Chapter 12 Regression of Boston House prices.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Baseline neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "import pandas\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe = pandas.read_csv(\"Boston.csv\",header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataframe.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset[:,0:13]\n",
    "Y = dataset[:,13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def baseline_model():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(13,input_dim=13,kernel_initializer='normal',activation='relu'))\n",
    "    model.add(Dense(1,kernel_initializer=\"normal\"))\n",
    "    model.compile(loss = \"mean_squared_error\",optimizer=\"adam\")\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed=7\n",
    "numpy.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator=KerasRegressor(build_fn=baseline_model,epochs=100,batch_size=5,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/arvind/.local/lib/python3.6/site-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n"
     ]
    }
   ],
   "source": [
    "kfold = KFold(n_splits=10,random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0423 17:54:56.930763 140087948650304 deprecation.py:506] From /home/arvind/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1633: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "W0423 17:54:57.205882 140087948650304 deprecation.py:323] From /home/arvind/.local/lib/python3.6/site-packages/keras/optimizers.py:550: BaseResourceVariable.constraint (from tensorflow.python.ops.resource_variable_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Apply a constraint manually following the optimizer update step.\n",
      "W0423 17:54:57.402948 140087948650304 module_wrapper.py:136] From /home/arvind/.local/lib/python3.6/site-packages/tensorflow_core/python/util/module_wrapper.py:163: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
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      "456/456 [==============================] - 0s 215us/step - loss: 22.6151\n",
      "Epoch 91/100\n",
      "456/456 [==============================] - 0s 211us/step - loss: 23.5572\n",
      "Epoch 92/100\n",
      "456/456 [==============================] - 0s 210us/step - loss: 21.9156\n",
      "Epoch 93/100\n",
      "456/456 [==============================] - 0s 227us/step - loss: 22.7191\n",
      "Epoch 94/100\n",
      "456/456 [==============================] - 0s 201us/step - loss: 21.7474\n",
      "Epoch 95/100\n",
      "456/456 [==============================] - 0s 205us/step - loss: 21.6913\n",
      "Epoch 96/100\n",
      "456/456 [==============================] - 0s 209us/step - loss: 21.7903\n",
      "Epoch 97/100\n",
      "456/456 [==============================] - 0s 215us/step - loss: 21.2651\n",
      "Epoch 98/100\n",
      "456/456 [==============================] - 0s 201us/step - loss: 22.1124\n",
      "Epoch 99/100\n",
      "456/456 [==============================] - 0s 225us/step - loss: 21.5941\n",
      "Epoch 100/100\n",
      "456/456 [==============================] - 0s 200us/step - loss: 21.8186\n",
      "50/50 [==============================] - 0s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "results=cross_val_score(estimator,X,Y,cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline: -32.33 (21.83) MSE\n"
     ]
    }
   ],
   "source": [
    "print(\"Baseline: %.2f (%.2f) MSE\" %(results.mean(),results.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators.append(('standardize',StandardScaler()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators.append(('mlp',KerasRegressor(build_fn=baseline_model,epochs=50,batch_size=5,verbose=0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline=Pipeline(estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/arvind/.local/lib/python3.6/site-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n"
     ]
    }
   ],
   "source": [
    "kfold = KFold(n_splits=10,random_state=seed)\n",
    "results = cross_val_score(pipeline,X,Y,cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standardized: -23.29 (19.43) MSE\n"
     ]
    }
   ],
   "source": [
    "print(\"Standardized: %.2f (%.2f) MSE\" %(results.mean(),results.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deeper model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def larger_model():\n",
    "    model=Sequential()\n",
    "    model.add(Dense(13,input_dim=13,kernel_initializer='normal',activation='relu'))\n",
    "    model.add(Dense(6,kernel_initializer='normal',activation='relu'))\n",
    "    model.add(Dense(1,kernel_initializer='normal'))\n",
    "    model.compile(loss='mean_squared_error',optimizer='adam')\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('mlp', <keras.wrappers.scikit_learn.KerasRegressor at 0x7f682599dcc0>)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimators.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators.append(('mlp',KerasRegressor(build_fn=larger_model,epochs=50,batch_size=5,verbose=0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline(estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = cross_val_score(pipeline,X,Y,cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Larger: -24.82 (21.34) MSE\n"
     ]
    }
   ],
   "source": [
    "print(\"Larger: %.2f (%.2f) MSE\"%(results.mean(),results.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate wider network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wider_model():\n",
    "    model=Sequential()\n",
    "    model.add(Dense(20,input_dim=13,kernel_initializer='normal',activation='relu'))\n",
    "    model.add(Dense(1,kernel_initializer=\"normal\"))\n",
    "    model.compile(loss='mean_squared_error',optimizer='adam')\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('mlp', <keras.wrappers.scikit_learn.KerasRegressor at 0x7f6825ac8828>)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimators.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators.append((\"mlp\",KerasRegressor(build_fn=wider_model,epochs=100,batch_size=5,verbose=0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline(estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "results=cross_val_score(pipeline,X,Y,cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Wider: %.2f\")"
   ]
  }
 ],
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