{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e7d28c78",
   "metadata": {},
   "source": [
    "# A first artificial neural network with tensorflow and keras"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4f75b33",
   "metadata": {},
   "source": [
    "In this notebook we will apply a simple neural network using tensorflow and keras.\n",
    "The ANN we wish to implement consists of 3 inputs and one ouput."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ea70e2d8-56f4-4657-938e-4686c09b7410",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We import the required python packages\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07ea3bf6",
   "metadata": {},
   "source": [
    "We need to import the dataset using the numpy loadtxt function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4573c373-4317-4ab4-bb98-3e7d6a507cb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.loadtxt('Data_Example_1_small.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e363744",
   "metadata": {},
   "source": [
    "From the dataset we need to extract the input and ouput needed to train the ANN. The three first columns contains the input variables while the last column contains the output variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7f11c78e-97d2-4f83-80f1-e9ecd40cb574",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_data, output_data  = data[:,0:3], data[:,-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ea906b3",
   "metadata": {},
   "source": [
    "We can visualize the data we imported:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c2378be8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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MyB5QAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 3000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (30, 10)\n",
    "fig, axs = plt.subplots(1,3)\n",
    "axs[0].scatter(input_data[:,0],output_data)\n",
    "axs[0].set_xlabel('x1')\n",
    "axs[0].set_ylabel('y')\n",
    "axs[0].grid()\n",
    "axs[1].scatter(input_data[:,1],output_data)\n",
    "axs[1].set_xlabel('x2')\n",
    "axs[1].set_ylabel('y')\n",
    "axs[1].grid()\n",
    "axs[2].scatter(input_data[:,2],output_data)\n",
    "axs[2].set_xlabel('x3')\n",
    "axs[2].set_ylabel('y')\n",
    "axs[2].grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6af28532",
   "metadata": {},
   "source": [
    "We then build our first ANN using keras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2c5751af-7e86-4a6d-a88c-43333d4616bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = tf.keras.Input(shape=(3,)) # We define the number of inputs of the ANN\n",
    "#\n",
    "x = tf.keras.layers.Dense(1000, activation='relu')(inputs) # This add a layer of of neurons with the relu activation function\n",
    "#\n",
    "outputs = tf.keras.layers.Dense(1, activation='linear')(x) # We define here the output layer with one ouput and the linear activation function\n",
    "#\n",
    "model = tf.keras.Model(inputs=inputs, outputs=outputs) # We setup the inputs and outputs of the ANN\n",
    "#\n",
    "model.compile(loss='mse', optimizer='adam') # We compile the ANN model specifying the loss metric and optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a39ed78b",
   "metadata": {},
   "source": [
    "Once the ANN model has been built we need to train (fit) it to the dataset we imported. Here we have frozen the number of epochs to 1000.\n",
    "We can also print the minimum and maximum error committed during the training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ce23fefb-90fa-4ce1-a3f9-effce1e975cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 0.1864  \n",
      "Epoch 2/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0440 \n",
      "Epoch 3/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0225 \n",
      "Epoch 4/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0379 \n",
      "Epoch 5/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0248 \n",
      "Epoch 6/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0145 \n",
      "Epoch 7/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0129 \n",
      "Epoch 8/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0138 \n",
      "Epoch 9/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0112 \n",
      "Epoch 10/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0087 \n",
      "Epoch 11/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0082 \n",
      "Epoch 12/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0078 \n",
      "Epoch 13/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0057 \n",
      "Epoch 14/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0055 \n",
      "Epoch 15/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0050 \n",
      "Epoch 16/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0046 \n",
      "Epoch 17/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0040 \n",
      "Epoch 18/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0042 \n",
      "Epoch 19/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0035 \n",
      "Epoch 20/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0034 \n",
      "Epoch 21/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0032 \n",
      "Epoch 22/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0029 \n",
      "Epoch 23/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0027 \n",
      "Epoch 24/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0025 \n",
      "Epoch 25/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0022 \n",
      "Epoch 26/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0022 \n",
      "Epoch 27/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0019 \n",
      "Epoch 28/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0021 \n",
      "Epoch 29/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0021 \n",
      "Epoch 30/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0018 \n",
      "Epoch 31/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0016 \n",
      "Epoch 32/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0015 \n",
      "Epoch 33/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0018 \n",
      "Epoch 34/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0019 \n",
      "Epoch 35/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0016 \n",
      "Epoch 36/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0015 \n",
      "Epoch 37/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0016 \n",
      "Epoch 38/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0013 \n",
      "Epoch 39/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0013 \n",
      "Epoch 40/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0012 \n",
      "Epoch 41/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0012     \n",
      "Epoch 42/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0012 \n",
      "Epoch 43/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0011     \n",
      "Epoch 44/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0012 \n",
      "Epoch 45/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0012 \n",
      "Epoch 46/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0011 \n",
      "Epoch 47/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 9.0791e-04 \n",
      "Epoch 48/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0010 \n",
      "Epoch 49/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 9.8283e-04\n",
      "Epoch 50/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 9.7343e-04\n",
      "Epoch 51/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.9421e-04\n",
      "Epoch 52/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.9420e-04 \n",
      "Epoch 53/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 9.6530e-04\n",
      "Epoch 54/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 8.1917e-04 \n",
      "Epoch 55/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 9.1316e-04 \n",
      "Epoch 56/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 8.8331e-04 \n",
      "Epoch 57/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.7663e-04 \n",
      "Epoch 58/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.9670e-04 \n",
      "Epoch 59/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 7.3924e-04 \n",
      "Epoch 60/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 7.4824e-04 \n",
      "Epoch 61/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.8154e-04 \n",
      "Epoch 62/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 7.3248e-04 \n",
      "Epoch 63/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 7.0255e-04 \n",
      "Epoch 64/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 7.4499e-04\n",
      "Epoch 65/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.4699e-04 \n",
      "Epoch 66/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.6763e-04 \n",
      "Epoch 67/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 7.2161e-04 \n",
      "Epoch 68/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.5920e-04 \n",
      "Epoch 69/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.5266e-04 \n",
      "Epoch 70/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.0852e-04 \n",
      "Epoch 71/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.4563e-04 \n",
      "Epoch 72/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 6.4831e-04 \n",
      "Epoch 73/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 5.5435e-04 \n",
      "Epoch 74/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.5873e-04 \n",
      "Epoch 75/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.3842e-04 \n",
      "Epoch 76/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.0055e-04 \n",
      "Epoch 77/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.9172e-04 \n",
      "Epoch 78/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.5094e-04 \n",
      "Epoch 79/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.1482e-04 \n",
      "Epoch 80/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 5.4158e-04\n",
      "Epoch 81/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.7612e-04 \n",
      "Epoch 82/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.8388e-04 \n",
      "Epoch 83/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.4171e-04 \n",
      "Epoch 84/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 4.7785e-04 \n",
      "Epoch 85/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.4501e-04 \n",
      "Epoch 86/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.5225e-04 \n",
      "Epoch 87/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 4.2937e-04 \n",
      "Epoch 88/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.7656e-04 \n",
      "Epoch 89/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.3134e-04 \n",
      "Epoch 90/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.7943e-04 \n",
      "Epoch 91/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.3965e-04 \n",
      "Epoch 92/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.7136e-04 \n",
      "Epoch 93/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 3.1976e-04 \n",
      "Epoch 94/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 3.8774e-04 \n",
      "Epoch 95/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.0976e-04 \n",
      "Epoch 96/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.1606e-04 \n",
      "Epoch 97/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.9321e-04 \n",
      "Epoch 98/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.3665e-04 \n",
      "Epoch 99/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.9135e-04 \n",
      "Epoch 100/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 3.1637e-04\n",
      "Epoch 101/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.5675e-04 \n",
      "Epoch 102/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.7900e-04 \n",
      "Epoch 103/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.5651e-04 \n",
      "Epoch 104/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.5233e-04 \n",
      "Epoch 105/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.4488e-04 \n",
      "Epoch 106/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.3785e-04 \n",
      "Epoch 107/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.5988e-04 \n",
      "Epoch 108/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.3935e-04 \n",
      "Epoch 109/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.2045e-04 \n",
      "Epoch 110/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.8762e-04 \n",
      "Epoch 111/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.2220e-04 \n",
      "Epoch 112/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.8331e-04 \n",
      "Epoch 113/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.0097e-04 \n",
      "Epoch 114/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.8458e-04 \n",
      "Epoch 115/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.5719e-04 \n",
      "Epoch 116/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.6598e-04 \n",
      "Epoch 117/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.5780e-04 \n",
      "Epoch 118/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.6136e-04 \n",
      "Epoch 119/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 1.4381e-04 \n",
      "Epoch 120/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.1552e-04 \n",
      "Epoch 121/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 2.0441e-04 \n",
      "Epoch 122/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 1.9408e-04 \n",
      "Epoch 123/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.5504e-04 \n",
      "Epoch 124/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.5753e-04 \n",
      "Epoch 125/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 1.6115e-04 \n",
      "Epoch 126/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 1.4912e-04 \n",
      "Epoch 127/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.5742e-04 \n",
      "Epoch 128/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.5332e-04 \n",
      "Epoch 129/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.2634e-04\n",
      "Epoch 130/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.3746e-04 \n",
      "Epoch 131/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 1.0821e-04 \n",
      "Epoch 132/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.5283e-04 \n",
      "Epoch 133/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.6050e-04 \n",
      "Epoch 134/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.1462e-04 \n",
      "Epoch 135/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.2233e-04 \n",
      "Epoch 136/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.1313e-04 \n",
      "Epoch 137/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 1.2526e-04 \n",
      "Epoch 138/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.0465e-04 \n",
      "Epoch 139/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.1098e-04 \n",
      "Epoch 140/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 8.5167e-05 \n",
      "Epoch 141/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 9.5768e-05 \n",
      "Epoch 142/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 9.0177e-05 \n",
      "Epoch 143/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.8040e-05 \n",
      "Epoch 144/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 8.7005e-05 \n",
      "Epoch 145/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 9.9218e-05 \n",
      "Epoch 146/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 9.4951e-05 \n",
      "Epoch 147/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.2808e-05 \n",
      "Epoch 148/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 7.4700e-05 \n",
      "Epoch 149/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 8.5029e-05 \n",
      "Epoch 150/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.6612e-05 \n",
      "Epoch 151/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.3495e-05 \n",
      "Epoch 152/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.4690e-05 \n",
      "Epoch 153/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.9477e-05 \n",
      "Epoch 154/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.2477e-05 \n",
      "Epoch 155/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.7431e-05 \n",
      "Epoch 156/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.6401e-05 \n",
      "Epoch 157/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.4895e-05 \n",
      "Epoch 158/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 7.2166e-05\n",
      "Epoch 159/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.3754e-05 \n",
      "Epoch 160/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 7.0603e-05 \n",
      "Epoch 161/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 9.5562e-05 \n",
      "Epoch 162/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 7.5634e-05 \n",
      "Epoch 163/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.1533e-05 \n",
      "Epoch 164/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.2747e-05 \n",
      "Epoch 165/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.7271e-05 \n",
      "Epoch 166/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.6407e-05 \n",
      "Epoch 167/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 5.3947e-05 \n",
      "Epoch 168/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.0122e-05 \n",
      "Epoch 169/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.6846e-05 \n",
      "Epoch 170/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 5.7783e-05 \n",
      "Epoch 171/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.3788e-05 \n",
      "Epoch 172/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 7.2458e-05 \n",
      "Epoch 173/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.9144e-05 \n",
      "Epoch 174/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 6.5390e-05 \n",
      "Epoch 175/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.5653e-05 \n",
      "Epoch 176/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 9.5166e-05 \n",
      "Epoch 177/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.8580e-05 \n",
      "Epoch 178/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.1493e-05 \n",
      "Epoch 179/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.4885e-05 \n",
      "Epoch 180/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.7199e-05 \n",
      "Epoch 181/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.7684e-05 \n",
      "Epoch 182/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.0982e-05 \n",
      "Epoch 183/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.0293e-05 \n",
      "Epoch 184/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.4560e-05 \n",
      "Epoch 185/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.0062e-05 \n",
      "Epoch 186/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.0315e-05 \n",
      "Epoch 187/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.7569e-05 \n",
      "Epoch 188/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 4.0622e-05 \n",
      "Epoch 189/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.0745e-05 \n",
      "Epoch 190/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.3558e-05 \n",
      "Epoch 191/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 5.5562e-05 \n",
      "Epoch 192/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 4.9471e-05 \n",
      "Epoch 193/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.8436e-05 \n",
      "Epoch 194/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.5031e-05 \n",
      "Epoch 195/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.3059e-05 \n",
      "Epoch 196/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.7948e-05 \n",
      "Epoch 197/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.9412e-05 \n",
      "Epoch 198/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.7367e-05 \n",
      "Epoch 199/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.6897e-05 \n",
      "Epoch 200/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.0157e-05 \n",
      "Epoch 201/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 4.6137e-05 \n",
      "Epoch 202/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.3944e-05 \n",
      "Epoch 203/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 3.4334e-05 \n",
      "Epoch 204/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.4099e-05 \n",
      "Epoch 205/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 3.6555e-05 \n",
      "Epoch 206/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.9510e-05 \n",
      "Epoch 207/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.1711e-05 \n",
      "Epoch 208/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.4459e-05 \n",
      "Epoch 209/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.4669e-05 \n",
      "Epoch 210/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 4.1468e-05 \n",
      "Epoch 211/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 7.1427e-05 \n",
      "Epoch 212/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.7424e-05 \n",
      "Epoch 213/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.4725e-05 \n",
      "Epoch 214/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 3.7075e-05 \n",
      "Epoch 215/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.2060e-05 \n",
      "Epoch 216/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.9028e-05 \n",
      "Epoch 217/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.9450e-05 \n",
      "Epoch 218/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.1478e-05 \n",
      "Epoch 219/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.1099e-05 \n",
      "Epoch 220/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.9174e-05 \n",
      "Epoch 221/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 3.1269e-05 \n",
      "Epoch 222/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.3896e-05 \n",
      "Epoch 223/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.8432e-05 \n",
      "Epoch 224/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.7454e-05 \n",
      "Epoch 225/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.8535e-05 \n",
      "Epoch 226/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.1435e-05 \n",
      "Epoch 227/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 3.0291e-05 \n",
      "Epoch 228/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.8933e-05 \n",
      "Epoch 229/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.5321e-05 \n",
      "Epoch 230/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.3521e-05 \n",
      "Epoch 231/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.2982e-05 \n",
      "Epoch 232/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.2770e-05 \n",
      "Epoch 233/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.5752e-05 \n",
      "Epoch 234/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.8773e-05 \n",
      "Epoch 235/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.5066e-05 \n",
      "Epoch 236/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.7370e-05 \n",
      "Epoch 237/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.8528e-05 \n",
      "Epoch 238/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 3.3418e-05 \n",
      "Epoch 239/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 3.2571e-05 \n",
      "Epoch 240/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.5218e-05 \n",
      "Epoch 241/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.4942e-05 \n",
      "Epoch 242/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.6601e-05 \n",
      "Epoch 243/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.8550e-05 \n",
      "Epoch 244/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 3.3517e-05 \n",
      "Epoch 245/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.7484e-05 \n",
      "Epoch 246/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.8062e-05 \n",
      "Epoch 247/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.2878e-05 \n",
      "Epoch 248/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.5318e-05 \n",
      "Epoch 249/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.3864e-05\n",
      "Epoch 250/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.6413e-05 \n",
      "Epoch 251/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.2439e-05 \n",
      "Epoch 252/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.7738e-05 \n",
      "Epoch 253/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.5548e-05 \n",
      "Epoch 254/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.3310e-05 \n",
      "Epoch 255/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 3.2170e-05\n",
      "Epoch 256/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.7421e-05 \n",
      "Epoch 257/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.0491e-05 \n",
      "Epoch 258/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.6592e-05 \n",
      "Epoch 259/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.7992e-05 \n",
      "Epoch 260/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.3256e-05 \n",
      "Epoch 261/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.8578e-05 \n",
      "Epoch 262/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 3.0653e-05 \n",
      "Epoch 263/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.9782e-05 \n",
      "Epoch 264/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.2421e-05\n",
      "Epoch 265/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 3.3822e-05\n",
      "Epoch 266/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.3824e-05 \n",
      "Epoch 267/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.3103e-05 \n",
      "Epoch 268/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.4184e-05 \n",
      "Epoch 269/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.2926e-05 \n",
      "Epoch 270/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.8785e-05 \n",
      "Epoch 271/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.3708e-05 \n",
      "Epoch 272/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.6185e-05 \n",
      "Epoch 273/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.0351e-05 \n",
      "Epoch 274/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.3659e-05 \n",
      "Epoch 275/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.5749e-05 \n",
      "Epoch 276/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.4647e-05 \n",
      "Epoch 277/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.2689e-05 \n",
      "Epoch 278/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.1985e-05 \n",
      "Epoch 279/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.8176e-05 \n",
      "Epoch 280/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.5920e-05\n",
      "Epoch 281/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.5681e-05 \n",
      "Epoch 282/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.4076e-05 \n",
      "Epoch 283/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.6375e-05 \n",
      "Epoch 284/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.0334e-05 \n",
      "Epoch 285/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.8876e-05 \n",
      "Epoch 286/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.2732e-05 \n",
      "Epoch 287/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.5801e-05 \n",
      "Epoch 288/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.6056e-05 \n",
      "Epoch 289/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.7067e-05 \n",
      "Epoch 290/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.7046e-05 \n",
      "Epoch 291/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.2862e-05 \n",
      "Epoch 292/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 2.1405e-05 \n",
      "Epoch 293/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.6036e-05 \n",
      "Epoch 294/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.5670e-05 \n",
      "Epoch 295/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.8594e-05\n",
      "Epoch 296/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.8413e-05 \n",
      "Epoch 297/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.9843e-05 \n",
      "Epoch 298/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.2180e-05 \n",
      "Epoch 299/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.5961e-05 \n",
      "Epoch 300/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.1637e-05 \n",
      "Epoch 301/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.7704e-05 \n",
      "Epoch 302/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.6860e-05 \n",
      "Epoch 303/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.6788e-05 \n",
      "Epoch 304/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.0767e-05 \n",
      "Epoch 305/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.8275e-05 \n",
      "Epoch 306/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.3252e-05 \n",
      "Epoch 307/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.3811e-05 \n",
      "Epoch 308/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.4111e-05 \n",
      "Epoch 309/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.7942e-05 \n",
      "Epoch 310/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.5817e-05 \n",
      "Epoch 311/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.7377e-05 \n",
      "Epoch 312/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.3224e-05 \n",
      "Epoch 313/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.7092e-05 \n",
      "Epoch 314/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.7729e-05 \n",
      "Epoch 315/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.7147e-05\n",
      "Epoch 316/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.5892e-05 \n",
      "Epoch 317/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6440e-05\n",
      "Epoch 318/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 1.4251e-05 \n",
      "Epoch 319/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 1.4010e-05 \n",
      "Epoch 320/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.2699e-05 \n",
      "Epoch 321/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.2560e-05 \n",
      "Epoch 322/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.9012e-05 \n",
      "Epoch 323/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6621e-05\n",
      "Epoch 324/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.3614e-05 \n",
      "Epoch 325/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.5417e-05\n",
      "Epoch 326/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.4189e-05 \n",
      "Epoch 327/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.3411e-05 \n",
      "Epoch 328/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.4646e-05 \n",
      "Epoch 329/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.5506e-05 \n",
      "Epoch 330/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.7031e-05 \n",
      "Epoch 331/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.3381e-05 \n",
      "Epoch 332/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.3714e-05 \n",
      "Epoch 333/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.5303e-05 \n",
      "Epoch 334/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.2615e-05 \n",
      "Epoch 335/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.1901e-05 \n",
      "Epoch 336/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4597e-05 \n",
      "Epoch 337/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.5661e-05\n",
      "Epoch 338/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.5623e-05 \n",
      "Epoch 339/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.6473e-05 \n",
      "Epoch 340/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.6840e-05 \n",
      "Epoch 341/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.5253e-05 \n",
      "Epoch 342/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.8347e-05 \n",
      "Epoch 343/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.8004e-05 \n",
      "Epoch 344/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.1698e-05 \n",
      "Epoch 345/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.3303e-05 \n",
      "Epoch 346/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.1647e-05 \n",
      "Epoch 347/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.0157e-05 \n",
      "Epoch 348/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.3958e-05 \n",
      "Epoch 349/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.2015e-05 \n",
      "Epoch 350/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.1917e-05 \n",
      "Epoch 351/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.2134e-05 \n",
      "Epoch 352/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.2002e-05 \n",
      "Epoch 353/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.0835e-05 \n",
      "Epoch 354/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3250e-05 \n",
      "Epoch 355/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.0364e-05 \n",
      "Epoch 356/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.0429e-05 \n",
      "Epoch 357/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.0693e-05 \n",
      "Epoch 358/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.8495e-05 \n",
      "Epoch 359/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.2539e-05 \n",
      "Epoch 360/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.6808e-05 \n",
      "Epoch 361/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.1396e-05 \n",
      "Epoch 362/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.9390e-05 \n",
      "Epoch 363/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.0235e-05 \n",
      "Epoch 364/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.1452e-05 \n",
      "Epoch 365/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.1386e-05 \n",
      "Epoch 366/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.4024e-05 \n",
      "Epoch 367/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.7975e-05 \n",
      "Epoch 368/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 4.6803e-05 \n",
      "Epoch 369/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.3200e-05 \n",
      "Epoch 370/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.6862e-05 \n",
      "Epoch 371/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.3044e-05 \n",
      "Epoch 372/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.3684e-05 \n",
      "Epoch 373/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.7223e-05 \n",
      "Epoch 374/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.3110e-05 \n",
      "Epoch 375/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.3799e-05 \n",
      "Epoch 376/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.3324e-05 \n",
      "Epoch 377/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6395e-05\n",
      "Epoch 378/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.5688e-05 \n",
      "Epoch 379/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.2065e-05 \n",
      "Epoch 380/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.3284e-05 \n",
      "Epoch 381/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.3224e-05 \n",
      "Epoch 382/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 8.2212e-06 \n",
      "Epoch 383/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 9.9952e-06 \n",
      "Epoch 384/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 8.1907e-06 \n",
      "Epoch 385/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 8.9031e-06 \n",
      "Epoch 386/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 9.5568e-06 \n",
      "Epoch 387/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.0639e-05 \n",
      "Epoch 388/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 8.4591e-06 \n",
      "Epoch 389/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6217e-05\n",
      "Epoch 390/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 3.1903e-05 \n",
      "Epoch 391/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.3822e-05 \n",
      "Epoch 392/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 5.4650e-05 \n",
      "Epoch 393/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.5455e-05 \n",
      "Epoch 394/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.2697e-05 \n",
      "Epoch 395/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.2968e-05 \n",
      "Epoch 396/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.5709e-05 \n",
      "Epoch 397/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.5491e-05 \n",
      "Epoch 398/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.3111e-05 \n",
      "Epoch 399/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.8850e-06 \n",
      "Epoch 400/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.4368e-05 \n",
      "Epoch 401/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 1.5991e-05\n",
      "Epoch 402/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.2660e-05 \n",
      "Epoch 403/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 8.4858e-06 \n",
      "Epoch 404/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.0248e-05\n",
      "Epoch 405/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 9.0677e-06 \n",
      "Epoch 406/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 9.5411e-06 \n",
      "Epoch 407/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.9788e-05 \n",
      "Epoch 408/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.0619e-05 \n",
      "Epoch 409/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 8.1255e-06\n",
      "Epoch 410/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6321e-05 \n",
      "Epoch 411/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.1521e-05 \n",
      "Epoch 412/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 7.5793e-06 \n",
      "Epoch 413/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 8.5466e-06 \n",
      "Epoch 414/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 8.2439e-06\n",
      "Epoch 415/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 9.5477e-06 \n",
      "Epoch 416/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 7.7720e-06 \n",
      "Epoch 417/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.5597e-05\n",
      "Epoch 418/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.2375e-05 \n",
      "Epoch 419/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 7.4406e-06 \n",
      "Epoch 420/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.2784e-05 \n",
      "Epoch 421/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.2777e-05 \n",
      "Epoch 422/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.0058e-05\n",
      "Epoch 423/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 8.8745e-06 \n",
      "Epoch 424/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.2577e-05\n",
      "Epoch 425/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.1544e-05\n",
      "Epoch 426/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.8046e-05\n",
      "Epoch 427/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.7266e-05\n",
      "Epoch 428/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.1926e-05 \n",
      "Epoch 429/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.1799e-05\n",
      "Epoch 430/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 9.4356e-06 \n",
      "Epoch 431/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 8.5007e-06 \n",
      "Epoch 432/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.7878e-06 \n",
      "Epoch 433/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 6.8022e-06 \n",
      "Epoch 434/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.6642e-06 \n",
      "Epoch 435/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 5.4934e-06\n",
      "Epoch 436/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 5.9977e-06\n",
      "Epoch 437/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 6.3721e-06 \n",
      "Epoch 438/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 9.2593e-06 \n",
      "Epoch 439/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.0049e-05 \n",
      "Epoch 440/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.5354e-05 \n",
      "Epoch 441/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 9.7378e-06 \n",
      "Epoch 442/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.1768e-05 \n",
      "Epoch 443/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 8.1183e-06 \n",
      "Epoch 444/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.4915e-06 \n",
      "Epoch 445/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.6872e-06 \n",
      "Epoch 446/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 5.7755e-06 \n",
      "Epoch 447/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.9754e-06 \n",
      "Epoch 448/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.6617e-06 \n",
      "Epoch 449/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 8.8047e-06 \n",
      "Epoch 450/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.0155e-06 \n",
      "Epoch 451/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 4.3742e-06 \n",
      "Epoch 452/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 6.5864e-06 \n",
      "Epoch 453/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.2578e-06 \n",
      "Epoch 454/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 5.8563e-06\n",
      "Epoch 455/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.9618e-06 \n",
      "Epoch 456/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.1447e-05 \n",
      "Epoch 457/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 7.9629e-06 \n",
      "Epoch 458/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.7571e-05 \n",
      "Epoch 459/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.8438e-05\n",
      "Epoch 460/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 7.1775e-06\n",
      "Epoch 461/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.3253e-05 \n",
      "Epoch 462/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.5618e-06 \n",
      "Epoch 463/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 8.4064e-06\n",
      "Epoch 464/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 5.7737e-06 \n",
      "Epoch 465/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 4.2462e-06 \n",
      "Epoch 466/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 5.4166e-06 \n",
      "Epoch 467/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.4497e-06 \n",
      "Epoch 468/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.1920e-06 \n",
      "Epoch 469/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 4.2071e-06\n",
      "Epoch 470/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.3474e-06 \n",
      "Epoch 471/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 3.9069e-06 \n",
      "Epoch 472/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 5.1810e-06 \n",
      "Epoch 473/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.0709e-06 \n",
      "Epoch 474/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 7.6537e-06 \n",
      "Epoch 475/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.1104e-05 \n",
      "Epoch 476/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 8.7959e-06 \n",
      "Epoch 477/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 6.7394e-06 \n",
      "Epoch 478/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.7366e-06 \n",
      "Epoch 479/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.0126e-06 \n",
      "Epoch 480/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.8574e-06 \n",
      "Epoch 481/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 5.5826e-06\n",
      "Epoch 482/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.9726e-05\n",
      "Epoch 483/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 1.6894e-05\n",
      "Epoch 484/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 7.6347e-06\n",
      "Epoch 485/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.2642e-05 \n",
      "Epoch 486/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.8407e-05 \n",
      "Epoch 487/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 9.4692e-06 \n",
      "Epoch 488/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.9809e-05\n",
      "Epoch 489/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.9240e-06 \n",
      "Epoch 490/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 9.0821e-06 \n",
      "Epoch 491/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.3462e-06 \n",
      "Epoch 492/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 7.5380e-06\n",
      "Epoch 493/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.5693e-06 \n",
      "Epoch 494/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.6061e-06 \n",
      "Epoch 495/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.8548e-06 \n",
      "Epoch 496/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 4.8801e-06 \n",
      "Epoch 497/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.4057e-06 \n",
      "Epoch 498/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 5.9558e-06 \n",
      "Epoch 499/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 7.5976e-06 \n",
      "Epoch 500/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.3653e-06 \n",
      "Epoch 501/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.8830e-06 \n",
      "Epoch 502/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.8736e-06 \n",
      "Epoch 503/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.3205e-06 \n",
      "Epoch 504/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.6220e-06 \n",
      "Epoch 505/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 3.8508e-06\n",
      "Epoch 506/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 3.8182e-06 \n",
      "Epoch 507/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.2073e-06 \n",
      "Epoch 508/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.9246e-06 \n",
      "Epoch 509/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.2440e-06 \n",
      "Epoch 510/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 5.9617e-06 \n",
      "Epoch 511/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.5523e-06 \n",
      "Epoch 512/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 5.6970e-06 \n",
      "Epoch 513/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 4.8768e-06 \n",
      "Epoch 514/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.5836e-06 \n",
      "Epoch 515/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.7882e-06 \n",
      "Epoch 516/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.0504e-06 \n",
      "Epoch 517/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.6496e-06 \n",
      "Epoch 518/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.4907e-06 \n",
      "Epoch 519/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.6317e-06 \n",
      "Epoch 520/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 2.7843e-06 \n",
      "Epoch 521/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.7306e-06 \n",
      "Epoch 522/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.0961e-06 \n",
      "Epoch 523/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 2.9702e-06 \n",
      "Epoch 524/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.8718e-06 \n",
      "Epoch 525/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 4.5416e-06 \n",
      "Epoch 526/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 5.1071e-06 \n",
      "Epoch 527/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 3.1480e-06 \n",
      "Epoch 528/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.5290e-06 \n",
      "Epoch 529/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.7563e-06 \n",
      "Epoch 530/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 8.6908e-06 \n",
      "Epoch 531/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.0538e-05 \n",
      "Epoch 532/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.2911e-06 \n",
      "Epoch 533/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.1942e-06 \n",
      "Epoch 534/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 7.8673e-06 \n",
      "Epoch 535/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.7040e-06 \n",
      "Epoch 536/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.4222e-05 \n",
      "Epoch 537/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.7272e-05 \n",
      "Epoch 538/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.6991e-06 \n",
      "Epoch 539/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.9705e-06 \n",
      "Epoch 540/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.5731e-06 \n",
      "Epoch 541/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.9839e-06 \n",
      "Epoch 542/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.9014e-06 \n",
      "Epoch 543/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.3295e-06 \n",
      "Epoch 544/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.6015e-06 \n",
      "Epoch 545/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.5169e-06 \n",
      "Epoch 546/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.3667e-06 \n",
      "Epoch 547/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.2694e-06 \n",
      "Epoch 548/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 7.4502e-06 \n",
      "Epoch 549/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 7.1767e-06 \n",
      "Epoch 550/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 6.7991e-06 \n",
      "Epoch 551/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.5018e-06 \n",
      "Epoch 552/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 8.4612e-06 \n",
      "Epoch 553/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.7156e-06 \n",
      "Epoch 554/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 3.6711e-06 \n",
      "Epoch 555/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 2.6200e-06 \n",
      "Epoch 556/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.1079e-06 \n",
      "Epoch 557/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 2.8368e-06 \n",
      "Epoch 558/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.7889e-06 \n",
      "Epoch 559/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.9164e-06\n",
      "Epoch 560/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.3198e-06 \n",
      "Epoch 561/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.0490e-06 \n",
      "Epoch 562/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 7.4545e-06 \n",
      "Epoch 563/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 8.3995e-06 \n",
      "Epoch 564/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.8870e-06 \n",
      "Epoch 565/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.1293e-06 \n",
      "Epoch 566/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 8.3319e-06 \n",
      "Epoch 567/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.7462e-06 \n",
      "Epoch 568/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.5790e-06 \n",
      "Epoch 569/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 9.3068e-06 \n",
      "Epoch 570/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.7693e-06 \n",
      "Epoch 571/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.5528e-06\n",
      "Epoch 572/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.6887e-06 \n",
      "Epoch 573/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.2335e-06 \n",
      "Epoch 574/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.1901e-06 \n",
      "Epoch 575/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.2852e-06 \n",
      "Epoch 576/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.3849e-06 \n",
      "Epoch 577/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.2942e-06 \n",
      "Epoch 578/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.3358e-06 \n",
      "Epoch 579/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.7619e-06 \n",
      "Epoch 580/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 5.5886e-06 \n",
      "Epoch 581/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.7173e-06 \n",
      "Epoch 582/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 7.2514e-06\n",
      "Epoch 583/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 4.0787e-06 \n",
      "Epoch 584/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.5837e-06 \n",
      "Epoch 585/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.7809e-06 \n",
      "Epoch 586/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.0692e-06 \n",
      "Epoch 587/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.2080e-06 \n",
      "Epoch 588/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.9167e-06 \n",
      "Epoch 589/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.7841e-06 \n",
      "Epoch 590/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.7811e-06 \n",
      "Epoch 591/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 5.4409e-06 \n",
      "Epoch 592/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.8805e-06 \n",
      "Epoch 593/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.9807e-06 \n",
      "Epoch 594/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.7398e-06 \n",
      "Epoch 595/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 3.9384e-06 \n",
      "Epoch 596/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.5862e-06 \n",
      "Epoch 597/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.7278e-06 \n",
      "Epoch 598/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.3585e-06 \n",
      "Epoch 599/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 1.0760e-05 \n",
      "Epoch 600/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.7026e-06 \n",
      "Epoch 601/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 8.2241e-06 \n",
      "Epoch 602/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 9.9880e-06 \n",
      "Epoch 603/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 5.1946e-06 \n",
      "Epoch 604/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.7161e-06 \n",
      "Epoch 605/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 3.4084e-06 \n",
      "Epoch 606/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.4208e-06 \n",
      "Epoch 607/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.7737e-06 \n",
      "Epoch 608/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.8652e-06 \n",
      "Epoch 609/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.9311e-06 \n",
      "Epoch 610/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.2906e-06 \n",
      "Epoch 611/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.0693e-06 \n",
      "Epoch 612/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 2.6149e-06 \n",
      "Epoch 613/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.0607e-06 \n",
      "Epoch 614/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 3.2213e-06 \n",
      "Epoch 615/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.0039e-06 \n",
      "Epoch 616/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.3136e-06 \n",
      "Epoch 617/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.3324e-06 \n",
      "Epoch 618/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.5347e-06 \n",
      "Epoch 619/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.8586e-06 \n",
      "Epoch 620/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.3423e-06 \n",
      "Epoch 621/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.5358e-06 \n",
      "Epoch 622/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.9172e-06 \n",
      "Epoch 623/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.7619e-06 \n",
      "Epoch 624/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.9616e-06 \n",
      "Epoch 625/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.7085e-06 \n",
      "Epoch 626/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.7302e-06 \n",
      "Epoch 627/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.8350e-06 \n",
      "Epoch 628/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.0240e-06 \n",
      "Epoch 629/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.8117e-06 \n",
      "Epoch 630/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.8277e-06 \n",
      "Epoch 631/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.3921e-06 \n",
      "Epoch 632/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.5684e-06 \n",
      "Epoch 633/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.8345e-06 \n",
      "Epoch 634/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.8362e-06 \n",
      "Epoch 635/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 1.6261e-06 \n",
      "Epoch 636/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.2445e-06 \n",
      "Epoch 637/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.4042e-06 \n",
      "Epoch 638/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.5685e-06\n",
      "Epoch 639/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.9272e-06 \n",
      "Epoch 640/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.5840e-06 \n",
      "Epoch 641/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.9507e-06 \n",
      "Epoch 642/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 1.8380e-06 \n",
      "Epoch 643/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.5081e-06 \n",
      "Epoch 644/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.4568e-06 \n",
      "Epoch 645/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.9034e-06 \n",
      "Epoch 646/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.2749e-06 \n",
      "Epoch 647/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.2449e-06 \n",
      "Epoch 648/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.9635e-06 \n",
      "Epoch 649/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.1018e-06 \n",
      "Epoch 650/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.4062e-06 \n",
      "Epoch 651/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 2.3975e-06 \n",
      "Epoch 652/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.4476e-06 \n",
      "Epoch 653/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.1567e-06 \n",
      "Epoch 654/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.1543e-06 \n",
      "Epoch 655/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.6797e-06 \n",
      "Epoch 656/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 1.6971e-06 \n",
      "Epoch 657/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.9143e-06 \n",
      "Epoch 658/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.8953e-06 \n",
      "Epoch 659/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.5735e-06 \n",
      "Epoch 660/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 2.2571e-06 \n",
      "Epoch 661/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.8822e-06 \n",
      "Epoch 662/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.2687e-06 \n",
      "Epoch 663/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.1163e-06 \n",
      "Epoch 664/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.6503e-06 \n",
      "Epoch 665/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.9553e-06 \n",
      "Epoch 666/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.2289e-05\n",
      "Epoch 667/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.2822e-05 \n",
      "Epoch 668/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.4622e-05 \n",
      "Epoch 669/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.2552e-05 \n",
      "Epoch 670/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.5762e-05 \n",
      "Epoch 671/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 9.4611e-06 \n",
      "Epoch 672/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.0450e-05 \n",
      "Epoch 673/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 8.5458e-06 \n",
      "Epoch 674/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 7.3915e-06 \n",
      "Epoch 675/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 8.5954e-06 \n",
      "Epoch 676/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.8454e-06 \n",
      "Epoch 677/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.0601e-06 \n",
      "Epoch 678/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.5975e-06 \n",
      "Epoch 679/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.9537e-06 \n",
      "Epoch 680/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.2085e-06 \n",
      "Epoch 681/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.4179e-06 \n",
      "Epoch 682/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.8344e-06 \n",
      "Epoch 683/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.0034e-06 \n",
      "Epoch 684/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.8819e-06 \n",
      "Epoch 685/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.5315e-06 \n",
      "Epoch 686/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.1496e-06 \n",
      "Epoch 687/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.0719e-06 \n",
      "Epoch 688/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.7264e-06 \n",
      "Epoch 689/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.2837e-06 \n",
      "Epoch 690/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.7142e-06 \n",
      "Epoch 691/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 6.0628e-06 \n",
      "Epoch 692/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.9117e-06 \n",
      "Epoch 693/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.9844e-06 \n",
      "Epoch 694/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.3993e-06 \n",
      "Epoch 695/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.8159e-06 \n",
      "Epoch 696/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.2360e-06 \n",
      "Epoch 697/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.9459e-06 \n",
      "Epoch 698/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.3636e-06 \n",
      "Epoch 699/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.3283e-06 \n",
      "Epoch 700/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.8184e-06\n",
      "Epoch 701/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.1013e-06 \n",
      "Epoch 702/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.1885e-06 \n",
      "Epoch 703/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.7524e-06 \n",
      "Epoch 704/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.7818e-06 \n",
      "Epoch 705/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.9630e-06 \n",
      "Epoch 706/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 4.0101e-06 \n",
      "Epoch 707/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 2.2956e-06 \n",
      "Epoch 708/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.1672e-06 \n",
      "Epoch 709/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.8356e-06 \n",
      "Epoch 710/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 9.0605e-06 \n",
      "Epoch 711/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.2844e-05 \n",
      "Epoch 712/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.3092e-05 \n",
      "Epoch 713/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.2071e-05 \n",
      "Epoch 714/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.2912e-05 \n",
      "Epoch 715/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.1044e-05 \n",
      "Epoch 716/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.4109e-05 \n",
      "Epoch 717/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.8163e-05 \n",
      "Epoch 718/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.0690e-05 \n",
      "Epoch 719/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.7708e-05 \n",
      "Epoch 720/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.4283e-05 \n",
      "Epoch 721/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.7845e-05 \n",
      "Epoch 722/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.3723e-05 \n",
      "Epoch 723/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.4045e-05\n",
      "Epoch 724/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.6194e-05 \n",
      "Epoch 725/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.4823e-05 \n",
      "Epoch 726/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.3088e-05 \n",
      "Epoch 727/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.4169e-05 \n",
      "Epoch 728/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 7.0255e-06 \n",
      "Epoch 729/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.2534e-05 \n",
      "Epoch 730/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.5898e-05 \n",
      "Epoch 731/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.0949e-05 \n",
      "Epoch 732/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.0382e-05 \n",
      "Epoch 733/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 8.0896e-06 \n",
      "Epoch 734/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.3886e-05 \n",
      "Epoch 735/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.2621e-05 \n",
      "Epoch 736/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 8.9309e-06 \n",
      "Epoch 737/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 9.4252e-06 \n",
      "Epoch 738/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.0607e-05 \n",
      "Epoch 739/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.7770e-05 \n",
      "Epoch 740/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 5.0129e-06 \n",
      "Epoch 741/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.0517e-05 \n",
      "Epoch 742/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.4198e-06 \n",
      "Epoch 743/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.4988e-06\n",
      "Epoch 744/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.8047e-06 \n",
      "Epoch 745/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 5.3794e-06 \n",
      "Epoch 746/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.7509e-06 \n",
      "Epoch 747/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.7885e-06 \n",
      "Epoch 748/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.3495e-06 \n",
      "Epoch 749/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 3.9232e-06 \n",
      "Epoch 750/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.8799e-06 \n",
      "Epoch 751/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.0901e-06 \n",
      "Epoch 752/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.4944e-06 \n",
      "Epoch 753/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 3.6111e-06 \n",
      "Epoch 754/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.1929e-06 \n",
      "Epoch 755/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 7.8744e-06 \n",
      "Epoch 756/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.1246e-06 \n",
      "Epoch 757/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.3158e-05 \n",
      "Epoch 758/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.0137e-05 \n",
      "Epoch 759/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 8.2169e-06 \n",
      "Epoch 760/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 8.0175e-06 \n",
      "Epoch 761/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 1.2747e-05 \n",
      "Epoch 762/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.2183e-05 \n",
      "Epoch 763/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.3844e-05 \n",
      "Epoch 764/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.4555e-05 \n",
      "Epoch 765/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.6484e-05 \n",
      "Epoch 766/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.0431e-05 \n",
      "Epoch 767/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.2736e-05\n",
      "Epoch 768/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3777e-05 \n",
      "Epoch 769/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.2669e-05 \n",
      "Epoch 770/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 8.9111e-06 \n",
      "Epoch 771/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.1085e-05 \n",
      "Epoch 772/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.1088e-06 \n",
      "Epoch 773/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.2411e-06 \n",
      "Epoch 774/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.7985e-06 \n",
      "Epoch 775/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.5731e-06 \n",
      "Epoch 776/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.2067e-06 \n",
      "Epoch 777/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.1967e-06 \n",
      "Epoch 778/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.7719e-06 \n",
      "Epoch 779/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.6987e-06 \n",
      "Epoch 780/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.0321e-06 \n",
      "Epoch 781/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.9125e-06 \n",
      "Epoch 782/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.8542e-06 \n",
      "Epoch 783/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.0884e-06 \n",
      "Epoch 784/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.1018e-06 \n",
      "Epoch 785/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.9408e-06 \n",
      "Epoch 786/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.2542e-06 \n",
      "Epoch 787/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 4.1087e-06 \n",
      "Epoch 788/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.3695e-06 \n",
      "Epoch 789/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.0446e-06 \n",
      "Epoch 790/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 4.0345e-06\n",
      "Epoch 791/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.0624e-06 \n",
      "Epoch 792/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.0943e-06 \n",
      "Epoch 793/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.3078e-06 \n",
      "Epoch 794/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.3180e-06 \n",
      "Epoch 795/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.8848e-06 \n",
      "Epoch 796/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.5505e-06 \n",
      "Epoch 797/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.9292e-06 \n",
      "Epoch 798/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.6924e-06 \n",
      "Epoch 799/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.9525e-06 \n",
      "Epoch 800/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.4028e-06 \n",
      "Epoch 801/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.2438e-06 \n",
      "Epoch 802/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 9.3064e-07 \n",
      "Epoch 803/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 9.8957e-07 \n",
      "Epoch 804/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.4502e-06\n",
      "Epoch 805/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 8.5319e-07 \n",
      "Epoch 806/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.7161e-06 \n",
      "Epoch 807/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.3525e-06 \n",
      "Epoch 808/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 8.5847e-06 \n",
      "Epoch 809/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.1095e-06 \n",
      "Epoch 810/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 8.6838e-06 \n",
      "Epoch 811/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 6.6293e-06 \n",
      "Epoch 812/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.3855e-05 \n",
      "Epoch 813/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 8.7322e-06 \n",
      "Epoch 814/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 9.8728e-06 \n",
      "Epoch 815/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.4912e-06 \n",
      "Epoch 816/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 6.6976e-06\n",
      "Epoch 817/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 7.4110e-06 \n",
      "Epoch 818/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.7365e-06 \n",
      "Epoch 819/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 8.4000e-06 \n",
      "Epoch 820/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 3.2914e-06 \n",
      "Epoch 821/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.1481e-06 \n",
      "Epoch 822/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.0432e-06 \n",
      "Epoch 823/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.3719e-06 \n",
      "Epoch 824/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.8827e-06\n",
      "Epoch 825/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.0206e-05 \n",
      "Epoch 826/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 8.9966e-06 \n",
      "Epoch 827/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 6.2167e-06 \n",
      "Epoch 828/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.0832e-05 \n",
      "Epoch 829/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.0685e-05 \n",
      "Epoch 830/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.0648e-06 \n",
      "Epoch 831/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 4.5029e-06 \n",
      "Epoch 832/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.4052e-06 \n",
      "Epoch 833/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.4441e-06 \n",
      "Epoch 834/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.6120e-06 \n",
      "Epoch 835/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.8977e-06 \n",
      "Epoch 836/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.6255e-06 \n",
      "Epoch 837/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.8670e-06 \n",
      "Epoch 838/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 2.1632e-06 \n",
      "Epoch 839/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.9480e-06\n",
      "Epoch 840/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.4498e-06 \n",
      "Epoch 841/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.1393e-06 \n",
      "Epoch 842/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.9062e-06 \n",
      "Epoch 843/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6707e-06\n",
      "Epoch 844/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.7183e-06 \n",
      "Epoch 845/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.1813e-06 \n",
      "Epoch 846/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.4905e-06 \n",
      "Epoch 847/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 4.3421e-06 \n",
      "Epoch 848/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.5949e-06 \n",
      "Epoch 849/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.0388e-06 \n",
      "Epoch 850/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.2834e-07 \n",
      "Epoch 851/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.3224e-07 \n",
      "Epoch 852/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.6268e-07 \n",
      "Epoch 853/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.0300e-06 \n",
      "Epoch 854/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.1880e-06 \n",
      "Epoch 855/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.9851e-06 \n",
      "Epoch 856/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 9.5541e-07 \n",
      "Epoch 857/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 8.0900e-07 \n",
      "Epoch 858/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 8.2201e-07 \n",
      "Epoch 859/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.2732e-06 \n",
      "Epoch 860/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4370e-06 \n",
      "Epoch 861/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.1779e-06 \n",
      "Epoch 862/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 7.2195e-07\n",
      "Epoch 863/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.0262e-06 \n",
      "Epoch 864/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.3294e-06 \n",
      "Epoch 865/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.8465e-07 \n",
      "Epoch 866/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.5103e-06\n",
      "Epoch 867/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.2957e-06 \n",
      "Epoch 868/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.3841e-06 \n",
      "Epoch 869/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.4671e-06 \n",
      "Epoch 870/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 9.3316e-07 \n",
      "Epoch 871/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.7511e-06 \n",
      "Epoch 872/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.0469e-06 \n",
      "Epoch 873/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 8.2529e-07\n",
      "Epoch 874/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.7572e-06 \n",
      "Epoch 875/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.7149e-06\n",
      "Epoch 876/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.9681e-06 \n",
      "Epoch 877/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.1716e-06 \n",
      "Epoch 878/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 3.6542e-06 \n",
      "Epoch 879/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 8.1271e-06 \n",
      "Epoch 880/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.3910e-06 \n",
      "Epoch 881/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.7076e-06 \n",
      "Epoch 882/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 9.1523e-06 \n",
      "Epoch 883/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 8.2521e-06\n",
      "Epoch 884/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 6.7043e-06 \n",
      "Epoch 885/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.9452e-06 \n",
      "Epoch 886/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.1599e-06 \n",
      "Epoch 887/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.6143e-06 \n",
      "Epoch 888/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.8048e-06 \n",
      "Epoch 889/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.5840e-06 \n",
      "Epoch 890/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.5148e-06 \n",
      "Epoch 891/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.3518e-05 \n",
      "Epoch 892/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 8.3979e-06 \n",
      "Epoch 893/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 7.2315e-06\n",
      "Epoch 894/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.9547e-05 \n",
      "Epoch 895/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.5114e-05 \n",
      "Epoch 896/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.2284e-05 \n",
      "Epoch 897/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.1761e-05 \n",
      "Epoch 898/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.2282e-05 \n",
      "Epoch 899/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.6758e-05 \n",
      "Epoch 900/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.6937e-05 \n",
      "Epoch 901/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.0868e-05 \n",
      "Epoch 902/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.3649e-05 \n",
      "Epoch 903/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.3699e-05 \n",
      "Epoch 904/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 8.9316e-06 \n",
      "Epoch 905/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 6.5307e-06 \n",
      "Epoch 906/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.7572e-06 \n",
      "Epoch 907/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.7474e-06 \n",
      "Epoch 908/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 7.7728e-06 \n",
      "Epoch 909/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.2587e-05 \n",
      "Epoch 910/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.6986e-06 \n",
      "Epoch 911/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.2497e-05 \n",
      "Epoch 912/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.4422e-05 \n",
      "Epoch 913/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.8230e-05 \n",
      "Epoch 914/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.4606e-05 \n",
      "Epoch 915/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.6631e-05 \n",
      "Epoch 916/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 9.7853e-06 \n",
      "Epoch 917/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 8.5378e-06 \n",
      "Epoch 918/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.8542e-05 \n",
      "Epoch 919/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 9.6043e-06 \n",
      "Epoch 920/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.6733e-05 \n",
      "Epoch 921/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.8363e-05 \n",
      "Epoch 922/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.6889e-05 \n",
      "Epoch 923/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.1091e-05 \n",
      "Epoch 924/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 9.5084e-06 \n",
      "Epoch 925/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 4.7364e-06 \n",
      "Epoch 926/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.8930e-06 \n",
      "Epoch 927/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.6038e-06 \n",
      "Epoch 928/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.7046e-06 \n",
      "Epoch 929/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 4.7247e-06 \n",
      "Epoch 930/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 5.1892e-06\n",
      "Epoch 931/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.3197e-06 \n",
      "Epoch 932/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 7.2359e-06 \n",
      "Epoch 933/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.9561e-06 \n",
      "Epoch 934/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.0407e-06 \n",
      "Epoch 935/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.6655e-06 \n",
      "Epoch 936/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.2037e-06 \n",
      "Epoch 937/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 8.4649e-06 \n",
      "Epoch 938/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.5370e-06 \n",
      "Epoch 939/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.1661e-06 \n",
      "Epoch 940/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 4.7621e-06 \n",
      "Epoch 941/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.1978e-06 \n",
      "Epoch 942/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.1554e-06 \n",
      "Epoch 943/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.5903e-06 \n",
      "Epoch 944/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.0566e-06 \n",
      "Epoch 945/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 9.9364e-06 \n",
      "Epoch 946/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.0657e-05 \n",
      "Epoch 947/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.4218e-05 \n",
      "Epoch 948/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.0172e-05 \n",
      "Epoch 949/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.6062e-05 \n",
      "Epoch 950/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 7.5326e-06 \n",
      "Epoch 951/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.1216e-05 \n",
      "Epoch 952/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.4709e-05 \n",
      "Epoch 953/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.4623e-05 \n",
      "Epoch 954/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 4.9089e-05 \n",
      "Epoch 955/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.5599e-05 \n",
      "Epoch 956/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 7.4705e-05 \n",
      "Epoch 957/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.4920e-05 \n",
      "Epoch 958/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.1801e-05 \n",
      "Epoch 959/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.4387e-06 \n",
      "Epoch 960/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 3.0957e-06 \n",
      "Epoch 961/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 3.2011e-06 \n",
      "Epoch 962/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.4521e-06 \n",
      "Epoch 963/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.3785e-06 \n",
      "Epoch 964/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.6119e-06 \n",
      "Epoch 965/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 9.8684e-06 \n",
      "Epoch 966/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 7.6129e-06 \n",
      "Epoch 967/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 6.9854e-06 \n",
      "Epoch 968/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.3422e-05 \n",
      "Epoch 969/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 6.8500e-06 \n",
      "Epoch 970/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.0071e-05 \n",
      "Epoch 971/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.0294e-05\n",
      "Epoch 972/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.1861e-05 \n",
      "Epoch 973/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.7095e-05 \n",
      "Epoch 974/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.4835e-05 \n",
      "Epoch 975/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3.2015e-05 \n",
      "Epoch 976/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.0460e-05 \n",
      "Epoch 977/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 4.1357e-05 \n",
      "Epoch 978/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.8371e-05 \n",
      "Epoch 979/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.4570e-05 \n",
      "Epoch 980/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.5906e-05 \n",
      "Epoch 981/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.3150e-05 \n",
      "Epoch 982/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.4536e-05 \n",
      "Epoch 983/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.3985e-05 \n",
      "Epoch 984/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.6758e-05 \n",
      "Epoch 985/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.0710e-05 \n",
      "Epoch 986/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 9.1180e-06 \n",
      "Epoch 987/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.8084e-05 \n",
      "Epoch 988/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 8.9812e-06 \n",
      "Epoch 989/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1.1485e-05 \n",
      "Epoch 990/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 9.4130e-06 \n",
      "Epoch 991/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 5.6441e-06 \n",
      "Epoch 992/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 3.6421e-06 \n",
      "Epoch 993/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 3.7034e-06 \n",
      "Epoch 994/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.7829e-06 \n",
      "Epoch 995/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 2.4065e-05 \n",
      "Epoch 996/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.0293e-05 \n",
      "Epoch 997/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 7.5987e-06 \n",
      "Epoch 998/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.6095e-06 \n",
      "Epoch 999/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.1852e-05 \n",
      "Epoch 1000/1000\n",
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 1.2325e-05 \n",
      "Minimum error : 5.083048790766043e-07, Maximum error: 0.1760162115097046\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(input_data, output_data, epochs=1000,verbose=1)\n",
    "print('Minimum error : {}, Maximum error: {}'.format(np.min(history.history['loss']),np.max(history.history['loss'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b81ec389",
   "metadata": {},
   "source": [
    "We can print the number of parameters in the ANN model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddfb054b",
   "metadata": {},
   "source": [
    "Once we have trained the ANN we can use it to do a prediction, here with the input data used for the training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b5c58de5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(input_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f5f6c86",
   "metadata": {},
   "source": [
    "The next cell plots the results from the ANN. The left graph shows the evolution of the loss metric as function of epochs while the right graph shows the predicted values compared to the measured data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "245ec6e3-0d98-4a58-8ca9-02ec0aae8a9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (15, 7)\n",
    "fig,axs = plt.subplots(1,2)\n",
    "axs[0].semilogy(history.history['loss'],'k',label='training')\n",
    "axs[0].set_xlabel('Epoch')\n",
    "axs[0].set_ylabel('Error')\n",
    "axs[0].legend()\n",
    "axs[0].grid()\n",
    "# Plot predictions from ANN\n",
    "axs[1].scatter(output_data,prediction,c='k',label='Predictions')\n",
    "axs[1].plot([np.min(output_data),np.max(output_data)],[np.min(prediction),np.max(prediction)],'r',label='Reference')\n",
    "axs[1].set_xlabel('Measured')\n",
    "axs[1].set_ylabel('Predicted')\n",
    "axs[1].legend()\n",
    "axs[1].grid()\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "506c7d01",
   "metadata": {},
   "source": [
    "It is good practice to save the trained ANN since big networks and/or large amount of data can be time consuming to train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "95aed764-cb95-43cb-a6e8-d94fdfece4a9",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Invalid filepath extension for saving. Please add either a `.keras` extension for the native Keras format (recommended) or a `.h5` extension. Use `model.export(filepath)` if you want to export a SavedModel for use with TFLite/TFServing/etc. Received: filepath=model_small.",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mmodel_small\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[39m, in \u001b[36mfilter_traceback.<locals>.error_handler\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m    119\u001b[39m     filtered_tb = _process_traceback_frames(e.__traceback__)\n\u001b[32m    120\u001b[39m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[32m    121\u001b[39m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m122\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m e.with_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m    123\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m    124\u001b[39m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\keras\\src\\saving\\saving_api.py:114\u001b[39m, in \u001b[36msave_model\u001b[39m\u001b[34m(model, filepath, overwrite, zipped, **kwargs)\u001b[39m\n\u001b[32m    110\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(filepath).endswith((\u001b[33m\"\u001b[39m\u001b[33m.h5\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33m.hdf5\u001b[39m\u001b[33m\"\u001b[39m)):\n\u001b[32m    111\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m legacy_h5_format.save_model_to_hdf5(\n\u001b[32m    112\u001b[39m         model, filepath, overwrite, include_optimizer\n\u001b[32m    113\u001b[39m     )\n\u001b[32m--> \u001b[39m\u001b[32m114\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m    115\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mInvalid filepath extension for saving. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    116\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mPlease add either a `.keras` extension for the native Keras \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    117\u001b[39m     \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mformat (recommended) or a `.h5` extension. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    118\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mUse `model.export(filepath)` if you want to export a SavedModel \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    119\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mfor use with TFLite/TFServing/etc. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    120\u001b[39m     \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mReceived: filepath=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfilepath\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    121\u001b[39m )\n",
      "\u001b[31mValueError\u001b[39m: Invalid filepath extension for saving. Please add either a `.keras` extension for the native Keras format (recommended) or a `.h5` extension. Use `model.export(filepath)` if you want to export a SavedModel for use with TFLite/TFServing/etc. Received: filepath=model_small."
     ]
    }
   ],
   "source": [
    "model.save('model_small')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6b6bfb1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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