ANN-Part 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "deafd453",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib as mpl \n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9175db61",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf \n",
    "from tensorflow import keras "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "278e7517",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "housing = fetch_california_housing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "46088ca2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']\n"
     ]
    }
   ],
   "source": [
    "print(housing.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c9b3b16e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train_full, X_test, y_train_full, y_test = train_test_split(housing.data, housing.target, random_state = 42)\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X_train_full, y_train_full, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e57c2274",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\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": 52,
   "id": "162c1742",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6b666367",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11610, 8)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "92bdb3a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation='relu', input_shape=[8]),\n",
    "    keras.layers.Dense(30, activation='relu'),\n",
    "    keras.layers.Dense(1)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "07756b3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 30)                270       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 30)                930       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 31        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,231\n",
      "Trainable params: 1,231\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4017b9c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eeavlab/anaconda3/lib/python3.9/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(SGD, self).__init__(name, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=\"mean_squared_error\",\n",
    "              optimizer = keras.optimizers.SGD(lr = 1e-3),\n",
    "              metrics=['mae']\n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e94e1ec0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3602 - mae: 0.4260 - val_loss: 0.3476 - val_mae: 0.4143\n",
      "Epoch 2/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3590 - mae: 0.4250 - val_loss: 0.3744 - val_mae: 0.4199\n",
      "Epoch 3/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3581 - mae: 0.4250 - val_loss: 0.3512 - val_mae: 0.4128\n",
      "Epoch 4/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3572 - mae: 0.4239 - val_loss: 0.3422 - val_mae: 0.4140\n",
      "Epoch 5/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3562 - mae: 0.4233 - val_loss: 0.3449 - val_mae: 0.4133\n",
      "Epoch 6/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3554 - mae: 0.4231 - val_loss: 0.3488 - val_mae: 0.4111\n",
      "Epoch 7/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3546 - mae: 0.4220 - val_loss: 0.3414 - val_mae: 0.4113\n",
      "Epoch 8/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3538 - mae: 0.4217 - val_loss: 0.3392 - val_mae: 0.4103\n",
      "Epoch 9/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3531 - mae: 0.4213 - val_loss: 0.3524 - val_mae: 0.4100\n",
      "Epoch 10/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3523 - mae: 0.4207 - val_loss: 0.3499 - val_mae: 0.4092\n",
      "Epoch 11/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3517 - mae: 0.4199 - val_loss: 0.3310 - val_mae: 0.4074\n",
      "Epoch 12/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3509 - mae: 0.4196 - val_loss: 0.3804 - val_mae: 0.4121\n",
      "Epoch 13/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3504 - mae: 0.4191 - val_loss: 0.3429 - val_mae: 0.4105\n",
      "Epoch 14/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3495 - mae: 0.4185 - val_loss: 0.3323 - val_mae: 0.4083\n",
      "Epoch 15/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3489 - mae: 0.4180 - val_loss: 0.3608 - val_mae: 0.4114\n",
      "Epoch 16/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3484 - mae: 0.4182 - val_loss: 0.3282 - val_mae: 0.4063\n",
      "Epoch 17/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3479 - mae: 0.4177 - val_loss: 0.3539 - val_mae: 0.4085\n",
      "Epoch 18/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3472 - mae: 0.4170 - val_loss: 0.3364 - val_mae: 0.4054\n",
      "Epoch 19/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3464 - mae: 0.4163 - val_loss: 0.3322 - val_mae: 0.4056\n",
      "Epoch 20/20\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3460 - mae: 0.4162 - val_loss: 0.3880 - val_mae: 0.4113\n"
     ]
    }
   ],
   "source": [
    "model_history = model.fit(X_train, y_train, epochs=20, validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ffe744a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162/162 [==============================] - 0s 1ms/step - loss: 0.3449 - mae: 0.4167\n"
     ]
    }
   ],
   "source": [
    "mae_test = model.evaluate(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8f21bace",
   "metadata": {},
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     "data": {
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       " 'val_loss': [0.34764066338539124,\n",
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    "model_history.history"
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  {
   "cell_type": "code",
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   "id": "bfab596d",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(model_history.history).plot(figsize=(8,5))\n",
    "plt.grid(True)\n",
    "plt.gca().set_ylim(0,1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6123db66",
   "metadata": {},
   "source": [
    "# Functional API \n",
    "\n",
    "we will try out a wide and deep model which is not like the sequential model \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "42541ba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bfbc7295",
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "fdb98e0a",
   "metadata": {},
   "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.models.Model(inputs=[input_], outputs=[output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e63db3c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_3 (InputLayer)           [(None, 8)]          0           []                               \n",
      "                                                                                                  \n",
      " dense_6 (Dense)                (None, 30)           270         ['input_3[0][0]']                \n",
      "                                                                                                  \n",
      " dense_7 (Dense)                (None, 30)           930         ['dense_6[0][0]']                \n",
      "                                                                                                  \n",
      " concatenate_2 (Concatenate)    (None, 38)           0           ['input_3[0][0]',                \n",
      "                                                                  'dense_7[0][0]']                \n",
      "                                                                                                  \n",
      " dense_8 (Dense)                (None, 1)            39          ['concatenate_2[0][0]']          \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 1,239\n",
      "Trainable params: 1,239\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f91606f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eeavlab/anaconda3/lib/python3.9/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(SGD, self).__init__(name, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=\"mean_squared_error\",\n",
    "              optimizer = keras.optimizers.SGD(lr = 1e-3),\n",
    "              metrics=['mae']\n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c4f94c0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "Epoch 1/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 1.8422 - mae: 0.9135 - val_loss: 1.7479 - val_mae: 0.6461\n",
      "Epoch 2/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.7258 - mae: 0.6194 - val_loss: 0.7584 - val_mae: 0.5884\n",
      "Epoch 3/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.6472 - mae: 0.5835 - val_loss: 0.6156 - val_mae: 0.5509\n",
      "Epoch 4/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.5944 - mae: 0.5572 - val_loss: 0.5442 - val_mae: 0.5291\n",
      "Epoch 5/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.5538 - mae: 0.5361 - val_loss: 0.5013 - val_mae: 0.5067\n",
      "Epoch 6/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.5217 - mae: 0.5201 - val_loss: 0.4749 - val_mae: 0.4918\n",
      "Epoch 7/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4970 - mae: 0.5071 - val_loss: 0.4519 - val_mae: 0.4811\n",
      "Epoch 8/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4784 - mae: 0.4968 - val_loss: 0.4399 - val_mae: 0.4731\n",
      "Epoch 9/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4633 - mae: 0.4895 - val_loss: 0.4288 - val_mae: 0.4664\n",
      "Epoch 10/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4515 - mae: 0.4832 - val_loss: 0.4316 - val_mae: 0.4620\n",
      "Epoch 11/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4425 - mae: 0.4780 - val_loss: 0.4132 - val_mae: 0.4593\n",
      "Epoch 12/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4348 - mae: 0.4743 - val_loss: 0.4353 - val_mae: 0.4571\n",
      "Epoch 13/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4287 - mae: 0.4710 - val_loss: 0.4309 - val_mae: 0.4573\n",
      "Epoch 14/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4232 - mae: 0.4680 - val_loss: 0.4025 - val_mae: 0.4536\n",
      "Epoch 15/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4185 - mae: 0.4660 - val_loss: 0.4114 - val_mae: 0.4509\n",
      "Epoch 16/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4144 - mae: 0.4642 - val_loss: 0.4002 - val_mae: 0.4489\n",
      "Epoch 17/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4107 - mae: 0.4618 - val_loss: 0.4188 - val_mae: 0.4473\n",
      "Epoch 18/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4075 - mae: 0.4600 - val_loss: 0.4173 - val_mae: 0.4452\n",
      "Epoch 19/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4039 - mae: 0.4577 - val_loss: 0.3970 - val_mae: 0.4422\n",
      "Epoch 20/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.4013 - mae: 0.4560 - val_loss: 0.4554 - val_mae: 0.4467\n",
      "Epoch 21/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3988 - mae: 0.4546 - val_loss: 0.4178 - val_mae: 0.4447\n",
      "Epoch 22/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3960 - mae: 0.4533 - val_loss: 0.4498 - val_mae: 0.4456\n",
      "Epoch 23/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3940 - mae: 0.4520 - val_loss: 0.3677 - val_mae: 0.4324\n",
      "Epoch 24/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3915 - mae: 0.4498 - val_loss: 0.3936 - val_mae: 0.4362\n",
      "Epoch 25/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3891 - mae: 0.4492 - val_loss: 0.4368 - val_mae: 0.4378\n",
      "Epoch 26/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3876 - mae: 0.4478 - val_loss: 0.4156 - val_mae: 0.4359\n",
      "Epoch 27/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3859 - mae: 0.4464 - val_loss: 0.3611 - val_mae: 0.4306\n",
      "Epoch 28/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3839 - mae: 0.4450 - val_loss: 0.3906 - val_mae: 0.4349\n",
      "Epoch 29/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3823 - mae: 0.4443 - val_loss: 0.3771 - val_mae: 0.4325\n",
      "Epoch 30/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3803 - mae: 0.4431 - val_loss: 0.4261 - val_mae: 0.4333\n",
      "Epoch 31/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3788 - mae: 0.4419 - val_loss: 0.3910 - val_mae: 0.4298\n",
      "Epoch 32/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3772 - mae: 0.4407 - val_loss: 0.4194 - val_mae: 0.4356\n",
      "Epoch 33/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3765 - mae: 0.4406 - val_loss: 0.3645 - val_mae: 0.4282\n",
      "Epoch 34/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3747 - mae: 0.4391 - val_loss: 0.3579 - val_mae: 0.4264\n",
      "Epoch 35/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3733 - mae: 0.4381 - val_loss: 0.4034 - val_mae: 0.4314\n",
      "Epoch 36/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3723 - mae: 0.4377 - val_loss: 0.3488 - val_mae: 0.4223\n",
      "Epoch 37/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3709 - mae: 0.4362 - val_loss: 0.3664 - val_mae: 0.4263\n",
      "Epoch 38/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3696 - mae: 0.4358 - val_loss: 0.3600 - val_mae: 0.4245\n",
      "Epoch 39/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3685 - mae: 0.4353 - val_loss: 0.3511 - val_mae: 0.4211\n",
      "Epoch 40/40\n",
      "363/363 [==============================] - 1s 2ms/step - loss: 0.3672 - mae: 0.4342 - val_loss: 0.3710 - val_mae: 0.4211\n"
     ]
    }
   ],
   "source": [
    "model_history = model.fit(X_train, y_train, epochs = 40, validation_data=(X_valid,y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "60e15db6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162/162 [==============================] - 0s 1ms/step - loss: 0.3710 - mae: 0.4318\n"
     ]
    }
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   "source": [
    "mae_test = model.evaluate(X_test, y_test)"
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  {
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