mindspore_yolo/tf13_keras_regression-hp-search-sklearn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.0\n",
      "sys.version_info(major=3, minor=6, micro=9, releaselevel='final', serial=0)\n",
      "matplotlib 3.2.1\n",
      "numpy 1.18.5\n",
      "pandas 1.0.4\n",
      "sklearn 0.23.1\n",
      "tensorflow 2.2.0\n",
      "tensorflow.keras 2.3.0-tf\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "\n",
    "print(tf.__version__)\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, tf, keras:\n",
    "    print(module.__name__, module.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _california_housing_dataset:\n",
      "\n",
      "California Housing dataset\n",
      "--------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 20640\n",
      "\n",
      "    :Number of Attributes: 8 numeric, predictive attributes and the target\n",
      "\n",
      "    :Attribute Information:\n",
      "        - MedInc        median income in block\n",
      "        - HouseAge      median house age in block\n",
      "        - AveRooms      average number of rooms\n",
      "        - AveBedrms     average number of bedrooms\n",
      "        - Population    block population\n",
      "        - AveOccup      average house occupancy\n",
      "        - Latitude      house block latitude\n",
      "        - Longitude     house block longitude\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "This dataset was obtained from the StatLib repository.\n",
      "http://lib.stat.cmu.edu/datasets/\n",
      "\n",
      "The target variable is the median house value for California districts.\n",
      "\n",
      "This dataset was derived from the 1990 U.S. census, using one row per census\n",
      "block group. A block group is the smallest geographical unit for which the U.S.\n",
      "Census Bureau publishes sample data (a block group typically has a population\n",
      "of 600 to 3,000 people).\n",
      "\n",
      "It can be downloaded/loaded using the\n",
      ":func:`sklearn.datasets.fetch_california_housing` function.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
      "      Statistics and Probability Letters, 33 (1997) 291-297\n",
      "\n",
      "(20640, 8)\n",
      "(20640,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "print(housing.DESCR)\n",
    "print(housing.data.shape)\n",
    "print(housing.target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11610, 8) (11610,)\n",
      "(3870, 8) (3870,)\n",
      "(5160, 8) (5160,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train_all, x_test, y_train_all, y_test = train_test_split(\n",
    "    housing.data, housing.target, random_state = 7)\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(\n",
    "    x_train_all, y_train_all, random_state = 11)\n",
    "print(x_train.shape, y_train.shape)\n",
    "print(x_valid.shape, y_valid.shape)\n",
    "print(x_test.shape, y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "x_train_scaled = scaler.fit_transform(x_train)\n",
    "x_valid_scaled = scaler.transform(x_valid)\n",
    "x_test_scaled = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 1.3733 - val_loss: 0.7411\n",
      "Epoch 2/10\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.6284 - val_loss: 0.6279\n",
      "Epoch 3/10\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.5568 - val_loss: 0.5713\n",
      "Epoch 4/10\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.5257 - val_loss: 0.5383\n",
      "Epoch 5/10\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4986 - val_loss: 0.5133\n",
      "Epoch 6/10\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4869 - val_loss: 0.5010\n",
      "Epoch 7/10\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4706 - val_loss: 0.4821\n",
      "Epoch 8/10\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4531 - val_loss: 0.4714\n",
      "Epoch 9/10\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4455 - val_loss: 0.4670\n",
      "Epoch 10/10\n",
      "363/363 [==============================] - 1s 3ms/step - loss: 0.4368 - val_loss: 0.4615\n"
     ]
    }
   ],
   "source": [
    "# RandomizedSearchCV\n",
    "# 1. 因为是sklearn的接口，转化为sklearn的model\n",
    "# 2. 定义参数集合\n",
    "# 3. 搜索参数\n",
    "\n",
    "def build_model(hidden_layers = 1,\n",
    "                layer_size = 30,\n",
    "                learning_rate = 3e-3):\n",
    "    model = keras.models.Sequential()\n",
    "    #因为不知道第一个输入的shape是多大的，因此我们需要单独从for循环里拿出来，for循环里的是输出再次作为输入\n",
    "    model.add(keras.layers.Dense(layer_size, activation='relu',\n",
    "                                 input_shape=x_train.shape[1:]))\n",
    "    for _ in range(hidden_layers - 1):\n",
    "        model.add(keras.layers.Dense(layer_size,\n",
    "                                     activation = 'relu'))\n",
    "    model.add(keras.layers.Dense(1))\n",
    "    optimizer = keras.optimizers.SGD(learning_rate)\n",
    "    model.compile(loss = 'mse', optimizer = optimizer)\n",
    "    return model\n",
    "\n",
    "#KerasRegressor返回一个sk的model,build_fn是一个回调函数\n",
    "sklearn_model = tf.keras.wrappers.scikit_learn.KerasRegressor(\n",
    "    build_fn = build_model)\n",
    "callbacks = [keras.callbacks.EarlyStopping(patience=5, min_delta=1e-2)]\n",
    "#下面只是先对sk封装tf模型的一个测试\n",
    "history = sklearn_model.fit(x_train_scaled, y_train,\n",
    "                            epochs = 10,\n",
    "                            validation_data = (x_valid_scaled, y_valid),\n",
    "                            callbacks = callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_learning_curves(history):\n",
    "    pd.DataFrame(history.history).plot(figsize=(8, 5))\n",
    "    plt.grid(True)\n",
    "    plt.gca().set_ylim(0, 1)\n",
    "    plt.show()\n",
    "plot_learning_curves(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "291/291 [==============================] - 2s 6ms/step - loss: 1.1076 - val_loss: 1.0160\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "291/291 [==============================] - 1s 3ms/step - loss: 6.1835 - val_loss: 5.5398\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "73/73 [==============================] - 0s 817us/step - loss: 0.4205\n",
      "Epoch 1/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 1.0686 - val_loss: 0.6908\n",
      "Epoch 2/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.5946 - val_loss: 0.5710\n",
      "Epoch 3/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.5093 - val_loss: 0.5047\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.4572 - val_loss: 0.4585\n",
      "Epoch 5/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.4262 - val_loss: 0.4433\n",
      "73/73 [==============================] - 0s 798us/step - loss: 0.4275\n",
      "Epoch 1/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 1.1227 - val_loss: 0.7027\n",
      "Epoch 2/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.5751 - val_loss: 0.5824\n",
      "Epoch 3/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.4987 - val_loss: 0.5099\n",
      "Epoch 4/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.4569 - val_loss: 0.4724\n",
      "Epoch 5/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.4282 - val_loss: 0.4401\n",
      "73/73 [==============================] - 0s 779us/step - loss: 0.4202\n",
      "Epoch 1/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 1.3362 - val_loss: 0.6359\n",
      "Epoch 2/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.5624 - val_loss: 0.5943\n",
      "Epoch 3/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.5819 - val_loss: 0.5202\n",
      "Epoch 4/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.4860 - val_loss: 0.4772\n",
      "Epoch 5/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.4429 - val_loss: 0.4553\n",
      "73/73 [==============================] - 0s 896us/step - loss: 0.4583\n",
      "Epoch 1/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 1.0628 - val_loss: 0.6888\n",
      "Epoch 2/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.5495 - val_loss: 0.5319\n",
      "Epoch 3/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.4531 - val_loss: 0.4723\n",
      "Epoch 4/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.4088 - val_loss: 0.4239\n",
      "Epoch 5/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.3857 - val_loss: 0.4092\n",
      "73/73 [==============================] - 0s 782us/step - loss: 0.4066\n",
      "Epoch 1/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.9645 - val_loss: 0.5424\n",
      "Epoch 2/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.4844 - val_loss: 0.4623\n",
      "Epoch 3/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.4253 - val_loss: 0.4155\n",
      "Epoch 4/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.3982 - val_loss: 0.4078\n",
      "Epoch 5/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.3823 - val_loss: 0.4521\n",
      "73/73 [==============================] - 0s 746us/step - loss: 0.4314\n",
      "Epoch 1/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.9178 - val_loss: 0.5626\n",
      "Epoch 2/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.4787 - val_loss: 0.4673\n",
      "Epoch 3/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.4256 - val_loss: 0.4214\n",
      "Epoch 4/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.3978 - val_loss: 0.3964\n",
      "Epoch 5/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.3774 - val_loss: 0.3914\n",
      "73/73 [==============================] - 0s 757us/step - loss: 0.3849\n",
      "Epoch 1/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.7389 - val_loss: 0.5297\n",
      "Epoch 2/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.4580 - val_loss: 0.4932\n",
      "Epoch 3/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.4165 - val_loss: 0.4136\n",
      "Epoch 4/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.3978 - val_loss: 0.4246\n",
      "Epoch 5/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.3865 - val_loss: 0.4346\n",
      "73/73 [==============================] - 0s 750us/step - loss: 0.4057\n",
      "Epoch 1/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.8434 - val_loss: 0.5559\n",
      "Epoch 2/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.4721 - val_loss: 0.4607\n",
      "Epoch 3/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.4149 - val_loss: 0.4158\n",
      "Epoch 4/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.3888 - val_loss: 0.4170\n",
      "Epoch 5/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.3753 - val_loss: 0.3879\n",
      "73/73 [==============================] - 0s 765us/step - loss: 0.3910\n",
      "Epoch 1/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.8391 - val_loss: 0.5323\n",
      "Epoch 2/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.4518 - val_loss: 0.4936\n",
      "Epoch 3/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.4083 - val_loss: 0.4271\n",
      "Epoch 4/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.3850 - val_loss: 0.4405\n",
      "Epoch 5/5\n",
      "291/291 [==============================] - 0s 1ms/step - loss: 0.3731 - val_loss: 0.3860\n",
      "73/73 [==============================] - 0s 749us/step - loss: 0.3935\n",
      "Epoch 1/5\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.6946 - val_loss: 0.5043\n",
      "Epoch 2/5\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.4602 - val_loss: 0.4307\n",
      "Epoch 3/5\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.4124 - val_loss: 0.4057\n",
      "Epoch 4/5\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3900 - val_loss: 0.3942\n",
      "Epoch 5/5\n",
      "363/363 [==============================] - 0s 1ms/step - loss: 0.3799 - val_loss: 0.3794\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=<tensorflow.python.keras.wrappers.scikit_learn.KerasRegressor object at 0x7f73c6ff9048>,\n",
       "             param_grid={'hidden_layers': [1, 2, 3, 4],\n",
       "                         'layer_size': [5, 10, 20, 30],\n",
       "                         'learning_rate': [0.0001, 5e-05, 0.001, 0.005, 0.01]})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scipy也是sk中的\n",
    "from scipy.stats import reciprocal\n",
    "# 分布函数\n",
    "# f(x) = 1/(x*log(b/a)) a <= x <= b\n",
    "\n",
    "#sk 0.21.3版本可以用这种列表\n",
    "# param_distribution = {\n",
    "#     \"hidden_layers\":[1, 2, 3, 4],\n",
    "#     \"layer_size\": np.arange(1, 100),\n",
    "#     \"learning_rate\": reciprocal(1e-4, 1e-2),\n",
    "# }\n",
    "#最新版本只能用普通列表\n",
    "param_distribution = {\n",
    "    \"hidden_layers\": [1, 2, 3, 4],\n",
    "    \"layer_size\": [5, 10, 20, 30],\n",
    "    \"learning_rate\": [1e-4, 5e-5, 1e-3, 5e-3, 1e-2],\n",
    "}\n",
    "\n",
    "from sklearn.model_selection import RandomizedSearchCV,GridSearchCV\n",
    "\n",
    "#随机搜索\n",
    "# random_search_cv = RandomizedSearchCV(sklearn_model,\n",
    "#                                       param_distribution)\n",
    "grid_search_cv =GridSearchCV(sklearn_model,param_distribution)\n",
    "# random_search_cv.fit(x_train_scaled, y_train, epochs = 5,\n",
    "#                      validation_data = (x_valid_scaled, y_valid),\n",
    "#                      callbacks = callbacks)\n",
    "\n",
    "grid_search_cv.fit(x_train_scaled, y_train, epochs = 5,\n",
    "                     validation_data = (x_valid_scaled, y_valid),\n",
    "                     callbacks = callbacks)\n",
    "# cross_validation: 训练集分成n份，n-1训练，最后一份验证."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'hidden_layers': 3, 'layer_size': 30, 'learning_rate': 0.01}\n",
      "-0.3978272318840027\n",
      "<tensorflow.python.keras.wrappers.scikit_learn.KerasRegressor object at 0x7f738bb95400>\n"
     ]
    }
   ],
   "source": [
    "# print(random_search_cv.best_params_)\n",
    "# print(random_search_cv.best_score_)\n",
    "# print(random_search_cv.best_estimator_)\n",
    "\n",
    "print(grid_search_cv.best_params_)\n",
    "print(grid_search_cv.best_score_)\n",
    "print(grid_search_cv.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162/162 [==============================] - 0s 770us/step - loss: 0.3876\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.38763508200645447"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#拿最佳的模型\n",
    "# model = random_search_cv.best_estimator_.model\n",
    "\n",
    "# model.evaluate(x_test_scaled, y_test)\n",
    "\n",
    "model = grid_search_cv.best_estimator_.model\n",
    "model.evaluate(x_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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