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FirstModel

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FirstModel
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4d62143b-6a33-4c02-8b1d-3d48d1256737",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "matplotlib.use(\"TkAgg\")\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d77530d4-e76b-4958-961e-a08bb19a7977",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = fetch_california_housing()\n",
    "x_train_full, x_test, y_train_full, y_test = train_test_split(housing.data,housing.target)\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(x_train_full, y_train_full)\n",
    "\n",
    "scaler=StandardScaler()\n",
    "x_train = scaler.fit_transform(x_train)\n",
    "x_valid = scaler.transform(x_valid)\n",
    "x_test=scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55bd5b4b-8f34-4ade-89b3-35cfcbe61d7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([keras.layers.Dense(30, activation = \"relu\", input_shape=x_train.shape[1:]), keras.layers.Dense(1)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "736db746-f0b9-4a4b-b90f-6ae3cda03447",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "input_ = keras.layers.Input(shape=x_train.shape[1:])\n",
    "hidden1 = keras.layers.Dense(30, activation=\"relu\")(input_)\n",
    "hidden2 = keras.layers.Dense(30, activation=\"relu\")(hidden1)\n",
    "concat = keras.layers.Concatenate()([input_,hidden2])\n",
    "output = keras.layers.Dense(1)(concat)\n",
    "model = keras.Model(inputs=[input_], outputs=[output])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "af7e711e-d8df-44d6-bf50-70ee09ee2ae6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 1.7751 - val_loss: 1.3058\n",
      "Epoch 2/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.8318 - val_loss: 0.8696\n",
      "Epoch 3/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.7257 - val_loss: 0.7567\n",
      "Epoch 4/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6701 - val_loss: 0.6548\n",
      "Epoch 5/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.6205 - val_loss: 0.6165\n",
      "Epoch 6/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5843 - val_loss: 0.5791\n",
      "Epoch 7/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5507 - val_loss: 0.5629\n",
      "Epoch 8/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5270 - val_loss: 0.5499\n",
      "Epoch 9/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5061 - val_loss: 0.5127\n",
      "Epoch 10/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4899 - val_loss: 0.4930\n",
      "Epoch 11/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4746 - val_loss: 0.4816\n",
      "Epoch 12/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4634 - val_loss: 0.4685\n",
      "Epoch 13/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4539 - val_loss: 0.4632\n",
      "Epoch 14/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4478 - val_loss: 0.4546\n",
      "Epoch 15/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4418 - val_loss: 0.4485\n",
      "Epoch 16/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4374 - val_loss: 0.4442\n",
      "Epoch 17/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4337 - val_loss: 0.4400\n",
      "Epoch 18/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4304 - val_loss: 0.4377\n",
      "Epoch 19/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4274 - val_loss: 0.4378\n",
      "Epoch 20/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4257 - val_loss: 0.4346\n",
      "\u001b[1m162/162\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.4405\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step\n",
      "[[1.3971645]\n",
      " [3.2480707]\n",
      " [2.94071  ]]\n"
     ]
    }
   ],
   "source": [
    "x_train_A, x_train_B = x_train[:, :5], x_train[:, 5:]\n",
    "x_valid_A, x_valid_B = x_valid[:, :5], x_valid[:, 5:]\n",
    "x_test_A, x_test_B = x_test[:, :5], x_test[:, 5:]\n",
    "x_new_A, x_new_B = x_test_A[:3], x_test_B[:3]\n",
    "\n",
    "# Construir modelo\n",
    "input_A = keras.layers.Input(shape=[5], name=\"wide_input\")\n",
    "input_B = keras.layers.Input(shape=[3], name=\"deep_input\")\n",
    "\n",
    "hidden1 = keras.layers.Dense(30, activation=\"relu\")(input_B)\n",
    "hidden2 = keras.layers.Dense(30, activation=\"relu\")(hidden1)\n",
    "\n",
    "concat = keras.layers.Concatenate()([input_A, hidden2])\n",
    "output = keras.layers.Dense(1, name=\"output\")(concat)\n",
    "\n",
    "model = keras.Model(inputs=[input_A, input_B], outputs=[output])\n",
    "\n",
    "# Compilar\n",
    "model.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(learning_rate=1e-3))\n",
    "\n",
    "# Treinar\n",
    "history = model.fit(\n",
    "    (x_train_A, x_train_B), y_train,\n",
    "    epochs=20,\n",
    "    validation_data=((x_valid_A, x_valid_B), y_valid)\n",
    ")\n",
    "\n",
    "# Avaliar\n",
    "mse_test = model.evaluate((x_test_A, x_test_B), y_test)\n",
    "\n",
    "# Predizer\n",
    "y_pred = model.predict((x_new_A, x_new_B))\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7835c49d-10bf-44cb-a90b-1cb808ce0be7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"mean_squared_error\", optimizer=\"adam\")\n",
    "history = model.fit(x_train, y_train, epochs=20, validation_data=(x_valid, y_valid))\n",
    "mse_test = model.evaluate(x_test, y_test)\n",
    "\n",
    "x_new = x_test[:3]\n",
    "y_pred = model.predict(x_new)\n",
    "\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f052ffc0-841f-4044-a40d-7c57216e0dc6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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