Redes_Neurais_Mini_Projeto 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eLpeTjalKY0O"
   },
   "source": [
    "## IF702 - REDES NEURAIS\n",
    "\n",
    "**Grupo:**\n",
    "- Adriano Felipe Cabral Filho (afcf)\n",
    "- Felipe Rodrigues de Souza (frs3)\n",
    "- Lucas Vinícius José da Silva (lvjs)\n",
    "- Pedro José Carneiro de Souza (pjcs) \n",
    "- Vitor Sousa Silva (vss2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vvKzsWFyttnd"
   },
   "source": [
    "# Reconhecimento de Dígitos com CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S1BzDG9xK572"
   },
   "source": [
    "##Importação de datasets e tratamento dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "e9B2D8d0LeiW"
   },
   "outputs": [],
   "source": [
    "!pip install keras-tuner --quiet\n",
    "!pip install optuna --quiet\n",
    "!pip install ipython-autotime --quiet\n",
    "from keras_tuner import RandomSearch\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "import optuna\n",
    "\n",
    "#Loading Data\n",
    "(train_images,train_labels),(test_images,test_labels)=tf.keras.datasets.mnist.load_data()\n",
    "\n",
    "#Reshape data\n",
    "train_images=train_images.reshape((60000,28,28,1))\n",
    "test_images=test_images.reshape((10000,28,28,1))\n",
    "\n",
    "#Normalize\n",
    "train_images,test_images=train_images/255.0 ,test_images/255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4LCkaTc1Lr4z"
   },
   "source": [
    "## Construindo a CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EIUE565V8mOz",
    "outputId": "6d0c8fc2-7750-49ec-a9ff-76ddcd665027"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 3, 3, 64)          36928     \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 576)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 64)                36928     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 93,322\n",
      "Trainable params: 93,322\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Building a CNN Model\n",
    "model=tf.keras.models.Sequential()\n",
    "model.add(tf.keras.layers.Conv2D(32, (3,3),activation='relu',input_shape=(28,28,1)))\n",
    "model.add(tf.keras.layers.MaxPooling2D((2,2)))\n",
    "model.add(tf.keras.layers.Conv2D(64, (3,3),activation='relu'))\n",
    "model.add(tf.keras.layers.MaxPooling2D((2,2)))\n",
    "model.add(tf.keras.layers.Conv2D(64,(3,3),activation='relu'))\n",
    "model.add(tf.keras.layers.Flatten())\n",
    "model.add(tf.keras.layers.Dense(64, activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
    "\n",
    "model.summary()\n",
    "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "L0yvwniM2GDs",
    "outputId": "8c65afd0-8516-467a-b28d-f7a9bba799ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6000/6000 [==============================] - 16s 2ms/step - loss: 0.1172 - accuracy: 0.9637\n"
     ]
    }
   ],
   "source": [
    "model.fit(train_images,train_labels,epochs=1,batch_size=10)\n",
    "scores = model.evaluate(test_images,test_labels, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "d5F0Mkma-kaq"
   },
   "outputs": [],
   "source": [
    "def build_model(hp):\n",
    "    # create model object\n",
    "    model=tf.keras.models.Sequential()\n",
    "    model.add(tf.keras.layers.Conv2D(32, (3,3),activation='relu',input_shape=(28,28,1)))\n",
    "    model.add(tf.keras.layers.MaxPooling2D((2,2)))\n",
    "    model.add(tf.keras.layers.Conv2D(64, (3,3),activation='relu'))\n",
    "    model.add(tf.keras.layers.MaxPooling2D((2,2)))\n",
    "    model.add(tf.keras.layers.Conv2D(64,(3,3),activation='relu'))\n",
    "    model.add(tf.keras.layers.Flatten())\n",
    "    model.add(tf.keras.layers.Dense(64, activation='relu'))\n",
    "    model.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
    "\n",
    "    model.summary()\n",
    "    model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics='accuracy')\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lgiIPkbq-mXA",
    "outputId": "5c8dd7ff-827c-415f-e74c-8efea03fff53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Reloading Oracle from existing project ./untitled_project/trial/untitled_project/oracle.json\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 3, 3, 64)          36928     \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 576)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 64)                36928     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 93,322\n",
      "Trainable params: 93,322\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "INFO:tensorflow:Reloading Tuner from ./untitled_project/trial/untitled_project/tuner0.json\n",
      "INFO:tensorflow:Oracle triggered exit\n"
     ]
    }
   ],
   "source": [
    "#creating randomsearch object\n",
    "tuner = RandomSearch(build_model,\n",
    "                    objective='val_accuracy',\n",
    "                    max_trials = 5, directory='./untitled_project/trial/')\n",
    "# search best parameter\n",
    "tuner.search(train_images,train_labels,epochs=3,validation_data=(train_images,train_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gYj97807VRWO"
   },
   "outputs": [],
   "source": [
    "best_hps=tuner.get_best_hyperparameters(num_trials=1)[0]\n",
    "best_hps.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QXdyYnkg1tIx",
    "outputId": "c52d6056-b96b-413c-bd77-233773e9c535"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Reloading Oracle from existing project ./untitled_project/trial2/untitled_project/oracle.json\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 3, 3, 128)         73856     \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 1152)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               147584    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 241,546\n",
      "Trainable params: 241,546\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "INFO:tensorflow:Reloading Tuner from ./untitled_project/trial2/untitled_project/tuner0.json\n",
      "INFO:tensorflow:Oracle triggered exit\n"
     ]
    }
   ],
   "source": [
    "# Sugestão de Adriano (128 layers na 3a CNN)\n",
    "def build_model2(hp):\n",
    "    # create model object\n",
    "    model=tf.keras.models.Sequential()\n",
    "    model.add(tf.keras.layers.Conv2D(32, (3,3),activation='relu',input_shape=(28,28,1)))\n",
    "    model.add(tf.keras.layers.MaxPooling2D((2,2)))\n",
    "    model.add(tf.keras.layers.Conv2D(64, (3,3),activation='relu'))\n",
    "    model.add(tf.keras.layers.MaxPooling2D((2,2)))\n",
    "    model.add(tf.keras.layers.Conv2D(128,(3,3),activation='relu'))\n",
    "    model.add(tf.keras.layers.Flatten())\n",
    "    model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
    "    model.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
    "\n",
    "    model.summary()\n",
    "    model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics='accuracy')\n",
    "    return model\n",
    "\n",
    "tuner2 = RandomSearch(build_model2,\n",
    "                    objective='val_accuracy',\n",
    "                    max_trials = 5,directory='./untitled_project/trial2/')\n",
    "\n",
    "tuner2.search(train_images,train_labels,epochs=3,validation_data=(train_images,train_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ycnJu_q46qPc",
    "outputId": "07f69063-4048-4adf-b443-aca0489091c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Reloading Oracle from existing project ./untitled_project/trial3/untitled_project/oracle.json\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 27, 27, 32)        160       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 12, 12, 64)        8256      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 5, 5, 128)         32896     \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 3200)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               409728    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 452,330\n",
      "Trainable params: 452,330\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "INFO:tensorflow:Reloading Tuner from ./untitled_project/trial3/untitled_project/tuner0.json\n",
      "INFO:tensorflow:Oracle triggered exit\n"
     ]
    }
   ],
   "source": [
    "# Sugestão de Adriano (128 layers na 3a CNN com dilation_rate menor=2)\n",
    "def build_model3(hp):\n",
    "    # create model object\n",
    "    model=tf.keras.models.Sequential()\n",
    "    model.add(tf.keras.layers.Conv2D(32, (2,2),activation='relu',input_shape=(28,28,1)))\n",
    "    model.add(tf.keras.layers.MaxPooling2D((2,2)))\n",
    "    model.add(tf.keras.layers.Conv2D(64, (2,2),activation='relu'))\n",
    "    model.add(tf.keras.layers.MaxPooling2D((2,2)))\n",
    "    model.add(tf.keras.layers.Conv2D(128,(2,2),activation='relu'))\n",
    "    model.add(tf.keras.layers.Flatten())\n",
    "    model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
    "    model.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
    "\n",
    "    model.summary()\n",
    "    model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics='accuracy')\n",
    "    return model\n",
    "\n",
    "tuner3 = RandomSearch(build_model3,\n",
    "                    objective='val_accuracy',\n",
    "                    max_trials = 5, directory='./untitled_project/trial3/')\n",
    "\n",
    "tuner3.search(train_images,train_labels,epochs=3,validation_data=(train_images,train_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9pMW85Er4L7b"
   },
   "outputs": [],
   "source": [
    "from keras.layers import LSTM\n",
    "from sklearn.metrics import accuracy_score, plot_confusion_matrix, confusion_matrix, classification_report\n",
    "\n",
    "(train_images,train_labels),(test_images,test_labels)=tf.keras.datasets.mnist.load_data()\n",
    "\n",
    "#Reshape data\n",
    "train_images=train_images.reshape((60000,28,28,1))\n",
    "test_images=test_images.reshape((10000,28,28,1))\n",
    "\n",
    "#Normalize\n",
    "train_images,test_images=train_images/255.0 ,test_images/255.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RWh03yw8KDjc"
   },
   "source": [
    "## Redes utilizadas\n",
    "\n",
    "- Camadas finais de adensamento com função de ativação 'relu' e de categorização com função de ativação 'softmax'.\n",
    "\n",
    "### CNN1:\n",
    "- Neurônios: 32 à 128\n",
    "- Dilatação: 1 ou 2\n",
    "- Camadas CNN: 2\n",
    "\n",
    "### CNN2:\n",
    "- Neurônios: 32 à 128\n",
    "- Dilatação: 1 ou 2\n",
    "- Camadas CNN: 3\n",
    "\n",
    "### CNN3:\n",
    "- Neurônios: 32 à 128\n",
    "- Dilatação: 1\n",
    "- Camadas CNN: 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pu1tgIQ0Jj6a"
   },
   "outputs": [],
   "source": [
    "def cnn(trial):\n",
    "  (x_train, y_train), (x_test, y_test) = (train_images,train_labels),(test_images,test_labels)\n",
    "  \n",
    "  activation = trial.suggest_categorical('activation', ['sigmoid', 'softmax', 'tanh', 'relu','softplus','softsign','selu','elu','exponential'])\n",
    "  \n",
    "  n_neurons = trial.suggest_int('n_neurons', 16, 64)\n",
    "  dilation = trial.suggest_int('dilation', 1, 2)\n",
    "  loss = trial.suggest_categorical('loss', ['sparse_categorical_crossentropy'])\n",
    "  optimizer = trial.suggest_categorical('optimizer', ['sgd', 'adam','rmsprop','adamax'])\n",
    "  \n",
    "  print(\"Função de ativação: {}\\nNúmero de neurônios: {}\\nOtimizador: {}\".format(activation,n_neurons,optimizer))\n",
    "  model = keras.models.Sequential()\n",
    "\n",
    "  model.add(tf.keras.layers.Conv2D(n_neurons, (dilation,dilation),activation=activation,input_shape=(28,28,1)))\n",
    "  model.add(tf.keras.layers.MaxPooling2D((dilation,dilation)))\n",
    "  model.add(tf.keras.layers.Conv2D(n_neurons*2, (dilation,dilation),activation=activation))\n",
    "  model.add(tf.keras.layers.Flatten())\n",
    "  model.add(tf.keras.layers.Dense(n_neurons*2, activation='relu'))\n",
    "  model.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
    "  model.compile(loss=loss,optimizer=optimizer,metrics=[\"accuracy\"])\n",
    "\n",
    "  history = model.fit(x_train, y_train, epochs=8, verbose=1)\n",
    "  y_pred = np.argmax(model.predict(x_test), axis=-1)\n",
    "  y_true = y_test\n",
    "  return accuracy_score(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bUdiNEiPKy6O",
    "outputId": "5a33d9b9-79df-41d5-cb1d-db9293be6e6e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:13:11,446]\u001b[0m A new study created in memory with name: no-name-0212a617-dc7d-4797-b20d-8c84dd82265d\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: selu\n",
      "Número de neurônios: 32\n",
      "Otimizador: sgd\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3251 - accuracy: 0.9087\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1599 - accuracy: 0.9535\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1184 - accuracy: 0.9643\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0939 - accuracy: 0.9720\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0782 - accuracy: 0.9766\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0670 - accuracy: 0.9797\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0577 - accuracy: 0.9825\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0505 - accuracy: 0.9842\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:13:49,505]\u001b[0m Trial 0 finished with value: 0.9729 and parameters: {'activation': 'selu', 'n_neurons': 32, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'sgd'}. Best is trial 0 with value: 0.9729.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: softplus\n",
      "Número de neurônios: 24\n",
      "Otimizador: rmsprop\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 2.4701 - accuracy: 0.1115\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3014 - accuracy: 0.1124\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3014 - accuracy: 0.1124\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3013 - accuracy: 0.1124\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:14:32,475]\u001b[0m Trial 1 finished with value: 0.1135 and parameters: {'activation': 'softplus', 'n_neurons': 24, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'rmsprop'}. Best is trial 0 with value: 0.9729.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: selu\n",
      "Número de neurônios: 20\n",
      "Otimizador: adamax\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2900 - accuracy: 0.9158\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1507 - accuracy: 0.9556\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1080 - accuracy: 0.9675\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0821 - accuracy: 0.9762\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0661 - accuracy: 0.9802\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0534 - accuracy: 0.9839\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0443 - accuracy: 0.9865\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0360 - accuracy: 0.9891\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:15:04,483]\u001b[0m Trial 2 finished with value: 0.9794 and parameters: {'activation': 'selu', 'n_neurons': 20, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adamax'}. Best is trial 2 with value: 0.9794.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: softplus\n",
      "Número de neurônios: 61\n",
      "Otimizador: adamax\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 2.6075 - accuracy: 0.1106\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 9s 5ms/step - loss: 2.3014 - accuracy: 0.1124\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 9s 5ms/step - loss: 2.3012 - accuracy: 0.1124\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 9s 5ms/step - loss: 2.3012 - accuracy: 0.1124\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 9s 5ms/step - loss: 2.3012 - accuracy: 0.1124\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 9s 5ms/step - loss: 2.3012 - accuracy: 0.1124\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 9s 5ms/step - loss: 2.3012 - accuracy: 0.1124\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 9s 5ms/step - loss: 2.3012 - accuracy: 0.1124\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:16:19,574]\u001b[0m Trial 3 finished with value: 0.1135 and parameters: {'activation': 'softplus', 'n_neurons': 61, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adamax'}. Best is trial 2 with value: 0.9794.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: softsign\n",
      "Número de neurônios: 39\n",
      "Otimizador: sgd\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5266 - accuracy: 0.8632\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2326 - accuracy: 0.9309\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1798 - accuracy: 0.9467\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1489 - accuracy: 0.9560\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1286 - accuracy: 0.9615\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1136 - accuracy: 0.9665\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1025 - accuracy: 0.9700\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0937 - accuracy: 0.9723\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:16:51,268]\u001b[0m Trial 4 finished with value: 0.9689 and parameters: {'activation': 'softsign', 'n_neurons': 39, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'sgd'}. Best is trial 2 with value: 0.9794.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: softsign\n",
      "Número de neurônios: 39\n",
      "Otimizador: adam\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1654 - accuracy: 0.9506\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0634 - accuracy: 0.9801\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0464 - accuracy: 0.9858\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0346 - accuracy: 0.9889\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0272 - accuracy: 0.9908\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0220 - accuracy: 0.9931\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0193 - accuracy: 0.9935\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0175 - accuracy: 0.9942\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:17:24,593]\u001b[0m Trial 5 finished with value: 0.9823 and parameters: {'activation': 'softsign', 'n_neurons': 39, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adam'}. Best is trial 5 with value: 0.9823.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: relu\n",
      "Número de neurônios: 21\n",
      "Otimizador: rmsprop\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1621 - accuracy: 0.9503\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0589 - accuracy: 0.9821\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0427 - accuracy: 0.9874\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0349 - accuracy: 0.9892\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0289 - accuracy: 0.9915\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0258 - accuracy: 0.9925\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0224 - accuracy: 0.9938\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0197 - accuracy: 0.9940\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:18:00,618]\u001b[0m Trial 6 finished with value: 0.987 and parameters: {'activation': 'relu', 'n_neurons': 21, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'rmsprop'}. Best is trial 6 with value: 0.987.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: softsign\n",
      "Número de neurônios: 42\n",
      "Otimizador: rmsprop\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2303 - accuracy: 0.9307\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1269 - accuracy: 0.9637\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1018 - accuracy: 0.9710\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0848 - accuracy: 0.9756\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0757 - accuracy: 0.9779\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0679 - accuracy: 0.9804\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0633 - accuracy: 0.9819\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0595 - accuracy: 0.9838\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:19:04,305]\u001b[0m Trial 7 finished with value: 0.9704 and parameters: {'activation': 'softsign', 'n_neurons': 42, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'rmsprop'}. Best is trial 6 with value: 0.987.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: selu\n",
      "Número de neurônios: 17\n",
      "Otimizador: adamax\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2846 - accuracy: 0.9161\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1798 - accuracy: 0.9464\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1489 - accuracy: 0.9559\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1279 - accuracy: 0.9622\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1163 - accuracy: 0.9648\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1066 - accuracy: 0.9677\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0976 - accuracy: 0.9704\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0898 - accuracy: 0.9722\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:19:39,487]\u001b[0m Trial 8 finished with value: 0.9591 and parameters: {'activation': 'selu', 'n_neurons': 17, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adamax'}. Best is trial 6 with value: 0.987.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: exponential\n",
      "Número de neurônios: 34\n",
      "Otimizador: rmsprop\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.5519 - accuracy: 0.8497\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1497 - accuracy: 0.9552\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1068 - accuracy: 0.9675\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0886 - accuracy: 0.9735\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0768 - accuracy: 0.9769\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0700 - accuracy: 0.9790\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0641 - accuracy: 0.9809\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0601 - accuracy: 0.9825\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:20:15,531]\u001b[0m Trial 9 finished with value: 0.9809 and parameters: {'activation': 'exponential', 'n_neurons': 34, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'rmsprop'}. Best is trial 6 with value: 0.987.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: relu\n",
      "Número de neurônios: 51\n",
      "Otimizador: adam\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1215 - accuracy: 0.9631\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0409 - accuracy: 0.9875\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0242 - accuracy: 0.9921\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0168 - accuracy: 0.9944\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0120 - accuracy: 0.9962\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0095 - accuracy: 0.9968\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0070 - accuracy: 0.9976\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0060 - accuracy: 0.9979\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:20:51,066]\u001b[0m Trial 10 finished with value: 0.9882 and parameters: {'activation': 'relu', 'n_neurons': 51, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adam'}. Best is trial 10 with value: 0.9882.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: relu\n",
      "Número de neurônios: 53\n",
      "Otimizador: adam\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1203 - accuracy: 0.9638\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0412 - accuracy: 0.9872\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0228 - accuracy: 0.9926\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0158 - accuracy: 0.9948\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0116 - accuracy: 0.9962\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0092 - accuracy: 0.9970\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0066 - accuracy: 0.9980\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0054 - accuracy: 0.9981\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:21:26,741]\u001b[0m Trial 11 finished with value: 0.9886 and parameters: {'activation': 'relu', 'n_neurons': 53, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adam'}. Best is trial 11 with value: 0.9886.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: relu\n",
      "Número de neurônios: 56\n",
      "Otimizador: adam\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1241 - accuracy: 0.9613\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0410 - accuracy: 0.9875\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0250 - accuracy: 0.9919\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0175 - accuracy: 0.9944\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0123 - accuracy: 0.9961\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0088 - accuracy: 0.9972\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0081 - accuracy: 0.9972\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0061 - accuracy: 0.9981\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:22:03,102]\u001b[0m Trial 12 finished with value: 0.9893 and parameters: {'activation': 'relu', 'n_neurons': 56, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adam'}. Best is trial 12 with value: 0.9893.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: relu\n",
      "Número de neurônios: 62\n",
      "Otimizador: adam\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1140 - accuracy: 0.9655\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0387 - accuracy: 0.9877\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0228 - accuracy: 0.9927\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0157 - accuracy: 0.9946\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0112 - accuracy: 0.9962\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0095 - accuracy: 0.9969\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0074 - accuracy: 0.9972\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0057 - accuracy: 0.9983\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:22:39,955]\u001b[0m Trial 13 finished with value: 0.9862 and parameters: {'activation': 'relu', 'n_neurons': 62, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adam'}. Best is trial 12 with value: 0.9893.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: elu\n",
      "Número de neurônios: 52\n",
      "Otimizador: adam\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1505 - accuracy: 0.9547\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0621 - accuracy: 0.9810\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0421 - accuracy: 0.9866\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0361 - accuracy: 0.9883\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0280 - accuracy: 0.9911\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0227 - accuracy: 0.9926\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0220 - accuracy: 0.9928\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0185 - accuracy: 0.9941\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:23:14,891]\u001b[0m Trial 14 finished with value: 0.9822 and parameters: {'activation': 'elu', 'n_neurons': 52, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adam'}. Best is trial 12 with value: 0.9893.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: tanh\n",
      "Número de neurônios: 53\n",
      "Otimizador: adam\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1474 - accuracy: 0.9549\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0628 - accuracy: 0.9804\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0419 - accuracy: 0.9865\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0340 - accuracy: 0.9890\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0287 - accuracy: 0.9908\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0219 - accuracy: 0.9927\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0205 - accuracy: 0.9934\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0166 - accuracy: 0.9949\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:23:50,908]\u001b[0m Trial 15 finished with value: 0.9828 and parameters: {'activation': 'tanh', 'n_neurons': 53, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adam'}. Best is trial 12 with value: 0.9893.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "study_1 = optuna.create_study(direction='maximize')\n",
    "study_1.optimize(cnn, n_trials=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uLT_drmGJjtA"
   },
   "outputs": [],
   "source": [
    "def cnn2(trial):\n",
    "  (x_train, y_train), (x_test, y_test) = (train_images,train_labels),(test_images,test_labels)\n",
    "  \n",
    "  activation = trial.suggest_categorical('activation', ['sigmoid', 'softmax', 'tanh', 'relu','softplus','softsign','selu','elu','exponential'])\n",
    "  \n",
    "  n_neurons = trial.suggest_int('n_neurons', 16, 64)\n",
    "  dilation = trial.suggest_int('dilation', 1, 2)\n",
    "  loss = trial.suggest_categorical('loss', ['sparse_categorical_crossentropy'])\n",
    "  optimizer = trial.suggest_categorical('optimizer', ['sgd', 'adam','rmsprop','adamax'])\n",
    "  \n",
    "  print(\"Função de ativação: {}\\nNúmero de neurônios: {}\\nOtimizador: {}\".format(activation,n_neurons,optimizer))\n",
    "  model = keras.models.Sequential()\n",
    "\n",
    "  model.add(tf.keras.layers.Conv2D(n_neurons, (dilation,dilation),activation=activation,input_shape=(28,28,1)))\n",
    "  model.add(tf.keras.layers.MaxPooling2D((dilation,dilation)))\n",
    "\n",
    "  # ----------------- ADICIONAMOS MAIS UMA CAMADA CNN ----------------- #\n",
    "  model.add(tf.keras.layers.Conv2D(n_neurons*2, (dilation,dilation),activation=activation))\n",
    "  model.add(tf.keras.layers.MaxPooling2D((dilation,dilation)))\n",
    "  # ----------------- ADICIONAMOS MAIS UMA CAMADA CNN ----------------- #\n",
    "\n",
    "  model.add(tf.keras.layers.Conv2D(n_neurons*2, (dilation,dilation),activation=activation))\n",
    "  model.add(tf.keras.layers.Flatten())\n",
    "  model.add(tf.keras.layers.Dense(n_neurons*2, activation='relu'))\n",
    "  model.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
    "  model.compile(loss=loss,optimizer=optimizer,metrics=[\"accuracy\"])\n",
    "\n",
    "  history = model.fit(x_train, y_train, epochs=8, verbose=1)\n",
    "  y_pred = np.argmax(model.predict(x_test), axis=-1)\n",
    "  y_true = y_test\n",
    "  return accuracy_score(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QemAZJPJKxOv",
    "outputId": "34e3354e-a322-4e14-ec37-94ace2e102c0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:23:50,941]\u001b[0m A new study created in memory with name: no-name-fd4b0ef8-84d3-496c-9361-d91ac0a9eb1f\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: softmax\n",
      "Número de neurônios: 20\n",
      "Otimizador: rmsprop\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3023 - accuracy: 0.1119\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3014 - accuracy: 0.1124\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3014 - accuracy: 0.1124\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3014 - accuracy: 0.1124\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3014 - accuracy: 0.1124\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 2.3014 - accuracy: 0.1124\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:24:45,081]\u001b[0m Trial 0 finished with value: 0.1135 and parameters: {'activation': 'softmax', 'n_neurons': 20, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'rmsprop'}. Best is trial 0 with value: 0.1135.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: selu\n",
      "Número de neurônios: 36\n",
      "Otimizador: adamax\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2375 - accuracy: 0.9299\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1321 - accuracy: 0.9595\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0994 - accuracy: 0.9701\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0822 - accuracy: 0.9750\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0676 - accuracy: 0.9788\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0559 - accuracy: 0.9821\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0483 - accuracy: 0.9845\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0416 - accuracy: 0.9865\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:25:46,116]\u001b[0m Trial 1 finished with value: 0.9689 and parameters: {'activation': 'selu', 'n_neurons': 36, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adamax'}. Best is trial 1 with value: 0.9689.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: softplus\n",
      "Número de neurônios: 45\n",
      "Otimizador: rmsprop\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 1.4710 - accuracy: 0.6766\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 0.3919 - accuracy: 0.8848\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 0.3430 - accuracy: 0.8993\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 0.3180 - accuracy: 0.9072\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 0.3034 - accuracy: 0.9124\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 0.2923 - accuracy: 0.9146\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 0.2843 - accuracy: 0.9153\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 0.2780 - accuracy: 0.9183\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:27:13,336]\u001b[0m Trial 2 finished with value: 0.9213 and parameters: {'activation': 'softplus', 'n_neurons': 45, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'rmsprop'}. Best is trial 1 with value: 0.9689.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: exponential\n",
      "Número de neurônios: 16\n",
      "Otimizador: adamax\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 1.0665 - accuracy: 0.6704\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3611 - accuracy: 0.8900\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2351 - accuracy: 0.9291\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1743 - accuracy: 0.9471\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1379 - accuracy: 0.9580\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1161 - accuracy: 0.9638\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1019 - accuracy: 0.9689\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0904 - accuracy: 0.9724\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:27:49,889]\u001b[0m Trial 3 finished with value: 0.9751 and parameters: {'activation': 'exponential', 'n_neurons': 16, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adamax'}. Best is trial 3 with value: 0.9751.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: selu\n",
      "Número de neurônios: 41\n",
      "Otimizador: adamax\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1678 - accuracy: 0.9504\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0572 - accuracy: 0.9830\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0401 - accuracy: 0.9877\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0299 - accuracy: 0.9911\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0234 - accuracy: 0.9927\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0183 - accuracy: 0.9945\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0142 - accuracy: 0.9958\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0111 - accuracy: 0.9966\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:28:28,012]\u001b[0m Trial 4 finished with value: 0.9893 and parameters: {'activation': 'selu', 'n_neurons': 41, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adamax'}. Best is trial 4 with value: 0.9893.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: tanh\n",
      "Número de neurônios: 20\n",
      "Otimizador: rmsprop\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 7s 3ms/step - loss: 0.2742 - accuracy: 0.9190\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1547 - accuracy: 0.9554\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1275 - accuracy: 0.9624\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1114 - accuracy: 0.9679\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0990 - accuracy: 0.9707\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0908 - accuracy: 0.9742\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0836 - accuracy: 0.9758\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0775 - accuracy: 0.9780\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:29:16,820]\u001b[0m Trial 5 finished with value: 0.9695 and parameters: {'activation': 'tanh', 'n_neurons': 20, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'rmsprop'}. Best is trial 4 with value: 0.9893.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: softplus\n",
      "Número de neurônios: 53\n",
      "Otimizador: adamax\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 1.2615 - accuracy: 0.5430\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1927 - accuracy: 0.9420\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1027 - accuracy: 0.9682\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0716 - accuracy: 0.9783\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0562 - accuracy: 0.9833\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0443 - accuracy: 0.9857\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0356 - accuracy: 0.9888\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0302 - accuracy: 0.9899\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:29:58,533]\u001b[0m Trial 6 finished with value: 0.9879 and parameters: {'activation': 'softplus', 'n_neurons': 53, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adamax'}. Best is trial 4 with value: 0.9893.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: sigmoid\n",
      "Número de neurônios: 63\n",
      "Otimizador: sgd\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 2.3220 - accuracy: 0.1121\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 2.3012 - accuracy: 0.1124\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 2.3013 - accuracy: 0.1124\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:31:18,595]\u001b[0m Trial 7 finished with value: 0.1135 and parameters: {'activation': 'sigmoid', 'n_neurons': 63, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'sgd'}. Best is trial 4 with value: 0.9893.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: exponential\n",
      "Número de neurônios: 63\n",
      "Otimizador: adam\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 2.2449 - accuracy: 0.8125\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 0.2702 - accuracy: 0.9238\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 0.2185 - accuracy: 0.9388\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 0.1784 - accuracy: 0.9499\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 0.1528 - accuracy: 0.9570\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 0.1329 - accuracy: 0.9614\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 0.1188 - accuracy: 0.9653\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 0.1057 - accuracy: 0.9687\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:32:47,390]\u001b[0m Trial 8 finished with value: 0.961 and parameters: {'activation': 'exponential', 'n_neurons': 63, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adam'}. Best is trial 4 with value: 0.9893.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: elu\n",
      "Número de neurônios: 19\n",
      "Otimizador: adamax\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.3001 - accuracy: 0.9099\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1013 - accuracy: 0.9699\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0705 - accuracy: 0.9783\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0550 - accuracy: 0.9830\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0465 - accuracy: 0.9859\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0403 - accuracy: 0.9878\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0354 - accuracy: 0.9892\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0307 - accuracy: 0.9906\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:33:23,811]\u001b[0m Trial 9 finished with value: 0.9878 and parameters: {'activation': 'elu', 'n_neurons': 19, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adamax'}. Best is trial 4 with value: 0.9893.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: selu\n",
      "Número de neurônios: 35\n",
      "Otimizador: sgd\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4111 - accuracy: 0.8804\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1319 - accuracy: 0.9608\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0902 - accuracy: 0.9733\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0707 - accuracy: 0.9790\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0587 - accuracy: 0.9826\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0508 - accuracy: 0.9844\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0448 - accuracy: 0.9863\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0397 - accuracy: 0.9879\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:33:57,578]\u001b[0m Trial 10 finished with value: 0.9843 and parameters: {'activation': 'selu', 'n_neurons': 35, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'sgd'}. Best is trial 4 with value: 0.9893.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: softplus\n",
      "Número de neurônios: 49\n",
      "Otimizador: adamax\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3054 - accuracy: 0.1103\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3012 - accuracy: 0.1124\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3012 - accuracy: 0.1124\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3012 - accuracy: 0.1124\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3012 - accuracy: 0.1124\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3012 - accuracy: 0.1124\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 2.3012 - accuracy: 0.1124\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:34:38,332]\u001b[0m Trial 11 finished with value: 0.1135 and parameters: {'activation': 'softplus', 'n_neurons': 49, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adamax'}. Best is trial 4 with value: 0.9893.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: softsign\n",
      "Número de neurônios: 53\n",
      "Otimizador: adamax\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2033 - accuracy: 0.9384\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0616 - accuracy: 0.9813\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0401 - accuracy: 0.9879\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0304 - accuracy: 0.9908\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0242 - accuracy: 0.9927\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0190 - accuracy: 0.9942\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0154 - accuracy: 0.9952\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0127 - accuracy: 0.9961\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:35:20,639]\u001b[0m Trial 12 finished with value: 0.9917 and parameters: {'activation': 'softsign', 'n_neurons': 53, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adamax'}. Best is trial 12 with value: 0.9917.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: softsign\n",
      "Número de neurônios: 55\n",
      "Otimizador: adam\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1319 - accuracy: 0.9592\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0500 - accuracy: 0.9847\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0400 - accuracy: 0.9873\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0314 - accuracy: 0.9898\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0273 - accuracy: 0.9915\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0243 - accuracy: 0.9925\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0224 - accuracy: 0.9933\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0198 - accuracy: 0.9937\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:36:01,848]\u001b[0m Trial 13 finished with value: 0.9813 and parameters: {'activation': 'softsign', 'n_neurons': 55, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adam'}. Best is trial 12 with value: 0.9917.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: softsign\n",
      "Número de neurônios: 31\n",
      "Otimizador: adamax\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2554 - accuracy: 0.9264\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0827 - accuracy: 0.9759\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0575 - accuracy: 0.9829\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0450 - accuracy: 0.9864\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0370 - accuracy: 0.9889\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0315 - accuracy: 0.9906\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0274 - accuracy: 0.9917\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0238 - accuracy: 0.9930\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:36:43,751]\u001b[0m Trial 14 finished with value: 0.99 and parameters: {'activation': 'softsign', 'n_neurons': 31, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adamax'}. Best is trial 12 with value: 0.9917.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: softsign\n",
      "Número de neurônios: 29\n",
      "Otimizador: adamax\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2614 - accuracy: 0.9236\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0841 - accuracy: 0.9745\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0599 - accuracy: 0.9824\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0471 - accuracy: 0.9858\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0388 - accuracy: 0.9882\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0328 - accuracy: 0.9902\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0283 - accuracy: 0.9914\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0246 - accuracy: 0.9924\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:37:22,545]\u001b[0m Trial 15 finished with value: 0.9886 and parameters: {'activation': 'softsign', 'n_neurons': 29, 'dilation': 2, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adamax'}. Best is trial 12 with value: 0.9917.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "study_2 = optuna.create_study(direction='maximize')\n",
    "study_2.optimize(cnn2, n_trials=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "aQWlb8cv8qXX"
   },
   "outputs": [],
   "source": [
    "def cnn3(trial):\n",
    "  (x_train, y_train), (x_test, y_test) = (train_images,train_labels),(test_images,test_labels)\n",
    "  \n",
    "  activation = trial.suggest_categorical('activation', ['sigmoid', 'softmax', 'tanh', 'relu','softplus','softsign','selu','elu','exponential'])\n",
    "  \n",
    "  n_neurons = trial.suggest_int('n_neurons', 16, 64)\n",
    "  # Redução de dilation\n",
    "  dilation = trial.suggest_int('dilation', 1, 1)\n",
    "  loss = trial.suggest_categorical('loss', ['sparse_categorical_crossentropy'])\n",
    "  optimizer = trial.suggest_categorical('optimizer', ['sgd', 'adam','rmsprop','adamax'])\n",
    "  \n",
    "  print(\"Função de ativação: {}\\nNúmero de neurônios: {}\\nOtimizador: {}\".format(activation,n_neurons,optimizer))\n",
    "  model = keras.models.Sequential()\n",
    "\n",
    "  model.add(tf.keras.layers.Conv2D(n_neurons, (dilation,dilation),activation=activation,input_shape=(28,28,1)))\n",
    "  model.add(tf.keras.layers.MaxPooling2D((dilation,dilation)))\n",
    "  \n",
    "  # ----------------- ADICIONAMOS MAIS TRÊS CAMADA CNN ----------------- #\n",
    "  model.add(tf.keras.layers.Conv2D(n_neurons*2, (dilation,dilation),activation=activation))\n",
    "  model.add(tf.keras.layers.MaxPooling2D((dilation,dilation)))\n",
    "  model.add(tf.keras.layers.Conv2D(n_neurons*2, (dilation,dilation),activation=activation))\n",
    "  model.add(tf.keras.layers.MaxPooling2D((dilation,dilation)))\n",
    "  model.add(tf.keras.layers.Conv2D(n_neurons*2, (dilation,dilation),activation=activation))\n",
    "  model.add(tf.keras.layers.MaxPooling2D((dilation,dilation)))\n",
    "  # ----------------- ADICIONAMOS MAIS TRÊS CAMADA CNN ----------------- #\n",
    "\n",
    "  model.add(tf.keras.layers.Conv2D(n_neurons*2, (dilation,dilation),activation=activation))\n",
    "  model.add(tf.keras.layers.Flatten())\n",
    "  model.add(tf.keras.layers.Dense(n_neurons*2, activation='relu'))\n",
    "  model.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
    "  model.compile(loss=loss,optimizer=optimizer,metrics=[\"accuracy\"])\n",
    "\n",
    "  history = model.fit(x_train, y_train, epochs=8, verbose=1)\n",
    "  y_pred = np.argmax(model.predict(x_test), axis=-1)\n",
    "  y_true = y_test\n",
    "  return accuracy_score(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UPBYNgTv8k3i",
    "outputId": "1090d1c7-c7b0-416d-c4c1-39086486a673"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:37:22,583]\u001b[0m A new study created in memory with name: no-name-bb39e995-7f55-4052-b3f5-188b7096ee06\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: sigmoid\n",
      "Número de neurônios: 43\n",
      "Otimizador: adamax\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 14s 7ms/step - loss: 2.3924 - accuracy: 0.1113\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 2.3012 - accuracy: 0.1124\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 2.3012 - accuracy: 0.1124\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 2.3012 - accuracy: 0.1124\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 2.3012 - accuracy: 0.1124\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 2.3012 - accuracy: 0.1124\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 2.3012 - accuracy: 0.1124\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:39:08,714]\u001b[0m Trial 0 finished with value: 0.1135 and parameters: {'activation': 'sigmoid', 'n_neurons': 43, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adamax'}. Best is trial 0 with value: 0.1135.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: softsign\n",
      "Número de neurônios: 32\n",
      "Otimizador: rmsprop\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 11s 5ms/step - loss: 0.2440 - accuracy: 0.9276\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 10s 6ms/step - loss: 0.1273 - accuracy: 0.9620\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.1019 - accuracy: 0.9704\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0877 - accuracy: 0.9744\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0781 - accuracy: 0.9776\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0697 - accuracy: 0.9795\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0646 - accuracy: 0.9809\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0596 - accuracy: 0.9834\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:40:32,414]\u001b[0m Trial 1 finished with value: 0.9725 and parameters: {'activation': 'softsign', 'n_neurons': 32, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'rmsprop'}. Best is trial 1 with value: 0.9725.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: tanh\n",
      "Número de neurônios: 37\n",
      "Otimizador: rmsprop\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 13s 6ms/step - loss: 0.2619 - accuracy: 0.9236\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.1307 - accuracy: 0.9622\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.1046 - accuracy: 0.9690\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0871 - accuracy: 0.9748\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0779 - accuracy: 0.9776\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0694 - accuracy: 0.9804\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0642 - accuracy: 0.9819\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0610 - accuracy: 0.9833\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:42:10,612]\u001b[0m Trial 2 finished with value: 0.9713 and parameters: {'activation': 'tanh', 'n_neurons': 37, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'rmsprop'}. Best is trial 1 with value: 0.9725.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: softmax\n",
      "Número de neurônios: 42\n",
      "Otimizador: rmsprop\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 18s 9ms/step - loss: 2.3041 - accuracy: 0.1110\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 2.3014 - accuracy: 0.1124\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 2.3014 - accuracy: 0.1124\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 2.3014 - accuracy: 0.1124\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 2.3014 - accuracy: 0.1124\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:44:34,662]\u001b[0m Trial 3 finished with value: 0.1135 and parameters: {'activation': 'softmax', 'n_neurons': 42, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'rmsprop'}. Best is trial 1 with value: 0.9725.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: elu\n",
      "Número de neurônios: 41\n",
      "Otimizador: sgd\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.2901 - accuracy: 0.9162\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.1269 - accuracy: 0.9626\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0892 - accuracy: 0.9730\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0702 - accuracy: 0.9783\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0539 - accuracy: 0.9832\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0453 - accuracy: 0.9853\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0375 - accuracy: 0.9878\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0297 - accuracy: 0.9903\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:46:57,749]\u001b[0m Trial 4 finished with value: 0.9704 and parameters: {'activation': 'elu', 'n_neurons': 41, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'sgd'}. Best is trial 1 with value: 0.9725.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: elu\n",
      "Número de neurônios: 60\n",
      "Otimizador: rmsprop\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 18s 9ms/step - loss: 0.2762 - accuracy: 0.9220\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 0.1356 - accuracy: 0.9622\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 0.1099 - accuracy: 0.9700\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 0.0954 - accuracy: 0.9747\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 0.0876 - accuracy: 0.9774\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 0.0799 - accuracy: 0.9791\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 0.0765 - accuracy: 0.9816\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 0.0732 - accuracy: 0.9816\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:49:17,789]\u001b[0m Trial 5 finished with value: 0.9647 and parameters: {'activation': 'elu', 'n_neurons': 60, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'rmsprop'}. Best is trial 1 with value: 0.9725.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: relu\n",
      "Número de neurônios: 38\n",
      "Otimizador: sgd\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 11s 5ms/step - loss: 0.4595 - accuracy: 0.8709\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.1462 - accuracy: 0.9557\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.1027 - accuracy: 0.9687\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0822 - accuracy: 0.9745\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0660 - accuracy: 0.9801\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0554 - accuracy: 0.9827\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0487 - accuracy: 0.9848\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0410 - accuracy: 0.9872\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:50:39,762]\u001b[0m Trial 6 finished with value: 0.9744 and parameters: {'activation': 'relu', 'n_neurons': 38, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'sgd'}. Best is trial 6 with value: 0.9744.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: softmax\n",
      "Número de neurônios: 64\n",
      "Otimizador: rmsprop\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 22s 11ms/step - loss: 2.3059 - accuracy: 0.1114\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 21s 11ms/step - loss: 2.3014 - accuracy: 0.1124\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 21s 11ms/step - loss: 2.3014 - accuracy: 0.1124\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 21s 11ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 21s 11ms/step - loss: 2.3014 - accuracy: 0.1124\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 21s 11ms/step - loss: 2.3014 - accuracy: 0.1124\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 21s 11ms/step - loss: 2.3014 - accuracy: 0.1124\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 21s 11ms/step - loss: 2.3013 - accuracy: 0.1124\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:53:32,748]\u001b[0m Trial 7 finished with value: 0.1135 and parameters: {'activation': 'softmax', 'n_neurons': 64, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'rmsprop'}. Best is trial 6 with value: 0.9744.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: exponential\n",
      "Número de neurônios: 19\n",
      "Otimizador: rmsprop\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 9s 4ms/step - loss: nan - accuracy: 0.0987\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: nan - accuracy: 0.0987\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: nan - accuracy: 0.0987\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: nan - accuracy: 0.0987\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: nan - accuracy: 0.0987\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: nan - accuracy: 0.0987\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: nan - accuracy: 0.0987\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: nan - accuracy: 0.0987\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:54:38,064]\u001b[0m Trial 8 finished with value: 0.098 and parameters: {'activation': 'exponential', 'n_neurons': 19, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'rmsprop'}. Best is trial 6 with value: 0.9744.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: selu\n",
      "Número de neurônios: 41\n",
      "Otimizador: sgd\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.2199 - accuracy: 0.9355\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.1050 - accuracy: 0.9680\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0762 - accuracy: 0.9767\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0595 - accuracy: 0.9809\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0476 - accuracy: 0.9846\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0388 - accuracy: 0.9877\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0320 - accuracy: 0.9900\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0254 - accuracy: 0.9921\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:56:14,523]\u001b[0m Trial 9 finished with value: 0.9758 and parameters: {'activation': 'selu', 'n_neurons': 41, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'sgd'}. Best is trial 9 with value: 0.9758.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: selu\n",
      "Número de neurônios: 52\n",
      "Otimizador: adam\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 14s 7ms/step - loss: 0.2547 - accuracy: 0.9246\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.1457 - accuracy: 0.9568\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.1187 - accuracy: 0.9653\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.1009 - accuracy: 0.9703\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0822 - accuracy: 0.9748\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0780 - accuracy: 0.9765\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0675 - accuracy: 0.9796\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0662 - accuracy: 0.9797\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:58:03,140]\u001b[0m Trial 10 finished with value: 0.9691 and parameters: {'activation': 'selu', 'n_neurons': 52, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adam'}. Best is trial 9 with value: 0.9758.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: relu\n",
      "Número de neurônios: 27\n",
      "Otimizador: sgd\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.4663 - accuracy: 0.8668\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1615 - accuracy: 0.9533\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1166 - accuracy: 0.9649\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0932 - accuracy: 0.9715\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0801 - accuracy: 0.9749\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0678 - accuracy: 0.9791\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0599 - accuracy: 0.9813\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0528 - accuracy: 0.9833\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 13:59:04,956]\u001b[0m Trial 11 finished with value: 0.9747 and parameters: {'activation': 'relu', 'n_neurons': 27, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'sgd'}. Best is trial 9 with value: 0.9758.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: relu\n",
      "Número de neurônios: 23\n",
      "Otimizador: sgd\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.5265 - accuracy: 0.8563\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1801 - accuracy: 0.9474\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1340 - accuracy: 0.9602\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1083 - accuracy: 0.9672\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0931 - accuracy: 0.9714\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0809 - accuracy: 0.9750\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0732 - accuracy: 0.9773\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0645 - accuracy: 0.9804\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 14:00:03,935]\u001b[0m Trial 12 finished with value: 0.9698 and parameters: {'activation': 'relu', 'n_neurons': 23, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'sgd'}. Best is trial 9 with value: 0.9758.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: softplus\n",
      "Número de neurônios: 27\n",
      "Otimizador: sgd\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 2.3300 - accuracy: 0.1117\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 2.3012 - accuracy: 0.1124\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 2.3013 - accuracy: 0.1124\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 2.3013 - accuracy: 0.1124\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 14:01:07,859]\u001b[0m Trial 13 finished with value: 0.1135 and parameters: {'activation': 'softplus', 'n_neurons': 27, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'sgd'}. Best is trial 9 with value: 0.9758.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: selu\n",
      "Número de neurônios: 16\n",
      "Otimizador: sgd\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2681 - accuracy: 0.9192\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1500 - accuracy: 0.9547\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1193 - accuracy: 0.9638\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1042 - accuracy: 0.9685\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0915 - accuracy: 0.9719\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0842 - accuracy: 0.9745\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0756 - accuracy: 0.9763\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0708 - accuracy: 0.9783\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 14:01:55,763]\u001b[0m Trial 14 finished with value: 0.9657 and parameters: {'activation': 'selu', 'n_neurons': 16, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'sgd'}. Best is trial 9 with value: 0.9758.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Função de ativação: selu\n",
      "Número de neurônios: 49\n",
      "Otimizador: adam\n",
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.2563 - accuracy: 0.9254\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.1484 - accuracy: 0.9554\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.1156 - accuracy: 0.9656\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0980 - accuracy: 0.9706\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0896 - accuracy: 0.9720\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0717 - accuracy: 0.9774\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0668 - accuracy: 0.9790\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0588 - accuracy: 0.9815\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2021-11-05 14:03:41,345]\u001b[0m Trial 15 finished with value: 0.9717 and parameters: {'activation': 'selu', 'n_neurons': 49, 'dilation': 1, 'loss': 'sparse_categorical_crossentropy', 'optimizer': 'adam'}. Best is trial 9 with value: 0.9758.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "study_3 = optuna.create_study(direction='maximize')\n",
    "study_3.optimize(cnn3, n_trials=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gTREgJ3DMLQx"
   },
   "source": [
    "##Gráficos de resultados do processo de otimização"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "Sl9Lw4NpMc79",
    "outputId": "1c786b4c-40a9-4a8b-b1e8-b84068c95195"
   },
   "outputs": [
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    "### Resultados do plot de otimização de cada estudo\n",
    "Como podemos ver acima, nossa primeira rede de CNN obteve melhor desempenho em sua otimização, dado que foi mais consistente (o primeiro trial do CNN2 e CNN3 foi um outlier)."
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   "source": [
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    "### Resultado da comparação de parâmetros\n",
    "- Funções de ativação: no geral, todas as funções de otimização apresentaram bons resultados (como visto no primeiro estudo). Nos outros dois estudos é visível que as funções \"softmax\", \"softplus\" e \"sigmoid\" foram as piores.\n",
    "- Dilatação: no terceiro estudo, a rede permitiu apenas uso da dilatação com 1, portanto, vamos desconsiderá-lo. Os outros dois estudos apresentaram melhores resultados quando a dilatação foi 2.\n",
    "- A função de perda utilizada foi a mesma em todos.\n",
    "- O número de neurônios foi um parâmetro bastante inconstante, não aparentou ser um diferencial em nenhum dos estudos.\n",
    "- O otimizador que mais se destacou foi o \"adam\", e em segundo o \"sgd\", os outros demonstraram ser igualmente bons, com um pouco mais de outliers."
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     "metadata": {},
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   ],
   "source": [
    "optuna.visualization.plot_param_importances(study_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 328
    },
    "id": "EMSCCkDeMmFK",
    "outputId": "adf13e4e-5436-4533-ca51-27a8b5b916a6"
   },
   "outputs": [
    {
     "ename": "ZeroDivisionError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-eba610d4746b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moptuna\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisualization\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_param_importances\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstudy_3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/optuna/visualization/_param_importances.py\u001b[0m in \u001b[0;36mplot_param_importances\u001b[0;34m(study, evaluator, params, target, target_name)\u001b[0m\n\u001b[1;32m    111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m     importances = optuna.importance.get_param_importances(\n\u001b[0;32m--> 113\u001b[0;31m         \u001b[0mstudy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mevaluator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    114\u001b[0m     )\n\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/optuna/importance/__init__.py\u001b[0m in \u001b[0;36mget_param_importances\u001b[0;34m(study, evaluator, params, target)\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Evaluator must be a subclass of BaseImportanceEvaluator.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 93\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mevaluator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstudy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/optuna/importance/_fanova/_evaluator.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, study, params, target)\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrans_values\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0msearch_spaces\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrans_bounds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m             \u001b[0mcolumn_to_encoded_columns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumn_to_encoded_columns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    127\u001b[0m         )\n\u001b[1;32m    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/optuna/importance/_fanova/_fanova.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, search_spaces, column_to_encoded_columns)\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trees\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_FanovaTree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msearch_spaces\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_column_to_encoded_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumn_to_encoded_columns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variances\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/optuna/importance/_fanova/_fanova.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trees\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_FanovaTree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msearch_spaces\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_column_to_encoded_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumn_to_encoded_columns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variances\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/optuna/importance/_fanova/_tree.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, tree, search_spaces)\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_search_spaces\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msearch_spaces\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m         \u001b[0mstatistics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_precompute_statistics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m         \u001b[0msplit_midpoints\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit_sizes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_precompute_split_midpoints_and_sizes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0msubtree_active_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_precompute_subtree_active_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/optuna/importance/_fanova/_tree.py\u001b[0m in \u001b[0;36m_precompute_statistics\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    180\u001b[0m                     \u001b[0mchild_values\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatistics\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mchild_node_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m                     \u001b[0mchild_weights\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatistics\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mchild_node_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m                 \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maverage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchild_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mchild_weights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m                 \u001b[0mweight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchild_weights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m                 \u001b[0mstatistics\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36maverage\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36maverage\u001b[0;34m(a, axis, weights, returned)\u001b[0m\n\u001b[1;32m    408\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscl\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m             raise ZeroDivisionError(\n\u001b[0;32m--> 410\u001b[0;31m                 \"Weights sum to zero, can't be normalized\")\n\u001b[0m\u001b[1;32m    411\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    412\u001b[0m         \u001b[0mavg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmultiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwgt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresult_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mscl\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mZeroDivisionError\u001b[0m: Weights sum to zero, can't be normalized"
     ]
    }
   ],
   "source": [
    "optuna.visualization.plot_param_importances(study_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YAhGkRyDsSZS"
   },
   "source": [
    "### Diferenciais\n",
    "\n",
    "A função de ativação foi alta nos dois primeiros estudos, sendo um diferencial importante, os outros fatores pouco impactaram. No terceiro estudo houve algum problema no optuna que impossibilitou a análise."
   ]
  }
 ],
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