MLP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Otimizando os hiper-parâmetros c/ Random Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importando as libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from scipy.stats import reciprocal\n",
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Aquisição dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_mnist = keras.datasets.mnist\n",
    "(X_train, y_train), (X_test, y_test) = number_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(60000,)\n",
      "(10000, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Pré-processamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_valid, X_train = X_train[:5000] / 255., X_train[5000:] / 255.\n",
    "y_valid, y_train = y_train[:5000], y_train[5000:]\n",
    "X_test = X_test / 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55000, 28, 28)\n",
      "(55000,)\n",
      "(10000, 28, 28)\n",
      "(10000,)\n",
      "(5000, 28, 28)\n",
      "(5000,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)\n",
    "print(X_valid.shape)\n",
    "print(y_valid.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Construindo e treinando o modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(n_hidden=1, n_neurons=300, learning_rate=3e-3, input_shape=[28, 28]):\n",
    "    model = keras.models.Sequential()\n",
    "    model.add(keras.layers.Flatten(input_shape= input_shape))\n",
    "    for layer in range(n_hidden):\n",
    "        model.add(keras.layers.Dense(n_neurons, activation=\"relu\"))    \n",
    "    model.add(keras.layers.Dense(10, activation=\"softmax\"))\n",
    "    optimizer = keras.optimizers.SGD(lr=learning_rate)\n",
    "    model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer, metrics=[\"accuracy\"]) \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.wrappers.scikit_learn.KerasClassifier(build_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "1719/1719 [==============================] - 5s 2ms/step - loss: 1.5201 - accuracy: 0.6130 - val_loss: 0.5938 - val_accuracy: 0.8656\n",
      "Epoch 2/30\n",
      "1719/1719 [==============================] - 3s 2ms/step - loss: 0.5518 - accuracy: 0.8659 - val_loss: 0.4298 - val_accuracy: 0.8918\n",
      "Epoch 3/30\n",
      "1719/1719 [==============================] - 3s 2ms/step - loss: 0.4307 - accuracy: 0.8863 - val_loss: 0.3683 - val_accuracy: 0.9024\n",
      "Epoch 4/30\n",
      "1719/1719 [==============================] - 3s 2ms/step - loss: 0.3742 - accuracy: 0.8992 - val_loss: 0.3346 - val_accuracy: 0.9108\n",
      "Epoch 5/30\n",
      "1719/1719 [==============================] - 3s 2ms/step - loss: 0.3472 - accuracy: 0.9049 - val_loss: 0.3117 - val_accuracy: 0.9158\n",
      "Epoch 6/30\n",
      "1719/1719 [==============================] - 3s 2ms/step - loss: 0.3253 - accuracy: 0.9107 - val_loss: 0.2950 - val_accuracy: 0.9212\n",
      "Epoch 7/30\n",
      "1719/1719 [==============================] - 3s 2ms/step - loss: 0.3073 - accuracy: 0.9158 - val_loss: 0.2822 - val_accuracy: 0.9234\n",
      "Epoch 8/30\n",
      "1719/1719 [==============================] - 3s 2ms/step - loss: 0.2917 - accuracy: 0.9190 - val_loss: 0.2703 - val_accuracy: 0.9260\n",
      "Epoch 9/30\n",
      "1719/1719 [==============================] - 3s 2ms/step - loss: 0.2806 - accuracy: 0.9230 - val_loss: 0.2608 - val_accuracy: 0.9300\n",
      "Epoch 10/30\n",
      "1719/1719 [==============================] - 3s 2ms/step - loss: 0.2759 - accuracy: 0.9248 - val_loss: 0.2514 - val_accuracy: 0.9310\n",
      "Epoch 11/30\n",
      "1719/1719 [==============================] - 3s 2ms/step - loss: 0.2672 - accuracy: 0.9268 - val_loss: 0.2433 - val_accuracy: 0.9336\n",
      "Epoch 12/30\n",
      "1719/1719 [==============================] - 3s 2ms/step - loss: 0.2554 - accuracy: 0.9282 - val_loss: 0.2362 - val_accuracy: 0.9362\n",
      "Epoch 13/30\n",
      "1719/1719 [==============================] - 4s 2ms/step - loss: 0.2497 - accuracy: 0.9308 - val_loss: 0.2297 - val_accuracy: 0.9364\n",
      "Epoch 14/30\n",
      "1719/1719 [==============================] - 4s 2ms/step - loss: 0.2356 - accuracy: 0.9344 - val_loss: 0.2236 - val_accuracy: 0.9384\n",
      "Epoch 15/30\n",
      "1719/1719 [==============================] - 3s 2ms/step - loss: 0.2270 - accuracy: 0.9360 - val_loss: 0.2183 - val_accuracy: 0.9396\n",
      "Epoch 16/30\n",
      "1719/1719 [==============================] - 4s 2ms/step - loss: 0.2300 - accuracy: 0.9357 - val_loss: 0.2124 - val_accuracy: 0.9402\n",
      "Epoch 17/30\n",
      "1719/1719 [==============================] - 3s 2ms/step - loss: 0.2210 - accuracy: 0.9384 - val_loss: 0.2078 - val_accuracy: 0.9424\n",
      "Epoch 18/30\n",
      "1719/1719 [==============================] - 3s 2ms/step - loss: 0.2102 - accuracy: 0.9414 - val_loss: 0.2033 - val_accuracy: 0.9424\n",
      "Epoch 19/30\n",
      "1719/1719 [==============================] - 3s 2ms/step - loss: 0.2071 - accuracy: 0.9417 - val_loss: 0.1981 - val_accuracy: 0.9438\n",
      "Epoch 20/30\n",
      "1719/1719 [==============================] - 5s 3ms/step - loss: 0.2019 - accuracy: 0.9445 - val_loss: 0.1940 - val_accuracy: 0.9462\n",
      "Epoch 21/30\n",
      "1719/1719 [==============================] - 4s 2ms/step - loss: 0.2006 - accuracy: 0.9435 - val_loss: 0.1897 - val_accuracy: 0.9470\n",
      "Epoch 22/30\n",
      "1719/1719 [==============================] - 5s 3ms/step - loss: 0.1969 - accuracy: 0.9448 - val_loss: 0.1866 - val_accuracy: 0.9478\n",
      "Epoch 23/30\n",
      "1719/1719 [==============================] - 6s 3ms/step - loss: 0.1947 - accuracy: 0.9448 - val_loss: 0.1821 - val_accuracy: 0.9496\n",
      "Epoch 24/30\n",
      "1719/1719 [==============================] - 4s 3ms/step - loss: 0.1858 - accuracy: 0.9477 - val_loss: 0.1793 - val_accuracy: 0.9512\n",
      "Epoch 25/30\n",
      "1719/1719 [==============================] - 5s 3ms/step - loss: 0.1834 - accuracy: 0.9484 - val_loss: 0.1752 - val_accuracy: 0.9508\n",
      "Epoch 26/30\n",
      "1719/1719 [==============================] - 5s 3ms/step - loss: 0.1848 - accuracy: 0.9481 - val_loss: 0.1730 - val_accuracy: 0.9516\n",
      "Epoch 27/30\n",
      "1719/1719 [==============================] - 5s 3ms/step - loss: 0.1752 - accuracy: 0.9521 - val_loss: 0.1694 - val_accuracy: 0.9522\n",
      "Epoch 28/30\n",
      "1719/1719 [==============================] - 5s 3ms/step - loss: 0.1701 - accuracy: 0.9519 - val_loss: 0.1662 - val_accuracy: 0.9542\n",
      "Epoch 29/30\n",
      "1719/1719 [==============================] - 4s 3ms/step - loss: 0.1701 - accuracy: 0.9536 - val_loss: 0.1634 - val_accuracy: 0.9546\n",
      "Epoch 30/30\n",
      "1719/1719 [==============================] - 4s 3ms/step - loss: 0.1688 - accuracy: 0.9532 - val_loss: 0.1607 - val_accuracy: 0.9552\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x29ad5e00d90>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=30,\n",
    "              validation_data=(X_valid, y_valid),\n",
    "              callbacks=[keras.callbacks.EarlyStopping(patience=10)]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Otimizando os parâmetros com o RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "jScpDtYyDIWC",
    "outputId": "66a55208-e317-47dd-9bd8-877ec33ecc21"
   },
   "outputs": [],
   "source": [
    "param_distribs = {\n",
    "    \"n_hidden\": [1, 2, 3, 4],\n",
    "    \"n_neurons\": np.arange(268, 400).tolist(),\n",
    "    \"learning_rate\": reciprocal(3e-4, 3e-2).rvs(1000).tolist(),\n",
    "}\n",
    "\n",
    "rnd_search_cv = RandomizedSearchCV(model, param_distribs, n_iter=10, scoring='accuracy', cv=3, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 6s 4ms/step - loss: 2.2516 - accuracy: 0.1772 - val_loss: 1.8456 - val_accuracy: 0.5880\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 1.7573 - accuracy: 0.6391 - val_loss: 1.4657 - val_accuracy: 0.7416\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 1.4095 - accuracy: 0.7468 - val_loss: 1.1925 - val_accuracy: 0.7892\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 1.1592 - accuracy: 0.7942 - val_loss: 1.0046 - val_accuracy: 0.8144\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.9860 - accuracy: 0.8141 - val_loss: 0.8748 - val_accuracy: 0.8322\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.8647 - accuracy: 0.8276 - val_loss: 0.7823 - val_accuracy: 0.8436\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.7726 - accuracy: 0.8442 - val_loss: 0.7137 - val_accuracy: 0.8512\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.7108 - accuracy: 0.8491 - val_loss: 0.6612 - val_accuracy: 0.8552\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.6603 - accuracy: 0.8560 - val_loss: 0.6198 - val_accuracy: 0.8606\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.6296 - accuracy: 0.8568 - val_loss: 0.5859 - val_accuracy: 0.8646\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.5882 - accuracy: 0.8633 - val_loss: 0.5577 - val_accuracy: 0.8694\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.5668 - accuracy: 0.8678 - val_loss: 0.5342 - val_accuracy: 0.8718\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.5421 - accuracy: 0.8701 - val_loss: 0.5140 - val_accuracy: 0.8750\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.5303 - accuracy: 0.8695 - val_loss: 0.4966 - val_accuracy: 0.8780\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.5064 - accuracy: 0.8739 - val_loss: 0.4812 - val_accuracy: 0.8804\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4897 - accuracy: 0.8768 - val_loss: 0.4677 - val_accuracy: 0.8822\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4772 - accuracy: 0.8801 - val_loss: 0.4557 - val_accuracy: 0.8854\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.4685 - accuracy: 0.8788 - val_loss: 0.4447 - val_accuracy: 0.8862\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.4581 - accuracy: 0.8837 - val_loss: 0.4350 - val_accuracy: 0.8890\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.4499 - accuracy: 0.8841 - val_loss: 0.4262 - val_accuracy: 0.8892\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4410 - accuracy: 0.8861 - val_loss: 0.4180 - val_accuracy: 0.8916\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4344 - accuracy: 0.8876 - val_loss: 0.4104 - val_accuracy: 0.8928\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.4250 - accuracy: 0.8890 - val_loss: 0.4037 - val_accuracy: 0.8938\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4201 - accuracy: 0.8890 - val_loss: 0.3972 - val_accuracy: 0.8968\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4101 - accuracy: 0.8909 - val_loss: 0.3914 - val_accuracy: 0.8958\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4085 - accuracy: 0.8912 - val_loss: 0.3860 - val_accuracy: 0.8976\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.4095 - accuracy: 0.8897 - val_loss: 0.3808 - val_accuracy: 0.8992\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3942 - accuracy: 0.8948 - val_loss: 0.3759 - val_accuracy: 0.9002\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.3861 - accuracy: 0.8973 - val_loss: 0.3714 - val_accuracy: 0.9008\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.3874 - accuracy: 0.8958 - val_loss: 0.3671 - val_accuracy: 0.9012\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.0004497801025474755, n_hidden=1, n_neurons=394; total time= 2.5min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 2.1865 - accuracy: 0.2085 - val_loss: 1.8227 - val_accuracy: 0.6070\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 1.7262 - accuracy: 0.6453 - val_loss: 1.4499 - val_accuracy: 0.7386\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 1.3851 - accuracy: 0.7444 - val_loss: 1.1836 - val_accuracy: 0.7848\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 1.1457 - accuracy: 0.7826 - val_loss: 1.0015 - val_accuracy: 0.8082\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.9776 - accuracy: 0.8034 - val_loss: 0.8752 - val_accuracy: 0.8270\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.8604 - accuracy: 0.8226 - val_loss: 0.7846 - val_accuracy: 0.8362\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.7757 - accuracy: 0.8328 - val_loss: 0.7171 - val_accuracy: 0.8448\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.7140 - accuracy: 0.8388 - val_loss: 0.6652 - val_accuracy: 0.8524\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.6669 - accuracy: 0.8454 - val_loss: 0.6240 - val_accuracy: 0.8570\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.6283 - accuracy: 0.8540 - val_loss: 0.5905 - val_accuracy: 0.8642\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.5934 - accuracy: 0.8577 - val_loss: 0.5626 - val_accuracy: 0.8684\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.5711 - accuracy: 0.8596 - val_loss: 0.5391 - val_accuracy: 0.8736\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.5488 - accuracy: 0.8641 - val_loss: 0.5190 - val_accuracy: 0.8770\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.5243 - accuracy: 0.8685 - val_loss: 0.5015 - val_accuracy: 0.8802\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.5080 - accuracy: 0.8728 - val_loss: 0.4861 - val_accuracy: 0.8822\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4975 - accuracy: 0.8740 - val_loss: 0.4724 - val_accuracy: 0.8858\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4860 - accuracy: 0.8740 - val_loss: 0.4603 - val_accuracy: 0.8876\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4719 - accuracy: 0.8793 - val_loss: 0.4496 - val_accuracy: 0.8898\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4599 - accuracy: 0.8808 - val_loss: 0.4395 - val_accuracy: 0.8918\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4440 - accuracy: 0.8840 - val_loss: 0.4306 - val_accuracy: 0.8944\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4385 - accuracy: 0.8858 - val_loss: 0.4224 - val_accuracy: 0.8958\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4293 - accuracy: 0.8870 - val_loss: 0.4147 - val_accuracy: 0.8972\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4261 - accuracy: 0.8868 - val_loss: 0.4079 - val_accuracy: 0.8992\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4080 - accuracy: 0.8926 - val_loss: 0.4015 - val_accuracy: 0.8986\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4083 - accuracy: 0.8914 - val_loss: 0.3953 - val_accuracy: 0.9008\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4079 - accuracy: 0.8918 - val_loss: 0.3899 - val_accuracy: 0.9008\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.3981 - accuracy: 0.8941 - val_loss: 0.3845 - val_accuracy: 0.9022\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.3895 - accuracy: 0.8966 - val_loss: 0.3796 - val_accuracy: 0.9026\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.3871 - accuracy: 0.8956 - val_loss: 0.3752 - val_accuracy: 0.9028\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.3839 - accuracy: 0.8971 - val_loss: 0.3706 - val_accuracy: 0.9044\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.0004497801025474755, n_hidden=1, n_neurons=394; total time= 1.7min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 2.1372 - accuracy: 0.3095 - val_loss: 1.7635 - val_accuracy: 0.6092\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 1.6757 - accuracy: 0.6364 - val_loss: 1.4026 - val_accuracy: 0.7376\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 1.3538 - accuracy: 0.7398 - val_loss: 1.1475 - val_accuracy: 0.7890\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 1.1208 - accuracy: 0.7826 - val_loss: 0.9723 - val_accuracy: 0.8188\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.9591 - accuracy: 0.8076 - val_loss: 0.8499 - val_accuracy: 0.8350\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.8454 - accuracy: 0.8290 - val_loss: 0.7614 - val_accuracy: 0.8474\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.7693 - accuracy: 0.8378 - val_loss: 0.6952 - val_accuracy: 0.8548\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.7079 - accuracy: 0.8458 - val_loss: 0.6440 - val_accuracy: 0.8626\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.6600 - accuracy: 0.8513 - val_loss: 0.6034 - val_accuracy: 0.8670\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.6253 - accuracy: 0.8566 - val_loss: 0.5701 - val_accuracy: 0.8728\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.5904 - accuracy: 0.8632 - val_loss: 0.5429 - val_accuracy: 0.8752\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.5672 - accuracy: 0.8665 - val_loss: 0.5196 - val_accuracy: 0.8788\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.5403 - accuracy: 0.8720 - val_loss: 0.4999 - val_accuracy: 0.8818\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.5163 - accuracy: 0.8727 - val_loss: 0.4827 - val_accuracy: 0.8838\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.5056 - accuracy: 0.8745 - val_loss: 0.4678 - val_accuracy: 0.8862\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4890 - accuracy: 0.8787 - val_loss: 0.4545 - val_accuracy: 0.8890\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4840 - accuracy: 0.8781 - val_loss: 0.4428 - val_accuracy: 0.8912\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4648 - accuracy: 0.8813 - val_loss: 0.4324 - val_accuracy: 0.8924\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4547 - accuracy: 0.8830 - val_loss: 0.4230 - val_accuracy: 0.8940\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4483 - accuracy: 0.8849 - val_loss: 0.4144 - val_accuracy: 0.8948\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4370 - accuracy: 0.8865 - val_loss: 0.4066 - val_accuracy: 0.8964\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4347 - accuracy: 0.8870 - val_loss: 0.3994 - val_accuracy: 0.9000\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4152 - accuracy: 0.8939 - val_loss: 0.3927 - val_accuracy: 0.9008\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4195 - accuracy: 0.8893 - val_loss: 0.3868 - val_accuracy: 0.9026\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4084 - accuracy: 0.8939 - val_loss: 0.3811 - val_accuracy: 0.9026\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4047 - accuracy: 0.8920 - val_loss: 0.3760 - val_accuracy: 0.9038\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.3984 - accuracy: 0.8925 - val_loss: 0.3707 - val_accuracy: 0.9046\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.4001 - accuracy: 0.8922 - val_loss: 0.3664 - val_accuracy: 0.9050\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.3884 - accuracy: 0.8957 - val_loss: 0.3619 - val_accuracy: 0.9060\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.3775 - accuracy: 0.9002 - val_loss: 0.3580 - val_accuracy: 0.9070\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.0004497801025474755, n_hidden=1, n_neurons=394; total time= 1.8min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 7s 5ms/step - loss: 2.2478 - accuracy: 0.2273 - val_loss: 2.0327 - val_accuracy: 0.5060\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 1.9653 - accuracy: 0.5660 - val_loss: 1.7566 - val_accuracy: 0.6846\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 1.6880 - accuracy: 0.7128 - val_loss: 1.4741 - val_accuracy: 0.7548\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 1.4146 - accuracy: 0.7682 - val_loss: 1.2195 - val_accuracy: 0.7948\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 1.1782 - accuracy: 0.7974 - val_loss: 1.0196 - val_accuracy: 0.8124\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.9874 - accuracy: 0.8193 - val_loss: 0.8738 - val_accuracy: 0.8278\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.8536 - accuracy: 0.8304 - val_loss: 0.7682 - val_accuracy: 0.8396\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.7617 - accuracy: 0.8377 - val_loss: 0.6907 - val_accuracy: 0.8494\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.6865 - accuracy: 0.8491 - val_loss: 0.6322 - val_accuracy: 0.8578\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.6327 - accuracy: 0.8560 - val_loss: 0.5864 - val_accuracy: 0.8644\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.5940 - accuracy: 0.8591 - val_loss: 0.5494 - val_accuracy: 0.8696\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.5568 - accuracy: 0.8656 - val_loss: 0.5197 - val_accuracy: 0.8750\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.5264 - accuracy: 0.8690 - val_loss: 0.4947 - val_accuracy: 0.8784\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.5075 - accuracy: 0.8707 - val_loss: 0.4734 - val_accuracy: 0.8820\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4861 - accuracy: 0.8762 - val_loss: 0.4548 - val_accuracy: 0.8844\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4685 - accuracy: 0.8799 - val_loss: 0.4391 - val_accuracy: 0.8872\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4489 - accuracy: 0.8849 - val_loss: 0.4254 - val_accuracy: 0.8898\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4396 - accuracy: 0.8846 - val_loss: 0.4130 - val_accuracy: 0.8924\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4284 - accuracy: 0.8865 - val_loss: 0.4022 - val_accuracy: 0.8932\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4188 - accuracy: 0.8885 - val_loss: 0.3925 - val_accuracy: 0.8956\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4090 - accuracy: 0.8910 - val_loss: 0.3834 - val_accuracy: 0.8968\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4036 - accuracy: 0.8914 - val_loss: 0.3753 - val_accuracy: 0.8990\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3918 - accuracy: 0.8947 - val_loss: 0.3681 - val_accuracy: 0.8998\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3861 - accuracy: 0.8929 - val_loss: 0.3612 - val_accuracy: 0.9012\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3746 - accuracy: 0.8981 - val_loss: 0.3550 - val_accuracy: 0.9026\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3737 - accuracy: 0.8976 - val_loss: 0.3495 - val_accuracy: 0.9048\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3657 - accuracy: 0.8986 - val_loss: 0.3440 - val_accuracy: 0.9054\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3625 - accuracy: 0.8980 - val_loss: 0.3388 - val_accuracy: 0.9076\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3621 - accuracy: 0.8977 - val_loss: 0.3341 - val_accuracy: 0.9080\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3505 - accuracy: 0.9019 - val_loss: 0.3301 - val_accuracy: 0.9098\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.0003731496412403024, n_hidden=2, n_neurons=307; total time= 2.5min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 7s 5ms/step - loss: 2.2656 - accuracy: 0.1712 - val_loss: 2.0704 - val_accuracy: 0.4770\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 2.0115 - accuracy: 0.5304 - val_loss: 1.8047 - val_accuracy: 0.6682\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 1.7403 - accuracy: 0.6793 - val_loss: 1.5168 - val_accuracy: 0.7378\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 1.4573 - accuracy: 0.7427 - val_loss: 1.2516 - val_accuracy: 0.7734\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 1.2076 - accuracy: 0.7746 - val_loss: 1.0424 - val_accuracy: 0.8008\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 1.0168 - accuracy: 0.8004 - val_loss: 0.8897 - val_accuracy: 0.8176\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.8699 - accuracy: 0.8216 - val_loss: 0.7795 - val_accuracy: 0.8314\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.7734 - accuracy: 0.8299 - val_loss: 0.6991 - val_accuracy: 0.8442\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.6951 - accuracy: 0.8411 - val_loss: 0.6376 - val_accuracy: 0.8524\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.6467 - accuracy: 0.8454 - val_loss: 0.5901 - val_accuracy: 0.8620\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.5963 - accuracy: 0.8552 - val_loss: 0.5522 - val_accuracy: 0.8676\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.5509 - accuracy: 0.8639 - val_loss: 0.5215 - val_accuracy: 0.8750\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.5306 - accuracy: 0.8645 - val_loss: 0.4961 - val_accuracy: 0.8804\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.5052 - accuracy: 0.8725 - val_loss: 0.4745 - val_accuracy: 0.8836\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4781 - accuracy: 0.8782 - val_loss: 0.4558 - val_accuracy: 0.8866\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4701 - accuracy: 0.8774 - val_loss: 0.4401 - val_accuracy: 0.8892\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4573 - accuracy: 0.8793 - val_loss: 0.4265 - val_accuracy: 0.8910\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4362 - accuracy: 0.8832 - val_loss: 0.4144 - val_accuracy: 0.8938\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.4242 - accuracy: 0.8873 - val_loss: 0.4036 - val_accuracy: 0.8958\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.4096 - accuracy: 0.8920 - val_loss: 0.3941 - val_accuracy: 0.8974\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.4119 - accuracy: 0.8884 - val_loss: 0.3857 - val_accuracy: 0.8984\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4029 - accuracy: 0.8892 - val_loss: 0.3778 - val_accuracy: 0.8998\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.3962 - accuracy: 0.8915 - val_loss: 0.3706 - val_accuracy: 0.9004\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3858 - accuracy: 0.8968 - val_loss: 0.3638 - val_accuracy: 0.9026\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3778 - accuracy: 0.8984 - val_loss: 0.3577 - val_accuracy: 0.9034\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3749 - accuracy: 0.8971 - val_loss: 0.3523 - val_accuracy: 0.9044\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3700 - accuracy: 0.8988 - val_loss: 0.3472 - val_accuracy: 0.9054\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3617 - accuracy: 0.8997 - val_loss: 0.3424 - val_accuracy: 0.9070\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3570 - accuracy: 0.9011 - val_loss: 0.3378 - val_accuracy: 0.9084\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3522 - accuracy: 0.9024 - val_loss: 0.3340 - val_accuracy: 0.9102\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.0003731496412403024, n_hidden=2, n_neurons=307; total time= 2.4min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 8s 5ms/step - loss: 2.2657 - accuracy: 0.1691 - val_loss: 2.0949 - val_accuracy: 0.3986\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 2.0362 - accuracy: 0.4713 - val_loss: 1.8583 - val_accuracy: 0.6244\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 1.7945 - accuracy: 0.6458 - val_loss: 1.5986 - val_accuracy: 0.7126\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 1.5405 - accuracy: 0.7215 - val_loss: 1.3427 - val_accuracy: 0.7680\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 1.2926 - accuracy: 0.7698 - val_loss: 1.1255 - val_accuracy: 0.7940\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 1.0888 - accuracy: 0.7970 - val_loss: 0.9590 - val_accuracy: 0.8148\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.9452 - accuracy: 0.8138 - val_loss: 0.8358 - val_accuracy: 0.8270\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.8291 - accuracy: 0.8275 - val_loss: 0.7447 - val_accuracy: 0.8406\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.7461 - accuracy: 0.8399 - val_loss: 0.6757 - val_accuracy: 0.8520\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.6832 - accuracy: 0.8488 - val_loss: 0.6227 - val_accuracy: 0.8602\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.6340 - accuracy: 0.8533 - val_loss: 0.5797 - val_accuracy: 0.8684\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.5940 - accuracy: 0.8616 - val_loss: 0.5450 - val_accuracy: 0.8736\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.5659 - accuracy: 0.8622 - val_loss: 0.5164 - val_accuracy: 0.8786\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.5314 - accuracy: 0.8715 - val_loss: 0.4925 - val_accuracy: 0.8822\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.5028 - accuracy: 0.8761 - val_loss: 0.4719 - val_accuracy: 0.8858\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4939 - accuracy: 0.8782 - val_loss: 0.4543 - val_accuracy: 0.8888\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4695 - accuracy: 0.8829 - val_loss: 0.4387 - val_accuracy: 0.8906\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4684 - accuracy: 0.8814 - val_loss: 0.4255 - val_accuracy: 0.8924\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4498 - accuracy: 0.8832 - val_loss: 0.4136 - val_accuracy: 0.8958\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4417 - accuracy: 0.8865 - val_loss: 0.4032 - val_accuracy: 0.8964\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4272 - accuracy: 0.8879 - val_loss: 0.3941 - val_accuracy: 0.8994\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4121 - accuracy: 0.8911 - val_loss: 0.3855 - val_accuracy: 0.8986\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4078 - accuracy: 0.8915 - val_loss: 0.3776 - val_accuracy: 0.9012\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.4018 - accuracy: 0.8926 - val_loss: 0.3707 - val_accuracy: 0.9032\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3902 - accuracy: 0.8954 - val_loss: 0.3641 - val_accuracy: 0.9044\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3805 - accuracy: 0.8987 - val_loss: 0.3579 - val_accuracy: 0.9046\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3766 - accuracy: 0.8982 - val_loss: 0.3525 - val_accuracy: 0.9064\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3737 - accuracy: 0.8989 - val_loss: 0.3476 - val_accuracy: 0.9074\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.3634 - accuracy: 0.9023 - val_loss: 0.3427 - val_accuracy: 0.9074\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.3734 - accuracy: 0.8976 - val_loss: 0.3379 - val_accuracy: 0.9084\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.0003731496412403024, n_hidden=2, n_neurons=307; total time= 2.5min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 11s 8ms/step - loss: 1.0508 - accuracy: 0.7121 - val_loss: 0.2613 - val_accuracy: 0.9266\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2378 - accuracy: 0.9302 - val_loss: 0.1779 - val_accuracy: 0.9526\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1710 - accuracy: 0.9485 - val_loss: 0.1423 - val_accuracy: 0.9598\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1298 - accuracy: 0.9615 - val_loss: 0.1355 - val_accuracy: 0.9590\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0992 - accuracy: 0.9713 - val_loss: 0.1032 - val_accuracy: 0.9692\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0826 - accuracy: 0.9766 - val_loss: 0.1130 - val_accuracy: 0.9674\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0732 - accuracy: 0.9784 - val_loss: 0.0934 - val_accuracy: 0.9724\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.0551 - accuracy: 0.9837 - val_loss: 0.0939 - val_accuracy: 0.9734\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0456 - accuracy: 0.9880 - val_loss: 0.0888 - val_accuracy: 0.9744\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0382 - accuracy: 0.9896 - val_loss: 0.0864 - val_accuracy: 0.9768\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0300 - accuracy: 0.9921 - val_loss: 0.0910 - val_accuracy: 0.9752\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0249 - accuracy: 0.9939 - val_loss: 0.0878 - val_accuracy: 0.9766\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0192 - accuracy: 0.9952 - val_loss: 0.0887 - val_accuracy: 0.9764\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0168 - accuracy: 0.9962 - val_loss: 0.0848 - val_accuracy: 0.9774\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 9s 7ms/step - loss: 0.0113 - accuracy: 0.9982 - val_loss: 0.0853 - val_accuracy: 0.9794\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0090 - accuracy: 0.9985 - val_loss: 0.0891 - val_accuracy: 0.9780\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0079 - accuracy: 0.9990 - val_loss: 0.0906 - val_accuracy: 0.9786\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0060 - accuracy: 0.9992 - val_loss: 0.0905 - val_accuracy: 0.9778\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0041 - accuracy: 0.9997 - val_loss: 0.0933 - val_accuracy: 0.9780\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0036 - accuracy: 0.9999 - val_loss: 0.0935 - val_accuracy: 0.9794\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0027 - accuracy: 0.9999 - val_loss: 0.0938 - val_accuracy: 0.9792\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0970 - val_accuracy: 0.9784\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0963 - val_accuracy: 0.9790\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9782\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.0181071653260448, n_hidden=4, n_neurons=362; total time= 3.3min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 11s 8ms/step - loss: 1.0502 - accuracy: 0.7112 - val_loss: 0.2742 - val_accuracy: 0.9194\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2373 - accuracy: 0.9302 - val_loss: 0.1823 - val_accuracy: 0.9492\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1786 - accuracy: 0.9479 - val_loss: 0.1430 - val_accuracy: 0.9572\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1318 - accuracy: 0.9601 - val_loss: 0.1270 - val_accuracy: 0.9624\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1037 - accuracy: 0.9695 - val_loss: 0.1105 - val_accuracy: 0.9694\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0846 - accuracy: 0.9745 - val_loss: 0.1057 - val_accuracy: 0.9670\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0690 - accuracy: 0.9799 - val_loss: 0.0902 - val_accuracy: 0.9752\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.0550 - accuracy: 0.9833 - val_loss: 0.0862 - val_accuracy: 0.9730\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0443 - accuracy: 0.9867 - val_loss: 0.0799 - val_accuracy: 0.9768\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 9s 8ms/step - loss: 0.0371 - accuracy: 0.9900 - val_loss: 0.0910 - val_accuracy: 0.9720\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0340 - accuracy: 0.9908 - val_loss: 0.0962 - val_accuracy: 0.9734\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0246 - accuracy: 0.9942 - val_loss: 0.0809 - val_accuracy: 0.9782\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0177 - accuracy: 0.9965 - val_loss: 0.0809 - val_accuracy: 0.9786\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0139 - accuracy: 0.9975 - val_loss: 0.0818 - val_accuracy: 0.9784\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0103 - accuracy: 0.9983 - val_loss: 0.0819 - val_accuracy: 0.9774\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0094 - accuracy: 0.9986 - val_loss: 0.0858 - val_accuracy: 0.9772\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0071 - accuracy: 0.9990 - val_loss: 0.0850 - val_accuracy: 0.9782\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0058 - accuracy: 0.9995 - val_loss: 0.0848 - val_accuracy: 0.9774\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0039 - accuracy: 0.9999 - val_loss: 0.0870 - val_accuracy: 0.9784\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.0181071653260448, n_hidden=4, n_neurons=362; total time= 2.6min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 11s 8ms/step - loss: 1.0442 - accuracy: 0.7131 - val_loss: 0.2462 - val_accuracy: 0.9314\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2517 - accuracy: 0.9256 - val_loss: 0.2119 - val_accuracy: 0.9388\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1762 - accuracy: 0.9487 - val_loss: 0.1440 - val_accuracy: 0.9578\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1326 - accuracy: 0.9616 - val_loss: 0.1279 - val_accuracy: 0.9642\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1046 - accuracy: 0.9695 - val_loss: 0.1076 - val_accuracy: 0.9684\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0822 - accuracy: 0.9767 - val_loss: 0.1028 - val_accuracy: 0.9724\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0687 - accuracy: 0.9800 - val_loss: 0.0940 - val_accuracy: 0.9722\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 9s 8ms/step - loss: 0.0536 - accuracy: 0.9845 - val_loss: 0.0978 - val_accuracy: 0.9746\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0463 - accuracy: 0.9866 - val_loss: 0.0883 - val_accuracy: 0.9754\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0351 - accuracy: 0.9899 - val_loss: 0.0882 - val_accuracy: 0.9740\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0297 - accuracy: 0.9928 - val_loss: 0.0832 - val_accuracy: 0.9750\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0236 - accuracy: 0.9939 - val_loss: 0.0871 - val_accuracy: 0.9754\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0196 - accuracy: 0.9950 - val_loss: 0.0851 - val_accuracy: 0.9768\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.0160 - accuracy: 0.9971 - val_loss: 0.0930 - val_accuracy: 0.9750\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0127 - accuracy: 0.9978 - val_loss: 0.0923 - val_accuracy: 0.9772\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0105 - accuracy: 0.9982 - val_loss: 0.0866 - val_accuracy: 0.9792\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0079 - accuracy: 0.9985 - val_loss: 0.0863 - val_accuracy: 0.9778\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0056 - accuracy: 0.9993 - val_loss: 0.0886 - val_accuracy: 0.9794\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0045 - accuracy: 0.9995 - val_loss: 0.0870 - val_accuracy: 0.9786\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0033 - accuracy: 0.9999 - val_loss: 0.0904 - val_accuracy: 0.9784\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0030 - accuracy: 0.9998 - val_loss: 0.0916 - val_accuracy: 0.9782\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.0181071653260448, n_hidden=4, n_neurons=362; total time= 2.8min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 10s 7ms/step - loss: 2.2305 - accuracy: 0.2413 - val_loss: 1.8159 - val_accuracy: 0.7082\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 1.6399 - accuracy: 0.7173 - val_loss: 1.0942 - val_accuracy: 0.7916\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.9933 - accuracy: 0.7993 - val_loss: 0.7158 - val_accuracy: 0.8304\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.6881 - accuracy: 0.8375 - val_loss: 0.5566 - val_accuracy: 0.8604\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.5524 - accuracy: 0.8594 - val_loss: 0.4745 - val_accuracy: 0.8770\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.4713 - accuracy: 0.8752 - val_loss: 0.4227 - val_accuracy: 0.8892\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.4374 - accuracy: 0.8840 - val_loss: 0.3867 - val_accuracy: 0.8976\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.3977 - accuracy: 0.8906 - val_loss: 0.3609 - val_accuracy: 0.9034\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3701 - accuracy: 0.8963 - val_loss: 0.3419 - val_accuracy: 0.9088\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3553 - accuracy: 0.8994 - val_loss: 0.3246 - val_accuracy: 0.9106\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.3336 - accuracy: 0.9055 - val_loss: 0.3126 - val_accuracy: 0.9136\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3215 - accuracy: 0.9086 - val_loss: 0.3010 - val_accuracy: 0.9172\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3044 - accuracy: 0.9142 - val_loss: 0.2910 - val_accuracy: 0.9194\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2984 - accuracy: 0.9169 - val_loss: 0.2824 - val_accuracy: 0.9208\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2868 - accuracy: 0.9197 - val_loss: 0.2750 - val_accuracy: 0.9232\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2845 - accuracy: 0.9191 - val_loss: 0.2693 - val_accuracy: 0.9234\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2770 - accuracy: 0.9206 - val_loss: 0.2629 - val_accuracy: 0.9276\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2677 - accuracy: 0.9230 - val_loss: 0.2566 - val_accuracy: 0.9284\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2616 - accuracy: 0.9261 - val_loss: 0.2512 - val_accuracy: 0.9290\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2544 - accuracy: 0.9283 - val_loss: 0.2449 - val_accuracy: 0.9318\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2570 - accuracy: 0.9245 - val_loss: 0.2405 - val_accuracy: 0.9338\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2525 - accuracy: 0.9283 - val_loss: 0.2377 - val_accuracy: 0.9360\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2411 - accuracy: 0.9308 - val_loss: 0.2326 - val_accuracy: 0.9366\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2355 - accuracy: 0.9335 - val_loss: 0.2282 - val_accuracy: 0.9384\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2367 - accuracy: 0.9309 - val_loss: 0.2257 - val_accuracy: 0.9382\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2302 - accuracy: 0.9350 - val_loss: 0.2215 - val_accuracy: 0.9390\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2265 - accuracy: 0.9351 - val_loss: 0.2175 - val_accuracy: 0.9398\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2176 - accuracy: 0.9374 - val_loss: 0.2147 - val_accuracy: 0.9400\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2155 - accuracy: 0.9375 - val_loss: 0.2131 - val_accuracy: 0.9412\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2117 - accuracy: 0.9385 - val_loss: 0.2079 - val_accuracy: 0.9410\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.0009416537242803311, n_hidden=3, n_neurons=386; total time= 3.8min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 9s 7ms/step - loss: 2.2060 - accuracy: 0.2667 - val_loss: 1.8701 - val_accuracy: 0.6184\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 1.7038 - accuracy: 0.6693 - val_loss: 1.1681 - val_accuracy: 0.7680\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 1.0363 - accuracy: 0.7876 - val_loss: 0.7389 - val_accuracy: 0.8292\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.6931 - accuracy: 0.8342 - val_loss: 0.5697 - val_accuracy: 0.8544\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.5525 - accuracy: 0.8586 - val_loss: 0.4818 - val_accuracy: 0.8712\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.4789 - accuracy: 0.8704 - val_loss: 0.4289 - val_accuracy: 0.8848\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.4323 - accuracy: 0.8823 - val_loss: 0.3934 - val_accuracy: 0.8914\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3984 - accuracy: 0.8868 - val_loss: 0.3676 - val_accuracy: 0.8964\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3845 - accuracy: 0.8924 - val_loss: 0.3473 - val_accuracy: 0.9026\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3508 - accuracy: 0.9013 - val_loss: 0.3317 - val_accuracy: 0.9074\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3357 - accuracy: 0.9036 - val_loss: 0.3199 - val_accuracy: 0.9104\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3261 - accuracy: 0.9072 - val_loss: 0.3077 - val_accuracy: 0.9136\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3096 - accuracy: 0.9098 - val_loss: 0.3011 - val_accuracy: 0.9172\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3018 - accuracy: 0.9140 - val_loss: 0.2899 - val_accuracy: 0.9216\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2942 - accuracy: 0.9162 - val_loss: 0.2835 - val_accuracy: 0.9234\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2886 - accuracy: 0.9186 - val_loss: 0.2771 - val_accuracy: 0.9220\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2784 - accuracy: 0.9211 - val_loss: 0.2703 - val_accuracy: 0.9256\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2799 - accuracy: 0.9198 - val_loss: 0.2637 - val_accuracy: 0.9268\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2617 - accuracy: 0.9248 - val_loss: 0.2604 - val_accuracy: 0.9262\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2684 - accuracy: 0.9239 - val_loss: 0.2536 - val_accuracy: 0.9288\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2529 - accuracy: 0.9296 - val_loss: 0.2488 - val_accuracy: 0.9290\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2513 - accuracy: 0.9299 - val_loss: 0.2444 - val_accuracy: 0.9316\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2532 - accuracy: 0.9280 - val_loss: 0.2404 - val_accuracy: 0.9326\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2428 - accuracy: 0.9324 - val_loss: 0.2367 - val_accuracy: 0.9328\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2381 - accuracy: 0.9333 - val_loss: 0.2328 - val_accuracy: 0.9336\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2318 - accuracy: 0.9328 - val_loss: 0.2306 - val_accuracy: 0.9364\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2245 - accuracy: 0.9372 - val_loss: 0.2239 - val_accuracy: 0.9358\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.2194 - accuracy: 0.9373 - val_loss: 0.2208 - val_accuracy: 0.9368\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2224 - accuracy: 0.9360 - val_loss: 0.2169 - val_accuracy: 0.9388\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2132 - accuracy: 0.9396 - val_loss: 0.2148 - val_accuracy: 0.9396\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.0009416537242803311, n_hidden=3, n_neurons=386; total time= 3.6min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 9s 7ms/step - loss: 2.2204 - accuracy: 0.2280 - val_loss: 1.8546 - val_accuracy: 0.6534\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 1.6888 - accuracy: 0.6987 - val_loss: 1.1496 - val_accuracy: 0.7930\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 1.0337 - accuracy: 0.8014 - val_loss: 0.7256 - val_accuracy: 0.8410\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.6853 - accuracy: 0.8431 - val_loss: 0.5520 - val_accuracy: 0.8672\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.5514 - accuracy: 0.8620 - val_loss: 0.4642 - val_accuracy: 0.8856\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.4674 - accuracy: 0.8780 - val_loss: 0.4133 - val_accuracy: 0.8952\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.4301 - accuracy: 0.8857 - val_loss: 0.3808 - val_accuracy: 0.8998\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3927 - accuracy: 0.8938 - val_loss: 0.3560 - val_accuracy: 0.9060\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3681 - accuracy: 0.8986 - val_loss: 0.3373 - val_accuracy: 0.9092\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3486 - accuracy: 0.9031 - val_loss: 0.3238 - val_accuracy: 0.9126\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3406 - accuracy: 0.9050 - val_loss: 0.3127 - val_accuracy: 0.9128\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3237 - accuracy: 0.9091 - val_loss: 0.3008 - val_accuracy: 0.9166\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3182 - accuracy: 0.9111 - val_loss: 0.2927 - val_accuracy: 0.9182\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3023 - accuracy: 0.9142 - val_loss: 0.2842 - val_accuracy: 0.9224\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2919 - accuracy: 0.9175 - val_loss: 0.2757 - val_accuracy: 0.9234\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2861 - accuracy: 0.9171 - val_loss: 0.2692 - val_accuracy: 0.9238\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2813 - accuracy: 0.9195 - val_loss: 0.2645 - val_accuracy: 0.9256\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.2657 - accuracy: 0.9256 - val_loss: 0.2594 - val_accuracy: 0.9268\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2680 - accuracy: 0.9233 - val_loss: 0.2523 - val_accuracy: 0.9292\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2642 - accuracy: 0.9242 - val_loss: 0.2487 - val_accuracy: 0.9292\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2560 - accuracy: 0.9267 - val_loss: 0.2438 - val_accuracy: 0.9326\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2457 - accuracy: 0.9305 - val_loss: 0.2396 - val_accuracy: 0.9332\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2482 - accuracy: 0.9287 - val_loss: 0.2348 - val_accuracy: 0.9356\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2484 - accuracy: 0.9300 - val_loss: 0.2323 - val_accuracy: 0.9370\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2433 - accuracy: 0.9303 - val_loss: 0.2271 - val_accuracy: 0.9362\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2309 - accuracy: 0.9350 - val_loss: 0.2233 - val_accuracy: 0.9384\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2291 - accuracy: 0.9338 - val_loss: 0.2191 - val_accuracy: 0.9396\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2224 - accuracy: 0.9374 - val_loss: 0.2165 - val_accuracy: 0.9396\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2216 - accuracy: 0.9369 - val_loss: 0.2125 - val_accuracy: 0.9402\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2214 - accuracy: 0.9389 - val_loss: 0.2106 - val_accuracy: 0.9398\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.0009416537242803311, n_hidden=3, n_neurons=386; total time= 3.6min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 6s 4ms/step - loss: 1.4668 - accuracy: 0.6270 - val_loss: 0.5754 - val_accuracy: 0.8664\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.5369 - accuracy: 0.8673 - val_loss: 0.4206 - val_accuracy: 0.8932\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.4189 - accuracy: 0.8890 - val_loss: 0.3628 - val_accuracy: 0.9002\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.3702 - accuracy: 0.8990 - val_loss: 0.3292 - val_accuracy: 0.9120\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.3421 - accuracy: 0.9055 - val_loss: 0.3081 - val_accuracy: 0.9168\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.3154 - accuracy: 0.9132 - val_loss: 0.2924 - val_accuracy: 0.9206\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2998 - accuracy: 0.9160 - val_loss: 0.2794 - val_accuracy: 0.9236\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2846 - accuracy: 0.9229 - val_loss: 0.2689 - val_accuracy: 0.9278\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2767 - accuracy: 0.9219 - val_loss: 0.2587 - val_accuracy: 0.9286\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2597 - accuracy: 0.9282 - val_loss: 0.2509 - val_accuracy: 0.9306\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.2525 - accuracy: 0.9300 - val_loss: 0.2428 - val_accuracy: 0.9330\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2451 - accuracy: 0.9323 - val_loss: 0.2363 - val_accuracy: 0.9340\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2379 - accuracy: 0.9334 - val_loss: 0.2290 - val_accuracy: 0.9368\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2362 - accuracy: 0.9339 - val_loss: 0.2239 - val_accuracy: 0.9386\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2280 - accuracy: 0.9371 - val_loss: 0.2175 - val_accuracy: 0.9402\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2197 - accuracy: 0.9398 - val_loss: 0.2135 - val_accuracy: 0.9420\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2151 - accuracy: 0.9404 - val_loss: 0.2095 - val_accuracy: 0.9444\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2070 - accuracy: 0.9438 - val_loss: 0.2039 - val_accuracy: 0.9432\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2004 - accuracy: 0.9443 - val_loss: 0.1980 - val_accuracy: 0.9454\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.1979 - accuracy: 0.9464 - val_loss: 0.1951 - val_accuracy: 0.9466\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.1957 - accuracy: 0.9458 - val_loss: 0.1909 - val_accuracy: 0.9482\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.1942 - accuracy: 0.9455 - val_loss: 0.1866 - val_accuracy: 0.9496\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.1813 - accuracy: 0.9495 - val_loss: 0.1835 - val_accuracy: 0.9500\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.1801 - accuracy: 0.9512 - val_loss: 0.1801 - val_accuracy: 0.9524\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.1734 - accuracy: 0.9519 - val_loss: 0.1773 - val_accuracy: 0.9528\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.1719 - accuracy: 0.9519 - val_loss: 0.1745 - val_accuracy: 0.9540\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.1651 - accuracy: 0.9546 - val_loss: 0.1712 - val_accuracy: 0.9548\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.1603 - accuracy: 0.9572 - val_loss: 0.1684 - val_accuracy: 0.9562\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.1631 - accuracy: 0.9546 - val_loss: 0.1663 - val_accuracy: 0.9562\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.1535 - accuracy: 0.9587 - val_loss: 0.1634 - val_accuracy: 0.9574\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.004659899078229261, n_hidden=1, n_neurons=357; total time= 2.0min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 6s 4ms/step - loss: 1.4928 - accuracy: 0.6225 - val_loss: 0.5824 - val_accuracy: 0.8642\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.5417 - accuracy: 0.8675 - val_loss: 0.4265 - val_accuracy: 0.8898\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.4226 - accuracy: 0.8887 - val_loss: 0.3686 - val_accuracy: 0.9000\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.3778 - accuracy: 0.8955 - val_loss: 0.3358 - val_accuracy: 0.9086\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.3413 - accuracy: 0.9073 - val_loss: 0.3147 - val_accuracy: 0.9142\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.3196 - accuracy: 0.9120 - val_loss: 0.2985 - val_accuracy: 0.9176\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.2991 - accuracy: 0.9179 - val_loss: 0.2853 - val_accuracy: 0.9204\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.2944 - accuracy: 0.9188 - val_loss: 0.2745 - val_accuracy: 0.9224\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.2814 - accuracy: 0.9226 - val_loss: 0.2654 - val_accuracy: 0.9262\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.2676 - accuracy: 0.9257 - val_loss: 0.2560 - val_accuracy: 0.9312\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2598 - accuracy: 0.9285 - val_loss: 0.2490 - val_accuracy: 0.9332\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2473 - accuracy: 0.9331 - val_loss: 0.2409 - val_accuracy: 0.9350\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.2462 - accuracy: 0.9319 - val_loss: 0.2357 - val_accuracy: 0.9374\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.2346 - accuracy: 0.9352 - val_loss: 0.2291 - val_accuracy: 0.9392\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.2330 - accuracy: 0.9361 - val_loss: 0.2237 - val_accuracy: 0.9398\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.2156 - accuracy: 0.9412 - val_loss: 0.2183 - val_accuracy: 0.9406\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.2146 - accuracy: 0.9404 - val_loss: 0.2128 - val_accuracy: 0.9420\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.2088 - accuracy: 0.9429 - val_loss: 0.2081 - val_accuracy: 0.9436\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.2061 - accuracy: 0.9440 - val_loss: 0.2045 - val_accuracy: 0.9444\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.1994 - accuracy: 0.9459 - val_loss: 0.2001 - val_accuracy: 0.9464\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.1906 - accuracy: 0.9492 - val_loss: 0.1953 - val_accuracy: 0.9488\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.1879 - accuracy: 0.9488 - val_loss: 0.1920 - val_accuracy: 0.9478\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.1859 - accuracy: 0.9488 - val_loss: 0.1887 - val_accuracy: 0.9502\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.1841 - accuracy: 0.9499 - val_loss: 0.1841 - val_accuracy: 0.9518\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.1831 - accuracy: 0.9493 - val_loss: 0.1815 - val_accuracy: 0.9522\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.1788 - accuracy: 0.9517 - val_loss: 0.1782 - val_accuracy: 0.9522\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.1687 - accuracy: 0.9533 - val_loss: 0.1749 - val_accuracy: 0.9536\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.1610 - accuracy: 0.9560 - val_loss: 0.1723 - val_accuracy: 0.9544\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.1592 - accuracy: 0.9563 - val_loss: 0.1684 - val_accuracy: 0.9552\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.1530 - accuracy: 0.9584 - val_loss: 0.1673 - val_accuracy: 0.9552\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.004659899078229261, n_hidden=1, n_neurons=357; total time= 2.0min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 1.4972 - accuracy: 0.6060 - val_loss: 0.5640 - val_accuracy: 0.8712\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.5360 - accuracy: 0.8713 - val_loss: 0.4131 - val_accuracy: 0.8914\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.4140 - accuracy: 0.8931 - val_loss: 0.3583 - val_accuracy: 0.9048\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.3764 - accuracy: 0.8983 - val_loss: 0.3263 - val_accuracy: 0.9126\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.3468 - accuracy: 0.9052 - val_loss: 0.3057 - val_accuracy: 0.9188\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.3147 - accuracy: 0.9126 - val_loss: 0.2894 - val_accuracy: 0.9218\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.3020 - accuracy: 0.9161 - val_loss: 0.2765 - val_accuracy: 0.9288\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2902 - accuracy: 0.9183 - val_loss: 0.2662 - val_accuracy: 0.9290\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2755 - accuracy: 0.9229 - val_loss: 0.2562 - val_accuracy: 0.9330\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2659 - accuracy: 0.9272 - val_loss: 0.2475 - val_accuracy: 0.9346\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.2587 - accuracy: 0.9271 - val_loss: 0.2398 - val_accuracy: 0.9364\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.2499 - accuracy: 0.9309 - val_loss: 0.2322 - val_accuracy: 0.9382\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2409 - accuracy: 0.9337 - val_loss: 0.2274 - val_accuracy: 0.9386\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2312 - accuracy: 0.9349 - val_loss: 0.2202 - val_accuracy: 0.9410\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 0.2191 - accuracy: 0.9397 - val_loss: 0.2148 - val_accuracy: 0.9406\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.2181 - accuracy: 0.9389 - val_loss: 0.2092 - val_accuracy: 0.9436\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.2111 - accuracy: 0.9421 - val_loss: 0.2044 - val_accuracy: 0.9436\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.2105 - accuracy: 0.9403 - val_loss: 0.2000 - val_accuracy: 0.9450\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.2047 - accuracy: 0.9422 - val_loss: 0.1954 - val_accuracy: 0.9464\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.1941 - accuracy: 0.9464 - val_loss: 0.1911 - val_accuracy: 0.9486\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.1953 - accuracy: 0.9451 - val_loss: 0.1881 - val_accuracy: 0.9472\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.1920 - accuracy: 0.9450 - val_loss: 0.1841 - val_accuracy: 0.9498\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.1767 - accuracy: 0.9492 - val_loss: 0.1809 - val_accuracy: 0.9510\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.1808 - accuracy: 0.9480 - val_loss: 0.1772 - val_accuracy: 0.9522\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.1785 - accuracy: 0.9499 - val_loss: 0.1739 - val_accuracy: 0.9538\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.1692 - accuracy: 0.9523 - val_loss: 0.1712 - val_accuracy: 0.9548\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.1708 - accuracy: 0.9523 - val_loss: 0.1677 - val_accuracy: 0.9552\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.1635 - accuracy: 0.9536 - val_loss: 0.1647 - val_accuracy: 0.9572\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.1605 - accuracy: 0.9561 - val_loss: 0.1627 - val_accuracy: 0.9566\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.1532 - accuracy: 0.9568 - val_loss: 0.1609 - val_accuracy: 0.9574\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.004659899078229261, n_hidden=1, n_neurons=357; total time= 2.1min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 8s 6ms/step - loss: 2.2875 - accuracy: 0.1594 - val_loss: 2.2466 - val_accuracy: 0.2432\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 2.2335 - accuracy: 0.2801 - val_loss: 2.1852 - val_accuracy: 0.3738\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 2.1688 - accuracy: 0.4084 - val_loss: 2.1009 - val_accuracy: 0.4930\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 2.0776 - accuracy: 0.5188 - val_loss: 1.9768 - val_accuracy: 0.5890\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 1.9399 - accuracy: 0.6125 - val_loss: 1.7948 - val_accuracy: 0.6622\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 1.7462 - accuracy: 0.6766 - val_loss: 1.5515 - val_accuracy: 0.7132\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 1.5006 - accuracy: 0.7130 - val_loss: 1.2835 - val_accuracy: 0.7448\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 1.2397 - accuracy: 0.7460 - val_loss: 1.0507 - val_accuracy: 0.7736\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 1.0189 - accuracy: 0.7774 - val_loss: 0.8791 - val_accuracy: 0.8020\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.8601 - accuracy: 0.8025 - val_loss: 0.7594 - val_accuracy: 0.8200\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.7578 - accuracy: 0.8168 - val_loss: 0.6752 - val_accuracy: 0.8324\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.6712 - accuracy: 0.8298 - val_loss: 0.6124 - val_accuracy: 0.8468\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.6133 - accuracy: 0.8433 - val_loss: 0.5649 - val_accuracy: 0.8582\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.5766 - accuracy: 0.8496 - val_loss: 0.5272 - val_accuracy: 0.8666\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.5402 - accuracy: 0.8567 - val_loss: 0.4971 - val_accuracy: 0.8726\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.5097 - accuracy: 0.8646 - val_loss: 0.4715 - val_accuracy: 0.8780\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.4853 - accuracy: 0.8685 - val_loss: 0.4505 - val_accuracy: 0.8828\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.4693 - accuracy: 0.8726 - val_loss: 0.4328 - val_accuracy: 0.8858\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.4377 - accuracy: 0.8822 - val_loss: 0.4171 - val_accuracy: 0.8888\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.4298 - accuracy: 0.8831 - val_loss: 0.4033 - val_accuracy: 0.8924\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.4123 - accuracy: 0.8874 - val_loss: 0.3911 - val_accuracy: 0.8942\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3944 - accuracy: 0.8912 - val_loss: 0.3807 - val_accuracy: 0.8976\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3897 - accuracy: 0.8916 - val_loss: 0.3714 - val_accuracy: 0.8992\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3874 - accuracy: 0.8919 - val_loss: 0.3632 - val_accuracy: 0.8998\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3682 - accuracy: 0.8971 - val_loss: 0.3559 - val_accuracy: 0.9016\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3630 - accuracy: 0.8978 - val_loss: 0.3478 - val_accuracy: 0.9032\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3589 - accuracy: 0.8995 - val_loss: 0.3418 - val_accuracy: 0.9044\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3549 - accuracy: 0.8995 - val_loss: 0.3354 - val_accuracy: 0.9066\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3491 - accuracy: 0.9005 - val_loss: 0.3301 - val_accuracy: 0.9084\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3427 - accuracy: 0.9023 - val_loss: 0.3246 - val_accuracy: 0.9086\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.00030589916396042916, n_hidden=4, n_neurons=285; total time= 2.9min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 8s 6ms/step - loss: 2.3047 - accuracy: 0.1213 - val_loss: 2.2438 - val_accuracy: 0.1604\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 2.2221 - accuracy: 0.2049 - val_loss: 2.1607 - val_accuracy: 0.3290\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 2.1335 - accuracy: 0.3843 - val_loss: 2.0536 - val_accuracy: 0.5296\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 2.0156 - accuracy: 0.5612 - val_loss: 1.9029 - val_accuracy: 0.6374\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 1.8605 - accuracy: 0.6474 - val_loss: 1.6962 - val_accuracy: 0.6906\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 1.6376 - accuracy: 0.6987 - val_loss: 1.4482 - val_accuracy: 0.7274\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 1.3866 - accuracy: 0.7321 - val_loss: 1.1989 - val_accuracy: 0.7580\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 1.1495 - accuracy: 0.7629 - val_loss: 0.9915 - val_accuracy: 0.7926\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.9599 - accuracy: 0.7932 - val_loss: 0.8375 - val_accuracy: 0.8190\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 0.8127 - accuracy: 0.8184 - val_loss: 0.7256 - val_accuracy: 0.8346\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.7144 - accuracy: 0.8303 - val_loss: 0.6441 - val_accuracy: 0.8512\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.6398 - accuracy: 0.8443 - val_loss: 0.5834 - val_accuracy: 0.8562\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 6s 6ms/step - loss: 0.5759 - accuracy: 0.8540 - val_loss: 0.5373 - val_accuracy: 0.8656\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.5339 - accuracy: 0.8626 - val_loss: 0.4999 - val_accuracy: 0.8722\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.5034 - accuracy: 0.8658 - val_loss: 0.4703 - val_accuracy: 0.8784\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.4782 - accuracy: 0.8712 - val_loss: 0.4471 - val_accuracy: 0.8826\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.4507 - accuracy: 0.8772 - val_loss: 0.4266 - val_accuracy: 0.8860\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.4336 - accuracy: 0.8804 - val_loss: 0.4091 - val_accuracy: 0.8908\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.4169 - accuracy: 0.8845 - val_loss: 0.3960 - val_accuracy: 0.8940\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.4054 - accuracy: 0.8876 - val_loss: 0.3828 - val_accuracy: 0.8976\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3896 - accuracy: 0.8921 - val_loss: 0.3720 - val_accuracy: 0.8984\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3877 - accuracy: 0.8914 - val_loss: 0.3616 - val_accuracy: 0.8988\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3716 - accuracy: 0.8943 - val_loss: 0.3531 - val_accuracy: 0.9032\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3688 - accuracy: 0.8937 - val_loss: 0.3454 - val_accuracy: 0.9048\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3588 - accuracy: 0.8963 - val_loss: 0.3383 - val_accuracy: 0.9058\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3472 - accuracy: 0.9001 - val_loss: 0.3320 - val_accuracy: 0.9056\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3386 - accuracy: 0.9021 - val_loss: 0.3254 - val_accuracy: 0.9076\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3393 - accuracy: 0.8999 - val_loss: 0.3203 - val_accuracy: 0.9102\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3330 - accuracy: 0.9046 - val_loss: 0.3143 - val_accuracy: 0.9112\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3256 - accuracy: 0.9050 - val_loss: 0.3098 - val_accuracy: 0.9144\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.00030589916396042916, n_hidden=4, n_neurons=285; total time= 3.0min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 8s 6ms/step - loss: 2.2895 - accuracy: 0.1370 - val_loss: 2.2404 - val_accuracy: 0.2994\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 2.2185 - accuracy: 0.3502 - val_loss: 2.1604 - val_accuracy: 0.4366\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 2.1333 - accuracy: 0.4761 - val_loss: 2.0516 - val_accuracy: 0.5402\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 2.0148 - accuracy: 0.5700 - val_loss: 1.8938 - val_accuracy: 0.6198\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 1.8385 - accuracy: 0.6389 - val_loss: 1.6722 - val_accuracy: 0.6804\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 5s 4ms/step - loss: 1.6128 - accuracy: 0.6866 - val_loss: 1.4044 - val_accuracy: 0.7364\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 1.3440 - accuracy: 0.7400 - val_loss: 1.1456 - val_accuracy: 0.7912\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 1.1104 - accuracy: 0.7836 - val_loss: 0.9376 - val_accuracy: 0.8134\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.9177 - accuracy: 0.8088 - val_loss: 0.7866 - val_accuracy: 0.8294\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.7777 - accuracy: 0.8288 - val_loss: 0.6808 - val_accuracy: 0.8462\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.6859 - accuracy: 0.8410 - val_loss: 0.6052 - val_accuracy: 0.8570\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.6148 - accuracy: 0.8517 - val_loss: 0.5494 - val_accuracy: 0.8654\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.5637 - accuracy: 0.8582 - val_loss: 0.5074 - val_accuracy: 0.8722\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.5200 - accuracy: 0.8672 - val_loss: 0.4752 - val_accuracy: 0.8786\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.4989 - accuracy: 0.8688 - val_loss: 0.4486 - val_accuracy: 0.8834\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.4677 - accuracy: 0.8752 - val_loss: 0.4278 - val_accuracy: 0.8874\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.4504 - accuracy: 0.8779 - val_loss: 0.4096 - val_accuracy: 0.8886\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.4352 - accuracy: 0.8818 - val_loss: 0.3945 - val_accuracy: 0.8948\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.4145 - accuracy: 0.8888 - val_loss: 0.3819 - val_accuracy: 0.8970\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.4046 - accuracy: 0.8910 - val_loss: 0.3711 - val_accuracy: 0.8994\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.3955 - accuracy: 0.8906 - val_loss: 0.3611 - val_accuracy: 0.9008\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.3817 - accuracy: 0.8936 - val_loss: 0.3520 - val_accuracy: 0.9034\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.3805 - accuracy: 0.8923 - val_loss: 0.3441 - val_accuracy: 0.9058\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.3739 - accuracy: 0.8948 - val_loss: 0.3365 - val_accuracy: 0.9068\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.3561 - accuracy: 0.9007 - val_loss: 0.3304 - val_accuracy: 0.9094\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.3583 - accuracy: 0.8989 - val_loss: 0.3238 - val_accuracy: 0.9110\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.3452 - accuracy: 0.9030 - val_loss: 0.3189 - val_accuracy: 0.9118\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 4s 4ms/step - loss: 0.3405 - accuracy: 0.9036 - val_loss: 0.3133 - val_accuracy: 0.9132\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.3309 - accuracy: 0.9063 - val_loss: 0.3083 - val_accuracy: 0.9172\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 5s 5ms/step - loss: 0.3286 - accuracy: 0.9069 - val_loss: 0.3040 - val_accuracy: 0.9164\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.00030589916396042916, n_hidden=4, n_neurons=285; total time= 2.8min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 9s 7ms/step - loss: 1.7705 - accuracy: 0.5223 - val_loss: 0.5071 - val_accuracy: 0.8504\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.4457 - accuracy: 0.8742 - val_loss: 0.3262 - val_accuracy: 0.9070\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3244 - accuracy: 0.9059 - val_loss: 0.2737 - val_accuracy: 0.9246\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2771 - accuracy: 0.9206 - val_loss: 0.2431 - val_accuracy: 0.9298\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2468 - accuracy: 0.9279 - val_loss: 0.2230 - val_accuracy: 0.9384\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2195 - accuracy: 0.9350 - val_loss: 0.1971 - val_accuracy: 0.9426\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1995 - accuracy: 0.9422 - val_loss: 0.1856 - val_accuracy: 0.9458\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.1801 - accuracy: 0.9485 - val_loss: 0.1752 - val_accuracy: 0.9512\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1650 - accuracy: 0.9521 - val_loss: 0.1592 - val_accuracy: 0.9550\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1533 - accuracy: 0.9556 - val_loss: 0.1517 - val_accuracy: 0.9556\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1375 - accuracy: 0.9617 - val_loss: 0.1394 - val_accuracy: 0.9610\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1308 - accuracy: 0.9616 - val_loss: 0.1323 - val_accuracy: 0.9636\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.1214 - accuracy: 0.9662 - val_loss: 0.1275 - val_accuracy: 0.9622\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1130 - accuracy: 0.9667 - val_loss: 0.1270 - val_accuracy: 0.9624\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1045 - accuracy: 0.9707 - val_loss: 0.1187 - val_accuracy: 0.9654\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0986 - accuracy: 0.9726 - val_loss: 0.1170 - val_accuracy: 0.9634\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.0893 - accuracy: 0.9746 - val_loss: 0.1126 - val_accuracy: 0.9668\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0871 - accuracy: 0.9756 - val_loss: 0.1068 - val_accuracy: 0.9696\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0808 - accuracy: 0.9774 - val_loss: 0.1043 - val_accuracy: 0.9708\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0726 - accuracy: 0.9793 - val_loss: 0.1005 - val_accuracy: 0.9714\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0682 - accuracy: 0.9816 - val_loss: 0.1028 - val_accuracy: 0.9700\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.0658 - accuracy: 0.9821 - val_loss: 0.0990 - val_accuracy: 0.9696\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0595 - accuracy: 0.9840 - val_loss: 0.0973 - val_accuracy: 0.9704\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.0557 - accuracy: 0.9852 - val_loss: 0.0952 - val_accuracy: 0.9716\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.0569 - accuracy: 0.9845 - val_loss: 0.0943 - val_accuracy: 0.9726\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.0498 - accuracy: 0.9872 - val_loss: 0.0930 - val_accuracy: 0.9722\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.0446 - accuracy: 0.9890 - val_loss: 0.0892 - val_accuracy: 0.9746\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.0437 - accuracy: 0.9882 - val_loss: 0.0883 - val_accuracy: 0.9750\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.0378 - accuracy: 0.9896 - val_loss: 0.0873 - val_accuracy: 0.9748\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.0363 - accuracy: 0.9912 - val_loss: 0.0896 - val_accuracy: 0.9738\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.004513305139109388, n_hidden=4, n_neurons=384; total time= 3.8min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 10s 7ms/step - loss: 1.8369 - accuracy: 0.4761 - val_loss: 0.4814 - val_accuracy: 0.8774\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.4304 - accuracy: 0.8835 - val_loss: 0.3230 - val_accuracy: 0.9094\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3258 - accuracy: 0.9063 - val_loss: 0.2755 - val_accuracy: 0.9204\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2716 - accuracy: 0.9228 - val_loss: 0.2468 - val_accuracy: 0.9298\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2417 - accuracy: 0.9293 - val_loss: 0.2174 - val_accuracy: 0.9364\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.2057 - accuracy: 0.9406 - val_loss: 0.2001 - val_accuracy: 0.9446\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1904 - accuracy: 0.9453 - val_loss: 0.1846 - val_accuracy: 0.9496\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1709 - accuracy: 0.9512 - val_loss: 0.1719 - val_accuracy: 0.9486\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1590 - accuracy: 0.9542 - val_loss: 0.1645 - val_accuracy: 0.9524\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1498 - accuracy: 0.9579 - val_loss: 0.1543 - val_accuracy: 0.9572\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1362 - accuracy: 0.9611 - val_loss: 0.1483 - val_accuracy: 0.9600\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1272 - accuracy: 0.9639 - val_loss: 0.1396 - val_accuracy: 0.9614\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1147 - accuracy: 0.9660 - val_loss: 0.1296 - val_accuracy: 0.9650\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1099 - accuracy: 0.9690 - val_loss: 0.1256 - val_accuracy: 0.9634\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1036 - accuracy: 0.9705 - val_loss: 0.1211 - val_accuracy: 0.9666\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0957 - accuracy: 0.9717 - val_loss: 0.1153 - val_accuracy: 0.9676\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0928 - accuracy: 0.9734 - val_loss: 0.1147 - val_accuracy: 0.9672\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0842 - accuracy: 0.9758 - val_loss: 0.1118 - val_accuracy: 0.9680\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0830 - accuracy: 0.9754 - val_loss: 0.1059 - val_accuracy: 0.9690\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0756 - accuracy: 0.9788 - val_loss: 0.1045 - val_accuracy: 0.9704\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0715 - accuracy: 0.9789 - val_loss: 0.1042 - val_accuracy: 0.9676\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0636 - accuracy: 0.9824 - val_loss: 0.1024 - val_accuracy: 0.9706\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0600 - accuracy: 0.9834 - val_loss: 0.1012 - val_accuracy: 0.9698\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0542 - accuracy: 0.9849 - val_loss: 0.0968 - val_accuracy: 0.9712\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.0524 - accuracy: 0.9852 - val_loss: 0.1031 - val_accuracy: 0.9694\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0493 - accuracy: 0.9869 - val_loss: 0.0928 - val_accuracy: 0.9724\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0437 - accuracy: 0.9888 - val_loss: 0.0927 - val_accuracy: 0.9728\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0430 - accuracy: 0.9890 - val_loss: 0.0959 - val_accuracy: 0.9720\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0399 - accuracy: 0.9907 - val_loss: 0.0914 - val_accuracy: 0.9738\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0376 - accuracy: 0.9906 - val_loss: 0.0894 - val_accuracy: 0.9738\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.004513305139109388, n_hidden=4, n_neurons=384; total time= 3.6min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 9s 7ms/step - loss: 1.8326 - accuracy: 0.5230 - val_loss: 0.4810 - val_accuracy: 0.8726\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.4406 - accuracy: 0.8783 - val_loss: 0.3191 - val_accuracy: 0.9066\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3276 - accuracy: 0.9052 - val_loss: 0.2836 - val_accuracy: 0.9176\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2796 - accuracy: 0.9197 - val_loss: 0.2486 - val_accuracy: 0.9282\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2387 - accuracy: 0.9314 - val_loss: 0.2224 - val_accuracy: 0.9358\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2194 - accuracy: 0.9357 - val_loss: 0.2022 - val_accuracy: 0.9404\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.1910 - accuracy: 0.9442 - val_loss: 0.1823 - val_accuracy: 0.9462\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1770 - accuracy: 0.9481 - val_loss: 0.1683 - val_accuracy: 0.9504\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1616 - accuracy: 0.9520 - val_loss: 0.1571 - val_accuracy: 0.9566\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1454 - accuracy: 0.9570 - val_loss: 0.1466 - val_accuracy: 0.9576\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1355 - accuracy: 0.9608 - val_loss: 0.1413 - val_accuracy: 0.9600\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1226 - accuracy: 0.9643 - val_loss: 0.1356 - val_accuracy: 0.9618\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 6s 5ms/step - loss: 0.1145 - accuracy: 0.9676 - val_loss: 0.1308 - val_accuracy: 0.9634\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1076 - accuracy: 0.9707 - val_loss: 0.1271 - val_accuracy: 0.9648\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1011 - accuracy: 0.9713 - val_loss: 0.1206 - val_accuracy: 0.9674\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0957 - accuracy: 0.9725 - val_loss: 0.1278 - val_accuracy: 0.9636\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0843 - accuracy: 0.9769 - val_loss: 0.1218 - val_accuracy: 0.9658\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0843 - accuracy: 0.9772 - val_loss: 0.1083 - val_accuracy: 0.9704\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0802 - accuracy: 0.9777 - val_loss: 0.1097 - val_accuracy: 0.9682\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0729 - accuracy: 0.9791 - val_loss: 0.1061 - val_accuracy: 0.9704\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0666 - accuracy: 0.9816 - val_loss: 0.0988 - val_accuracy: 0.9716\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0620 - accuracy: 0.9834 - val_loss: 0.0991 - val_accuracy: 0.9726\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0600 - accuracy: 0.9835 - val_loss: 0.0968 - val_accuracy: 0.9728\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0547 - accuracy: 0.9854 - val_loss: 0.1002 - val_accuracy: 0.9712\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0493 - accuracy: 0.9871 - val_loss: 0.0989 - val_accuracy: 0.9724\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0503 - accuracy: 0.9870 - val_loss: 0.0971 - val_accuracy: 0.9726\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0437 - accuracy: 0.9888 - val_loss: 0.0975 - val_accuracy: 0.9706\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0423 - accuracy: 0.9891 - val_loss: 0.0940 - val_accuracy: 0.9736\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0397 - accuracy: 0.9904 - val_loss: 0.0922 - val_accuracy: 0.9732\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.0380 - accuracy: 0.9911 - val_loss: 0.0925 - val_accuracy: 0.9722\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.004513305139109388, n_hidden=4, n_neurons=384; total time= 3.7min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 8s 6ms/step - loss: 2.0842 - accuracy: 0.3839 - val_loss: 1.1136 - val_accuracy: 0.7728\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.9179 - accuracy: 0.8018 - val_loss: 0.5630 - val_accuracy: 0.8642\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.5313 - accuracy: 0.8626 - val_loss: 0.4246 - val_accuracy: 0.8868\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.4220 - accuracy: 0.8858 - val_loss: 0.3647 - val_accuracy: 0.8986\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3679 - accuracy: 0.8993 - val_loss: 0.3288 - val_accuracy: 0.9086\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3427 - accuracy: 0.9020 - val_loss: 0.3050 - val_accuracy: 0.9138\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.3170 - accuracy: 0.9092 - val_loss: 0.2893 - val_accuracy: 0.9190\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2969 - accuracy: 0.9156 - val_loss: 0.2731 - val_accuracy: 0.9238\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2847 - accuracy: 0.9189 - val_loss: 0.2628 - val_accuracy: 0.9254\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.2709 - accuracy: 0.9219 - val_loss: 0.2510 - val_accuracy: 0.9298\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2639 - accuracy: 0.9249 - val_loss: 0.2438 - val_accuracy: 0.9312\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.2503 - accuracy: 0.9280 - val_loss: 0.2346 - val_accuracy: 0.9324\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.2389 - accuracy: 0.9321 - val_loss: 0.2264 - val_accuracy: 0.9368\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2285 - accuracy: 0.9351 - val_loss: 0.2193 - val_accuracy: 0.9390\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.2247 - accuracy: 0.9342 - val_loss: 0.2133 - val_accuracy: 0.9410\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2190 - accuracy: 0.9365 - val_loss: 0.2086 - val_accuracy: 0.9428\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2090 - accuracy: 0.9399 - val_loss: 0.2022 - val_accuracy: 0.9438\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.2061 - accuracy: 0.9409 - val_loss: 0.1975 - val_accuracy: 0.9442\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1955 - accuracy: 0.9438 - val_loss: 0.1912 - val_accuracy: 0.9466\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1917 - accuracy: 0.9453 - val_loss: 0.1892 - val_accuracy: 0.9462\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1875 - accuracy: 0.9456 - val_loss: 0.1853 - val_accuracy: 0.9472\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1805 - accuracy: 0.9479 - val_loss: 0.1802 - val_accuracy: 0.9514\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1780 - accuracy: 0.9495 - val_loss: 0.1774 - val_accuracy: 0.9502\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1698 - accuracy: 0.9529 - val_loss: 0.1754 - val_accuracy: 0.9520\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1700 - accuracy: 0.9522 - val_loss: 0.1704 - val_accuracy: 0.9522\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1609 - accuracy: 0.9537 - val_loss: 0.1667 - val_accuracy: 0.9530\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1524 - accuracy: 0.9553 - val_loss: 0.1623 - val_accuracy: 0.9560\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1523 - accuracy: 0.9565 - val_loss: 0.1603 - val_accuracy: 0.9566\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1434 - accuracy: 0.9605 - val_loss: 0.1567 - val_accuracy: 0.9564\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1486 - accuracy: 0.9574 - val_loss: 0.1548 - val_accuracy: 0.9584\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.0018794094675637445, n_hidden=3, n_neurons=388; total time= 3.7min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 9s 7ms/step - loss: 2.0877 - accuracy: 0.3624 - val_loss: 1.1356 - val_accuracy: 0.7912\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.9297 - accuracy: 0.8109 - val_loss: 0.5664 - val_accuracy: 0.8588\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.5320 - accuracy: 0.8646 - val_loss: 0.4257 - val_accuracy: 0.8862\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.4259 - accuracy: 0.8849 - val_loss: 0.3660 - val_accuracy: 0.8972\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3698 - accuracy: 0.8979 - val_loss: 0.3322 - val_accuracy: 0.9056\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.3328 - accuracy: 0.9070 - val_loss: 0.3079 - val_accuracy: 0.9130\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3162 - accuracy: 0.9119 - val_loss: 0.2907 - val_accuracy: 0.9176\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3013 - accuracy: 0.9149 - val_loss: 0.2762 - val_accuracy: 0.9220\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 6s 6ms/step - loss: 0.2779 - accuracy: 0.9237 - val_loss: 0.2644 - val_accuracy: 0.9264\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2718 - accuracy: 0.9239 - val_loss: 0.2554 - val_accuracy: 0.9278\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.2592 - accuracy: 0.9276 - val_loss: 0.2459 - val_accuracy: 0.9302\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2499 - accuracy: 0.9306 - val_loss: 0.2379 - val_accuracy: 0.9326\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.2390 - accuracy: 0.9337 - val_loss: 0.2297 - val_accuracy: 0.9346\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2301 - accuracy: 0.9354 - val_loss: 0.2237 - val_accuracy: 0.9382\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2164 - accuracy: 0.9392 - val_loss: 0.2165 - val_accuracy: 0.9412\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2179 - accuracy: 0.9398 - val_loss: 0.2095 - val_accuracy: 0.9408\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2081 - accuracy: 0.9408 - val_loss: 0.2049 - val_accuracy: 0.9432\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.2051 - accuracy: 0.9411 - val_loss: 0.1995 - val_accuracy: 0.9438\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1960 - accuracy: 0.9453 - val_loss: 0.1932 - val_accuracy: 0.9450\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1877 - accuracy: 0.9479 - val_loss: 0.1898 - val_accuracy: 0.9454\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1791 - accuracy: 0.9506 - val_loss: 0.1861 - val_accuracy: 0.9480\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1762 - accuracy: 0.9510 - val_loss: 0.1808 - val_accuracy: 0.9490\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.1743 - accuracy: 0.9501 - val_loss: 0.1775 - val_accuracy: 0.9492\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.1727 - accuracy: 0.9520 - val_loss: 0.1722 - val_accuracy: 0.9512\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1651 - accuracy: 0.9518 - val_loss: 0.1696 - val_accuracy: 0.9520\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1538 - accuracy: 0.9584 - val_loss: 0.1654 - val_accuracy: 0.9546\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.1576 - accuracy: 0.9542 - val_loss: 0.1637 - val_accuracy: 0.9566\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 6s 6ms/step - loss: 0.1530 - accuracy: 0.9564 - val_loss: 0.1589 - val_accuracy: 0.9560\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1466 - accuracy: 0.9587 - val_loss: 0.1569 - val_accuracy: 0.9566\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1443 - accuracy: 0.9591 - val_loss: 0.1543 - val_accuracy: 0.9586\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.0018794094675637445, n_hidden=3, n_neurons=388; total time= 3.7min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 9s 7ms/step - loss: 2.1020 - accuracy: 0.3898 - val_loss: 1.1668 - val_accuracy: 0.7756\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.9666 - accuracy: 0.7932 - val_loss: 0.5636 - val_accuracy: 0.8642\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.5463 - accuracy: 0.8619 - val_loss: 0.4228 - val_accuracy: 0.8910\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.4319 - accuracy: 0.8817 - val_loss: 0.3643 - val_accuracy: 0.9034\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3730 - accuracy: 0.8973 - val_loss: 0.3297 - val_accuracy: 0.9092\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3459 - accuracy: 0.9038 - val_loss: 0.3049 - val_accuracy: 0.9154\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3208 - accuracy: 0.9124 - val_loss: 0.2865 - val_accuracy: 0.9208\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3027 - accuracy: 0.9143 - val_loss: 0.2723 - val_accuracy: 0.9236\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2887 - accuracy: 0.9169 - val_loss: 0.2621 - val_accuracy: 0.9248\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2733 - accuracy: 0.9211 - val_loss: 0.2516 - val_accuracy: 0.9296\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2631 - accuracy: 0.9264 - val_loss: 0.2424 - val_accuracy: 0.9328\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2519 - accuracy: 0.9297 - val_loss: 0.2336 - val_accuracy: 0.9358\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2365 - accuracy: 0.9331 - val_loss: 0.2287 - val_accuracy: 0.9338\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2278 - accuracy: 0.9356 - val_loss: 0.2202 - val_accuracy: 0.9390\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2221 - accuracy: 0.9374 - val_loss: 0.2129 - val_accuracy: 0.9398\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2217 - accuracy: 0.9371 - val_loss: 0.2067 - val_accuracy: 0.9416\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 6s 6ms/step - loss: 0.2131 - accuracy: 0.9415 - val_loss: 0.2053 - val_accuracy: 0.9430\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2056 - accuracy: 0.9415 - val_loss: 0.1968 - val_accuracy: 0.9452\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1995 - accuracy: 0.9437 - val_loss: 0.1914 - val_accuracy: 0.9480\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1897 - accuracy: 0.9449 - val_loss: 0.1868 - val_accuracy: 0.9492\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1825 - accuracy: 0.9486 - val_loss: 0.1878 - val_accuracy: 0.9480\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1851 - accuracy: 0.9473 - val_loss: 0.1788 - val_accuracy: 0.9518\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.1750 - accuracy: 0.9513 - val_loss: 0.1754 - val_accuracy: 0.9530\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1720 - accuracy: 0.9512 - val_loss: 0.1709 - val_accuracy: 0.9526\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1674 - accuracy: 0.9525 - val_loss: 0.1670 - val_accuracy: 0.9546\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.1642 - accuracy: 0.9529 - val_loss: 0.1658 - val_accuracy: 0.9548\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1548 - accuracy: 0.9558 - val_loss: 0.1616 - val_accuracy: 0.9560\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1491 - accuracy: 0.9583 - val_loss: 0.1574 - val_accuracy: 0.9582\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1490 - accuracy: 0.9592 - val_loss: 0.1585 - val_accuracy: 0.9566\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.1404 - accuracy: 0.9600 - val_loss: 0.1512 - val_accuracy: 0.9584\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.0018794094675637445, n_hidden=3, n_neurons=388; total time= 3.7min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 9s 7ms/step - loss: 2.1745 - accuracy: 0.3385 - val_loss: 1.6700 - val_accuracy: 0.6994\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 1.4655 - accuracy: 0.7329 - val_loss: 0.9161 - val_accuracy: 0.8090\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.8203 - accuracy: 0.8259 - val_loss: 0.6216 - val_accuracy: 0.8498\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.6007 - accuracy: 0.8518 - val_loss: 0.4981 - val_accuracy: 0.8726\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.4929 - accuracy: 0.8705 - val_loss: 0.4312 - val_accuracy: 0.8876\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.4391 - accuracy: 0.8829 - val_loss: 0.3903 - val_accuracy: 0.8928\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3974 - accuracy: 0.8895 - val_loss: 0.3604 - val_accuracy: 0.8992\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.3721 - accuracy: 0.8975 - val_loss: 0.3394 - val_accuracy: 0.9072\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.3430 - accuracy: 0.9023 - val_loss: 0.3226 - val_accuracy: 0.9100\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3323 - accuracy: 0.9067 - val_loss: 0.3094 - val_accuracy: 0.9128\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3193 - accuracy: 0.9103 - val_loss: 0.2990 - val_accuracy: 0.9144\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3100 - accuracy: 0.9135 - val_loss: 0.2889 - val_accuracy: 0.9174\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2897 - accuracy: 0.9177 - val_loss: 0.2793 - val_accuracy: 0.9226\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2833 - accuracy: 0.9200 - val_loss: 0.2716 - val_accuracy: 0.9236\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.2829 - accuracy: 0.9213 - val_loss: 0.2646 - val_accuracy: 0.9250\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2654 - accuracy: 0.9237 - val_loss: 0.2588 - val_accuracy: 0.9254\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2680 - accuracy: 0.9230 - val_loss: 0.2527 - val_accuracy: 0.9292\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2659 - accuracy: 0.9242 - val_loss: 0.2469 - val_accuracy: 0.9300\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2515 - accuracy: 0.9265 - val_loss: 0.2420 - val_accuracy: 0.9314\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2481 - accuracy: 0.9270 - val_loss: 0.2371 - val_accuracy: 0.9328\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2437 - accuracy: 0.9303 - val_loss: 0.2324 - val_accuracy: 0.9332\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2389 - accuracy: 0.9314 - val_loss: 0.2280 - val_accuracy: 0.9338\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.2254 - accuracy: 0.9359 - val_loss: 0.2235 - val_accuracy: 0.9372\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2248 - accuracy: 0.9356 - val_loss: 0.2193 - val_accuracy: 0.9372\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2221 - accuracy: 0.9361 - val_loss: 0.2169 - val_accuracy: 0.9382\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2159 - accuracy: 0.9400 - val_loss: 0.2130 - val_accuracy: 0.9394\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2153 - accuracy: 0.9387 - val_loss: 0.2092 - val_accuracy: 0.9406\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2105 - accuracy: 0.9407 - val_loss: 0.2091 - val_accuracy: 0.9402\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2131 - accuracy: 0.9386 - val_loss: 0.2036 - val_accuracy: 0.9410\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2044 - accuracy: 0.9411 - val_loss: 0.2016 - val_accuracy: 0.9422\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.001041967455764514, n_hidden=3, n_neurons=398; total time= 3.8min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 10s 7ms/step - loss: 2.1894 - accuracy: 0.3194 - val_loss: 1.7359 - val_accuracy: 0.6964\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 1.5235 - accuracy: 0.7248 - val_loss: 0.9824 - val_accuracy: 0.7872\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.8787 - accuracy: 0.8092 - val_loss: 0.6559 - val_accuracy: 0.8442\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.6231 - accuracy: 0.8466 - val_loss: 0.5171 - val_accuracy: 0.8714\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.5057 - accuracy: 0.8689 - val_loss: 0.4401 - val_accuracy: 0.8864\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.4441 - accuracy: 0.8808 - val_loss: 0.3961 - val_accuracy: 0.8924\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3960 - accuracy: 0.8923 - val_loss: 0.3662 - val_accuracy: 0.9024\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3748 - accuracy: 0.8976 - val_loss: 0.3429 - val_accuracy: 0.9050\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3551 - accuracy: 0.8999 - val_loss: 0.3258 - val_accuracy: 0.9090\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3333 - accuracy: 0.9058 - val_loss: 0.3125 - val_accuracy: 0.9128\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3206 - accuracy: 0.9079 - val_loss: 0.3015 - val_accuracy: 0.9144\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3185 - accuracy: 0.9082 - val_loss: 0.2924 - val_accuracy: 0.9194\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3033 - accuracy: 0.9153 - val_loss: 0.2839 - val_accuracy: 0.9198\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2883 - accuracy: 0.9183 - val_loss: 0.2759 - val_accuracy: 0.9210\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2830 - accuracy: 0.9193 - val_loss: 0.2685 - val_accuracy: 0.9254\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2782 - accuracy: 0.9209 - val_loss: 0.2628 - val_accuracy: 0.9268\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2654 - accuracy: 0.9247 - val_loss: 0.2575 - val_accuracy: 0.9278\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2644 - accuracy: 0.9264 - val_loss: 0.2506 - val_accuracy: 0.9294\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2528 - accuracy: 0.9279 - val_loss: 0.2459 - val_accuracy: 0.9322\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2462 - accuracy: 0.9298 - val_loss: 0.2415 - val_accuracy: 0.9340\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2502 - accuracy: 0.9283 - val_loss: 0.2358 - val_accuracy: 0.9340\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2407 - accuracy: 0.9311 - val_loss: 0.2309 - val_accuracy: 0.9352\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2301 - accuracy: 0.9345 - val_loss: 0.2275 - val_accuracy: 0.9368\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2324 - accuracy: 0.9350 - val_loss: 0.2236 - val_accuracy: 0.9366\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2214 - accuracy: 0.9362 - val_loss: 0.2201 - val_accuracy: 0.9364\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2227 - accuracy: 0.9365 - val_loss: 0.2165 - val_accuracy: 0.9390\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2228 - accuracy: 0.9371 - val_loss: 0.2131 - val_accuracy: 0.9382\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2176 - accuracy: 0.9389 - val_loss: 0.2092 - val_accuracy: 0.9388\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2133 - accuracy: 0.9401 - val_loss: 0.2058 - val_accuracy: 0.9426\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2006 - accuracy: 0.9435 - val_loss: 0.2034 - val_accuracy: 0.9416\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.001041967455764514, n_hidden=3, n_neurons=398; total time= 4.1min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 10s 8ms/step - loss: 2.1967 - accuracy: 0.2803 - val_loss: 1.7960 - val_accuracy: 0.6832\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 1.6053 - accuracy: 0.7133 - val_loss: 1.0257 - val_accuracy: 0.7896\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.9241 - accuracy: 0.8057 - val_loss: 0.6611 - val_accuracy: 0.8438\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.6333 - accuracy: 0.8482 - val_loss: 0.5171 - val_accuracy: 0.8696\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 7s 7ms/step - loss: 0.5209 - accuracy: 0.8671 - val_loss: 0.4417 - val_accuracy: 0.8860\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.4527 - accuracy: 0.8811 - val_loss: 0.3975 - val_accuracy: 0.8948\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.4031 - accuracy: 0.8912 - val_loss: 0.3656 - val_accuracy: 0.9012\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.3740 - accuracy: 0.8976 - val_loss: 0.3444 - val_accuracy: 0.9074\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3559 - accuracy: 0.8997 - val_loss: 0.3249 - val_accuracy: 0.9126\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3412 - accuracy: 0.9043 - val_loss: 0.3118 - val_accuracy: 0.9158\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3186 - accuracy: 0.9107 - val_loss: 0.2991 - val_accuracy: 0.9170\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3147 - accuracy: 0.9117 - val_loss: 0.2895 - val_accuracy: 0.9216\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.3104 - accuracy: 0.9105 - val_loss: 0.2813 - val_accuracy: 0.9238\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2933 - accuracy: 0.9167 - val_loss: 0.2737 - val_accuracy: 0.9240\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2820 - accuracy: 0.9199 - val_loss: 0.2650 - val_accuracy: 0.9278\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2786 - accuracy: 0.9204 - val_loss: 0.2611 - val_accuracy: 0.9298\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2733 - accuracy: 0.9217 - val_loss: 0.2530 - val_accuracy: 0.9318\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2680 - accuracy: 0.9231 - val_loss: 0.2471 - val_accuracy: 0.9344\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2622 - accuracy: 0.9249 - val_loss: 0.2409 - val_accuracy: 0.9344\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2573 - accuracy: 0.9269 - val_loss: 0.2364 - val_accuracy: 0.9360\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2496 - accuracy: 0.9276 - val_loss: 0.2323 - val_accuracy: 0.9358\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2433 - accuracy: 0.9301 - val_loss: 0.2273 - val_accuracy: 0.9374\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2334 - accuracy: 0.9341 - val_loss: 0.2233 - val_accuracy: 0.9400\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2302 - accuracy: 0.9334 - val_loss: 0.2188 - val_accuracy: 0.9408\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2306 - accuracy: 0.9330 - val_loss: 0.2162 - val_accuracy: 0.9404\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2213 - accuracy: 0.9375 - val_loss: 0.2138 - val_accuracy: 0.9426\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2220 - accuracy: 0.9384 - val_loss: 0.2087 - val_accuracy: 0.9442\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 8s 7ms/step - loss: 0.2107 - accuracy: 0.9422 - val_loss: 0.2063 - val_accuracy: 0.9450\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2068 - accuracy: 0.9410 - val_loss: 0.2018 - val_accuracy: 0.9462\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 7s 6ms/step - loss: 0.2017 - accuracy: 0.9431 - val_loss: 0.1986 - val_accuracy: 0.9472\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.001041967455764514, n_hidden=3, n_neurons=398; total time= 3.9min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 6s 3ms/step - loss: 1.0088 - accuracy: 0.7443 - val_loss: 0.3592 - val_accuracy: 0.9044\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.3585 - accuracy: 0.8997 - val_loss: 0.2906 - val_accuracy: 0.9178\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.2960 - accuracy: 0.9174 - val_loss: 0.2543 - val_accuracy: 0.9304\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.2595 - accuracy: 0.9262 - val_loss: 0.2310 - val_accuracy: 0.9382\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.2307 - accuracy: 0.9348 - val_loss: 0.2117 - val_accuracy: 0.9422\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.2152 - accuracy: 0.9401 - val_loss: 0.1965 - val_accuracy: 0.9450\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1915 - accuracy: 0.9476 - val_loss: 0.1857 - val_accuracy: 0.9512\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1834 - accuracy: 0.9497 - val_loss: 0.1743 - val_accuracy: 0.9528\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1672 - accuracy: 0.9534 - val_loss: 0.1650 - val_accuracy: 0.9540\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1496 - accuracy: 0.9584 - val_loss: 0.1580 - val_accuracy: 0.9570\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1477 - accuracy: 0.9596 - val_loss: 0.1512 - val_accuracy: 0.9582\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1396 - accuracy: 0.9623 - val_loss: 0.1458 - val_accuracy: 0.9590\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1321 - accuracy: 0.9633 - val_loss: 0.1403 - val_accuracy: 0.9632\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1271 - accuracy: 0.9649 - val_loss: 0.1331 - val_accuracy: 0.9648\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1172 - accuracy: 0.9686 - val_loss: 0.1299 - val_accuracy: 0.9656\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1125 - accuracy: 0.9695 - val_loss: 0.1273 - val_accuracy: 0.9664\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1071 - accuracy: 0.9717 - val_loss: 0.1239 - val_accuracy: 0.9668\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.0984 - accuracy: 0.9732 - val_loss: 0.1179 - val_accuracy: 0.9680\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.0958 - accuracy: 0.9752 - val_loss: 0.1169 - val_accuracy: 0.9692\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.0973 - accuracy: 0.9743 - val_loss: 0.1141 - val_accuracy: 0.9690\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.0878 - accuracy: 0.9773 - val_loss: 0.1111 - val_accuracy: 0.9708\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.0846 - accuracy: 0.9781 - val_loss: 0.1096 - val_accuracy: 0.9694\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.0835 - accuracy: 0.9785 - val_loss: 0.1071 - val_accuracy: 0.9710\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.0804 - accuracy: 0.9791 - val_loss: 0.1058 - val_accuracy: 0.9714\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.0776 - accuracy: 0.9805 - val_loss: 0.1035 - val_accuracy: 0.9710\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.0729 - accuracy: 0.9815 - val_loss: 0.1027 - val_accuracy: 0.9724\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.0723 - accuracy: 0.9809 - val_loss: 0.0992 - val_accuracy: 0.9722\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.0663 - accuracy: 0.9846 - val_loss: 0.0988 - val_accuracy: 0.9718\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.0641 - accuracy: 0.9844 - val_loss: 0.0962 - val_accuracy: 0.9732\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.0641 - accuracy: 0.9837 - val_loss: 0.0959 - val_accuracy: 0.9734\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.015114765647573948, n_hidden=1, n_neurons=287; total time= 1.7min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 4s 3ms/step - loss: 1.0145 - accuracy: 0.7463 - val_loss: 0.3551 - val_accuracy: 0.9054\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.3517 - accuracy: 0.9026 - val_loss: 0.2927 - val_accuracy: 0.9212\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.2935 - accuracy: 0.9204 - val_loss: 0.2582 - val_accuracy: 0.9304\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.2567 - accuracy: 0.9284 - val_loss: 0.2356 - val_accuracy: 0.9354\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.2405 - accuracy: 0.9349 - val_loss: 0.2158 - val_accuracy: 0.9404\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.2175 - accuracy: 0.9400 - val_loss: 0.2026 - val_accuracy: 0.9434\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.2000 - accuracy: 0.9453 - val_loss: 0.1904 - val_accuracy: 0.9470\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1801 - accuracy: 0.9499 - val_loss: 0.1766 - val_accuracy: 0.9522\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1710 - accuracy: 0.9519 - val_loss: 0.1680 - val_accuracy: 0.9550\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1538 - accuracy: 0.9581 - val_loss: 0.1596 - val_accuracy: 0.9572\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1480 - accuracy: 0.9599 - val_loss: 0.1617 - val_accuracy: 0.9550\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1432 - accuracy: 0.9607 - val_loss: 0.1459 - val_accuracy: 0.9610\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1305 - accuracy: 0.9644 - val_loss: 0.1397 - val_accuracy: 0.9638\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1260 - accuracy: 0.9656 - val_loss: 0.1349 - val_accuracy: 0.9642\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1160 - accuracy: 0.9699 - val_loss: 0.1309 - val_accuracy: 0.9650\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1140 - accuracy: 0.9690 - val_loss: 0.1276 - val_accuracy: 0.9658\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 3s 3ms/step - loss: 0.1100 - accuracy: 0.9711 - val_loss: 0.1238 - val_accuracy: 0.9666\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.1014 - accuracy: 0.9731 - val_loss: 0.1210 - val_accuracy: 0.9670\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0978 - accuracy: 0.9734 - val_loss: 0.1174 - val_accuracy: 0.9682\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0933 - accuracy: 0.9758 - val_loss: 0.1146 - val_accuracy: 0.9692\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0893 - accuracy: 0.9771 - val_loss: 0.1117 - val_accuracy: 0.9712\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0881 - accuracy: 0.9773 - val_loss: 0.1107 - val_accuracy: 0.9688\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0830 - accuracy: 0.9777 - val_loss: 0.1075 - val_accuracy: 0.9706\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0799 - accuracy: 0.9784 - val_loss: 0.1046 - val_accuracy: 0.9716\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0758 - accuracy: 0.9808 - val_loss: 0.1031 - val_accuracy: 0.9728\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0708 - accuracy: 0.9812 - val_loss: 0.1026 - val_accuracy: 0.9714\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0730 - accuracy: 0.9812 - val_loss: 0.1016 - val_accuracy: 0.9712\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0659 - accuracy: 0.9831 - val_loss: 0.0988 - val_accuracy: 0.9716\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0663 - accuracy: 0.9837 - val_loss: 0.0971 - val_accuracy: 0.9738\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0643 - accuracy: 0.9833 - val_loss: 0.0957 - val_accuracy: 0.9744\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.015114765647573948, n_hidden=1, n_neurons=287; total time= 1.4min\n",
      "Epoch 1/30\n",
      "1146/1146 [==============================] - 3s 2ms/step - loss: 1.0627 - accuracy: 0.7310 - val_loss: 0.3564 - val_accuracy: 0.9062\n",
      "Epoch 2/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.3602 - accuracy: 0.9004 - val_loss: 0.2910 - val_accuracy: 0.9224\n",
      "Epoch 3/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.3012 - accuracy: 0.9146 - val_loss: 0.2608 - val_accuracy: 0.9278\n",
      "Epoch 4/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.2641 - accuracy: 0.9266 - val_loss: 0.2338 - val_accuracy: 0.9352\n",
      "Epoch 5/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.2372 - accuracy: 0.9336 - val_loss: 0.2163 - val_accuracy: 0.9414\n",
      "Epoch 6/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.2191 - accuracy: 0.9383 - val_loss: 0.1985 - val_accuracy: 0.9476\n",
      "Epoch 7/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.2011 - accuracy: 0.9420 - val_loss: 0.1845 - val_accuracy: 0.9502\n",
      "Epoch 8/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.1826 - accuracy: 0.9503 - val_loss: 0.1745 - val_accuracy: 0.9536\n",
      "Epoch 9/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.1776 - accuracy: 0.9505 - val_loss: 0.1648 - val_accuracy: 0.9562\n",
      "Epoch 10/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.1578 - accuracy: 0.9555 - val_loss: 0.1554 - val_accuracy: 0.9598\n",
      "Epoch 11/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.1523 - accuracy: 0.9569 - val_loss: 0.1498 - val_accuracy: 0.9598\n",
      "Epoch 12/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.1416 - accuracy: 0.9604 - val_loss: 0.1434 - val_accuracy: 0.9614\n",
      "Epoch 13/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.1255 - accuracy: 0.9642 - val_loss: 0.1355 - val_accuracy: 0.9634\n",
      "Epoch 14/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.1276 - accuracy: 0.9650 - val_loss: 0.1306 - val_accuracy: 0.9656\n",
      "Epoch 15/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.1205 - accuracy: 0.9684 - val_loss: 0.1263 - val_accuracy: 0.9674\n",
      "Epoch 16/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.1100 - accuracy: 0.9697 - val_loss: 0.1237 - val_accuracy: 0.9678\n",
      "Epoch 17/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.1040 - accuracy: 0.9726 - val_loss: 0.1188 - val_accuracy: 0.9682\n",
      "Epoch 18/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0996 - accuracy: 0.9740 - val_loss: 0.1169 - val_accuracy: 0.9682\n",
      "Epoch 19/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0963 - accuracy: 0.9735 - val_loss: 0.1127 - val_accuracy: 0.9704\n",
      "Epoch 20/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0911 - accuracy: 0.9750 - val_loss: 0.1116 - val_accuracy: 0.9702\n",
      "Epoch 21/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0901 - accuracy: 0.9762 - val_loss: 0.1092 - val_accuracy: 0.9704\n",
      "Epoch 22/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0836 - accuracy: 0.9779 - val_loss: 0.1065 - val_accuracy: 0.9712\n",
      "Epoch 23/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0818 - accuracy: 0.9792 - val_loss: 0.1033 - val_accuracy: 0.9716\n",
      "Epoch 24/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0760 - accuracy: 0.9805 - val_loss: 0.1030 - val_accuracy: 0.9722\n",
      "Epoch 25/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0749 - accuracy: 0.9801 - val_loss: 0.1007 - val_accuracy: 0.9716\n",
      "Epoch 26/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0720 - accuracy: 0.9815 - val_loss: 0.0988 - val_accuracy: 0.9726\n",
      "Epoch 27/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0673 - accuracy: 0.9827 - val_loss: 0.0971 - val_accuracy: 0.9740\n",
      "Epoch 28/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0679 - accuracy: 0.9828 - val_loss: 0.0953 - val_accuracy: 0.9738\n",
      "Epoch 29/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0603 - accuracy: 0.9850 - val_loss: 0.0945 - val_accuracy: 0.9730\n",
      "Epoch 30/30\n",
      "1146/1146 [==============================] - 2s 2ms/step - loss: 0.0617 - accuracy: 0.9844 - val_loss: 0.0924 - val_accuracy: 0.9748\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alexw\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END learning_rate=0.015114765647573948, n_hidden=1, n_neurons=287; total time=  59.6s\n",
      "Epoch 1/30\n",
      "1719/1719 [==============================] - 8s 4ms/step - loss: 0.9152 - accuracy: 0.7517 - val_loss: 0.2173 - val_accuracy: 0.9386\n",
      "Epoch 2/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2064 - accuracy: 0.9401 - val_loss: 0.1445 - val_accuracy: 0.9612\n",
      "Epoch 3/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.1354 - accuracy: 0.9606 - val_loss: 0.1221 - val_accuracy: 0.9648\n",
      "Epoch 4/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.1013 - accuracy: 0.9693 - val_loss: 0.0973 - val_accuracy: 0.9734\n",
      "Epoch 5/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.0800 - accuracy: 0.9754 - val_loss: 0.0888 - val_accuracy: 0.9734\n",
      "Epoch 6/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.0633 - accuracy: 0.9806 - val_loss: 0.0869 - val_accuracy: 0.9750\n",
      "Epoch 7/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.0518 - accuracy: 0.9850 - val_loss: 0.1030 - val_accuracy: 0.9718\n",
      "Epoch 8/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.0387 - accuracy: 0.9886 - val_loss: 0.0740 - val_accuracy: 0.9782\n",
      "Epoch 9/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.0304 - accuracy: 0.9911 - val_loss: 0.0834 - val_accuracy: 0.9772\n",
      "Epoch 10/30\n",
      "1719/1719 [==============================] - 8s 4ms/step - loss: 0.0271 - accuracy: 0.9924 - val_loss: 0.0711 - val_accuracy: 0.9806\n",
      "Epoch 11/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.0213 - accuracy: 0.9951 - val_loss: 0.0798 - val_accuracy: 0.9780\n",
      "Epoch 12/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.0175 - accuracy: 0.9958 - val_loss: 0.0716 - val_accuracy: 0.9816\n",
      "Epoch 13/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.0141 - accuracy: 0.9963 - val_loss: 0.0758 - val_accuracy: 0.9798\n",
      "Epoch 14/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.0120 - accuracy: 0.9974 - val_loss: 0.0779 - val_accuracy: 0.9812\n",
      "Epoch 15/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.0079 - accuracy: 0.9984 - val_loss: 0.0809 - val_accuracy: 0.9800\n",
      "Epoch 16/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.0065 - accuracy: 0.9989 - val_loss: 0.0788 - val_accuracy: 0.9816\n",
      "Epoch 17/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.0053 - accuracy: 0.9991 - val_loss: 0.0784 - val_accuracy: 0.9822\n",
      "Epoch 18/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.0038 - accuracy: 0.9997 - val_loss: 0.0758 - val_accuracy: 0.9828\n",
      "Epoch 19/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.0028 - accuracy: 0.9997 - val_loss: 0.0780 - val_accuracy: 0.9826\n",
      "Epoch 20/30\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.0024 - accuracy: 0.9998 - val_loss: 0.0810 - val_accuracy: 0.9840\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3,\n",
       "                   estimator=<tensorflow.python.keras.wrappers.scikit_learn.KerasClassifier object at 0x0000029AD5C33F70>,\n",
       "                   param_distributions={'learning_rate': [0.012244261547686505,\n",
       "                                                          0.02392460863595287,\n",
       "                                                          0.020111098777089338,\n",
       "                                                          0.004762211431961521,\n",
       "                                                          0.010392905078550537,\n",
       "                                                          0.0018603043619865288,\n",
       "                                                          0.0017369166054379242,\n",
       "                                                          0.0004955137402474184,\n",
       "                                                          0.0043300122358824...\n",
       "                                                          0.0009051629413466672,\n",
       "                                                          0.0003802356843261923,\n",
       "                                                          0.0018794094675637445,\n",
       "                                                          0.0014059767093864705,\n",
       "                                                          0.019380816803604536,\n",
       "                                                          0.0005787380830198248,\n",
       "                                                          0.0028058855331209017,\n",
       "                                                          0.02530912059622319, ...],\n",
       "                                        'n_hidden': [1, 2, 3, 4],\n",
       "                                        'n_neurons': [268, 269, 270, 271, 272,\n",
       "                                                      273, 274, 275, 276, 277,\n",
       "                                                      278, 279, 280, 281, 282,\n",
       "                                                      283, 284, 285, 286, 287,\n",
       "                                                      288, 289, 290, 291, 292,\n",
       "                                                      293, 294, 295, 296, 297, ...]},\n",
       "                   scoring='accuracy', verbose=2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.fit(X_train, y_train, epochs=30,\n",
    "                  validation_data=(X_valid, y_valid),\n",
    "                  callbacks=[keras.callbacks.EarlyStopping(patience=10), \n",
    "                             keras.callbacks.ModelCheckpoint(\"melhor_modelo_mlp_hiperparameters2.h5\", save_best_only=True)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Avaliando o melhor modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = keras.models.load_model(\"melhor_modelo_mlp_hiperparameters2.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_31\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_31 (Flatten)         (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_110 (Dense)            (None, 362)               284170    \n",
      "_________________________________________________________________\n",
      "dense_111 (Dense)            (None, 362)               131406    \n",
      "_________________________________________________________________\n",
      "dense_112 (Dense)            (None, 362)               131406    \n",
      "_________________________________________________________________\n",
      "dense_113 (Dense)            (None, 362)               131406    \n",
      "_________________________________________________________________\n",
      "dense_114 (Dense)            (None, 10)                3630      \n",
      "=================================================================\n",
      "Total params: 682,018\n",
      "Trainable params: 682,018\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "best_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Meta: Acurácia: 0.9798 e Kappa:  0.9775466807563838\n",
    "y_pred = np.argmax(best_model.predict(X_test), axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia:  0.9793\n",
      "Kappa:  0.9769906167735203\n",
      "Matriz de confusão: \n",
      " [[ 965    0    4    1    1    3    1    1    3    1]\n",
      " [   0 1127    2    0    0    1    2    0    3    0]\n",
      " [   1    4 1013    2    2    0    1    5    4    0]\n",
      " [   0    1    7  992    0    2    0    3    3    2]\n",
      " [   0    0    5    0  970    0    1    3    0    3]\n",
      " [   2    0    0    9    2  869    4    1    3    2]\n",
      " [   4    2    1    1    4    8  935    0    3    0]\n",
      " [   1    5    7    2    1    0    0 1003    4    5]\n",
      " [   4    0    2    6    3    4    4    2  946    3]\n",
      " [   1    6    0    9   11    5    1    2    1  973]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, cohen_kappa_score\n",
    "print('Acurácia: ', accuracy_score(y_test,y_pred))\n",
    "print('Kappa: ', cohen_kappa_score(y_test,y_pred))\n",
    "print('Matriz de confusão: \\n', confusion_matrix(y_test,y_pred))"
   ]
  }
 ],
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