   "id": "f021e1d8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,8 +199,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "83695ce1",
+   "execution_count": 8,
+   "id": "f4a1b342",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -184,8 +214,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "92216c47",
+   "execution_count": 9,
+   "id": "1540ece8",
    "metadata": {},
    "outputs": [
     {
@@ -195,8 +225,8 @@
       "Loading data...\n",
       "../data.pickle found...\n",
       "768\n",
-      "CPU times: user 614 ms, sys: 2.53 s, total: 3.14 s\n",
-      "Wall time: 3.14 s\n"
+      "CPU times: user 535 ms, sys: 2.43 s, total: 2.97 s\n",
+      "Wall time: 2.97 s\n"
      ]
     }
    ],
@@ -220,8 +250,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "6f337a51",
+   "execution_count": 10,
+   "id": "25f648ae",
    "metadata": {
     "tags": []
    },
@@ -230,8 +260,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 97 µs, sys: 302 µs, total: 399 µs\n",
-      "Wall time: 402 µs\n"
+      "CPU times: user 343 µs, sys: 200 µs, total: 543 µs\n",
+      "Wall time: 549 µs\n"
      ]
     }
    ],
@@ -279,7 +309,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9500a64",
+   "id": "049c83fa",
    "metadata": {},
    "source": [
     "# Preprocessing"
@@ -287,8 +317,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "c7c9d655",
+   "execution_count": 11,
+   "id": "95a39c6e",
    "metadata": {
     "tags": []
    },
@@ -307,8 +337,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "f5b437a2",
+   "execution_count": 12,
+   "id": "5bc3de2b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -336,8 +366,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "f126154b",
+   "execution_count": 13,
+   "id": "ca4a71d9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -364,8 +394,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "d50c3391",
+   "execution_count": 14,
+   "id": "71aa29b6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -380,8 +410,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "60629469",
+   "execution_count": 15,
+   "id": "bdbada4a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -405,8 +435,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "4de2497d",
+   "execution_count": null,
+   "id": "612c5b39",
    "metadata": {
     "tags": []
    },
@@ -415,7 +445,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 96/96 [00:05<00:00, 16.44it/s] \n"
+      "100%|██████████| 96/96 [00:05<00:00,  9.51it/s] "
      ]
     }
    ],
@@ -441,8 +471,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "7b167a3c",
+   "execution_count": null,
+   "id": "d8dde568",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -452,8 +482,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "044a73e4",
+   "execution_count": null,
+   "id": "238e7d22",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -476,8 +506,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "7e7526e9",
+   "execution_count": null,
+   "id": "311ad27d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -494,33 +524,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "e9ace1d0",
+   "execution_count": null,
+   "id": "9ce4736b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 96/96 [00:14<00:00,  6.79it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 12.8 s, sys: 1.54 s, total: 14.3 s\n",
-      "Wall time: 14.1 s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "\n",
@@ -563,33 +570,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "a067392e",
+   "execution_count": null,
+   "id": "2684b1c6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f37f07b3fa0>]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "a = drop(cdata[cenario][0]['data'], False)\n",
     "a['left_OVRHandPrefab_pos_X'].plot()\n",
@@ -598,33 +582,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "b1567410",
+   "execution_count": null,
+   "id": "225ba8a8",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f37f0654970>]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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TfBbITcwlX5qHH6YTpzv9HMnJ8vO+9fJq9je30d7ZzZ3XLcprkyVFBdx5/SL+779v5dFtjVy3eOBpnP1p34wC9fAjycd08k32bXS8NePCZBHs1ZYUFfC5P13M3990AWOKC/N+v1svT831//ULDQFUJz4JJPDNbJmZbTezWjNbNcDrd5rZVjPbbGaPmdm8ILYbfdELlzhqbs3tSpSjIsT/eAoLjHe+aQaPbD3I4ZPBXY9fwpd34JtZIXAfcAOwGLjFzBb3We0FoMY5dyHwc+DefLcrMqQsh0SOt3bgNCRxhnddNJP2rm5e1Jz8WAmih38ZUOuc2+2cawfWACsyV3DOPe6ca0k/fRaYHcB240Pz8EPlZQ8fCPPznjulDIC6I1neFUvHlyIhiMCfBdRlPK9PLxvMR4DfDvSCma00sw1mtqGpqSmA0mIqguPMoy6HNjoz8NW2AFXjSxlTXEDdkZahV5bIGNWDtmb2F0AN8JWBXnfOrXbO1TjnaqqqqkaztHAouL3gZw8/3H3DzJg9qYy6ozkGvvZprwUxLbMBmJPxfHZ62RnMbClwF/B255yOBMkoyHIMv61j2GemxtmsiWPZd2zom61IdATRw18PLDSz+WZWAtwMrM1cwcwuAf4fcKNzrjGAbcaM5uGHqbm1I5DpjIEL+fOeOXEM+5uzHMPX8aVIyDvwnXOdwB3AQ8A24EHn3BYzu8fMbkyv9hVgPPAzM9tkZmsHeTvJir42Dy2XMfxOxhSnfxU0JNFrZsVYDp1sz7jOkERdIGfaOufWAev6LLs74/HSILYTPwoXH/T28NvDriSDB//xzJg4FoADzW1UVw58p6z+wq9bBqczbSW+shxlaG7toLTIwyGdkM2cmLomz75j2Q7riO8U+D7QPPxQnfB1DD/kz3tmRaqHv685iwO3Or4UCQr8KPLg6773cpyH3zuGryGJXj1X3VQPPz4U+GFScIfOOefpLJ3w940xxYVUji/JYaYO2qc9p8CXGBt6mKG1o4vObkdpsa4UPpCZE8fSoLn4saHA94Lm4Yfl1OnUlMOSIg9/FTz4vGdUjGG/hnRiw8O9XIamr81Dy66NWtpT924tKdQ8/IHMnDiWfcdadfermFDgS6K1tPf08MPvTZ/Bk4CdWTGWU+1dHG/L9qbmftQtA1PgS3xlMSTS08MvLvTtoK0fZqZPvqrP9SJq4iUFvg80Dz80PT38Yh/H8D34vOdO7rku/hCB78HxBhmaj3u5yKjpPWhbqF+FgfTcCGXPYfXw40B7eZiGO07ryfiu17Jso9aOniEd30688qOOirHFTCorZk+2N0LRvuk1Bb7EWDZj+B5Py/TE3Cnj2HP4VNhlSAC0l3tB8/DD0pIe0in2cUjHk8973uSyLIZ0/KhVzs7DvVxk9KiHP7R5U8rYd6yV9s7usEuRPGkvD9Vwxzs1Tjq07E+8Ki0qoKCnN+3LGLQnZUBqpk63y3ZqpkeFSz8KfImvrObhd1FWUoiGJAZ33vQJAGzZdzzkSiRfCnwfaB5+aFKB7+uF0/z4vM+bUU5pUQEv7D02+EqeHG+Qs1PgS6K1tHeme/gymOLCAt40q4JNdUfDLkXypMAPk+bhj5ws26ilvYuy0swevi9t60sdKRfMqmDb/hNDX0RN+6bXFPgSY9ldS6esuFBDEkOYN6WM1o4ujpzy6U7vkqtAAt/MlpnZdjOrNbNVA7xeamY/Tb/+nJlVB7HdxFEoBe71g7Ye8ujznj0pfU2do4NdG9+fWmVweQe+mRUC9wE3AIuBW8xscZ/VPgIcdc6dC3wD+HK+2xUJQv8hHRnInMmpq2YOeRE18VoQe/plQK1zbjeAma0BVgBbM9ZZAXw+/fjnwHfMzNxI3FWh9Rj87IOBv+2I6Brm1+Onvgp/vD/YWuLm4BYoLR9ytd4hnR4//Uso8OA/gHa/LmUwp7eHP3Dg72w8yUKg8zefoGjM0O0uQ6hcBMvvDfxtg9izZwF1Gc/rgT8ZbB3nXKeZNQNTgEOZK5nZSmAlwNy5c4dZjvPul+Ws5l0Jcy/Pbt2Jc2HRMmg5Eq2fMQyTquHcpWddxTnHibZOxpUWwbwrUn86TwOnR6XEs5r9Fjj32rCr6DWutIjpE8ZQe/DkgK9/4YUxfKLgTVxipn0zCJ0jc1tJD7oyr3POrQZWA9TU1Ayv9z92Etz+SJBl+aN4LPz5T8OuIjZOnO6kpb2L6RWlMGMxfGhd2CV5bfHMCWzd3//kq6YTp3mycSxXLr+fmqsWhFCZZCuIg7YNwJyM57PTywZcx8yKgArgcADbFhm2A81tAMyoGBtyJdFw/oxyahtP0tbRdcbyp3Y0AXDpvIkhVCW5CCLw1wMLzWy+mZUANwNr+6yzFrgt/fi9wO9HZPxeJAf7ewN/TMiVRMPiGRV0djtqG88c1vnp+joWVI7jotkTwylMspZ34DvnOoE7gIeAbcCDzrktZnaPmd2YXu0HwBQzqwXuBPpN3RQZbS/sTZ05Ol2Bn5XFM1PX1NlUd6x3WeOJNjbsOcKfXjiDIh8vMS1nCGQM3zm3DljXZ9ndGY/bgPcFsS2RoHzz0Z0ATC1X4GejekoZcyeX8fDWg/zFknkArN20j24HKy6ZFXJ1kg39lyyxdvJ0Z+9lfZtbO1j91C7u/8Nrva+XlxbpWvhZMjPeeeEMnqk9xMHjbbR1dPGT5/Zy0ZyJnFM1PuzyJAtezdJJoraOLk53dFNRVhx2KbG0/FtPs/dIC69+cTnXfu0JDp1Mnftw2fzJlBQW8IF0T1Wy82c1c/jeE7v4m5+9yGuHT1F3pJV/ePcFYZclWVLXJkSt7V1c/sXH+O6TtWGXElt702eG/v6Vxt6wB1j2zadp7+pm8jj9R5uL6spxXLWoiqd3HqK7G759yyXc8pbhnjMjo02BH6KxJYVcOncSv36hga5uTVoKWkt7Z+/jz/36ZWZUjGH9XUv5xDXn9i6fWFYSRmmR9oUVF/C37zyfh//XVdx40UwKCnQdnahQ4IfsPW+ezcHjp/mv2kNDryw52Xfs9bMV9zW38Y43TqeqvJRPX/8G3vfm2UDqW5bkZu6UMm5/24LUGcoSKQr8kF17/lQqxhbzqz/Wh11K7NT3ubLjommvX+PlY29fwJRxJVxz3tTRLkskNPovOmSlRYW89ZwpZ8xtlmA0HOsb+K/PJDl3ajkbP3fdaJckEir18D2wcFo5e4609DtlXfLTcLSVoozx5YXTdBVHSTYFvgcWTRuPc/Q7ZV2G779rD/HdJ3adcRZtxVjNyJFk05COB3rGlmsbT3LBrIqQq4mHTfXHABhfWsS3br6Y460d4RYk4gEFvgfmV46jtKiAF+uPcZNOUQ/E4hmp675MKithxcVqUxHQkI4XigsLuGz+ZH7/SiO6iGgwLj9nCp+8diF3vfP8sEsR8YYC3xPXL57GnsMt/aYSyvCUFhVy53WLNEQmkkGB74k3TE8NQexqGvzA7cY9R7n+G0+yZV/zaJUlIjGiwPfEuVNTc8TPNlNn454j7Dh4kmkTdDlfEcmdAt8Tk8eVUDm+lFcOnBh0nT/uOcbcyWVUji8dxcpEJC4U+B5ZPHMCW/b1v0l0j5camrl4zsTRK0hEYkWB75E3zpzAzoMnON3Z/4zbrm7H/uZW5k0pC6EyEYkDBb5HzpteTme3Y8/hln6vHTp5mm4HUzV+LyLDpMD3yOxJqd57wwBTMw8ebwNgugJfRIZJge+RqeWpg7GNJ9r6vXagWYEvIvnJK/DNbLKZPWJmO9N/TxpgnYvN7A9mtsXMNpvZn+WzzTir6gn846f7vbb70CkgdfMJEZHhyLeHvwp4zDm3EHgs/byvFuBW59wbgWXAN81sYp7bjaUxxYVUjC2m8UT/wN9x8ATTJpTqio8iMmz5Bv4K4Efpxz8Cbuq7gnNuh3NuZ/rxPqARqMpzu7E1tby0d7w+067Gk70nZ4mIDEe+gT/NObc//fgAMO1sK5vZZUAJsGuQ11ea2QYz29DU1JRnadFUVV7KoZNn9vCdc7x66BTzK8eFVJWIxMGQl0c2s0eB6QO8dFfmE+ecM7NBL/VoZjOAHwO3Oee6B1rHObcaWA1QU1OTyMtGVo4v7Xe7w6MtHRxv66R6igJfRIZvyMB3zi0d7DUzO2hmM5xz+9OB3jjIehOA/wTucs49O+xqE2CgHv6ew6kDtgp8EclHvkM6a4Hb0o9vA37TdwUzKwF+BdzvnPt5ntuLvcrxpbS0d3HqdGfvsp6bcc+ePDasskQkBvIN/C8B15nZTmBp+jlmVmNm30+v837gKuCDZrYp/efiPLcbWz1TMzN7+fvSgT9zogJfRIYvr1scOucOA9cOsHwDcHv68b8C/5rPdpKkcnwJAE0nTjMvPYSz71gb5aVFTBijKZkiMnw609YzsyelevF7j7x+PZ09h08xa5J69yKSHwW+Z+ZNGUdxobHj4Os3Qtlx8CRvmF4eYlUiEgcKfM8UFxawoHI8Ow+mboTS2t5Fw7FWzq3SSVcikh8FvocWThvPjsZU4Pfc43aBAl9E8qTA99CiaeXUHWnl1OlONu45CsAFsyaEXJWIRJ0C30OLpr1+Q/M/7DrM7Elje2fsiIgMlwLfQwunpQ7Qbj94ghfrj3Hp3H5XnRYRyZkC30PVU8YxsayYnzy3l/3NbdRUK/BFJH8KfA8VFhjXvGEqL6Yvonbt+We9CKmISFYU+J5aujgV8pPKipmlSyqISADyurSCjJxrzpvKx//HObz7kllhlyIiMaHA99SY4kI+847zwi5DRGJEQzoiIgmhwBcRSQgFvohIQijwRUQSQoEvIpIQCnwRkYRQ4IuIJIQCX0QkIcw5F3YNAzKzJmBPHm9RCRwKqJy4UhtlR+2UHbXT0EajjeY556oGesHbwM+XmW1wztWEXYfP1EbZUTtlR+00tLDbSEM6IiIJocAXEUmIOAf+6rALiAC1UXbUTtlROw0t1DaK7Ri+iIicKc49fBERyaDAFxFJiNgFvpktM7PtZlZrZqvCridsZvaamb1kZpvMbEN62WQze8TMdqb/npRebmb27XTbbTazS8OtfmSY2Q/NrNHMXs5YlnObmNlt6fV3mtltYfwsI2mQdvq8mTWk96dNZrY847XPpttpu5m9I2N5bH8nzWyOmT1uZlvNbIuZfSq93M/9yTkXmz9AIbALWACUAC8Ci8OuK+Q2eQ2o7LPsXmBV+vEq4Mvpx8uB3wIGLAGeC7v+EWqTq4BLgZeH2ybAZGB3+u9J6ceTwv7ZRqGdPg/8zQDrLk7/vpUC89O/h4Vx/50EZgCXph+XAzvSbeHl/hS3Hv5lQK1zbrdzrh1YA6wIuSYfrQB+lH78I+CmjOX3u5RngYlmNiOE+kaUc+4p4Eifxbm2yTuAR5xzR5xzR4FHgGUjXvwoGqSdBrMCWOOcO+2cexWoJfX7GOvfSefcfufcH9OPTwDbgFl4uj/FLfBnAXUZz+vTy5LMAQ+b2UYzW5leNs05tz/9+AAwLf04ye2Xa5skua3uSA9H/LBnqAK1E2ZWDVwCPIen+1PcAl/6u9I5dylwA/BxM7sq80WX+j6pubkZ1CZn9T3gHOBiYD/wtVCr8YSZjQd+Afy1c+545ms+7U9xC/wGYE7G89npZYnlnGtI/90I/IrUV+yDPUM16b8b06snuf1ybZNEtpVz7qBzrss51w38E6n9CRLcTmZWTCrsf+Kc+2V6sZf7U9wCfz2w0Mzmm1kJcDOwNuSaQmNm48ysvOcxcD3wMqk26ZkFcBvwm/TjtcCt6ZkES4DmjK+lcZdrmzwEXG9mk9LDGtenl8Van2M67ya1P0GqnW42s1Izmw8sBJ4n5r+TZmbAD4BtzrmvZ7zk5/4U9lHuEThqvpzUkfJdwF1h1xNyWywgNSviRWBLT3sAU4DHgJ3Ao8Dk9HID7ku33UtATdg/wwi1ywOkhiM6SI2VfmQ4bQJ8mNTByVrgQ2H/XKPUTj9Ot8NmUuE1I2P9u9LttB24IWN5bH8ngStJDddsBjal/yz3dX/SpRVERBIibkM6IiIyCAW+iEhCKPBFRBJCgS8ikhAKfBGRhFDgi4gkhAJfRCQh/j/j+cvIuyAsEgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "b = rem_low_acc(a, False)\n",
     "b['left_OVRHandPrefab_pos_X'].plot()\n",
@@ -633,33 +594,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "b04ce014",
+   "execution_count": null,
+   "id": "d42fc99a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f37f05d9310>]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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ui4HFvZbd3ce6V59+Wdng7jq7IkGO8U/Lt3DZ2aMZ2ziY7z+xkRe27+ejF02guXEQv1qxjUunjI6CKEVbIoWfFJobB/FnH2nlyw+t4KsPr+RD55/Fyq37+IdlL3PwaDfnnjmM4YF9uVkt0pdzJei5LXu5CPjRk5v5zAfHluUyY/KWTTvf5MuPruBtZzVy6Gj38SGBX63YdnydP/vISVMmpA83XDKRF7fnLoaxcHnuxLbzxw/n2x+fwfDF2jsPgQI9QRdMGAEr4bv/9hJDRo/nhraWIj+hwI/Lgec79gLwwvb9NNQZ93+2jXPPHMbj63fytV+somnYIC6cmObhlnRtbzPjax9p5cPvGMfrh7qY0TLyrWEW7YwEQYGeoAHR94e0jm/kL3+7nlkXjmPYoCJNnsKP42lzuKubuu5utu07xFXTm3h8/U5uaGvhg61nAjB5zBl8vK2FI909vX5SbRvHRZNGVbsE6ac0fsFF5tzx/ulsf/0Qd/58xSnX27LnIIe7enCF+ikNaqinzoyB9XXcc+35PHDzu7jrw28/YZ2BDXXF/3iKZIwCPUlRMF8yeTS3vXcav1qxrc8p1rvfPMKTG3dx8Gg33b0v2S4nqTfjlqvOZlrzMK4+b2zx8E7VkEGg21c7GqmnQK+QL7znbEYNHcC3lqwr+Pgvn9vK4aPdDBvUQEO9NkscaYpokTRQclSEMWLIAD59+WQeX9/J3gMnfv+0u/PQ0x2MGDqQhjJdlUYCkKpPDcWEVGvtUqBX0NXnjaXHYWmvi/MuXb+T1a++Tuu44VWqTESyQIFeQTNaRtLcOIh/fv7ECwr8w7KXaRo2iKlNZxDs+GrF9aOdNAYsGadAr6D6OuO6GeN57IUdxy/7tWX3Af593Q4+etF46jXcUhuC/cMSat21Q4FeYddf0kJXj/NA9P3T/+uRF6ivM/7wyqnVLSxEJY1B64+lZJ9O1K2EvOA576xGPvyOcSxY+hIHj3Tx65Xb+OIHz2XciCEodGpNQNvbLOBPFrVDe+hVcNfst9M4eAD3P76JK84Zw21XT6t2SSKSAdpDr4LxI4fw4B/9Hss27uK6GRMYkH/eufaC4ulXO6ltJdsU6FUytemM6KwWEZHy0JBLkrS3nbASxqBTNYkn0H6h/px6CvSK0DVFpQBtbykzBbqISEYo0FNHH2vj0UxRkd4U6CIiGaFAT5T2CBMV6kzRYLtFsIXXDAW6iEhGKNArIfaeZIr2IqUCAtreOiMnCAp0EZGMUKCnjc7EiEdT/0VOokBPksJZCgq0X6g/p54CXQIW6tR/kWQo0NNEoVNbgtreIdVauxToIiIZoUBPHY1TxqOp/yK9xQp0M5tpZuvMbIOZ3Vng8S+a2RozW2Fmj5rZ5PKXGiIFiBQQ7B+WUOuuHUUD3czqgfnALKAVmGtmrb1WexZoc/d3AA8B3yx3oSInCXXqv0hC4uyhXwpscPeN7n4EWAjMyV/B3R9z9wPR3WXAxPKWGTjNFJWCAtreQR3ArV1xAn0CsCXvfke0rC+3AI8UesDMbjWzdjNr7+zsjF+liIgUVdaDomb2aaAN+Fahx919gbu3uXtbc3NzOV86OzRMmSA1rmRbnItEbwVa8u5PjJadwMw+ANwFvNfdD5envMAFe/BLkhVov1B/Tr04e+jLgelmNtXMBgI3AovyVzCzi4C/Ba519x3lL1OkEM0UFclXNNDdvQuYBywB1gIPuvtqM7vXzK6NVvsWMAz4JzN7zswW9fF0NUoXiZYCgtrcQRVbs+IMueDui4HFvZbdnXf7A2WuS0RESqSZoiIiGaFATx0deCqqvwfndFBPMk6BnigFSKJCnSka7B+WUOuuHQr0StBMUSkooO2tA/ZBUKCLiGSEAl1EJCMU6GkT7PhqBfW7jdS2km0K9CQpnKWgQPuF+nPqKdAlYJr6L5JPgV4RmvovBQS1vUOqtXYp0EVEMkKBnjoapyxOM0VFClGgJ0oBIgUE+4cl1LprhwJdwhXq1H+RhCjQKyGog19SOQH1C/XhICjQRUQyQoEuIpIRCvQk9efgV7AHzCoo+DYKtP7g2z37FOgSMM0UFcmnQK8IzRSVAoLa3iHVWrsU6CIiGaFAFxHJCAV6ovpzEEkHnooLfOp/WuooWah11w4FuohIRijQK0EXiU5GSc2VxrZNY019COoAbu1SoIuIZIQCXUQkIxToSdJM0WQEf5HotNRRIvXN1FOgi4hkhAK9IjRTNBmlTP1Prop+C2p7h1Rr7VKgi4hkRKxAN7OZZrbOzDaY2Z0FHh9kZj+LHn/KzKaUvVIRETmlooFuZvXAfGAW0ArMNbPWXqvdAuxx93OAvwC+Ue5CRUTk1BpirHMpsMHdNwKY2UJgDrAmb505wD3R7YeA+8zM3BM4LP7Mj+HJ+8r+tIk4sKv0n+k6CPMvK38tWeI9/fu5TUvT0baH91e7gv7ZsTYd7ZcF7/0yXPCxsj9tnECfAGzJu98B9N6qx9dx9y4z2weMAXbmr2RmtwK3AkyaNKl/FQ8dDc3n9e9nq2HUVBgwJN66rdfB3lf6H1i15KwL4bzZ8de/7DZYvyS5eko1+APQdG61q4jvks/BgMHVriI7Bo9M5Gmt2E60mV0PzHT3z0f3PwNc5u7z8tZZFa3TEd1/KVpnZ6HnBGhra/P29vYy/AoiIrXDzJ5297ZCj8U5KLoVaMm7PzFaVnAdM2sARgD9GG8QEZH+ihPoy4HpZjbVzAYCNwKLeq2zCLgpun098G+JjJ+LiEifio6hR2Pi84AlQD3wA3dfbWb3Au3uvgj4PvBjM9sA7CYX+iIiUkFxDori7ouBxb2W3Z13+xBwQ3lLExGRUmimqIhIRijQRUQyQoEuIpIRCnQRkYwoOrEosRc26wRe7uePN9FrFqoUpHaKR+1UnNoonkq002R3by70QNUC/XSYWXtfM6XkLWqneNROxamN4ql2O2nIRUQkIxToIiIZEWqgL6h2AYFQO8WjdipObRRPVdspyDF0ERE5Wah76CIi0osCXUQkI4IL9GIXrK4lZrbZzFaa2XNm1h4tG21mvzGz9dH/o6LlZmbfjdpthZldXN3qk2NmPzCzHdGFV44tK7ldzOymaP31ZnZTodcKWR/tdI+ZbY361HNmNjvvsa9E7bTOzD6Utzyz70kzazGzx8xsjZmtNrM7ouXp7E/uHsw/cl/f+xJwNjAQeB5orXZdVWyPzUBTr2XfBO6Mbt8JfCO6PRt4BDDgcuCpatefYLu8B7gYWNXfdgFGAxuj/0dFt0dV+3erQDvdA/xJgXVbo/fbIGBq9D6sz/p7EhgHXBzdbgRejNoilf0ptD304xesdvcjwLELVstb5gA/jG7/ELgub/mPPGcZMNLMxlWhvsS5+1Jy38ufr9R2+RDwG3ff7e57gN8AMxMvvoL6aKe+zAEWuvthd98EbCD3fsz0e9Ldt7n7M9Ht/cBactdQTmV/Ci3QC12wekKVakkDB/7VzJ6OLsANcKa7b4tubwfOjG7XetuV2i613F7zouGCHxwbSkDthJlNAS4CniKl/Sm0QJcTXenuFwOzgNvN7D35D3rus57OS+1F7XJK3wOmATOAbcD/qWo1KWFmw4CfA//N3V/PfyxN/Sm0QI9zweqa4e5bo/93AA+T+/j72rGhlOj/HdHqtd52pbZLTbaXu7/m7t3u3gPcT65PQQ23k5kNIBfm/+ju/zdanMr+FFqgx7lgdU0wszPMrPHYbeAaYBUnXrD7JuCX0e1FwGejo/CXA/vyPjLWglLbZQlwjZmNioYdromWZVqv4yofJdenINdON5rZIDObCkwHfkfG35NmZuSumbzW3b+d91A6+1O1jyL346jzbHJHml8C7qp2PVVsh7PJnVHwPLD6WFsAY4BHgfXAb4HR0XID5kftthJoq/bvkGDb/JTccMFRcmOVt/SnXYA/JHfwbwNwc7V/rwq104+jdlhBLpzG5a1/V9RO64BZecsz+54EriQ3nLICeC76Nzut/UlT/0VEMiK0IRcREemDAl1EJCMU6CIiGaFAFxHJCAW6iEhGKNBFRDJCgS4ikhH/HyJEX7uYuMpYAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "c = norm(b, False)\n",
     "c['left_OVRHandPrefab_pos_X'].plot()\n",
@@ -668,33 +606,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "62e4eb23",
+   "execution_count": null,
+   "id": "7d58b0f5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f37f05524c0>]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "d = interpol(c, False)\n",
     "d['left_OVRHandPrefab_pos_X'].plot()\n",
@@ -703,29 +618,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "cc70c742",
+   "execution_count": null,
+   "id": "fb02ea59",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 123 µs, sys: 105 µs, total: 228 µs\n",
-      "Wall time: 238 µs\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(48, 48)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "train = np.array([x['data'] for x in pdata if x['session'] == 1])\n",
@@ -736,27 +632,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "7b44ef39",
+   "execution_count": null,
+   "id": "64b1f388",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 96/96 [00:36<00:00,  2.61it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(57800, 5, 336) (57800,) (37106, 5, 336) (37106,)\n",
-      "CPU times: user 1min 48s, sys: 15.1 s, total: 2min 3s\n",
-      "Wall time: 37.1 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "\n",
@@ -810,58 +689,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "27f1c824",
+   "execution_count": null,
+   "id": "4bf9d67f",
    "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Key: 1: 1347\n",
-      "Key: 2: 1583\n",
-      "Key: 3: 8568\n",
-      "Key: 4: 3034\n",
-      "Key: 5: 1960\n",
-      "Key: 6: 3311\n",
-      "Key: 7: 3971\n",
-      "Key: 8: 1407\n",
-      "Key: 9: 1135\n",
-      "Key: 10: 7466\n",
-      "Key: 11: 6494\n",
-      "Key: 12: 1813\n",
-      "Key: 13: 3596\n",
-      "Key: 14: 3260\n",
-      "Key: 15: 2825\n",
-      "Key: 16: 6030\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([<AxesSubplot:ylabel='0'>], dtype=object)"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Xy_train = list(zip(X_train, y_train))\n",
     "Xy_test = list(zip(X_test, y_test))\n",
@@ -874,55 +707,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "34e0af8d",
+   "execution_count": null,
+   "id": "e608f7f3",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Key: 1: 790\n",
-      "Key: 2: 59\n",
-      "Key: 3: 4330\n",
-      "Key: 4: 0\n",
-      "Key: 5: 545\n",
-      "Key: 6: 348\n",
-      "Key: 7: 5245\n",
-      "Key: 8: 3558\n",
-      "Key: 9: 2565\n",
-      "Key: 10: 4163\n",
-      "Key: 11: 3654\n",
-      "Key: 12: 2868\n",
-      "Key: 13: 2130\n",
-      "Key: 14: 2360\n",
-      "Key: 15: 2390\n",
-      "Key: 16: 2101\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([<AxesSubplot:ylabel='0'>], dtype=object)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Xy_test = list(zip(X_test, y_test))\n",
     "test_dict = {\"1\":[], \"2\":[],\"3\":[], \"4\":[], \"5\":[],\"6\":[], \"7\":[], \"8\":[],\"9\":[], \"10\":[], \"11\":[],\"12\":[], \"13\":[], \"14\":[], \"15\": [], \"16\": []}\n",
@@ -934,19 +724,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "id": "4b6d7b97",
+   "execution_count": null,
+   "id": "b681b93c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 348 ms, sys: 22.5 ms, total: 371 ms\n",
-      "Wall time: 370 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "\n",
@@ -960,8 +741,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "id": "90634662",
+   "execution_count": null,
+   "id": "630ad588",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -977,21 +758,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "id": "02c58b6d",
+   "execution_count": null,
+   "id": "3d127f9a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(57800, 5, 336)\n",
-      "(57800, 16)\n",
-      "(37106, 5, 336)\n",
-      "(37106, 16)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(X_train.shape)\n",
     "print(yy_train.shape)\n",
@@ -1001,7 +771,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff2da104",
+   "id": "5647746c",
    "metadata": {},
    "source": [
     "# Building Model"
@@ -1009,8 +779,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "a5ef1b0f",
+   "execution_count": null,
+   "id": "c5b4f772",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1048,13 +818,14 @@
     "        metrics=[\"acc\"],\n",
     "    )\n",
     "    \n",
+    "    model.summary()\n",
     "    return model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "id": "ccbb5d69",
+   "execution_count": null,
+   "id": "a4ad2401",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1063,8 +834,6 @@
     "def train_model(X_train, y_train, X_test, y_test):\n",
     "    model = build_model(X_train[0].shape, 16)\n",
     "    \n",
-    "    model.summary()\n",
-    "    \n",
     "    # Create a callback that saves the model's weights\n",
     "    model_checkpoint = ModelCheckpoint(filepath=checkpoint_path, monitor='loss', \n",
     "\t\t\tsave_best_only=True)\n",
@@ -1083,189 +852,30 @@
     "             )\n",
     "    \n",
     "    model.load_weights(checkpoint_path)\n",
-    "    return model, history\n"
+    "    return model, history"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "id": "2032334e",
+   "execution_count": null,
+   "id": "704b40fb",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "flatten (Flatten)            (None, 1680)              0         \n",
-      "_________________________________________________________________\n",
-      "dropout_10.0 (Dropout)       (None, 1680)              0         \n",
-      "_________________________________________________________________\n",
-      "batchNorm (BatchNormalizatio (None, 1680)              6720      \n",
-      "_________________________________________________________________\n",
-      "HiddenDropout_20 (Dropout)   (None, 1680)              0         \n",
-      "_________________________________________________________________\n",
-      "Hidden_2 (Dense)             (None, 186)               312666    \n",
-      "_________________________________________________________________\n",
-      "HiddenDropout_30 (Dropout)   (None, 186)               0         \n",
-      "_________________________________________________________________\n",
-      "Hidden_3 (Dense)             (None, 62)                11594     \n",
-      "_________________________________________________________________\n",
-      "Output (Dense)               (None, 16)                1008      \n",
-      "=================================================================\n",
-      "Total params: 331,988\n",
-      "Trainable params: 328,628\n",
-      "Non-trainable params: 3,360\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/50\n",
-      "1807/1807 - 7s - loss: 0.9538 - acc: 0.8336 - val_loss: 3.6780 - val_acc: 0.3465\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 2/50\n",
-      "1807/1807 - 6s - loss: 0.5806 - acc: 0.9273 - val_loss: 3.6350 - val_acc: 0.3550\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 3/50\n",
-      "1807/1807 - 6s - loss: 0.5180 - acc: 0.9391 - val_loss: 3.9988 - val_acc: 0.3526\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 4/50\n",
-      "1807/1807 - 6s - loss: 0.4804 - acc: 0.9477 - val_loss: 3.9750 - val_acc: 0.3467\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 5/50\n",
-      "1807/1807 - 6s - loss: 0.4737 - acc: 0.9476 - val_loss: 4.1912 - val_acc: 0.3614\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 6/50\n",
-      "1807/1807 - 6s - loss: 0.4542 - acc: 0.9515 - val_loss: 3.9345 - val_acc: 0.3631\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 7/50\n",
-      "1807/1807 - 6s - loss: 0.4476 - acc: 0.9516 - val_loss: 3.8092 - val_acc: 0.3848\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 8/50\n",
-      "1807/1807 - 7s - loss: 0.4408 - acc: 0.9528 - val_loss: 3.8813 - val_acc: 0.3970\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 9/50\n",
-      "1807/1807 - 7s - loss: 0.4283 - acc: 0.9537 - val_loss: 4.0705 - val_acc: 0.3612\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 10/50\n",
-      "1807/1807 - 6s - loss: 0.4218 - acc: 0.9543 - val_loss: 4.2684 - val_acc: 0.3690\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 11/50\n",
-      "1807/1807 - 6s - loss: 0.4198 - acc: 0.9563 - val_loss: 4.1950 - val_acc: 0.3870\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 12/50\n",
-      "1807/1807 - 6s - loss: 0.4149 - acc: 0.9553 - val_loss: 4.5124 - val_acc: 0.3501\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 13/50\n",
-      "1807/1807 - 6s - loss: 0.4078 - acc: 0.9571 - val_loss: 4.2129 - val_acc: 0.3756\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 14/50\n",
-      "1807/1807 - 6s - loss: 0.4025 - acc: 0.9578 - val_loss: 4.4079 - val_acc: 0.3655\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 15/50\n",
-      "1807/1807 - 6s - loss: 0.4055 - acc: 0.9575 - val_loss: 4.1757 - val_acc: 0.3840\n",
-      "Epoch 16/50\n",
-      "1807/1807 - 6s - loss: 0.4043 - acc: 0.9566 - val_loss: 4.3999 - val_acc: 0.3444\n",
-      "Epoch 17/50\n",
-      "1807/1807 - 6s - loss: 0.4010 - acc: 0.9566 - val_loss: 4.3559 - val_acc: 0.3768\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 18/50\n",
-      "1807/1807 - 6s - loss: 0.3933 - acc: 0.9594 - val_loss: 4.2061 - val_acc: 0.3845\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 19/50\n",
-      "1807/1807 - 6s - loss: 0.3968 - acc: 0.9579 - val_loss: 4.3063 - val_acc: 0.3800\n",
-      "Epoch 20/50\n",
-      "1807/1807 - 6s - loss: 0.3919 - acc: 0.9581 - val_loss: 4.3823 - val_acc: 0.3755\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 21/50\n",
-      "1807/1807 - 6s - loss: 0.3901 - acc: 0.9586 - val_loss: 4.3927 - val_acc: 0.3830\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 22/50\n",
-      "1807/1807 - 6s - loss: 0.3915 - acc: 0.9584 - val_loss: 4.2102 - val_acc: 0.3625\n",
-      "Epoch 23/50\n",
-      "1807/1807 - 6s - loss: 0.3854 - acc: 0.9585 - val_loss: 4.0813 - val_acc: 0.3962\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 24/50\n",
-      "1807/1807 - 6s - loss: 0.3871 - acc: 0.9587 - val_loss: 4.3345 - val_acc: 0.3638\n",
-      "Epoch 25/50\n",
-      "1807/1807 - 6s - loss: 0.3815 - acc: 0.9594 - val_loss: 4.2709 - val_acc: 0.3893\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 26/50\n",
-      "1807/1807 - 6s - loss: 0.3767 - acc: 0.9606 - val_loss: 4.4886 - val_acc: 0.3754\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 27/50\n",
-      "1807/1807 - 6s - loss: 0.3803 - acc: 0.9604 - val_loss: 4.2969 - val_acc: 0.3838\n",
-      "Epoch 28/50\n",
-      "1807/1807 - 6s - loss: 0.3804 - acc: 0.9595 - val_loss: 4.5919 - val_acc: 0.3615\n",
-      "Epoch 29/50\n",
-      "1807/1807 - 6s - loss: 0.3783 - acc: 0.9598 - val_loss: 4.5624 - val_acc: 0.3457\n",
-      "Epoch 30/50\n",
-      "1807/1807 - 6s - loss: 0.3762 - acc: 0.9595 - val_loss: 4.2288 - val_acc: 0.3515\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 31/50\n",
-      "1807/1807 - 6s - loss: 0.3771 - acc: 0.9609 - val_loss: 4.1085 - val_acc: 0.3797\n",
-      "Epoch 32/50\n",
-      "1807/1807 - 6s - loss: 0.3698 - acc: 0.9619 - val_loss: 4.1579 - val_acc: 0.3847\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 33/50\n",
-      "1807/1807 - 6s - loss: 0.3763 - acc: 0.9601 - val_loss: 4.4391 - val_acc: 0.3858\n",
-      "Epoch 34/50\n",
-      "1807/1807 - 6s - loss: 0.3725 - acc: 0.9607 - val_loss: 4.1958 - val_acc: 0.3683\n",
-      "Epoch 35/50\n",
-      "1807/1807 - 6s - loss: 0.3709 - acc: 0.9604 - val_loss: 4.1139 - val_acc: 0.3646\n",
-      "Epoch 36/50\n",
-      "1807/1807 - 6s - loss: 0.3673 - acc: 0.9618 - val_loss: 4.2390 - val_acc: 0.3969\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 37/50\n",
-      "1807/1807 - 6s - loss: 0.3686 - acc: 0.9616 - val_loss: 4.3510 - val_acc: 0.3647\n",
-      "Epoch 38/50\n",
-      "1807/1807 - 6s - loss: 0.3633 - acc: 0.9623 - val_loss: 4.4228 - val_acc: 0.3852\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 39/50\n",
-      "1807/1807 - 6s - loss: 0.3719 - acc: 0.9609 - val_loss: 4.2608 - val_acc: 0.3747\n",
-      "Epoch 40/50\n",
-      "1807/1807 - 6s - loss: 0.3668 - acc: 0.9608 - val_loss: 4.3543 - val_acc: 0.3529\n",
-      "Epoch 41/50\n",
-      "1807/1807 - 6s - loss: 0.3612 - acc: 0.9620 - val_loss: 4.0086 - val_acc: 0.3695\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 42/50\n",
-      "1807/1807 - 6s - loss: 0.3680 - acc: 0.9613 - val_loss: 4.2026 - val_acc: 0.3929\n",
-      "Epoch 43/50\n",
-      "1807/1807 - 6s - loss: 0.3576 - acc: 0.9633 - val_loss: 4.3106 - val_acc: 0.3515\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 44/50\n",
-      "1807/1807 - 6s - loss: 0.3651 - acc: 0.9605 - val_loss: 4.2105 - val_acc: 0.3962\n",
-      "Epoch 45/50\n",
-      "1807/1807 - 6s - loss: 0.3610 - acc: 0.9618 - val_loss: 4.4887 - val_acc: 0.3635\n",
-      "Epoch 46/50\n",
-      "1807/1807 - 6s - loss: 0.3681 - acc: 0.9606 - val_loss: 4.1679 - val_acc: 0.3470\n",
-      "Epoch 47/50\n",
-      "1807/1807 - 6s - loss: 0.3635 - acc: 0.9610 - val_loss: 4.1243 - val_acc: 0.4065\n",
-      "Epoch 48/50\n",
-      "1807/1807 - 6s - loss: 0.3568 - acc: 0.9624 - val_loss: 4.1263 - val_acc: 0.3691\n",
-      "INFO:tensorflow:Assets written to: training_1/cp.ckpt/assets\n",
-      "Epoch 49/50\n",
-      "1807/1807 - 6s - loss: 0.3585 - acc: 0.9622 - val_loss: 3.9652 - val_acc: 0.4077\n",
-      "Epoch 50/50\n",
-      "1807/1807 - 6s - loss: 0.3595 - acc: 0.9624 - val_loss: 4.0055 - val_acc: 0.3855\n",
-      "CPU times: user 8min 47s, sys: 46.3 s, total: 9min 34s\n",
-      "Wall time: 5min 33s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
-    "if 'model' not in locals():\n",
+    "\n",
+    "if not os.path.isdir(checkpoint_dir) or create_new:\n",
     "    tf.keras.backend.clear_session()\n",
     "    model, history = train_model(np.array(X_train), np.array(yy_train), np.array(X_test), np.array(yy_test))\n",
     "else:\n",
     "    print(\"Loaded weights...\")\n",
+    "    model = build_model(X_train[0].shape, 16)\n",
     "    model.load_weights(checkpoint_path)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3125ff7f",
+   "id": "03971701",
    "metadata": {},
    "source": [
     "# Eval"
@@ -1273,8 +883,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "id": "03c2ed28",
+   "execution_count": null,
+   "id": "580b1b78",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1292,29 +902,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "id": "3a3df1c6",
+   "execution_count": null,
+   "id": "3749d475",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2.86 s, sys: 310 ms, total: 3.17 s\n",
-      "Wall time: 2.36 s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(43, 43)"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "\n",
@@ -1326,29 +917,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "id": "ac38f7f4",
+   "execution_count": null,
+   "id": "c3c48d92",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 3.85 s, sys: 350 ms, total: 4.2 s\n",
-      "Wall time: 2.93 s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(47, 47)"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "\n",
@@ -1361,76 +933,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "626fa67e",
+   "execution_count": null,
+   "id": "80f0ac46",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},\n",
-       " {1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16})"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "set(ltrain), set(ltest)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "id": "3b643815",
+   "execution_count": null,
+   "id": "8daae77e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x504 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           1       1.00      0.67      0.80         3\n",
-      "           2       0.00      0.00      0.00         1\n",
-      "           3       0.29      0.67      0.40         3\n",
-      "           4       0.00      0.00      0.00         0\n",
-      "           5       0.00      0.00      0.00         3\n",
-      "           6       1.00      0.33      0.50         3\n",
-      "           7       0.75      1.00      0.86         3\n",
-      "           8       0.00      0.00      0.00         3\n",
-      "           9       1.00      0.33      0.50         3\n",
-      "          10       0.00      0.00      0.00         3\n",
-      "          11       0.20      0.33      0.25         3\n",
-      "          12       0.60      1.00      0.75         3\n",
-      "          13       0.38      1.00      0.55         3\n",
-      "          14       0.00      0.00      0.00         3\n",
-      "          15       0.00      0.00      0.00         3\n",
-      "          16       0.75      1.00      0.86         3\n",
-      "\n",
-      "    accuracy                           0.44        43\n",
-      "   macro avg       0.37      0.40      0.34        43\n",
-      "weighted avg       0.42      0.44      0.38        43\n",
-      "\n",
-      "CPU times: user 676 ms, sys: 176 ms, total: 852 ms\n",
-      "Wall time: 622 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "\n",
@@ -1456,35 +972,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "id": "645ca873",
+   "execution_count": null,
+   "id": "72055847",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "def plot_keras_history(history, name='', acc='acc'):\n",
     "    \"\"\"Plots keras history.\"\"\"\n",
@@ -1511,29 +1002,16 @@
     "    plt.legend()\n",
     "    plt.show()\n",
     "    plt.close()\n",
-    "plot_keras_history(history)"
+    "if 'history' in locals():\n",
+    "    plot_keras_history(history)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "id": "39e60eaa",
+   "execution_count": null,
+   "id": "deb72af6",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Scenario: SYN\n",
-      "Window Size: 5\n",
-      "Strides: 1\n",
-      "Epochs: 50\n",
-      "HiddenL Count: 3\n",
-      "Neuron Factor: 3\n",
-      "Drop Factor: 0.1\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(f'Scenario: {cenario}')\n",
     "print(f'Window Size: {win_sz}')\n",
@@ -1547,7 +1025,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7676ea0c",
+   "id": "9b2ad872",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/2-second-project/tdt/JNY25 1010/checkpoint b/2-second-project/tdt/JNY25 1010/checkpoint
deleted file mode 100644
index a3ed6e4..0000000
--- a/2-second-project/tdt/JNY25 1010/checkpoint	
+++ /dev/null
@@ -1,2 +0,0 @@
-model_checkpoint_path: "goat.weights"
-all_model_checkpoint_paths: "goat.weights"
diff --git a/2-second-project/tdt/JNY25 1010/goat.weights.data-00000-of-00001 b/2-second-project/tdt/JNY25 1010/goat.weights.data-00000-of-00001
deleted file mode 100644
index c5086e6..0000000
Binary files a/2-second-project/tdt/JNY25 1010/goat.weights.data-00000-of-00001 and /dev/null differ
diff --git a/2-second-project/tdt/JNY25 1010/goat.weights.index b/2-second-project/tdt/JNY25 1010/goat.weights.index
deleted file mode 100644
index 655765b..0000000
Binary files a/2-second-project/tdt/JNY25 1010/goat.weights.index and /dev/null differ
diff --git a/2-second-project/tdt/training_1/cp.ckpt/keras_metadata.pb b/2-second-project/tdt/training_1/cp.ckpt/keras_metadata.pb
deleted file mode 100644
index 3099b13..0000000
--- a/2-second-project/tdt/training_1/cp.ckpt/keras_metadata.pb
+++ /dev/null
@@ -1,12 +0,0 @@
-
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