Simon_482_Coding_Project_Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ca338525",
   "metadata": {},
   "source": [
    "# Alejandro Simon\n",
    "## CPSC 482 Coding Project\n",
    "### March 3rd, 2023\n",
    "\n",
    "# A Neural Network Approach to Predicting Ingredient Synergy\n",
    "\n",
    "Predicts user ratings based on recipe information"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b2ca1f",
   "metadata": {},
   "source": [
    "## CODE USED BELOW\n",
    "### Slightly different variations of the code below were used for the other models mentioned in the methods section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dddf2337",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import keras\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import normalize\n",
    "\n",
    "# Details on the dataset can be found at https://www.kaggle.com/datasets/shuyangli94/food-com-recipes-and-user-interactions\n",
    "# For nutrition information:\n",
    "# [calories (#), total fat (PDV), \n",
    "# sugar (PDV) , sodium (PDV) , protein (PDV) \n",
    "# , saturated fat (PDV) , and carbohydrates (PDV)]\n",
    "\n",
    "# Load the dataset\n",
    "path = r\"/Users/aleja/Documents/School/CPSC 482\"\n",
    "data_path = path + r\"/archive\"\n",
    "\n",
    "users = pd.read_csv(data_path+\"/RAW_interactions.csv\")\n",
    "recipes = pd.read_csv(data_path+\"/RAW_recipes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6fe1669f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>id</th>\n",
       "      <th>minutes</th>\n",
       "      <th>tags</th>\n",
       "      <th>nutrition</th>\n",
       "      <th>n_steps</th>\n",
       "      <th>description</th>\n",
       "      <th>ingredients</th>\n",
       "      <th>n_ingredients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>137739</td>\n",
       "      <td>55</td>\n",
       "      <td>['60-minutes-or-less', 'time-to-make', 'course...</td>\n",
       "      <td>[51.5, 0.0, 13.0, 0.0, 2.0, 0.0, 4.0]</td>\n",
       "      <td>11</td>\n",
       "      <td>autumn is my favorite time of year to cook! th...</td>\n",
       "      <td>['winter squash', 'mexican seasoning', 'mixed ...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       id  minutes                                               tags  \\\n",
       "0  137739       55  ['60-minutes-or-less', 'time-to-make', 'course...   \n",
       "\n",
       "                               nutrition  n_steps  \\\n",
       "0  [51.5, 0.0, 13.0, 0.0, 2.0, 0.0, 4.0]       11   \n",
       "\n",
       "                                         description  \\\n",
       "0  autumn is my favorite time of year to cook! th...   \n",
       "\n",
       "                                         ingredients  n_ingredients  \n",
       "0  ['winter squash', 'mexican seasoning', 'mixed ...              7  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get rid of unnecessary columns of data\n",
    "to_drop = ['name', 'contributor_id', 'submitted', 'steps']\n",
    "for header in to_drop:\n",
    "    recipes = recipes.drop(header,axis=1)\n",
    "recipes.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "61b3ee2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "231637\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tr/x4_qfn892xsb6l8flq60l05c0000gn/T/ipykernel_47648/1093409927.py:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  users = users.groupby('id').mean()\n"
     ]
    }
   ],
   "source": [
    "# Merge the users and recipes dataframes\n",
    "users = users.rename(columns={'recipe_id': 'id'})\n",
    "users = users.groupby('id').mean()\n",
    "print(len(users))\n",
    "df = pd.merge(users, recipes, on=\"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "511119b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop more of the unnecessary columns of data\n",
    "\n",
    "for header in users.head():\n",
    "    if header != 'id' and header != 'rating':\n",
    "        users = users.drop(header,axis=1)\n",
    "\n",
    "for header in recipes.head():\n",
    "    if header != 'id' and header != 'nutrition' and header != 'ingredients' and header != 'tags':\n",
    "        recipes = recipes.drop(header,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d03d9ae3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average and round the user ratings\n",
    "users = users.round(0)\n",
    "users['rating'] = users['rating'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bc69bf91",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tr/x4_qfn892xsb6l8flq60l05c0000gn/T/ipykernel_47648/4161499698.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train['rating'][train['rating'] <= 4] = 0\n",
      "/var/folders/tr/x4_qfn892xsb6l8flq60l05c0000gn/T/ipykernel_47648/4161499698.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train['rating'][train['rating'] > 4] = 1\n"
     ]
    }
   ],
   "source": [
    "train = pd.merge(users, recipes, on=\"id\")\n",
    "\n",
    "# Change the ratings into binary (1 for successful with an average score of five out of five \n",
    "# or 0 for unsuccesful with less ideal reviews)\n",
    "train['rating'][train['rating'] <= 4] = 0\n",
    "train['rating'][train['rating'] > 4] = 1\n",
    "\n",
    "def vectorize(inp):\n",
    "    \"\"\"\n",
    "    Vectorizes input\n",
    "    \"\"\"\n",
    "    pre_vect = [] # Store all of the possible inputs (get size and name)\n",
    "    for vecs in inp:\n",
    "        for vec in vecs:\n",
    "            if vec not in pre_vect:\n",
    "                pre_vect.append(vec)\n",
    "                \n",
    "    n = len(pre_vect)\n",
    "    vect = np.zeros((train.shape[0],n)).tolist() # This will contain 1 or 0 if the element is contained for a given recipe\n",
    "    for i,vecs in enumerate(inp):\n",
    "        for vec in vecs:\n",
    "            vect[i][pre_vect.index(vec)] = 1\n",
    "        vect[i] = normalize([vect[i]])[0]\n",
    "    return vect\n",
    "\n",
    "# Vectorize all of the possible inputs\n",
    "vect_ingr = vectorize(train['ingredients'])\n",
    "vect_tags = vectorize(train['tags'])\n",
    "\n",
    "# We don't actually use these because they don't help the model much\n",
    "# but I kept the code here for completeness\n",
    "vect_min = df['minutes'].to_numpy().tolist()\n",
    "vect_nsteps = df['n_steps'].to_numpy().tolist()\n",
    "vect_ningr = df['n_ingredients'].to_numpy().tolist()\n",
    "# vect_desc = vectorize(recipes['description'])\n",
    "\n",
    "# Drop now unnecessary columns of data\n",
    "train = train.drop('id',axis=1)\n",
    "train = train.drop('ingredients',axis=1)\n",
    "train = train.drop('tags',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "47f50bef",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Find the appropriate dimensions for the tensor\n",
    "n = max(len(vect_ingr[0]), len(vect_tags[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "93963d70",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get x and y data\n",
    "x, y = train.drop('rating',axis=1), train['rating'].to_numpy().tolist()\n",
    "\n",
    "# Convert the string that holds the list of ingredients to a list\n",
    "for i in range(np.shape(x)[0]):\n",
    "    x['nutrition'][i] = normalize([eval(x['nutrition'][i])])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "185bb7b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81628611, 0.01432919, 0.57316754, 0.0047764 , 0.02865838,\n",
       "       0.02865838, 0.05731675])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['nutrition'][0] # Double check output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2aeb0eb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Actually define the tensor for training and testing\n",
    "# Depending on what data we want we comment out rows of x_t and change the size of x_t \n",
    "# and the input shape to the model\n",
    "x_t = np.zeros((len(x),3,n)).tolist()\n",
    "for i,ing in enumerate(vect_ingr):\n",
    "    x_t[i][0] = np.pad(x['nutrition'][i],(0,n-len(x['nutrition'][i])),'constant',constant_values=(0,0)).tolist()\n",
    "    x_t[i][1] = ing\n",
    "    x_t[i][2] = np.pad(vect_tags[i],(0,n-len(vect_tags[i])),'constant',constant_values=(0,0)).tolist()\n",
    "    # x_t[i][3] = np.pad([vect_min[i]],(0,n-1),'constant',constant_values=(0,0)).tolist()\n",
    "    # x_t[i][4] = np.pad([vect_nsteps[i]],(0,n-1),'constant',constant_values=(0,0)).tolist()\n",
    "    # x_t[i][5] = np.pad([vect_ningr[i]],(0,n-1),'constant',constant_values=(0,0)).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "90d0b41b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data between a train and test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_t, y, test_size=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dbf62155",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-01 09:09:57.821202: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "# Define the model as outlined in the methods section\n",
    "from keras import backend as K\n",
    "\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(3,n)),\n",
    "    # keras.layers.Dense(1024, activation='relu'),\n",
    "    keras.layers.Dense(512, activation='relu'),\n",
    "    keras.layers.Dense(256, activation='relu'),\n",
    "    keras.layers.Dense(128, activation='relu'),\n",
    "    keras.layers.Dense(64, activation='relu'),\n",
    "    keras.layers.Dense(32, activation='relu'),\n",
    "    keras.layers.Dense(16, activation='relu'),\n",
    "    keras.layers.Dense(2, activation='softmax')\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Here we benefit from a lower learning rate\n",
    "K.set_value(model.optimizer.learning_rate, 1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "993d148f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6920 - accuracy: 0.5403 - val_loss: 0.6905 - val_accuracy: 0.5603\n",
      "Epoch 2/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6893 - accuracy: 0.5601 - val_loss: 0.6883 - val_accuracy: 0.5603\n",
      "Epoch 3/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6876 - accuracy: 0.5601 - val_loss: 0.6870 - val_accuracy: 0.5603\n",
      "Epoch 4/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6866 - accuracy: 0.5601 - val_loss: 0.6863 - val_accuracy: 0.5603\n",
      "Epoch 5/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6861 - accuracy: 0.5601 - val_loss: 0.6859 - val_accuracy: 0.5603\n",
      "Epoch 6/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6859 - accuracy: 0.5601 - val_loss: 0.6858 - val_accuracy: 0.5603\n",
      "Epoch 7/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6858 - accuracy: 0.5601 - val_loss: 0.6857 - val_accuracy: 0.5603\n",
      "Epoch 8/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6857 - accuracy: 0.5601 - val_loss: 0.6856 - val_accuracy: 0.5603\n",
      "Epoch 9/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6856 - accuracy: 0.5601 - val_loss: 0.6855 - val_accuracy: 0.5603\n",
      "Epoch 10/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6856 - accuracy: 0.5601 - val_loss: 0.6855 - val_accuracy: 0.5603\n",
      "Epoch 11/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6855 - accuracy: 0.5601 - val_loss: 0.6854 - val_accuracy: 0.5603\n",
      "Epoch 12/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6854 - accuracy: 0.5601 - val_loss: 0.6853 - val_accuracy: 0.5603\n",
      "Epoch 13/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6854 - accuracy: 0.5601 - val_loss: 0.6853 - val_accuracy: 0.5603\n",
      "Epoch 14/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6853 - accuracy: 0.5601 - val_loss: 0.6852 - val_accuracy: 0.5603\n",
      "Epoch 15/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6852 - accuracy: 0.5601 - val_loss: 0.6852 - val_accuracy: 0.5603\n",
      "Epoch 16/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6852 - accuracy: 0.5601 - val_loss: 0.6851 - val_accuracy: 0.5603\n",
      "Epoch 17/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6851 - accuracy: 0.5601 - val_loss: 0.6850 - val_accuracy: 0.5603\n",
      "Epoch 18/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6850 - accuracy: 0.5601 - val_loss: 0.6849 - val_accuracy: 0.5603\n",
      "Epoch 19/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6849 - accuracy: 0.5601 - val_loss: 0.6848 - val_accuracy: 0.5603\n",
      "Epoch 20/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6848 - accuracy: 0.5601 - val_loss: 0.6847 - val_accuracy: 0.5603\n",
      "Epoch 21/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6847 - accuracy: 0.5601 - val_loss: 0.6846 - val_accuracy: 0.5603\n",
      "Epoch 22/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6846 - accuracy: 0.5601 - val_loss: 0.6845 - val_accuracy: 0.5603\n",
      "Epoch 23/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6845 - accuracy: 0.5601 - val_loss: 0.6844 - val_accuracy: 0.5603\n",
      "Epoch 24/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6844 - accuracy: 0.5601 - val_loss: 0.6843 - val_accuracy: 0.5603\n",
      "Epoch 25/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6843 - accuracy: 0.5601 - val_loss: 0.6842 - val_accuracy: 0.5603\n",
      "Epoch 26/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6842 - accuracy: 0.5601 - val_loss: 0.6842 - val_accuracy: 0.5603\n",
      "Epoch 27/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6841 - accuracy: 0.5601 - val_loss: 0.6841 - val_accuracy: 0.5603\n",
      "Epoch 28/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6840 - accuracy: 0.5601 - val_loss: 0.6840 - val_accuracy: 0.5603\n",
      "Epoch 29/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6839 - accuracy: 0.5601 - val_loss: 0.6839 - val_accuracy: 0.5603\n",
      "Epoch 30/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6838 - accuracy: 0.5601 - val_loss: 0.6838 - val_accuracy: 0.5603\n",
      "Epoch 31/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6837 - accuracy: 0.5601 - val_loss: 0.6837 - val_accuracy: 0.5603\n",
      "Epoch 32/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6836 - accuracy: 0.5601 - val_loss: 0.6836 - val_accuracy: 0.5603\n",
      "Epoch 33/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6836 - accuracy: 0.5601 - val_loss: 0.6835 - val_accuracy: 0.5603\n",
      "Epoch 34/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6835 - accuracy: 0.5601 - val_loss: 0.6835 - val_accuracy: 0.5603\n",
      "Epoch 35/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6834 - accuracy: 0.5601 - val_loss: 0.6834 - val_accuracy: 0.5603\n",
      "Epoch 36/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6833 - accuracy: 0.5601 - val_loss: 0.6833 - val_accuracy: 0.5603\n",
      "Epoch 37/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6832 - accuracy: 0.5601 - val_loss: 0.6832 - val_accuracy: 0.5603\n",
      "Epoch 38/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6831 - accuracy: 0.5601 - val_loss: 0.6831 - val_accuracy: 0.5603\n",
      "Epoch 39/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6830 - accuracy: 0.5601 - val_loss: 0.6830 - val_accuracy: 0.5603\n",
      "Epoch 40/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6829 - accuracy: 0.5601 - val_loss: 0.6829 - val_accuracy: 0.5603\n",
      "Epoch 41/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6828 - accuracy: 0.5603 - val_loss: 0.6827 - val_accuracy: 0.5605\n",
      "Epoch 42/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6825 - accuracy: 0.5609 - val_loss: 0.6824 - val_accuracy: 0.5609\n",
      "Epoch 43/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6822 - accuracy: 0.5611 - val_loss: 0.6823 - val_accuracy: 0.5614\n",
      "Epoch 44/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6821 - accuracy: 0.5611 - val_loss: 0.6822 - val_accuracy: 0.5613\n",
      "Epoch 45/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6820 - accuracy: 0.5611 - val_loss: 0.6821 - val_accuracy: 0.5613\n",
      "Epoch 46/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6819 - accuracy: 0.5612 - val_loss: 0.6820 - val_accuracy: 0.5614\n",
      "Epoch 47/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6818 - accuracy: 0.5614 - val_loss: 0.6819 - val_accuracy: 0.5617\n",
      "Epoch 48/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6817 - accuracy: 0.5617 - val_loss: 0.6818 - val_accuracy: 0.5617\n",
      "Epoch 49/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6817 - accuracy: 0.5618 - val_loss: 0.6818 - val_accuracy: 0.5618\n",
      "Epoch 50/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6816 - accuracy: 0.5621 - val_loss: 0.6817 - val_accuracy: 0.5622\n",
      "Epoch 51/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6815 - accuracy: 0.5623 - val_loss: 0.6817 - val_accuracy: 0.5623\n",
      "Epoch 52/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6815 - accuracy: 0.5624 - val_loss: 0.6816 - val_accuracy: 0.5623\n",
      "Epoch 53/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6814 - accuracy: 0.5628 - val_loss: 0.6815 - val_accuracy: 0.5629\n",
      "Epoch 54/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6814 - accuracy: 0.5627 - val_loss: 0.6815 - val_accuracy: 0.5629\n",
      "Epoch 55/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6813 - accuracy: 0.5628 - val_loss: 0.6814 - val_accuracy: 0.5630\n",
      "Epoch 56/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6812 - accuracy: 0.5630 - val_loss: 0.6814 - val_accuracy: 0.5633\n",
      "Epoch 57/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6812 - accuracy: 0.5634 - val_loss: 0.6813 - val_accuracy: 0.5633\n",
      "Epoch 58/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6811 - accuracy: 0.5636 - val_loss: 0.6813 - val_accuracy: 0.5638\n",
      "Epoch 59/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6811 - accuracy: 0.5637 - val_loss: 0.6813 - val_accuracy: 0.5637\n",
      "Epoch 60/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6810 - accuracy: 0.5639 - val_loss: 0.6812 - val_accuracy: 0.5633\n",
      "Epoch 61/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6810 - accuracy: 0.5640 - val_loss: 0.6812 - val_accuracy: 0.5640\n",
      "Epoch 62/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6810 - accuracy: 0.5643 - val_loss: 0.6812 - val_accuracy: 0.5644\n",
      "Epoch 63/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6809 - accuracy: 0.5645 - val_loss: 0.6811 - val_accuracy: 0.5644\n",
      "Epoch 64/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6809 - accuracy: 0.5640 - val_loss: 0.6811 - val_accuracy: 0.5645\n",
      "Epoch 65/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6809 - accuracy: 0.5647 - val_loss: 0.6811 - val_accuracy: 0.5644\n",
      "Epoch 66/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6808 - accuracy: 0.5645 - val_loss: 0.6810 - val_accuracy: 0.5646\n",
      "Epoch 67/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6808 - accuracy: 0.5650 - val_loss: 0.6810 - val_accuracy: 0.5646\n",
      "Epoch 68/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6808 - accuracy: 0.5649 - val_loss: 0.6810 - val_accuracy: 0.5649\n",
      "Epoch 69/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6807 - accuracy: 0.5651 - val_loss: 0.6810 - val_accuracy: 0.5648\n",
      "Epoch 70/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6807 - accuracy: 0.5651 - val_loss: 0.6809 - val_accuracy: 0.5647\n",
      "Epoch 71/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6807 - accuracy: 0.5651 - val_loss: 0.6809 - val_accuracy: 0.5648\n",
      "Epoch 72/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6807 - accuracy: 0.5650 - val_loss: 0.6809 - val_accuracy: 0.5650\n",
      "Epoch 73/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6806 - accuracy: 0.5655 - val_loss: 0.6809 - val_accuracy: 0.5648\n",
      "Epoch 74/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6806 - accuracy: 0.5652 - val_loss: 0.6809 - val_accuracy: 0.5650\n",
      "Epoch 75/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6806 - accuracy: 0.5657 - val_loss: 0.6809 - val_accuracy: 0.5652\n",
      "Epoch 76/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6805 - accuracy: 0.5660 - val_loss: 0.6808 - val_accuracy: 0.5645\n",
      "Epoch 77/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6805 - accuracy: 0.5654 - val_loss: 0.6808 - val_accuracy: 0.5649\n",
      "Epoch 78/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6805 - accuracy: 0.5660 - val_loss: 0.6808 - val_accuracy: 0.5647\n",
      "Epoch 79/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6805 - accuracy: 0.5654 - val_loss: 0.6808 - val_accuracy: 0.5653\n",
      "Epoch 80/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6804 - accuracy: 0.5660 - val_loss: 0.6807 - val_accuracy: 0.5647\n",
      "Epoch 81/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6804 - accuracy: 0.5655 - val_loss: 0.6807 - val_accuracy: 0.5647\n",
      "Epoch 82/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6804 - accuracy: 0.5662 - val_loss: 0.6807 - val_accuracy: 0.5651\n",
      "Epoch 83/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6804 - accuracy: 0.5659 - val_loss: 0.6807 - val_accuracy: 0.5649\n",
      "Epoch 84/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6804 - accuracy: 0.5662 - val_loss: 0.6807 - val_accuracy: 0.5656\n",
      "Epoch 85/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6804 - accuracy: 0.5663 - val_loss: 0.6807 - val_accuracy: 0.5651\n",
      "Epoch 86/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6803 - accuracy: 0.5663 - val_loss: 0.6806 - val_accuracy: 0.5656\n",
      "Epoch 87/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6803 - accuracy: 0.5663 - val_loss: 0.6806 - val_accuracy: 0.5658\n",
      "Epoch 88/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6803 - accuracy: 0.5662 - val_loss: 0.6806 - val_accuracy: 0.5651\n",
      "Epoch 89/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6803 - accuracy: 0.5664 - val_loss: 0.6806 - val_accuracy: 0.5659\n",
      "Epoch 90/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6803 - accuracy: 0.5667 - val_loss: 0.6806 - val_accuracy: 0.5656\n",
      "Epoch 91/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6802 - accuracy: 0.5662 - val_loss: 0.6806 - val_accuracy: 0.5654\n",
      "Epoch 92/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6802 - accuracy: 0.5661 - val_loss: 0.6806 - val_accuracy: 0.5656\n",
      "Epoch 93/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6802 - accuracy: 0.5664 - val_loss: 0.6806 - val_accuracy: 0.5658\n",
      "Epoch 94/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6802 - accuracy: 0.5664 - val_loss: 0.6805 - val_accuracy: 0.5658\n",
      "Epoch 95/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6802 - accuracy: 0.5663 - val_loss: 0.6805 - val_accuracy: 0.5657\n",
      "Epoch 96/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6801 - accuracy: 0.5666 - val_loss: 0.6805 - val_accuracy: 0.5659\n",
      "Epoch 97/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6801 - accuracy: 0.5666 - val_loss: 0.6805 - val_accuracy: 0.5658\n",
      "Epoch 98/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6801 - accuracy: 0.5664 - val_loss: 0.6806 - val_accuracy: 0.5666\n",
      "Epoch 99/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6801 - accuracy: 0.5671 - val_loss: 0.6805 - val_accuracy: 0.5658\n",
      "Epoch 100/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6801 - accuracy: 0.5669 - val_loss: 0.6805 - val_accuracy: 0.5654\n",
      "Epoch 101/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6801 - accuracy: 0.5669 - val_loss: 0.6805 - val_accuracy: 0.5655\n",
      "Epoch 102/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6801 - accuracy: 0.5671 - val_loss: 0.6805 - val_accuracy: 0.5660\n",
      "Epoch 103/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6800 - accuracy: 0.5667 - val_loss: 0.6804 - val_accuracy: 0.5658\n",
      "Epoch 104/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6800 - accuracy: 0.5667 - val_loss: 0.6804 - val_accuracy: 0.5654\n",
      "Epoch 105/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6800 - accuracy: 0.5672 - val_loss: 0.6805 - val_accuracy: 0.5657\n",
      "Epoch 106/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6800 - accuracy: 0.5671 - val_loss: 0.6804 - val_accuracy: 0.5654\n",
      "Epoch 107/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6800 - accuracy: 0.5672 - val_loss: 0.6804 - val_accuracy: 0.5656\n",
      "Epoch 108/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6800 - accuracy: 0.5672 - val_loss: 0.6804 - val_accuracy: 0.5657\n",
      "Epoch 109/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6799 - accuracy: 0.5681 - val_loss: 0.6805 - val_accuracy: 0.5655\n",
      "Epoch 110/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6799 - accuracy: 0.5670 - val_loss: 0.6804 - val_accuracy: 0.5660\n",
      "Epoch 111/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6799 - accuracy: 0.5673 - val_loss: 0.6804 - val_accuracy: 0.5665\n",
      "Epoch 112/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6799 - accuracy: 0.5680 - val_loss: 0.6804 - val_accuracy: 0.5658\n",
      "Epoch 113/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6799 - accuracy: 0.5675 - val_loss: 0.6804 - val_accuracy: 0.5656\n",
      "Epoch 114/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6799 - accuracy: 0.5676 - val_loss: 0.6803 - val_accuracy: 0.5662\n",
      "Epoch 115/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6799 - accuracy: 0.5675 - val_loss: 0.6804 - val_accuracy: 0.5664\n",
      "Epoch 116/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6799 - accuracy: 0.5679 - val_loss: 0.6803 - val_accuracy: 0.5657\n",
      "Epoch 117/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6798 - accuracy: 0.5673 - val_loss: 0.6803 - val_accuracy: 0.5655\n",
      "Epoch 118/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6798 - accuracy: 0.5677 - val_loss: 0.6803 - val_accuracy: 0.5656\n",
      "Epoch 119/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6798 - accuracy: 0.5676 - val_loss: 0.6803 - val_accuracy: 0.5662\n",
      "Epoch 120/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6798 - accuracy: 0.5678 - val_loss: 0.6803 - val_accuracy: 0.5661\n",
      "Epoch 121/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6798 - accuracy: 0.5680 - val_loss: 0.6803 - val_accuracy: 0.5667\n",
      "Epoch 122/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6798 - accuracy: 0.5673 - val_loss: 0.6803 - val_accuracy: 0.5664\n",
      "Epoch 123/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6798 - accuracy: 0.5681 - val_loss: 0.6803 - val_accuracy: 0.5660\n",
      "Epoch 124/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6797 - accuracy: 0.5680 - val_loss: 0.6803 - val_accuracy: 0.5667\n",
      "Epoch 125/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6797 - accuracy: 0.5684 - val_loss: 0.6803 - val_accuracy: 0.5666\n",
      "Epoch 126/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6797 - accuracy: 0.5683 - val_loss: 0.6803 - val_accuracy: 0.5661\n",
      "Epoch 127/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6797 - accuracy: 0.5681 - val_loss: 0.6803 - val_accuracy: 0.5671\n",
      "Epoch 128/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6797 - accuracy: 0.5686 - val_loss: 0.6803 - val_accuracy: 0.5669\n",
      "Epoch 129/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6797 - accuracy: 0.5683 - val_loss: 0.6802 - val_accuracy: 0.5666\n",
      "Epoch 130/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6797 - accuracy: 0.5683 - val_loss: 0.6802 - val_accuracy: 0.5669\n",
      "Epoch 131/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6797 - accuracy: 0.5685 - val_loss: 0.6802 - val_accuracy: 0.5663\n",
      "Epoch 132/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6796 - accuracy: 0.5686 - val_loss: 0.6802 - val_accuracy: 0.5663\n",
      "Epoch 133/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6796 - accuracy: 0.5686 - val_loss: 0.6802 - val_accuracy: 0.5669\n",
      "Epoch 134/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6796 - accuracy: 0.5685 - val_loss: 0.6802 - val_accuracy: 0.5662\n",
      "Epoch 135/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6796 - accuracy: 0.5686 - val_loss: 0.6802 - val_accuracy: 0.5663\n",
      "Epoch 136/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6796 - accuracy: 0.5685 - val_loss: 0.6802 - val_accuracy: 0.5670\n",
      "Epoch 137/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6796 - accuracy: 0.5688 - val_loss: 0.6802 - val_accuracy: 0.5674\n",
      "Epoch 138/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6796 - accuracy: 0.5688 - val_loss: 0.6802 - val_accuracy: 0.5673\n",
      "Epoch 139/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6796 - accuracy: 0.5687 - val_loss: 0.6802 - val_accuracy: 0.5665\n",
      "Epoch 140/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6796 - accuracy: 0.5691 - val_loss: 0.6802 - val_accuracy: 0.5671\n",
      "Epoch 141/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6795 - accuracy: 0.5688 - val_loss: 0.6802 - val_accuracy: 0.5677\n",
      "Epoch 142/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6795 - accuracy: 0.5691 - val_loss: 0.6802 - val_accuracy: 0.5671\n",
      "Epoch 143/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6795 - accuracy: 0.5689 - val_loss: 0.6802 - val_accuracy: 0.5671\n",
      "Epoch 144/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6795 - accuracy: 0.5691 - val_loss: 0.6802 - val_accuracy: 0.5666\n",
      "Epoch 145/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6795 - accuracy: 0.5686 - val_loss: 0.6802 - val_accuracy: 0.5665\n",
      "Epoch 146/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6795 - accuracy: 0.5695 - val_loss: 0.6802 - val_accuracy: 0.5672\n",
      "Epoch 147/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6795 - accuracy: 0.5692 - val_loss: 0.6801 - val_accuracy: 0.5666\n",
      "Epoch 148/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6795 - accuracy: 0.5690 - val_loss: 0.6801 - val_accuracy: 0.5671\n",
      "Epoch 149/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6795 - accuracy: 0.5691 - val_loss: 0.6801 - val_accuracy: 0.5673\n",
      "Epoch 150/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6794 - accuracy: 0.5695 - val_loss: 0.6801 - val_accuracy: 0.5674\n",
      "Epoch 151/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6794 - accuracy: 0.5689 - val_loss: 0.6801 - val_accuracy: 0.5666\n",
      "Epoch 152/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6794 - accuracy: 0.5692 - val_loss: 0.6801 - val_accuracy: 0.5668\n",
      "Epoch 153/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6794 - accuracy: 0.5694 - val_loss: 0.6801 - val_accuracy: 0.5673\n",
      "Epoch 154/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6794 - accuracy: 0.5692 - val_loss: 0.6801 - val_accuracy: 0.5665\n",
      "Epoch 155/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6794 - accuracy: 0.5694 - val_loss: 0.6801 - val_accuracy: 0.5670\n",
      "Epoch 156/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6794 - accuracy: 0.5697 - val_loss: 0.6801 - val_accuracy: 0.5669\n",
      "Epoch 157/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6794 - accuracy: 0.5692 - val_loss: 0.6801 - val_accuracy: 0.5670\n",
      "Epoch 158/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6794 - accuracy: 0.5690 - val_loss: 0.6801 - val_accuracy: 0.5667\n",
      "Epoch 159/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6794 - accuracy: 0.5692 - val_loss: 0.6801 - val_accuracy: 0.5670\n",
      "Epoch 160/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6794 - accuracy: 0.5693 - val_loss: 0.6801 - val_accuracy: 0.5673\n",
      "Epoch 161/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6793 - accuracy: 0.5692 - val_loss: 0.6801 - val_accuracy: 0.5672\n",
      "Epoch 162/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6793 - accuracy: 0.5690 - val_loss: 0.6801 - val_accuracy: 0.5671\n",
      "Epoch 163/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6793 - accuracy: 0.5693 - val_loss: 0.6801 - val_accuracy: 0.5670\n",
      "Epoch 164/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6793 - accuracy: 0.5695 - val_loss: 0.6801 - val_accuracy: 0.5670\n",
      "Epoch 165/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6793 - accuracy: 0.5692 - val_loss: 0.6801 - val_accuracy: 0.5675\n",
      "Epoch 166/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6793 - accuracy: 0.5694 - val_loss: 0.6801 - val_accuracy: 0.5672\n",
      "Epoch 167/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6793 - accuracy: 0.5700 - val_loss: 0.6801 - val_accuracy: 0.5675\n",
      "Epoch 168/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6793 - accuracy: 0.5698 - val_loss: 0.6801 - val_accuracy: 0.5668\n",
      "Epoch 169/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6793 - accuracy: 0.5696 - val_loss: 0.6801 - val_accuracy: 0.5675\n",
      "Epoch 170/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6792 - accuracy: 0.5698 - val_loss: 0.6801 - val_accuracy: 0.5671\n",
      "Epoch 171/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6792 - accuracy: 0.5693 - val_loss: 0.6801 - val_accuracy: 0.5666\n",
      "Epoch 172/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6792 - accuracy: 0.5692 - val_loss: 0.6801 - val_accuracy: 0.5672\n",
      "Epoch 173/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6792 - accuracy: 0.5692 - val_loss: 0.6801 - val_accuracy: 0.5678\n",
      "Epoch 174/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6792 - accuracy: 0.5692 - val_loss: 0.6801 - val_accuracy: 0.5673\n",
      "Epoch 175/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6792 - accuracy: 0.5698 - val_loss: 0.6800 - val_accuracy: 0.5672\n",
      "Epoch 176/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6792 - accuracy: 0.5695 - val_loss: 0.6800 - val_accuracy: 0.5679\n",
      "Epoch 177/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6792 - accuracy: 0.5695 - val_loss: 0.6800 - val_accuracy: 0.5677\n",
      "Epoch 178/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6792 - accuracy: 0.5691 - val_loss: 0.6800 - val_accuracy: 0.5679\n",
      "Epoch 179/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6792 - accuracy: 0.5695 - val_loss: 0.6800 - val_accuracy: 0.5672\n",
      "Epoch 180/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6792 - accuracy: 0.5697 - val_loss: 0.6800 - val_accuracy: 0.5677\n",
      "Epoch 181/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6791 - accuracy: 0.5695 - val_loss: 0.6800 - val_accuracy: 0.5677\n",
      "Epoch 182/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6791 - accuracy: 0.5699 - val_loss: 0.6800 - val_accuracy: 0.5675\n",
      "Epoch 183/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6791 - accuracy: 0.5696 - val_loss: 0.6800 - val_accuracy: 0.5675\n",
      "Epoch 184/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6791 - accuracy: 0.5698 - val_loss: 0.6800 - val_accuracy: 0.5671\n",
      "Epoch 185/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6791 - accuracy: 0.5694 - val_loss: 0.6800 - val_accuracy: 0.5671\n",
      "Epoch 186/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6791 - accuracy: 0.5695 - val_loss: 0.6800 - val_accuracy: 0.5676\n",
      "Epoch 187/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6791 - accuracy: 0.5697 - val_loss: 0.6800 - val_accuracy: 0.5675\n",
      "Epoch 188/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6791 - accuracy: 0.5699 - val_loss: 0.6800 - val_accuracy: 0.5675\n",
      "Epoch 189/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6791 - accuracy: 0.5701 - val_loss: 0.6800 - val_accuracy: 0.5678\n",
      "Epoch 190/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6791 - accuracy: 0.5698 - val_loss: 0.6800 - val_accuracy: 0.5672\n",
      "Epoch 191/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6790 - accuracy: 0.5701 - val_loss: 0.6800 - val_accuracy: 0.5675\n",
      "Epoch 192/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6790 - accuracy: 0.5701 - val_loss: 0.6800 - val_accuracy: 0.5678\n",
      "Epoch 193/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6790 - accuracy: 0.5700 - val_loss: 0.6800 - val_accuracy: 0.5677\n",
      "Epoch 194/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6790 - accuracy: 0.5698 - val_loss: 0.6800 - val_accuracy: 0.5675\n",
      "Epoch 195/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6790 - accuracy: 0.5697 - val_loss: 0.6800 - val_accuracy: 0.5669\n",
      "Epoch 196/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6790 - accuracy: 0.5696 - val_loss: 0.6800 - val_accuracy: 0.5669\n",
      "Epoch 197/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6790 - accuracy: 0.5699 - val_loss: 0.6800 - val_accuracy: 0.5680\n",
      "Epoch 198/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6790 - accuracy: 0.5703 - val_loss: 0.6800 - val_accuracy: 0.5678\n",
      "Epoch 199/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6790 - accuracy: 0.5706 - val_loss: 0.6800 - val_accuracy: 0.5677\n",
      "Epoch 200/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6789 - accuracy: 0.5699 - val_loss: 0.6800 - val_accuracy: 0.5670\n",
      "Epoch 201/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6789 - accuracy: 0.5696 - val_loss: 0.6800 - val_accuracy: 0.5675\n",
      "Epoch 202/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6789 - accuracy: 0.5701 - val_loss: 0.6800 - val_accuracy: 0.5676\n",
      "Epoch 203/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6789 - accuracy: 0.5699 - val_loss: 0.6800 - val_accuracy: 0.5670\n",
      "Epoch 204/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6789 - accuracy: 0.5700 - val_loss: 0.6800 - val_accuracy: 0.5669\n",
      "Epoch 205/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6789 - accuracy: 0.5701 - val_loss: 0.6800 - val_accuracy: 0.5677\n",
      "Epoch 206/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6789 - accuracy: 0.5701 - val_loss: 0.6800 - val_accuracy: 0.5675\n",
      "Epoch 207/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6789 - accuracy: 0.5699 - val_loss: 0.6800 - val_accuracy: 0.5669\n",
      "Epoch 208/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6789 - accuracy: 0.5698 - val_loss: 0.6800 - val_accuracy: 0.5674\n",
      "Epoch 209/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6789 - accuracy: 0.5703 - val_loss: 0.6800 - val_accuracy: 0.5672\n",
      "Epoch 210/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6788 - accuracy: 0.5701 - val_loss: 0.6800 - val_accuracy: 0.5669\n",
      "Epoch 211/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6789 - accuracy: 0.5702 - val_loss: 0.6800 - val_accuracy: 0.5677\n",
      "Epoch 212/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6788 - accuracy: 0.5702 - val_loss: 0.6800 - val_accuracy: 0.5678\n",
      "Epoch 213/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6788 - accuracy: 0.5702 - val_loss: 0.6800 - val_accuracy: 0.5669\n",
      "Epoch 214/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6788 - accuracy: 0.5707 - val_loss: 0.6800 - val_accuracy: 0.5667\n",
      "Epoch 215/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6788 - accuracy: 0.5705 - val_loss: 0.6800 - val_accuracy: 0.5678\n",
      "Epoch 216/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6788 - accuracy: 0.5704 - val_loss: 0.6800 - val_accuracy: 0.5664\n",
      "Epoch 217/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6788 - accuracy: 0.5701 - val_loss: 0.6800 - val_accuracy: 0.5669\n",
      "Epoch 218/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6788 - accuracy: 0.5700 - val_loss: 0.6800 - val_accuracy: 0.5676\n",
      "Epoch 219/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6788 - accuracy: 0.5704 - val_loss: 0.6800 - val_accuracy: 0.5677\n",
      "Epoch 220/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6788 - accuracy: 0.5704 - val_loss: 0.6800 - val_accuracy: 0.5679\n",
      "Epoch 221/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6787 - accuracy: 0.5703 - val_loss: 0.6800 - val_accuracy: 0.5667\n",
      "Epoch 222/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6788 - accuracy: 0.5703 - val_loss: 0.6799 - val_accuracy: 0.5675\n",
      "Epoch 223/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6787 - accuracy: 0.5706 - val_loss: 0.6799 - val_accuracy: 0.5674\n",
      "Epoch 224/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6787 - accuracy: 0.5708 - val_loss: 0.6800 - val_accuracy: 0.5666\n",
      "Epoch 225/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6787 - accuracy: 0.5711 - val_loss: 0.6799 - val_accuracy: 0.5670\n",
      "Epoch 226/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6787 - accuracy: 0.5707 - val_loss: 0.6799 - val_accuracy: 0.5673\n",
      "Epoch 227/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6787 - accuracy: 0.5706 - val_loss: 0.6799 - val_accuracy: 0.5674\n",
      "Epoch 228/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6787 - accuracy: 0.5707 - val_loss: 0.6800 - val_accuracy: 0.5680\n",
      "Epoch 229/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6787 - accuracy: 0.5707 - val_loss: 0.6799 - val_accuracy: 0.5675\n",
      "Epoch 230/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6787 - accuracy: 0.5711 - val_loss: 0.6800 - val_accuracy: 0.5673\n",
      "Epoch 231/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6787 - accuracy: 0.5707 - val_loss: 0.6799 - val_accuracy: 0.5676\n",
      "Epoch 232/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6787 - accuracy: 0.5709 - val_loss: 0.6799 - val_accuracy: 0.5675\n",
      "Epoch 233/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6786 - accuracy: 0.5705 - val_loss: 0.6799 - val_accuracy: 0.5676\n",
      "Epoch 234/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6786 - accuracy: 0.5706 - val_loss: 0.6799 - val_accuracy: 0.5676\n",
      "Epoch 235/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6786 - accuracy: 0.5709 - val_loss: 0.6799 - val_accuracy: 0.5675\n",
      "Epoch 236/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6786 - accuracy: 0.5704 - val_loss: 0.6800 - val_accuracy: 0.5674\n",
      "Epoch 237/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6786 - accuracy: 0.5707 - val_loss: 0.6799 - val_accuracy: 0.5667\n",
      "Epoch 238/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6786 - accuracy: 0.5717 - val_loss: 0.6800 - val_accuracy: 0.5673\n",
      "Epoch 239/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6786 - accuracy: 0.5706 - val_loss: 0.6799 - val_accuracy: 0.5675\n",
      "Epoch 240/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6786 - accuracy: 0.5704 - val_loss: 0.6799 - val_accuracy: 0.5675\n",
      "Epoch 241/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6786 - accuracy: 0.5706 - val_loss: 0.6800 - val_accuracy: 0.5677\n",
      "Epoch 242/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6786 - accuracy: 0.5708 - val_loss: 0.6799 - val_accuracy: 0.5678\n",
      "Epoch 243/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6785 - accuracy: 0.5707 - val_loss: 0.6799 - val_accuracy: 0.5679\n",
      "Epoch 244/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6785 - accuracy: 0.5709 - val_loss: 0.6799 - val_accuracy: 0.5679\n",
      "Epoch 245/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6785 - accuracy: 0.5708 - val_loss: 0.6799 - val_accuracy: 0.5676\n",
      "Epoch 246/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6785 - accuracy: 0.5710 - val_loss: 0.6799 - val_accuracy: 0.5673\n",
      "Epoch 247/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6785 - accuracy: 0.5710 - val_loss: 0.6799 - val_accuracy: 0.5673\n",
      "Epoch 248/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6785 - accuracy: 0.5710 - val_loss: 0.6799 - val_accuracy: 0.5680\n",
      "Epoch 249/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6785 - accuracy: 0.5711 - val_loss: 0.6799 - val_accuracy: 0.5670\n",
      "Epoch 250/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6785 - accuracy: 0.5708 - val_loss: 0.6799 - val_accuracy: 0.5677\n",
      "Epoch 251/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6785 - accuracy: 0.5710 - val_loss: 0.6799 - val_accuracy: 0.5675\n",
      "Epoch 252/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6784 - accuracy: 0.5712 - val_loss: 0.6799 - val_accuracy: 0.5674\n",
      "Epoch 253/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6784 - accuracy: 0.5714 - val_loss: 0.6799 - val_accuracy: 0.5673\n",
      "Epoch 254/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6784 - accuracy: 0.5713 - val_loss: 0.6799 - val_accuracy: 0.5679\n",
      "Epoch 255/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6784 - accuracy: 0.5713 - val_loss: 0.6799 - val_accuracy: 0.5674\n",
      "Epoch 256/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6784 - accuracy: 0.5710 - val_loss: 0.6799 - val_accuracy: 0.5676\n",
      "Epoch 257/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6784 - accuracy: 0.5717 - val_loss: 0.6799 - val_accuracy: 0.5676\n",
      "Epoch 258/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6784 - accuracy: 0.5717 - val_loss: 0.6799 - val_accuracy: 0.5674\n",
      "Epoch 259/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6784 - accuracy: 0.5713 - val_loss: 0.6799 - val_accuracy: 0.5673\n",
      "Epoch 260/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6784 - accuracy: 0.5713 - val_loss: 0.6799 - val_accuracy: 0.5675\n",
      "Epoch 261/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6784 - accuracy: 0.5711 - val_loss: 0.6799 - val_accuracy: 0.5677\n",
      "Epoch 262/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6783 - accuracy: 0.5716 - val_loss: 0.6800 - val_accuracy: 0.5668\n",
      "Epoch 263/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6783 - accuracy: 0.5713 - val_loss: 0.6799 - val_accuracy: 0.5678\n",
      "Epoch 264/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6783 - accuracy: 0.5713 - val_loss: 0.6799 - val_accuracy: 0.5675\n",
      "Epoch 265/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6783 - accuracy: 0.5717 - val_loss: 0.6799 - val_accuracy: 0.5678\n",
      "Epoch 266/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6783 - accuracy: 0.5716 - val_loss: 0.6799 - val_accuracy: 0.5677\n",
      "Epoch 267/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6783 - accuracy: 0.5715 - val_loss: 0.6799 - val_accuracy: 0.5677\n",
      "Epoch 268/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6783 - accuracy: 0.5711 - val_loss: 0.6799 - val_accuracy: 0.5677\n",
      "Epoch 269/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6783 - accuracy: 0.5714 - val_loss: 0.6799 - val_accuracy: 0.5670\n",
      "Epoch 270/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6782 - accuracy: 0.5718 - val_loss: 0.6800 - val_accuracy: 0.5674\n",
      "Epoch 271/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6782 - accuracy: 0.5714 - val_loss: 0.6799 - val_accuracy: 0.5678\n",
      "Epoch 272/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6782 - accuracy: 0.5719 - val_loss: 0.6799 - val_accuracy: 0.5678\n",
      "Epoch 273/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6782 - accuracy: 0.5715 - val_loss: 0.6799 - val_accuracy: 0.5671\n",
      "Epoch 274/1000\n",
      "1303/1303 [==============================] - 3s 3ms/step - loss: 0.6782 - accuracy: 0.5716 - val_loss: 0.6799 - val_accuracy: 0.5676\n",
      "Epoch 275/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6782 - accuracy: 0.5717 - val_loss: 0.6799 - val_accuracy: 0.5677\n",
      "Epoch 276/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6782 - accuracy: 0.5715 - val_loss: 0.6799 - val_accuracy: 0.5670\n",
      "Epoch 277/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6782 - accuracy: 0.5716 - val_loss: 0.6799 - val_accuracy: 0.5669\n",
      "Epoch 278/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6782 - accuracy: 0.5713 - val_loss: 0.6799 - val_accuracy: 0.5678\n",
      "Epoch 279/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6782 - accuracy: 0.5721 - val_loss: 0.6799 - val_accuracy: 0.5672\n",
      "Epoch 280/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6782 - accuracy: 0.5717 - val_loss: 0.6799 - val_accuracy: 0.5675\n",
      "Epoch 281/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6781 - accuracy: 0.5719 - val_loss: 0.6799 - val_accuracy: 0.5679\n",
      "Epoch 282/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6781 - accuracy: 0.5718 - val_loss: 0.6799 - val_accuracy: 0.5675\n",
      "Epoch 283/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6781 - accuracy: 0.5721 - val_loss: 0.6799 - val_accuracy: 0.5671\n",
      "Epoch 284/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6781 - accuracy: 0.5723 - val_loss: 0.6799 - val_accuracy: 0.5674\n",
      "Epoch 285/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6781 - accuracy: 0.5725 - val_loss: 0.6799 - val_accuracy: 0.5679\n",
      "Epoch 286/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6781 - accuracy: 0.5720 - val_loss: 0.6799 - val_accuracy: 0.5675\n",
      "Epoch 287/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6781 - accuracy: 0.5717 - val_loss: 0.6798 - val_accuracy: 0.5676\n",
      "Epoch 288/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6781 - accuracy: 0.5717 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 289/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6781 - accuracy: 0.5724 - val_loss: 0.6799 - val_accuracy: 0.5671\n",
      "Epoch 290/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6780 - accuracy: 0.5722 - val_loss: 0.6798 - val_accuracy: 0.5674\n",
      "Epoch 291/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6780 - accuracy: 0.5721 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 292/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6780 - accuracy: 0.5719 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 293/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6780 - accuracy: 0.5726 - val_loss: 0.6799 - val_accuracy: 0.5680\n",
      "Epoch 294/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6780 - accuracy: 0.5719 - val_loss: 0.6798 - val_accuracy: 0.5672\n",
      "Epoch 295/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6780 - accuracy: 0.5718 - val_loss: 0.6799 - val_accuracy: 0.5680\n",
      "Epoch 296/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6780 - accuracy: 0.5722 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 297/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6780 - accuracy: 0.5725 - val_loss: 0.6798 - val_accuracy: 0.5672\n",
      "Epoch 298/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6780 - accuracy: 0.5724 - val_loss: 0.6798 - val_accuracy: 0.5680\n",
      "Epoch 299/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6779 - accuracy: 0.5725 - val_loss: 0.6799 - val_accuracy: 0.5674\n",
      "Epoch 300/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6779 - accuracy: 0.5722 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 301/1000\n",
      "1303/1303 [==============================] - 3s 3ms/step - loss: 0.6779 - accuracy: 0.5724 - val_loss: 0.6798 - val_accuracy: 0.5681\n",
      "Epoch 302/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6779 - accuracy: 0.5729 - val_loss: 0.6798 - val_accuracy: 0.5676\n",
      "Epoch 303/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6779 - accuracy: 0.5723 - val_loss: 0.6798 - val_accuracy: 0.5672\n",
      "Epoch 304/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6779 - accuracy: 0.5725 - val_loss: 0.6798 - val_accuracy: 0.5674\n",
      "Epoch 305/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6779 - accuracy: 0.5722 - val_loss: 0.6798 - val_accuracy: 0.5674\n",
      "Epoch 306/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6779 - accuracy: 0.5724 - val_loss: 0.6799 - val_accuracy: 0.5675\n",
      "Epoch 307/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6778 - accuracy: 0.5724 - val_loss: 0.6798 - val_accuracy: 0.5674\n",
      "Epoch 308/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6778 - accuracy: 0.5725 - val_loss: 0.6798 - val_accuracy: 0.5676\n",
      "Epoch 309/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6778 - accuracy: 0.5730 - val_loss: 0.6798 - val_accuracy: 0.5674\n",
      "Epoch 310/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6778 - accuracy: 0.5726 - val_loss: 0.6798 - val_accuracy: 0.5674\n",
      "Epoch 311/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6778 - accuracy: 0.5729 - val_loss: 0.6799 - val_accuracy: 0.5671\n",
      "Epoch 312/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6778 - accuracy: 0.5730 - val_loss: 0.6798 - val_accuracy: 0.5674\n",
      "Epoch 313/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6778 - accuracy: 0.5725 - val_loss: 0.6798 - val_accuracy: 0.5676\n",
      "Epoch 314/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6778 - accuracy: 0.5731 - val_loss: 0.6798 - val_accuracy: 0.5680\n",
      "Epoch 315/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6778 - accuracy: 0.5730 - val_loss: 0.6798 - val_accuracy: 0.5673\n",
      "Epoch 316/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6777 - accuracy: 0.5727 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 317/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6777 - accuracy: 0.5726 - val_loss: 0.6798 - val_accuracy: 0.5680\n",
      "Epoch 318/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6777 - accuracy: 0.5731 - val_loss: 0.6799 - val_accuracy: 0.5671\n",
      "Epoch 319/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6777 - accuracy: 0.5725 - val_loss: 0.6798 - val_accuracy: 0.5684\n",
      "Epoch 320/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6777 - accuracy: 0.5725 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 321/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6777 - accuracy: 0.5724 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 322/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6777 - accuracy: 0.5732 - val_loss: 0.6798 - val_accuracy: 0.5683\n",
      "Epoch 323/1000\n",
      "1303/1303 [==============================] - 3s 3ms/step - loss: 0.6777 - accuracy: 0.5725 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 324/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6777 - accuracy: 0.5725 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 325/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6776 - accuracy: 0.5729 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 326/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6776 - accuracy: 0.5730 - val_loss: 0.6798 - val_accuracy: 0.5676\n",
      "Epoch 327/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6776 - accuracy: 0.5728 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 328/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6776 - accuracy: 0.5729 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 329/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6776 - accuracy: 0.5733 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 330/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6776 - accuracy: 0.5729 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 331/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6776 - accuracy: 0.5727 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 332/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6776 - accuracy: 0.5728 - val_loss: 0.6798 - val_accuracy: 0.5680\n",
      "Epoch 333/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6775 - accuracy: 0.5728 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 334/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6775 - accuracy: 0.5733 - val_loss: 0.6798 - val_accuracy: 0.5680\n",
      "Epoch 335/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6775 - accuracy: 0.5733 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 336/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6775 - accuracy: 0.5734 - val_loss: 0.6798 - val_accuracy: 0.5681\n",
      "Epoch 337/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6775 - accuracy: 0.5731 - val_loss: 0.6799 - val_accuracy: 0.5674\n",
      "Epoch 338/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6775 - accuracy: 0.5739 - val_loss: 0.6798 - val_accuracy: 0.5674\n",
      "Epoch 339/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6775 - accuracy: 0.5733 - val_loss: 0.6798 - val_accuracy: 0.5675\n",
      "Epoch 340/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6774 - accuracy: 0.5737 - val_loss: 0.6798 - val_accuracy: 0.5683\n",
      "Epoch 341/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6774 - accuracy: 0.5736 - val_loss: 0.6798 - val_accuracy: 0.5676\n",
      "Epoch 342/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6774 - accuracy: 0.5735 - val_loss: 0.6798 - val_accuracy: 0.5681\n",
      "Epoch 343/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6774 - accuracy: 0.5740 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 344/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6774 - accuracy: 0.5738 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 345/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6774 - accuracy: 0.5734 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 346/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6774 - accuracy: 0.5737 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 347/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6774 - accuracy: 0.5738 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 348/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6774 - accuracy: 0.5733 - val_loss: 0.6798 - val_accuracy: 0.5682\n",
      "Epoch 349/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6773 - accuracy: 0.5733 - val_loss: 0.6798 - val_accuracy: 0.5682\n",
      "Epoch 350/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6773 - accuracy: 0.5740 - val_loss: 0.6798 - val_accuracy: 0.5682\n",
      "Epoch 351/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6773 - accuracy: 0.5739 - val_loss: 0.6798 - val_accuracy: 0.5684\n",
      "Epoch 352/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6773 - accuracy: 0.5741 - val_loss: 0.6798 - val_accuracy: 0.5681\n",
      "Epoch 353/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6773 - accuracy: 0.5743 - val_loss: 0.6798 - val_accuracy: 0.5681\n",
      "Epoch 354/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6773 - accuracy: 0.5737 - val_loss: 0.6798 - val_accuracy: 0.5681\n",
      "Epoch 355/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6773 - accuracy: 0.5741 - val_loss: 0.6798 - val_accuracy: 0.5681\n",
      "Epoch 356/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6773 - accuracy: 0.5740 - val_loss: 0.6798 - val_accuracy: 0.5681\n",
      "Epoch 357/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6772 - accuracy: 0.5741 - val_loss: 0.6798 - val_accuracy: 0.5681\n",
      "Epoch 358/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6772 - accuracy: 0.5738 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 359/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6772 - accuracy: 0.5742 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 360/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6772 - accuracy: 0.5743 - val_loss: 0.6798 - val_accuracy: 0.5683\n",
      "Epoch 361/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6772 - accuracy: 0.5741 - val_loss: 0.6798 - val_accuracy: 0.5685\n",
      "Epoch 362/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6772 - accuracy: 0.5739 - val_loss: 0.6798 - val_accuracy: 0.5681\n",
      "Epoch 363/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6772 - accuracy: 0.5740 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 364/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6772 - accuracy: 0.5739 - val_loss: 0.6798 - val_accuracy: 0.5682\n",
      "Epoch 365/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6771 - accuracy: 0.5741 - val_loss: 0.6798 - val_accuracy: 0.5680\n",
      "Epoch 366/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6771 - accuracy: 0.5744 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 367/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6771 - accuracy: 0.5736 - val_loss: 0.6798 - val_accuracy: 0.5684\n",
      "Epoch 368/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6771 - accuracy: 0.5740 - val_loss: 0.6798 - val_accuracy: 0.5681\n",
      "Epoch 369/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6771 - accuracy: 0.5741 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 370/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6771 - accuracy: 0.5742 - val_loss: 0.6798 - val_accuracy: 0.5680\n",
      "Epoch 371/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6771 - accuracy: 0.5745 - val_loss: 0.6797 - val_accuracy: 0.5682\n",
      "Epoch 372/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6770 - accuracy: 0.5742 - val_loss: 0.6798 - val_accuracy: 0.5680\n",
      "Epoch 373/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6770 - accuracy: 0.5748 - val_loss: 0.6797 - val_accuracy: 0.5681\n",
      "Epoch 374/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6770 - accuracy: 0.5745 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 375/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6770 - accuracy: 0.5738 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 376/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6770 - accuracy: 0.5742 - val_loss: 0.6797 - val_accuracy: 0.5676\n",
      "Epoch 377/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6770 - accuracy: 0.5740 - val_loss: 0.6798 - val_accuracy: 0.5680\n",
      "Epoch 378/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6770 - accuracy: 0.5744 - val_loss: 0.6797 - val_accuracy: 0.5679\n",
      "Epoch 379/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6769 - accuracy: 0.5745 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 380/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6769 - accuracy: 0.5744 - val_loss: 0.6797 - val_accuracy: 0.5677\n",
      "Epoch 381/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6769 - accuracy: 0.5746 - val_loss: 0.6797 - val_accuracy: 0.5678\n",
      "Epoch 382/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6769 - accuracy: 0.5744 - val_loss: 0.6797 - val_accuracy: 0.5676\n",
      "Epoch 383/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6769 - accuracy: 0.5751 - val_loss: 0.6798 - val_accuracy: 0.5681\n",
      "Epoch 384/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6769 - accuracy: 0.5745 - val_loss: 0.6797 - val_accuracy: 0.5676\n",
      "Epoch 385/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6769 - accuracy: 0.5752 - val_loss: 0.6798 - val_accuracy: 0.5674\n",
      "Epoch 386/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6768 - accuracy: 0.5751 - val_loss: 0.6797 - val_accuracy: 0.5676\n",
      "Epoch 387/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6768 - accuracy: 0.5741 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 388/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6768 - accuracy: 0.5748 - val_loss: 0.6798 - val_accuracy: 0.5684\n",
      "Epoch 389/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6768 - accuracy: 0.5747 - val_loss: 0.6798 - val_accuracy: 0.5686\n",
      "Epoch 390/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6768 - accuracy: 0.5747 - val_loss: 0.6798 - val_accuracy: 0.5676\n",
      "Epoch 391/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6768 - accuracy: 0.5747 - val_loss: 0.6797 - val_accuracy: 0.5676\n",
      "Epoch 392/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6768 - accuracy: 0.5748 - val_loss: 0.6797 - val_accuracy: 0.5677\n",
      "Epoch 393/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6767 - accuracy: 0.5745 - val_loss: 0.6797 - val_accuracy: 0.5677\n",
      "Epoch 394/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6767 - accuracy: 0.5750 - val_loss: 0.6797 - val_accuracy: 0.5678\n",
      "Epoch 395/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6767 - accuracy: 0.5750 - val_loss: 0.6798 - val_accuracy: 0.5681\n",
      "Epoch 396/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6767 - accuracy: 0.5749 - val_loss: 0.6797 - val_accuracy: 0.5676\n",
      "Epoch 397/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6767 - accuracy: 0.5750 - val_loss: 0.6797 - val_accuracy: 0.5675\n",
      "Epoch 398/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6767 - accuracy: 0.5753 - val_loss: 0.6799 - val_accuracy: 0.5667\n",
      "Epoch 399/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6767 - accuracy: 0.5752 - val_loss: 0.6797 - val_accuracy: 0.5682\n",
      "Epoch 400/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6766 - accuracy: 0.5745 - val_loss: 0.6797 - val_accuracy: 0.5677\n",
      "Epoch 401/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6766 - accuracy: 0.5747 - val_loss: 0.6797 - val_accuracy: 0.5674\n",
      "Epoch 402/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6766 - accuracy: 0.5752 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 403/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6766 - accuracy: 0.5749 - val_loss: 0.6797 - val_accuracy: 0.5680\n",
      "Epoch 404/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6766 - accuracy: 0.5750 - val_loss: 0.6797 - val_accuracy: 0.5677\n",
      "Epoch 405/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6766 - accuracy: 0.5750 - val_loss: 0.6797 - val_accuracy: 0.5675\n",
      "Epoch 406/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6766 - accuracy: 0.5745 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 407/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6765 - accuracy: 0.5754 - val_loss: 0.6798 - val_accuracy: 0.5683\n",
      "Epoch 408/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6765 - accuracy: 0.5749 - val_loss: 0.6797 - val_accuracy: 0.5677\n",
      "Epoch 409/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6765 - accuracy: 0.5745 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 410/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6765 - accuracy: 0.5751 - val_loss: 0.6798 - val_accuracy: 0.5669\n",
      "Epoch 411/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6765 - accuracy: 0.5750 - val_loss: 0.6798 - val_accuracy: 0.5681\n",
      "Epoch 412/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6765 - accuracy: 0.5752 - val_loss: 0.6797 - val_accuracy: 0.5678\n",
      "Epoch 413/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6764 - accuracy: 0.5752 - val_loss: 0.6797 - val_accuracy: 0.5675\n",
      "Epoch 414/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6764 - accuracy: 0.5748 - val_loss: 0.6797 - val_accuracy: 0.5681\n",
      "Epoch 415/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6764 - accuracy: 0.5757 - val_loss: 0.6798 - val_accuracy: 0.5672\n",
      "Epoch 416/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6764 - accuracy: 0.5751 - val_loss: 0.6797 - val_accuracy: 0.5679\n",
      "Epoch 417/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6764 - accuracy: 0.5752 - val_loss: 0.6798 - val_accuracy: 0.5681\n",
      "Epoch 418/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6764 - accuracy: 0.5751 - val_loss: 0.6797 - val_accuracy: 0.5679\n",
      "Epoch 419/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6764 - accuracy: 0.5751 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 420/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6763 - accuracy: 0.5751 - val_loss: 0.6798 - val_accuracy: 0.5674\n",
      "Epoch 421/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6763 - accuracy: 0.5749 - val_loss: 0.6797 - val_accuracy: 0.5679\n",
      "Epoch 422/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6763 - accuracy: 0.5754 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 423/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6763 - accuracy: 0.5752 - val_loss: 0.6797 - val_accuracy: 0.5678\n",
      "Epoch 424/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6763 - accuracy: 0.5755 - val_loss: 0.6797 - val_accuracy: 0.5680\n",
      "Epoch 425/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6763 - accuracy: 0.5754 - val_loss: 0.6797 - val_accuracy: 0.5681\n",
      "Epoch 426/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6762 - accuracy: 0.5754 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 427/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6762 - accuracy: 0.5749 - val_loss: 0.6797 - val_accuracy: 0.5680\n",
      "Epoch 428/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6762 - accuracy: 0.5761 - val_loss: 0.6798 - val_accuracy: 0.5683\n",
      "Epoch 429/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6762 - accuracy: 0.5756 - val_loss: 0.6797 - val_accuracy: 0.5678\n",
      "Epoch 430/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6762 - accuracy: 0.5757 - val_loss: 0.6798 - val_accuracy: 0.5676\n",
      "Epoch 431/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6762 - accuracy: 0.5750 - val_loss: 0.6798 - val_accuracy: 0.5682\n",
      "Epoch 432/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6762 - accuracy: 0.5761 - val_loss: 0.6797 - val_accuracy: 0.5678\n",
      "Epoch 433/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6762 - accuracy: 0.5756 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 434/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6761 - accuracy: 0.5757 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 435/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6761 - accuracy: 0.5758 - val_loss: 0.6798 - val_accuracy: 0.5680\n",
      "Epoch 436/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6761 - accuracy: 0.5753 - val_loss: 0.6798 - val_accuracy: 0.5684\n",
      "Epoch 437/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6761 - accuracy: 0.5754 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 438/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6761 - accuracy: 0.5759 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 439/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6760 - accuracy: 0.5759 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 440/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6760 - accuracy: 0.5755 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 441/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6760 - accuracy: 0.5759 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 442/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6760 - accuracy: 0.5756 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 443/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6760 - accuracy: 0.5761 - val_loss: 0.6798 - val_accuracy: 0.5681\n",
      "Epoch 444/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6760 - accuracy: 0.5756 - val_loss: 0.6798 - val_accuracy: 0.5681\n",
      "Epoch 445/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6760 - accuracy: 0.5760 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 446/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6759 - accuracy: 0.5759 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 447/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6759 - accuracy: 0.5766 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 448/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6759 - accuracy: 0.5758 - val_loss: 0.6798 - val_accuracy: 0.5676\n",
      "Epoch 449/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6759 - accuracy: 0.5766 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 450/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6758 - accuracy: 0.5766 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 451/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6759 - accuracy: 0.5757 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 452/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6758 - accuracy: 0.5761 - val_loss: 0.6798 - val_accuracy: 0.5680\n",
      "Epoch 453/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6758 - accuracy: 0.5765 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 454/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6758 - accuracy: 0.5767 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 455/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6758 - accuracy: 0.5766 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 456/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6758 - accuracy: 0.5761 - val_loss: 0.6799 - val_accuracy: 0.5676\n",
      "Epoch 457/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6757 - accuracy: 0.5767 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 458/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6757 - accuracy: 0.5767 - val_loss: 0.6799 - val_accuracy: 0.5680\n",
      "Epoch 459/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6757 - accuracy: 0.5765 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 460/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6757 - accuracy: 0.5766 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 461/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6757 - accuracy: 0.5762 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 462/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6757 - accuracy: 0.5767 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 463/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6756 - accuracy: 0.5761 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 464/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6756 - accuracy: 0.5766 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 465/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6756 - accuracy: 0.5768 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 466/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6756 - accuracy: 0.5767 - val_loss: 0.6799 - val_accuracy: 0.5680\n",
      "Epoch 467/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6756 - accuracy: 0.5766 - val_loss: 0.6798 - val_accuracy: 0.5680\n",
      "Epoch 468/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6756 - accuracy: 0.5764 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 469/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6755 - accuracy: 0.5761 - val_loss: 0.6798 - val_accuracy: 0.5676\n",
      "Epoch 470/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6755 - accuracy: 0.5764 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 471/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6755 - accuracy: 0.5765 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 472/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6755 - accuracy: 0.5765 - val_loss: 0.6798 - val_accuracy: 0.5682\n",
      "Epoch 473/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6755 - accuracy: 0.5769 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 474/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6755 - accuracy: 0.5762 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 475/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6754 - accuracy: 0.5769 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 476/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6754 - accuracy: 0.5775 - val_loss: 0.6798 - val_accuracy: 0.5675\n",
      "Epoch 477/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6754 - accuracy: 0.5772 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 478/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6754 - accuracy: 0.5769 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 479/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6754 - accuracy: 0.5771 - val_loss: 0.6798 - val_accuracy: 0.5683\n",
      "Epoch 480/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6753 - accuracy: 0.5767 - val_loss: 0.6798 - val_accuracy: 0.5676\n",
      "Epoch 481/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6753 - accuracy: 0.5769 - val_loss: 0.6798 - val_accuracy: 0.5680\n",
      "Epoch 482/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6753 - accuracy: 0.5768 - val_loss: 0.6799 - val_accuracy: 0.5677\n",
      "Epoch 483/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6753 - accuracy: 0.5774 - val_loss: 0.6798 - val_accuracy: 0.5679\n",
      "Epoch 484/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6753 - accuracy: 0.5775 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 485/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6752 - accuracy: 0.5772 - val_loss: 0.6799 - val_accuracy: 0.5678\n",
      "Epoch 486/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6752 - accuracy: 0.5771 - val_loss: 0.6798 - val_accuracy: 0.5678\n",
      "Epoch 487/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6752 - accuracy: 0.5775 - val_loss: 0.6798 - val_accuracy: 0.5680\n",
      "Epoch 488/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6752 - accuracy: 0.5771 - val_loss: 0.6798 - val_accuracy: 0.5675\n",
      "Epoch 489/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6752 - accuracy: 0.5770 - val_loss: 0.6799 - val_accuracy: 0.5679\n",
      "Epoch 490/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6752 - accuracy: 0.5777 - val_loss: 0.6798 - val_accuracy: 0.5677\n",
      "Epoch 491/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6751 - accuracy: 0.5778 - val_loss: 0.6798 - val_accuracy: 0.5676\n",
      "Epoch 492/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6751 - accuracy: 0.5773 - val_loss: 0.6799 - val_accuracy: 0.5676\n",
      "Epoch 493/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6751 - accuracy: 0.5779 - val_loss: 0.6799 - val_accuracy: 0.5680\n",
      "Epoch 494/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6751 - accuracy: 0.5773 - val_loss: 0.6799 - val_accuracy: 0.5675\n",
      "Epoch 495/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6751 - accuracy: 0.5773 - val_loss: 0.6799 - val_accuracy: 0.5672\n",
      "Epoch 496/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6750 - accuracy: 0.5772 - val_loss: 0.6799 - val_accuracy: 0.5678\n",
      "Epoch 497/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6750 - accuracy: 0.5778 - val_loss: 0.6799 - val_accuracy: 0.5678\n",
      "Epoch 498/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6750 - accuracy: 0.5771 - val_loss: 0.6799 - val_accuracy: 0.5680\n",
      "Epoch 499/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6750 - accuracy: 0.5782 - val_loss: 0.6799 - val_accuracy: 0.5674\n",
      "Epoch 500/1000\n",
      "1303/1303 [==============================] - 3s 3ms/step - loss: 0.6750 - accuracy: 0.5773 - val_loss: 0.6799 - val_accuracy: 0.5674\n",
      "Epoch 501/1000\n",
      "1303/1303 [==============================] - 3s 3ms/step - loss: 0.6749 - accuracy: 0.5773 - val_loss: 0.6799 - val_accuracy: 0.5677\n",
      "Epoch 502/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6749 - accuracy: 0.5781 - val_loss: 0.6799 - val_accuracy: 0.5674\n",
      "Epoch 503/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6749 - accuracy: 0.5778 - val_loss: 0.6799 - val_accuracy: 0.5676\n",
      "Epoch 504/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6749 - accuracy: 0.5775 - val_loss: 0.6800 - val_accuracy: 0.5680\n",
      "Epoch 505/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6749 - accuracy: 0.5781 - val_loss: 0.6799 - val_accuracy: 0.5672\n",
      "Epoch 506/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6749 - accuracy: 0.5779 - val_loss: 0.6799 - val_accuracy: 0.5677\n",
      "Epoch 507/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6748 - accuracy: 0.5778 - val_loss: 0.6799 - val_accuracy: 0.5674\n",
      "Epoch 508/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6748 - accuracy: 0.5788 - val_loss: 0.6799 - val_accuracy: 0.5682\n",
      "Epoch 509/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6748 - accuracy: 0.5773 - val_loss: 0.6799 - val_accuracy: 0.5681\n",
      "Epoch 510/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6748 - accuracy: 0.5781 - val_loss: 0.6799 - val_accuracy: 0.5672\n",
      "Epoch 511/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6747 - accuracy: 0.5776 - val_loss: 0.6799 - val_accuracy: 0.5680\n",
      "Epoch 512/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6747 - accuracy: 0.5778 - val_loss: 0.6799 - val_accuracy: 0.5669\n",
      "Epoch 513/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6747 - accuracy: 0.5781 - val_loss: 0.6799 - val_accuracy: 0.5679\n",
      "Epoch 514/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6747 - accuracy: 0.5782 - val_loss: 0.6799 - val_accuracy: 0.5672\n",
      "Epoch 515/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6747 - accuracy: 0.5783 - val_loss: 0.6799 - val_accuracy: 0.5676\n",
      "Epoch 516/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6746 - accuracy: 0.5779 - val_loss: 0.6802 - val_accuracy: 0.5672\n",
      "Epoch 517/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6747 - accuracy: 0.5781 - val_loss: 0.6799 - val_accuracy: 0.5670\n",
      "Epoch 518/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6746 - accuracy: 0.5776 - val_loss: 0.6799 - val_accuracy: 0.5670\n",
      "Epoch 519/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6746 - accuracy: 0.5783 - val_loss: 0.6800 - val_accuracy: 0.5674\n",
      "Epoch 520/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6746 - accuracy: 0.5780 - val_loss: 0.6799 - val_accuracy: 0.5677\n",
      "Epoch 521/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6745 - accuracy: 0.5784 - val_loss: 0.6800 - val_accuracy: 0.5677\n",
      "Epoch 522/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6745 - accuracy: 0.5783 - val_loss: 0.6801 - val_accuracy: 0.5682\n",
      "Epoch 523/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6745 - accuracy: 0.5779 - val_loss: 0.6800 - val_accuracy: 0.5680\n",
      "Epoch 524/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6745 - accuracy: 0.5784 - val_loss: 0.6801 - val_accuracy: 0.5679\n",
      "Epoch 525/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6745 - accuracy: 0.5775 - val_loss: 0.6800 - val_accuracy: 0.5669\n",
      "Epoch 526/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6745 - accuracy: 0.5784 - val_loss: 0.6800 - val_accuracy: 0.5680\n",
      "Epoch 527/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6744 - accuracy: 0.5787 - val_loss: 0.6800 - val_accuracy: 0.5672\n",
      "Epoch 528/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6744 - accuracy: 0.5787 - val_loss: 0.6800 - val_accuracy: 0.5679\n",
      "Epoch 529/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6744 - accuracy: 0.5782 - val_loss: 0.6800 - val_accuracy: 0.5669\n",
      "Epoch 530/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6744 - accuracy: 0.5785 - val_loss: 0.6800 - val_accuracy: 0.5670\n",
      "Epoch 531/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6744 - accuracy: 0.5786 - val_loss: 0.6800 - val_accuracy: 0.5670\n",
      "Epoch 532/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6743 - accuracy: 0.5785 - val_loss: 0.6800 - val_accuracy: 0.5674\n",
      "Epoch 533/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6743 - accuracy: 0.5784 - val_loss: 0.6802 - val_accuracy: 0.5675\n",
      "Epoch 534/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6743 - accuracy: 0.5786 - val_loss: 0.6800 - val_accuracy: 0.5680\n",
      "Epoch 535/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6743 - accuracy: 0.5789 - val_loss: 0.6800 - val_accuracy: 0.5671\n",
      "Epoch 536/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6742 - accuracy: 0.5783 - val_loss: 0.6800 - val_accuracy: 0.5675\n",
      "Epoch 537/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6742 - accuracy: 0.5787 - val_loss: 0.6800 - val_accuracy: 0.5672\n",
      "Epoch 538/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6742 - accuracy: 0.5786 - val_loss: 0.6800 - val_accuracy: 0.5679\n",
      "Epoch 539/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6742 - accuracy: 0.5791 - val_loss: 0.6800 - val_accuracy: 0.5680\n",
      "Epoch 540/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6742 - accuracy: 0.5783 - val_loss: 0.6801 - val_accuracy: 0.5677\n",
      "Epoch 541/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6742 - accuracy: 0.5789 - val_loss: 0.6801 - val_accuracy: 0.5675\n",
      "Epoch 542/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6741 - accuracy: 0.5790 - val_loss: 0.6800 - val_accuracy: 0.5672\n",
      "Epoch 543/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6741 - accuracy: 0.5792 - val_loss: 0.6801 - val_accuracy: 0.5679\n",
      "Epoch 544/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6741 - accuracy: 0.5789 - val_loss: 0.6800 - val_accuracy: 0.5677\n",
      "Epoch 545/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6741 - accuracy: 0.5794 - val_loss: 0.6801 - val_accuracy: 0.5680\n",
      "Epoch 546/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6740 - accuracy: 0.5792 - val_loss: 0.6800 - val_accuracy: 0.5683\n",
      "Epoch 547/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6740 - accuracy: 0.5793 - val_loss: 0.6800 - val_accuracy: 0.5675\n",
      "Epoch 548/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6740 - accuracy: 0.5796 - val_loss: 0.6800 - val_accuracy: 0.5681\n",
      "Epoch 549/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6740 - accuracy: 0.5793 - val_loss: 0.6800 - val_accuracy: 0.5673\n",
      "Epoch 550/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6739 - accuracy: 0.5791 - val_loss: 0.6801 - val_accuracy: 0.5672\n",
      "Epoch 551/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6739 - accuracy: 0.5792 - val_loss: 0.6801 - val_accuracy: 0.5674\n",
      "Epoch 552/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6739 - accuracy: 0.5795 - val_loss: 0.6801 - val_accuracy: 0.5672\n",
      "Epoch 553/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6739 - accuracy: 0.5795 - val_loss: 0.6801 - val_accuracy: 0.5673\n",
      "Epoch 554/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6739 - accuracy: 0.5799 - val_loss: 0.6801 - val_accuracy: 0.5680\n",
      "Epoch 555/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6738 - accuracy: 0.5799 - val_loss: 0.6801 - val_accuracy: 0.5671\n",
      "Epoch 556/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6738 - accuracy: 0.5798 - val_loss: 0.6801 - val_accuracy: 0.5671\n",
      "Epoch 557/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6738 - accuracy: 0.5799 - val_loss: 0.6801 - val_accuracy: 0.5679\n",
      "Epoch 558/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6738 - accuracy: 0.5794 - val_loss: 0.6801 - val_accuracy: 0.5670\n",
      "Epoch 559/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6737 - accuracy: 0.5796 - val_loss: 0.6802 - val_accuracy: 0.5676\n",
      "Epoch 560/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6737 - accuracy: 0.5803 - val_loss: 0.6801 - val_accuracy: 0.5681\n",
      "Epoch 561/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6737 - accuracy: 0.5798 - val_loss: 0.6801 - val_accuracy: 0.5680\n",
      "Epoch 562/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6737 - accuracy: 0.5796 - val_loss: 0.6801 - val_accuracy: 0.5674\n",
      "Epoch 563/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6737 - accuracy: 0.5802 - val_loss: 0.6801 - val_accuracy: 0.5672\n",
      "Epoch 564/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6736 - accuracy: 0.5799 - val_loss: 0.6802 - val_accuracy: 0.5670\n",
      "Epoch 565/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6736 - accuracy: 0.5802 - val_loss: 0.6801 - val_accuracy: 0.5678\n",
      "Epoch 566/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6736 - accuracy: 0.5803 - val_loss: 0.6802 - val_accuracy: 0.5676\n",
      "Epoch 567/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6736 - accuracy: 0.5804 - val_loss: 0.6802 - val_accuracy: 0.5673\n",
      "Epoch 568/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6736 - accuracy: 0.5808 - val_loss: 0.6804 - val_accuracy: 0.5681\n",
      "Epoch 569/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6736 - accuracy: 0.5795 - val_loss: 0.6802 - val_accuracy: 0.5679\n",
      "Epoch 570/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6735 - accuracy: 0.5806 - val_loss: 0.6802 - val_accuracy: 0.5680\n",
      "Epoch 571/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6735 - accuracy: 0.5810 - val_loss: 0.6802 - val_accuracy: 0.5680\n",
      "Epoch 572/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6734 - accuracy: 0.5804 - val_loss: 0.6805 - val_accuracy: 0.5662\n",
      "Epoch 573/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6734 - accuracy: 0.5801 - val_loss: 0.6803 - val_accuracy: 0.5673\n",
      "Epoch 574/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6734 - accuracy: 0.5801 - val_loss: 0.6802 - val_accuracy: 0.5674\n",
      "Epoch 575/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6734 - accuracy: 0.5807 - val_loss: 0.6803 - val_accuracy: 0.5669\n",
      "Epoch 576/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6734 - accuracy: 0.5804 - val_loss: 0.6802 - val_accuracy: 0.5677\n",
      "Epoch 577/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6734 - accuracy: 0.5799 - val_loss: 0.6802 - val_accuracy: 0.5676\n",
      "Epoch 578/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6733 - accuracy: 0.5803 - val_loss: 0.6802 - val_accuracy: 0.5677\n",
      "Epoch 579/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6733 - accuracy: 0.5808 - val_loss: 0.6802 - val_accuracy: 0.5674\n",
      "Epoch 580/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6733 - accuracy: 0.5809 - val_loss: 0.6804 - val_accuracy: 0.5679\n",
      "Epoch 581/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6733 - accuracy: 0.5807 - val_loss: 0.6803 - val_accuracy: 0.5671\n",
      "Epoch 582/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6732 - accuracy: 0.5809 - val_loss: 0.6802 - val_accuracy: 0.5678\n",
      "Epoch 583/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6732 - accuracy: 0.5814 - val_loss: 0.6802 - val_accuracy: 0.5672\n",
      "Epoch 584/1000\n",
      "1303/1303 [==============================] - 4s 3ms/step - loss: 0.6732 - accuracy: 0.5801 - val_loss: 0.6803 - val_accuracy: 0.5678\n",
      "Epoch 585/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6732 - accuracy: 0.5812 - val_loss: 0.6803 - val_accuracy: 0.5673\n",
      "Epoch 586/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6731 - accuracy: 0.5809 - val_loss: 0.6803 - val_accuracy: 0.5676\n",
      "Epoch 587/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6731 - accuracy: 0.5807 - val_loss: 0.6803 - val_accuracy: 0.5674\n",
      "Epoch 588/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6731 - accuracy: 0.5814 - val_loss: 0.6803 - val_accuracy: 0.5678\n",
      "Epoch 589/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6731 - accuracy: 0.5803 - val_loss: 0.6803 - val_accuracy: 0.5673\n",
      "Epoch 590/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6731 - accuracy: 0.5808 - val_loss: 0.6803 - val_accuracy: 0.5672\n",
      "Epoch 591/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6730 - accuracy: 0.5814 - val_loss: 0.6803 - val_accuracy: 0.5673\n",
      "Epoch 592/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6730 - accuracy: 0.5811 - val_loss: 0.6803 - val_accuracy: 0.5672\n",
      "Epoch 593/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6730 - accuracy: 0.5817 - val_loss: 0.6803 - val_accuracy: 0.5673\n",
      "Epoch 594/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6729 - accuracy: 0.5816 - val_loss: 0.6803 - val_accuracy: 0.5676\n",
      "Epoch 595/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6729 - accuracy: 0.5813 - val_loss: 0.6803 - val_accuracy: 0.5672\n",
      "Epoch 596/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6729 - accuracy: 0.5817 - val_loss: 0.6804 - val_accuracy: 0.5671\n",
      "Epoch 597/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6728 - accuracy: 0.5817 - val_loss: 0.6804 - val_accuracy: 0.5676\n",
      "Epoch 598/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6729 - accuracy: 0.5814 - val_loss: 0.6803 - val_accuracy: 0.5672\n",
      "Epoch 599/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6728 - accuracy: 0.5818 - val_loss: 0.6804 - val_accuracy: 0.5670\n",
      "Epoch 600/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6728 - accuracy: 0.5821 - val_loss: 0.6804 - val_accuracy: 0.5668\n",
      "Epoch 601/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6728 - accuracy: 0.5812 - val_loss: 0.6804 - val_accuracy: 0.5672\n",
      "Epoch 602/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6728 - accuracy: 0.5819 - val_loss: 0.6804 - val_accuracy: 0.5673\n",
      "Epoch 603/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6727 - accuracy: 0.5818 - val_loss: 0.6804 - val_accuracy: 0.5670\n",
      "Epoch 604/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6727 - accuracy: 0.5814 - val_loss: 0.6804 - val_accuracy: 0.5669\n",
      "Epoch 605/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6727 - accuracy: 0.5812 - val_loss: 0.6804 - val_accuracy: 0.5678\n",
      "Epoch 606/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6726 - accuracy: 0.5819 - val_loss: 0.6804 - val_accuracy: 0.5672\n",
      "Epoch 607/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6726 - accuracy: 0.5815 - val_loss: 0.6806 - val_accuracy: 0.5670\n",
      "Epoch 608/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6726 - accuracy: 0.5821 - val_loss: 0.6805 - val_accuracy: 0.5670\n",
      "Epoch 609/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6726 - accuracy: 0.5826 - val_loss: 0.6804 - val_accuracy: 0.5669\n",
      "Epoch 610/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6725 - accuracy: 0.5822 - val_loss: 0.6805 - val_accuracy: 0.5670\n",
      "Epoch 611/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6725 - accuracy: 0.5829 - val_loss: 0.6805 - val_accuracy: 0.5671\n",
      "Epoch 612/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6725 - accuracy: 0.5825 - val_loss: 0.6804 - val_accuracy: 0.5669\n",
      "Epoch 613/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6725 - accuracy: 0.5821 - val_loss: 0.6805 - val_accuracy: 0.5672\n",
      "Epoch 614/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6724 - accuracy: 0.5820 - val_loss: 0.6805 - val_accuracy: 0.5670\n",
      "Epoch 615/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6724 - accuracy: 0.5830 - val_loss: 0.6805 - val_accuracy: 0.5671\n",
      "Epoch 616/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6724 - accuracy: 0.5824 - val_loss: 0.6807 - val_accuracy: 0.5666\n",
      "Epoch 617/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6723 - accuracy: 0.5826 - val_loss: 0.6806 - val_accuracy: 0.5678\n",
      "Epoch 618/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6724 - accuracy: 0.5827 - val_loss: 0.6806 - val_accuracy: 0.5669\n",
      "Epoch 619/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6723 - accuracy: 0.5828 - val_loss: 0.6806 - val_accuracy: 0.5669\n",
      "Epoch 620/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6723 - accuracy: 0.5832 - val_loss: 0.6806 - val_accuracy: 0.5670\n",
      "Epoch 621/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6722 - accuracy: 0.5829 - val_loss: 0.6806 - val_accuracy: 0.5667\n",
      "Epoch 622/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6723 - accuracy: 0.5825 - val_loss: 0.6805 - val_accuracy: 0.5670\n",
      "Epoch 623/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6722 - accuracy: 0.5829 - val_loss: 0.6806 - val_accuracy: 0.5672\n",
      "Epoch 624/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6722 - accuracy: 0.5829 - val_loss: 0.6806 - val_accuracy: 0.5669\n",
      "Epoch 625/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6722 - accuracy: 0.5822 - val_loss: 0.6806 - val_accuracy: 0.5670\n",
      "Epoch 626/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6721 - accuracy: 0.5832 - val_loss: 0.6806 - val_accuracy: 0.5670\n",
      "Epoch 627/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6721 - accuracy: 0.5820 - val_loss: 0.6806 - val_accuracy: 0.5668\n",
      "Epoch 628/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6721 - accuracy: 0.5831 - val_loss: 0.6807 - val_accuracy: 0.5666\n",
      "Epoch 629/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6721 - accuracy: 0.5837 - val_loss: 0.6807 - val_accuracy: 0.5677\n",
      "Epoch 630/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6720 - accuracy: 0.5830 - val_loss: 0.6807 - val_accuracy: 0.5668\n",
      "Epoch 631/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6720 - accuracy: 0.5831 - val_loss: 0.6806 - val_accuracy: 0.5669\n",
      "Epoch 632/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6720 - accuracy: 0.5839 - val_loss: 0.6807 - val_accuracy: 0.5663\n",
      "Epoch 633/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6720 - accuracy: 0.5836 - val_loss: 0.6806 - val_accuracy: 0.5669\n",
      "Epoch 634/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6719 - accuracy: 0.5832 - val_loss: 0.6807 - val_accuracy: 0.5663\n",
      "Epoch 635/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6719 - accuracy: 0.5839 - val_loss: 0.6808 - val_accuracy: 0.5665\n",
      "Epoch 636/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6718 - accuracy: 0.5834 - val_loss: 0.6807 - val_accuracy: 0.5669\n",
      "Epoch 637/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6719 - accuracy: 0.5839 - val_loss: 0.6808 - val_accuracy: 0.5667\n",
      "Epoch 638/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6718 - accuracy: 0.5839 - val_loss: 0.6807 - val_accuracy: 0.5669\n",
      "Epoch 639/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6718 - accuracy: 0.5843 - val_loss: 0.6807 - val_accuracy: 0.5671\n",
      "Epoch 640/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6718 - accuracy: 0.5840 - val_loss: 0.6808 - val_accuracy: 0.5670\n",
      "Epoch 641/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6717 - accuracy: 0.5834 - val_loss: 0.6807 - val_accuracy: 0.5668\n",
      "Epoch 642/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6717 - accuracy: 0.5840 - val_loss: 0.6808 - val_accuracy: 0.5663\n",
      "Epoch 643/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6717 - accuracy: 0.5841 - val_loss: 0.6808 - val_accuracy: 0.5666\n",
      "Epoch 644/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6717 - accuracy: 0.5842 - val_loss: 0.6808 - val_accuracy: 0.5670\n",
      "Epoch 645/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6716 - accuracy: 0.5842 - val_loss: 0.6808 - val_accuracy: 0.5672\n",
      "Epoch 646/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6716 - accuracy: 0.5843 - val_loss: 0.6809 - val_accuracy: 0.5672\n",
      "Epoch 647/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6716 - accuracy: 0.5843 - val_loss: 0.6808 - val_accuracy: 0.5670\n",
      "Epoch 648/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6716 - accuracy: 0.5845 - val_loss: 0.6809 - val_accuracy: 0.5664\n",
      "Epoch 649/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6716 - accuracy: 0.5843 - val_loss: 0.6808 - val_accuracy: 0.5662\n",
      "Epoch 650/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6715 - accuracy: 0.5849 - val_loss: 0.6810 - val_accuracy: 0.5670\n",
      "Epoch 651/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6715 - accuracy: 0.5849 - val_loss: 0.6809 - val_accuracy: 0.5673\n",
      "Epoch 652/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6715 - accuracy: 0.5847 - val_loss: 0.6810 - val_accuracy: 0.5661\n",
      "Epoch 653/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6714 - accuracy: 0.5849 - val_loss: 0.6808 - val_accuracy: 0.5665\n",
      "Epoch 654/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6714 - accuracy: 0.5842 - val_loss: 0.6809 - val_accuracy: 0.5665\n",
      "Epoch 655/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6714 - accuracy: 0.5849 - val_loss: 0.6809 - val_accuracy: 0.5665\n",
      "Epoch 656/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6714 - accuracy: 0.5853 - val_loss: 0.6809 - val_accuracy: 0.5674\n",
      "Epoch 657/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6713 - accuracy: 0.5857 - val_loss: 0.6809 - val_accuracy: 0.5669\n",
      "Epoch 658/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6713 - accuracy: 0.5847 - val_loss: 0.6809 - val_accuracy: 0.5672\n",
      "Epoch 659/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6713 - accuracy: 0.5850 - val_loss: 0.6810 - val_accuracy: 0.5664\n",
      "Epoch 660/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6713 - accuracy: 0.5848 - val_loss: 0.6809 - val_accuracy: 0.5663\n",
      "Epoch 661/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6712 - accuracy: 0.5847 - val_loss: 0.6810 - val_accuracy: 0.5673\n",
      "Epoch 662/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6712 - accuracy: 0.5850 - val_loss: 0.6809 - val_accuracy: 0.5665\n",
      "Epoch 663/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6712 - accuracy: 0.5853 - val_loss: 0.6810 - val_accuracy: 0.5665\n",
      "Epoch 664/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6712 - accuracy: 0.5846 - val_loss: 0.6810 - val_accuracy: 0.5662\n",
      "Epoch 665/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6711 - accuracy: 0.5850 - val_loss: 0.6810 - val_accuracy: 0.5671\n",
      "Epoch 666/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6711 - accuracy: 0.5853 - val_loss: 0.6810 - val_accuracy: 0.5668\n",
      "Epoch 667/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6711 - accuracy: 0.5847 - val_loss: 0.6810 - val_accuracy: 0.5662\n",
      "Epoch 668/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6710 - accuracy: 0.5855 - val_loss: 0.6810 - val_accuracy: 0.5669\n",
      "Epoch 669/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6710 - accuracy: 0.5848 - val_loss: 0.6810 - val_accuracy: 0.5669\n",
      "Epoch 670/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6710 - accuracy: 0.5854 - val_loss: 0.6810 - val_accuracy: 0.5666\n",
      "Epoch 671/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6709 - accuracy: 0.5855 - val_loss: 0.6811 - val_accuracy: 0.5656\n",
      "Epoch 672/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6709 - accuracy: 0.5853 - val_loss: 0.6811 - val_accuracy: 0.5667\n",
      "Epoch 673/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6709 - accuracy: 0.5852 - val_loss: 0.6812 - val_accuracy: 0.5654\n",
      "Epoch 674/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6709 - accuracy: 0.5858 - val_loss: 0.6811 - val_accuracy: 0.5662\n",
      "Epoch 675/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6708 - accuracy: 0.5853 - val_loss: 0.6811 - val_accuracy: 0.5669\n",
      "Epoch 676/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6708 - accuracy: 0.5852 - val_loss: 0.6811 - val_accuracy: 0.5669\n",
      "Epoch 677/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6708 - accuracy: 0.5862 - val_loss: 0.6812 - val_accuracy: 0.5672\n",
      "Epoch 678/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6708 - accuracy: 0.5857 - val_loss: 0.6811 - val_accuracy: 0.5670\n",
      "Epoch 679/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6707 - accuracy: 0.5865 - val_loss: 0.6811 - val_accuracy: 0.5663\n",
      "Epoch 680/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6707 - accuracy: 0.5852 - val_loss: 0.6811 - val_accuracy: 0.5669\n",
      "Epoch 681/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6707 - accuracy: 0.5860 - val_loss: 0.6812 - val_accuracy: 0.5670\n",
      "Epoch 682/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6706 - accuracy: 0.5858 - val_loss: 0.6812 - val_accuracy: 0.5668\n",
      "Epoch 683/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6706 - accuracy: 0.5862 - val_loss: 0.6812 - val_accuracy: 0.5668\n",
      "Epoch 684/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6706 - accuracy: 0.5858 - val_loss: 0.6812 - val_accuracy: 0.5666\n",
      "Epoch 685/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6706 - accuracy: 0.5860 - val_loss: 0.6812 - val_accuracy: 0.5667\n",
      "Epoch 686/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6705 - accuracy: 0.5862 - val_loss: 0.6812 - val_accuracy: 0.5669\n",
      "Epoch 687/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6705 - accuracy: 0.5859 - val_loss: 0.6813 - val_accuracy: 0.5674\n",
      "Epoch 688/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6705 - accuracy: 0.5860 - val_loss: 0.6812 - val_accuracy: 0.5668\n",
      "Epoch 689/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6704 - accuracy: 0.5864 - val_loss: 0.6816 - val_accuracy: 0.5642\n",
      "Epoch 690/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6705 - accuracy: 0.5866 - val_loss: 0.6812 - val_accuracy: 0.5665\n",
      "Epoch 691/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6704 - accuracy: 0.5864 - val_loss: 0.6812 - val_accuracy: 0.5665\n",
      "Epoch 692/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6704 - accuracy: 0.5864 - val_loss: 0.6813 - val_accuracy: 0.5664\n",
      "Epoch 693/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6704 - accuracy: 0.5867 - val_loss: 0.6814 - val_accuracy: 0.5656\n",
      "Epoch 694/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6703 - accuracy: 0.5861 - val_loss: 0.6813 - val_accuracy: 0.5667\n",
      "Epoch 695/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6703 - accuracy: 0.5867 - val_loss: 0.6814 - val_accuracy: 0.5657\n",
      "Epoch 696/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6702 - accuracy: 0.5863 - val_loss: 0.6813 - val_accuracy: 0.5662\n",
      "Epoch 697/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6703 - accuracy: 0.5868 - val_loss: 0.6813 - val_accuracy: 0.5668\n",
      "Epoch 698/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6702 - accuracy: 0.5866 - val_loss: 0.6813 - val_accuracy: 0.5667\n",
      "Epoch 699/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6702 - accuracy: 0.5867 - val_loss: 0.6813 - val_accuracy: 0.5665\n",
      "Epoch 700/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6701 - accuracy: 0.5864 - val_loss: 0.6814 - val_accuracy: 0.5659\n",
      "Epoch 701/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6701 - accuracy: 0.5873 - val_loss: 0.6815 - val_accuracy: 0.5673\n",
      "Epoch 702/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6701 - accuracy: 0.5867 - val_loss: 0.6815 - val_accuracy: 0.5652\n",
      "Epoch 703/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6701 - accuracy: 0.5866 - val_loss: 0.6815 - val_accuracy: 0.5667\n",
      "Epoch 704/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6700 - accuracy: 0.5877 - val_loss: 0.6814 - val_accuracy: 0.5659\n",
      "Epoch 705/1000\n",
      "1303/1303 [==============================] - 3s 3ms/step - loss: 0.6700 - accuracy: 0.5871 - val_loss: 0.6815 - val_accuracy: 0.5652\n",
      "Epoch 706/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6700 - accuracy: 0.5870 - val_loss: 0.6815 - val_accuracy: 0.5660\n",
      "Epoch 707/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6699 - accuracy: 0.5874 - val_loss: 0.6815 - val_accuracy: 0.5656\n",
      "Epoch 708/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6699 - accuracy: 0.5876 - val_loss: 0.6815 - val_accuracy: 0.5655\n",
      "Epoch 709/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6699 - accuracy: 0.5876 - val_loss: 0.6815 - val_accuracy: 0.5664\n",
      "Epoch 710/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6698 - accuracy: 0.5874 - val_loss: 0.6815 - val_accuracy: 0.5657\n",
      "Epoch 711/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6699 - accuracy: 0.5869 - val_loss: 0.6815 - val_accuracy: 0.5664\n",
      "Epoch 712/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6698 - accuracy: 0.5874 - val_loss: 0.6815 - val_accuracy: 0.5662\n",
      "Epoch 713/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6698 - accuracy: 0.5872 - val_loss: 0.6816 - val_accuracy: 0.5651\n",
      "Epoch 714/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6698 - accuracy: 0.5879 - val_loss: 0.6817 - val_accuracy: 0.5669\n",
      "Epoch 715/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6697 - accuracy: 0.5882 - val_loss: 0.6815 - val_accuracy: 0.5658\n",
      "Epoch 716/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6697 - accuracy: 0.5872 - val_loss: 0.6816 - val_accuracy: 0.5660\n",
      "Epoch 717/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6697 - accuracy: 0.5878 - val_loss: 0.6816 - val_accuracy: 0.5660\n",
      "Epoch 718/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6696 - accuracy: 0.5873 - val_loss: 0.6816 - val_accuracy: 0.5665\n",
      "Epoch 719/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6696 - accuracy: 0.5878 - val_loss: 0.6816 - val_accuracy: 0.5658\n",
      "Epoch 720/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6696 - accuracy: 0.5876 - val_loss: 0.6816 - val_accuracy: 0.5659\n",
      "Epoch 721/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6695 - accuracy: 0.5882 - val_loss: 0.6816 - val_accuracy: 0.5661\n",
      "Epoch 722/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6695 - accuracy: 0.5883 - val_loss: 0.6816 - val_accuracy: 0.5656\n",
      "Epoch 723/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6695 - accuracy: 0.5880 - val_loss: 0.6817 - val_accuracy: 0.5657\n",
      "Epoch 724/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6694 - accuracy: 0.5880 - val_loss: 0.6817 - val_accuracy: 0.5655\n",
      "Epoch 725/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6694 - accuracy: 0.5884 - val_loss: 0.6817 - val_accuracy: 0.5655\n",
      "Epoch 726/1000\n",
      "1303/1303 [==============================] - 4s 3ms/step - loss: 0.6694 - accuracy: 0.5875 - val_loss: 0.6817 - val_accuracy: 0.5660\n",
      "Epoch 727/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6694 - accuracy: 0.5878 - val_loss: 0.6818 - val_accuracy: 0.5651\n",
      "Epoch 728/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6693 - accuracy: 0.5877 - val_loss: 0.6817 - val_accuracy: 0.5653\n",
      "Epoch 729/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6693 - accuracy: 0.5877 - val_loss: 0.6818 - val_accuracy: 0.5661\n",
      "Epoch 730/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6693 - accuracy: 0.5882 - val_loss: 0.6817 - val_accuracy: 0.5655\n",
      "Epoch 731/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6692 - accuracy: 0.5889 - val_loss: 0.6817 - val_accuracy: 0.5656\n",
      "Epoch 732/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6692 - accuracy: 0.5882 - val_loss: 0.6818 - val_accuracy: 0.5656\n",
      "Epoch 733/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6692 - accuracy: 0.5883 - val_loss: 0.6818 - val_accuracy: 0.5656\n",
      "Epoch 734/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6691 - accuracy: 0.5882 - val_loss: 0.6818 - val_accuracy: 0.5662\n",
      "Epoch 735/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6691 - accuracy: 0.5881 - val_loss: 0.6818 - val_accuracy: 0.5661\n",
      "Epoch 736/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6691 - accuracy: 0.5884 - val_loss: 0.6821 - val_accuracy: 0.5635\n",
      "Epoch 737/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6691 - accuracy: 0.5885 - val_loss: 0.6820 - val_accuracy: 0.5644\n",
      "Epoch 738/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6690 - accuracy: 0.5889 - val_loss: 0.6818 - val_accuracy: 0.5654\n",
      "Epoch 739/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6690 - accuracy: 0.5889 - val_loss: 0.6819 - val_accuracy: 0.5658\n",
      "Epoch 740/1000\n",
      "1303/1303 [==============================] - 3s 3ms/step - loss: 0.6689 - accuracy: 0.5887 - val_loss: 0.6819 - val_accuracy: 0.5649\n",
      "Epoch 741/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6689 - accuracy: 0.5887 - val_loss: 0.6819 - val_accuracy: 0.5653\n",
      "Epoch 742/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6689 - accuracy: 0.5889 - val_loss: 0.6819 - val_accuracy: 0.5655\n",
      "Epoch 743/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6689 - accuracy: 0.5890 - val_loss: 0.6819 - val_accuracy: 0.5655\n",
      "Epoch 744/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6689 - accuracy: 0.5888 - val_loss: 0.6820 - val_accuracy: 0.5650\n",
      "Epoch 745/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6688 - accuracy: 0.5893 - val_loss: 0.6819 - val_accuracy: 0.5653\n",
      "Epoch 746/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6688 - accuracy: 0.5887 - val_loss: 0.6820 - val_accuracy: 0.5657\n",
      "Epoch 747/1000\n",
      "1303/1303 [==============================] - 3s 3ms/step - loss: 0.6688 - accuracy: 0.5886 - val_loss: 0.6820 - val_accuracy: 0.5659\n",
      "Epoch 748/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6687 - accuracy: 0.5889 - val_loss: 0.6820 - val_accuracy: 0.5651\n",
      "Epoch 749/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6687 - accuracy: 0.5891 - val_loss: 0.6820 - val_accuracy: 0.5650\n",
      "Epoch 750/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6687 - accuracy: 0.5899 - val_loss: 0.6822 - val_accuracy: 0.5641\n",
      "Epoch 751/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6686 - accuracy: 0.5893 - val_loss: 0.6820 - val_accuracy: 0.5651\n",
      "Epoch 752/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6686 - accuracy: 0.5898 - val_loss: 0.6822 - val_accuracy: 0.5662\n",
      "Epoch 753/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6686 - accuracy: 0.5890 - val_loss: 0.6820 - val_accuracy: 0.5654\n",
      "Epoch 754/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6685 - accuracy: 0.5898 - val_loss: 0.6821 - val_accuracy: 0.5651\n",
      "Epoch 755/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6685 - accuracy: 0.5899 - val_loss: 0.6821 - val_accuracy: 0.5650\n",
      "Epoch 756/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6685 - accuracy: 0.5888 - val_loss: 0.6821 - val_accuracy: 0.5649\n",
      "Epoch 757/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6684 - accuracy: 0.5898 - val_loss: 0.6821 - val_accuracy: 0.5651\n",
      "Epoch 758/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6684 - accuracy: 0.5894 - val_loss: 0.6821 - val_accuracy: 0.5653\n",
      "Epoch 759/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6684 - accuracy: 0.5893 - val_loss: 0.6824 - val_accuracy: 0.5633\n",
      "Epoch 760/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6683 - accuracy: 0.5900 - val_loss: 0.6822 - val_accuracy: 0.5649\n",
      "Epoch 761/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6683 - accuracy: 0.5899 - val_loss: 0.6823 - val_accuracy: 0.5654\n",
      "Epoch 762/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6683 - accuracy: 0.5901 - val_loss: 0.6822 - val_accuracy: 0.5649\n",
      "Epoch 763/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6683 - accuracy: 0.5899 - val_loss: 0.6822 - val_accuracy: 0.5653\n",
      "Epoch 764/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6682 - accuracy: 0.5906 - val_loss: 0.6826 - val_accuracy: 0.5630\n",
      "Epoch 765/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6682 - accuracy: 0.5897 - val_loss: 0.6823 - val_accuracy: 0.5643\n",
      "Epoch 766/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6682 - accuracy: 0.5899 - val_loss: 0.6822 - val_accuracy: 0.5649\n",
      "Epoch 767/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6681 - accuracy: 0.5901 - val_loss: 0.6823 - val_accuracy: 0.5651\n",
      "Epoch 768/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6681 - accuracy: 0.5905 - val_loss: 0.6823 - val_accuracy: 0.5653\n",
      "Epoch 769/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6681 - accuracy: 0.5899 - val_loss: 0.6823 - val_accuracy: 0.5652\n",
      "Epoch 770/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6680 - accuracy: 0.5904 - val_loss: 0.6823 - val_accuracy: 0.5648\n",
      "Epoch 771/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6680 - accuracy: 0.5912 - val_loss: 0.6824 - val_accuracy: 0.5651\n",
      "Epoch 772/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6680 - accuracy: 0.5912 - val_loss: 0.6823 - val_accuracy: 0.5647\n",
      "Epoch 773/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6679 - accuracy: 0.5907 - val_loss: 0.6825 - val_accuracy: 0.5655\n",
      "Epoch 774/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6679 - accuracy: 0.5902 - val_loss: 0.6824 - val_accuracy: 0.5653\n",
      "Epoch 775/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6679 - accuracy: 0.5898 - val_loss: 0.6824 - val_accuracy: 0.5649\n",
      "Epoch 776/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6679 - accuracy: 0.5909 - val_loss: 0.6825 - val_accuracy: 0.5634\n",
      "Epoch 777/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6678 - accuracy: 0.5901 - val_loss: 0.6825 - val_accuracy: 0.5640\n",
      "Epoch 778/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6678 - accuracy: 0.5904 - val_loss: 0.6825 - val_accuracy: 0.5649\n",
      "Epoch 779/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6678 - accuracy: 0.5906 - val_loss: 0.6825 - val_accuracy: 0.5646\n",
      "Epoch 780/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6677 - accuracy: 0.5910 - val_loss: 0.6826 - val_accuracy: 0.5657\n",
      "Epoch 781/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6677 - accuracy: 0.5900 - val_loss: 0.6829 - val_accuracy: 0.5624\n",
      "Epoch 782/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6677 - accuracy: 0.5911 - val_loss: 0.6825 - val_accuracy: 0.5647\n",
      "Epoch 783/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6675 - accuracy: 0.5906 - val_loss: 0.6825 - val_accuracy: 0.5649\n",
      "Epoch 784/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6676 - accuracy: 0.5908 - val_loss: 0.6831 - val_accuracy: 0.5618\n",
      "Epoch 785/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6675 - accuracy: 0.5914 - val_loss: 0.6826 - val_accuracy: 0.5637\n",
      "Epoch 786/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6675 - accuracy: 0.5914 - val_loss: 0.6826 - val_accuracy: 0.5649\n",
      "Epoch 787/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6674 - accuracy: 0.5913 - val_loss: 0.6826 - val_accuracy: 0.5645\n",
      "Epoch 788/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6675 - accuracy: 0.5911 - val_loss: 0.6826 - val_accuracy: 0.5650\n",
      "Epoch 789/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6674 - accuracy: 0.5912 - val_loss: 0.6827 - val_accuracy: 0.5639\n",
      "Epoch 790/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6674 - accuracy: 0.5915 - val_loss: 0.6826 - val_accuracy: 0.5645\n",
      "Epoch 791/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6674 - accuracy: 0.5913 - val_loss: 0.6826 - val_accuracy: 0.5642\n",
      "Epoch 792/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6673 - accuracy: 0.5912 - val_loss: 0.6827 - val_accuracy: 0.5640\n",
      "Epoch 793/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6673 - accuracy: 0.5920 - val_loss: 0.6828 - val_accuracy: 0.5630\n",
      "Epoch 794/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6673 - accuracy: 0.5915 - val_loss: 0.6827 - val_accuracy: 0.5644\n",
      "Epoch 795/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6672 - accuracy: 0.5924 - val_loss: 0.6827 - val_accuracy: 0.5644\n",
      "Epoch 796/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6672 - accuracy: 0.5915 - val_loss: 0.6828 - val_accuracy: 0.5646\n",
      "Epoch 797/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6672 - accuracy: 0.5918 - val_loss: 0.6828 - val_accuracy: 0.5643\n",
      "Epoch 798/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6671 - accuracy: 0.5914 - val_loss: 0.6828 - val_accuracy: 0.5645\n",
      "Epoch 799/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6671 - accuracy: 0.5919 - val_loss: 0.6828 - val_accuracy: 0.5646\n",
      "Epoch 800/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6670 - accuracy: 0.5916 - val_loss: 0.6829 - val_accuracy: 0.5637\n",
      "Epoch 801/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6670 - accuracy: 0.5919 - val_loss: 0.6828 - val_accuracy: 0.5646\n",
      "Epoch 802/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6670 - accuracy: 0.5921 - val_loss: 0.6828 - val_accuracy: 0.5641\n",
      "Epoch 803/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6669 - accuracy: 0.5922 - val_loss: 0.6829 - val_accuracy: 0.5649\n",
      "Epoch 804/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6669 - accuracy: 0.5919 - val_loss: 0.6829 - val_accuracy: 0.5647\n",
      "Epoch 805/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6669 - accuracy: 0.5919 - val_loss: 0.6829 - val_accuracy: 0.5642\n",
      "Epoch 806/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6668 - accuracy: 0.5920 - val_loss: 0.6829 - val_accuracy: 0.5637\n",
      "Epoch 807/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6668 - accuracy: 0.5925 - val_loss: 0.6830 - val_accuracy: 0.5646\n",
      "Epoch 808/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6668 - accuracy: 0.5921 - val_loss: 0.6830 - val_accuracy: 0.5645\n",
      "Epoch 809/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6667 - accuracy: 0.5928 - val_loss: 0.6830 - val_accuracy: 0.5646\n",
      "Epoch 810/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6667 - accuracy: 0.5924 - val_loss: 0.6830 - val_accuracy: 0.5648\n",
      "Epoch 811/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6667 - accuracy: 0.5920 - val_loss: 0.6831 - val_accuracy: 0.5631\n",
      "Epoch 812/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6666 - accuracy: 0.5928 - val_loss: 0.6832 - val_accuracy: 0.5650\n",
      "Epoch 813/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6666 - accuracy: 0.5922 - val_loss: 0.6830 - val_accuracy: 0.5646\n",
      "Epoch 814/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6665 - accuracy: 0.5932 - val_loss: 0.6833 - val_accuracy: 0.5623\n",
      "Epoch 815/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6665 - accuracy: 0.5926 - val_loss: 0.6831 - val_accuracy: 0.5640\n",
      "Epoch 816/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6665 - accuracy: 0.5926 - val_loss: 0.6831 - val_accuracy: 0.5647\n",
      "Epoch 817/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6665 - accuracy: 0.5925 - val_loss: 0.6831 - val_accuracy: 0.5638\n",
      "Epoch 818/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6664 - accuracy: 0.5931 - val_loss: 0.6833 - val_accuracy: 0.5622\n",
      "Epoch 819/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6664 - accuracy: 0.5927 - val_loss: 0.6832 - val_accuracy: 0.5640\n",
      "Epoch 820/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6664 - accuracy: 0.5930 - val_loss: 0.6832 - val_accuracy: 0.5641\n",
      "Epoch 821/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6663 - accuracy: 0.5935 - val_loss: 0.6832 - val_accuracy: 0.5647\n",
      "Epoch 822/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6663 - accuracy: 0.5928 - val_loss: 0.6832 - val_accuracy: 0.5638\n",
      "Epoch 823/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6662 - accuracy: 0.5932 - val_loss: 0.6833 - val_accuracy: 0.5646\n",
      "Epoch 824/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6662 - accuracy: 0.5934 - val_loss: 0.6834 - val_accuracy: 0.5627\n",
      "Epoch 825/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6662 - accuracy: 0.5931 - val_loss: 0.6834 - val_accuracy: 0.5620\n",
      "Epoch 826/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6662 - accuracy: 0.5933 - val_loss: 0.6833 - val_accuracy: 0.5641\n",
      "Epoch 827/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6662 - accuracy: 0.5929 - val_loss: 0.6833 - val_accuracy: 0.5638\n",
      "Epoch 828/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6661 - accuracy: 0.5933 - val_loss: 0.6835 - val_accuracy: 0.5620\n",
      "Epoch 829/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6661 - accuracy: 0.5937 - val_loss: 0.6836 - val_accuracy: 0.5644\n",
      "Epoch 830/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6660 - accuracy: 0.5936 - val_loss: 0.6836 - val_accuracy: 0.5647\n",
      "Epoch 831/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6660 - accuracy: 0.5934 - val_loss: 0.6834 - val_accuracy: 0.5639\n",
      "Epoch 832/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6660 - accuracy: 0.5940 - val_loss: 0.6834 - val_accuracy: 0.5643\n",
      "Epoch 833/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6659 - accuracy: 0.5943 - val_loss: 0.6835 - val_accuracy: 0.5620\n",
      "Epoch 834/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6659 - accuracy: 0.5935 - val_loss: 0.6834 - val_accuracy: 0.5639\n",
      "Epoch 835/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6658 - accuracy: 0.5937 - val_loss: 0.6835 - val_accuracy: 0.5643\n",
      "Epoch 836/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6658 - accuracy: 0.5934 - val_loss: 0.6835 - val_accuracy: 0.5631\n",
      "Epoch 837/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6658 - accuracy: 0.5942 - val_loss: 0.6835 - val_accuracy: 0.5644\n",
      "Epoch 838/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6657 - accuracy: 0.5946 - val_loss: 0.6835 - val_accuracy: 0.5642\n",
      "Epoch 839/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6657 - accuracy: 0.5937 - val_loss: 0.6839 - val_accuracy: 0.5608\n",
      "Epoch 840/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6657 - accuracy: 0.5940 - val_loss: 0.6836 - val_accuracy: 0.5642\n",
      "Epoch 841/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6656 - accuracy: 0.5939 - val_loss: 0.6836 - val_accuracy: 0.5628\n",
      "Epoch 842/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6656 - accuracy: 0.5943 - val_loss: 0.6836 - val_accuracy: 0.5642\n",
      "Epoch 843/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6655 - accuracy: 0.5946 - val_loss: 0.6836 - val_accuracy: 0.5643\n",
      "Epoch 844/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6655 - accuracy: 0.5941 - val_loss: 0.6837 - val_accuracy: 0.5628\n",
      "Epoch 845/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6655 - accuracy: 0.5944 - val_loss: 0.6838 - val_accuracy: 0.5619\n",
      "Epoch 846/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6654 - accuracy: 0.5943 - val_loss: 0.6837 - val_accuracy: 0.5627\n",
      "Epoch 847/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6654 - accuracy: 0.5949 - val_loss: 0.6837 - val_accuracy: 0.5627\n",
      "Epoch 848/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6654 - accuracy: 0.5943 - val_loss: 0.6837 - val_accuracy: 0.5629\n",
      "Epoch 849/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6653 - accuracy: 0.5949 - val_loss: 0.6840 - val_accuracy: 0.5612\n",
      "Epoch 850/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6653 - accuracy: 0.5949 - val_loss: 0.6838 - val_accuracy: 0.5642\n",
      "Epoch 851/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6653 - accuracy: 0.5946 - val_loss: 0.6838 - val_accuracy: 0.5643\n",
      "Epoch 852/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6653 - accuracy: 0.5949 - val_loss: 0.6842 - val_accuracy: 0.5599\n",
      "Epoch 853/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6652 - accuracy: 0.5950 - val_loss: 0.6838 - val_accuracy: 0.5631\n",
      "Epoch 854/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6652 - accuracy: 0.5947 - val_loss: 0.6839 - val_accuracy: 0.5642\n",
      "Epoch 855/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6651 - accuracy: 0.5947 - val_loss: 0.6839 - val_accuracy: 0.5643\n",
      "Epoch 856/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6651 - accuracy: 0.5943 - val_loss: 0.6839 - val_accuracy: 0.5621\n",
      "Epoch 857/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6651 - accuracy: 0.5951 - val_loss: 0.6839 - val_accuracy: 0.5637\n",
      "Epoch 858/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6650 - accuracy: 0.5953 - val_loss: 0.6840 - val_accuracy: 0.5642\n",
      "Epoch 859/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6650 - accuracy: 0.5955 - val_loss: 0.6839 - val_accuracy: 0.5634\n",
      "Epoch 860/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6650 - accuracy: 0.5952 - val_loss: 0.6840 - val_accuracy: 0.5633\n",
      "Epoch 861/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6649 - accuracy: 0.5945 - val_loss: 0.6840 - val_accuracy: 0.5633\n",
      "Epoch 862/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6649 - accuracy: 0.5951 - val_loss: 0.6840 - val_accuracy: 0.5637\n",
      "Epoch 863/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6648 - accuracy: 0.5950 - val_loss: 0.6842 - val_accuracy: 0.5617\n",
      "Epoch 864/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6648 - accuracy: 0.5955 - val_loss: 0.6841 - val_accuracy: 0.5639\n",
      "Epoch 865/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6648 - accuracy: 0.5948 - val_loss: 0.6841 - val_accuracy: 0.5634\n",
      "Epoch 866/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6647 - accuracy: 0.5962 - val_loss: 0.6841 - val_accuracy: 0.5628\n",
      "Epoch 867/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6647 - accuracy: 0.5956 - val_loss: 0.6842 - val_accuracy: 0.5639\n",
      "Epoch 868/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6647 - accuracy: 0.5958 - val_loss: 0.6845 - val_accuracy: 0.5601\n",
      "Epoch 869/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6646 - accuracy: 0.5954 - val_loss: 0.6842 - val_accuracy: 0.5619\n",
      "Epoch 870/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6646 - accuracy: 0.5961 - val_loss: 0.6842 - val_accuracy: 0.5632\n",
      "Epoch 871/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6645 - accuracy: 0.5960 - val_loss: 0.6843 - val_accuracy: 0.5644\n",
      "Epoch 872/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6645 - accuracy: 0.5960 - val_loss: 0.6843 - val_accuracy: 0.5618\n",
      "Epoch 873/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6645 - accuracy: 0.5960 - val_loss: 0.6843 - val_accuracy: 0.5629\n",
      "Epoch 874/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6645 - accuracy: 0.5963 - val_loss: 0.6844 - val_accuracy: 0.5613\n",
      "Epoch 875/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6644 - accuracy: 0.5962 - val_loss: 0.6844 - val_accuracy: 0.5619\n",
      "Epoch 876/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6643 - accuracy: 0.5954 - val_loss: 0.6843 - val_accuracy: 0.5627\n",
      "Epoch 877/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6644 - accuracy: 0.5960 - val_loss: 0.6845 - val_accuracy: 0.5616\n",
      "Epoch 878/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6643 - accuracy: 0.5966 - val_loss: 0.6844 - val_accuracy: 0.5639\n",
      "Epoch 879/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6643 - accuracy: 0.5964 - val_loss: 0.6844 - val_accuracy: 0.5623\n",
      "Epoch 880/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6642 - accuracy: 0.5960 - val_loss: 0.6844 - val_accuracy: 0.5624\n",
      "Epoch 881/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6642 - accuracy: 0.5956 - val_loss: 0.6844 - val_accuracy: 0.5629\n",
      "Epoch 882/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6641 - accuracy: 0.5965 - val_loss: 0.6845 - val_accuracy: 0.5618\n",
      "Epoch 883/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6641 - accuracy: 0.5964 - val_loss: 0.6845 - val_accuracy: 0.5631\n",
      "Epoch 884/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6641 - accuracy: 0.5958 - val_loss: 0.6845 - val_accuracy: 0.5624\n",
      "Epoch 885/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6641 - accuracy: 0.5966 - val_loss: 0.6845 - val_accuracy: 0.5628\n",
      "Epoch 886/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6640 - accuracy: 0.5968 - val_loss: 0.6845 - val_accuracy: 0.5624\n",
      "Epoch 887/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6640 - accuracy: 0.5964 - val_loss: 0.6847 - val_accuracy: 0.5643\n",
      "Epoch 888/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6639 - accuracy: 0.5973 - val_loss: 0.6847 - val_accuracy: 0.5619\n",
      "Epoch 889/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6639 - accuracy: 0.5966 - val_loss: 0.6847 - val_accuracy: 0.5631\n",
      "Epoch 890/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6639 - accuracy: 0.5970 - val_loss: 0.6847 - val_accuracy: 0.5614\n",
      "Epoch 891/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6638 - accuracy: 0.5965 - val_loss: 0.6847 - val_accuracy: 0.5618\n",
      "Epoch 892/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6637 - accuracy: 0.5969 - val_loss: 0.6850 - val_accuracy: 0.5594\n",
      "Epoch 893/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6637 - accuracy: 0.5966 - val_loss: 0.6847 - val_accuracy: 0.5629\n",
      "Epoch 894/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6637 - accuracy: 0.5963 - val_loss: 0.6849 - val_accuracy: 0.5639\n",
      "Epoch 895/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6636 - accuracy: 0.5977 - val_loss: 0.6848 - val_accuracy: 0.5629\n",
      "Epoch 896/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6637 - accuracy: 0.5970 - val_loss: 0.6848 - val_accuracy: 0.5617\n",
      "Epoch 897/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6636 - accuracy: 0.5972 - val_loss: 0.6850 - val_accuracy: 0.5639\n",
      "Epoch 898/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6635 - accuracy: 0.5957 - val_loss: 0.6849 - val_accuracy: 0.5618\n",
      "Epoch 899/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6635 - accuracy: 0.5972 - val_loss: 0.6850 - val_accuracy: 0.5611\n",
      "Epoch 900/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6635 - accuracy: 0.5973 - val_loss: 0.6849 - val_accuracy: 0.5618\n",
      "Epoch 901/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6634 - accuracy: 0.5968 - val_loss: 0.6849 - val_accuracy: 0.5629\n",
      "Epoch 902/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6634 - accuracy: 0.5971 - val_loss: 0.6850 - val_accuracy: 0.5623\n",
      "Epoch 903/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6634 - accuracy: 0.5978 - val_loss: 0.6850 - val_accuracy: 0.5628\n",
      "Epoch 904/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6634 - accuracy: 0.5977 - val_loss: 0.6850 - val_accuracy: 0.5618\n",
      "Epoch 905/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6633 - accuracy: 0.5970 - val_loss: 0.6850 - val_accuracy: 0.5631\n",
      "Epoch 906/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6633 - accuracy: 0.5976 - val_loss: 0.6850 - val_accuracy: 0.5614\n",
      "Epoch 907/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6632 - accuracy: 0.5973 - val_loss: 0.6850 - val_accuracy: 0.5616\n",
      "Epoch 908/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6632 - accuracy: 0.5980 - val_loss: 0.6851 - val_accuracy: 0.5613\n",
      "Epoch 909/1000\n",
      "1303/1303 [==============================] - 4s 3ms/step - loss: 0.6631 - accuracy: 0.5984 - val_loss: 0.6852 - val_accuracy: 0.5598\n",
      "Epoch 910/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6631 - accuracy: 0.5976 - val_loss: 0.6852 - val_accuracy: 0.5612\n",
      "Epoch 911/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6630 - accuracy: 0.5972 - val_loss: 0.6852 - val_accuracy: 0.5611\n",
      "Epoch 912/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6631 - accuracy: 0.5977 - val_loss: 0.6852 - val_accuracy: 0.5619\n",
      "Epoch 913/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6630 - accuracy: 0.5980 - val_loss: 0.6852 - val_accuracy: 0.5620\n",
      "Epoch 914/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6630 - accuracy: 0.5976 - val_loss: 0.6858 - val_accuracy: 0.5648\n",
      "Epoch 915/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6629 - accuracy: 0.5985 - val_loss: 0.6854 - val_accuracy: 0.5592\n",
      "Epoch 916/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6629 - accuracy: 0.5984 - val_loss: 0.6853 - val_accuracy: 0.5619\n",
      "Epoch 917/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6628 - accuracy: 0.5981 - val_loss: 0.6853 - val_accuracy: 0.5628\n",
      "Epoch 918/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6628 - accuracy: 0.5976 - val_loss: 0.6854 - val_accuracy: 0.5627\n",
      "Epoch 919/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6627 - accuracy: 0.5985 - val_loss: 0.6854 - val_accuracy: 0.5609\n",
      "Epoch 920/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6627 - accuracy: 0.5982 - val_loss: 0.6854 - val_accuracy: 0.5621\n",
      "Epoch 921/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6627 - accuracy: 0.5982 - val_loss: 0.6854 - val_accuracy: 0.5613\n",
      "Epoch 922/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6626 - accuracy: 0.5983 - val_loss: 0.6855 - val_accuracy: 0.5620\n",
      "Epoch 923/1000\n",
      "1303/1303 [==============================] - 4s 3ms/step - loss: 0.6626 - accuracy: 0.5981 - val_loss: 0.6857 - val_accuracy: 0.5592\n",
      "Epoch 924/1000\n",
      "1303/1303 [==============================] - 3s 3ms/step - loss: 0.6625 - accuracy: 0.5981 - val_loss: 0.6856 - val_accuracy: 0.5627\n",
      "Epoch 925/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6625 - accuracy: 0.5980 - val_loss: 0.6857 - val_accuracy: 0.5636\n",
      "Epoch 926/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6625 - accuracy: 0.5987 - val_loss: 0.6856 - val_accuracy: 0.5606\n",
      "Epoch 927/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6624 - accuracy: 0.5985 - val_loss: 0.6857 - val_accuracy: 0.5629\n",
      "Epoch 928/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6624 - accuracy: 0.5986 - val_loss: 0.6857 - val_accuracy: 0.5594\n",
      "Epoch 929/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6624 - accuracy: 0.5992 - val_loss: 0.6856 - val_accuracy: 0.5610\n",
      "Epoch 930/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6623 - accuracy: 0.5986 - val_loss: 0.6857 - val_accuracy: 0.5610\n",
      "Epoch 931/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6623 - accuracy: 0.5990 - val_loss: 0.6858 - val_accuracy: 0.5591\n",
      "Epoch 932/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6622 - accuracy: 0.5983 - val_loss: 0.6858 - val_accuracy: 0.5591\n",
      "Epoch 933/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6622 - accuracy: 0.5992 - val_loss: 0.6863 - val_accuracy: 0.5647\n",
      "Epoch 934/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6622 - accuracy: 0.5996 - val_loss: 0.6858 - val_accuracy: 0.5603\n",
      "Epoch 935/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6622 - accuracy: 0.5996 - val_loss: 0.6858 - val_accuracy: 0.5612\n",
      "Epoch 936/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6621 - accuracy: 0.5993 - val_loss: 0.6859 - val_accuracy: 0.5628\n",
      "Epoch 937/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6621 - accuracy: 0.5995 - val_loss: 0.6859 - val_accuracy: 0.5598\n",
      "Epoch 938/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6620 - accuracy: 0.5998 - val_loss: 0.6859 - val_accuracy: 0.5626\n",
      "Epoch 939/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6620 - accuracy: 0.6001 - val_loss: 0.6859 - val_accuracy: 0.5599\n",
      "Epoch 940/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6620 - accuracy: 0.5996 - val_loss: 0.6859 - val_accuracy: 0.5617\n",
      "Epoch 941/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6619 - accuracy: 0.5996 - val_loss: 0.6860 - val_accuracy: 0.5610\n",
      "Epoch 942/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6618 - accuracy: 0.6004 - val_loss: 0.6861 - val_accuracy: 0.5591\n",
      "Epoch 943/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6618 - accuracy: 0.5994 - val_loss: 0.6860 - val_accuracy: 0.5623\n",
      "Epoch 944/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6618 - accuracy: 0.5999 - val_loss: 0.6860 - val_accuracy: 0.5610\n",
      "Epoch 945/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6617 - accuracy: 0.6000 - val_loss: 0.6868 - val_accuracy: 0.5561\n",
      "Epoch 946/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6617 - accuracy: 0.5996 - val_loss: 0.6861 - val_accuracy: 0.5608\n",
      "Epoch 947/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6617 - accuracy: 0.5991 - val_loss: 0.6862 - val_accuracy: 0.5599\n",
      "Epoch 948/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6616 - accuracy: 0.5995 - val_loss: 0.6866 - val_accuracy: 0.5571\n",
      "Epoch 949/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6616 - accuracy: 0.5996 - val_loss: 0.6862 - val_accuracy: 0.5597\n",
      "Epoch 950/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6616 - accuracy: 0.5999 - val_loss: 0.6862 - val_accuracy: 0.5601\n",
      "Epoch 951/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6615 - accuracy: 0.6005 - val_loss: 0.6862 - val_accuracy: 0.5600\n",
      "Epoch 952/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6614 - accuracy: 0.5998 - val_loss: 0.6863 - val_accuracy: 0.5597\n",
      "Epoch 953/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6614 - accuracy: 0.6002 - val_loss: 0.6864 - val_accuracy: 0.5588\n",
      "Epoch 954/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6614 - accuracy: 0.6008 - val_loss: 0.6862 - val_accuracy: 0.5608\n",
      "Epoch 955/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6614 - accuracy: 0.6006 - val_loss: 0.6864 - val_accuracy: 0.5610\n",
      "Epoch 956/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6613 - accuracy: 0.6007 - val_loss: 0.6863 - val_accuracy: 0.5594\n",
      "Epoch 957/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6613 - accuracy: 0.6005 - val_loss: 0.6864 - val_accuracy: 0.5588\n",
      "Epoch 958/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6612 - accuracy: 0.6003 - val_loss: 0.6864 - val_accuracy: 0.5597\n",
      "Epoch 959/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6612 - accuracy: 0.6002 - val_loss: 0.6864 - val_accuracy: 0.5615\n",
      "Epoch 960/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6611 - accuracy: 0.6013 - val_loss: 0.6865 - val_accuracy: 0.5591\n",
      "Epoch 961/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6611 - accuracy: 0.6010 - val_loss: 0.6865 - val_accuracy: 0.5615\n",
      "Epoch 962/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6610 - accuracy: 0.6006 - val_loss: 0.6865 - val_accuracy: 0.5602\n",
      "Epoch 963/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6610 - accuracy: 0.6009 - val_loss: 0.6868 - val_accuracy: 0.5629\n",
      "Epoch 964/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6610 - accuracy: 0.6008 - val_loss: 0.6866 - val_accuracy: 0.5597\n",
      "Epoch 965/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6609 - accuracy: 0.6003 - val_loss: 0.6866 - val_accuracy: 0.5595\n",
      "Epoch 966/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6609 - accuracy: 0.6012 - val_loss: 0.6866 - val_accuracy: 0.5596\n",
      "Epoch 967/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6608 - accuracy: 0.6016 - val_loss: 0.6866 - val_accuracy: 0.5606\n",
      "Epoch 968/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6608 - accuracy: 0.6015 - val_loss: 0.6867 - val_accuracy: 0.5606\n",
      "Epoch 969/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6607 - accuracy: 0.6015 - val_loss: 0.6867 - val_accuracy: 0.5591\n",
      "Epoch 970/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6607 - accuracy: 0.6016 - val_loss: 0.6868 - val_accuracy: 0.5590\n",
      "Epoch 971/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6607 - accuracy: 0.6009 - val_loss: 0.6869 - val_accuracy: 0.5578\n",
      "Epoch 972/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6606 - accuracy: 0.6018 - val_loss: 0.6868 - val_accuracy: 0.5592\n",
      "Epoch 973/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6606 - accuracy: 0.6021 - val_loss: 0.6869 - val_accuracy: 0.5610\n",
      "Epoch 974/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6605 - accuracy: 0.6025 - val_loss: 0.6869 - val_accuracy: 0.5595\n",
      "Epoch 975/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6605 - accuracy: 0.6009 - val_loss: 0.6868 - val_accuracy: 0.5606\n",
      "Epoch 976/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6605 - accuracy: 0.6016 - val_loss: 0.6869 - val_accuracy: 0.5601\n",
      "Epoch 977/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6604 - accuracy: 0.6019 - val_loss: 0.6870 - val_accuracy: 0.5612\n",
      "Epoch 978/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6604 - accuracy: 0.6015 - val_loss: 0.6870 - val_accuracy: 0.5608\n",
      "Epoch 979/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6603 - accuracy: 0.6022 - val_loss: 0.6870 - val_accuracy: 0.5607\n",
      "Epoch 980/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6603 - accuracy: 0.6014 - val_loss: 0.6872 - val_accuracy: 0.5576\n",
      "Epoch 981/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6603 - accuracy: 0.6023 - val_loss: 0.6872 - val_accuracy: 0.5585\n",
      "Epoch 982/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6602 - accuracy: 0.6016 - val_loss: 0.6871 - val_accuracy: 0.5583\n",
      "Epoch 983/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6602 - accuracy: 0.6019 - val_loss: 0.6872 - val_accuracy: 0.5599\n",
      "Epoch 984/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6601 - accuracy: 0.6022 - val_loss: 0.6872 - val_accuracy: 0.5581\n",
      "Epoch 985/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6601 - accuracy: 0.6024 - val_loss: 0.6873 - val_accuracy: 0.5579\n",
      "Epoch 986/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6600 - accuracy: 0.6018 - val_loss: 0.6872 - val_accuracy: 0.5595\n",
      "Epoch 987/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6600 - accuracy: 0.6028 - val_loss: 0.6872 - val_accuracy: 0.5594\n",
      "Epoch 988/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6599 - accuracy: 0.6022 - val_loss: 0.6878 - val_accuracy: 0.5550\n",
      "Epoch 989/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6599 - accuracy: 0.6020 - val_loss: 0.6873 - val_accuracy: 0.5606\n",
      "Epoch 990/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6599 - accuracy: 0.6025 - val_loss: 0.6873 - val_accuracy: 0.5584\n",
      "Epoch 991/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6598 - accuracy: 0.6016 - val_loss: 0.6874 - val_accuracy: 0.5582\n",
      "Epoch 992/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6598 - accuracy: 0.6024 - val_loss: 0.6874 - val_accuracy: 0.5587\n",
      "Epoch 993/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6598 - accuracy: 0.6024 - val_loss: 0.6876 - val_accuracy: 0.5614\n",
      "Epoch 994/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6597 - accuracy: 0.6024 - val_loss: 0.6875 - val_accuracy: 0.5581\n",
      "Epoch 995/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6596 - accuracy: 0.6031 - val_loss: 0.6878 - val_accuracy: 0.5620\n",
      "Epoch 996/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6597 - accuracy: 0.6027 - val_loss: 0.6875 - val_accuracy: 0.5586\n",
      "Epoch 997/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6596 - accuracy: 0.6032 - val_loss: 0.6875 - val_accuracy: 0.5585\n",
      "Epoch 998/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6596 - accuracy: 0.6031 - val_loss: 0.6876 - val_accuracy: 0.5584\n",
      "Epoch 999/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6595 - accuracy: 0.6026 - val_loss: 0.6876 - val_accuracy: 0.5591\n",
      "Epoch 1000/1000\n",
      "1303/1303 [==============================] - 3s 2ms/step - loss: 0.6594 - accuracy: 0.6026 - val_loss: 0.6878 - val_accuracy: 0.5607\n",
      "2896/2896 - 2s - loss: 0.6868 - accuracy: 0.5631 - 2s/epoch - 565us/step\n"
     ]
    }
   ],
   "source": [
    "# Perform training as outlined in the method and results sections\n",
    "model.fit(np.asarray(x_train), np.asarray(y_train), epochs=1000, batch_size=64, shuffle=True, validation_split=0.4)\n",
    "test_loss, test_acc = model.evaluate(np.asarray(x_test), np.asarray(y_test), verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7544e739",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4344/4344 [==============================] - 3s 567us/step\n"
     ]
    }
   ],
   "source": [
    "# Test some predictions\n",
    "predictions_single = model.predict(np.asarray(x_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "25ca5131",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2241657193017801"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert probabilities to actual predictions\n",
    "pred = [predictions_single[i].argmax() for i in range(len(predictions_single))]\n",
    "np.sum(np.array(pred) - np.array(y_train)) / len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49d0fd93",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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