18 - fano_fitting_hybrid_qnn_regressors_qiskit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hybrid Variational Neural Network for Fano fitting (Quantum-Classical Variational Neural Network)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Perform a regression to fit a Fano resonance with Pennylane framework</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p> Let's fit a <a href=\"https://en.wikipedia.org/wiki/Fano_resonance\">Fano resonance</a>, this signal appears on superconducting resonators when photons<sup><a href=\"https://arxiv.org/abs/2207.03829\">[1]</a></sup> interact with spins at low temperatures (from 4K to 10mK in a dilution refrigerator)</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### References:\n",
    "\n",
    "> https://arxiv.org/abs/2207.03829\n",
    ">\n",
    "> https://qiskit.org/textbook/ch-applications/vqe-molecules.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "from qiskit import QuantumCircuit\n",
    "from math import asin, sqrt, ceil\n",
    "from qiskit.opflow import Z, CircuitStateFn, StateFn, PauliExpectation\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy.ndimage import gaussian_filter\n",
    "from scipy.signal import detrend\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from tensorflow.keras.optimizers import Adam, SGD\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Dense, Layer\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [10, 7]\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.executing_eagerly()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fano_data = pd.read_csv(\"./datasets/signal_080_peak_1_fano_adjust.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func_fano(x, xc, w, q, y0):\n",
    "    y = y0+A*(2*(x-xc)/w+q)*(2*(x-xc)/w+q)/(2*(x-xc)/w*2*(x-xc)/w+1)/(1+q*q)\n",
    "    return y\n",
    "\n",
    "def scale(arr, lvalue = -np.pi, rvalue = np.pi):\n",
    "    return np.interp(arr, (arr.min(), arr.max()), (lvalue, rvalue))\n",
    "\n",
    "def remove_outlier(df_in, col_name, quartiles, rng_arr):\n",
    "    q1 = df_in[col_name].quantile(quartiles[0])\n",
    "    q3 = df_in[col_name].quantile(quartiles[1])\n",
    "    iqr = q3-q1 #Interquartile range\n",
    "    fence_low  = q1-rng_arr[0]*iqr\n",
    "    fence_high = q3+rng_arr[1]*iqr\n",
    "    df_out = df_in.loc[(df_in[col_name] > fence_low) & (df_in[col_name] < fence_high)]\n",
    "    return df_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def smooth(data):\n",
    "    #return data\n",
    "    #return scipy.ndimage.gaussian_filter(data, sigma=30, mode='reflect', truncate=18.0)\n",
    "    \n",
    "    return gaussian_filter(data, sigma=15, mode='reflect', truncate=18.0)\n",
    "    #return scipy.ndimage.gaussian_filter(data, sigma=15, mode='reflect', truncate=15.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x630 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 514,
       "width": 762
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_samples = 20\n",
    "eps = 0.2\n",
    "lb, ub = -1, 1\n",
    "\n",
    "y0 = fano_data[\"y0\"].values[0]\n",
    "A = fano_data[\"A\"].values[0]\n",
    "xc = fano_data[\"xc\"].values[0]\n",
    "q = fano_data[\"q\"].values[0]\n",
    "w = fano_data[\"w\"].values[0]\n",
    "\n",
    "f = lambda x: func_fano(x, xc, w, q, y0)\n",
    "\n",
    "X_ = fano_data[\"signal_original_x\"].values.reshape(-1,1)\n",
    "\n",
    "# Create Gaussian distribution (mean = 0, std = 1)\n",
    "std_scaler = StandardScaler()\n",
    "std_scaler.fit(X_)\n",
    "X_ = std_scaler.transform(X_)\n",
    "\n",
    "#y_ = f(X_)\n",
    "\n",
    "y_ = fano_data[\"signal_original_y\"].values\n",
    "\n",
    "# select a better range of data to fit\n",
    "rng = range(250,700)\n",
    "\n",
    "X_ = X_.take(rng, axis=0)\n",
    "y_ = y_.take(rng, axis=0)\n",
    "\n",
    "# random indices for points selection\n",
    "#indices = sorted(np.random.choice(range(X_.shape[0]), num_samples, replace=False))\n",
    "\n",
    "#indices = np.array([19,  40, 156, 159, 173, 175, 191, 207, 213, 253, 358, 359, 448, 543, 559, 613, 651, 657, 737, 754])\n",
    "indices = [22, 54, 125, 129, 153, 158, 204, 205, 229, 248, 254, 268, 287, 310, 311, 320, 324, 325, 412, 428]\n",
    "\n",
    "XX = X_[indices].reshape(-1,1)\n",
    "yy = y_[indices].reshape(-1,1)\n",
    "\n",
    "#df = pd.DataFrame(zip(X[:,0].flatten().tolist(), y[:,0].flatten().tolist()), columns = [\"X\", \"y\"])\n",
    "#new_df = remove_outlier(df, \"y\", [0.25, 0.75], [0.05, 0.05])\n",
    "#X = new_df[\"X\"].values.reshape(-1,1)\n",
    "#y = new_df[\"y\"].values.reshape(-1,1)\n",
    "\n",
    "yy = detrend(yy)\n",
    "\n",
    "#XX = scale(XX, 0, 1)\n",
    "XX = scale(XX) # from -pi to pi\n",
    "yy = scale(yy, -1, 1)\n",
    "\n",
    "#X_ = scale(X_, 0, 1)\n",
    "X_ = scale(X_) # from -pi to pi\n",
    "y_ = scale(y_, -1, 1)\n",
    "\n",
    "plt.figure(num = \"Fano adjust data\", figsize = (10, 7), dpi=90, facecolor='w', edgecolor='k')\n",
    "plt.plot(X_, y_, \"r--\", label='Tx signal', linewidth = 1)\n",
    "plt.plot(XX, yy, \"bo\", label = 'Points to fit')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prob_to_angle(prob):\n",
    "    return 2*asin(sqrt(prob))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def quantum_layer(initial_parameters, num_qubits):\n",
    "\n",
    "    expectations = []\n",
    "    \n",
    "    for i in range(len(initial_parameters)):\n",
    "        \n",
    "        quantumCircuit = QuantumCircuit(num_qubits)\n",
    "\n",
    "        quantumCircuit.h(range(num_qubits))\n",
    "    \n",
    "        angles = initial_parameters[i]\n",
    "        \n",
    "        for j in range(len(angles)):\n",
    "            #quantumCircuit.ry(prob_to_angle(float(angles[j])), j)\n",
    "            #quantumCircuit.ry(angles[j] * np.pi, j)\n",
    "            quantumCircuit.ry(angles[j], j)\n",
    "\n",
    "        #quantumCircuit.h(range(num_qubits))\n",
    " \n",
    "        operator = Z\n",
    "        for _ in range(num_qubits - 1):\n",
    "            operator @= Z\n",
    "          \n",
    "        psi = StateFn(quantumCircuit)\n",
    "        #psi = CircuitStateFn(quantumCircuit)\n",
    "        #expectation_value = psi.adjoint().compose(operator).compose(psi).eval() \n",
    "        expectation_value = (~psi @ operator @ psi).eval()\n",
    "        #expectation_value = PauliExpectation().convert(expectation_value).eval()\n",
    "\n",
    "        expectations.append(expectation_value)\n",
    " \n",
    "    diff = num_qubits - len(expectations)\n",
    "    \n",
    "    if diff > 0:\n",
    "        for _ in range(diff):\n",
    "            expectations.append(0j)\n",
    "    \n",
    "    return expectations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Linear(Layer):\n",
    "    \n",
    "    def __init__(self, output_size, batch_size = 10, units = 1, input_dim=1, num_qubits = 1, **kwargs):\n",
    "        \n",
    "        super(Linear, self).__init__(**kwargs)\n",
    "        #self.threshold = 0.5\n",
    "        self.num_qubits = num_qubits\n",
    "        self.output_size = output_size\n",
    "\n",
    "        self.w = self.add_weight(\n",
    "            shape=(input_dim, units), initializer=\"random_normal\", trainable=True\n",
    "        )\n",
    "        \n",
    "        self.b = self.add_weight(shape=(units,), initializer=\"zeros\", trainable=True)\n",
    "\n",
    "    def build(self,input_shape): \n",
    "        self.input_size = input_shape[1]        \n",
    "        self.w = self.add_weight(shape=(self.input_size,\n",
    "                                          self.output_size),\n",
    "                                   initializer='random_normal',\n",
    "                                   trainable=True)\n",
    "\n",
    "    def get_config(self):\n",
    "        config = super(Linear, self).get_config()\n",
    "        return config\n",
    "\n",
    "    def call(self, inputs):\n",
    "        \n",
    "        if (tf.executing_eagerly()):\n",
    "            \n",
    "            final_output = []\n",
    "        \n",
    "            for i in range(inputs.shape[0]):\n",
    "                pred = quantum_layer(inputs.numpy(), self.num_qubits)\n",
    "                final_output.append(list(pred))\n",
    "            \n",
    "            out_layer = tf.keras.layers.Dense(3, activation='relu')\n",
    "            \n",
    "            return tf.convert_to_tensor(final_output)\n",
    "        \n",
    "        return inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_qubits = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sequential_model():\n",
    "\n",
    "    layers_arr = []\n",
    "    layers_arr.append(Dense(n_qubits, activation = 'linear', input_shape = [1], name = \"dense1\"))\n",
    "    layers_arr.append(Linear(output_size = n_qubits, units = 1, input_dim = n_qubits, num_qubits = n_qubits, name = \"quantum1\"))\n",
    "    #layers_arr.append(Dense(1, activation=\"linear\", name = \"dense2\"))   \n",
    "    layers_arr.append(Dense(1, name =\"dense3\"))\n",
    "\n",
    "    return Sequential(layers_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "model = sequential_model()\n",
    "    \n",
    "#optimizer = Adam(learning_rate=0.01)\n",
    "optimizer = SGD(learning_rate=0.01)\n",
    "\n",
    "#model.compile(loss='mse', optimizer = optimizer, metrics = ['mae', 'mse', 'acc'], run_eagerly=True)\n",
    "model.compile(optimizer, loss='mse', metrics=[\"accuracy\"], run_eagerly=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense1 (Dense)               (None, 1)                 2         \n",
      "_________________________________________________________________\n",
      "quantum1 (Linear)            (None, 1)                 2         \n",
      "_________________________________________________________________\n",
      "dense3 (Dense)               (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 6\n",
      "Trainable params: 6\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 1s - loss: 2.2947 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 5/15 [=========>....................] - ETA: 0s - loss: 2.5177 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 9/15 [=================>............] - ETA: 0s - loss: 2.8941 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "13/15 [=========================>....] - ETA: 0s - loss: 2.4264 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 18ms/step - loss: 2.4152 - accuracy: 0.0000e+00 - val_loss: 2.4845 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.5474 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 2.0942 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 1.7105 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 1.7988 - accuracy: 0.0000e+00 - val_loss: 1.8996 - val_accuracy: 0.0000e+00\n",
      "Epoch 3/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 1.5700 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 1.1135 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 1.5400 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 1.3421 - accuracy: 0.0000e+00 - val_loss: 1.4413 - val_accuracy: 0.0000e+00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 2.3590 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 1.2006 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 1.0277 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 1.0070 - accuracy: 0.0000e+00 - val_loss: 1.1114 - val_accuracy: 0.0000e+00\n",
      "Epoch 5/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 1.8173 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.8735 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.8627 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.7599 - accuracy: 0.0000e+00 - val_loss: 0.8657 - val_accuracy: 0.0000e+00\n",
      "Epoch 6/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.2210 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.5797 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.6274 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.5801 - accuracy: 0.0000e+00 - val_loss: 0.6673 - val_accuracy: 0.0000e+00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.1693 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.4572 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.3661 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.4457 - accuracy: 0.0000e+00 - val_loss: 0.5154 - val_accuracy: 0.0000e+00\n",
      "Epoch 8/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0579 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.3557 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.3542 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.3464 - accuracy: 0.0000e+00 - val_loss: 0.3989 - val_accuracy: 0.0000e+00\n",
      "Epoch 9/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0898 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.2505 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.2640 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 16ms/step - loss: 0.2735 - accuracy: 0.0000e+00 - val_loss: 0.3134 - val_accuracy: 0.0000e+00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.2224 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.2578 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.2324 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.2196 - accuracy: 0.0000e+00 - val_loss: 0.2459 - val_accuracy: 0.0000e+00\n",
      "Epoch 11/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0122 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.1197 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.1923 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 16ms/step - loss: 0.1797 - accuracy: 0.0000e+00 - val_loss: 0.1964 - val_accuracy: 0.0000e+00\n",
      "Epoch 12/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.5112 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.1896 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.1595 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.1503 - accuracy: 0.0000e+00 - val_loss: 0.1568 - val_accuracy: 0.6000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0789 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.1382 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.1354 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.1282 - accuracy: 0.0667 - val_loss: 0.1255 - val_accuracy: 0.6000\n",
      "Epoch 14/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0343 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.1291 - accuracy: 0.1667    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.1261 - accuracy: 0.0909WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.1126 - accuracy: 0.1333 - val_loss: 0.1038 - val_accuracy: 0.6000\n",
      "Epoch 15/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0344 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0533 - accuracy: 0.1667    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0546 - accuracy: 0.1818WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.1008 - accuracy: 0.2000 - val_loss: 0.0886 - val_accuracy: 0.6000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.3000 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.1120 - accuracy: 0.3333    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "10/15 [===================>..........] - ETA: 0s - loss: 0.0907 - accuracy: 0.3000WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0920 - accuracy: 0.2000 - val_loss: 0.0757 - val_accuracy: 0.6000\n",
      "Epoch 17/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.3066 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0906 - accuracy: 0.1667    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.1074 - accuracy: 0.1818WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0855 - accuracy: 0.2000 - val_loss: 0.0650 - val_accuracy: 0.6000\n",
      "Epoch 18/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0015 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0173 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 9/15 [=================>............] - ETA: 0s - loss: 0.0797 - accuracy: 0.1111    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "14/15 [===========================>..] - ETA: 0s - loss: 0.0861 - accuracy: 0.2143WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 17ms/step - loss: 0.0808 - accuracy: 0.2000 - val_loss: 0.0579 - val_accuracy: 0.6000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 8.6632e-04 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 5/15 [=========>....................] - ETA: 0s - loss: 0.0911 - accuracy: 0.0000e+00    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 9/15 [=================>............] - ETA: 0s - loss: 0.0753 - accuracy: 0.1111    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "13/15 [=========================>....] - ETA: 0s - loss: 0.0840 - accuracy: 0.1538WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 18ms/step - loss: 0.0775 - accuracy: 0.2000 - val_loss: 0.0510 - val_accuracy: 0.6000\n",
      "Epoch 20/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.1346 - accuracy: 1.0000WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0424 - accuracy: 0.3333WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0293 - accuracy: 0.1818WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0747 - accuracy: 0.2000 - val_loss: 0.0477 - val_accuracy: 0.6000\n",
      "Epoch 21/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0145 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0130 - accuracy: 0.1667    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0533 - accuracy: 0.1818WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0731 - accuracy: 0.2000 - val_loss: 0.0437 - val_accuracy: 0.6000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.3290 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0974 - accuracy: 0.3333    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0608 - accuracy: 0.2727WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0717 - accuracy: 0.2000 - val_loss: 0.0402 - val_accuracy: 0.6000\n",
      "Epoch 23/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0322 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0256 - accuracy: 0.1667    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0906 - accuracy: 0.1818WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0706 - accuracy: 0.2000 - val_loss: 0.0377 - val_accuracy: 0.6000\n",
      "Epoch 24/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.1023 - accuracy: 1.0000WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0968 - accuracy: 0.1667WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0918 - accuracy: 0.1818WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0699 - accuracy: 0.2000 - val_loss: 0.0354 - val_accuracy: 0.6000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 5.6748e-07 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 5/15 [=========>....................] - ETA: 0s - loss: 0.0709 - accuracy: 0.0000e+00    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "10/15 [===================>..........] - ETA: 0s - loss: 0.0848 - accuracy: 0.1000    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "14/15 [===========================>..] - ETA: 0s - loss: 0.0719 - accuracy: 0.1429WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 17ms/step - loss: 0.0693 - accuracy: 0.2000 - val_loss: 0.0328 - val_accuracy: 0.6000\n",
      "Epoch 26/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0247 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0899 - accuracy: 0.3333    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0597 - accuracy: 0.2727WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0689 - accuracy: 0.2000 - val_loss: 0.0320 - val_accuracy: 0.6000\n",
      "Epoch 27/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0019 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0109 - accuracy: 0.1667    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0903 - accuracy: 0.1818WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0686 - accuracy: 0.2000 - val_loss: 0.0305 - val_accuracy: 0.6000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0027 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0651 - accuracy: 0.1667    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0506 - accuracy: 0.1818WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0684 - accuracy: 0.2000 - val_loss: 0.0297 - val_accuracy: 0.6000\n",
      "Epoch 29/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0210 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0055 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0775 - accuracy: 0.1818    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 14ms/step - loss: 0.0682 - accuracy: 0.2000 - val_loss: 0.0287 - val_accuracy: 0.6000\n",
      "Epoch 30/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 7.3827e-05 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0689 - accuracy: 0.3333        WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0511 - accuracy: 0.1818WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0681 - accuracy: 0.2000 - val_loss: 0.0280 - val_accuracy: 0.6000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 31/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0023 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.1322 - accuracy: 0.1667    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0850 - accuracy: 0.0909WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0679 - accuracy: 0.2000 - val_loss: 0.0267 - val_accuracy: 0.6000\n",
      "Epoch 32/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0180 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0856 - accuracy: 0.1667    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0571 - accuracy: 0.2727WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0679 - accuracy: 0.2000 - val_loss: 0.0270 - val_accuracy: 0.6000\n",
      "Epoch 33/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 1.9084e-04 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0125 - accuracy: 0.3333        WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0509 - accuracy: 0.1818WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0678 - accuracy: 0.2000 - val_loss: 0.0272 - val_accuracy: 0.6000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 34/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0054 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0742 - accuracy: 0.1667    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0550 - accuracy: 0.1818WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0678 - accuracy: 0.2000 - val_loss: 0.0272 - val_accuracy: 0.6000\n",
      "Epoch 35/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0168 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0656 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0573 - accuracy: 0.1818    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0678 - accuracy: 0.2000 - val_loss: 0.0269 - val_accuracy: 0.6000\n",
      "Epoch 36/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.1151 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0835 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0823 - accuracy: 0.1818    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0677 - accuracy: 0.2000 - val_loss: 0.0261 - val_accuracy: 0.6000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 37/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0017 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0792 - accuracy: 0.3333    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0490 - accuracy: 0.2727WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0678 - accuracy: 0.2000 - val_loss: 0.0259 - val_accuracy: 0.6000\n",
      "Epoch 38/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0064 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0329 - accuracy: 0.1667    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0867 - accuracy: 0.2727WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0677 - accuracy: 0.2000 - val_loss: 0.0260 - val_accuracy: 0.6000\n",
      "Epoch 39/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 2.6384e-04 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0260 - accuracy: 0.0000e+00    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0515 - accuracy: 0.1818    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 16ms/step - loss: 0.0678 - accuracy: 0.2000 - val_loss: 0.0258 - val_accuracy: 0.6000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 40/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.1186 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0869 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0911 - accuracy: 0.2727    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0678 - accuracy: 0.2000 - val_loss: 0.0253 - val_accuracy: 0.6000\n",
      "Epoch 41/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0210 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0787 - accuracy: 0.3333    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0901 - accuracy: 0.2727WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 16ms/step - loss: 0.0678 - accuracy: 0.2000 - val_loss: 0.0250 - val_accuracy: 0.6000\n",
      "Epoch 42/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0177 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 5/15 [=========>....................] - ETA: 0s - loss: 0.1037 - accuracy: 0.2000    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 9/15 [=================>............] - ETA: 0s - loss: 0.0666 - accuracy: 0.2222WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "14/15 [===========================>..] - ETA: 0s - loss: 0.0710 - accuracy: 0.2143WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 17ms/step - loss: 0.0678 - accuracy: 0.2000 - val_loss: 0.0247 - val_accuracy: 0.6000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 43/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0045 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0354 - accuracy: 0.1667    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0886 - accuracy: 0.2727WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0677 - accuracy: 0.2000 - val_loss: 0.0250 - val_accuracy: 0.6000\n",
      "Epoch 44/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0176 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0081 - accuracy: 0.1667    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0550 - accuracy: 0.1818WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0677 - accuracy: 0.2000 - val_loss: 0.0252 - val_accuracy: 0.6000\n",
      "Epoch 45/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0158 - accuracy: 1.0000WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0854 - accuracy: 0.1667WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0520 - accuracy: 0.0909WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0677 - accuracy: 0.2000 - val_loss: 0.0251 - val_accuracy: 0.6000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 46/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0156 - accuracy: 1.0000WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 5/15 [=========>....................] - ETA: 0s - loss: 0.0316 - accuracy: 0.2000WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "10/15 [===================>..........] - ETA: 0s - loss: 0.0190 - accuracy: 0.1000WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0676 - accuracy: 0.2000 - val_loss: 0.0257 - val_accuracy: 0.6000\n",
      "Epoch 47/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.1219 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0263 - accuracy: 0.1667    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0592 - accuracy: 0.2727WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 16ms/step - loss: 0.0676 - accuracy: 0.2000 - val_loss: 0.0262 - val_accuracy: 0.6000\n",
      "Epoch 48/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.3510 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 4/15 [=======>......................] - ETA: 0s - loss: 0.1820 - accuracy: 0.2500    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 8/15 [===============>..............] - ETA: 0s - loss: 0.0977 - accuracy: 0.1250WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "13/15 [=========================>....] - ETA: 0s - loss: 0.0680 - accuracy: 0.2308WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 18ms/step - loss: 0.0677 - accuracy: 0.2000 - val_loss: 0.0250 - val_accuracy: 0.6000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 49/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0155 - accuracy: 1.0000WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0308 - accuracy: 0.3333WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0836 - accuracy: 0.1818WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0677 - accuracy: 0.2000 - val_loss: 0.0253 - val_accuracy: 0.6000\n",
      "Epoch 50/50\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0156 - accuracy: 0.0000e+00WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      " 6/15 [===========>..................] - ETA: 0s - loss: 0.0246 - accuracy: 0.5000    WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "11/15 [=====================>........] - ETA: 0s - loss: 0.0594 - accuracy: 0.2727WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['dense1/kernel:0', 'dense1/bias:0', 'Variable:0', 'quantum1/Variable:0'] when minimizing the loss.\n",
      "15/15 [==============================] - 0s 15ms/step - loss: 0.0677 - accuracy: 0.2000 - val_loss: 0.0259 - val_accuracy: 0.6000\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(XX, yy, epochs=50, batch_size=n_qubits, validation_split = 0.25)\n",
    "\n",
    "#history = model.fit(XX, yy, epochs = 50, batch_size=n_qubits, validation_split = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 440,
       "width": 608
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def split_batch_predict(data, num_qubits, model):\n",
    "    \n",
    "    splitted_X = None\n",
    "\n",
    "    if num_qubits > 1:\n",
    "        num_sections = ceil(X_.shape[0] / num_qubits)\n",
    "        splitted_X = np.array_split(data.flatten(), num_sections)\n",
    "    else:\n",
    "        splitted_X = data\n",
    "\n",
    "    y_prediction = []\n",
    "\n",
    "    for arr in splitted_X:\n",
    "        pred = model.predict(arr)\n",
    "        y_prediction.append(pred.tolist())\n",
    "    \n",
    "    return np.array(y_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = split_batch_predict(X_, n_qubits, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_smooth = smooth(y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 900x630 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 514,
       "width": 754
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(num = \"Fano adjust QML 1\", figsize = (10, 7), dpi=90, facecolor='w', edgecolor='k')\n",
    "\n",
    "# plot data\n",
    "plt.plot(XX, yy, \"bo\", label = 'Points to fit')\n",
    "\n",
    "# plot target function\n",
    "plt.plot(X_, y_, \"r--\", label = 'Tx signal')\n",
    "\n",
    "# plot prediction\n",
    "plt.plot(X_, y_predict.flatten(), \"g-\", label = 'Fano prediction raw')\n",
    "plt.plot(X_, y_predict_smooth.flatten(), \"--\", color='#d7901b', linewidth=2.5, label = 'Fano prediction smooth')\n",
    "plt.legend(loc = 'upper left', fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.8782064640\n"
     ]
    }
   ],
   "source": [
    "print(\"Score: %.10f\" % r2_score(y_, y_predict.flatten()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.21.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.10.4</td></tr><tr><td><code>qiskit-ignis</code></td><td>0.6.0</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.19.2</td></tr><tr><td><code>qiskit</code></td><td>0.37.0</td></tr><tr><td><code>qiskit-nature</code></td><td>0.4.3</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.4.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.7.7</td></tr><tr><td>Python compiler</td><td>MSC v.1916 64 bit (AMD64)</td></tr><tr><td>Python build</td><td>default, May  6 2020 11:45:54</td></tr><tr><td>OS</td><td>Windows</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>15.872657775878906</td></tr><tr><td colspan='2'>Tue Aug 16 13:38:35 2022 Hora de verano romance</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2022.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import qiskit.tools.jupyter\n",
    "\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}